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<title>Replication Study: Transcriptional amplification in tumor cells with elevated c-Myc
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<article itemscope="" itemtype="http://schema.org/Article" data-itemscope="root">
<h1 itemprop="headline">Replication Study: Transcriptional amplification in tumor cells with
elevated c-Myc</h1>
<meta itemprop="image"
content="https://via.placeholder.com/1200x714/dbdbdb/4a4a4a.png?text=Replication%20Study:%20Transcriptional%20amplification%20in%20tumor%20cells%20with%20elevated%20c-Myc">
<ol data-itemprop="authors">
<li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
<meta itemprop="name" content="L Michelle Lewis"><span data-itemprop="givenNames"><span
itemprop="givenName">L</span><span itemprop="givenName">Michelle</span></span><span
data-itemprop="familyNames"><span itemprop="familyName">Lewis</span></span><span
data-itemprop="affiliations"><a itemprop="affiliation"
href="#author-organization-1">1</a></span>
</li>
<li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
<meta itemprop="name" content="Meredith C Edwards"><span
data-itemprop="givenNames"><span itemprop="givenName">Meredith</span><span
itemprop="givenName">C</span></span><span data-itemprop="familyNames"><span
itemprop="familyName">Edwards</span></span><span data-itemprop="affiliations"><a
itemprop="affiliation" href="#author-organization-1">1</a></span>
</li>
<li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
<meta itemprop="name" content="Zachary R Meyers"><span data-itemprop="givenNames"><span
itemprop="givenName">Zachary</span><span itemprop="givenName">R</span></span><span
data-itemprop="familyNames"><span itemprop="familyName">Meyers</span></span><span
data-itemprop="affiliations"><a itemprop="affiliation"
href="#author-organization-1">1</a></span>
</li>
<li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
<meta itemprop="name" content="C Conover Talbot"><span data-itemprop="givenNames"><span
itemprop="givenName">C</span><span itemprop="givenName">Conover</span></span><span
data-itemprop="familyNames"><span itemprop="familyName">Talbot</span></span><span
data-itemprop="affiliations"><a itemprop="affiliation"
href="#author-organization-2">2</a></span>
</li>
<li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
<meta itemprop="name" content="Haiping Hao"><span data-itemprop="givenNames"><span
itemprop="givenName">Haiping</span></span><span data-itemprop="familyNames"><span
itemprop="familyName">Hao</span></span><span data-itemprop="affiliations"><a
itemprop="affiliation" href="#author-organization-2">2</a></span>
</li>
<li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
<meta itemprop="name" content="David Blum"><span data-itemprop="givenNames"><span
itemprop="givenName">David</span></span><span data-itemprop="familyNames"><span
itemprop="familyName">Blum</span></span><span data-itemprop="affiliations"><a
itemprop="affiliation" href="#author-organization-1">1</a></span>
</li>
<li itemscope="" itemtype="http://schema.org/Organization" itemprop="author"><span
itemprop="name">Reproducibility Project: Cancer Biology</span></li>
<li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
<meta itemprop="name" content="Elizabeth Iorns"><span data-itemprop="givenNames"><span
itemprop="givenName">Elizabeth</span></span><span data-itemprop="familyNames"><span
itemprop="familyName">Iorns</span></span><span data-itemprop="affiliations"><a
itemprop="affiliation" href="#author-organization-3">3</a></span>
</li>
<li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
<meta itemprop="name" content="Rachel Tsui"><span data-itemprop="givenNames"><span
itemprop="givenName">Rachel</span></span><span data-itemprop="familyNames"><span
itemprop="familyName">Tsui</span></span><span data-itemprop="affiliations"><a
itemprop="affiliation" href="#author-organization-3">3</a></span>
</li>
<li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
<meta itemprop="name" content="Alexandria Denis"><span data-itemprop="givenNames"><span
itemprop="givenName">Alexandria</span></span><span data-itemprop="familyNames"><span
itemprop="familyName">Denis</span></span><span data-itemprop="affiliations"><a
itemprop="affiliation" href="#author-organization-4">4</a></span>
</li>
<li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
<meta itemprop="name" content="Nicole Perfito"><span data-itemprop="givenNames"><span
itemprop="givenName">Nicole</span></span><span data-itemprop="familyNames"><span
itemprop="familyName">Perfito</span></span><span data-itemprop="affiliations"><a
itemprop="affiliation" href="#author-organization-3">3</a></span>
</li>
<li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
<meta itemprop="name" content="Timothy M Errington"><span
data-itemprop="givenNames"><span itemprop="givenName">Timothy</span><span
itemprop="givenName">M</span></span><span data-itemprop="familyNames"><span
itemprop="familyName">Errington</span></span><span data-itemprop="affiliations"><a
itemprop="affiliation" href="#author-organization-4">4</a></span>
</li>
</ol>
<ol data-itemprop="affiliations">
<li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-1"
id="author-organization-1"><span itemprop="name">University of Georgia, Bioexpression
and Fermentation Facility</span>
<address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address">
<span itemprop="addressLocality">Georgia</span><span itemprop="addressCountry">United
States</span></address>
</li>
<li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-2"
id="author-organization-2"><span itemprop="name">Johns Hopkins University, Deep
Sequencing and Microarray Core Facility</span>
<address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address">
<span itemprop="addressLocality">Maryland</span><span itemprop="addressCountry">United
States</span></address>
</li>
<li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-3"
id="author-organization-3"><span itemprop="name">Science Exchange</span>
<address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address">
<span itemprop="addressLocality">Palo Alto</span><span
itemprop="addressCountry">United States</span></address>
</li>
<li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-4"
id="author-organization-4"><span itemprop="name">Center for Open Science</span>
<address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address">
<span itemprop="addressLocality">Charlottesville</span><span
itemprop="addressCountry">United States</span></address>
</li>
</ol><span itemscope="" itemtype="http://schema.org/Organization" itemprop="publisher">
<meta itemprop="name" content="Unknown"><span itemscope=""
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content="https://via.placeholder.com/600x60/dbdbdb/4a4a4a.png?text=Unknown">
</span>
</span><time itemprop="datePublished" datetime="2018-01-09">2018-01-09</time>
<ul data-itemprop="identifiers">
<li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier">
<meta itemprop="propertyID"
content="https://registry.identifiers.org/registry/publisher-id"><span
itemprop="name">publisher-id</span><span itemprop="value">30274</span>
</li>
<li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier">
<meta itemprop="propertyID" content="https://registry.identifiers.org/registry/doi">
<span itemprop="name">doi</span><span itemprop="value">10.7554/eLife.30274</span>
</li>
<li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier">
<meta itemprop="propertyID"
content="https://registry.identifiers.org/registry/elocation-id"><span
itemprop="name">elocation-id</span><span itemprop="value">e30274</span>
</li>
</ul>
<section data-itemprop="description">
<h2 data-itemtype="http://schema.stenci.la/Heading">Abstract</h2>
<meta itemprop="description"
content="As part of the Reproducibility Project: Cancer Biology, we published a Registered Report (Blum et al., 2015), that described how we intended to replicate selected experiments from the paper ‘Transcriptional amplification in tumor cells with elevated c-Myc’ (Lin et al., 2012). Here we report the results. We found overexpression of c-Myc increased total levels of RNA in P493-6 Burkitt’s lymphoma cells; however, while the effect was in the same direction as the original study (Figure 3E; Lin et al., 2012), statistical significance and the size of the effect varied between the original study and the two different lots of serum tested in this replication. Digital gene expression analysis for a set of genes was also performed on P493-6 cells before and after c-Myc overexpression. Transcripts from genes that were active before c-Myc induction increased in expression following c-Myc overexpression, similar to the original study (Figure 3F; Lin et al., 2012). Transcripts from genes that were silent before c-Myc induction also increased in expression following c-Myc overexpression, while the original study concluded elevated c-Myc had no effect on silent genes (Figure 3F; Lin et al., 2012). Treating the data as paired, we found a statistically significant increase in gene expression for both active and silent genes upon c-Myc induction, with the change in gene expression greater for active genes compared to silent genes. Finally, we report meta-analyses for each result.">
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">As part of the <a
href="https://osf.io/e81xl/wiki/home/" itemscope=""
itemtype="http://schema.stenci.la/Link">Reproducibility Project: Cancer Biology</a>,
we published a Registered Report (Blum et al., 2015), that described how we intended to
replicate selected experiments from the paper ‘Transcriptional amplification in tumor
cells with elevated c-Myc’ (Lin et al., 2012). Here we report the results. We found
overexpression of c-Myc increased total levels of RNA in P493-6 Burkitt’s lymphoma
cells; however, while the effect was in the same direction as the original study (Figure
3E; Lin et al., 2012), statistical significance and the size of the effect varied
between the original study and the two different lots of serum tested in this
replication. Digital gene expression analysis for a set of genes was also performed on
P493-6 cells before and after c-Myc overexpression. Transcripts from genes that were
active before c-Myc induction increased in expression following c-Myc overexpression,
similar to the original study (Figure 3F; Lin et al., 2012). Transcripts from genes that
were silent before c-Myc induction also increased in expression following c-Myc
overexpression, while the original study concluded elevated c-Myc had no effect on
silent genes (Figure 3F; Lin et al., 2012). Treating the data as paired, we found a
statistically significant increase in gene expression for both active and silent genes
upon c-Myc induction, with the change in gene expression greater for active genes
compared to silent genes. Finally, we report meta-analyses for each result.</p>
</section>
<h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="introduction">Introduction
</h2>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The <a
href="https://osf.io/e81xl/wiki/home/" itemscope=""
itemtype="http://schema.stenci.la/Link">Reproducibility Project: Cancer Biology</a>
(RP:CB) is a collaboration between the <a href="https://centerforopenscience.org/"
itemscope="" itemtype="http://schema.stenci.la/Link">Center for Open Science</a> and <a
href="https://www.scienceexchange.com/" itemscope=""
itemtype="http://schema.stenci.la/Link">Science Exchange</a> that seeks to address
concerns about reproducibility in scientific research by conducting replications of
selected experiments from a number of high-profile papers in the field of cancer biology
(<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib6">Errington et
al., 2014</a></cite>). For each of these papers a Registered Report detailing the
proposed experimental designs and protocols for the replications was peer reviewed and
published prior to data collection. The present paper is a Replication Study that reports
the results of the replication experiments detailed in the Registered Report (<cite
itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib3">Blum et al.,
2015</a></cite>) for a 2012 paper by Lin et al., and uses a number of approaches to
compare the outcomes of the original experiments and the replications.</p>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">In 2012, Lin et al. reported
results that the c-Myc transcription factor, a potent oncogene that is frequently
overexpressed in a large percentage of cancers, globally amplifies the expression of
actively transcribed genes, opposed to regulating specific target genes. Using the P493-6
cell line, a model for <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">MYC</em> activation in Burkitt’s lymphoma,
total levels of RNA per cell were reported to increase when c-Myc was highly expressed
compared to conditions where c-Myc expression was low. Additionally, active genes in cells
with low c-Myc levels were reported to increase in expression upon c-Myc induction, in
contrast to genes that were silent under low c-Myc conditions that did not change.</p>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The Registered Report for the
2012 paper by Lin et al. described the experiments to be replicated (Figure 1B and 3E–F),
and summarized the current evidence for these findings (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib3">Blum et al., 2015</a></cite>).
Since that publication there have been additional studies investigating the ability c-Myc
to influence the global gene expression output of cells. Similar to Lin et al. other
studies have reported c-Myc dependent amplification of cellular RNA (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib9">Hart et al., 2014</a></cite>;
<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib12">Hsu et al.,
2015</a></cite>; <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib19">Nie et al., 2012</a></cite>; <cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib27">Sabò et al., 2014</a></cite>),
although this observation was not reported in all biological systems (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib7">Fagnocchi et al.,
2016</a></cite>; <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib27">Sabò et al., 2014</a></cite>; <cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib35">Walz et al., 2014</a></cite>).
It has been suggested c-Myc regulates specific genes that indirectly lead to RNA
amplification (<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib27">Sabò et al., 2014</a></cite>; <cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib26">Sabò and Amati,
2014</a></cite>; <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib35">Walz et al., 2014</a></cite>). This has also been suggested of MYCN
(<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib5">Duffy et al.,
2015</a></cite>). The reported differences could be a result of the intrinsic
variation between cell lines in maintaining the transcriptome (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib32">Trakhtenberg et al.,
2016</a></cite>). Indeed, a recent study reported that distinct transcriptional
regulation can be accounted for by differences in promoter affinity under different c-Myc
expression levels (<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib18">Lorenzin et al., 2016</a></cite>).</p>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The outcome measures reported
in this Replication Study will be aggregated with those from the other Replication Studies
to create a dataset that will be examined to provide evidence about reproducibility of
cancer biology research, and to identify factors that influence reproducibility more
generally.</p>
<h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="results-and-discussion">
Results and discussion</h2>
<stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
data-programminglanguage="r">
<pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
slot="text"><code>##############################################################################
# The R code in this executable research article is from https://osf.io/tfd57/
# and associated files.
# Only code necessary to reproduce the article is included here.
# See the link above for more details
# Code edited only to remove extraneous outputs and readability
##############################################################################
# Load packages
library(cowplot)
library(ggplot2)
library(lsmeans)
library(reshape)
library(Rmisc)
# Load data
dat <- read.csv("Study_48_Figure_2_Supplemental_Tables.csv", header=T)
data2 <- read.csv("Study_48_Protocol_2_Data.csv", header=T)
comb.means <- read.csv("Study_48_Protocols_3_4_Combined_Means.csv", header=T)
meta <- read.csv("Study_48_Meta_Analysis.csv", header = T)
################################################################################
# Constants from https://osf.io/9wmq8/
zero <- c(4.394, 4.076, 4.286)
one <- c(4.114, 4.286, 3.712)
twentyfour <- c(5.868, 5.112, 5.424)
################################################################################
# Statistical analyses from Study_48_Protocol_2_Analysis.R https://osf.io/u7a5h/
#creates new column calculating RNA in 100uL
data2$RNA.100uL <- data2$Average.RNA.Concentration*100
##calculates RNA per cell
data2$RNA.per.cell <- data2$RNA.100uL/data2$Total.Cells.Harvested
#calculates RNA per 1000 cells
data2$value <- data2$RNA.per.cell*1000
########## Lot 1 Analysis ##########
####################################
#shapiro test for normality on lot 1 data by time
norm1 <- sapply(unique(data2$Time), function(x)
shapiro.test(data2[which(data2$Lot=="1" & data2$Time==x),]$value)) #all data normal
#time as character
data2$Time <- as.character(as.factor((data2$Time)))
#one-way ANOVA comparing total RNA (ng/1000 cells) in cells cultured 0 hr, 1 hr, and 24 hr from tet release.
fit1 <- aov(value ~ Time, data=data2[which(data2$Lot=="1"),])
invisible(ref1 <- lsmeans(fit1, "Time"))
c_list <- list(c1 = c(-1,0,1)) # contrast 0hr to 24 hr
contrast1 <- summary(contrast(ref1, c_list))
########## Lot 2 Analysis ###########
#####################################
#shapiro test for normality on lot 2 data by time
norm2 <- sapply(unique(data2$Time), function(x)
shapiro.test(data2[which(data2$Lot=="2" & data2$Time==x),]$value)) #all data normal
#one-way ANOVA comparing total RNA (ng/1000 cells) in cells cultured 0 hr, 1 hr, and 24 hr from tet release.
