{ "type": "Article", "authors": [ { "type": "Person", "affiliations": [ { "type": "Organization", "address": { "type": "PostalAddress", "addressCountry": "United States", "addressLocality": "Georgia" }, "name": "University of Georgia, Bioexpression and Fermentation Facility" } ], "familyNames": [ "Lewis" ], "givenNames": [ "L", "Michelle" ] }, { "type": "Person", "affiliations": [ { "type": "Organization", "address": { "type": "PostalAddress", "addressCountry": "United States", "addressLocality": "Georgia" }, "name": "University of Georgia, Bioexpression and Fermentation Facility" } ], "familyNames": [ "Edwards" ], "givenNames": [ "Meredith", "C" ] }, { "type": "Person", "affiliations": [ { "type": "Organization", "address": { "type": "PostalAddress", "addressCountry": "United States", "addressLocality": "Georgia" }, "name": "University of Georgia, Bioexpression and Fermentation Facility" } ], "familyNames": [ "Meyers" ], "givenNames": [ "Zachary", "R" ] }, { "type": "Person", "affiliations": [ { "type": "Organization", "address": { "type": "PostalAddress", "addressCountry": "United States", "addressLocality": "Maryland" }, "name": "Johns Hopkins University, Deep Sequencing and Microarray Core Facility" } ], "familyNames": [ "Talbot" ], "givenNames": [ "C", "Conover" ], "honorificSuffix": "Jr" }, { "type": "Person", "affiliations": [ { "type": "Organization", "address": { "type": "PostalAddress", "addressCountry": "United States", "addressLocality": "Maryland" }, "name": "Johns Hopkins University, Deep Sequencing and Microarray Core Facility" } ], "familyNames": [ "Hao" ], "givenNames": [ "Haiping" ] }, { "type": "Person", "affiliations": [ { "type": "Organization", "address": { "type": "PostalAddress", "addressCountry": "United States", "addressLocality": "Georgia" }, "name": "University of Georgia, Bioexpression and Fermentation Facility" } ], "familyNames": [ "Blum" ], "givenNames": [ "David" ] }, { "type": "Organization", "contactPoints": [ { "type": "ContactPoint", "emails": [ "tim@cos.io", "nicole@scienceexchange.com" ] } ], "name": "Reproducibility Project: Cancer Biology" }, { "type": "Person", "affiliations": [ { "type": "Organization", "address": { "type": "PostalAddress", "addressCountry": "United States", "addressLocality": "Palo Alto" }, "name": "Science Exchange" } ], "familyNames": [ "Iorns" ], "givenNames": [ "Elizabeth" ] }, { "type": "Person", "affiliations": [ { "type": "Organization", "address": { "type": "PostalAddress", "addressCountry": "United States", "addressLocality": "Palo Alto" }, "name": "Science Exchange" } ], "familyNames": [ "Tsui" ], "givenNames": [ "Rachel" ] }, { "type": "Person", "affiliations": [ { "type": "Organization", "address": { "type": "PostalAddress", "addressCountry": "United States", "addressLocality": "Charlottesville" }, "name": "Center for Open Science" } ], "familyNames": [ "Denis" ], "givenNames": [ "Alexandria" ] }, { "type": "Person", "affiliations": [ { "type": "Organization", "address": { "type": "PostalAddress", "addressCountry": "United States", "addressLocality": "Palo Alto" }, "name": "Science Exchange" } ], "familyNames": [ "Perfito" ], "givenNames": [ "Nicole" ] }, { "type": "Person", "affiliations": [ { "type": "Organization", "address": { "type": "PostalAddress", "addressCountry": "United States", "addressLocality": "Charlottesville" }, "name": "Center for Open Science" } ], "familyNames": [ "Errington" ], "givenNames": [ "Timothy", "M" ] } ], "dateAccepted": { "type": "Date", "value": "2017-11-16" }, "datePublished": { "type": "Date", "value": "2018-01-09" }, "dateReceived": { "type": "Date", "value": "2017-07-10" }, "description": [ { "type": "Paragraph", "content": [ "As part of the ", { "type": "Link", "target": "https://osf.io/e81xl/wiki/home/", "content": [ "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." ] } ], "editors": [ { "type": "Person", "affiliations": [ { "type": "Organization", "address": { "type": "PostalAddress", "addressCountry": "United States" }, "name": "Howard Hughes Medical Institute, University of Massachusetts Medical School" } ], "familyNames": [ "Green" ], "givenNames": [ "Michael", "R" ] } ], "fundedBy": [ { "type": "MonetaryGrant", "funders": [ { "type": "Organization", "name": "Laura and John Arnold Foundation" } ], "identifiers": [] } ], "identifiers": [ { "type": "PropertyValue", "name": "publisher-id", "propertyID": "https://registry.identifiers.org/registry/publisher-id", "value": 30274 }, { "type": "PropertyValue", "name": "doi", "propertyID": "https://registry.identifiers.org/registry/doi", "value": "10.7554/eLife.30274" }, { "type": "PropertyValue", "name": "elocation-id", "propertyID": "https://registry.identifiers.org/registry/elocation-id", "value": "e30274" } ], "isPartOf": { "type": 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unrestricted use and redistribution provided that the original author and source are credited." ] } ] } ], "references": [ { "type": "Article", "id": "bib1", "authors": [ { "type": "Person", "familyNames": [ "Altman" ], "givenNames": [ "DG" ] }, { "type": "Person", "familyNames": [ "Royston" ], "givenNames": [ "P" ] } ], "datePublished": { "type": "Date", "value": "2006" }, "isPartOf": { "type": "PublicationVolume", "isPartOf": { "type": "Periodical", "name": "BMJ" }, "volumeNumber": 332 }, "title": "The cost of dichotomising continuous variables" }, { "type": "Article", "id": "bib2", "authors": [ { "type": "Person", "familyNames": [ "Biggs" ], "givenNames": [ "R" ] }, { "type": "Person", "familyNames": [ "Macmillan" ], "givenNames": [ "RL" ] } ], "datePublished": { "type": "Date", "value": "1948" }, "isPartOf": { "type": "PublicationVolume", "isPartOf": { "type": "Periodical", "name": "Journal of Clinical Pathology" }, "volumeNumber": 1 }, "pageEnd": 291, "pageStart": 288, "title": 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"M" ] } ], "datePublished": { "type": "Date", "value": "2014" }, "isPartOf": { "type": "PublicationVolume", "isPartOf": { "type": "Periodical", "name": "Nature" }, "volumeNumber": 511 }, "pageEnd": 487, "pageStart": 483, "title": "Activation and repression by oncogenic MYC shape tumour-specific gene expression profiles" } ], "title": "Replication Study: Transcriptional amplification in tumor cells with elevated c-Myc", "content": [ { "type": "Heading", "depth": 1, "content": [ "Introduction" ] }, { "type": "Paragraph", "content": [ "The ", { "type": "Link", "target": "https://osf.io/e81xl/wiki/home/", "content": [ "Reproducibility Project: Cancer Biology" ] }, " (RP:CB) is a collaboration between the ", { "type": "Link", "target": "https://centerforopenscience.org/", "content": [ "Center for Open Science" ] }, " and ", { "type": "Link", "target": "https://www.scienceexchange.com/", "content": [ "Science Exchange" ] }, " 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 (", { "type": "Cite", "target": "bib6" }, "). 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 (", { "type": "Cite", "target": "bib3" }, ") 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." ] }, { "type": "Paragraph", "content": [ "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 ", { "type": "Emphasis", "content": [ "MYC" ] }, " 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." ] }, { "type": "Paragraph", "content": [ "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 (", { "type": "Cite", "target": "bib3" }, "). 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 (", { "type": "Cite", "target": "bib9" }, "; ", { "type": "Cite", "target": "bib12" }, "; ", { "type": "Cite", "target": "bib19" }, "; ", { "type": "Cite", "target": "bib27" }, "), although this observation was not reported in all biological systems (", { "type": "Cite", "target": "bib7" }, "; ", { "type": "Cite", "target": "bib27" }, "; ", { "type": "Cite", "target": "bib35" }, "). It has been suggested c-Myc regulates specific genes that indirectly lead to RNA amplification (", { "type": "Cite", "target": "bib27" }, "; ", { "type": "Cite", "target": "bib26" }, "; ", { "type": "Cite", "target": "bib35" }, "). This has also been suggested of MYCN (", { "type": "Cite", "target": "bib5" }, "). The reported differences could be a result of the intrinsic variation between cell lines in maintaining the transcriptome (", { "type": "Cite", "target": "bib32" }, "). Indeed, a recent study reported that distinct transcriptional regulation can be accounted for by differences in promoter affinity under different c-Myc expression levels (", { "type": "Cite", "target": "bib18" }, ")." ] }, { "type": "Paragraph", "content": [ "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." ] }, { "type": "Heading", "depth": 1, "content": [ "Results and discussion" ] }, { "type": "CodeChunk", "duration": 1.494, "programmingLanguage": "r", "text": "##############################################################################\n# The R code in this executable research article is from https://osf.io/tfd57/\n# and associated files.\n# Only code necessary to reproduce the article is included here.\n# See the link above for more details\n# Code edited only to remove extraneous outputs and readability\n##############################################################################\n\n# Load packages\n\nlibrary(cowplot)\nlibrary(ggplot2)\nlibrary(lsmeans)\nlibrary(reshape)\nlibrary(Rmisc)\n\n# Load data\n\ndat <- read.csv(\"Study_48_Figure_2_Supplemental_Tables.csv\", header=T)\ndata2 <- read.csv(\"Study_48_Protocol_2_Data.csv\", header=T)\ncomb.means <- read.csv(\"Study_48_Protocols_3_4_Combined_Means.csv\", header=T)\nmeta <- read.csv(\"Study_48_Meta_Analysis.csv\", header = T)\n\n################################################################################\n# Constants from https://osf.io/9wmq8/\n\nzero <- c(4.394, 4.076, 4.286)\none <- c(4.114, 4.286, 3.712)\ntwentyfour <- c(5.868, 5.112, 5.424)\n\n################################################################################\n# Statistical analyses from Study_48_Protocol_2_Analysis.R https://osf.io/u7a5h/\n\n#creates new column calculating RNA in 100uL\ndata2$RNA.100uL <- data2$Average.RNA.Concentration*100\n\n##calculates RNA per cell\ndata2$RNA.per.cell <- data2$RNA.100uL/data2$Total.Cells.Harvested\n\n#calculates RNA per 1000 cells\ndata2$value <- data2$RNA.per.cell*1000\n\n########## Lot 1 Analysis ##########\n####################################\n\n#shapiro test for normality on lot 1 data by time\nnorm1 <- sapply(unique(data2$Time), function(x) \n shapiro.test(data2[which(data2$Lot==\"1\" & data2$Time==x),]$value)) #all data normal\n\n#time as character\ndata2$Time <- as.character(as.factor((data2$Time)))\n\n#one-way ANOVA comparing total RNA (ng/1000 cells) in cells cultured 0 hr, 1 hr, and 24 hr from tet release.\nfit1 <- aov(value ~ Time, data=data2[which(data2$Lot==\"1\"),])\ninvisible(ref1 <- lsmeans(fit1, \"Time\"))\nc_list <- list(c1 = c(-1,0,1)) # contrast 0hr to 24 hr\n\ncontrast1 <- summary(contrast(ref1, c_list))\n\n########## Lot 2 Analysis ###########\n#####################################\n\n#shapiro test for normality on lot 2 data by time\nnorm2 <- sapply(unique(data2$Time), function(x) \n shapiro.test(data2[which(data2$Lot==\"2\" & data2$Time==x),]$value)) #all data normal\n\n#one-way ANOVA comparing total RNA (ng/1000 cells) in cells cultured 0 hr, 1 hr, and 24 hr from tet release.