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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
<meta name="author" content="Shannon E. Ellis" />
<title>Summarizing Sex in the SRA</title>
<link href="data:text/css;charset=utf-8,pre%20%2Eoperator%2C%0Apre%20%2Eparen%20%7B%0Acolor%3A%20rgb%28104%2C%20118%2C%20135%29%0A%7D%0Apre%20%2Eliteral%20%7B%0Acolor%3A%20%23990073%0A%7D%0Apre%20%2Enumber%20%7B%0Acolor%3A%20%23099%3B%0A%7D%0Apre%20%2Ecomment%20%7B%0Acolor%3A%20%23998%3B%0Afont%2Dstyle%3A%20italic%0A%7D%0Apre%20%2Ekeyword%20%7B%0Acolor%3A%20%23900%3B%0Afont%2Dweight%3A%20bold%0A%7D%0Apre%20%2Eidentifier%20%7B%0Acolor%3A%20rgb%280%2C%200%2C%200%29%3B%0A%7D%0Apre%20%2Estring%20%7B%0Acolor%3A%20%23d14%3B%0A%7D%0A" rel="stylesheet" />
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<h1 class="title">Summarizing Sex in the SRA</h1>
<h4 class="author"><em>Shannon E. Ellis</em></h4>
</div>
<h1>Contents</h1>
<div id="TOC">
<ul>
<li><a href="#load-data"><span class="toc-section-number">1</span> Load data</a></li>
<li><a href="#recount-breakdown"><span class="toc-section-number">2</span> Recount breakdown</a></li>
<li><a href="#tcga-and-gtex-breakdown"><span class="toc-section-number">3</span> TCGA and GTEx Breakdown</a></li>
<li><a href="#analyze-sex-across-the-sra"><span class="toc-section-number">4</span> Analyze sex across the SRA</a></li>
<li><a href="#looking-at-sex-broken-down-by-project"><span class="toc-section-number">5</span> Looking at sex broken down by project</a></li>
<li><a href="#vignette-information"><span class="toc-section-number">6</span> Vignette information</a></li>
<li><a href="#code-for-creating-the-vignette"><span class="toc-section-number">7</span> Code for creating the vignette</a></li>
</ul>
</div>
<pre class="r"><code>library(extrafont)
library(gridExtra)
library(dplyr)
## load colors
bright= c(red=rgb(222,45,38, maxColorValue=255), #de2d26
pink=rgb( 255, 102, 153, maxColorValue=255), #ff6699
orange=rgb(232,121,12, maxColorValue=255), #e8790c
yellow=rgb(255,222,13, maxColorValue=255), #ffde0d
green=rgb(12,189,24, maxColorValue=255), #0cbd18
teal=rgb(59,196,199, maxColorValue=255), #3bc4c7
blue=rgb(58,158,234, maxColorValue=255), #3a9eea
purple=rgb(148,12,232, maxColorValue=255)) #940ce8 </code></pre>
<div id="load-data" class="section level1">
<h1><span class="header-section-number">1</span> Load data</h1>
<pre class="r"><code>## load predicted phenotypes
load('/dcl01/leek/data/sellis/barcoding/output/PredictedPhenotypes_v0.0.06.rda')
df = PredictedPhenotypes #70479
df$predicted_sex <- as.factor(tolower(df$predicted_sex))
## load SRA metadata
load('/dcl01/leek/data/recount-website/metadata/metadata_sra.Rdata')
metadata <- metadata[!is.na(metadata$bigwig_path), ]
sra_meta = metadata
rm(metadata)
pd = read_csv("https://raw.githubusercontent.com/nellore/runs/master/sra/v2/hg38/SraRunInfo.csv")
sra_meta = left_join(as.data.frame(sra_meta),pd,by=c("run"="Run","sample"="Sample"))
colnames(sra_meta)[4] <- "sample_id"
meta = left_join(sra_meta,df)</code></pre>
</div>
<div id="recount-breakdown" class="section level1">
<h1><span class="header-section-number">2</span> Recount breakdown</h1>