fit2 <- aov(value ~ Time, data=data2[which(data2$Lot=="2"),])
invisible(ref2 <- lsmeans(fit2, "Time"))
c_list2 <- list(c2 = c(-1,0,1)) # contrast 0hr to 24 hr
contrast2 <- summary(contrast(ref2, c_list2))
################################################################################
# Subsets on Lot/time/active/silent from https://osf.io/2yj6v/
active_0hr_l1 <- comb.means[which(comb.means$Status=="Active" & comb.means$Measure=="Mean_0HR_C1"),]$final.mean
active_1hr_l1 <- comb.means[which(comb.means$Status=="Active" & comb.means$Measure=="Mean_1HR_C1"),]$final.mean
active_24hr_l1 <- comb.means[which(comb.means$Status=="Active" & comb.means$Measure=="Mean_24HR_C1"),]$final.mean
active_0hr_l2 <- comb.means[which(comb.means$Status=="Active" & comb.means$Measure=="Mean_0HR_C2"),]$final.mean
active_1hr_l2 <- comb.means[which(comb.means$Status=="Active" & comb.means$Measure=="Mean_1HR_C2"),]$final.mean
active_24hr_l2 <- comb.means[which(comb.means$Status=="Active" & comb.means$Measure=="Mean_24HR_C2"),]$final.mean
silent_0hr_l1 <- comb.means[which(comb.means$Status=="Silent" & comb.means$Measure=="Mean_0HR_C1"),]$final.mean
silent_1hr_l1 <- comb.means[which(comb.means$Status=="Silent" & comb.means$Measure=="Mean_1HR_C1"),]$final.mean
silent_24hr_l1 <- comb.means[which(comb.means$Status=="Silent" & comb.means$Measure=="Mean_24HR_C1"),]$final.mean
silent_0hr_l2 <- comb.means[which(comb.means$Status=="Silent" & comb.means$Measure=="Mean_0HR_C2"),]$final.mean
silent_1hr_l2 <- comb.means[which(comb.means$Status=="Silent" & comb.means$Measure=="Mean_1HR_C2"),]$final.mean
silent_24hr_l2 <- comb.means[which(comb.means$Status=="Silent" & comb.means$Measure=="Mean_24HR_C2"),]$final.mean
</code></pre>
</stencila-code-chunk>
<h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
id="conditional-expression-of-c-myc-in-the-b-cell-line-p493-6">Conditional expression of
c-Myc in the B-cell line P493-6</h3>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">To test the effects of
increased levels of c-Myc on gene expression we used the same human P493-6 B cell line of
Burkitt’s lymphoma that contains a conditional tetracycline-repressive <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">MYC</em> transgene (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib23">Pajic et al., 2000</a></cite>;
<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib30">Schuhmacher et
al., 1999</a></cite>) as the original study. We performed Western blot analysis to
confirm c-Myc expression could be reduced to very low levels and then reactivated after
removal of tetracycline. This is comparable to what was reported in Figure 1B of <cite
itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib17">Lin et al.,
2012</a></cite> and described in Protocol 1 in the Registered Report (<cite
itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib3">Blum et al.,
2015</a></cite>). Since proliferation of P493-6 cells depend on c-Myc expression and
the presence of serum (<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib23">Pajic et al., 2000</a></cite>; <cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib30">Schuhmacher et al.,
1999</a></cite>), with serum reported to stimulate a majority of genes independent of
c-Myc (<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib28">Schlosser et al., 2005</a></cite>), we maintained these cells in
separate lots of serum to assess whether the results differed. For cells maintained in
both lots of serum, treatment with tetracycline resulted in a strong decrease in c-Myc
protein levels (<a href="#fig1a" itemscope=""
itemtype="http://schema.stenci.la/Link">Figure 1A</a>). After removal of tetracycline,
c-Myc levels increased over time approaching the levels observed in tetracycline-free
conditions.</p>
<figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig1a" title="Figure 1A">
<label data-itemprop="label">Figure 1A</label><img src="index.html.media/fig1a.png" alt=""
itemscope="" itemtype="http://schema.org/ImageObject">
<figcaption>
<h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
id="induction-of-c-myc-in-p493-6-cells-and-impact-on-total-rna-levels">Induction of
c-Myc in P493-6 cells and impact on total RNA levels.</h3>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">P493-6 cells were grown in
the presence of tetracycline (Tet) for 72 hr and switched into Tet-free growth medium
to induce c-Myc expression. Cells were cultured in two separate lots of serum. <strong
itemscope="" itemtype="http://schema.stenci.la/Strong">(A)</strong> Representative
Western blot using an anti-c-Myc antibody (top panels) or an anti-ß-Actin antibody
(bottom panel). Two exposures of the anti-c-Myc antibody are presented to facilitate
detection of c-Myc.</p>
</figcaption>
</figure>
<figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig1b" title="Figure 1B">
<label data-itemprop="label">Figure 1B</label>
<stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
data-programminglanguage="r">
<pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
slot="text"><code>#' @width 17
#' @height 10
#creates new column calculating RNA in 100uL
data2$RNA.100uL <- data2$Average.RNA.Concentration*100
##calculates RNA per cell
data2$RNA.per.cell <- data2$RNA.100uL/data2$Total.Cells.Harvested
#calculates RNA per 1000 cells
data2$value <- data2$RNA.per.cell*1000
#classifies time as character
data2$Time <- as.character(data2$Time)
########## subsets and summarizes Data ##########
#subsets data on lot 1
lot1dat <- data2[which(data2$Lot=="1"),]
#subsets data on lot 2
lot2dat <- data2[which(data2$Lot=="2"),]
#summarizes lot 1 data
lot1sum <- summarySE(data=lot1dat, measurevar = "value", groupvars = "Time")
#summarizes lot 2 data
lot2sum <- summarySE(data=lot2dat, measurevar = "value", groupvars = "Time")
########## Generates bar plot for lot 1 ##########
##################################################
plot.lot1 <- ggplot(lot1sum, aes(x=Time, y=lot1sum$value, fill=Time)) +
geom_bar(stat="identity", width=.8, color = "black") +
geom_errorbar(aes(x=Time, ymin=value-se, ymax=value+se),
width=.20)+
coord_cartesian(ylim=c(0,2.5)) +
scale_fill_manual(values = c("grey30", "grey30","grey30")) +
ylab(expression(paste("Total RNA (ng) \n per 1,000 cells"))) +
scale_y_continuous(expand = c(0,0),
limits = c(0,6),
breaks = c(0, .5, 1.0, 1.5, 2.0, 2.5),
labels = c("0.0", "0.5", "1.0", "1.5", "2.0", "2.5")) +
scale_x_discrete(labels = c("0hr", "1hr", "24hr")) +
theme(plot.margin = unit(c(1,1,1,2), "lines"),
axis.ticks.length = unit(0.25, "cm"),
axis.text.x = element_text(size=15, color = "black"),
axis.text.y = element_text(size = 15, color = "black"),
axis.title.y = element_text(size = 20),
axis.title.x = element_blank(),
panel.background = element_blank(),
axis.line.y = element_line(),
legend.position = "none",
axis.line.x = element_line())
########## Generates bar plot for lot 1 ##########
##################################################
plot.lot2 <- ggplot(lot2sum, aes(x=Time, y=lot2sum$value, fill=Time)) +
geom_bar(stat="identity", width=.8, color = "black") +
geom_errorbar(aes(x=Time, ymin=value-se, ymax=value+se),
width=.20)+
coord_cartesian(ylim=c(0,2.5)) +
scale_fill_manual(values = c("grey30","grey30","grey30")) +
ylab(expression("Total RNA (ng) \n per 1,000 cells")) +
scale_y_continuous(expand = c(0,0),
limits = c(0,6),
breaks = c(0, .5, 1.0, 1.5, 2.0, 2.5),
labels = c("0.0", "0.5", "1.0", "1.5", "2.0", "2.5")) +
scale_x_discrete(labels = c("0hr", "1hr", "24hr")) +
theme(plot.margin = unit(c(1,1,1,2), "lines"),
axis.text.x = element_text(size=15, color = "black"),
axis.text.y = element_text(size = 15, color = "black"),
axis.title.y = element_text(size = 20),
axis.title.x = element_blank(),
panel.background = element_blank(),
axis.line.y = element_line(),
legend.position = "none",
axis.line.x = element_line())
Figure_1B <- plot_grid(plot.lot1, plot.lot2, labels = c("Lot 1", "Lot 2"), label_size = 20, hjust = .01)
Figure_1B</code></pre>
<figure slot="outputs"><img src="index.html.media/0" alt="" itemscope=""
itemtype="http://schema.org/ImageObject"></figure>
</stencila-code-chunk>
<figcaption>
<h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
id="induction-of-c-myc-in-p493-6-cells-and-impact-on-total-rna-levels-1">Induction of
c-Myc in P493-6 cells and impact on total RNA levels.</h3>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">P493-6 cells were grown in
the presence of tetracycline (Tet) for 72 hr and switched into Tet-free growth medium
to induce c-Myc expression. Cells were cultured in two separate lots of serum. <strong
itemscope="" itemtype="http://schema.stenci.la/Strong">(B)</strong> Quantification
of total RNA levels (ng of total RNA per 1,000 cells) for cells at 0, 1, and 24 hr
after release from Tet. Means reported and error bars represent s.e.m. from
<stencila-code-expression programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">length(subset(data2, Lot==1 & Time==0)$value)</code><output
slot="output"><span data-itemtype="http://schema.org/Number">3</span></output>
</stencila-code-expression> independent biological repeats. For serum lot one, one-way
ANOVA on total RNA levels of all groups; <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">F</em>(<stencila-code-expression
programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">summary(fit1)[[1]][["Df"]][1]</code><output slot="output"><span
data-itemtype="http://schema.org/Number">2</span></output>
</stencila-code-expression>, <stencila-code-expression programming-language="r"
itemscope="" itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">summary(fit1)[[1]][["Df"]][2]</code><output slot="output"><span
data-itemtype="http://schema.org/Number">6</span></output>
</stencila-code-expression>) = <stencila-code-expression programming-language="r"
itemscope="" itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">round(summary(fit1)[[1]][["F value"]][1],digits=2)</code><output
slot="output"><span data-itemtype="http://schema.org/Number">1.25</span></output>
</stencila-code-expression>, <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">p</em> = <stencila-code-expression
programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">sub('^(-)?0[.]','\\1.', round(summary(fit1)[[1]][["Pr(>F)"]][1],
digits = 3))</code><output slot="output">.353</output></stencila-code-expression>.
Planned contrast between 0 hr and 24 hr; <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">t</em>(<stencila-code-expression
programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">contrast1$df</code><output slot="output"><span
data-itemtype="http://schema.org/Number">6</span></output>
</stencila-code-expression>) = <stencila-code-expression programming-language="r"
itemscope="" itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">round(contrast1$t.ratio,2)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">1.02</span></output>
</stencila-code-expression>, <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">p</em> = <stencila-code-expression
programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">sub('^(-)?0[.]','\\1.',round(contrast1$p.value,3))</code><output
slot="output">.347</output></stencila-code-expression> with <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">a priori</em> alpha level = .05. For
serum lot two, one-way ANOVA on total RNA levels of all groups; <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">F</em>(<stencila-code-expression
programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">summary(fit2)[[1]][["Df"]][1]</code><output slot="output"><span
data-itemtype="http://schema.org/Number">2</span></output>
</stencila-code-expression>, <stencila-code-expression programming-language="r"
itemscope="" itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">summary(fit2)[[1]][["Df"]][2]</code><output slot="output"><span
data-itemtype="http://schema.org/Number">6</span></output>
</stencila-code-expression>) = <stencila-code-expression programming-language="r"
itemscope="" itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">round(summary(fit2)[[1]][["F value"]][1],digits=2)</code><output
slot="output"><span data-itemtype="http://schema.org/Number">21.87</span></output>
</stencila-code-expression>, <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">p</em> = <stencila-code-expression
programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">sub('^(-)?0[.]','\\1.', round(summary(fit2)[[1]][["Pr(>F)"]][1],
digits = 5))</code><output slot="output">.00176</output>
</stencila-code-expression>. Planned contrast between 0 hr and 24 hr; <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">t</em>(<stencila-code-expression
programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">contrast2$df</code><output slot="output"><span
data-itemtype="http://schema.org/Number">6</span></output>
</stencila-code-expression>) = <stencila-code-expression programming-language="r"
itemscope="" itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">round(contrast2$t.ratio,2)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">5.03</span></output>
</stencila-code-expression>, <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">p</em> = <stencila-code-expression
programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">sub('^(-)?0[.]','\\1.',round(contrast2$p.value,4))</code><output
slot="output">.0024</output></stencila-code-expression> with <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">a priori</em> alpha level = .05.
Additional details for this experiment can be found at <a
href="https://osf.io/tfd57/." itemscope=""
itemtype="http://schema.stenci.la/Link">https://osf.io/tfd57/</a>.</p>
</figcaption>
</figure>
<h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
id="total-rna-levels-following-c-myc-overexpression">Total RNA levels following c-Myc
overexpression</h3>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We sought to independently
replicate whether increased levels of c-Myc resulted in increased absolute levels of RNA.
This experiment is similar to what was reported in Figure 3E of <cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib17">Lin et al., 2012</a></cite> and
used the same extraction method for total RNA quantification, which was described in
Protocol 2 in the Registered Report (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib3">Blum et al., 2015</a></cite>).
Total RNA was isolated from P493-6 cells 0, 1, and 24 hr after tetracycline release and
the amount of RNA per 1,000 cells was quantified (<a href="#fig1" itemscope=""
itemtype="http://schema.stenci.la/Link">Figure 1B</a>). We found that under conditions
where c-Myc expression was low (0 hr), there was a mean of <stencila-code-expression
programming-language="r" itemscope="" itemtype="http://schema.stenci.la/CodeExpression">
<code class="r" slot="text">round(mean(subset(data2, Lot==1 &
Time==0)$value),2)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">1.55</span></output>
</stencila-code-expression> ng total RNA per 1,000 cells (ng/1 k cells) [n =
<stencila-code-expression programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">length(subset(data2, Lot==1 & Time==0)$value)</code><output
slot="output"><span data-itemtype="http://schema.org/Number">3</span></output>
</stencila-code-expression>, <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">SD</em> = <stencila-code-expression
programming-language="r" itemscope="" itemtype="http://schema.stenci.la/CodeExpression">
<code class="r" slot="text">formatC(sd(subset(data2, Lot==1 &
Time==0)$value),2,format="f")</code><output slot="output">0.20</output>
</stencila-code-expression>], which increased to <stencila-code-expression
programming-language="r" itemscope="" itemtype="http://schema.stenci.la/CodeExpression">
<code class="r" slot="text">round(mean(subset(data2, Lot==1 &
Time==24)$value),2)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">1.77</span></output>
</stencila-code-expression> ng/1 k cells [n = <stencila-code-expression
programming-language="r" itemscope="" itemtype="http://schema.stenci.la/CodeExpression">
<code class="r" slot="text">length(subset(data2, Lot==1 &
Time==24)$value)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">3</span></output>
</stencila-code-expression>, <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">SD</em> = <stencila-code-expression
programming-language="r" itemscope="" itemtype="http://schema.stenci.la/CodeExpression">
<code class="r" slot="text">round(sd(subset(data2, Lot==1 &
Time==24)$value),2)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">0.31</span></output>
</stencila-code-expression>] when c-Myc expression was high (24 hr), a
<stencila-code-expression programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">round(mean(subset(data2, Lot==1 & Time==24)$value)/mean(subset(data2,
Lot==1 & Time==0)$value),2)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">1.14</span></output>
</stencila-code-expression> times increase, for serum lot one, which was not statistically
significant (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">t</em>(
<stencila-code-expression programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">contrast1$df</code><output slot="output"><span
data-itemtype="http://schema.org/Number">6</span></output>
</stencila-code-expression>) = <stencila-code-expression programming-language="r"
itemscope="" itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">round(contrast1$t.ratio,2)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">1.02</span></output>
</stencila-code-expression>, <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">p</em>=<stencila-code-expression
programming-language="r" itemscope="" itemtype="http://schema.stenci.la/CodeExpression">
<code class="r"
slot="text">sub('^(-)?0[.]','\\1.',round(contrast1$p.value,3))</code><output
slot="output">.347</output></stencila-code-expression>). Serum lot two changed from a
mean of <stencila-code-expression programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">round(mean(subset(data2, Lot==2 & Time==0)$value),2)</code><output
slot="output"><span data-itemtype="http://schema.org/Number">1.57</span></output>
</stencila-code-expression> ng/1 k cells [n = <stencila-code-expression
programming-language="r" itemscope="" itemtype="http://schema.stenci.la/CodeExpression">
<code class="r" slot="text">length(subset(data2, Lot==2 &
Time==0)$value)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">3</span></output>
</stencila-code-expression>, <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">SD</em> = <stencila-code-expression
programming-language="r" itemscope="" itemtype="http://schema.stenci.la/CodeExpression">
<code class="r" slot="text">round(sd(subset(data2, Lot==2 &
Time==0)$value),2)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">0.21</span></output>
</stencila-code-expression>] at 0 hr to <stencila-code-expression programming-language="r"
itemscope="" itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">round(mean(subset(data2, Lot==2 & Time==24)$value),2)</code><output
slot="output"><span data-itemtype="http://schema.org/Number">2.25</span></output>
</stencila-code-expression> ng/1 k cells [n = <stencila-code-expression
programming-language="r" itemscope="" itemtype="http://schema.stenci.la/CodeExpression">
<code class="r" slot="text">length(subset(data2, Lot==2 &
Time==24)$value)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">3</span></output>
</stencila-code-expression>, <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">SD</em> = <stencila-code-expression
programming-language="r" itemscope="" itemtype="http://schema.stenci.la/CodeExpression">
<code class="r" slot="text">round(sd(subset(data2, Lot==2 &
Time==24)$value),2)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">0.19</span></output>
</stencila-code-expression>] at 24 hr, a <stencila-code-expression
programming-language="r" itemscope="" itemtype="http://schema.stenci.la/CodeExpression">
<code class="r" slot="text">round(mean(subset(data2, Lot==2 &
Time==24)$value)/mean(subset(data2, Lot==2 & Time==0)$value),2)</code><output
slot="output"><span data-itemtype="http://schema.org/Number">1.43</span></output>
</stencila-code-expression> times increase, which was statistically significant (<em
itemscope="" itemtype="http://schema.stenci.la/Emphasis">t</em>(
<stencila-code-expression programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">contrast2$df</code><output slot="output"><span
data-itemtype="http://schema.org/Number">6</span></output>
</stencila-code-expression> = <stencila-code-expression programming-language="r"
itemscope="" itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">round(contrast2$t.ratio,2)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">5.03</span></output>
</stencila-code-expression>, <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">p</em>=<stencila-code-expression
programming-language="r" itemscope="" itemtype="http://schema.stenci.la/CodeExpression">
<code class="r"
slot="text">sub('^(-)?0[.]','\\1.',round(contrast2$p.value,4))</code><output
slot="output">.0024</output></stencila-code-expression>). This compares to the
original study, which reported a mean of <stencila-code-expression
programming-language="r" itemscope="" itemtype="http://schema.stenci.la/CodeExpression">
<code class="r" slot="text">round(mean(zero),2)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">4.25</span></output>
</stencila-code-expression> ng/1 k cells at 0 hr, which increased to
<stencila-code-expression programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">round(mean(twentyfour),2)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">5.47</span></output>
</stencila-code-expression> ng/1 k cells at 24 hr, a <stencila-code-expression
programming-language="r" itemscope="" itemtype="http://schema.stenci.la/CodeExpression">
<code class="r" slot="text">round(mean(twentyfour)/mean(zero),2)</code><output
slot="output"><span data-itemtype="http://schema.org/Number">1.29</span></output>
</stencila-code-expression> times increase in total RNA levels. In both studies there was
a minor decrease at 1 hr after tetracycline release when c-Myc levels begin to become
detectable. Total RNA per 1,000 cells at 0 hr were much lower in this replication attempt
than those reported in the original study, although changes in total RNA levels were in
the same direction following c-Myc expression. Similarly, another independent study that
measured total RNA from P493-6 cells reported a different level at 0 hr (~3 ng/1 k cells),
while also reporting increased levels following c-Myc expression (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib27">Sabò et al., 2014</a></cite>).