\nfit2 <- aov(value ~ Time, data=data2[which(data2$Lot==\"2\"),])\ninvisible(ref2 <- lsmeans(fit2, \"Time\"))\nc_list2 <- list(c2 = c(-1,0,1)) # contrast 0hr to 24 hr\n\ncontrast2 <- summary(contrast(ref2, c_list2))\n\n################################################################################\n# Subsets on Lot/time/active/silent from https://osf.io/2yj6v/\n\nactive_0hr_l1 <- comb.means[which(comb.means$Status==\"Active\" & comb.means$Measure==\"Mean_0HR_C1\"),]$final.mean\nactive_1hr_l1 <- comb.means[which(comb.means$Status==\"Active\" & comb.means$Measure==\"Mean_1HR_C1\"),]$final.mean\nactive_24hr_l1 <- comb.means[which(comb.means$Status==\"Active\" & comb.means$Measure==\"Mean_24HR_C1\"),]$final.mean\n\nactive_0hr_l2 <- comb.means[which(comb.means$Status==\"Active\" & comb.means$Measure==\"Mean_0HR_C2\"),]$final.mean\nactive_1hr_l2 <- comb.means[which(comb.means$Status==\"Active\" & comb.means$Measure==\"Mean_1HR_C2\"),]$final.mean\nactive_24hr_l2 <- comb.means[which(comb.means$Status==\"Active\" & comb.means$Measure==\"Mean_24HR_C2\"),]$final.mean\n\nsilent_0hr_l1 <- comb.means[which(comb.means$Status==\"Silent\" & comb.means$Measure==\"Mean_0HR_C1\"),]$final.mean\nsilent_1hr_l1 <- comb.means[which(comb.means$Status==\"Silent\" & comb.means$Measure==\"Mean_1HR_C1\"),]$final.mean\nsilent_24hr_l1 <- comb.means[which(comb.means$Status==\"Silent\" & comb.means$Measure==\"Mean_24HR_C1\"),]$final.mean\n\nsilent_0hr_l2 <- comb.means[which(comb.means$Status==\"Silent\" & comb.means$Measure==\"Mean_0HR_C2\"),]$final.mean\nsilent_1hr_l2 <- comb.means[which(comb.means$Status==\"Silent\" & comb.means$Measure==\"Mean_1HR_C2\"),]$final.mean\nsilent_24hr_l2 <- comb.means[which(comb.means$Status==\"Silent\" & comb.means$Measure==\"Mean_24HR_C2\"),]$final.mean\n" }, { "type": "Heading", "depth": 2, "content": [ "Conditional expression of c-Myc in the B-cell line P493-6" ] }, { "type": "Paragraph", "content": [ "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 ", { "type": "Emphasis", "content": [ "MYC" ] }, " transgene (", { "type": "Cite", "target": "bib23" }, "; ", { "type": "Cite", "target": "bib30" }, ") 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 ", { "type": "Cite", "target": "bib17" }, " and described in Protocol 1 in the Registered Report (", { "type": "Cite", "target": "bib3" }, "). Since proliferation of P493-6 cells depend on c-Myc expression and the presence of serum (", { "type": "Cite", "target": "bib23" }, "; ", { "type": "Cite", "target": "bib30" }, "), with serum reported to stimulate a majority of genes independent of c-Myc (", { "type": "Cite", "target": "bib28" }, "), 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 (", { "type": "Link", "target": "#fig1a", "content": [ "Figure 1A" ] }, "). After removal of tetracycline, c-Myc levels increased over time approaching the levels observed in tetracycline-free conditions." ] }, { "type": "Figure", "id": "fig1a", "caption": [ { "type": "Heading", "depth": 2, "content": [ "Induction of c-Myc in P493-6 cells and impact on total RNA levels." ] }, { "type": "Paragraph", "content": [ "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. ", { "type": "Strong", "content": [ "(A)" ] }, " 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." ] } ], "label": "Figure 1A", "content": [ { "type": "ImageObject", "contentUrl": "article.json.media/fig1a.png" } ] }, { "type": "CodeChunk", "id": "fig1b", "caption": [ { "type": "Heading", "depth": 2, "content": [ "Induction of c-Myc in P493-6 cells and impact on total RNA levels." ] }, { "type": "Paragraph", "content": [ "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. ", { "type": "Strong", "content": [ "(B)" ] }, " 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 ", { "type": "CodeExpression", "duration": 0.003, "output": 3, "programmingLanguage": "r", "text": "length(subset(data2, Lot==1 & Time==0)$value)" }, " independent biological repeats. For serum lot one, one-way ANOVA on total RNA levels of all groups; ", { "type": "Emphasis", "content": [ "F" ] }, "(", { "type": "CodeExpression", "duration": 0.004, "output": 2, "programmingLanguage": "r", "text": "summary(fit1)[[1]][[\"Df\"]][1]" }, ", ", { "type": "CodeExpression", "duration": 0.003, "output": 6, "programmingLanguage": "r", "text": "summary(fit1)[[1]][[\"Df\"]][2]" }, ") = ", { "type": "CodeExpression", "duration": 0.004, "output": 1.25, "programmingLanguage": "r", "text": "round(summary(fit1)[[1]][[\"F value\"]][1],digits=2)" }, ", ", { "type": "Emphasis", "content": [ "p" ] }, " = ", { "type": "CodeExpression", "duration": 0.003, "output": ".353", "programmingLanguage": "r", "text": "sub('^(-)?0[.]','\\\\1.', round(summary(fit1)[[1]][[\"Pr(>F)\"]][1], digits = 3))" }, ". Planned contrast between 0 hr and 24 hr; ", { "type": "Emphasis", "content": [ "t" ] }, "(", { "type": "CodeExpression", "duration": 0.002, "output": 6, "programmingLanguage": "r", "text": "contrast1$df" }, ") = ", { "type": "CodeExpression", "duration": 0.003, "output": 1.02, "programmingLanguage": "r", "text": "round(contrast1$t.ratio,2)" }, ", ", { "type": "Emphasis", "content": [ "p" ] }, " = ", { "type": "CodeExpression", "duration": 0.003, "output": ".347", "programmingLanguage": "r", "text": "sub('^(-)?0[.]','\\\\1.',round(contrast1$p.value,3))" }, " with ", { "type": "Emphasis", "content": [ "a priori" ] }, " alpha level = .05. For serum lot two, one-way ANOVA on total RNA levels of all groups; ", { "type": "Emphasis", "content": [ "F" ] }, "(", { "type": "CodeExpression", "duration": 0.003, "output": 2, "programmingLanguage": "r", "text": "summary(fit2)[[1]][[\"Df\"]][1]" }, ", ", { "type": "CodeExpression", "duration": 0.003, "output": 6, "programmingLanguage": "r", "text": "summary(fit2)[[1]][[\"Df\"]][2]" }, ") = ", { "type": "CodeExpression", "duration": 0.004, "output": 21.87, "programmingLanguage": "r", "text": "round(summary(fit2)[[1]][[\"F value\"]][1],digits=2)" }, ", ", { "type": "Emphasis", "content": [ "p" ] }, " = ", { "type": "CodeExpression", "duration": 0.003, "output": ".00176", "programmingLanguage": "r", "text": "sub('^(-)?0[.]','\\\\1.', round(summary(fit2)[[1]][[\"Pr(>F)\"]][1], digits = 5))" }, ". Planned contrast between 0 hr and 24 hr; ", { "type": "Emphasis", "content": [ "t" ] }, "(", { "type": "CodeExpression", "duration": 0.002, "output": 6, "programmingLanguage": "r", "text": "contrast2$df" }, ") = ", { "type": "CodeExpression", "duration": 0.003, "output": 5.03, "programmingLanguage": "r", "text": "round(contrast2$t.ratio,2)" }, ", ", { "type": "Emphasis", "content": [ "p" ] }, " = ", { "type": "CodeExpression", "duration": 0.003, "output": ".0024", "programmingLanguage": "r", "text": "sub('^(-)?0[.]','\\\\1.',round(contrast2$p.value,4))" }, " with ", { "type": "Emphasis", "content": [ "a priori" ] }, " alpha level = .05. Additional details for this experiment can be found at ", { "type": "Link", "target": "https://osf.io/tfd57/.", "content": [ "https://osf.io/tfd57/" ] }, "." ] } ], "duration": 0.281, "label": "Figure 1B", "outputs": [ { "type": "ImageObject", "contentUrl": 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" } ], "programmingLanguage": "r", "text": "#' @width 17\n#' @height 10\n\n#creates new column calculating RNA in 100uL\ndata2$RNA.100uL <- data2$Average.RNA.Concentration*100\n\n##calculates RNA per cell\ndata2$RNA.per.cell <- data2$RNA.100uL/data2$Total.Cells.Harvested\n\n#calculates RNA per 1000 cells\ndata2$value <- data2$RNA.per.cell*1000\n\n#classifies time as character\ndata2$Time <- as.character(data2$Time)\n\n########## subsets and summarizes Data ##########\n\n#subsets data on lot 1\nlot1dat <- data2[which(data2$Lot==\"1\"),]\n#subsets data on lot 2\nlot2dat <- data2[which(data2$Lot==\"2\"),]\n\n#summarizes lot 1 data\nlot1sum <- summarySE(data=lot1dat, measurevar = \"value\", groupvars = \"Time\")\n#summarizes lot 2 data\nlot2sum <- summarySE(data=lot2dat, measurevar = \"value\", groupvars = \"Time\")\n\n########## Generates bar plot for lot 1 ##########\n##################################################\n\nplot.lot1 <- ggplot(lot1sum, aes(x=Time, y=lot1sum$value, fill=Time)) +\n geom_bar(stat=\"identity\", width=.8, color = \"black\") +\n geom_errorbar(aes(x=Time, ymin=value-se, ymax=value+se),\n width=.20)+\n coord_cartesian(ylim=c(0,2.5)) +\n scale_fill_manual(values = c(\"grey30\", \"grey30\",\"grey30\")) +\n ylab(expression(paste(\"Total RNA (ng) \\n per 1,000 cells\"))) +\n scale_y_continuous(expand = c(0,0),\n limits = c(0,6),\n breaks = c(0, .5, 1.0, 1.5, 2.0, 2.5),\n labels = c(\"0.0\", \"0.5\", \"1.0\", \"1.5\", \"2.0\", \"2.5\")) +\n scale_x_discrete(labels = c(\"0hr\", \"1hr\", \"24hr\")) +\n theme(plot.margin = unit(c(1,1,1,2), \"lines\"),\n axis.ticks.length = unit(0.25, \"cm\"),\n axis.text.x = element_text(size=15, color = \"black\"),\n axis.text.y = element_text(size = 15, color = \"black\"),\n axis.title.y = element_text(size = 20),\n axis.title.x = element_blank(),\n panel.background = element_blank(),\n axis.line.y = element_line(),\n legend.position = \"none\",\n axis.line.x = element_line())\n\n########## Generates bar plot for lot 1 ##########\n##################################################\n\nplot.lot2 <- ggplot(lot2sum, aes(x=Time, y=lot2sum$value, fill=Time)) +\n geom_bar(stat=\"identity\", width=.8, color = \"black\") +\n geom_errorbar(aes(x=Time, ymin=value-se, ymax=value+se),\n width=.20)+\n coord_cartesian(ylim=c(0,2.5)) +\n scale_fill_manual(values = c(\"grey30\",\"grey30\",\"grey30\")) +\n ylab(expression(\"Total RNA (ng) \\n per 1,000 cells\")) +\n scale_y_continuous(expand = c(0,0),\n limits = c(0,6),\n breaks = c(0, .5, 1.0, 1.5, 2.0, 2.5),\n labels = c(\"0.0\", \"0.5\", \"1.0\", \"1.5\", \"2.0\", \"2.5\")) +\n scale_x_discrete(labels = c(\"0hr\", \"1hr\", \"24hr\")) +\n theme(plot.margin = unit(c(1,1,1,2), \"lines\"),\n axis.text.x = element_text(size=15, color = \"black\"),\n axis.text.y = element_text(size = 15, color = \"black\"),\n axis.title.y = element_text(size = 20),\n axis.title.x = element_blank(),\n panel.background = element_blank(),\n axis.line.y = element_line(),\n legend.position = \"none\",\n axis.line.x = element_line())\n\nFigure_1B <- plot_grid(plot.lot1, plot.lot2, labels = c(\"Lot 1\", \"Lot 2\"), label_size = 20, hjust = .01)\nFigure_1B" }, { "type": "Heading", "depth": 2, "content": [ "Total RNA levels following c-Myc overexpression" ] }, { "type": "Paragraph", "content": [ "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 ", { "type": "Cite", "target": "bib17" }, " and used the same extraction method for total RNA quantification, which was described in Protocol 2 in the Registered Report (", { "type": "Cite", "target": "bib3" }, "). 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 (", { "type": "Link", "target": "#fig1", "content": [ "Figure 1B" ] }, "). We found that under conditions where c-Myc expression was low (0 hr), there was a mean of ", { "type": "CodeExpression", "duration": 0.004, "output": 1.55, "programmingLanguage": "r", "text": "round(mean(subset(data2, Lot==1 & Time==0)$value),2)" }, " ng total RNA per 1,000 cells (ng/1 k cells) [n = ", { "type": "CodeExpression", "duration": 0.003, "output": 3, "programmingLanguage": "r", "text": "length(subset(data2, Lot==1 & Time==0)$value)" }, ", ", { "type": "Emphasis", "content": [ "SD" ] }, " = ", { "type": "CodeExpression", "duration": 0.004, "output": "0.20", "programmingLanguage": "r", "text": "formatC(sd(subset(data2, Lot==1 & Time==0)$value),2,format=\"f\")" }, "], which increased to ", { "type": "CodeExpression", "duration": 0.003, "output": 1.77, "programmingLanguage": "r", "text": "round(mean(subset(data2, Lot==1 & Time==24)$value),2)" }, " ng/1 k cells [n = ", { "type": "CodeExpression", "duration": 0.003, "output": 3, "programmingLanguage": "r", "text": "length(subset(data2, Lot==1 & Time==24)$value)" }, ", ", { "type": "Emphasis", "content": [ "SD" ] }, " = ", { "type": "CodeExpression", "duration": 0.003, "output": 0.31, "programmingLanguage": "r", "text": "round(sd(subset(data2, Lot==1 & Time==24)$value),2)" }, "] when c-Myc expression was high (24 hr), a ", { "type": "CodeExpression", "duration": 0.003, "output": 1.14, "programmingLanguage": "r", "text": "round(mean(subset(data2, Lot==1 & Time==24)$value)/mean(subset(data2, Lot==1 & Time==0)$value),2)" }, " times increase, for serum lot one, which was not statistically significant (", { "type": "Emphasis", "content": [ "t" ] }, "(", { "type": "CodeExpression", "duration": 0.003, "output": 6, "programmingLanguage": "r", "text": "contrast1$df" }, ") = ", { "type": "CodeExpression", "duration": 0.002, "output": 1.02, "programmingLanguage": "r", "text": "round(contrast1$t.ratio,2)" }, ", ", { "type": "Emphasis", "content": [ "p" ] }, "=", { "type": "CodeExpression", "duration": 0.003, "output": ".347", "programmingLanguage": "r", "text": "sub('^(-)?0[.]','\\\\1.',round(contrast1$p.value,3))" }, "). Serum lot two changed from a mean of ", { "type": "CodeExpression", "duration": 0.003, "output": 1.57, "programmingLanguage": "r", "text": "round(mean(subset(data2, Lot==2 & Time==0)$value),2)" }, " ng/1 k cells [n = ", { "type": "CodeExpression", "duration": 0.003, "output": 3, "programmingLanguage": "r", "text": "length(subset(data2, Lot==2 & Time==0)$value)" }, ", ", { "type": "Emphasis", "content": [ "SD" ] }, " = ", { "type": "CodeExpression", "duration": 0.003, "output": 0.21, "programmingLanguage": "r", "text": "round(sd(subset(data2, Lot==2 & Time==0)$value),2)" }, "] at 0 hr to ", { "type": "CodeExpression", "duration": 0.002, "output": 2.25, "programmingLanguage": "r", "text": "round(mean(subset(data2, Lot==2 & Time==24)$value),2)" }, " ng/1 k cells [n = ", { "type": "CodeExpression", "duration": 0.005, "output": 3, "programmingLanguage": "r", "text": "length(subset(data2, Lot==2 & Time==24)$value)" }, ", ", { "type": "Emphasis", "content": [ "SD" ] }, " = ", { "type": "CodeExpression", "duration": 0.003, "output": 0.19, "programmingLanguage": "r", "text": "round(sd(subset(data2, Lot==2 & Time==24)$value),2)" }, "] at 24 hr, a ", { "type": "CodeExpression", "duration": 0.003, "output": 1.43, "programmingLanguage": "r", "text": "round(mean(subset(data2, Lot==2 & Time==24)$value)/mean(subset(data2, Lot==2 & Time==0)$value),2)" }, " times increase, which was statistically significant (", { "type": "Emphasis", "content": [ "t" ] }, "(", { "type": "CodeExpression", "duration": 0.003, "output": 6, "programmingLanguage": "r", "text": "contrast2$df" }, " = ", { "type": "CodeExpression", "duration": 0.003, "output": 5.03, "programmingLanguage": "r", "text": "round(contrast2$t.ratio,2)" }, ", ", { "type": "Emphasis", "content": [ "p" ] }, "=", { "type": "CodeExpression", "duration": 0.003, "output": ".0024", "programmingLanguage": "r", "text": "sub('^(-)?0[.]','\\\\1.',round(contrast2$p.value,4))" }, "). This compares to the original study, which reported a mean of ", { "type": "CodeExpression", "duration": 0.003, "output": 4.25, "programmingLanguage": "r", "text": "round(mean(zero),2)" }, " ng/1 k cells at 0 hr, which increased to ", { "type": "CodeExpression", "duration": 0.003, "output": 5.47, "programmingLanguage": "r", "text": "round(mean(twentyfour),2)" }, " ng/1 k cells at 24 hr, a ", { "type": "CodeExpression", "duration": 0.003, "output": 1.29, "programmingLanguage": "r", "text": "round(mean(twentyfour)/mean(zero),2)" }, " 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 (", { "type": "Cite", "target": "bib27" }, "). There are multiple possible explanations for these differences, such as variation in RNA expression during cell culture passage (", { "type": "Cite", "target": "bib11" }, "), low yield of the RNA isolation procedure (e.g. incomplete homogenization), or the high variance associated with manual cell counts using a hemacytometer (", { "type": "Cite", "target": "bib2" }, "; ", { "type": "Cite", "target": "bib20" }, "). 