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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>Identifying a Study of Interest</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">Identifying a Study of Interest</h1>
<h4 class="author"><em>Shannon E. Ellis</em></h4>
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<h1>Contents</h1>
<div id="TOC">
<ul>
<li><a href="#load-predicted-phenotypes"><span class="toc-section-number">1</span> Load predicted phenotypes</a></li>
<li><a href="#load-sra-reported-metadata"><span class="toc-section-number">2</span> Load SRA reported metadata</a></li>
<li><a href="#identify-a-study-of-interest"><span class="toc-section-number">3</span> Identify a study of interest</a></li>
<li><a href="#decide-which-of-possible-projects-to-use"><span class="toc-section-number">4</span> Decide which of possible projects to use</a></li>
<li><a href="#vignette-information"><span class="toc-section-number">5</span> Vignette information</a></li>
</ul>
</div>
<div id="load-predicted-phenotypes" class="section level1">
<h1><span class="header-section-number">1</span> Load predicted phenotypes</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))</code></pre>
</div>
<div id="load-sra-reported-metadata" class="section level1">
<h1><span class="header-section-number">2</span> Load SRA reported metadata</h1>
<pre class="r"><code>## load SRA metadata
load('/dcl01/leek/data/recount-website/metadata/metadata_sra.Rdata')
metadata <- metadata[!is.na(metadata$bigwig_path), ]
sra_meta = metadata
rm(metadata)
pd = read_csv("https://raw.githubusercontent.com/nellore/runs/master/sra/v2/hg38/SraRunInfo.csv")
sra_meta = left_join(as.data.frame(sra_meta),pd,by=c("run"="Run","sample"="Sample"))
colnames(sra_meta)[4] <- "sample_id"
## combine metadata and predicted phentypes
meta = left_join(sra_meta,df)</code></pre>
</div>
<div id="identify-a-study-of-interest" class="section level1">
<h1><span class="header-section-number">3</span> Identify a study of interest</h1>
<pre class="r"><code>## Looking for a paired end sequencing study
## predicted_samplesource == tissue
## where sex was not reported
## N >=20
## but first, let's get studies where cancer is mentioned in the reported metadata
df <- meta[grep("cancer", meta$characteristics),]
## now let's be sure to include all samples from these studies (in case the controls don't have the word cancer in 'characteristics')
projects = unique(df$project)
df <- meta[meta$project %in% projects,]
## now: let's filter on criteria of interest
(possible_projects <- df %>% dplyr::filter(predicted_sequencingstrategy=="PAIRED", is.na(reported_sex), predicted_samplesource=="tissue") %>% group_by(project) %>% dplyr::summarise(Count = n()) %>% dplyr::filter(Count>=20)) </code></pre>
<pre><code>## # A tibble: 6 x 2
## project Count
## <chr> <int>
## 1 SRP002628 29
## 2 SRP007461 84
## 3 SRP027530 20
## 4 SRP029880 54
## 5 SRP042620 143
## 6 SRP055438 33</code></pre>
<pre class="r"><code> ## checking out total sample size
N=c()
for(i in 1:nrow(possible_projects)){
a <- print(meta[meta$project==possible_projects$project[i],] %>% nrow())
N = c(N,a)
}</code></pre>
<pre><code>## [1] 30
## [1] 203
## [1] 20
## [1] 54
## [1] 167
## [1] 33</code></pre>
<pre class="r"><code>## if samples meeting criteria and total study sample size are not the same
## means that some samples within study do not meet above critera
## will filter out these studies to only include studies in which all samples meet above criteria
possible_projects <- cbind(possible_projects,N)
(possible_projects = possible_projects %>% dplyr::filter(Count==N))</code></pre>
<pre><code>## project Count N
## 1 SRP027530 20 20
## 2 SRP029880 54 54
## 3 SRP055438 33 33</code></pre>
<pre class="r"><code>## let's look at sex breakdown in each study
for(i in 1:nrow(possible_projects)){
print(meta[meta$project==possible_projects$project[i],"predicted_sex"] %>% table())
}</code></pre>
<pre><code>## .