<pre class="r"><code>## overall breakdown
(sex_recount <- df %>% dplyr::filter(!is.na(dataset)) %>% group_by(predicted_sex) %>% dplyr::summarise(n = n()) %>% mutate(freq = n / sum(n))) </code></pre>
<pre><code>## # A tibble: 3 x 3
## predicted_sex n freq
## <fctr> <int> <dbl>
## 1 female 35311 0.50101449
## 2 male 31461 0.44638829
## 3 unassigned 3707 0.05259723</code></pre>
<pre class="r"><code>b <- ggplot(data = sex_recount, aes(x=predicted_sex, y = freq,label = freq)) +
labs(y="Proportion",x="Predicted Sex",title="") +
geom_bar(stat="identity", aes(fill = predicted_sex),position="dodge") +
# geom_text(aes(fill=predicted_sex),size = 12, position = position_dodge(width = 0.9),colour="black") +
scale_fill_manual(values=c("#940CE8", "#0CBD18", "grey48"))+
theme_bw()+
theme(legend.title=element_blank(),legend.text=element_text(size=16),plot.title = element_text(hjust = 0.5),text = element_text(size=16), panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),axis.text=element_text(colour="black"))
# pdf('/dcl01/leek/data/sellis/barcoding/plots/Sex_recount.pdf',family="Roboto Condensed",width=15,height=15)
plot(b)</code></pre>
<p><img src="data:image/png;base64,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" /></p>
<pre class="r"><code># dev.off()</code></pre>
</div>
<div id="tcga-and-gtex-breakdown" class="section level1">
<h1><span class="header-section-number">3</span> TCGA and GTEx Breakdown</h1>
<pre class="r"><code>## get breakdown across projects
(sex_recount2 <- df %>% dplyr::filter(dataset=="tcga" | dataset=="gtex") %>% group_by(dataset,predicted_sex) %>% dplyr::summarise(n = n()) %>% mutate(freq = n / sum(n))) </code></pre>
<pre><code>## # A tibble: 4 x 4
## # Groups: dataset [2]
## dataset predicted_sex n freq
## <fctr> <fctr> <int> <dbl>
## 1 gtex female 3505 0.3674775
## 2 gtex male 6033 0.6325225
## 3 tcga female 5944 0.5267636
## 4 tcga male 5340 0.4732364</code></pre>
<pre class="r"><code>## Overall summary
b <- ggplot(data = sex_recount2, aes(x=dataset, y = freq,label = freq)) +
labs(y="Proportion",x="Data Set",title="Predicted Sex") +
geom_bar(stat="identity", aes(fill = predicted_sex),position="dodge") +
# geom_text(aes(fill=predicted_sex),size = 12, position = position_dodge(width = 0.9),colour="black") +
scale_fill_manual(values=c("#940CE8", "#0CBD18"))+
theme_bw()+
theme(legend.title=element_blank(),legend.text=element_text(size=16),plot.title = element_text(hjust = 0.5),text = element_text(size=16), panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),axis.text=element_text(colour="black"))
# pdf('/dcl01/leek/data/sellis/barcoding/plots/Sex_recount_summary.pdf',family="Roboto Condensed",width=15,height=15)
plot(b)</code></pre>
<p><img src="data:image/png;base64,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" /></p>
<pre class="r"><code># dev.off()</code></pre>
</div>
<div id="analyze-sex-across-the-sra" class="section level1">
<h1><span class="header-section-number">4</span> Analyze sex across the SRA</h1>
<pre class="r"><code>## Sex Breakdown w/n SRA
(sex_SRA <- meta %>% group_by(predicted_sex) %>% select(predicted_sex) %>% dplyr::summarise(Count = n())) </code></pre>