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." ] }, { "type": "Heading", "depth": 2, "content": [ "Digital gene expression following c-Myc overexpression" ] }, { "type": "Paragraph", "content": [ "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 ", { "type": "Cite", "target": "bib17" }, " and described in Protocols 3–4 in the Registered Report (", { "type": "Cite", "target": "bib3" }, "). 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 ", { "type": "CodeExpression", "duration": 0.003, "output": 708, "programmingLanguage": "r", "text": "length(active_0hr_l1)" }, " active genes with a median expression of ", { "type": "CodeExpression", "duration": 0.003, "output": "4.70", "programmingLanguage": "r", "text": "formatC(median(active_0hr_l1),2,format=\"f\")" }, ", and ", { "type": "CodeExpression", "duration": 0.003, "output": 580, "programmingLanguage": "r", "text": "length(silent_0hr_l1)" }, " silent genes with a median expression of ", { "type": "CodeExpression", "duration": 0.003, "output": 0.032, "programmingLanguage": "r", "text": "round(median(silent_0hr_l1),3)" }, ", for serum lot one. For active genes, ", { "type": "CodeExpression", "duration": 0.003, "output": 75, "programmingLanguage": "r", "text": "round(length(which((active_1hr_l1-active_0hr_l1)>0))/length(active_0hr_l1)*100)" }, "% of the genes increased from 0 hr to 1 hr, ", { "type": "CodeExpression", "duration": 0.004, "output": 68, "programmingLanguage": "r", "text": "round(length(which((active_24hr_l1-active_0hr_l1)>0))/length(active_0hr_l1)*100)" }, "% increased from 0 hr to 24 hr, and ", { "type": "CodeExpression", "duration": 0.003, "output": 59, "programmingLanguage": "r", "text": "round(length(which((active_24hr_l1-active_1hr_l1)>0))/length(active_0hr_l1)*100)" }, "% increased from 1 hr to 24 hr upon c-Myc induction. This corresponds to a ", { "type": "CodeExpression", "duration": 0.003, "output": 1.11, "programmingLanguage": "r", "text": "round(median(active_1hr_l1)/median(active_0hr_l1),2)" }, ", ", { "type": "CodeExpression", "duration": 0.003, "output": "1.50", "programmingLanguage": "r", "text": "formatC(median(active_24hr_l1)/median(active_0hr_l1),2,format=\"f\")" }, ", and ", { "type": "CodeExpression", "duration": 0.003, "output": 1.36, "programmingLanguage": "r", "text": "round(median(active_24hr_l1)/median(active_1hr_l1),2)" }, " times increase in median expression, respectively (", { "type": "Link", "target": "#fig2", "content": [ "Figure 2" ] }, ", ", { "type": "Link", "target": "#fig2s1", "content": [ "Figure 2—figure supplement 1" ] }, "). For silent genes, ", { "type": "CodeExpression", "duration": 0.002, "output": 74, "programmingLanguage": "r", "text": "round(length(which((silent_1hr_l1-silent_0hr_l1)>0))/length(silent_0hr_l1)*100)" }, "% of the genes increased from 0 hr to 1 hr, ", { "type": "CodeExpression", "duration": 0.002, "output": 66, "programmingLanguage": "r", "text": "round(length(which((silent_24hr_l1-silent_0hr_l1)>0))/length(silent_0hr_l1)*100)" }, "% increased from 0 hr to 24 hr, and ", { "type": "CodeExpression", "duration": 0.003, "output": 50, "programmingLanguage": "r", "text": "round(length(which((silent_24hr_l1-silent_1hr_l1)>0))/length(silent_0hr_l1)*100)" }, "% increased from 1 hr to 24 hr, corresponding to a ", { "type": "CodeExpression", "duration": 0.003, "output": 1.19, "programmingLanguage": "r", "text": "round(median(silent_1hr_l1)/median(silent_0hr_l1),2)" }, " and ", { "type": "CodeExpression", "duration": 0.003, "output": 1.13, "programmingLanguage": "r", "text": "round(median(silent_24hr_l1)/median(silent_0hr_l1),2)" }, " times increase, and a ", { "type": "CodeExpression", "duration": 0.003, "output": 0.05, "programmingLanguage": "r", "text": "abs(round((median(silent_24hr_l1)-median(silent_1hr_l1))/(median(silent_24hr_l1)),2))" }, " times decrease in median expression, respectively (", { "type": "Link", "target": "#fig2", "content": [ "Figure 2" ] }, ", ", { "type": "Link", "target": "#fig2s1", "content": [ "Figure 2—figure supplement 1" ] }, "). 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 (", { "type": "Link", "target": "#fig2", "content": [ "Figure 2" ] }, ", ", { "type": "Link", "target": "#fig2s1", "content": [ "Figure 2—figure supplement 1" ] }, "). 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)." ] }, { "type": "CodeChunk", "id": "fig2", "caption": [ { "type": "Heading", "depth": 2, "content": [ "Digital gene expression analysis." ] }, { "type": "Paragraph", "content": [ "P493-6 cells grown in the presence of tetracycline (Tet) for 72 hr for repression of the conditional p", { "type": "Emphasis", "content": [ "myc" ] }, "-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 (", { "type": "CodeExpression", "duration": 0.009, "output": "1,308", "programmingLanguage": "r", "text": "prettyNum(length(unique(comb.means$Accession)), big.mark=\",\")" }, " 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 = ", { "type": "CodeExpression", "duration": 0.003, "output": 708, "programmingLanguage": "r", "text": "length(active_0hr_l1)" }, ", silent genes = ", { "type": "CodeExpression", "duration": 0.003, "output": 580, "programmingLanguage": "r", "text": "length(silent_0hr_l1)" }, ". Cells grown in serum lot two: active genes = ", { "type": "CodeExpression", "duration": 0.003, "output": 719, "programmingLanguage": "r", "text": "length(active_0hr_l2)" }, ", silent genes = ", { "type": "CodeExpression", "duration": 0.002, "output": 573, "programmingLanguage": "r", "text": "length(silent_0hr_l2)" }, ". Confirmatory analysis is reported in ", { "type": "Link", "target": "#table1", "content": [ "Table 1" ] }, " and exploratory statistical analysis is reported in ", { "type": "Link", "target": "#table2", "content": [ "Table 2" ] }, " and ", { "type": "Link", "target": "#table3", "content": [ "Table 3" ] }, ". Additional details for this experiment can be found at ", { "type": "Link", "target": "https://osf.io/fn2y4/.", "content": [ "https://osf.io/fn2y4/" ] }, ".List of Reporter CodeSets and gene expression values." ] } ], "duration": 1.779, "label": "Figure 2", "outputs": [ { "type": "ImageObject", "contentUrl": 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" } ], "programmingLanguage": "r", "text": "#' @width 18\n#' @height 24\n\ncomb.means <- comb.means[which(comb.means$Status!=\"NA\"),] #removes NA status genes\ncomb.means$lstat <- interaction(comb.means$Time, comb.means$Status) #creates interaction variable between lot and status called 'lstat'\ncomb.means$Time <- as.character(comb.means$Time) #creates a column for Time\n\nactive <- comb.means[which(comb.means$Status==\"Active\"),] #subsets all data on Active gene status\nsilent <- comb.means[which(comb.means$Status==\"Silent\"),] #subsets all data on Silent gene status\n\n#create summary data for graph lot 1\nactive.sum1 <- summarySE(active[which(active$Lot==\"C1\"),], measurevar=\"final.mean\", groupvars=\"Time\")\nsilent.sum1 <- summarySE(silent[which(silent$Lot==\"C1\"),], measurevar=\"final.mean\", groupvars=\"Time\")\n\n#create summary data for graph lot 2\nactive.sum2 <- summarySE(active[which(active$Lot==\"C2\"),], measurevar=\"final.mean\", groupvars=\"Time\")\nsilent.sum2 <- summarySE(silent[which(silent$Lot==\"C2\"),], measurevar=\"final.mean\", groupvars=\"Time\")\n\n########## Plots Active Genes/ Lot 1 LOG SCALE ##########\n#########################################################\n\nlog_activeplot1 <- ggplot(active[which(active$Lot==\"C1\"),], aes(x=Time, y = final.mean)) +\n stat_boxplot(geom ='errorbar', width=0.5) +\n geom_boxplot(aes(fill=Time), outlier.shape = 16, outlier.size = 1.5, outlier.colour = \"black\", colour = \"black\") +\n scale_fill_manual(values=c(\"red\", \"red\", \"red\"))+\n ylab(\"Transcripts/Cell\") +\n ggtitle(\"Active genes\")+\n xlab(element_blank()) +\n scale_x_discrete(labels=c(\"0hr\", \"1hr\", \"24hr\")) +\n scale_y_continuous(expand = c(.01,.01),\n trans = \"log2\",\n limits = c(2^-10,2^11),\n breaks = c( 2^-9,2^-5,2^-1,2^3,2^7,2^11),\n labels = c(bquote(\"2\"^\"-9\"),bquote(\"2\"^\"-5\"),\n bquote(\"2\"^\"-1\"),bquote(\"2\"^\"3\"),\n bquote(\"2\"^\"7\"),bquote(\"2\"^\"11\"))) +\n theme_bw()+\n theme(legend.position = \"none\",\n axis.ticks.length = unit(0.2, \"cm\"),\n plot.title = element_text(color = \"black\", size = 15, hjust = .5),\n plot.margin = unit(c(1,1,1,1),\"cm\"),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n panel.background = element_rect(colour = \"black\", size=1.8),\n axis.title = element_text(colour = \"black\", size = 15),\n axis.text.x = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5)),\n axis.text.y = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5), hjust = 0),\n axis.title.x = element_blank(),\n axis.title.y = element_text(colour=\"black\",size=15,margin = margin(r=10, unit=\"pt\")))\n\n\n########## Plots Active Genes/ Lot 1 LINEAR SCALE ##########\n############################################################\n\nlinear_activeplot1 <- ggplot(active[which(active$Lot==\"C1\" & active$final.mean<=100),], aes(x=Time, y = final.mean)) +\n stat_boxplot(geom ='errorbar', width=0.5 ) +\n geom_boxplot(aes(fill=Time), outlier.shape = 16, outlier.size = 1.5, outlier.colour = \"black\", colour = \"black\") +\n scale_fill_manual(values=c(\"red\", \"red\", \"red\"))+\n ggtitle(\"Active genes\")+\n ylab(\"Transcripts/Cell\") +\n xlab(element_blank()) +\n scale_x_discrete(labels=c(\"0hr\", \"1hr\", \"24hr\")) +\n scale_y_continuous(expand = c(0,0),\n limits = c(-5, 105),\n breaks = c(0,20,40,60,80,100)) +\n theme_bw()+\n theme(legend.position = \"none\",\n axis.ticks.length = unit(0.2, \"cm\"),\n plot.title = element_text(color = \"black\", size = 15, hjust = .5),\n plot.margin = unit(c(1,1,1,1),\"cm\"),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n panel.background = element_rect(colour = \"black\", size=1.8),\n axis.title = element_text(colour = \"black\", size = 15),\n axis.text.x = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5)),\n axis.text.y = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5), hjust = 0),\n axis.title.x = element_blank(),\n axis.title.y = element_text(colour=\"black\",size=15,margin = margin(r=10, unit=\"pt\")))\n\n\n########## Plots Active Genes/ Lot 2 LOG SCALE ##########\n#########################################################\n\nlog_activeplot2 <- ggplot(active[which(active$Lot==\"C2\"),], aes(x=Time, y = final.mean)) +\n stat_boxplot(geom ='errorbar', width=0.5 ) +\n geom_boxplot(aes(fill=Time), outlier.shape = 16, outlier.size = 1.5, outlier.colour = \"black\", colour = \"black\") +\n scale_fill_manual(values=c(\"red\", \"red\", \"red\"))+\n ylab(\"Transcripts/Cell\") +\n ggtitle(\"Active genes\")+\n xlab(element_blank()) +\n scale_x_discrete(labels=c(\"0hr\", \"1hr\", \"24hr\")) +\n scale_y_continuous(expand = c(.01,.01),\n trans = \"log2\",\n limits = c(2^-10,2^11),\n breaks = c( 2^-9,2^-5,2^-1,2^3,2^7,2^11),\n labels = c(bquote(\"2\"^\"-9\"),bquote(\"2\"^\"-5\"),\n bquote(\"2\"^\"-1\"),bquote(\"2\"^\"3\"),\n bquote(\"2\"^\"7\"),bquote(\"2\"^\"11\"))) +\n theme_bw()+\n theme(legend.position = \"none\",\n axis.ticks.length = unit(0.2, \"cm\"),\n plot.title = element_text(color = \"black\", size = 15, hjust = .5),\n plot.margin = unit(c(1,1,1,1),\"cm\"),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n panel.background = element_rect(colour = \"black\", size=1.8),\n axis.title = element_text(colour = \"black\", size = 15),\n axis.text.x = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5)),\n axis.text.y = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5), hjust = 0),\n