There are multiple possible explanations for these differences, such as variation in RNA
expression during cell culture passage (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib11">Hiorns et al.,
2004</a></cite>), low yield of the RNA isolation procedure (e.g. incomplete
homogenization), or the high variance associated with manual cell counts using a
hemacytometer (<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib2">Biggs and Macmillan, 1948</a></cite>; <cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib20">Nielson et al.,
1991</a></cite>). To summarize, for this experiment we found results that were in the
same direction as the original study and not statistically significant for serum lot one,
while statistically significant for serum lot two.</p>
<h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
id="digital-gene-expression-following-c-myc-overexpression">Digital gene expression
following c-Myc overexpression</h3>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">To test whether c-Myc
expression amplifies the existing gene expression program, digital gene expression
analysis using the NanoString nCounter platform was performed on a set of genes from
multiple functional categories. This experiment is similar to what was reported in Figure
3F and Table S1 of <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib17">Lin et al., 2012</a></cite> and described in Protocols 3–4 in the
Registered Report (<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib3">Blum et al., 2015</a></cite>). We quantified mRNA levels/cell of 1369
genes, of which 1212 were the same genes as the 1338 genes interrogated in the original
study. We used the same criteria as the original study to classify a gene as silent
(expression was less than 0.5 transcript/cell at time 0 hr) or active (more than one
transcript/cell at time 0 hr). In cells with low levels of c-Myc (0 hr) there were
<stencila-code-expression programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">length(active_0hr_l1)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">708</span></output>
</stencila-code-expression> active genes with a median expression of
<stencila-code-expression programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">formatC(median(active_0hr_l1),2,format="f")</code><output
slot="output">4.70</output></stencila-code-expression>, and <stencila-code-expression
programming-language="r" itemscope="" itemtype="http://schema.stenci.la/CodeExpression">
<code class="r" slot="text">length(silent_0hr_l1)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">580</span></output>
</stencila-code-expression> silent genes with a median expression of
<stencila-code-expression programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">round(median(silent_0hr_l1),3)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">0.032</span></output>
</stencila-code-expression>, for serum lot one. For active genes,
<stencila-code-expression programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">round(length(which((active_1hr_l1-active_0hr_l1)>0))/length(active_0hr_l1)*100)</code><output
slot="output"><span data-itemtype="http://schema.org/Number">75</span></output>
</stencila-code-expression>% of the genes increased from 0 hr to 1 hr,
<stencila-code-expression programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">round(length(which((active_24hr_l1-active_0hr_l1)>0))/length(active_0hr_l1)*100)</code><output
slot="output"><span data-itemtype="http://schema.org/Number">68</span></output>
</stencila-code-expression>% increased from 0 hr to 24 hr, and <stencila-code-expression
programming-language="r" itemscope="" itemtype="http://schema.stenci.la/CodeExpression">
<code class="r"
slot="text">round(length(which((active_24hr_l1-active_1hr_l1)>0))/length(active_0hr_l1)*100)</code><output
slot="output"><span data-itemtype="http://schema.org/Number">59</span></output>
</stencila-code-expression>% increased from 1 hr to 24 hr upon c-Myc induction. This
corresponds to a <stencila-code-expression programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">round(median(active_1hr_l1)/median(active_0hr_l1),2)</code><output
slot="output"><span data-itemtype="http://schema.org/Number">1.11</span></output>
</stencila-code-expression>, <stencila-code-expression programming-language="r"
itemscope="" itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">formatC(median(active_24hr_l1)/median(active_0hr_l1),2,format="f")</code><output
slot="output">1.50</output></stencila-code-expression>, and <stencila-code-expression
programming-language="r" itemscope="" itemtype="http://schema.stenci.la/CodeExpression">
<code class="r"
slot="text">round(median(active_24hr_l1)/median(active_1hr_l1),2)</code><output
slot="output"><span data-itemtype="http://schema.org/Number">1.36</span></output>
</stencila-code-expression> times increase in median expression, respectively (<a
href="#fig2" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2</a>, <a
href="#fig2s1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2—figure
supplement 1</a>). For silent genes, <stencila-code-expression programming-language="r"
itemscope="" itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">round(length(which((silent_1hr_l1-silent_0hr_l1)>0))/length(silent_0hr_l1)*100)</code><output
slot="output"><span data-itemtype="http://schema.org/Number">74</span></output>
</stencila-code-expression>% of the genes increased from 0 hr to 1 hr,
<stencila-code-expression programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">round(length(which((silent_24hr_l1-silent_0hr_l1)>0))/length(silent_0hr_l1)*100)</code><output
slot="output"><span data-itemtype="http://schema.org/Number">66</span></output>
</stencila-code-expression>% increased from 0 hr to 24 hr, and <stencila-code-expression
programming-language="r" itemscope="" itemtype="http://schema.stenci.la/CodeExpression">
<code class="r"
slot="text">round(length(which((silent_24hr_l1-silent_1hr_l1)>0))/length(silent_0hr_l1)*100)</code><output
slot="output"><span data-itemtype="http://schema.org/Number">50</span></output>
</stencila-code-expression>% increased from 1 hr to 24 hr, corresponding to a
<stencila-code-expression programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">round(median(silent_1hr_l1)/median(silent_0hr_l1),2)</code><output
slot="output"><span data-itemtype="http://schema.org/Number">1.19</span></output>
</stencila-code-expression> and <stencila-code-expression programming-language="r"
itemscope="" itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">round(median(silent_24hr_l1)/median(silent_0hr_l1),2)</code><output
slot="output"><span data-itemtype="http://schema.org/Number">1.13</span></output>
</stencila-code-expression> times increase, and a <stencila-code-expression
programming-language="r" itemscope="" itemtype="http://schema.stenci.la/CodeExpression">
<code class="r"
slot="text">abs(round((median(silent_24hr_l1)-median(silent_1hr_l1))/(median(silent_24hr_l1)),2))</code><output
slot="output"><span data-itemtype="http://schema.org/Number">0.05</span></output>
</stencila-code-expression> times decrease in median expression, respectively (<a
href="#fig2" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2</a>, <a
href="#fig2s1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2—figure
supplement 1</a>). Serum lot two gave similar results, although there were variations in
the number of genes identified as silent or active as well as the degree of increase among
the conditions (<a href="#fig2" itemscope=""
itemtype="http://schema.stenci.la/Link">Figure 2</a>, <a href="#fig2s1" itemscope=""
itemtype="http://schema.stenci.la/Link">Figure 2—figure supplement 1</a>). This compares
to the original study that identified 755 active genes with a median expression of 7.06,
and 514 silent genes with a median expression of 0.00 (more than half the silent genes did
not have a reported expression value). Active genes in the original study, increased 91%
from 0 hr to 1 hr, 96% from 0 hr to 24 hr, and 87% from 1 hr to 24 hr upon c-Myc
induction, corresponding to a 1.23, 2.45, and 1.99 times increase in median expression,
respectively. Silent genes in the original study, increased 23% from 0 hr to 1 hr, 29%
from 0 hr to 24 hr, and 30% from 1 hr to 24 hr, with the median expression unchanged among
conditions. In addition, we further examined the extent of overlap of active and silent
genes between the original study and this replication attempt. Of the 1212 genes that were
interrogated in both studies, 88.8% (603/679) of the active genes we identified in serum
lot one were also active in the original study (90.1% (612/679) for serum lot two). For
silent genes, 96.4% (456/473) of the genes we identified as silent in serum lot one were
common with the silent genes identified in the original study (95.8% (453/473) for serum
lot two).</p>
<figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2" title="Figure 2">
<label data-itemprop="label">Figure 2</label>
<stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
data-programminglanguage="r">
<pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
slot="text"><code>#' @width 18
#' @height 24
comb.means <- comb.means[which(comb.means$Status!="NA"),] #removes NA status genes
comb.means$lstat <- interaction(comb.means$Time, comb.means$Status) #creates interaction variable between lot and status called 'lstat'
comb.means$Time <- as.character(comb.means$Time) #creates a column for Time
active <- comb.means[which(comb.means$Status=="Active"),] #subsets all data on Active gene status
silent <- comb.means[which(comb.means$Status=="Silent"),] #subsets all data on Silent gene status
#create summary data for graph lot 1
active.sum1 <- summarySE(active[which(active$Lot=="C1"),], measurevar="final.mean", groupvars="Time")
silent.sum1 <- summarySE(silent[which(silent$Lot=="C1"),], measurevar="final.mean", groupvars="Time")
#create summary data for graph lot 2
active.sum2 <- summarySE(active[which(active$Lot=="C2"),], measurevar="final.mean", groupvars="Time")
silent.sum2 <- summarySE(silent[which(silent$Lot=="C2"),], measurevar="final.mean", groupvars="Time")
########## Plots Active Genes/ Lot 1 LOG SCALE ##########
#########################################################
log_activeplot1 <- ggplot(active[which(active$Lot=="C1"),], aes(x=Time, y = final.mean)) +
stat_boxplot(geom ='errorbar', width=0.5) +
geom_boxplot(aes(fill=Time), outlier.shape = 16, outlier.size = 1.5, outlier.colour = "black", colour = "black") +
scale_fill_manual(values=c("red", "red", "red"))+
ylab("Transcripts/Cell") +
ggtitle("Active genes")+
xlab(element_blank()) +
scale_x_discrete(labels=c("0hr", "1hr", "24hr")) +
scale_y_continuous(expand = c(.01,.01),
trans = "log2",
limits = c(2^-10,2^11),
breaks = c( 2^-9,2^-5,2^-1,2^3,2^7,2^11),
labels = c(bquote("2"^"-9"),bquote("2"^"-5"),
bquote("2"^"-1"),bquote("2"^"3"),
bquote("2"^"7"),bquote("2"^"11"))) +
theme_bw()+
theme(legend.position = "none",
axis.ticks.length = unit(0.2, "cm"),
plot.title = element_text(color = "black", size = 15, hjust = .5),
plot.margin = unit(c(1,1,1,1),"cm"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "black", size=1.8),
axis.title = element_text(colour = "black", size = 15),
axis.text.x = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5)),
axis.text.y = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5), hjust = 0),
axis.title.x = element_blank(),
axis.title.y = element_text(colour="black",size=15,margin = margin(r=10, unit="pt")))
########## Plots Active Genes/ Lot 1 LINEAR SCALE ##########
############################################################
linear_activeplot1 <- ggplot(active[which(active$Lot=="C1" & active$final.mean<=100),], aes(x=Time, y = final.mean)) +
stat_boxplot(geom ='errorbar', width=0.5 ) +
geom_boxplot(aes(fill=Time), outlier.shape = 16, outlier.size = 1.5, outlier.colour = "black", colour = "black") +
scale_fill_manual(values=c("red", "red", "red"))+
ggtitle("Active genes")+
ylab("Transcripts/Cell") +
xlab(element_blank()) +
scale_x_discrete(labels=c("0hr", "1hr", "24hr")) +
scale_y_continuous(expand = c(0,0),
limits = c(-5, 105),
breaks = c(0,20,40,60,80,100)) +
theme_bw()+
theme(legend.position = "none",
axis.ticks.length = unit(0.2, "cm"),
plot.title = element_text(color = "black", size = 15, hjust = .5),
plot.margin = unit(c(1,1,1,1),"cm"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "black", size=1.8),
axis.title = element_text(colour = "black", size = 15),
axis.text.x = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5)),
axis.text.y = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5), hjust = 0),
axis.title.x = element_blank(),
axis.title.y = element_text(colour="black",size=15,margin = margin(r=10, unit="pt")))
########## Plots Active Genes/ Lot 2 LOG SCALE ##########
#########################################################
log_activeplot2 <- ggplot(active[which(active$Lot=="C2"),], aes(x=Time, y = final.mean)) +
stat_boxplot(geom ='errorbar', width=0.5 ) +
geom_boxplot(aes(fill=Time), outlier.shape = 16, outlier.size = 1.5, outlier.colour = "black", colour = "black") +
scale_fill_manual(values=c("red", "red", "red"))+
ylab("Transcripts/Cell") +
ggtitle("Active genes")+
xlab(element_blank()) +
scale_x_discrete(labels=c("0hr", "1hr", "24hr")) +
scale_y_continuous(expand = c(.01,.01),
trans = "log2",
limits = c(2^-10,2^11),
breaks = c( 2^-9,2^-5,2^-1,2^3,2^7,2^11),
labels = c(bquote("2"^"-9"),bquote("2"^"-5"),
bquote("2"^"-1"),bquote("2"^"3"),
bquote("2"^"7"),bquote("2"^"11"))) +
theme_bw()+
theme(legend.position = "none",
axis.ticks.length = unit(0.2, "cm"),
plot.title = element_text(color = "black", size = 15, hjust = .5),
plot.margin = unit(c(1,1,1,1),"cm"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "black", size=1.8),
axis.title = element_text(colour = "black", size = 15),
axis.text.x = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5)),
axis.text.y = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5), hjust = 0),
axis.title.x = element_blank(),
axis.title.y = element_text(colour="black",size=15,margin = margin(r=10, unit="pt")))
########## Plots Active Genes/ Lot 2 LINEAR SCALE ##########
############################################################
linear_activeplot2 <- ggplot(active[which(active$Lot=="C2" & active$final.mean<=100),], aes(x=Time, y = final.mean)) +
stat_boxplot(geom ='errorbar', width=0.5 ) +
geom_boxplot(aes(fill=Time), outlier.shape = 16, outlier.size = 1.5, outlier.colour = "black", colour = "black") +
scale_fill_manual(values=c("red", "red", "red"))+
ggtitle("Active genes")+
ylab("Transcripts/Cell") +
xlab(element_blank()) +
scale_x_discrete(labels=c("0hr", "1hr", "24hr")) +
scale_y_continuous(expand = c(0,0),
limits = c(-5, 105),
breaks = c(0,20,40,60,80,100)) +
theme_bw()+
theme(legend.position = "none",
axis.ticks.length = unit(0.2, "cm"),
plot.title = element_text(color = "black", size = 15, hjust = .5),
plot.margin = unit(c(1,1,1,1),"cm"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "black", size=1.8),
axis.title = element_text(colour = "black", size = 15),
axis.text.x = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5)),
axis.text.y = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5), hjust = 0),
axis.title.x = element_blank(),
axis.title.y = element_text(colour="black",size=15,margin = margin(r=10, unit="pt")))
########## Plots Silent Genes/ Lot 1 LOG SCALE ##########
#########################################################
log_silentplot1 <- ggplot(silent[which(silent$Lot=="C1"),], aes(x=Time, y = final.mean)) +
stat_boxplot(geom ='errorbar', width=0.5 ) +
geom_boxplot(aes(fill=Time), outlier.shape = 16, outlier.size = 1.5, outlier.colour = "black", colour = "black") +
scale_fill_manual(values=c("red", "red", "red"))+
ylab("Transcripts/Cell") +
ggtitle("Silent genes")+
xlab(element_blank()) +
scale_x_discrete(labels=c("0hr", "1hr", "24hr")) +
scale_y_continuous(expand = c(.01,.01),
trans = "log2",
limits = c(2^-10,2^11),
breaks = c( 2^-9,2^-5,2^-1,2^3,2^7,2^11),
labels = c(bquote("2"^"-9"),bquote("2"^"-5"),
bquote("2"^"-1"),bquote("2"^"3"),
bquote("2"^"7"),bquote("2"^"11"))) +
theme_bw()+
theme(legend.position = "none",
axis.ticks.length = unit(0.2, "cm"),
plot.title = element_text(color = "black", size = 15, hjust = .5),
plot.margin = unit(c(1,1,1,1),"cm"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "black", size=1.8),
axis.title = element_text(colour = "black", size = 15),
axis.text.x = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5)),
axis.text.y = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5), hjust = 0),
axis.title.x = element_blank(),
axis.title.y = element_text(colour="black",size=15,margin = margin(r=10, unit="pt")))
########## Plots Silent Genes/ Lot 1 LINEAR SCALE ##########
############################################################
linear_silentplot1 <- ggplot(silent[which(silent$Lot=="C1" & silent$final.mean<=100),], aes(x=Time, y = final.mean)) +
stat_boxplot(geom ='errorbar', width=0.5 ) +
geom_boxplot(aes(fill=Time), outlier.shape = 16, outlier.size = 1.5, outlier.colour = "black", colour = "black") +
scale_fill_manual(values=c("red", "red", "red"))+
ggtitle("Silent genes")+
ylab("Transcripts/Cell") +
xlab(element_blank()) +
scale_x_discrete(labels=c("0hr", "1hr", "24hr")) +
scale_y_continuous(expand = c(0,0),
limits = c(-5, 105),