<pre><code>## # A tibble: 3 x 2
## predicted_sex Count
## <fctr> <int>
## 1 female 25862
## 2 male 20088
## 3 unassigned 3707</code></pre>
<pre class="r"><code>## Overall summary
# pdf('/dcl01/leek/data/sellis/barcoding/plots/Sex_SRA_summary.pdf',family="Roboto Condensed",width=15,height=15)
# plot(ggplot(data = sex_SRA, aes(x=predicted_sex, y = Count,label = Count)) +
# labs(y="No. of Samples",x="Data Set",title="Predicted Sex") +
# geom_bar(stat="identity", aes(fill = predicted_sex),position="dodge") +
# # geom_text(aes(fill=predicted_sex),size = 12, position = position_dodge(width = 0.9),colour="black") +
# scale_fill_manual(values=c("#940CE8", "#0CBD18", "grey48"))+
# theme_bw()+
# theme(legend.title=element_blank(),legend.text=element_text(size=60),plot.title = element_text(hjust = 0.5),text = element_text(size=48), panel.border = element_blank(), panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),axis.text=element_text(colour="black")))
# dev.off()
plot(ggplot(data = sex_SRA, aes(x=predicted_sex, y = Count,label = Count)) +
labs(y="No. of Samples",x="Data Set",title="Predicted Sex") +
geom_bar(stat="identity", aes(fill = predicted_sex),position="dodge") +
geom_text(aes(fill=predicted_sex),size = 6, position = position_dodge(width = 0.9),colour="black") +
scale_fill_manual(values=c("#940CE8", "#0CBD18", "grey48"))+
theme_bw()+
theme(legend.title=element_blank(),legend.text=element_text(size=12),plot.title = element_text(hjust = 0.5),text = element_text(size=12), panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),axis.text=element_text(colour="black")))</code></pre>
<p><img 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" /></p>
</div>
<div id="looking-at-sex-broken-down-by-project" class="section level1">
<h1><span class="header-section-number">5</span> Looking at sex broken down by project</h1>
<pre class="r"><code>## Broken down by project type
## combine to look at sex across SRA
(projorder <- meta %>% group_by(ProjectID) %>% summarise(n=n()) %>% arrange(n) %>% mutate(index=1:nrow(.)))</code></pre>
<pre><code>## # A tibble: 1,991 x 3
## ProjectID n index
## <int> <int> <int>
## 1 33835 1 1
## 2 33851 1 2
## 3 33865 1 3
## 4 63201 1 4
## 5 80133 1 5
## 6 80159 1 6
## 7 124917 1 7
## 8 125353 1 8
## 9 126491 1 9
## 10 128253 1 10
## # ... with 1,981 more rows</code></pre>
<pre class="r"><code>(proj <- meta %>% group_by(ProjectID,predicted_sex) %>% summarise(n=n()) %>% mutate(prop= n/sum(n)))</code></pre>
<pre><code>## # A tibble: 2,829 x 4
## # Groups: ProjectID [1,991]
## ProjectID predicted_sex n prop
## <int> <fctr> <int> <dbl>
## 1 0 female 224 0.369028007
## 2 0 male 191 0.314662273
## 3 0 unassigned 192 0.316309720
## 4 30709 female 414 0.776735460
## 5 30709 male 118 0.221388368
## 6 30709 unassigned 1 0.001876173
## 7 33835 female 1 1.000000000
## 8 33851 female 1 1.000000000
## 9 33865 female 1 1.000000000
## 10 34535 female 203 0.377323420
## # ... with 2,819 more rows</code></pre>
<pre class="r"><code>test = left_join(proj,projorder, by="ProjectID") %>% arrange(index)
scale <- function(X){
(X - min(X))/diff(range(X))+0.001