axis.title.x = element_blank(),\n axis.title.y = element_text(colour=\"black\",size=15,margin = margin(r=10, unit=\"pt\")))\n\n\n########## Plots Active Genes/ Lot 2 LINEAR SCALE ##########\n############################################################\n\nlinear_activeplot2 <- ggplot(active[which(active$Lot==\"C2\" & active$final.mean<=100),], aes(x=Time, y = final.mean)) +\n stat_boxplot(geom ='errorbar', width=0.5 ) +\n geom_boxplot(aes(fill=Time), outlier.shape = 16, outlier.size = 1.5, outlier.colour = \"black\", colour = \"black\") +\n scale_fill_manual(values=c(\"red\", \"red\", \"red\"))+\n ggtitle(\"Active genes\")+\n ylab(\"Transcripts/Cell\") +\n xlab(element_blank()) +\n scale_x_discrete(labels=c(\"0hr\", \"1hr\", \"24hr\")) +\n scale_y_continuous(expand = c(0,0),\n limits = c(-5, 105),\n breaks = c(0,20,40,60,80,100)) +\n theme_bw()+\n theme(legend.position = \"none\",\n axis.ticks.length = unit(0.2, \"cm\"),\n plot.title = element_text(color = \"black\", size = 15, hjust = .5),\n plot.margin = unit(c(1,1,1,1),\"cm\"),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n panel.background = element_rect(colour = \"black\", size=1.8),\n axis.title = element_text(colour = \"black\", size = 15),\n axis.text.x = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5)),\n axis.text.y = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5), hjust = 0),\n axis.title.x = element_blank(),\n axis.title.y = element_text(colour=\"black\",size=15,margin = margin(r=10, unit=\"pt\")))\n\n\n########## Plots Silent Genes/ Lot 1 LOG SCALE ##########\n#########################################################\n\nlog_silentplot1 <- ggplot(silent[which(silent$Lot==\"C1\"),], aes(x=Time, y = final.mean)) +\n stat_boxplot(geom ='errorbar', width=0.5 ) +\n geom_boxplot(aes(fill=Time), outlier.shape = 16, outlier.size = 1.5, outlier.colour = \"black\", colour = \"black\") +\n scale_fill_manual(values=c(\"red\", \"red\", \"red\"))+\n ylab(\"Transcripts/Cell\") +\n ggtitle(\"Silent genes\")+\n xlab(element_blank()) +\n scale_x_discrete(labels=c(\"0hr\", \"1hr\", \"24hr\")) +\n scale_y_continuous(expand = c(.01,.01),\n trans = \"log2\",\n limits = c(2^-10,2^11),\n breaks = c( 2^-9,2^-5,2^-1,2^3,2^7,2^11),\n labels = c(bquote(\"2\"^\"-9\"),bquote(\"2\"^\"-5\"),\n bquote(\"2\"^\"-1\"),bquote(\"2\"^\"3\"),\n bquote(\"2\"^\"7\"),bquote(\"2\"^\"11\"))) +\n theme_bw()+\n theme(legend.position = \"none\",\n axis.ticks.length = unit(0.2, \"cm\"),\n plot.title = element_text(color = \"black\", size = 15, hjust = .5),\n plot.margin = unit(c(1,1,1,1),\"cm\"),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n panel.background = element_rect(colour = \"black\", size=1.8),\n axis.title = element_text(colour = \"black\", size = 15),\n axis.text.x = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5)),\n axis.text.y = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5), hjust = 0),\n axis.title.x = element_blank(),\n axis.title.y = element_text(colour=\"black\",size=15,margin = margin(r=10, unit=\"pt\")))\n\n\n########## Plots Silent Genes/ Lot 1 LINEAR SCALE ##########\n############################################################\n\nlinear_silentplot1 <- ggplot(silent[which(silent$Lot==\"C1\" & silent$final.mean<=100),], aes(x=Time, y = final.mean)) +\n stat_boxplot(geom ='errorbar', width=0.5 ) +\n geom_boxplot(aes(fill=Time), outlier.shape = 16, outlier.size = 1.5, outlier.colour = \"black\", colour = \"black\") +\n scale_fill_manual(values=c(\"red\", \"red\", \"red\"))+\n ggtitle(\"Silent genes\")+\n ylab(\"Transcripts/Cell\") +\n xlab(element_blank()) +\n scale_x_discrete(labels=c(\"0hr\", \"1hr\", \"24hr\")) +\n scale_y_continuous(expand = c(0,0),\n limits = c(-5, 105),\n breaks = c(0,20,40,60,80,100)) +\n theme_bw()+\n theme(legend.position = \"none\",\n axis.ticks.length = unit(0.2, \"cm\"),\n plot.title = element_text(color = \"black\", size = 15, hjust = .5),\n plot.margin = unit(c(1,1,1,1),\"cm\"),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n panel.background = element_rect(colour = \"black\", size=1.8),\n axis.title = element_text(colour = \"black\", size = 15),\n axis.text.x = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5)),\n axis.text.y = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5), hjust = 0),\n axis.title.x = element_blank(),\n axis.title.y = element_text(colour=\"black\",size=15,margin = margin(r=10, unit=\"pt\")))\n\n\n########## Plots Silent Genes/ Lot 2 LOG SCALE ##########\n#########################################################\n\n#plots active cohort 1\nlog_silentplot2 <- ggplot(silent[which(silent$Lot==\"C2\"),], aes(x=Time, y = final.mean)) +\n stat_boxplot(geom ='errorbar', width=0.5 ) +\n geom_boxplot(aes(fill=Time), outlier.shape = 16, outlier.size = 1.5, outlier.colour = \"black\", colour = \"black\") +\n scale_fill_manual(values=c(\"red\", \"red\", \"red\"))+\n ylab(\"Transcripts/Cell\") +\n ggtitle(\"Silent genes\")+\n xlab(element_blank()) +\n scale_x_discrete(labels=c(\"0hr\", \"1hr\", \"24hr\")) +\n scale_y_continuous(expand = c(.01,.01),\n trans = \"log2\",\n limits = c(2^-10,2^11),\n breaks = c( 2^-9,2^-5,2^-1,2^3,2^7,2^11),\n labels = c(bquote(\"2\"^\"-9\"),bquote(\"2\"^\"-5\"),\n bquote(\"2\"^\"-1\"),bquote(\"2\"^\"3\"),\n bquote(\"2\"^\"7\"),bquote(\"2\"^\"11\"))) +\n theme_bw()+\n theme(legend.position = \"none\",\n axis.ticks.length = unit(0.2, \"cm\"),\n plot.title = element_text(color = \"black\", size = 15, hjust = .5),\n plot.margin = unit(c(1,1,1,1),\"cm\"),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n panel.background = element_rect(colour = \"black\", size=1.8),\n axis.title = element_text(colour = \"black\", size = 15),\n axis.text.x = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5)),\n axis.text.y = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5), hjust = 0),\n axis.title.x = element_blank(),\n axis.title.y = element_text(colour=\"black\",size=15,margin = margin(r=10, unit=\"pt\")))\n\n\n########## Plots Silent Genes/ Lot 2 LINEAR SCALE ##########\n############################################################\n\nlinear_silentplot2 <- ggplot(silent[which(silent$Lot==\"C2\" & silent$final.mean<=100),], aes(x=Time, y = final.mean)) +\n stat_boxplot(geom ='errorbar', width=0.5 ) +\n geom_boxplot(aes(fill=Time), outlier.shape = 16, outlier.size = 1.5, outlier.colour = \"black\", colour = \"black\") +\n scale_fill_manual(values=c(\"red\", \"red\", \"red\")) +\n ggtitle(\"Silent genes\")+\n ylab(\"Transcripts/Cell\") +\n xlab(element_blank()) +\n scale_x_discrete(labels=c(\"0hr\", \"1hr\", \"24hr\")) +\n scale_y_continuous(expand = c(0,0),\n limits = c(-5, 105),\n breaks = c(0,20,40,60,80,100)) +\n theme_bw()+\n theme(legend.position = \"none\",\n axis.ticks.length = unit(0.2, \"cm\"),\n plot.title = element_text(color = \"black\", size = 15, hjust = .5),\n plot.margin = unit(c(1,1,1,1.88),\"cm\"),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n panel.background = element_rect(colour = \"black\", size=1.8),\n axis.title = element_text(colour = \"black\", size = 15),\n axis.text.x = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5)),\n axis.text.y = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5), hjust = 0),\n axis.title.x = element_blank(),\n axis.title.y = element_text(colour=\"black\",size=15,margin = margin(r=10, unit=\"pt\")))\n\n###########################################################################################\n###########################################################################################\n\n#plots all comparisons for Lot 1 silent\nlot1_silent <- subset(comb.means, comb.means$Lot==\"C1\" & comb.means$Status==\"Silent\")\ntime0 <- subset(lot1_silent, lot1_silent$Time==\"0HR\")\ntime1 <- subset(lot1_silent, lot1_silent$Time==\"1HR\")\ntime24 <- subset(lot1_silent, lot1_silent$Time==\"24HR\")\nratio <- c(((log2(time1$final.mean))-(log2(time0$final.mean))),\n ((log2(time24$final.mean))-(log2(time0$final.mean))),\n ((log2(time24$final.mean))-(log2(time1$final.mean))))\nlot1_silentdat <- as.data.frame(cbind(as.character(lot1_silent[,1]),as.numeric(as.character(ratio))))\nlot1_silentdat$V1 <- as.factor(lot1_silentdat$V1)\nlot1_silentdat$V3 <- c(rep(\"diff1\",nrow(lot1_silent)/3),rep(\"diff2\",nrow(lot1_silent)/3),rep(\"diff3\",nrow(lot1_silent)/3))\nlot1_silentdat$V3 <- as.factor(lot1_silentdat$V3)\nlot1_silentdat$V2 <- as.numeric(as.character(lot1_silentdat$V2))\ncolnames(lot1_silentdat) <- c(\"Gene\",\"ratio\",\"comparison\")\n\nplot_lot1_silent <- ggplot(lot1_silentdat, aes(x=comparison, y = ratio)) +\n stat_boxplot(geom ='errorbar', width=0.5) +\n geom_boxplot(aes(fill=comparison), outlier.shape = 16, outlier.size = 1.5, outlier.colour = \"black\", colour = \"black\") +\n scale_fill_manual(values=c(\"red\", \"red\", \"red\"))+\n ylab(\"log2 (ratio)\") +\n xlab(element_blank()) +\n scale_x_discrete(labels=c(\"1 hr vs. \\n 0 hr\", \"24 hr vs. \\n 0 hr\", \"24hr vs. \\n 1 hr\")) +\n scale_y_continuous(expand = c(0,0),\n limits = c(-4.5,6.5),\n breaks = c(-4, -2, 0, 2, 4, 6),\n labels = c(\"-4\",\"-2\",\"0\",\"2\",\"4\",\"6\")) +\n geom_hline(yintercept = 0) +\n ggtitle(\"Silent genes\")+\n theme_bw()+\n theme(legend.position = \"none\",\n axis.ticks.length = unit(0.2, \"cm\"),\n plot.title = element_text(color = \"black\", size = 15, hjust = .5),\n plot.margin = unit(c(1,1,1,1),\"cm\"),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n panel.background = element_rect(colour = \"black\", size=1.8),\n axis.title = element_text(colour = \"black\", size = 15),\n axis.text.x = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5)),\n axis.text.y = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5)),\n axis.title.x = element_blank(),\n axis.title.y = element_text(colour=\"black\",size=15,margin = margin(r=10, unit=\"pt\")))\n\n####################################################\n####################################################\n\n#plots all comparisons for Lot 2 silent\nlot2_silent <- subset(comb.means, comb.means$Lot==\"C2\" & comb.means$Status==\"Silent\")\ntime0 <- subset(lot2_silent, lot2_silent$Time==\"0HR\")\ntime1 <- subset(lot2_silent, lot2_silent$Time==\"1HR\")\ntime24 <- subset(lot2_silent, lot2_silent$Time==\"24HR\")\nratio <- c(((log2(time1$final.mean))-(log2(time0$final.mean))),\n ((log2(time24$final.mean))-(log2(time0$final.mean))),\n ((log2(time24$final.mean))-(log2(time1$final.mean))))\nlot2_silentdat <- as.data.frame(cbind(as.character(lot2_silent[,1]),as.numeric(as.character(ratio))))\nlot2_silentdat$V1 <- as.factor(lot2_silentdat$V1)\nlot2_silentdat$V3 <- c(rep(\"diff1\",nrow(lot2_silent)/3),rep(\"diff2\",nrow(lot2_silent)/3),rep(\"diff3\",nrow(lot2_silent)/3))\nlot2_silentdat$V3 <- as.factor(lot2_silentdat$V3)\nlot2_silentdat$V2 <- as.numeric(as.character(lot2_silentdat$V2))\ncolnames(lot2_silentdat) <- c(\"Gene\",\"ratio\",\"comparison\")\n\nplot_lot2_silent <- ggplot(lot2_silentdat, aes(x=comparison, y = ratio)) +\n stat_boxplot(geom ='errorbar', width=0.5) +\n geom_boxplot(aes(fill=comparison), outlier.shape = 16, outlier.size = 1.5, outlier.colour = \"black\", colour = \"black\") +\n scale_fill_manual(values=c(\"red\", \"red\", \"red\"))+\n ylab(\"log2 (ratio)\") +\n xlab(element_blank()) +\n scale_x_discrete(labels=c(\"1 hr vs. \\n 0 hr\", \"24 hr vs. \\n 0 hr\", \"24hr vs. \\n 1 hr\")) +\n scale_y_continuous(expand = c(0,0),\n limits = c(-4.5,6.5),\n breaks = c(-4, -2, 0, 2, 4, 6),\n labels = c(\"-4\",\"-2\",\"0\",\"2\",\"4\",\"6\")) +\n geom_hline(yintercept = 0) +\n ggtitle(\"Silent genes\")+\n theme_bw()+\n theme(legend.position = \"none\",\n axis.ticks.length = unit(0.2, \"cm\"),\n plot.title = element_text(color = \"black\", size = 15, hjust = .5),\n plot.margin = unit(c(1,1,1,1),\"cm\"),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n panel.background = element_rect(colour = \"black\", size=1.8),\n axis.title = element_text(colour = \"black\", size = 15),\n axis.text.x = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5)),\n axis.text.y = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5)),\n axis.title.x = element_blank(),\n axis.title.y = element_text(colour=\"black\",size=15,margin = margin(r=10, unit=\"pt\")))\n\n####################################################\n####################################################\n\n#plots all comparisons for Lot 1 active\nlot1_active <- subset(comb.means, comb.means$Lot==\"C1\" & comb.means$Status==\"Active\")\ntime0 <- subset(lot1_active, lot1_active$Time==\"0HR\")\ntime1 <- subset(lot1_active, lot1_active$Time==\"1HR\")\ntime24 <- subset(lot1_active, lot1_active$Time==\"24HR\")\nratio <- c(((log2(time1$final.mean))-(log2(time0$final.mean))),\n ((log2(time24$final.mean))-(log2(time0$final.mean))),\n ((log2(time24$final.mean))-(log2(time1$final.mean))))\nlot1_activedat <- as.data.frame(cbind(as.character(lot1_active[,1]),as.numeric(as.character(ratio))))\nlot1_activedat$V1 <- as.factor(lot1_activedat$V1)\nlot1_activedat$V3 <- c(rep(\"diff1\",nrow(lot1_active)/3),rep(\"diff2\",nrow(lot1_active)/3),rep(\"diff3\",nrow(lot1_active)/3))\nlot1_activedat$V3 <- as.factor(lot1_activedat$V3)\nlot1_activedat$V2 <- as.numeric(as.character(lot1_activedat$V2))\ncolnames(lot1_activedat) <- c(\"Gene\",\"ratio\",\"comparison\")\n\nplot_lot1_active <- ggplot(lot1_activedat, aes(x=comparison, y = ratio)) +\n stat_boxplot(geom ='errorbar', width=0.5) +\n geom_boxplot(aes(fill=comparison), outlier.shape = 16, outlier.size = 1.5, outlier.colour = \"black\", colour = \"black\") +\n scale_fill_manual(values=c(\"red\", \"red\", \"red\"))+\n ylab(\"log2 (ratio)\") +\n xlab(element_blank()) +\n scale_x_discrete(labels=c(\"1 hr vs. \\n 0 hr\", \"24 hr vs. \\n 0 hr\", \"24hr vs. \\n 1 hr\")) +\n scale_y_continuous(expand = c(0,0),\n limits = c(-4.5,6.5),\n breaks = c(-4, -2, 0, 2, 4, 6),\n labels = c(\"-4\",\"-2\",\"0\",\"2\",\"4\",\"6\")) +\n geom_hline(yintercept = 0) +\n ggtitle(\"Active genes\")+\n theme_bw()+\n theme(legend.position = \"none\",\n axis.ticks.length = unit(0.2, \"cm\"),\n plot.title = element_text(color = \"black\", size = 15, hjust = .5),\n plot.margin = unit(c(1,1,1,1),\"cm\"),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n panel.background = element_rect(colour = \"black\", size=1.8),\n axis.title = element_text(colour = \"black\", size = 15),\n axis.text.x = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5)),\n axis.text.y = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5)),\n axis.title.x = element_blank(),\n axis.title.y = element_text(colour=\"black\",size=15,margin = margin(r=10, unit=\"pt\")))\n\n####################################################\n####################################################\n\n#plots all comparisons for Lot 2 active\nlot2_active <- subset(comb.means, comb.means$Lot==\"C2\" & comb.means$Status==\"Active\")\ntime0 <- subset(lot2_active, lot2_active$Time==\"0HR\")\ntime1 <- subset(lot2_active, lot2_active$Time==\"1HR\")\ntime24 <- subset(lot2_active, lot2_active$Time==\"24HR\")\nratio <- c(((log2(time1$final.mean))-(log2(time0$final.mean))),\n ((log2(time24$final.mean))-(log2(time0$final.mean))),\n ((log2(time24$final.mean))-(log2(time1$final.mean))))\nlot2_activedat <- as.data.frame(cbind(as.character(lot2_active[,1]),as.numeric(as.character(ratio))))\nlot2_activedat$V1 <- as.factor(lot2_activedat$V1)\nlot2_activedat$V3 <- c(rep(\"diff1\",nrow(lot2_active)/3),rep(\"diff2\",nrow(lot2_active)/3),rep(\"diff3\",nrow(lot2_active)/3))\nlot2_activedat$V3 <- as.factor(lot2_activedat$V3)\nlot2_activedat$V2 <- as.numeric(as.character(lot2_activedat$V2))\ncolnames(lot2_activedat) <- c(\"Gene\",\"ratio\",\"comparison\")\n\nplot_lot2_active <- ggplot(lot2_activedat, aes(x=comparison, y = ratio)) +\n stat_boxplot(geom ='errorbar', width=0.5) +\n geom_boxplot(aes(fill=comparison), outlier.shape = 16, outlier.size = 1.5, outlier.colour = \"black\", colour = \"black\") +\n scale_fill_manual(values=c(\"red\", \"red\", \"red\"))+\n ylab(\"log2 (ratio)\") +\n xlab(element_blank()) +\n scale_x_discrete(labels=c(\"1 hr vs. \\n 0 hr\", \"24 hr vs. \\n 0 hr\", \"24hr vs. \\n 1 hr\")) +\n scale_y_continuous(expand = c(0,0),\n limits = c(-4.5,6.5),\n breaks = c(-4, -2, 0, 2, 4, 6),\n labels = c(\"-4\",\"-2\",\"0\",\"2\",\"4\",\"6\")) +\n geom_hline(yintercept = 0) +\n ggtitle(\"Active genes\")+\n theme_bw()+\n theme(legend.position = \"none\",\n axis.ticks.length = unit(0.2, \"cm\"),\n plot.title = element_text(color = \"black\", size = 15, hjust = .5),\n plot.margin = unit(c(1,1,1,1),\"cm\"),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n panel.background = element_rect(colour = \"black\", size=1.8),\n axis.title = element_text(colour = \"black\", size = 15),\n axis.text.x = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5)),\n axis.text.y = element_text(colour=\"black\",size=15, margin=margin(.5,.5,.5,.5)),\n axis.title.x = element_blank(),\n axis.title.y = element_text(colour=\"black\",size=15, margin = margin(r=10, unit=\"pt\")))\n\n###########################################################################################\n###########################################################################################\n\n#combines lot 1 linear scale plots\nlinear_lot1 <- plot_grid(linear_activeplot1, linear_silentplot1, ncol = 2, align = \"h\")\n#combines lot 2 linear scale plots\nlinear_lot2 <- plot_grid(linear_activeplot2, linear_silentplot2, ncol = 2, align = \"h\")\n\n#combines lot 1 log scale plots\nlog_lot1 <- plot_grid(log_activeplot1, log_silentplot1, ncol = 2, align = \"h\", labels = c(\"A\",\"B\"))\n#combines lot 2 log scale plots\nlog_lot2 <- plot_grid(log_activeplot2, log_silentplot2, ncol = 2, align = \"h\", labels = c(\"E\",\"F\"))\n\n#combines lot 1 ratio plots\nratio_lot1 <- plot_grid(plot_lot1_active, plot_lot1_silent, ncol = 2, align = \"h\", labels = c(\"C\",\"D\"))\n#combines lot 2 ratio plots\nratio_lot2 <- plot_grid(plot_lot2_active, plot_lot2_silent, ncol = 2, align = \"h\", labels = c(\"G\",\"H\"))\n\n#combines Linear Scale Plots\nLinear <- plot_grid(linear_lot1,linear_lot2, nrow = 2, align = \"h\", labels = c(\"Lot 1\",\"Lot 2\"), label_size = 20)\nfigure_2 <- plot_grid(Linear,ncol = 1,rel_heights = c(0.1,1))\nfigure_2" }, { "type": "Figure", "id": "fig2s1", "caption": [ { "type": "Heading", "depth": 2, "content": [ "Logarithmic expression of genes." ] }, { "type": "Paragraph", "content": [ "This is the same experiment as in ", { "type": "Link", "target": "#fig2", "content": [ "Figure 2" ] }, ". (", { "type": "Strong", "content": [ "A–B, E–F" ] }, ") Gene expression data plotted on a log", { "type": "Subscript", "content": [ "2" ] }, " transformed scale for active (", { "type": "Strong", "content": [ "A, E" ] }, ") and silent (", { "type": "Strong", "content": [ "B, F" ] }, ") genes at 0, 1, and 24 hr after release from Tet for both lots of serum. (", { "type": "Strong", "content": [ "C–D, G–H" ] }, ") Box and whisker plots showing gene expression changes (log", { "type": "Subscript", "content": [ "2" ] }, " ratio) between the indicated times for active (", { "type": "Strong", "content": [ "C, G" ] }, ") and silent (", { "type": "Strong", "content": [ "D, H" ] }, ") 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 ", { "type": "Link", "target": "https://osf.io/fn2y4/.", "content": [ "https://osf.io/fn2y4/" ] }, "." ] } ], "label": "Figure 2—figure supplement 1", "content": [ { "type": "ImageObject", "contentUrl": "article.json.media/fig2-figsupp1.jpg" } ] }, { "type": "Figure", "id": "fig2s2", "caption": [ { "type": "Heading", "depth": 2, "content": [ "Comparison of gene expression data as continuous." ] }, { "type": "Paragraph", "content": [ "This is the same experiment as in ", { "type": "Link", "target": "#fig2", "content": [ "Figure 2" ] }, ". (", { "type": "Strong", "content": [ "A–C, E–G" ] }, ") Scatter plots of log", { "type": "Subscript", "content": [ "2" ] }, " 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. (", { "type": "Strong", "content": [ "D, H" ] }, ") Box and whisker plots showing gene expression changes (log", { "type": "Subscript", "content": [ "2" ] }, " 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 ", { "type": "Link", "target": "https://osf.io/fn2y4/.", "content": [ "https://osf.io/fn2y4/" ] }, "." ] } ], "label": "Figure 2—figure supplement 2", "content": [ { "type": "ImageObject", "contentUrl": "article.json.media/fig2-figsupp2.jpg" } ] }, { "type": "Paragraph", "content": [ "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 (", { "type": "Cite", "target": "bib3" }, "). 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 (", { "type": "Cite", "target": "bib3" }, "). 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 (", { "type": "Link", "target": "#table1", "content": [ "Table 1" ] }, "). 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 (", { "type": "Link", "target": "#table1", "content": [ "Table 1" ] }, "). 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 (", { "type": "Link", "target": "#table2", "content": [ "Table 2" ] }, ")." ] }, { "type": "CodeChunk", "id": "table1", "caption": [ { "type": "Heading", "depth": 2, "content": [ "Confirmatory statistical tests" ] } ], "duration": 0.032, "label": "Table 1", "outputs": [ { "type": "Datatable", "columns": [ { "type": "DatatableColumn", "name": "Genes", "validator": { "type": "ArrayValidator", "itemsValidator": { "type": "StringValidator" } }, "values": [ "Active", "", "", "", "", "", "", "", "", "Silent", "", "", "", "", "", "", "", "" ] }, { "type": "DatatableColumn", "name": "Comparison", "validator": { "type": "ArrayValidator", "itemsValidator": { "type": "StringValidator" } }, "values": [ "0hr vs 1hr", "", "", "0hr vs 24hr", "", "", "1hr vs 24hr", "", "", "0hr vs 1hr", "", "", "0hr vs 24hr", "", "", "1hr vs 24hr", "", "" ] }, { "type": "DatatableColumn", "name": "Study", "validator": { "type": "ArrayValidator", "itemsValidator": { "type": "StringValidator" } }, "values": [ "RP:CB Lot 1", "RP:CB Lot 2", "Lin et al., 2012", "RP:CB Lot 1", "RP:CB Lot 2", "Lin et al., 2012", "RP:CB Lot 1", "RP:CB Lot 2", "Lin et al., 2012", "RP:CB Lot 1", "RP:CB Lot 2", "Lin et al., 2012", "RP:CB Lot 1", "RP:CB Lot 2", "Lin et al., 2012", "RP:CB Lot 1", "RP:CB Lot 2", "Lin et al., 2012" ] }, { "type": "DatatableColumn", "name": "Z value", "validator": { "type": "ArrayValidator", "itemsValidator": { "type": "NumberValidator" } }, "values": [ 14.86, 11.83, 21.17, 9.922, 12.77, 23.26, 4.742, 10.04, 23.26, 12.61, 7.05, -1.998, 8.328, 8.156, 3.179, 0.6853, 4.436, 5.806 ] }, { "type": "DatatableColumn", "name": "P value", "validator": { "type": "ArrayValidator", "itemsValidator": { "type": "NumberValidator" } }, "values": [ 1.36e-55, 2.83e-34, 1.05e-130, 3.67e-24, 3.77e-40, 3.33e-184, 0.00000192, 9.91e-25, 3.33e-184, 7.11e-40, 9.29e-13, 0.0457, 2.22e-17, 1.03e-16, 0.0014, 0.493, 0.00000835, 4.11e-9 ] }, { "type": "DatatableColumn", "name": "Sample size (n)", "validator": { "type": "ArrayValidator", "itemsValidator": { "type": "NumberValidator" } }, "values": [ 708, 719, 755, 708, 719, 755, 708, 719, 755, 580, 572, 274, 579, 572, 276, 580, 573, 236 ] } ] } ], "programmingLanguage": "r", "text": "table1_active <- data.frame(dat[c(1:5)]) #subsets on active genes\ntable1_active <- melt(table1_active,id.vars = c(\"Study\",\"Label\")) #melts on Study and Label Variables\ntable1_active$interaction <- interaction(table1_active$Study,table1_active$variable)\ntable1_active <- reshape(table1_active, idvar=\"interaction\", timevar = \"Label\",direction=\"wide\")\ntable1_active <- table1_active[,c(2:4,7,10)]\n\ntable1_silent <- data.frame(dat[c(1:2,6:8)]) #subsets on silent genes\ntable1_silent <- melt(table1_silent,id.vars = c(\"Study\",\"Label\")) #melts on Study and Label Variables\ntable1_silent$interaction <- interaction(table1_silent$Study,table1_silent$variable)\ntable1_silent <- reshape(table1_silent, idvar=\"interaction\", timevar = \"Label\",direction=\"wide\")\ntable1_silent <- table1_silent[,c(2:4,7,10)]\n\ntable1 <- rbind(table1_active,table1_silent) #combines silent and active into one data frame\n\n#changes column names\ncolnames(table1) <- c(\"Study\",\"Comparison\",\"Z value\",\"P value\",\"Sample size (n)\")\n\n#creates comparison column/ renames comparisons\ntable1$Comparison <- rep(c(rep(\"0hr vs 1hr\",3),rep(\"0hr vs 24hr\",3),rep(\"1hr vs 24hr\",3)),2)\nrownames(table1) <- NULL #deletes row names\ntable1$Study <- as.character(table1$Study) #Makes 'study' variable as character\ntable1[c(2:3,5:6,8:9,11:12,14:15,17:18),2] <- c(\"\",\"\")\n\n#label active and silent categories\ntable1$Genes <- c(c(\"Active\",rep(c(\"\"),8)),c(\"Silent\",rep(c(\"\"),8)))\ntable1 <- table1[,c(6,2,1,3:5)]\n\ntable1" }, { "type": "Paragraph", "content": [ "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 ", { "type": "Emphasis", "content": [ "p" ] }, " values are reported with an ", { "type": "Emphasis", "content": [ "a priori" ] }, " significance threshold of ", { "type": "CodeExpression", "duration": 0.003, "output": ".0167", "programmingLanguage": "r", "text": "sub('^(-)?0[.]','\\\\1.',round(0.05/3, digits = 4))" }, ". Sample sizes reported are based on the sample size used in the tests. Additional details for this experiment can be found at ", { "type": "Link", "target": "https://osf.io/fn2y4/", "content": [ "https://osf.io/fn2y4/" ] }, "." ] }, { "type": "CodeChunk", "id": "table2", "caption": [ { "type": "Heading", "depth": 2, "content": [ "Exploratory statistical tests" ] } ], "duration": 0.032, "label": "Table 2", "outputs": [ { "type": "Datatable", "columns": [ { "type": "DatatableColumn", "name": "Genes", "validator": { "type": "ArrayValidator", "itemsValidator": { "type": "StringValidator" } }, "values": [ "Active", "", "", "", "", "", "", "", "", "Silent", "", "", "", "", "", "", "", "" ] }, { "type": "DatatableColumn", "name": "Comparison", "validator": { "type": "ArrayValidator", "itemsValidator": { "type": "StringValidator" } }, "values": [ "0hr vs 1hr", "", "", "0hr vs 24hr", "", "", "1hr vs 24hr", "", "", "0hr vs 1hr", "", "", "0hr vs 24hr", "", "", "1hr vs 24hr", "", "" ] }, { "type": "DatatableColumn", "name": "Study", "validator": { "type": "ArrayValidator", "itemsValidator": { "type": "StringValidator" } }, "values": [ "RP:CB Lot 1", "RP:CB Lot 2", "Lin et al., 2012", "RP:CB Lot 1", "RP:CB Lot 2", "Lin et al., 2012", "RP:CB Lot 1", "RP:CB Lot 2", "Lin et al., 2012", "RP:CB Lot 1", "RP:CB Lot 2", "Lin et al., 2012", "RP:CB Lot 1", "RP:CB Lot 2", "Lin et al., 2012", "RP:CB Lot 1", "RP:CB Lot 2", "Lin et al., 2012" ] }, { "type": "DatatableColumn", "name": "W value", "validator": { "type": "ArrayValidator", "itemsValidator": { "type": "NumberValidator" } }, "values": [ 270378, 274696, 318799, 300774, 324564, 400999, 281679, 308954, 372714, 187682, 174695, 127104, 185804, 184470, 132082, 166122, 173608, 136443 ] }, { "type": "DatatableColumn", "name": "P value", "validator": { "type": "ArrayValidator", "itemsValidator": { "type": "NumberValidator" } }, "values": [ 0.0103, 0.0395, 0.0001, 7.16e-11, 4.74e-17, 1.16e-42, 0.0001, 1.45e-10, 4.11e-25, 0.0006, 0.0602, 0.236, 0.002, 0.0003, 0.997, 0.716, 0.0918, 0.295 ] }, { "type": "DatatableColumn", "name": "Sample size (n)", "validator": { "type": "ArrayValidator", "itemsValidator": { "type": "NumberValidator" } }, "values": [ 1416, 1438, 1510, 1416, 1438, 1510, 1416, 1438, 1510, 1160, 1146, 1028, 1160, 1146, 1028, 1160, 1146, 1028 ] } ] } ], "programmingLanguage": "r", "text": "#Subsets on only Wilcoxon Rank-Sum Tests\nWilcoxonRS <- dat[c(1,9:12)]\ntable2_active <- melt(WilcoxonRS,id.vars = c(\"Study\",\"Label.1\")) #melts on Study and Label Variables\ntable2_active$interaction <- interaction(table2_active$Study,table2_active$variable)\ntable2_active <- reshape(table2_active, idvar=\"interaction\", timevar = \"Label.1\",direction=\"wide\")\ntable2_active <- table2_active[,c(2:4,7,10)]\n\ntable2_silent <- data.frame(dat[c(1,9,13:15)]) #subsets on silent genes\ntable2_silent <- melt(table2_silent,id.vars = c(\"Study\",\"Label.1\")) #melts on Study and Label Variables\ntable2_silent$interaction <- interaction(table2_silent$Study,table2_silent$variable)\ntable2_silent <- reshape(table2_silent, idvar=\"interaction\", timevar = \"Label.1\",direction=\"wide\")\ntable2_silent <- table2_silent[,c(2:4,7,10)]\n\ntable2 <- rbind(table2_active,table2_silent) #combines silent and active into one data frame\n\n#changes column names\ncolnames(table2) <- c(\"Study\",\"Comparison\",\"W value\",\"P value\",\"Sample size (n)\")\n\n#creates comparison column/ renames comparisons\ntable2$Comparison <- rep(c(rep(\"0hr vs 1hr\",3),rep(\"0hr vs 24hr\",3),rep(\"1hr vs 24hr\",3)),2)\nrownames(table2) <- NULL #deletes row names\ntable2$Study <- as.character(table2$Study) #Makes 'study' variable as character\ntable2[c(2:3,5:6,8:9,11:12,14:15,17:18),2] <- c(\"\",\"\")\n\n#label active and silent categories\ntable2$Genes <- c(c(\"Active\",rep(c(\"\"),8)),c(\"Silent\",rep(c(\"\"),8)))\ntable2 <- table2[,c(6,2,1,3:5)]\n\ntable2" }, { "type": "Paragraph", "content": [ "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 ", { "type": "Emphasis", "content": [ "p" ] }, " 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 ", { "type": "Link", "target": "https://osf.io/fn2y4/", "content": [ "https://osf.io/fn2y4/" ] }, "." ] }, { "type": "Paragraph", "content": [ "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 (", { "type": "Cite", "target": "bib8" }, "; ", { "type": "Cite", "target": "bib21" }, "). 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 (", { "type": "Link", "target": "#table3", "content": [ "Table 3" ] }, "). 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." ] }, { "type": "CodeChunk", "id": "table3", "caption": [ { "type": "Heading", "depth": 2, "content": [ "Exploratory statistical tests" ] } ], "duration": 0.022, "label": "Table 3", "outputs": [ { "type": "Datatable", "columns": [ { "type": "DatatableColumn", "name": "Comparison", "validator": { "type": "ArrayValidator", "itemsValidator": { "type": "StringValidator" } }, "values": [ "0hr vs 1hr", "", "", "0hr vs 24hr", "", "", "1hr vs 24hr", "", "" ] }, { "type": "DatatableColumn", "name": "Study", "validator": { "type": "ArrayValidator", "itemsValidator": { "type": "StringValidator" } }, "values": [ "RP:CB Lot 1", "RP:CB Lot 2", "Lin et al., 2012", "RP:CB Lot 1", "RP:CB Lot 2", "Lin et al., 2012", "RP:CB Lot 1", "RP:CB Lot 2", "Lin et al., 2012" ] }, { "type": "DatatableColumn", "name": "W value", "validator": { "type": "ArrayValidator", "itemsValidator": { "type": "EnumValidator", "values": [ "1.1e-27", "1269", "1.27e-130", "1288", "1292", "278646", "303897", "349351", "7.78e-50", "1.14e-163", "272441", "292865", "368182", "5.18e-24", "7.4e-39", "235077", "272028", "332069", "3.73e-23", "5.72e-104", "7.45e-06" ] } }, "values": [ "303897", "278646", "349351", "272441", "292865", "368182", "235077", "272028", "332069" ] }, { "type": "DatatableColumn", "name": "P value", "validator": { "type": "ArrayValidator", "itemsValidator": { "type": "EnumValidator", "values": [ "1.1e-27", "1269", "1.27e-130", "1288", "1292", "278646", "303897", "349351", "7.78e-50", "1.14e-163", "272441", "292865", "368182", "5.18e-24", "7.4e-39", "235077", "272028", "332069", "3.73e-23", "5.72e-104", "7.45e-06" ] } }, "values": [ "7.78e-50", "1.1e-27", "1.27e-130", "5.18e-24", "7.4e-39", "1.14e-163", "7.45e-06", "3.73e-23", "5.72e-104" ] }, { "type": "DatatableColumn", "name": "Sample size (n)", "validator": { "type": "ArrayValidator", "itemsValidator": { "type": "EnumValidator", "values": [ "1.1e-27", "1269", "1.27e-130", "1288", "1292", "278646", "303897", "349351", "7.78e-50", "1.14e-163", "272441", "292865", "368182", "5.18e-24", "7.4e-39", "235077", "272028", "332069", "3.73e-23", "5.72e-104", "7.45e-06" ] } }, "values": [ "1288", "1292", "1269", "1288", "1292", "1269", "1288", "1292", "1269" ] } ] } ], "programmingLanguage": "r", "text": "\ndiff <- dat[c(1,16:19)]\n\n# only needed if first column consists of numbers\ndiff[[1]] <- as.character(diff[[1]])\ndiff[2,3:5] <- as.character(diff[2,3:5])\n\ntable3 <- melt(diff,id.vars = c(\"Study\",\"Label.2\")) #melts on Study and Label Variables\ntable3$interaction <- interaction(table3$Study,table3$variable)\ntable3 <- reshape(table3, idvar=\"interaction\", timevar = \"Label.2\",direction=\"wide\")\ntable3 <- table3[,c(2:4,7,10)]\n\n#changes column names\ncolnames(table3) <- c(\"Study\",\"Comparison\",\"W value\",\"P value\",\"Sample size (n)\")\n#creates comparison column/ renames comparisons\ntable3$Comparison <- rep(c(rep(\"0hr vs 1hr\",3),rep(\"0hr vs 24hr\",3),rep(\"1hr vs 24hr\",3)))\nrownames(table3) <- NULL #deletes row names\ntable3$Study <- as.character(table3$Study) #Makes 'study' variable as character\ntable3[c(2:3,5:6,8:9),2] <- c(\"\",\"\")\ntable3 <- table3[,c(2,1,3:5)]\n\ntable3" }, { "type": "Paragraph", "content": [ "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 ", { "type": "Emphasis", "content": [ "p" ] }, " values are reported. Sample sizes reported are based on treating genes as unpaired between conditions. Additional details for this experiment can be found at ", { "type": "Link", "target": "https://osf.io/fn2y4/", "content": [ "https://osf.io/fn2y4/" ] }, "." ] }, { "type": "Paragraph", "content": [ "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 (", { "type": "Cite", "target": "bib1" }, "; ", { "type": "Cite", "target": "bib4" }, "). 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 (", { "type": "Cite", "target": "bib27" }, "; ", { "type": "Cite", "target": "bib35" }, "). 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 (", { "type": "Cite", "target": "bib27" }, "). 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 (", { "type": "Link", "target": "#fig2s2", "content": [ "Figure 2—figure supplement 2" ] }, "). 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." ] }, { "type": "Heading", "depth": 2, "content": [ "Meta-analyses of original and replicated effects" ] }, { "type": "Paragraph", "content": [ "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 (", { "type": "Cite", "target": "bib3" }, "). 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 ", { "type": "Emphasis", "content": [ "d" ] }, " is the standardized difference between two means using the pooled sample standard deviation. The effect size ", { "type": "Emphasis", "content": [ "r" ] }, " 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 (", { "type": "Cite", "target": "bib6" }, "; ", { "type": "Cite", "target": "bib33" }, "). 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. ", { "type": "Emphasis", "content": [ "SD" ] }, ") and sample size." ] }, { "type": "Paragraph", "content": [ "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 ", { "type": "Emphasis", "content": [ "d" ] }, " = 4.19, 95% CI [0.94, 7.37] for the data reported in Figure 3E of the original study (", { "type": "Cite", "target": "bib17" }, "). This compares to ", { "type": "Emphasis", "content": [ "d" ] }, " = 0.83, 95% CI [−0.91, 2.48] for serum lot one and ", { "type": "Emphasis", "content": [ "d" ] }, " = 4.11, 95% CI [0.90, 7.23] for serum lot two reported in this study. A meta-analysis (", { "type": "Link", "target": "#fig3", "content": [ "Figure 3A" ] }, ") of these effects resulted in ", { "type": "Emphasis", "content": [ "d" ] }, " = 2.52, 95% CI [0.01, 5.03], which was statistically significant (", { "type": "Emphasis", "content": [ "p" ] }, "=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." ] }, { "type": "CodeChunk", "id": "fig3", "caption": [ { "type": "Heading", "depth": 2, "content": [ "Meta-analyses of each effect." ] }, { "type": "Paragraph", "content": [ "Effect size and 95% confidence interval are presented for ", { "type": "Cite", "target": "bib17" }, ", this replication study (RP:CB), and a random effects meta-analysis of those two effects. Cohen’s ", { "type": "Emphasis", "content": [ "d" ] }, " 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 ", { "type": "Emphasis", "content": [ "r" ] }, " 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 ", { "type": "Cite", "target": "bib17" }, " and this replication attempt are reported under the study name. (", { "type": "Strong", "content": [ "A" ] }, ") Total RNA levels in P493-6 cells 0 hr compared to 24 hr after release from tetracycline (meta-analysis ", { "type": "Emphasis", "content": [ "p" ] }, " = 0.0488). (", { "type": "Strong", "content": [ "B" ] }, ") Gene expression of active or silent genes are shown for all comparisons. Active genes: 0 hr compared to 1 hr (meta-analysis ", { "type": "Emphasis", "content": [ "p" ] }, " = 1.12x10", { "type": "Superscript", "content": [ "-7" ] }, "), 0 hr compared to 24 hr (meta-analysis ", { "type": "Emphasis", "content": [ "p" ] }, " = 7.01x10", { "type": "Superscript", "content": [ "-4" ] }, "), 1 hr compared to 24 hr (meta-analysis ", { "type": "Emphasis", "content": [ "p" ] }, " = 0.0129). Silent genes: 0 hr compared to 1 hr (meta-analysis ", { "type": "Emphasis", "content": [ "p" ] }, " = 0.203), 0 hr compared to 24 hr (meta-analysis ", { "type": "Emphasis", "content": [ "p" ] }, " = 7.10x10", { "type": "Superscript", "content": [ "-17" ] }, "), 1 hr compared to 24 hr (meta-analysis ", { "type": "Emphasis", "content": [ "p" ] }, " = 0.0571). Additional details for these meta-analyses can be found at ", { "type": "Link", "target": "https://osf.io/5yscz/", "content": [ "https://osf.io/5yscz/" ] }, "." ] } ], "duration": 1.212, "label": "Figure 3", "outputs": [ { "type": "ImageObject", "contentUrl": 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" } ], "programmingLanguage": "r", "text": "#' @width 40\n#' @height 50\n\n#re-orders the data frame\nmeta <- meta[order(meta$study),]\nmeta <- meta[order(meta$comparison),]\n\n#subsets data to plot results\nd1 <- subset(meta[1:4,]) #Active 0v1\nd2 <- subset(meta[5:8,]) #Active 0v24\nd3 <- subset(meta[9:12,]) #Active 1v24\n\nd4 <- subset(meta[13:16,]) #Silent 0v1\nd5 <- subset(meta[17:20,]) #Silent 0v24\nd6 <- subset(meta[21:24,]) #Silent 1v24\n\nd7 <- subset(meta[25:28,]) #protocol 2 oh vs. 24hr\n\n#Plots for protocol 3 analyses:\n########################### Active 0hr vs. 1hr #####################################\n\n#re-order the levels for plotting\ndesired_order <- c(\"Meta-Analysis\",\"RP:CB Lot 2\",\"RP:CB Lot 1\", \"Lin et al., 2012\" )\n\n#re-orders data for plotting\nd1$study <- factor(as.character(d1$study), levels=desired_order)\nd1 <- d1[order(d1$study),]\n\na1 <- ggplot(data=d1,aes(x=estimate,y=d1$study)) +\n geom_point(size=5, colour=\"black\", fill = \"black\", shape = c(22,21,21,23)) +\n geom_errorbarh(aes(xmin=CI.lower,xmax=CI.upper, height = .1)) +\n geom_vline(xintercept=0,linetype=\"dashed\") +\n coord_cartesian(xlim=c(-.2,1)) +\n scale_x_continuous(breaks= c(-.2,0,.2,.4,.6,.8,1)) +\n ylab(d1$comparison[1])+\n xlab(NULL)+\n scale_y_discrete(labels = c(paste(as.character(d1$study[1])),\n gsub(\"x\", \"\\n\",paste(as.character(d1$study[2]),\n paste(\"x (n =\", as.character(d1$N[2]),\")\"))),\n gsub(\"x\", \"\\n\",paste(as.character(d1$study[3]),\n paste(\"x (n =\", as.character(d1$N[3]),\")\"))),\n gsub(\"x\", \"\\n\",paste(as.character(d1$study[4]),\n paste(\"x (n =\", as.character(d1$N[4]),\")\"))))) +\n theme(panel.background = element_blank(),\n axis.ticks.x=element_blank(),\n axis.title.x = element_blank(),\n axis.text.x = element_blank(),\n axis.text.y = element_text(size=15),\n legend.position=\"none\",\n axis.line.x = element_blank(),\n axis.title.y = element_text(size=15,margin=margin(0,30,0,0)),\n axis.line.y = element_line(),\n plot.margin = unit(c(.5 ,2,.075,.5), \"in\"))\na1 <- ggdraw(a1)+\n draw_text(paste(\"r\",\"[\",\"L.CI\", \",\", \"U.CI\", \"]\"), x = .84, y = 0.91, fontface=\"bold\")+\n draw_text(paste(formatC(d1$estimate[[4]], digits = 2, format = \"f\"),\n \"[\",\n formatC(d1$CI.lower[[4]],digits = 2, format = \"f\"),\n \",\",\n formatC(d1$CI.upper[[4]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.785, size = 12, hjust=0)+\n draw_text(paste(formatC(d1$estimate[[3]], digits = 2, format = \"f\"),\n \"[\",\n formatC(d1$CI.lower[[3]],digits = 2, format = \"f\"),\n \",\",\n formatC(d1$CI.upper[[3]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.575, size = 12, hjust=0)+\n draw_text(paste(formatC(d1$estimate[[2]],digits = 2, format = \"f\"),\n \"[\",\n formatC(d1$CI.lower[[2]],digits = 2, format = \"f\"),\n \",\",\n formatC(d1$CI.upper[[2]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.3575, size = 12, hjust=0)+\n draw_text(paste(formatC(d1$estimate[[1]],digits = 2, format = \"f\"),\n \"[\",\n formatC(d1$CI.lower[[1]],digits = 2, format = \"f\"),\n \",\",\n formatC(d1$CI.upper[[1]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.154, size = 12, hjust=0)\n\n########################## Active 0hr vs. 24hr #####################################\n\n#re-order the levels for plotting\nd2$study <- factor(as.character(d2$study), levels=desired_order)\nd2 <- d2[order(d2$study),]\n\na2 <- ggplot(data=d2,aes(x=estimate,y=d2$study)) +\n geom_point(size=5, colour=\"black\", fill = \"black\", shape = c(22,21,21,23)) +\n geom_errorbarh(aes(xmin=CI.lower,xmax=CI.upper, height = .1)) +\n geom_vline(xintercept=0,linetype=\"dashed\") +\n coord_cartesian(xlim=c(-.2,1)) +\n scale_x_continuous(breaks= c(-.2,0,.2,.4,.6,.8,1)) +\n ylab(d2$comparison[1])+\n xlab(NULL)+\n scale_y_discrete(labels = c(paste(as.character(d2$study[1])),\n gsub(\"x\", \"\\n\",paste(as.character(d2$study[2]),\n paste(\"x (n =\", as.character(d2$N[2]),\")\"))),\n gsub(\"x\", \"\\n\",paste(as.character(d2$study[3]),\n paste(\"x (n =\", as.character(d2$N[3]),\")\"))),\n gsub(\"x\", \"\\n\",paste(as.character(d2$study[4]),\n paste(\"x (n =\", as.character(d2$N[4]),\")\")))))+\n theme(panel.background = element_blank(),\n axis.ticks.x=element_blank(),\n axis.title.x = element_blank(),\n axis.text.x = element_blank(),\n axis.text.y = element_text(size=15),\n legend.position=\"none\",\n axis.line.x = element_blank(),\n axis.title.y = element_text(size=15,margin=margin(0,30,0,0)),\n axis.line.y = element_line(),\n plot.margin = unit(c(.22,2,.22,.5), \"in\"))\na2 <- ggdraw(a2)+\n draw_text(paste(formatC(d2$estimate[[4]], digits = 2, format = \"f\"),\n \"[\",\n formatC(d2$CI.lower[[4]],digits = 2, format = \"f\"),\n \",\",\n formatC(d2$CI.upper[[4]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.829, size = 12, hjust=0)+\n draw_text(paste(formatC(d2$estimate[[3]], digits = 2, format = \"f\"),\n \"[\",\n formatC(d2$CI.lower[[3]],digits = 2, format = \"f\"),\n \",\",\n formatC(d2$CI.upper[[3]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.615, size = 12, hjust=0)+\n draw_text(paste(formatC(d2$estimate[[2]],digits = 2, format = \"f\"),\n \"[\",\n formatC(d2$CI.lower[[2]],digits = 2, format = \"f\"),\n \",\",\n formatC(d2$CI.upper[[2]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.395, size = 12, hjust=0)+\n draw_text(paste(formatC(d2$estimate[[1]],digits = 2, format = \"f\"),\n \"[\",\n formatC(d2$CI.lower[[1]],digits = 2, format = \"f\"),\n \",\",\n formatC(d2$CI.upper[[1]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.183, size = 12, hjust=0)\n\n########################### Active 1hr vs. 24hr #####################################\n\n#re-order the levels for plotting\nd3$study <- factor(as.character(d3$study), levels=desired_order)\nd3 <- d3[order(d3$study),]\n\na3 <- ggplot(data=d3,aes(x=estimate,y=d3$study))+\n geom_point(size=5, colour=\"black\", fill = \"black\", shape = c(22,21,21,23)) +\n geom_errorbarh(aes(xmin=CI.lower,xmax=CI.upper, height = .1)) +\n geom_vline(xintercept=0,linetype=\"dashed\") +\n coord_cartesian(xlim=c(-.2,1)) +\n scale_x_continuous(breaks= c(-.2,0,.2,.4,.6,.8,1)) +\n ylab(d3$comparison[1]) +\n xlab(\"r\") +\n scale_y_discrete(labels = c(paste(as.character(d3$study[1])),\n gsub(\"x\", \"\\n\",paste(as.character(d3$study[2]),\n paste(\"x (n =\", as.character(d3$N[2]),\")\"))),\n gsub(\"x\", \"\\n\",paste(as.character(d3$study[3]),\n paste(\"x (n =\", as.character(d3$N[3]),\")\"))),\n gsub(\"x\", \"\\n\",paste(as.character(d3$study[4]),\n paste(\"x (n =\", as.character(d3$N[4]),\")\"))))) +\n theme(panel.background = element_blank(),\n legend.position=\"none\",\n axis.line.x = element_line(),\n axis.title.y = element_text(size=15,margin=margin(0,30,0,0)),\n axis.text.y = element_text(size=15),\n axis.title.x = element_text(size=15),\n axis.line.y = element_line(),\n plot.margin = unit(c(.1, 2,0,.5), \"in\"))\na3 <- ggdraw(a3)+\n draw_text(paste(formatC(d3$estimate[[4]], digits = 2, format = \"f\"),\n \"[\",\n formatC(d3$CI.lower[[4]],digits = 2, format = \"f\"),\n \",\",\n formatC(d3$CI.upper[[4]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.858, size = 12, hjust=0)+\n draw_text(paste(formatC(d3$estimate[[3]], digits = 2, format = \"f\"),\n \"[\",\n formatC(d3$CI.lower[[3]],digits = 2, format = \"f\"),\n \",\",\n formatC(d3$CI.upper[[3]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.6445, size = 12, hjust=0)+\n draw_text(paste(formatC(d3$estimate[[2]],digits = 2, format = \"f\"),\n \"[\",\n formatC(d3$CI.lower[[2]],digits = 2, format = \"f\"),\n \",\",\n formatC(d3$CI.upper[[2]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.432, size = 12, hjust=0)+\n draw_text(paste(formatC(d3$estimate[[1]],digits = 2, format = \"f\"),\n \"[\",\n formatC(d3$CI.lower[[1]],digits = 2, format = \"f\"),\n \",\",\n formatC(d3$CI.upper[[1]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.218, size = 12, hjust=0)\n\n#Plots for protocol 3 analyses Silent genes:\n########################### Silent 0hr vs. 1hr #####################################\n\n#re-order the levels for plotting\nd4$study <- factor(as.character(d4$study), levels=desired_order)\nd4 <- d4[order(d4$study),]\n\ns1 <- ggplot(data=d4,aes(x=estimate,y=d4$study)) +\n geom_point(size=5, colour=\"black\", fill = \"black\", shape = c(22,21,21,23)) +\n geom_errorbarh(aes(xmin=CI.lower,xmax=CI.upper, height = .1)) +\n geom_vline(xintercept=0,linetype=\"dashed\") +\n coord_cartesian(xlim=c(-.2,1)) +\n scale_x_continuous(breaks= c(-.2,0,.2,.4,.6,.8,1)) +\n ylab(d4$comparison[1])+\n xlab(NULL)+\n scale_y_discrete(labels = c(paste(as.character(d4$study[1])),\n gsub(\"x\", \"\\n\",paste(as.character(d4$study[2]),\n paste(\"x (n =\", as.character(d4$N[2]),\")\"))),\n gsub(\"x\", \"\\n\",paste(as.character(d4$study[3]),\n paste(\"x (n =\", as.character(d4$N[3]),\")\"))),\n gsub(\"x\", \"\\n\",paste(as.character(d4$study[4]),\n paste(\"x (n =\", as.character(d4$N[4]),\")\")))))+\n theme(panel.background = element_blank(),\n axis.ticks.x=element_blank(),\n axis.title.x = element_blank(),\n axis.text.x = element_blank(),\n axis.text.y = element_text(size=15),\n legend.position=\"none\",\n axis.line.x = element_blank(),\n axis.title.y = element_text(size=15,margin=margin(0,30,0,0)),\n axis.line.y = element_line(),\n plot.margin = unit(c(.5 ,2,.075,.5), \"in\"))\ns1 <- ggdraw(s1)+\n draw_text(paste(\"r\",\"[\",\"L.CI\", \",\", \"U.CI\", \"]\"), x = .84, y = 0.91, fontface=\"bold\")+\n draw_text(paste(formatC(d4$estimate[[4]], digits = 2, format = \"f\"),\n \"[\",\n formatC(d4$CI.lower[[4]],digits = 2, format = \"f\"),\n \",\",\n formatC(d4$CI.upper[[4]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.785, size = 12, hjust=0)+\n draw_text(paste(formatC(d4$estimate[[3]], digits = 2, format = \"f\"),\n \"[\",\n formatC(d4$CI.lower[[3]],digits = 2, format = \"f\"),\n \",\",\n formatC(d4$CI.upper[[3]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.575, size = 12, hjust=0)+\n draw_text(paste(formatC(d4$estimate[[2]],digits = 2, format = \"f\"),\n \"[\",\n formatC(d4$CI.lower[[2]],digits = 2, format = \"f\"),\n \",\",\n formatC(d4$CI.upper[[2]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.3575, size = 12, hjust=0)+\n draw_text(paste(formatC(d4$estimate[[1]],digits = 2, format = \"f\"),\n \"[\",\n formatC(d4$CI.lower[[1]],digits = 2, format = \"f\"),\n \",\",\n formatC(d4$CI.upper[[1]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.154, size = 12, hjust=0)\n\n\n########################## Silent 0hr vs. 24hr #####################################\n\n#re-order the levels for plotting\nd5$study <- factor(as.character(d5$study), levels=desired_order)\nd5 <- d5[order(d5$study),]\n\ns2 <- ggplot(data=d5,aes(x=estimate,y=d5$study)) +\n geom_point(size=5, colour=\"black\", fill = \"black\", shape = c(22,21,21,23)) +\n geom_errorbarh(aes(xmin=CI.lower,xmax=CI.upper, height = .1)) +\n geom_vline(xintercept=0,linetype=\"dashed\") +\n coord_cartesian(xlim=c(-.2,1)) +\n scale_x_continuous(breaks= c(-.2,0,.2,.4,.6,.8,1)) +\n ylab(d5$comparison[1])+\n xlab(NULL)+\n scale_y_discrete(labels = c(paste(as.character(d5$study[1])),\n gsub(\"x\", \"\\n\",paste(as.character(d5$study[2]),\n paste(\"x (n =\", as.character(d5$N[2]),\")\"))),\n gsub(\"x\", \"\\n\",paste(as.character(d5$study[3]),\n paste(\"x (n =\", as.character(d5$N[3]),\")\"))),\n gsub(\"x\", \"\\n\",paste(as.character(d5$study[4]),\n paste(\"x (n =\", as.character(d5$N[4]),\")\")))))+\n theme(panel.background = element_blank(),\n axis.ticks.x=element_blank(),\n axis.title.x = element_blank(),\n axis.text.x = element_blank(),\n axis.text.y = element_text(size=15),\n legend.position=\"none\",\n axis.line.x = element_blank(),\n axis.title.y = element_text(size=15,margin=margin(0,30,0,0)),\n axis.line.y = element_line(),\n plot.margin = unit(c(.22,2,.22,.5), \"in\"))\ns2 <- ggdraw(s2)+\n draw_text(paste(formatC(d5$estimate[[4]], digits = 2, format = \"f\"),\n \"[\",\n formatC(d5$CI.lower[[4]],digits = 2, format = \"f\"),\n \",\",\n formatC(d5$CI.upper[[4]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.829, size = 12, hjust=0)+\n draw_text(paste(formatC(d5$estimate[[3]], digits = 2, format = \"f\"),\n \"[\",\n formatC(d5$CI.lower[[3]],digits = 2, format = \"f\"),\n \",\",\n formatC(d5$CI.upper[[3]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.615, size = 12, hjust=0)+\n draw_text(paste(formatC(d5$estimate[[2]],digits = 2, format = \"f\"),\n \"[\",\n formatC(d5$CI.lower[[2]],digits = 2, format = \"f\"),\n \",\",\n formatC(d5$CI.upper[[2]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.395, size = 12, hjust=0)+\n draw_text(paste(formatC(d5$estimate[[1]],digits = 2, format = \"f\"),\n \"[\",\n formatC(d5$CI.lower[[1]],digits = 2, format = \"f\"),\n \",\",\n formatC(d5$CI.upper[[1]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.183, size = 12, hjust=0)\n\n########################## Silent 1hr vs. 24hr #####################################\n\n#re-order the levels for plotting\nd6$study <- factor(as.character(d6$study), levels=desired_order)\nd6 <- d6[order(d6$study),]\n\ns3 <- ggplot(data=d6,aes(x=estimate,y=factor(d6$study)))+\n geom_point(size=5, colour=\"black\", fill = \"black\", shape = c(22,21,21,23)) +\n geom_errorbarh(aes(xmin=CI.lower,xmax=CI.upper, height = .1)) +\n geom_vline(xintercept=0,linetype=\"dashed\") +\n coord_cartesian(xlim=c(-.2,1)) +\n scale_x_continuous(breaks= c(-.2,0,.2,.4,.6,.8,1)) +\n ylab(d6$comparison[1]) +\n xlab(\"r\") +\n scale_y_discrete(labels = c(paste(as.character(d6$study[1])),\n gsub(\"x\", \"\\n\",paste(as.character(d6$study[2]),\n paste(\"x (n =\", as.character(d6$N[2]),\")\"))),\n gsub(\"x\", \"\\n\",paste(as.character(d6$study[3]),\n paste(\"x (n =\", as.character(d6$N[3]),\")\"))),\n gsub(\"x\", \"\\n\",paste(as.character(d6$study[4]),\n paste(\"x (n =\", as.character(d6$N[4]),\")\"))))) +\n theme(panel.background = element_blank(),\n legend.position=\"none\",\n axis.line.x = element_line(),\n axis.title.y = element_text(size=15,margin=margin(0,30,0,0)),\n axis.text.y = element_text(size=15),\n axis.title.x = element_text(size=15),\n axis.line.y = element_line(),\n plot.margin = unit(c(.1, 2,0,.5), \"in\"))\ns3 <- ggdraw(s3)+\n draw_text(paste(formatC(d6$estimate[[4]], digits = 2, format = \"f\"),\n \"[\",\n