breaks = c(0,20,40,60,80,100)) +
theme_bw()+
theme(legend.position = "none",
axis.ticks.length = unit(0.2, "cm"),
plot.title = element_text(color = "black", size = 15, hjust = .5),
plot.margin = unit(c(1,1,1,1),"cm"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "black", size=1.8),
axis.title = element_text(colour = "black", size = 15),
axis.text.x = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5)),
axis.text.y = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5), hjust = 0),
axis.title.x = element_blank(),
axis.title.y = element_text(colour="black",size=15,margin = margin(r=10, unit="pt")))
########## Plots Silent Genes/ Lot 2 LOG SCALE ##########
#########################################################
#plots active cohort 1
log_silentplot2 <- ggplot(silent[which(silent$Lot=="C2"),], aes(x=Time, y = final.mean)) +
stat_boxplot(geom ='errorbar', width=0.5 ) +
geom_boxplot(aes(fill=Time), outlier.shape = 16, outlier.size = 1.5, outlier.colour = "black", colour = "black") +
scale_fill_manual(values=c("red", "red", "red"))+
ylab("Transcripts/Cell") +
ggtitle("Silent genes")+
xlab(element_blank()) +
scale_x_discrete(labels=c("0hr", "1hr", "24hr")) +
scale_y_continuous(expand = c(.01,.01),
trans = "log2",
limits = c(2^-10,2^11),
breaks = c( 2^-9,2^-5,2^-1,2^3,2^7,2^11),
labels = c(bquote("2"^"-9"),bquote("2"^"-5"),
bquote("2"^"-1"),bquote("2"^"3"),
bquote("2"^"7"),bquote("2"^"11"))) +
theme_bw()+
theme(legend.position = "none",
axis.ticks.length = unit(0.2, "cm"),
plot.title = element_text(color = "black", size = 15, hjust = .5),
plot.margin = unit(c(1,1,1,1),"cm"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "black", size=1.8),
axis.title = element_text(colour = "black", size = 15),
axis.text.x = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5)),
axis.text.y = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5), hjust = 0),
axis.title.x = element_blank(),
axis.title.y = element_text(colour="black",size=15,margin = margin(r=10, unit="pt")))
########## Plots Silent Genes/ Lot 2 LINEAR SCALE ##########
############################################################
linear_silentplot2 <- ggplot(silent[which(silent$Lot=="C2" & silent$final.mean<=100),], aes(x=Time, y = final.mean)) +
stat_boxplot(geom ='errorbar', width=0.5 ) +
geom_boxplot(aes(fill=Time), outlier.shape = 16, outlier.size = 1.5, outlier.colour = "black", colour = "black") +
scale_fill_manual(values=c("red", "red", "red")) +
ggtitle("Silent genes")+
ylab("Transcripts/Cell") +
xlab(element_blank()) +
scale_x_discrete(labels=c("0hr", "1hr", "24hr")) +
scale_y_continuous(expand = c(0,0),
limits = c(-5, 105),
breaks = c(0,20,40,60,80,100)) +
theme_bw()+
theme(legend.position = "none",
axis.ticks.length = unit(0.2, "cm"),
plot.title = element_text(color = "black", size = 15, hjust = .5),
plot.margin = unit(c(1,1,1,1.88),"cm"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "black", size=1.8),
axis.title = element_text(colour = "black", size = 15),
axis.text.x = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5)),
axis.text.y = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5), hjust = 0),
axis.title.x = element_blank(),
axis.title.y = element_text(colour="black",size=15,margin = margin(r=10, unit="pt")))
###########################################################################################
###########################################################################################
#plots all comparisons for Lot 1 silent
lot1_silent <- subset(comb.means, comb.means$Lot=="C1" & comb.means$Status=="Silent")
time0 <- subset(lot1_silent, lot1_silent$Time=="0HR")
time1 <- subset(lot1_silent, lot1_silent$Time=="1HR")
time24 <- subset(lot1_silent, lot1_silent$Time=="24HR")
ratio <- c(((log2(time1$final.mean))-(log2(time0$final.mean))),
((log2(time24$final.mean))-(log2(time0$final.mean))),
((log2(time24$final.mean))-(log2(time1$final.mean))))
lot1_silentdat <- as.data.frame(cbind(as.character(lot1_silent[,1]),as.numeric(as.character(ratio))))
lot1_silentdat$V1 <- as.factor(lot1_silentdat$V1)
lot1_silentdat$V3 <- c(rep("diff1",nrow(lot1_silent)/3),rep("diff2",nrow(lot1_silent)/3),rep("diff3",nrow(lot1_silent)/3))
lot1_silentdat$V3 <- as.factor(lot1_silentdat$V3)
lot1_silentdat$V2 <- as.numeric(as.character(lot1_silentdat$V2))
colnames(lot1_silentdat) <- c("Gene","ratio","comparison")
plot_lot1_silent <- ggplot(lot1_silentdat, aes(x=comparison, y = ratio)) +
stat_boxplot(geom ='errorbar', width=0.5) +
geom_boxplot(aes(fill=comparison), outlier.shape = 16, outlier.size = 1.5, outlier.colour = "black", colour = "black") +
scale_fill_manual(values=c("red", "red", "red"))+
ylab("log2 (ratio)") +
xlab(element_blank()) +
scale_x_discrete(labels=c("1 hr vs. \n 0 hr", "24 hr vs. \n 0 hr", "24hr vs. \n 1 hr")) +
scale_y_continuous(expand = c(0,0),
limits = c(-4.5,6.5),
breaks = c(-4, -2, 0, 2, 4, 6),
labels = c("-4","-2","0","2","4","6")) +
geom_hline(yintercept = 0) +
ggtitle("Silent genes")+
theme_bw()+
theme(legend.position = "none",
axis.ticks.length = unit(0.2, "cm"),
plot.title = element_text(color = "black", size = 15, hjust = .5),
plot.margin = unit(c(1,1,1,1),"cm"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "black", size=1.8),
axis.title = element_text(colour = "black", size = 15),
axis.text.x = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5)),
axis.text.y = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5)),
axis.title.x = element_blank(),
axis.title.y = element_text(colour="black",size=15,margin = margin(r=10, unit="pt")))
####################################################
####################################################
#plots all comparisons for Lot 2 silent
lot2_silent <- subset(comb.means, comb.means$Lot=="C2" & comb.means$Status=="Silent")
time0 <- subset(lot2_silent, lot2_silent$Time=="0HR")
time1 <- subset(lot2_silent, lot2_silent$Time=="1HR")
time24 <- subset(lot2_silent, lot2_silent$Time=="24HR")
ratio <- c(((log2(time1$final.mean))-(log2(time0$final.mean))),
((log2(time24$final.mean))-(log2(time0$final.mean))),
((log2(time24$final.mean))-(log2(time1$final.mean))))
lot2_silentdat <- as.data.frame(cbind(as.character(lot2_silent[,1]),as.numeric(as.character(ratio))))
lot2_silentdat$V1 <- as.factor(lot2_silentdat$V1)
lot2_silentdat$V3 <- c(rep("diff1",nrow(lot2_silent)/3),rep("diff2",nrow(lot2_silent)/3),rep("diff3",nrow(lot2_silent)/3))
lot2_silentdat$V3 <- as.factor(lot2_silentdat$V3)
lot2_silentdat$V2 <- as.numeric(as.character(lot2_silentdat$V2))
colnames(lot2_silentdat) <- c("Gene","ratio","comparison")
plot_lot2_silent <- ggplot(lot2_silentdat, aes(x=comparison, y = ratio)) +
stat_boxplot(geom ='errorbar', width=0.5) +
geom_boxplot(aes(fill=comparison), outlier.shape = 16, outlier.size = 1.5, outlier.colour = "black", colour = "black") +
scale_fill_manual(values=c("red", "red", "red"))+
ylab("log2 (ratio)") +
xlab(element_blank()) +
scale_x_discrete(labels=c("1 hr vs. \n 0 hr", "24 hr vs. \n 0 hr", "24hr vs. \n 1 hr")) +
scale_y_continuous(expand = c(0,0),
limits = c(-4.5,6.5),
breaks = c(-4, -2, 0, 2, 4, 6),
labels = c("-4","-2","0","2","4","6")) +
geom_hline(yintercept = 0) +
ggtitle("Silent genes")+
theme_bw()+
theme(legend.position = "none",
axis.ticks.length = unit(0.2, "cm"),
plot.title = element_text(color = "black", size = 15, hjust = .5),
plot.margin = unit(c(1,1,1,1),"cm"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "black", size=1.8),
axis.title = element_text(colour = "black", size = 15),
axis.text.x = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5)),
axis.text.y = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5)),
axis.title.x = element_blank(),
axis.title.y = element_text(colour="black",size=15,margin = margin(r=10, unit="pt")))
####################################################
####################################################
#plots all comparisons for Lot 1 active
lot1_active <- subset(comb.means, comb.means$Lot=="C1" & comb.means$Status=="Active")
time0 <- subset(lot1_active, lot1_active$Time=="0HR")
time1 <- subset(lot1_active, lot1_active$Time=="1HR")
time24 <- subset(lot1_active, lot1_active$Time=="24HR")
ratio <- c(((log2(time1$final.mean))-(log2(time0$final.mean))),
((log2(time24$final.mean))-(log2(time0$final.mean))),
((log2(time24$final.mean))-(log2(time1$final.mean))))
lot1_activedat <- as.data.frame(cbind(as.character(lot1_active[,1]),as.numeric(as.character(ratio))))
lot1_activedat$V1 <- as.factor(lot1_activedat$V1)
lot1_activedat$V3 <- c(rep("diff1",nrow(lot1_active)/3),rep("diff2",nrow(lot1_active)/3),rep("diff3",nrow(lot1_active)/3))
lot1_activedat$V3 <- as.factor(lot1_activedat$V3)
lot1_activedat$V2 <- as.numeric(as.character(lot1_activedat$V2))
colnames(lot1_activedat) <- c("Gene","ratio","comparison")
plot_lot1_active <- ggplot(lot1_activedat, aes(x=comparison, y = ratio)) +
stat_boxplot(geom ='errorbar', width=0.5) +
geom_boxplot(aes(fill=comparison), outlier.shape = 16, outlier.size = 1.5, outlier.colour = "black", colour = "black") +
scale_fill_manual(values=c("red", "red", "red"))+
ylab("log2 (ratio)") +
xlab(element_blank()) +
scale_x_discrete(labels=c("1 hr vs. \n 0 hr", "24 hr vs. \n 0 hr", "24hr vs. \n 1 hr")) +
scale_y_continuous(expand = c(0,0),
limits = c(-4.5,6.5),
breaks = c(-4, -2, 0, 2, 4, 6),
labels = c("-4","-2","0","2","4","6")) +
geom_hline(yintercept = 0) +
ggtitle("Active genes")+
theme_bw()+
theme(legend.position = "none",
axis.ticks.length = unit(0.2, "cm"),
plot.title = element_text(color = "black", size = 15, hjust = .5),
plot.margin = unit(c(1,1,1,1),"cm"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "black", size=1.8),
axis.title = element_text(colour = "black", size = 15),
axis.text.x = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5)),
axis.text.y = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5)),
axis.title.x = element_blank(),
axis.title.y = element_text(colour="black",size=15,margin = margin(r=10, unit="pt")))
####################################################
####################################################
#plots all comparisons for Lot 2 active
lot2_active <- subset(comb.means, comb.means$Lot=="C2" & comb.means$Status=="Active")
time0 <- subset(lot2_active, lot2_active$Time=="0HR")
time1 <- subset(lot2_active, lot2_active$Time=="1HR")
time24 <- subset(lot2_active, lot2_active$Time=="24HR")
ratio <- c(((log2(time1$final.mean))-(log2(time0$final.mean))),
((log2(time24$final.mean))-(log2(time0$final.mean))),
((log2(time24$final.mean))-(log2(time1$final.mean))))
lot2_activedat <- as.data.frame(cbind(as.character(lot2_active[,1]),as.numeric(as.character(ratio))))
lot2_activedat$V1 <- as.factor(lot2_activedat$V1)
lot2_activedat$V3 <- c(rep("diff1",nrow(lot2_active)/3),rep("diff2",nrow(lot2_active)/3),rep("diff3",nrow(lot2_active)/3))
lot2_activedat$V3 <- as.factor(lot2_activedat$V3)
lot2_activedat$V2 <- as.numeric(as.character(lot2_activedat$V2))
colnames(lot2_activedat) <- c("Gene","ratio","comparison")
plot_lot2_active <- ggplot(lot2_activedat, aes(x=comparison, y = ratio)) +
stat_boxplot(geom ='errorbar', width=0.5) +
geom_boxplot(aes(fill=comparison), outlier.shape = 16, outlier.size = 1.5, outlier.colour = "black", colour = "black") +
scale_fill_manual(values=c("red", "red", "red"))+
ylab("log2 (ratio)") +
xlab(element_blank()) +
scale_x_discrete(labels=c("1 hr vs. \n 0 hr", "24 hr vs. \n 0 hr", "24hr vs. \n 1 hr")) +
scale_y_continuous(expand = c(0,0),
limits = c(-4.5,6.5),
breaks = c(-4, -2, 0, 2, 4, 6),
labels = c("-4","-2","0","2","4","6")) +
geom_hline(yintercept = 0) +
ggtitle("Active genes")+
theme_bw()+
theme(legend.position = "none",
axis.ticks.length = unit(0.2, "cm"),
plot.title = element_text(color = "black", size = 15, hjust = .5),
plot.margin = unit(c(1,1,1,1),"cm"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "black", size=1.8),
axis.title = element_text(colour = "black", size = 15),
axis.text.x = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5)),
axis.text.y = element_text(colour="black",size=15, margin=margin(.5,.5,.5,.5)),
axis.title.x = element_blank(),
axis.title.y = element_text(colour="black",size=15, margin = margin(r=10, unit="pt")))
###########################################################################################
###########################################################################################
#combines lot 1 linear scale plots
linear_lot1 <- plot_grid(linear_activeplot1, linear_silentplot1, ncol = 2, align = "h")
#combines lot 2 linear scale plots
linear_lot2 <- plot_grid(linear_activeplot2, linear_silentplot2, ncol = 2, align = "h")
#combines lot 1 log scale plots
log_lot1 <- plot_grid(log_activeplot1, log_silentplot1, ncol = 2, align = "h", labels = c("A","B"))
#combines lot 2 log scale plots
log_lot2 <- plot_grid(log_activeplot2, log_silentplot2, ncol = 2, align = "h", labels = c("E","F"))
#combines lot 1 ratio plots
ratio_lot1 <- plot_grid(plot_lot1_active, plot_lot1_silent, ncol = 2, align = "h", labels = c("C","D"))
#combines lot 2 ratio plots
ratio_lot2 <- plot_grid(plot_lot2_active, plot_lot2_silent, ncol = 2, align = "h", labels = c("G","H"))
#combines Linear Scale Plots
Linear <- plot_grid(linear_lot1,linear_lot2, nrow = 2, align = "h", labels = c("Lot 1","Lot 2"), label_size = 20)
figure_2 <- plot_grid(Linear,ncol = 1,rel_heights = c(0.1,1))
figure_2</code></pre>
<figure slot="outputs"><img src="index.html.media/1" alt="" itemscope=""
itemtype="http://schema.org/ImageObject"></figure>
</stencila-code-chunk>
<figcaption>
<h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
id="digital-gene-expression-analysis">Digital gene expression analysis.</h3>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">P493-6 cells grown in the
presence of tetracycline (Tet) for 72 hr for repression of the conditional p<em
itemscope="" itemtype="http://schema.stenci.la/Emphasis">myc</em>-tet construct,
were switched into Tet-free growth medium to induce c-Myc expression. Cells were
cultured in two separate lots of serum. Transcripts/cell estimates from NanoString
nCounter gene expression assays (<stencila-code-expression programming-language="r"
itemscope="" itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">prettyNum(length(unique(comb.means$Accession)),
big.mark=",")</code><output slot="output">1,308</output>
</stencila-code-expression> genes assay) for active (left) and silent (right) genes at
0, 1, and 24 hr after release from Tet. Active genes expressed greater than 1
transcript/cell. Silent genes expressed less than 0.5 transcript/cell. Box and whisker
plots with median represented as the line through the box and whiskers representing
values within 1.5 IQR of the first and third quartile. Cells grown in serum lot one:
active genes = <stencila-code-expression programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">length(active_0hr_l1)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">708</span></output>
</stencila-code-expression>, silent genes = <stencila-code-expression
programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">length(silent_0hr_l1)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">580</span></output>
</stencila-code-expression>. Cells grown in serum lot two: active genes =
<stencila-code-expression programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">length(active_0hr_l2)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">719</span></output>
</stencila-code-expression>, silent genes = <stencila-code-expression
programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">length(silent_0hr_l2)</code><output slot="output"><span
data-itemtype="http://schema.org/Number">573</span></output>
</stencila-code-expression>. Confirmatory analysis is reported in <a href="#table1"
itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a> and exploratory
statistical analysis is reported in <a href="#table2" itemscope=""
itemtype="http://schema.stenci.la/Link">Table 2</a> and <a href="#table3"
itemscope="" itemtype="http://schema.stenci.la/Link">Table 3</a>. Additional details
for this experiment can be found at <a href="https://osf.io/fn2y4/." itemscope=""
itemtype="http://schema.stenci.la/Link">https://osf.io/fn2y4/</a>.List of Reporter
CodeSets and gene expression values.</p>
</figcaption>
</figure>
<figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2s1"
title="Figure 2—figure supplement 1"><label data-itemprop="label">Figure 2—figure
supplement 1</label><img src="index.html.media/fig2-figsupp1.jpg" alt="" itemscope=""
itemtype="http://schema.org/ImageObject">
<figcaption>
<h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
id="logarithmic-expression-of-genes">Logarithmic expression of genes.</h3>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">This is the same experiment
as in <a href="#fig2" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
2</a>. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">A–B,
E–F</strong>) Gene expression data plotted on a log<sub itemscope=""
itemtype="http://schema.stenci.la/Subscript">2</sub> transformed scale for active
(<strong itemscope="" itemtype="http://schema.stenci.la/Strong">A, E</strong>) and
silent (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">B, F</strong>)
genes at 0, 1, and 24 hr after release from Tet for both lots of serum. (<strong
itemscope="" itemtype="http://schema.stenci.la/Strong">C–D, G–H</strong>) Box and
whisker plots showing gene expression changes (log<sub itemscope=""