}
test$widths <- scale(test$n.y)
# pdf("plots/Sex_by_proj.pdf",width=100,family="Roboto Condensed")
# ggplot(test, aes(x = index, y = prop,fill=predicted_sex)) +
# geom_bar(stat='identity',aes(width=widths))+
# scale_fill_manual(values=c("#940CE8", "#0CBD18", "grey48"))
# # dev.off()
proj_summ <- proj %>% group_by(ProjectID) %>% summarise(projtype = paste(predicted_sex, collapse=","), times=length(predicted_sex))
proj_summ$type <- proj_summ$projtype
proj_summ$type[proj_summ$times>1] <- "mixed"
proj_summ$type[proj_summ$type=="female"] <- "female only"
proj_summ$type[proj_summ$type=="male"] <- "male only"
proj_summ$type[proj_summ$type=="unassigned"] <- "unassigned only"
d <- proj_summ %>% group_by(type) %>% summarise(Count = n())
# pdf('plots/Sex_SRA_ProjectType.pdf',width=12,height=12,family="Roboto Condensed")
ggplot(data = d, aes(x=type, y = Count,label = Count)) +
labs(y="No. of Projects",x="Project Type",title="Project Type Summary")+
geom_bar(stat="identity", aes(fill = type),position="dodge") +
geom_text(aes(fill=type),size = 6, position = position_dodge(width = 0.9),colour="black") +
scale_fill_manual(values=c("#940CE8", "#0CBD18","#E8790C", "grey48"))+
theme_bw()+
theme(legend.title=element_blank(),plot.title = element_text(hjust = 0.5),text = element_text(size=16), panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),axis.text=element_text(colour="black"))</code></pre>
<p><img 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" /></p>
<pre class="r"><code># dev.off()
# embed_fonts('plots/Sex_SRA_ProjectType.pdf',outfile='plots/Sex_SRA_ProjectType.pdf')</code></pre>
</div>
<div id="vignette-information" class="section level1">
<h1><span class="header-section-number">6</span> Vignette information</h1>
<pre class="r"><code>## Time spent creating this report:
diff(c(startTime, Sys.time()))</code></pre>
<pre><code>## Time difference of 9.063228 secs</code></pre>
<pre class="r"><code>## Date this report was generated
message(Sys.time())</code></pre>
<pre><code>## 2017-11-22 10:47:05</code></pre>
<pre class="r"><code>## Reproducibility info
options(width = 120)
devtools::session_info()</code></pre>
<pre><code>## Session info ----------------------------------------------------------------------------------------------------------</code></pre>
<pre><code>## setting value
## version R version 3.3.3 Patched (2017-03-15 r72696)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## tz <NA>
## date 2017-11-22</code></pre>
<pre><code>## Packages --------------------------------------------------------------------------------------------------------------</code></pre>
<pre><code>## package * version date source
## assertthat 0.2.0 2017-04-11 CRAN (R 3.3.1)
## backports 1.1.1 2017-09-25 CRAN (R 3.3.3)
## base * 3.3.3 2017-05-18 local
## bindr 0.1 2016-11-13 CRAN (R 3.3.3)
## bindrcpp * 0.2 2017-06-17 CRAN (R 3.3.3)
## BiocGenerics * 0.20.0 2016-10-20 Bioconductor
## BiocStyle * 2.2.1 2016-11-25 Bioconductor
## broom 0.4.2 2017-02-13 CRAN (R 3.3.1)
## cellranger 1.1.0 2016-07-27 CRAN (R 3.3.1)
## colorspace 1.3-2 2016-12-14 CRAN (R 3.3.1)
## curl 2.8.1 2017-07-21 CRAN (R 3.3.3)
## datasets * 