## female male unassigned
## 14 6 0
## .
## female male unassigned
## 9 45 0
## .
## female male unassigned
## 19 14 0</code></pre>
<pre class="r"><code># three studies match
# SRP027530
# SRP029880
# SRP055438</code></pre>
</div>
<div id="decide-which-of-possible-projects-to-use" class="section level1">
<h1><span class="header-section-number">4</span> Decide which of possible projects to use</h1>
<pre class="r"><code>## Study 1
## SRP027530
## https://trace.ncbi.nlm.nih.gov/Traces/sra/?study=SRP027530
## Note: Abstract metnions sex breakdown, so not best study of our purpose of including sex in an analysis where it was not previously included.
## Study 2
## SRP029880
## Publication here: http://onlinelibrary.wiley.com/doi/10.1016/j.molonc.2014.06.016/full
## Study 3
## SRP055438
## is a Chron's study; not actually a cancer study
## included in filtered studies b/c a few individuals have the word 'cancer' in their characteristics
grep("cancer",meta[meta$project==possible_projects$project[3],"characteristics"],val=T)</code></pre>
<pre><code>## [1] "c(\"age: 52\", \"Sex: male\", \"disease: Normal, Colon cancer\", \"disease_stage: NA\", \"inflamed: No\", \"montreal classification: NA\")"
## [2] "c(\"age: 44\", \"Sex: male\", \"disease: Normal, Colon cancer\", \"disease_stage: NA\", \"inflamed: No\", \"montreal classification: NA\")"
## [3] "c(\"age: 82\", \"Sex: female\", \"disease: Normal, Colon cancer\", \"disease_stage: NA\", \"inflamed: No\", \"montreal classification: NA\")"
## [4] "c(\"age: 52\", \"Sex: male\", \"disease: Normal, Hartmann reversal after colon cancer\", \"disease_stage: NA\", \"inflamed: No\", \"montreal classification: NA\")"</code></pre>
<pre class="r"><code>## will use Study 2: SRP029880</code></pre>
</div>
<div id="vignette-information" class="section level1">
<h1><span class="header-section-number">5</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 13.94796 secs</code></pre>
<pre class="r"><code>## Date this report was generated
message(Sys.time())</code></pre>
<pre><code>## 2017-09-28 21:55:46</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 3.4.2 Patched (2017-09-28 r73377)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## tz <NA>
## date 2017-09-28</code></pre>
<pre><code>## Packages --------------------------------------------------------------------------------------------------------------</code></pre>
<pre><code>## package * version date source
## assertthat 0.2.0 2017-04-11 CRAN (R 3.4.0)
## backports 1.1.1 2017-09-25 CRAN (R 3.4.2)
## base * 3.4.2 2017-09-28 local
## bindr 0.1 2016-11-13 CRAN (R 3.4.1)
## bindrcpp * 0.2 2017-06-17 CRAN (R 3.4.1)
## BiocGenerics * 0.23.1 2017-09-05 Bioconductor
## BiocStyle * 2.4.1 2017-08-02 Bioconductor
## broom 0.4.2 2017-02-13 CRAN (R 3.4.0)
## cellranger 1.1.0 2016-07-27 CRAN (R 3.4.0)
## colorspace 1.3-2 2016-12-14 CRAN (R 3.4.0)
## compiler 3.4.2 