itemtype="http://schema.stenci.la/Subscript">2</sub> ratio) between the indicated
times for active (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">C,
G</strong>) and silent (<strong itemscope=""
itemtype="http://schema.stenci.la/Strong">D, H</strong>) genes. Median represented
as the line through the box and whiskers representing values within 1.5 IQR of the
first and third quartile. Additional details for this experiment can be found at <a
href="https://osf.io/fn2y4/." itemscope=""
itemtype="http://schema.stenci.la/Link">https://osf.io/fn2y4/</a>.</p>
</figcaption>
</figure>
<figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2s2"
title="Figure 2—figure supplement 2"><label data-itemprop="label">Figure 2—figure
supplement 2</label><img src="index.html.media/fig2-figsupp2.jpg" alt="" itemscope=""
itemtype="http://schema.org/ImageObject">
<figcaption>
<h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
id="comparison-of-gene-expression-data-as-continuous">Comparison of gene expression
data as continuous.</h3>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">This is the same experiment
as in <a href="#fig2" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
2</a>. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">A–C,
E–G</strong>) Scatter plots of log<sub itemscope=""
itemtype="http://schema.stenci.la/Subscript">2</sub> transformed gene expression
data for all genes analyzed at the indicated times on the y and x axes for both lots
of serum. Active genes are blue, silent genes are red, and genes that are neither
active or silent (expression was more than 0.5 transcript/cell and less than one
transcript/cell at time 0 hr) are white. (<strong itemscope=""
itemtype="http://schema.stenci.la/Strong">D, H</strong>) Box and whisker plots
showing gene expression changes (log<sub itemscope=""
itemtype="http://schema.stenci.la/Subscript">2</sub> ratio) between the indicated
times for all genes analyzed for both lots of serum. Median represented as the line
through the box and whiskers representing values within 1.5 IQR of the first and third
quartile. Additional details for this experiment can be found at <a
href="https://osf.io/fn2y4/." itemscope=""
itemtype="http://schema.stenci.la/Link">https://osf.io/fn2y4/</a>.</p>
</figcaption>
</figure>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">To test whether active genes,
as well as silent genes, increased expression during c-Myc induction we performed the
confirmatory analysis as outlined in the Registered Report (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib3">Blum et al., 2015</a></cite>).
This analysis differed from what was reported in the original study by analyzing the data
as paired instead of unpaired. As suggested during peer review of the Registered Report,
this is because expression of the same gene, analyzed across different conditions, is not
independent (<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib3">Blum et al., 2015</a></cite>). We performed a Wilcoxon signed-rank test
on active genes comparing expression at 0 hr to 1 hr, 0 hr to 24 hr, and 1 hr to 24 hr,
which were statistically significant for cells grown in both lots of serum (<a
href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a>). The
same comparisons were performed on silent genes, which were also statistically
significant, with the exception of the silent gene comparison of 1 hr to 24 hr for serum
lot one. Considering this was not the test reported in the original study, we conducted
these paired analyses on the original data to provide a direct comparison. For both active
and silent genes c-Myc induction resulted in statistically significant increases in
expression, with the exception of the silent gene comparison from 0 hr to 1 hr (<a
href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a>). This
is in contrast to the results of the unpaired tests that were reported in the original
study where active genes were reported to have a statistically significant increase in
expression and silent genes were reported as not statistically significant for all
comparisons. We conducted an exploratory unpaired analysis on the replication data for
comparison, which resulted in statistically significant differences among the active gene
comparisons as well as half of the silent gene comparisons (<a href="#table2" itemscope=""
itemtype="http://schema.stenci.la/Link">Table 2</a>).</p>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph"><strong itemscope=""
itemtype="http://schema.stenci.la/Strong">Table 1. Confirmatory statistical
tests.</strong></p>
<stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
data-programminglanguage="r">
<pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
slot="text"><code>table1_active <- data.frame(dat[c(1:5)]) #subsets on active genes
table1_active <- melt(table1_active,id.vars = c("Study","Label")) #melts on Study and Label Variables
table1_active$interaction <- interaction(table1_active$Study,table1_active$variable)
table1_active <- reshape(table1_active, idvar="interaction", timevar = "Label",direction="wide")
table1_active <- table1_active[,c(2:4,7,10)]
table1_silent <- data.frame(dat[c(1:2,6:8)]) #subsets on silent genes
table1_silent <- melt(table1_silent,id.vars = c("Study","Label")) #melts on Study and Label Variables
table1_silent$interaction <- interaction(table1_silent$Study,table1_silent$variable)
table1_silent <- reshape(table1_silent, idvar="interaction", timevar = "Label",direction="wide")
table1_silent <- table1_silent[,c(2:4,7,10)]
table1 <- rbind(table1_active,table1_silent) #combines silent and active into one data frame
#changes column names
colnames(table1) <- c("Study","Comparison","Z value","P value","Sample size (n)")
#creates comparison column/ renames comparisons
table1$Comparison <- rep(c(rep("0hr vs 1hr",3),rep("0hr vs 24hr",3),rep("1hr vs 24hr",3)),2)
rownames(table1) <- NULL #deletes row names
table1$Study <- as.character(table1$Study) #Makes 'study' variable as character
table1[c(2:3,5:6,8:9,11:12,14:15,17:18),2] <- c("","")
#label active and silent categories
table1$Genes <- c(c("Active",rep(c(""),8)),c("Silent",rep(c(""),8)))
table1 <- table1[,c(6,2,1,3:5)]
table1</code></pre>
<figure slot="outputs">
<div itemscope="" itemtype="http://schema.stenci.la/Datatable">
<table>
<thead>
<tr>
<th>Genes</th>
<th>Comparison</th>
<th>Study</th>
<th>Z value</th>
<th>P value</th>
<th>Sample size (n)</th>
</tr>
</thead>
<tbody>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">Active</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0hr vs 1hr
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 1
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">14.86</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1.36e-55
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">708</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 2
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">11.83</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">2.83e-34
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">719</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">Lin et al.,
2012</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">21.17</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1.05e-130
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">755</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0hr vs 24hr
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 1
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">9.922</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">3.67e-24
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">708</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 2
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">12.77</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">3.77e-40
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">719</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">Lin et al.,
2012</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">23.26</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">3.33e-184
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">755</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1hr vs 24hr
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 1
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">4.742</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0.00000192
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">708</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 2
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">10.04</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">9.91e-25
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">719</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">Lin et al.,
2012</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">23.26</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">3.33e-184
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">755</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">Silent</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0hr vs 1hr
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 1
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">12.61</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">7.11e-40
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">580</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 2
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">7.05</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">9.29e-13
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">572</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">Lin et al.,
2012</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">-1.998</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0.0457</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">274</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0hr vs 24hr
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 1
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">8.328</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">2.22e-17
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">579</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 2
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">8.156</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1.03e-16
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">572</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">Lin et al.,
2012</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">3.179</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0.0014</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">276</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1hr vs 24hr
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 1
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0.6853</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0.493</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">580</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 2
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">4.436</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0.00000835
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">573</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">Lin et al.,
2012</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">5.806</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">4.11e-9</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">236</td>
</tr>
</tbody>
</table>
</div>
</figure>
</stencila-code-chunk>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">These confirmatory statistical
tests relate to the data presented in Figure 2. Wilcoxon signed-rank test, which treat the
data as paired, were conducted for the original study (Lin et al., 2012) and this
replication attempt (RP:CB). Uncorrected <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">p</em> values are reported with an <em
itemscope="" itemtype="http://schema.stenci.la/Emphasis">a priori</em> significance
threshold of <stencila-code-expression programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">sub('^(-)?0[.]','\\1.',round(0.05/3, digits = 4))</code><output
slot="output">.0167</output></stencila-code-expression>. Sample sizes reported are
based on the sample size used in the tests. Additional details for this experiment can be
found at <a href="https://osf.io/fn2y4/" itemscope=""
itemtype="http://schema.stenci.la/Link">https://osf.io/fn2y4/</a>.</p>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph"><strong itemscope=""
itemtype="http://schema.stenci.la/Strong">Table 2. Exploratory statistical
tests.</strong></p>
<stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
data-programminglanguage="r">
<pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
slot="text"><code>#Subsets on only Wilcoxon Rank-Sum Tests
WilcoxonRS <- dat[c(1,9:12)]
table2_active <- melt(WilcoxonRS,id.vars = c("Study","Label.1")) #melts on Study and Label Variables
table2_active$interaction <- interaction(table2_active$Study,table2_active$variable)
table2_active <- reshape(table2_active, idvar="interaction", timevar = "Label.1",direction="wide")
table2_active <- table2_active[,c(2:4,7,10)]
table2_silent <- data.frame(dat[c(1,9,13:15)]) #subsets on silent genes
table2_silent <- melt(table2_silent,id.vars = c("Study","Label.1")) #melts on Study and Label Variables
table2_silent$interaction <- interaction(table2_silent$Study,table2_silent$variable)
table2_silent <- reshape(table2_silent, idvar="interaction", timevar = "Label.1",direction="wide")
table2_silent <- table2_silent[,c(2:4,7,10)]
table2 <- rbind(table2_active,table2_silent) #combines silent and active into one data frame
#changes column names
colnames(table2) <- c("Study","Comparison","W value","P value","Sample size (n)")
#creates comparison column/ renames comparisons
table2$Comparison <- rep(c(rep("0hr vs 1hr",3),rep("0hr vs 24hr",3),rep("1hr vs 24hr",3)),2)
rownames(table2) <- NULL #deletes row names
table2$Study <- as.character(table2$Study) #Makes 'study' variable as character
table2[c(2:3,5:6,8:9,11:12,14:15,17:18),2] <- c("","")
#label active and silent categories
table2$Genes <- c(c("Active",rep(c(""),8)),c("Silent",rep(c(""),8)))
table2 <- table2[,c(6,2,1,3:5)]
table2</code></pre>
<figure slot="outputs">
<div itemscope="" itemtype="http://schema.stenci.la/Datatable">
<table>
<thead>
<tr>
<th>Genes</th>
<th>Comparison</th>
<th>Study</th>
<th>W value</th>
<th>P value</th>
<th>Sample size (n)</th>
</tr>
</thead>
<tbody>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">Active</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0hr vs 1hr
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 1
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">270378</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0.0103</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1416</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 2
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">274696</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0.0395</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1438</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">Lin et al.,
2012</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">318799</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0.0001</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1510</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0hr vs 24hr
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 1
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">300774</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">7.16e-11
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1416</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 2
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">324564</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">4.74e-17
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1438</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">Lin et al.,
2012</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">400999</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1.16e-42
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1510</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1hr vs 24hr
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 1
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">281679</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0.0001</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1416</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 2
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">308954</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1.45e-10
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1438</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">Lin et al.,
2012</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">372714</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">4.11e-25
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1510</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">Silent</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0hr vs 1hr
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 1
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">187682</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0.0006</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1160</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 2