3.3.3 2017-05-18 local
## devtools 1.13.3 2017-08-02 CRAN (R 3.3.3)
## digest 0.6.12 2017-01-27 CRAN (R 3.3.1)
## dplyr * 0.7.4 2017-09-28 CRAN (R 3.3.3)
## evaluate 0.10.1 2017-06-24 CRAN (R 3.3.3)
## extrafont * 0.17 2014-12-08 CRAN (R 3.3.1)
## extrafontdb 1.0 2012-06-11 CRAN (R 3.3.1)
## forcats 0.2.0 2017-01-23 CRAN (R 3.3.1)
## foreign 0.8-67 2016-09-13 CRAN (R 3.3.3)
## ggplot2 * 2.2.1 2016-12-30 CRAN (R 3.3.1)
## glue 1.1.1 2017-06-21 CRAN (R 3.3.3)
## graphics * 3.3.3 2017-05-18 local
## grDevices * 3.3.3 2017-05-18 local
## grid 3.3.3 2017-05-18 local
## gridExtra * 2.3 2017-09-09 CRAN (R 3.3.3)
## gtable 0.2.0 2016-02-26 CRAN (R 3.3.0)
## haven 1.1.0 2017-07-09 CRAN (R 3.3.3)
## hms 0.3 2016-11-22 CRAN (R 3.3.1)
## htmltools 0.3.6 2017-04-28 CRAN (R 3.3.1)
## httr 1.3.1 2017-08-20 CRAN (R 3.3.3)
## IRanges * 2.8.2 2017-05-22 Bioconductor
## jsonlite 1.5 2017-06-01 CRAN (R 3.3.3)
## knitr 1.17 2017-08-10 CRAN (R 3.3.3)
## labeling 0.3 2014-08-23 CRAN (R 3.3.0)
## lattice 0.20-34 2016-09-06 CRAN (R 3.3.3)
## lazyeval 0.2.0 2016-06-12 CRAN (R 3.3.1)
## lubridate 1.6.0 2016-09-13 CRAN (R 3.3.1)
## magrittr 1.5 2014-11-22 CRAN (R 3.3.0)
## memoise 1.1.0 2017-04-21 CRAN (R 3.3.1)
## methods * 3.3.3 2017-05-18 local
## mnormt 1.5-5 2016-10-15 CRAN (R 3.3.1)
## modelr 0.1.1 2017-07-24 CRAN (R 3.3.3)
## munsell 0.4.3 2016-02-13 CRAN (R 3.3.0)
## nlme 3.1-131 2017-02-06 CRAN (R 3.3.1)
## parallel * 3.3.3 2017-05-18 local
## pkgconfig 2.0.1 2017-03-21 CRAN (R 3.3.3)
## plyr 1.8.4 2016-06-08 cran (@1.8.4)
## psych 1.7.5 2017-05-03 cran (@1.7.5)
## purrr * 0.2.3 2017-08-02 cran (@0.2.3)
## R6 2.2.2 2017-06-17 CRAN (R 3.3.3)
## Rcpp 0.12.12 2017-07-15 CRAN (R 3.3.3)
## readr * 1.1.1 2017-05-16 CRAN (R 3.3.1)
## readxl 1.0.0 2017-04-18 CRAN (R 3.3.1)
## reshape2 1.4.2 2016-10-22 CRAN (R 3.3.1)
## rlang 0.1.2 2017-08-09 cran (@0.1.2)
## rmarkdown * 1.6 2017-06-15 CRAN (R 3.3.3)
## rprojroot 1.2 2017-01-16 CRAN (R 3.3.1)
## Rttf2pt1 1.3.4 2016-05-19 CRAN (R 3.3.1)
## rvest 0.3.2 2016-06-17 CRAN (R 3.3.1)
## S4Vectors * 0.12.2 2017-05-22 Bioconductor
## scales 0.5.0 2017-08-24 CRAN (R 3.3.3)
## stats * 3.3.3 2017-05-18 local
## stats4 * 3.3.3 2017-05-18 local
## stringi 1.1.5 2017-04-07 CRAN (R 3.3.1)
## stringr 1.2.0 2017-02-18 CRAN (R 3.3.1)
## tibble * 1.3.4 2017-08-22 cran (@1.3.4)
## tidyr * 0.7.1 2017-09-01 CRAN (R 3.3.3)
## tidyverse * 1.1.1 2017-01-27 CRAN (R 3.3.1)
## tools 3.3.3 2017-05-18 local
## utils * 3.3.3 2017-05-18 local
## withr 2.0.0 2017-07-28 CRAN (R 3.3.3)
## xml2 1.1.1 2017-01-24 CRAN (R 3.3.1)
## yaml 2.1.14 2016-11-12 CRAN (R 3.3.1)</code></pre>
</div>
<div id="code-for-creating-the-vignette" class="section level1">
<h1><span class="header-section-number">7</span> Code for creating the vignette</h1>
<pre class="r"><code>## Create the vignette
library('rmarkdown')
system.time(render('/dcl01/leek/data/sellis/barcoding/phenopredict_usecase/sex_summary.Rmd', 'BiocStyle::html_document'))
## Extract the R code
library('knitr')
knit('/dcl01/leek/data/sellis/barcoding/phenopredict_usecase/sex_summary.Rmd', tangle = TRUE)</code></pre>
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