formatC(d6$CI.lower[[4]],digits = 2, format = \"f\"),\n \",\",\n formatC(d6$CI.upper[[4]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.858, size = 12, hjust=0)+\n draw_text(paste(formatC(d6$estimate[[3]], digits = 2, format = \"f\"),\n \"[\",\n formatC(d6$CI.lower[[3]],digits = 2, format = \"f\"),\n \",\",\n formatC(d6$CI.upper[[3]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.6445, size = 12, hjust=0)+\n draw_text(paste(formatC(d6$estimate[[2]],digits = 2, format = \"f\"),\n \"[\",\n formatC(d6$CI.lower[[2]],digits = 2, format = \"f\"),\n \",\",\n formatC(d6$CI.upper[[2]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.432, size = 12, hjust=0)+\n draw_text(paste(formatC(d6$estimate[[1]],digits = 2, format = \"f\"),\n \"[\",\n formatC(d6$CI.lower[[1]],digits = 2, format = \"f\"),\n \",\",\n formatC(d6$CI.upper[[1]],digits = 2, format = \"f\"),\n \"]\"), x = 0.75, y = 0.218, size = 12, hjust=0)\n\n########################## Protocol 2 Meta Analysis #####################################\n\n#re-order the levels for plotting\nd7$study <- factor(as.character(d7$study), levels=desired_order)\nd7 <- d7[order(d7$study),]\n\npro2 <- ggplot(data=d7,aes(x=estimate,y=d7$study))+\n geom_point(size=5, colour=\"black\", fill = \"black\", shape = c(22,21,21,23)) +\n geom_errorbarh(aes(xmin=CI.lower,xmax=CI.upper, height = .1)) +\n geom_vline(xintercept=0,linetype=\"dashed\") +\n coord_cartesian(xlim=c(-1,8)) +\n scale_x_continuous(breaks= c(-1,0,1,2,3,4,5,6,7,8)) +\n ylab(d7$comparison[1]) +\n xlab(\"Cohen's\"~italic(\"d\")) +\n scale_y_discrete(labels = c(paste(as.character(d7$study[1])),\n gsub(\"x\", \"\\n\",paste(as.character(d7$study[2]),\n paste(\"x (n =\", as.character(d7$N[2]),\")\"))),\n gsub(\"x\", \"\\n\",paste(as.character(d7$study[3]),\n paste(\"x (n =\", as.character(d7$N[3]),\")\"))),\n gsub(\"x\", \"\\n\",paste(as.character(d7$study[4]),\n paste(\"x (n =\", as.character(d7$N[4]),\")\"))))) +\n theme(panel.background = element_blank(),\n legend.position=\"none\",\n axis.line.x = element_line(),\n axis.title.y = element_text(size=15,margin=margin(0,30,0,0)),\n axis.text.y = element_text(size=15),\n axis.line.y = element_line(),\n plot.margin = margin(.6, 8, .5, .55, \"in\"))\npro2 <- ggdraw(pro2)+\n draw_text(paste(\"Cohen's d\",\"[\",\"L.CI\", \",\", \"U.CI\", \"]\"), x = 0.568, y = 0.93, fontface=\"bold\") +\n draw_text(paste(formatC(d7$estimate[[4]], digits = 2, format = \"f\"),\n \"[\",\n formatC(d7$CI.lower[[4]],digits = 2, format = \"f\"),\n \",\",\n formatC(d7$CI.upper[[4]],digits = 2, format = \"f\"),\n \"]\"), x = 0.52, y = 0.79, size = 12, hjust=0)+\n draw_text(paste(formatC(d7$estimate[[3]], digits = 2, format = \"f\"),\n \"[\",\n formatC(d7$CI.lower[[3]],digits = 2, format = \"f\"),\n \",\",\n formatC(d7$CI.upper[[3]],digits = 2, format = \"f\"),\n \"]\"), x = 0.52, y = 0.62, size = 12, hjust=0)+\n draw_text(paste(formatC(d7$estimate[[2]],digits = 2, format = \"f\"),\n \"[\",\n formatC(d7$CI.lower[[2]],digits = 2, format = \"f\"),\n \",\",\n formatC(d7$CI.upper[[2]],digits = 2, format = \"f\"),\n \"]\"), x = 0.52, y = 0.455, size = 12, hjust=0)+\n draw_text(paste(formatC(d7$estimate[[1]],digits = 2, format = \"f\"),\n \"[\",\n formatC(d7$CI.lower[[1]],digits = 2, format = \"f\"),\n \",\",\n formatC(d7$CI.upper[[1]],digits = 2, format = \"f\"),\n \"]\"), x = 0.52, y = 0.285, size = 12, hjust=0)\n\n\n#Creates figure for protocol 3 meta-analyses\npro3 <- plot_grid(a1, s1, a2, s2, a3, s3, nrow = 3)\n\nfigure <- plot_grid(pro2, pro3, nrow = 2, rel_heights = c(1,3), labels = c(\"A\", \"B\"), label_size = 25)\nfigure_3 <- plot_grid(figure,ncol = 1,rel_heights = c(0.1,1))\nfigure_3" }, { "type": "Paragraph", "content": [ "There were six comparisons of the gene expression data, three for active genes and three for silent genes (", { "type": "Link", "target": "#fig3", "content": [ "Figure 3B" ] }, "). These calculations were performed analyzing the data as paired, for reasons discussed above and as prespecified in the Registered Report (", { "type": "Cite", "target": "bib3" }, "). 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 (", { "type": "Emphasis", "content": [ "p" ] }, "=1.12×10", { "type": "Superscript", "content": [ "−7" ] }, ", ", { "type": "Emphasis", "content": [ "p" ] }, "=7.01×10", { "type": "Superscript", "content": [ "−4" ] }, ", ", { "type": "Emphasis", "content": [ "p" ] }, "=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 ", { "type": "Emphasis", "content": [ "Q" ] }, " 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 (", { "type": "Emphasis", "content": [ "p" ] }, "=0.203, ", { "type": "Emphasis", "content": [ "p" ] }, "=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 ", { "type": "Emphasis", "content": [ "Q" ] }, " 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 (", { "type": "Emphasis", "content": [ "p" ] }, "=7.10×10", { "type": "Superscript", "content": [ "−17" ] }, "). 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." ] }, { "type": "Paragraph", "content": [ "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 (", { "type": "Cite", "target": "bib3" }, "). 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 (", { "type": "Cite", "target": "bib3" }, ") that could affect results, such as the analytical approach (", { "type": "Cite", "target": "bib31" }, ") and serum lot (", { "type": "Cite", "target": "bib15" }, "). Furthermore, other aspects were unknown or not easily controlled for. These include variables such as cell line genetic drift (", { "type": "Cite", "target": "bib13" }, "; ", { "type": "Cite", "target": "bib14" }, ") or changes in cellular volume that can impact overall transcript abundance (", { "type": "Cite", "target": "bib22" }, "). 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." ] }, { "type": "Heading", "depth": 1, "content": [ "Materials and methods" ] }, { "type": "Paragraph", "content": [ "As described in the Registered Report (", { "type": "Cite", "target": "bib3" }, "), we attempted a replication of the experiments reported in Figures 1B and 3E-F of ", { "type": "Cite", "target": "bib17" }, ". A detailed description of all protocols can be found in the Registered Report (", { "type": "Cite", "target": "bib3" }, "). Additional detailed experimental notes, data, and analysis are available on the Open Science Framework (OSF) (RRID:", { "type": "Link", "target": "https://scicrunch.org/resolver/SCR_003238", "content": [ "SCR_003238" ] }, ") (", { "type": "Link", "target": "https://osf.io/mokeb/", "content": [ "https://osf.io/mokeb/" ] }, "; ", { "type": "Cite", "target": "bib16" }, "). This includes the R Markdown file (", { "type": "Link", "target": "https://osf.io/vdrsh/", "content": [ "https://osf.io/vdrsh/" ] }, ") 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 (", { "type": "Cite", "target": "bib10" }, ")." ] }, { "type": "Heading", "depth": 2, "content": [ "Cell culture" ] }, { "type": "Paragraph", "content": [ "P493-6 cells (shared by Young lab, Whitehead Institute for Biomedical Research, RRID: ", { "type": "Link", "target": "https://scicrunch.org/resolver/CVCL_6783", "content": [ "CVCL_6783" ] }, ") 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", { "type": "Subscript", "content": [ "2" ] }, ". Quality control data for the cell line are available at ", { "type": "Link", "target": "https://osf.io/e6ftz/.", "content": [ "https://osf.io/e6ftz/." ] }, " 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)." ] }, { "type": "Paragraph", "content": [ "For repression of the conditional p", { "type": "Emphasis", "content": [ "myc" ] }, "-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 ", { "type": "Emphasis", "content": [ "MYC" ] }, " (", { "type": "Cite", "target": "bib30" }, "). For ", { "type": "Emphasis", "content": [ "MYC" ] }, " re-induction, cells were washed three times with growth medium and grown in tetracycline-free culture conditions." ] }, { "type": "Heading", "depth": 2, "content": [ "Western blot" ] }, { "type": "Paragraph", "content": [ "P493-6 cells were harvested at the indicated times and total cell lysates were prepared by pelleting ~1×10", { "type": "Superscript", "content": [ "7" ] }, " 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 (", { "type": "Cite", "target": "bib3" }, "). 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:", { "type": "Link", "target": "https://scicrunch.org/resolver/AB_731658", "content": [ "AB_731658" ] }, "); 1:5000 dilution in 5% w/v nonfat dry milk/TBST and mouse anti-ß-actin [clone AC-15] (Sigma-Aldrich, cat# A5441; RRID:", { "type": "Link", "target": "https://scicrunch.org/resolver/AB_476744", "content": [ "AB_476744" ] }, "); 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:", { "type": "Link", "target": "https://scicrunch.org/resolver/SCR_014249", "content": [ "SCR_014249" ] }, "), version 7.12.02 (Syngene, Frederick, Maryland) and quantified using ImageJ software (RRID:", { "type": "Link", "target": "https://scicrunch.org/resolver/SCR_003070", "content": [ "SCR_003070" ] }, "), version 1.50i (", { "type": "Cite", "target": "bib29" }, "). All images taken are available at ", { "type": "Link", "target": "https://osf.io/ujg7t/.", "content": [ "https://osf.io/ujg7t/." ] } ] }, { "type": "Heading", "depth": 2, "content": [ "RNA quantification" ] }, { "type": "Paragraph", "content": [ "P493-6 cells were harvested at the indicated times and total RNA extraction was performed by pelleting ~1×10", { "type": "Superscript", "content": [ "7" ] }, " 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", { "type": "Emphasis", "content": [ "g" ] }, " 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 ", { "type": "Link", "target": "https://osf.io/jh5r4/", "content": [ "https://osf.io/jh5r4/" ] }, ") 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." ] }, { "type": "Heading", "depth": 2, "content": [ "RNA extraction and NanoString nCounter digital gene expression assay" ] }, { "type": "Paragraph", "content": [ "P493-6 cells were harvested at the indicated times and 1 × 10", { "type": "Superscript", "content": [ "6" ] }, " 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 (", { "type": "Cite", "target": "bib3" }, "). Three nCounter Reporter CodeSets (nCounter GX Human Immunology Kit, nCounter GX Human Kinase Kit, nCounter Custom CodeSet) encompassing ", { "type": "CodeExpression", "duration": 0.004, "output": "1,308", "programmingLanguage": "r", "text": "prettyNum(length(unique(comb.means$Accession)), big.mark=\",\")" }, " 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 ", { "type": "Link", "target": "#fig2sdata1", "content": [ "Figure 2—source data 1" ] }, ". Additional files and analysis scripts are available at ", { "type": "Link", "target": "https://osf.io/fn2y4/.", "content": [ "https://osf.io/fn2y4/." ] } ] }, { "type": "Heading", "depth": 2, "content": [ "Statistical analysis" ] }, { "type": "Paragraph", "content": [ "Statistical analysis was performed with R software (RRID:", { "type": "Link", "target": "https://scicrunch.org/resolver/SCR_001905", "content": [ "SCR_001905" ] }, "), version 3.3.2 (", { "type": "Cite", "target": "bib25" }, "). All data, csv files, and analysis scripts are available on the OSF (", { "type": "Link", "target": "https://osf.io/mokeb/", "content": [ "https://osf.io/mokeb/" ] }, "). Confirmatory statistical analysis was pre-registered (", { "type": "Link", "target": "https://osf.io/nj8wb/", "content": [ "https://osf.io/nj8wb/" ] }, ") before the experimental work began as outlined in the Registered Report (", { "type": "Cite", "target": "bib3" }, "). Proposed analysis of gene expression data was conducted by the Wilcoxon signed-rank test using the method proposed by Pratt to handle zero differences (", { "type": "Cite", "target": "bib24" }, "), 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 ", { "type": "Emphasis", "content": [ "a priori" ] }, " significance threshold of. 05. A meta-analysis of a common original and replication effect size was performed with a random effects model and the ", { "type": "Emphasis", "content": [ "metafor" ] }, " package (", { "type": "Cite", "target": "bib34" }, ") (available at: ", { "type": "Link", "target": "https://osf.io/5yscz/", "content": [ "https://osf.io/5yscz/" ] }, "). The sample sizes reported in ", { "type": "Link", "target": "#table1", "content": [ "Table 1" ] }, " and ", { "type": "Link", "target": "#fig3", "content": [ "Figure 3" ] }, " 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 (", { "type": "Cite", "target": "bib24" }, "). The raw original study data were shared by the original authors with the summary data published in the Registered Report (", { "type": "Cite", "target": "bib3" }, ") and was used in the power calculations to determine the sample size for this study." ] }, { "type": "Heading", "depth": 2, "content": [ "Deviations from registered report" ] }, { "type": "Paragraph", "content": [ "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." ] } ] }