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">174695</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0.0602</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1146</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">Lin et al.,
2012</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">127104</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0.236</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1028</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0hr vs 24hr
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 1
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">185804</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0.002</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1160</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 2
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">184470</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0.0003</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1146</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">Lin et al.,
2012</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">132082</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0.997</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1028</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1hr vs 24hr
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 1
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">166122</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0.716</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1160</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 2
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">173608</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0.0918</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1146</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">Lin et al.,
2012</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">136443</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0.295</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1028</td>
</tr>
</tbody>
</table>
</div>
</figure>
</stencila-code-chunk>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">These exploratory statistical
tests relate to the data presented in Figure 2. Wilcoxon rank sum tests were conducted for
the original study (Lin et al., 2012) and this replication attempt (RP:CB) on the
difference in expression of active genes during c-Myc induction (e.g. from 0 hr to 24 hr)
compared to the difference in expression of silent genes over that same period (e.g. from
0 hr to 24 hr). Uncorrected <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">p</em> values are reported. Sample sizes
reported are based on number of active and silent genes used in the tests. Additional
details for this experiment can be found at <a href="https://osf.io/fn2y4/" itemscope=""
itemtype="http://schema.stenci.la/Link">https://osf.io/fn2y4/</a>.</p>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Importantly, though, the
question of whether the change in expression among active genes is different than silent
genes has not been tested. This would require a separate test on their difference (<cite
itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib8">Gelman and Stern,
2006</a></cite>; <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib21">Nieuwenhuis et al., 2011</a></cite>). To test whether active genes
increased in expression during c-Myc induction more than silent genes, we performed an
exploratory analysis on the difference in expression of active genes during c-Myc
induction (e.g. from 0 hr to 24 hr) compared to the difference in expression of silent
genes over that same period (e.g. from 0 hr to 24 hr). For both the original and
replication data, there was a statistically significant increase in expression of active
genes compared to silent genes (<a href="#table3" itemscope=""
itemtype="http://schema.stenci.la/Link">Table 3</a>). This suggests that active genes
and silent genes do not have similar rates of expression upon c-Myc induction. To
summarize, for this experiment we found results that were in the same direction as the
original study and suggest that while both active and silent genes increased in expression
upon c-Myc induction, the rate of increase was different.</p>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph"><strong itemscope=""
itemtype="http://schema.stenci.la/Strong">Table 3. Exploratory statistical
tests.</strong></p>
<stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
data-programminglanguage="r">
<pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
slot="text"><code>diff <- dat[c(1,16:19)]
# only needed if first column consists of numbers
diff[[1]] <- as.character(diff[[1]])
diff[2,3:5] <- as.character(diff[2,3:5])
table3 <- melt(diff,id.vars = c("Study","Label.2")) #melts on Study and Label Variables
table3$interaction <- interaction(table3$Study,table3$variable)
table3 <- reshape(table3, idvar="interaction", timevar = "Label.2",direction="wide")
table3 <- table3[,c(2:4,7,10)]
#changes column names
colnames(table3) <- c("Study","Comparison","W value","P value","Sample size (n)")
#creates comparison column/ renames comparisons
table3$Comparison <- rep(c(rep("0hr vs 1hr",3),rep("0hr vs 24hr",3),rep("1hr vs 24hr",3)))
rownames(table3) <- NULL #deletes row names
table3$Study <- as.character(table3$Study) #Makes 'study' variable as character
table3[c(2:3,5:6,8:9),2] <- c("","")
table3 <- table3[,c(2,1,3:5)]
table3</code></pre>
<figure slot="outputs">
<div itemscope="" itemtype="http://schema.stenci.la/Datatable">
<table>
<thead>
<tr>
<th>Comparison</th>
<th>Study</th>
<th>W value</th>
<th>P value</th>
<th>Sample size (n)</th>
</tr>
</thead>
<tbody>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0hr vs 1hr
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 1
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">303897</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">7.78e-50
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1288</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 2
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">278646</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1.1e-27</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1292</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">Lin et al.,
2012</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">349351</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1.27e-130
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1269</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0hr vs 24hr
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 1
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">272441</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">5.18e-24
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1288</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 2
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">292865</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">7.4e-39</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1292</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">Lin et al.,
2012</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">368182</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1.14e-163
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1269</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1hr vs 24hr
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 1
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">235077</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">7.45e-06
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1288</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">RP:CB Lot 2
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">272028</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">3.73e-23
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1292</td>
</tr>
<tr>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"></td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">Lin et al.,
2012</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">332069</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">5.72e-104
</td>
<td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1269</td>
</tr>
</tbody>
</table>
</div>
</figure>
</stencila-code-chunk>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">These exploratory statistical
tests relate to the data presented in Figure 2. Wilcoxon rank sum tests, which treat the
data as unpaired, were conducted for the original study (Lin et al., 2012) and this
replication attempt (RP:CB). Uncorrected <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">p</em> values are reported. Sample sizes
reported are based on treating genes as unpaired between conditions. Additional details
for this experiment can be found at <a href="https://osf.io/fn2y4/" itemscope=""
itemtype="http://schema.stenci.la/Link">https://osf.io/fn2y4/</a>.</p>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The original study and this
replication attempt used the same criteria to characterize a gene as silent or active, but
there are many negative consequences of dichotomizing continuous variables, such as
information loss, especially with a small gene set (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib1">Altman and Royston,
2006</a></cite>; <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib4">Cohen, 1983</a></cite>). Papers published after the original study took
an unbiased view by collecting comprehensive RNA-sequencing data to assess if the
transcriptional effects of c-Myc were direct or indirect, concluding c-Myc activates and
represses transcription of discrete gene sets, which in turn leads to induced RNA
amplification (<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib27">Sabò et al., 2014</a></cite>; <cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib35">Walz et al., 2014</a></cite>).
Furthermore, Sabò and colleagues also used NanoString technology to quantify a subset of
the differentially expressed genes identified by RNA-seq and observed similar results that
revealed upward shifts in gene expression upon c-Myc induction (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib27">Sabò et al., 2014</a></cite>).
However, instead of dichotomizing genes as active or silent, gene expression data was
presented as continuous. Similarly, we presented the digital gene expression data
generated during this replication attempt as continuous, which illustrates a general
pattern of overall increased gene expression following c-Myc induction (<a href="#fig2s2"
itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2—figure supplement 2</a>).
Importantly, though, these results are limited to the 1369 genes interrogated in this
study and may or may not reflect how the entire transcriptome of P493-6 cells respond to
c-Myc induction.</p>
<h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
id="meta-analyses-of-original-and-replicated-effects">Meta-analyses of original and
replicated effects</h3>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We performed a meta-analysis
using a random-effects model to combine each of the effects described above as
pre-specified in the confirmatory analysis plan (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib3">Blum et al., 2015</a></cite>).
To provide a standardized measure of the effect, a common effect size was calculated for
each effect from the original and replication studies. Cohen’s <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">d</em> is the standardized difference
between two means using the pooled sample standard deviation. The effect size <em
itemscope="" itemtype="http://schema.stenci.la/Emphasis">r</em> is a standardized
measure of the strength and direction of the association between two variables, in this
case time during c-Myc induction and gene expression. The estimate of the effect size of
one study, as well as the associated uncertainty (i.e. confidence interval), compared to
the effect size of the other study provides another approach to compare the original and
replication results (<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib6">Errington et al., 2014</a></cite>; <cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib33">Valentine et al.,
2011</a></cite>). Importantly, the width of the confidence interval for each study is
a reflection of not only the confidence level (e.g. 95%), but also variability of the
sample (e.g. <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">SD</em>) and
sample size.</p>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The comparison of total RNA
levels at low levels of c-Myc (0 hr) compared to high levels of c-Myc (24 hr) resulted in
<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">d</em> = 4.19, 95% CI [0.94,
7.37] for the data reported in Figure 3E of the original study (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib17">Lin et al., 2012</a></cite>).
This compares to <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">d</em> = 0.83, 95% CI [−0.91, 2.48] for
serum lot one and <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">d</em> = 4.11, 95% CI [0.90, 7.23] for serum
lot two reported in this study. A meta-analysis (<a href="#fig3" itemscope=""
itemtype="http://schema.stenci.la/Link">Figure 3A</a>) of these effects resulted in <em
itemscope="" itemtype="http://schema.stenci.la/Emphasis">d</em> = 2.52, 95% CI [0.01,
5.03], which was statistically significant (<em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">p</em>=0.0488). The original and replication
results are consistent when considering the direction of the effect, which suggests c-Myc
induction increases total RNA levels in P493-6 Burkitt’s lymphoma cells. Noticeably, there
was substantial within-study variation observed in this replication attempt, due the
different serum lots tested. The point estimate of serum lot one was not within the
confidence intervals of the original study and serum lot two, and vice versa; however the
point estimate of the original study and serum lot two were within the confidence
intervals of each other.</p>
<figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig3" title="Figure 3">
<label data-itemprop="label">Figure 3</label>
<stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
data-programminglanguage="r">
<pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
slot="text"><code>#' @width 40
#' @height 50
#re-orders the data frame
meta <- meta[order(meta$study),]
meta <- meta[order(meta$comparison),]
#subsets data to plot results
d1 <- subset(meta[1:4,]) #Active 0v1
d2 <- subset(meta[5:8,]) #Active 0v24
d3 <- subset(meta[9:12,]) #Active 1v24
d4 <- subset(meta[13:16,]) #Silent 0v1
d5 <- subset(meta[17:20,]) #Silent 0v24
d6 <- subset(meta[21:24,]) #Silent 1v24
d7 <- subset(meta[25:28,]) #protocol 2 oh vs. 24hr
#Plots for protocol 3 analyses:
########################### Active 0hr vs. 1hr #####################################
#re-order the levels for plotting
desired_order <- c("Meta-Analysis","RP:CB Lot 2","RP:CB Lot 1", "Lin et al., 2012" )
#re-orders data for plotting
d1$study <- factor(as.character(d1$study), levels=desired_order)
d1 <- d1[order(d1$study),]
a1 <- ggplot(data=d1,aes(x=estimate,y=d1$study)) +
geom_point(size=5, colour="black", fill = "black", shape = c(22,21,21,23)) +
geom_errorbarh(aes(xmin=CI.lower,xmax=CI.upper, height = .1)) +
geom_vline(xintercept=0,linetype="dashed") +
coord_cartesian(xlim=c(-.2,1)) +
scale_x_continuous(breaks= c(-.2,0,.2,.4,.6,.8,1)) +
ylab(d1$comparison[1])+
xlab(NULL)+
scale_y_discrete(labels = c(paste(as.character(d1$study[1])),
gsub("x", "\n",paste(as.character(d1$study[2]),
paste("x (n =", as.character(d1$N[2]),")"))),
gsub("x", "\n",paste(as.character(d1$study[3]),
paste("x (n =", as.character(d1$N[3]),")"))),
gsub("x", "\n",paste(as.character(d1$study[4]),
paste("x (n =", as.character(d1$N[4]),")"))))) +
theme(panel.background = element_blank(),
axis.ticks.x=element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(size=15),
legend.position="none",
axis.line.x = element_blank(),
axis.title.y = element_text(size=15,margin=margin(0,30,0,0)),
axis.line.y = element_line(),
plot.margin = unit(c(.5 ,2,.075,.5), "in"))
a1 <- ggdraw(a1)+
draw_text(paste("r","[","L.CI", ",", "U.CI", "]"), x = .84, y = 0.91, fontface="bold")+
draw_text(paste(formatC(d1$estimate[[4]], digits = 2, format = "f"),
"[",
formatC(d1$CI.lower[[4]],digits = 2, format = "f"),
",",
formatC(d1$CI.upper[[4]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.785, size = 12, hjust=0)+
draw_text(paste(formatC(d1$estimate[[3]], digits = 2, format = "f"),
"[",
formatC(d1$CI.lower[[3]],digits = 2, format = "f"),
",",
formatC(d1$CI.upper[[3]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.575, size = 12, hjust=0)+
draw_text(paste(formatC(d1$estimate[[2]],digits = 2, format = "f"),
"[",
formatC(d1$CI.lower[[2]],digits = 2, format = "f"),
",",
formatC(d1$CI.upper[[2]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.3575, size = 12, hjust=0)+
draw_text(paste(formatC(d1$estimate[[1]],digits = 2, format = "f"),
"[",
formatC(d1$CI.lower[[1]],digits = 2, format = "f"),
",",
formatC(d1$CI.upper[[1]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.154, size = 12, hjust=0)
########################## Active 0hr vs. 24hr #####################################
#re-order the levels for plotting
d2$study <- factor(as.character(d2$study), levels=desired_order)