2017-09-28 local
## curl 2.8.1 2017-07-21 CRAN (R 3.4.1)
## datasets * 3.4.2 2017-09-28 local
## devtools 1.13.3 2017-08-02 CRAN (R 3.4.1)
## digest 0.6.12 2017-01-27 CRAN (R 3.4.0)
## dplyr * 0.7.3 2017-09-09 CRAN (R 3.4.1)
## evaluate 0.10.1 2017-06-24 CRAN (R 3.4.1)
## forcats 0.2.0 2017-01-23 CRAN (R 3.4.1)
## foreign 0.8-69 2017-06-22 CRAN (R 3.4.2)
## ggplot2 * 2.2.1 2016-12-30 CRAN (R 3.4.0)
## glue 1.1.1 2017-06-21 CRAN (R 3.4.1)
## graphics * 3.4.2 2017-09-28 local
## grDevices * 3.4.2 2017-09-28 local
## grid 3.4.2 2017-09-28 local
## gtable 0.2.0 2016-02-26 CRAN (R 3.4.0)
## haven 1.1.0 2017-07-09 CRAN (R 3.4.1)
## hms 0.3 2016-11-22 CRAN (R 3.4.0)
## htmltools 0.3.6 2017-04-28 CRAN (R 3.4.0)
## httr 1.3.1 2017-08-20 CRAN (R 3.4.1)
## IRanges * 2.11.17 2017-09-22 Bioconductor
## jsonlite 1.5 2017-06-01 CRAN (R 3.4.1)
## knitr 1.17 2017-08-10 CRAN (R 3.4.1)
## lattice 0.20-35 2017-03-25 CRAN (R 3.4.2)
## lazyeval 0.2.0 2016-06-12 CRAN (R 3.4.0)
## lubridate 1.6.0 2016-09-13 CRAN (R 3.4.1)
## magrittr 1.5 2014-11-22 CRAN (R 3.4.0)
## memoise 1.1.0 2017-04-21 CRAN (R 3.4.0)
## methods * 3.4.2 2017-09-28 local
## mnormt 1.5-5 2016-10-15 CRAN (R 3.4.0)
## modelr 0.1.1 2017-07-24 CRAN (R 3.4.1)
## munsell 0.4.3 2016-02-13 CRAN (R 3.4.0)
## nlme 3.1-131 2017-02-06 CRAN (R 3.4.2)
## parallel * 3.4.2 2017-09-28 local
## pkgconfig 2.0.1 2017-03-21 CRAN (R 3.4.1)
## plyr 1.8.4 2016-06-08 CRAN (R 3.4.0)
## psych 1.7.5 2017-05-03 CRAN (R 3.4.0)
## purrr * 0.2.3 2017-08-02 CRAN (R 3.4.1)
## R6 2.2.2 2017-06-17 CRAN (R 3.4.1)
## Rcpp 0.12.13 2017-09-28 CRAN (R 3.4.2)
## readr * 1.1.1 2017-05-16 CRAN (R 3.4.0)
## readxl 1.0.0 2017-04-18 CRAN (R 3.4.0)
## reshape2 1.4.2 2016-10-22 CRAN (R 3.4.0)
## rlang 0.1.2 2017-08-09 CRAN (R 3.4.1)
## rmarkdown * 1.6 2017-06-15 CRAN (R 3.4.1)
## rprojroot 1.2 2017-01-16 CRAN (R 3.4.1)
## rvest 0.3.2 2016-06-17 CRAN (R 3.4.0)
## S4Vectors * 0.15.8 2017-09-15 Bioconductor
## scales 0.5.0 2017-08-24 CRAN (R 3.4.1)
## stats * 3.4.2 2017-09-28 local
## stats4 * 3.4.2 2017-09-28 local
## stringi 1.1.5 2017-04-07 CRAN (R 3.4.0)
## stringr 1.2.0 2017-02-18 CRAN (R 3.4.0)
## tibble * 1.3.4 2017-08-22 CRAN (R 3.4.1)
## tidyr * 0.7.1 2017-09-01 CRAN (R 3.4.1)
## tidyverse * 1.1.1 2017-01-27 CRAN (R 3.4.1)
## tools 3.4.2 2017-09-28 local
## utils * 3.4.2 2017-09-28 local
## withr 2.0.0 2017-07-28 CRAN (R 3.4.1)
## xml2 1.1.1 2017-01-24 CRAN (R 3.4.0)
## yaml 2.1.14 2016-11-12 CRAN (R 3.4.0)</code></pre>
<p>Code for creating the vignette</p>
<pre class="r"><code>## Create the vignette
library('rmarkdown')
system.time(render('/dcl01/leek/data/sellis/barcoding/phenopredict_usecase/identify_study.Rmd', 'BiocStyle::html_document'))
## Extract the R code
library('knitr')
knit('/dcl01/leek/data/sellis/barcoding/phenopredict_usecase/identify_study.Rmd', tangle = TRUE)</code></pre>
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