d2 <- d2[order(d2$study),]
a2 <- ggplot(data=d2,aes(x=estimate,y=d2$study)) +
geom_point(size=5, colour="black", fill = "black", shape = c(22,21,21,23)) +
geom_errorbarh(aes(xmin=CI.lower,xmax=CI.upper, height = .1)) +
geom_vline(xintercept=0,linetype="dashed") +
coord_cartesian(xlim=c(-.2,1)) +
scale_x_continuous(breaks= c(-.2,0,.2,.4,.6,.8,1)) +
ylab(d2$comparison[1])+
xlab(NULL)+
scale_y_discrete(labels = c(paste(as.character(d2$study[1])),
gsub("x", "\n",paste(as.character(d2$study[2]),
paste("x (n =", as.character(d2$N[2]),")"))),
gsub("x", "\n",paste(as.character(d2$study[3]),
paste("x (n =", as.character(d2$N[3]),")"))),
gsub("x", "\n",paste(as.character(d2$study[4]),
paste("x (n =", as.character(d2$N[4]),")")))))+
theme(panel.background = element_blank(),
axis.ticks.x=element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(size=15),
legend.position="none",
axis.line.x = element_blank(),
axis.title.y = element_text(size=15,margin=margin(0,30,0,0)),
axis.line.y = element_line(),
plot.margin = unit(c(.22,2,.22,.5), "in"))
a2 <- ggdraw(a2)+
draw_text(paste(formatC(d2$estimate[[4]], digits = 2, format = "f"),
"[",
formatC(d2$CI.lower[[4]],digits = 2, format = "f"),
",",
formatC(d2$CI.upper[[4]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.829, size = 12, hjust=0)+
draw_text(paste(formatC(d2$estimate[[3]], digits = 2, format = "f"),
"[",
formatC(d2$CI.lower[[3]],digits = 2, format = "f"),
",",
formatC(d2$CI.upper[[3]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.615, size = 12, hjust=0)+
draw_text(paste(formatC(d2$estimate[[2]],digits = 2, format = "f"),
"[",
formatC(d2$CI.lower[[2]],digits = 2, format = "f"),
",",
formatC(d2$CI.upper[[2]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.395, size = 12, hjust=0)+
draw_text(paste(formatC(d2$estimate[[1]],digits = 2, format = "f"),
"[",
formatC(d2$CI.lower[[1]],digits = 2, format = "f"),
",",
formatC(d2$CI.upper[[1]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.183, size = 12, hjust=0)
########################### Active 1hr vs. 24hr #####################################
#re-order the levels for plotting
d3$study <- factor(as.character(d3$study), levels=desired_order)
d3 <- d3[order(d3$study),]
a3 <- ggplot(data=d3,aes(x=estimate,y=d3$study))+
geom_point(size=5, colour="black", fill = "black", shape = c(22,21,21,23)) +
geom_errorbarh(aes(xmin=CI.lower,xmax=CI.upper, height = .1)) +
geom_vline(xintercept=0,linetype="dashed") +
coord_cartesian(xlim=c(-.2,1)) +
scale_x_continuous(breaks= c(-.2,0,.2,.4,.6,.8,1)) +
ylab(d3$comparison[1]) +
xlab("r") +
scale_y_discrete(labels = c(paste(as.character(d3$study[1])),
gsub("x", "\n",paste(as.character(d3$study[2]),
paste("x (n =", as.character(d3$N[2]),")"))),
gsub("x", "\n",paste(as.character(d3$study[3]),
paste("x (n =", as.character(d3$N[3]),")"))),
gsub("x", "\n",paste(as.character(d3$study[4]),
paste("x (n =", as.character(d3$N[4]),")"))))) +
theme(panel.background = element_blank(),
legend.position="none",
axis.line.x = element_line(),
axis.title.y = element_text(size=15,margin=margin(0,30,0,0)),
axis.text.y = element_text(size=15),
axis.title.x = element_text(size=15),
axis.line.y = element_line(),
plot.margin = unit(c(.1, 2,0,.5), "in"))
a3 <- ggdraw(a3)+
draw_text(paste(formatC(d3$estimate[[4]], digits = 2, format = "f"),
"[",
formatC(d3$CI.lower[[4]],digits = 2, format = "f"),
",",
formatC(d3$CI.upper[[4]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.858, size = 12, hjust=0)+
draw_text(paste(formatC(d3$estimate[[3]], digits = 2, format = "f"),
"[",
formatC(d3$CI.lower[[3]],digits = 2, format = "f"),
",",
formatC(d3$CI.upper[[3]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.6445, size = 12, hjust=0)+
draw_text(paste(formatC(d3$estimate[[2]],digits = 2, format = "f"),
"[",
formatC(d3$CI.lower[[2]],digits = 2, format = "f"),
",",
formatC(d3$CI.upper[[2]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.432, size = 12, hjust=0)+
draw_text(paste(formatC(d3$estimate[[1]],digits = 2, format = "f"),
"[",
formatC(d3$CI.lower[[1]],digits = 2, format = "f"),
",",
formatC(d3$CI.upper[[1]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.218, size = 12, hjust=0)
#Plots for protocol 3 analyses Silent genes:
########################### Silent 0hr vs. 1hr #####################################
#re-order the levels for plotting
d4$study <- factor(as.character(d4$study), levels=desired_order)
d4 <- d4[order(d4$study),]
s1 <- ggplot(data=d4,aes(x=estimate,y=d4$study)) +
geom_point(size=5, colour="black", fill = "black", shape = c(22,21,21,23)) +
geom_errorbarh(aes(xmin=CI.lower,xmax=CI.upper, height = .1)) +
geom_vline(xintercept=0,linetype="dashed") +
coord_cartesian(xlim=c(-.2,1)) +
scale_x_continuous(breaks= c(-.2,0,.2,.4,.6,.8,1)) +
ylab(d4$comparison[1])+
xlab(NULL)+
scale_y_discrete(labels = c(paste(as.character(d4$study[1])),
gsub("x", "\n",paste(as.character(d4$study[2]),
paste("x (n =", as.character(d4$N[2]),")"))),
gsub("x", "\n",paste(as.character(d4$study[3]),
paste("x (n =", as.character(d4$N[3]),")"))),
gsub("x", "\n",paste(as.character(d4$study[4]),
paste("x (n =", as.character(d4$N[4]),")")))))+
theme(panel.background = element_blank(),
axis.ticks.x=element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(size=15),
legend.position="none",
axis.line.x = element_blank(),
axis.title.y = element_text(size=15,margin=margin(0,30,0,0)),
axis.line.y = element_line(),
plot.margin = unit(c(.5 ,2,.075,.5), "in"))
s1 <- ggdraw(s1)+
draw_text(paste("r","[","L.CI", ",", "U.CI", "]"), x = .84, y = 0.91, fontface="bold")+
draw_text(paste(formatC(d4$estimate[[4]], digits = 2, format = "f"),
"[",
formatC(d4$CI.lower[[4]],digits = 2, format = "f"),
",",
formatC(d4$CI.upper[[4]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.785, size = 12, hjust=0)+
draw_text(paste(formatC(d4$estimate[[3]], digits = 2, format = "f"),
"[",
formatC(d4$CI.lower[[3]],digits = 2, format = "f"),
",",
formatC(d4$CI.upper[[3]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.575, size = 12, hjust=0)+
draw_text(paste(formatC(d4$estimate[[2]],digits = 2, format = "f"),
"[",
formatC(d4$CI.lower[[2]],digits = 2, format = "f"),
",",
formatC(d4$CI.upper[[2]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.3575, size = 12, hjust=0)+
draw_text(paste(formatC(d4$estimate[[1]],digits = 2, format = "f"),
"[",
formatC(d4$CI.lower[[1]],digits = 2, format = "f"),
",",
formatC(d4$CI.upper[[1]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.154, size = 12, hjust=0)
########################## Silent 0hr vs. 24hr #####################################
#re-order the levels for plotting
d5$study <- factor(as.character(d5$study), levels=desired_order)
d5 <- d5[order(d5$study),]
s2 <- ggplot(data=d5,aes(x=estimate,y=d5$study)) +
geom_point(size=5, colour="black", fill = "black", shape = c(22,21,21,23)) +
geom_errorbarh(aes(xmin=CI.lower,xmax=CI.upper, height = .1)) +
geom_vline(xintercept=0,linetype="dashed") +
coord_cartesian(xlim=c(-.2,1)) +
scale_x_continuous(breaks= c(-.2,0,.2,.4,.6,.8,1)) +
ylab(d5$comparison[1])+
xlab(NULL)+
scale_y_discrete(labels = c(paste(as.character(d5$study[1])),
gsub("x", "\n",paste(as.character(d5$study[2]),
paste("x (n =", as.character(d5$N[2]),")"))),
gsub("x", "\n",paste(as.character(d5$study[3]),
paste("x (n =", as.character(d5$N[3]),")"))),
gsub("x", "\n",paste(as.character(d5$study[4]),
paste("x (n =", as.character(d5$N[4]),")")))))+
theme(panel.background = element_blank(),
axis.ticks.x=element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(size=15),
legend.position="none",
axis.line.x = element_blank(),
axis.title.y = element_text(size=15,margin=margin(0,30,0,0)),
axis.line.y = element_line(),
plot.margin = unit(c(.22,2,.22,.5), "in"))
s2 <- ggdraw(s2)+
draw_text(paste(formatC(d5$estimate[[4]], digits = 2, format = "f"),
"[",
formatC(d5$CI.lower[[4]],digits = 2, format = "f"),
",",
formatC(d5$CI.upper[[4]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.829, size = 12, hjust=0)+
draw_text(paste(formatC(d5$estimate[[3]], digits = 2, format = "f"),
"[",
formatC(d5$CI.lower[[3]],digits = 2, format = "f"),
",",
formatC(d5$CI.upper[[3]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.615, size = 12, hjust=0)+
draw_text(paste(formatC(d5$estimate[[2]],digits = 2, format = "f"),
"[",
formatC(d5$CI.lower[[2]],digits = 2, format = "f"),
",",
formatC(d5$CI.upper[[2]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.395, size = 12, hjust=0)+
draw_text(paste(formatC(d5$estimate[[1]],digits = 2, format = "f"),
"[",
formatC(d5$CI.lower[[1]],digits = 2, format = "f"),
",",
formatC(d5$CI.upper[[1]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.183, size = 12, hjust=0)
########################## Silent 1hr vs. 24hr #####################################
#re-order the levels for plotting
d6$study <- factor(as.character(d6$study), levels=desired_order)
d6 <- d6[order(d6$study),]
s3 <- ggplot(data=d6,aes(x=estimate,y=factor(d6$study)))+
geom_point(size=5, colour="black", fill = "black", shape = c(22,21,21,23)) +
geom_errorbarh(aes(xmin=CI.lower,xmax=CI.upper, height = .1)) +
geom_vline(xintercept=0,linetype="dashed") +
coord_cartesian(xlim=c(-.2,1)) +
scale_x_continuous(breaks= c(-.2,0,.2,.4,.6,.8,1)) +
ylab(d6$comparison[1]) +
xlab("r") +
scale_y_discrete(labels = c(paste(as.character(d6$study[1])),
gsub("x", "\n",paste(as.character(d6$study[2]),
paste("x (n =", as.character(d6$N[2]),")"))),
gsub("x", "\n",paste(as.character(d6$study[3]),
paste("x (n =", as.character(d6$N[3]),")"))),
gsub("x", "\n",paste(as.character(d6$study[4]),
paste("x (n =", as.character(d6$N[4]),")"))))) +
theme(panel.background = element_blank(),
legend.position="none",
axis.line.x = element_line(),
axis.title.y = element_text(size=15,margin=margin(0,30,0,0)),
axis.text.y = element_text(size=15),
axis.title.x = element_text(size=15),
axis.line.y = element_line(),
plot.margin = unit(c(.1, 2,0,.5), "in"))
s3 <- ggdraw(s3)+
draw_text(paste(formatC(d6$estimate[[4]], digits = 2, format = "f"),
"[",
formatC(d6$CI.lower[[4]],digits = 2, format = "f"),
",",
formatC(d6$CI.upper[[4]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.858, size = 12, hjust=0)+
draw_text(paste(formatC(d6$estimate[[3]], digits = 2, format = "f"),
"[",
formatC(d6$CI.lower[[3]],digits = 2, format = "f"),
",",
formatC(d6$CI.upper[[3]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.6445, size = 12, hjust=0)+
draw_text(paste(formatC(d6$estimate[[2]],digits = 2, format = "f"),
"[",
formatC(d6$CI.lower[[2]],digits = 2, format = "f"),
",",
formatC(d6$CI.upper[[2]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.432, size = 12, hjust=0)+
draw_text(paste(formatC(d6$estimate[[1]],digits = 2, format = "f"),
"[",
formatC(d6$CI.lower[[1]],digits = 2, format = "f"),
",",
formatC(d6$CI.upper[[1]],digits = 2, format = "f"),
"]"), x = 0.75, y = 0.218, size = 12, hjust=0)
########################## Protocol 2 Meta Analysis #####################################
#re-order the levels for plotting
d7$study <- factor(as.character(d7$study), levels=desired_order)
d7 <- d7[order(d7$study),]
pro2 <- ggplot(data=d7,aes(x=estimate,y=d7$study))+
geom_point(size=5, colour="black", fill = "black", shape = c(22,21,21,23)) +
geom_errorbarh(aes(xmin=CI.lower,xmax=CI.upper, height = .1)) +
geom_vline(xintercept=0,linetype="dashed") +
coord_cartesian(xlim=c(-1,8)) +
scale_x_continuous(breaks= c(-1,0,1,2,3,4,5,6,7,8)) +
ylab(d7$comparison[1]) +
xlab("Cohen's"~italic("d")) +
scale_y_discrete(labels = c(paste(as.character(d7$study[1])),
gsub("x", "\n",paste(as.character(d7$study[2]),
paste("x (n =", as.character(d7$N[2]),")"))),
gsub("x", "\n",paste(as.character(d7$study[3]),
paste("x (n =", as.character(d7$N[3]),")"))),
gsub("x", "\n",paste(as.character(d7$study[4]),
paste("x (n =", as.character(d7$N[4]),")"))))) +
theme(panel.background = element_blank(),
legend.position="none",
axis.line.x = element_line(),
axis.title.y = element_text(size=15,margin=margin(0,30,0,0)),
axis.text.y = element_text(size=15),
axis.line.y = element_line(),
plot.margin = margin(.6, 8, .5, .55, "in"))
pro2 <- ggdraw(pro2)+
draw_text(paste("Cohen's d","[","L.CI", ",", "U.CI", "]"), x = 0.568, y = 0.93, fontface="bold") +
draw_text(paste(formatC(d7$estimate[[4]], digits = 2, format = "f"),
"[",
formatC(d7$CI.lower[[4]],digits = 2, format = "f"),
",",
formatC(d7$CI.upper[[4]],digits = 2, format = "f"),
"]"), x = 0.52, y = 0.79, size = 12, hjust=0)+
draw_text(paste(formatC(d7$estimate[[3]], digits = 2, format = "f"),
"[",
formatC(d7$CI.lower[[3]],digits = 2, format = "f"),
",",
formatC(d7$CI.upper[[3]],digits = 2, format = "f"),
"]"), x = 0.52, y = 0.62, size = 12, hjust=0)+
draw_text(paste(formatC(d7$estimate[[2]],digits = 2, format = "f"),
"[",
formatC(d7$CI.lower[[2]],digits = 2, format = "f"),
",",
formatC(d7$CI.upper[[2]],digits = 2, format = "f"),
"]"), x = 0.52, y = 0.455, size = 12, hjust=0)+
draw_text(paste(formatC(d7$estimate[[1]],digits = 2, format = "f"),
"[",
formatC(d7$CI.lower[[1]],digits = 2, format = "f"),
",",
formatC(d7$CI.upper[[1]],digits = 2, format = "f"),
"]"), x = 0.52, y = 0.285, size = 12, hjust=0)
#Creates figure for protocol 3 meta-analyses
pro3 <- plot_grid(a1, s1, a2, s2, a3, s3, nrow = 3)
figure <- plot_grid(pro2, pro3, nrow = 2, rel_heights = c(1,3), labels = c("A", "B"), label_size = 25)
figure_3 <- plot_grid(figure,ncol = 1,rel_heights = c(0.1,1))
figure_3</code></pre>
<figure slot="outputs"><img src="index.html.media/2" alt="" itemscope=""
itemtype="http://schema.org/ImageObject"></figure>
</stencila-code-chunk>
<figcaption>
<h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
id="meta-analyses-of-each-effect">Meta-analyses of each effect.</h3>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Effect size and 95%
confidence interval are presented for <cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib17">bib17</a></cite>, this
replication study (RP:CB), and a random effects meta-analysis of those two effects.
Cohen’s <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">d</em> is the
standardized difference between the two measurements, with a larger positive value
indicating total RNA levels are increased at 24 hr compared to 0 hr. The effect size
<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">r</em> is a standardized
measure of the correlation (strength and direction) of the association between gene
expression and c-Myc induction, with a larger positive value indicating gene
expression increased during the course of c-Myc induction. Sample sizes used in <cite
itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib17">bib17</a></cite> and this replication attempt are reported under the
study name. (<strong itemscope=""
itemtype="http://schema.stenci.la/Strong">A</strong>) Total RNA levels in P493-6
cells 0 hr compared to 24 hr after release from tetracycline (meta-analysis <em
itemscope="" itemtype="http://schema.stenci.la/Emphasis">p</em> = 0.0488). (<strong
itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>) Gene expression
of active or silent genes are shown for all comparisons. Active genes: 0 hr compared
to 1 hr (meta-analysis <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">p</em> = 1.12x10<sup itemscope=""
itemtype="http://schema.stenci.la/Superscript">-7</sup>), 0 hr compared to 24 hr
(meta-analysis <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">p</em> =
7.01x10<sup itemscope="" itemtype="http://schema.stenci.la/Superscript">-4</sup>), 1
hr compared to 24 hr (meta-analysis <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">p</em> = 0.0129). Silent genes: 0 hr
compared to 1 hr (meta-analysis <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">p</em> = 0.203), 0 hr compared to 24 hr
(meta-analysis <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">p</em> =
7.10x10<sup itemscope="" itemtype="http://schema.stenci.la/Superscript">-17</sup>), 1
hr compared to 24 hr (meta-analysis <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">p</em> = 0.0571). Additional details for
these meta-analyses can be found at <a href="https://osf.io/5yscz/" itemscope=""
itemtype="http://schema.stenci.la/Link">https://osf.io/5yscz/</a>.</p>
</figcaption>
</figure>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">There were six comparisons of
the gene expression data, three for active genes and three for silent genes (<a
href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3B</a>). These
calculations were performed analyzing the data as paired, for reasons discussed above and
as prespecified in the Registered Report (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib3">Blum et al., 2015</a></cite>).
For active genes, expression at 0 hr to 1 hr, 0 hr to 24 hr, and 1 hr to 24 hr the
meta-analyses were statistically significant (<em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">p</em>=1.12×10<sup itemscope=""
itemtype="http://schema.stenci.la/Superscript">−7</sup>, <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">p</em>=7.01×10<sup itemscope=""
itemtype="http://schema.stenci.la/Superscript">−4</sup>, <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">p</em>=0.0129, respectively). In all
comparisons the results were consistent when considering the direction of the effect;
however the effect size point estimate of each study (original, replication serum lot one,
replication serum lot two) was not within the confidence interval of the other studies.
Further, the large confidence intervals of the meta-analysis along with statistically
significant Cochran’s <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Q</em>
tests suggest heterogeneity between the original and replication studies. For silent
genes, the meta-analysis was not statistically significant for gene expression at 0 hr to
1 hr and 1 hr to 24 hr (<em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">p</em>=0.203, <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">p</em>=0.0571, respectively) and the effect
size point estimate of each study was not within the confidence interval of the other
studies. Similar to the active gene comparisons, the large confidence intervals of the
meta-analysis along with statistically significant Cochran’s <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">Q</em> tests suggest heterogeneity between
the studies. Furthermore, for the 0 hr to 1 hr comparison the original study and
replication studies were in opposite directions, while the 1 hr to 24 hr comparison was
consistent. Finally, the comparison between 0 hr and 24 hr for silent genes was consistent
when considering direction of the effect with a statistically significant meta-analysis
(<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">p</em>=7.10×10<sup
itemscope="" itemtype="http://schema.stenci.la/Superscript">−17</sup>). The point
estimate of the original study was not within the confidence intervals of the replication
studies; however both replication studies with different serum lots were within the
confidence intervals of the original study and each other. Overall, the gene expression
analysis indicates that the effect sizes observed from the two serum lots tested in this
replication attempt, although not identical, were more similar to each other than to the
original study.</p>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">This direct replication
provides an opportunity to understand the present evidence of these effects. Any known
differences, including reagents and protocol differences, were identified prior to
conducting the experimental work and described in the Registered Report (<cite
itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib3">Blum et al.,
2015</a></cite>). However, this is limited to what was obtainable from the original
paper and through communication with the original authors, which means there might be
particular features of the original experimental protocol that could be critical, but
unidentified. So while some aspects, such as the cell line, induction time course, and the
method used to measure gene expression were maintained, others were changed at the time of
study design (<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib3">Blum et al., 2015</a></cite>) that could affect results, such as the
analytical approach (<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib31">Silberzahn et al., 2017</a></cite>) and serum lot (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib15">Leek et al., 2010</a></cite>).
Furthermore, other aspects were unknown or not easily controlled for. These include
variables such as cell line genetic drift (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib13">Hughes et al., 2007</a></cite>;
<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib14">Kleensang et
al., 2016</a></cite>) or changes in cellular volume that can impact overall transcript
abundance (<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib22">Padovan-Merhar et al., 2015</a></cite>). Whether these or other factors
influence the outcomes of this study is open to hypothesizing and further investigation,
which is facilitated by direct replications and transparent reporting.</p>
<h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="materials-and-methods">
Materials and methods</h2>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">As described in the Registered
Report (<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib3">Blum et
al., 2015</a></cite>), we attempted a replication of the experiments reported in
Figures 1B and 3E-F of <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib17">Lin et al., 2012</a></cite>. A detailed description of all protocols can
be found in the Registered Report (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib3">Blum et al., 2015</a></cite>).
Additional detailed experimental notes, data, and analysis are available on the Open
Science Framework (OSF) (RRID:<a href="https://scicrunch.org/resolver/SCR_003238"
itemscope="" itemtype="http://schema.stenci.la/Link">SCR_003238</a>) (<a
href="https://osf.io/mokeb/" itemscope=""
itemtype="http://schema.stenci.la/Link">https://osf.io/mokeb/</a>; <cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib16">Lewis et al., 2017</a></cite>).
This includes the R Markdown file (<a href="https://osf.io/vdrsh/" itemscope=""
itemtype="http://schema.stenci.la/Link">https://osf.io/vdrsh/</a>) that was used to
compose this manuscript, which is a reproducible document linking the results in the
article directly to the data and code that produced them (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib10">Hartgerink, 2017</a></cite>).
</p>
<h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="cell-culture">Cell culture
</h3>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">P493-6 cells (shared by Young
lab, Whitehead Institute for Biomedical Research, RRID: <a
href="https://scicrunch.org/resolver/CVCL_6783" itemscope=""
itemtype="http://schema.stenci.la/Link">CVCL_6783</a>) were maintained in RPMI-1640
supplemented with 1% Ala-Gln and 10% tetracycline-free FBS (Clontech, Mountain View, CA,
cat# 631105, lot# 1: A15003, lot# 2: A15032). Cells were grown at 37°C in a humidified
atmosphere at 5% CO<sub itemscope="" itemtype="http://schema.stenci.la/Subscript">2</sub>.
Quality control data for the cell line are available at <a href="https://osf.io/e6ftz/."
itemscope="" itemtype="http://schema.stenci.la/Link">https://osf.io/e6ftz/.</a> This
includes results confirming the cell line was free of mycoplasma contamination (DDC
Medical, Fairfield, Ohio). Additionally, STR DNA profiling of the cell line was performed
(DDC Medical, Fairfield, Ohio).</p>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">For repression of the
conditional p<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">myc</em>-tet
construct in P493-6 cells, 0.1 µg/ml tetracycline (Sigma-Aldrich, St. Louis, MO, cat#
T7660) was added to the culture medium and cells were incubated for 72 hr. Under these
conditions, P493-6 cells did not proliferate due to a dependency on the expression of <em
itemscope="" itemtype="http://schema.stenci.la/Emphasis">MYC</em> (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib30">Schuhmacher et al.,
1999</a></cite>). For <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">MYC</em> re-induction, cells were washed
three times with growth medium and grown in tetracycline-free culture conditions.</p>
<h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="western-blot">Western blot
</h3>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">P493-6 cells were harvested at
the indicated times and total cell lysates were prepared by pelleting ~1×10<sup
itemscope="" itemtype="http://schema.stenci.la/Superscript">7</sup> cells (determined
with a C-chip disposable hemocytometer) at 4°C at 1,200 rpm for 5 min using a refrigerated
centrifuge (Eppendorf, Westbury, NY, model# 5810R). After cell pellets were washed once
with ice-cold 1X PBS, pellets were resuspended in RIPA lysis buffer containing 2X
SIGMAFAST Protease inhibitors and 2X Phosphatase inhibitor cocktails 2 and 3. Protein
concentrations were determined using the Bradford assay according to the manufacturer’s
instructions. Sample buffer was added to protein lysates and 50 µg of protein along with
protein ladder was resolved by SDS-PAGE and transferred to PVDF membrane as described in
the Registered Report (<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
href="#bib3">Blum et al., 2015</a></cite>). The membrane was blocked with 5% w/v
nonfat dry milk in 1X TBS with 0.2% Tween-20 (TBST). Membranes were probed with rabbit
anti-c-Myc [clone Y69] (Epitomics, Burlingame, CA, cat# 1472–1; RRID:<a
href="https://scicrunch.org/resolver/AB_731658" itemscope=""
itemtype="http://schema.stenci.la/Link">AB_731658</a>); 1:5000 dilution in 5% w/v nonfat
dry milk/TBST and mouse anti-ß-actin [clone AC-15] (Sigma-Aldrich, cat# A5441; RRID:<a
href="https://scicrunch.org/resolver/AB_476744" itemscope=""
itemtype="http://schema.stenci.la/Link">AB_476744</a>); 1:10,000 dilution in 5% w/v
nonfat dry milk/TBST. Each incubation was followed by washes with TBST and the appropriate
secondary antibody: HRP-conjugated donkey anti-rabbit (Sigma-Aldrich, cat# GERPN2124);
1:10,000 dilution in 5% w/v nonfat dry milk/TBST or HRP-conjugated sheep anti-mouse
(Sigma-Aldrich, cat# GERPN2124); 1:10,000 dilution in 5% w/v nonfat dry milk/TBST.
Membranes were washed with TBST and incubated with ECL Prime Chemiluminescent reagent
(Sigma-Aldrich, cat# GERPN2232) according to the manufacturer’s instructions. Western blot
images were acquired with G:BOX iChem XT and GeneSnap software (RRID:<a
href="https://scicrunch.org/resolver/SCR_014249" itemscope=""
itemtype="http://schema.stenci.la/Link">SCR_014249</a>), version 7.12.02 (Syngene,
Frederick, Maryland) and quantified using ImageJ software (RRID:<a
href="https://scicrunch.org/resolver/SCR_003070" itemscope=""
itemtype="http://schema.stenci.la/Link">SCR_003070</a>), version 1.50i (<cite
itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib29">Schneider et al.,
2012</a></cite>). All images taken are available at <a href="https://osf.io/ujg7t/."
itemscope="" itemtype="http://schema.stenci.la/Link">https://osf.io/ujg7t/.</a></p>
<h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="rna-quantification">RNA
quantification</h3>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">P493-6 cells were harvested at
the indicated times and total RNA extraction was performed by pelleting ~1×10<sup
itemscope="" itemtype="http://schema.stenci.la/Superscript">7</sup> cells (exact number
determined with a C-chip disposable hemocytometer) and homogenizing the sample in 1 ml Tri
Reagent (Sigma-Aldrich, cat# T9424) according to the manufacturer’s instructions. For each
sample 10% v/v miRNA Homogenate Additive was added, vortexed, and incubated on ice for 10
min. For each 1 ml of Tri Reagent, 100 µl of bromochloropropane was added, vortexed for
15–30 s, incubated for 5 min at RT, then centrifuged at 12,000x<em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">g</em> for 10 min at 4°C. The aqueous phase
was recovered and total RNA isolation was performed using the miRVana miRNA extraction kit
(Ambion, Waltham, MA, cat# AM1561) according to the manufacturer’s instructions. Recovered
RNA was eluated in 100 µl nuclease-free water. Total RNA concentrations and purity (data
available at <a href="https://osf.io/jh5r4/" itemscope=""
itemtype="http://schema.stenci.la/Link">https://osf.io/jh5r4/</a>) were measured using a
NanoDrop ND-1000 (Thermo Fisher Scientific, Waltham, Massachusetts) with the NanoDrop
Operating Software, version 3.3, and converted to ng per 1,000 cells.</p>
<h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
id="rna-extraction-and-nanostring-ncounter-digital-gene-expression-assay">RNA extraction
and NanoString nCounter digital gene expression assay</h3>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">P493-6 cells were harvested at
the indicated times and 1 × 10<sup itemscope=""
itemtype="http://schema.stenci.la/Superscript">6</sup> cells were collected (number
determined with a C-chip disposable hemocytometer) and lysed directly in 100 µl Buffer RLT
(Qiagen, Hilden, Germany, cat# 79216) supplemented with ß-mercaptoethanol to yield a
concentration of 10,000 cells per µl. This was performed four independent times. Multiple
4 µl aliquots were stored and shipped at −80°C with temperature monitored during shipping
to avoid freeze/thaw cycles. Lysates were processed according to the Cell Lysate Protocol
(nCounter Gene Expression Assay Manual, NanoString Technologies, Seattle, Washington)
according to the manufacturer’s instructions and as described in the Registered Report
(<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib3">Blum et al.,
2015</a></cite>). Three nCounter Reporter CodeSets (nCounter GX Human Immunology Kit,
nCounter GX Human Kinase Kit, nCounter Custom CodeSet) encompassing
<stencila-code-expression programming-language="r" itemscope=""
itemtype="http://schema.stenci.la/CodeExpression"><code class="r"
slot="text">prettyNum(length(unique(comb.means$Accession)),
big.mark=",")</code><output slot="output">1,308</output></stencila-code-expression>
genes across multiple functional categories were used. Following hybridization, samples
were immediately processed with the nCounter Analysis System (NanoString Technologies,
NCT-PREP-120). The count data was collected using the nCounter RCC Collector Worksheet
(NanoString Technologies), version 1.6.0 and then positive-, negative-, and housekeeping
gene-normalized per nCounter guidelines. Expression for each gene was averaged across the
four independent replicate samples. Additionally, for genes that appeared on multiple
CodeSets, expression values were averaged together to generate a single value for each
gene. A gene was defined as transcriptionally active if its average expression was above
one transcript/cell at 0 hr and transcriptionally silent if below 0.5 transcript/cell. A
list of all Reporter CodeSets and their expression values (transcripts/cell) are available
at <a href="#fig2sdata1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
2—source data 1</a>. Additional files and analysis scripts are available at <a
href="https://osf.io/fn2y4/." itemscope=""
itemtype="http://schema.stenci.la/Link">https://osf.io/fn2y4/.</a></p>
<h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="statistical-analysis">
Statistical analysis</h3>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Statistical analysis was
performed with R software (RRID:<a href="https://scicrunch.org/resolver/SCR_001905"
itemscope="" itemtype="http://schema.stenci.la/Link">SCR_001905</a>), version 3.3.2
(<cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib25">,
2017</a></cite>). All data, csv files, and analysis scripts are available on the OSF
(<a href="https://osf.io/mokeb/" itemscope=""
itemtype="http://schema.stenci.la/Link">https://osf.io/mokeb/</a>). Confirmatory
statistical analysis was pre-registered (<a href="https://osf.io/nj8wb/" itemscope=""
itemtype="http://schema.stenci.la/Link">https://osf.io/nj8wb/</a>) before the
experimental work began as outlined in the Registered Report (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib3">Blum et al., 2015</a></cite>).
Proposed analysis of gene expression data was conducted by the Wilcoxon signed-rank test
using the method proposed by Pratt to handle zero differences (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib24">Pratt, 1959</a></cite>), with
additional exploratory analysis performed using the Wilcoxon rank sum test as reported in
the original study and a Wilcoxon rank sum test on the difference in expression of active
genes during c-Myc induction (e.g. from 0 hr to 24 hr) compared to the difference in
expression of silent genes over that same period (e.g. from 0 hr to 24 hr). Data were
checked to ensure assumptions of statistical tests were met. When described in the
results, the Bonferroni correction, to account for multiple testings, was applied to the
alpha error by dividing the uncorrected value (.05) by the number of tests performed.
Although the Bonferroni method is conservative, it was accounted for in the power
calculations to ensure sample size was sufficient. In cases where the number of groups
were three and the sample sizes were evenly distributed among the groups, Fisher's LSD
test was performed resulting in an <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">a priori</em> significance threshold of. 05.
A meta-analysis of a common original and replication effect size was performed with a
random effects model and the <em itemscope=""
itemtype="http://schema.stenci.la/Emphasis">metafor</em> package (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib34">Viechtbauer, 2010</a></cite>)
(available at: <a href="https://osf.io/5yscz/" itemscope=""
itemtype="http://schema.stenci.la/Link">https://osf.io/5yscz/</a>). The sample sizes
reported in <a href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table
1</a> and <a href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
3</a> for the gene analysis comparisons is based on the sample size used in the Wilcoxon
signed-rank test, which removes samples with zero differences after ranking (<cite
itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib24">Pratt,
1959</a></cite>). The raw original study data were shared by the original authors with
the summary data published in the Registered Report (<cite itemscope=""
itemtype="http://schema.stenci.la/Cite"><a href="#bib3">Blum et al., 2015</a></cite>)
and was used in the power calculations to determine the sample size for this study.</p>
<h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
id="deviations-from-registered-report">Deviations from registered report</h3>
<p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The number of flasks, and thus
cells, was increased when tetracycline was added to P493-6 cells to account for the cells
not proliferating during this period (i.e. there were two Flask B’s as described in the
Registered Report, which were pooled prior to seeding). The proposed statistical analysis
for the western blot analysis (Protocol 1) described in the Registered Report was not
performed since the levels of normalized c-Myc at time 0 hr was at the limit of detection.
The number of genes analyzed in the original study, and thus listed in the Registered
Report, was reported incorrectly as 1388 instead of 1338 (data shared by original
authors). NanoString analysis was conducted using the nCounter RCC Collector Worksheet
instead of nSolver Analysis software. Additionally, the statistical tests reported
in Figure 3F of the original study incorrectly described the comparisons as between 0 hr
and 1 hr instead of between 0 hr and 24 hr (scripts shared by original authors). The
corrected values are described above for comparisons and used in the meta-analysis.
Additional materials and instrumentation not listed in the Registered Report, but needed
during experimentation are also listed.</p>
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