423 lines
910 KiB
HTML
423 lines
910 KiB
HTML
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<!DOCTYPE html>
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<html xmlns="http://www.w3.org/1999/xhtml">
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<head>
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<meta charset="utf-8" />
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<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
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<meta name="generator" content="pandoc" />
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<meta name="author" content="Corneel Vermeulen" />
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<meta name="date" content="2019-12-12" />
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<title>Sample workflow DESeq2 for Tessa</title>
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<script src="data:application/x-javascript;base64,LyohIGpRdWVyeSB2MS4xMS4zIHwgKGMpIDIwMDUsIDIwMTUgalF1ZXJ5IEZvdW5kYXRpb24sIEluYy4gfCBqcXVlcnkub3JnL2xpY2Vuc2UgKi8KIWZ1bmN0aW9uKGEsYil7Im9iamVjdCI9PXR5cGVvZiBtb2R1bGUmJiJvYmplY3QiPT10eXBlb2YgbW9kdWxlLmV4cG9ydHM/bW9kdWxlLmV4cG9ydHM9YS5kb2N1bWVudD9iKGEsITApOmZ1bmN0aW9uKGEpe2lmKCFhLmRvY3VtZW50KXRocm93IG5ldyBFcnJvcigialF1ZXJ5IHJlcXVpcmVzIGEgd2luZG93IHdpdGggYSBkb2N1bWVudCIpO3JldHVybiBiKGEpfTpiKGEpfSgidW5kZWZpbmVkIiE9dHlwZW9mIHdpbmRvdz93aW5kb3c6dGhpcyxmdW5jdGlvbihhLGIpe3ZhciBjPVtdLGQ9Yy5zbGljZSxlPWMuY29uY2F0LGY9Yy5wdXNoLGc9Yy5pbmRleE9mLGg9e30saT1oLnRvU3RyaW5nLGo9aC5oYXNPd25Qcm9wZXJ0eSxrPXt9LGw9IjEuMTEuMyIsbT1mdW5jdGlvbihhLGIpe3JldHVybiBuZXcgbS5mbi5pbml0KGEsYil9LG49L15bXHNcdUZFRkZceEEwXSt8W1xzXHVGRUZGXHhBMF0rJC9nLG89L14tbXMtLyxwPS8tKFtcZGEtel0pL2dpLHE9ZnVuY3Rpb24oYSxiKXtyZXR1cm4gYi50b1VwcGVyQ2FzZSgpfTttLmZuPW0ucHJvdG90eXBlPXtqcXVlcnk6bCxjb25zdHJ1Y3RvcjptLHNlbGVjdG9yOiIiLGxlbmd0aDowLHRvQXJyYXk6ZnVuY3Rpb24oKXtyZXR1cm4gZC5jYWxsKHRoaXMpfSxnZXQ6ZnVuY3Rpb24oYSl7cmV0dXJuIG51bGwhPWE/MD5hP3RoaXNbYSt0aGlzLmxlbmd0aF06dGhpc1thXTpkLmNhbGwodGhpcyl9LHB1c2hTdGFjazpmdW5jdGlvbihhKXt2YXIgYj1tLm1lcmdlKHRoaXMuY29uc3RydWN0b3IoKSxhKTtyZXR1cm4gYi5wcmV2T2JqZWN0PXRoaXMsYi5jb250ZXh0PXRoaXMuY29udGV4dCxifSxlYWNoOmZ1bmN0aW9uKGEsYil7cmV0dXJuIG0uZWFjaCh0aGlzLGEsYil9LG1hcDpmdW5jdGlvbihhKXtyZXR1cm4gdGhpcy5wdXNoU3RhY2sobS5tYXAodGhpcyxmdW5jdGlvbihiLGMpe3JldHVybiBhLmNhbGwoYixjLGIpfSkpfSxzbGljZTpmdW5jdGlvbigpe3JldHVybiB0aGlzLnB1c2hTdGFjayhkLmFwcGx5KHRoaXMsYXJndW1lbnRzKSl9LGZpcnN0OmZ1bmN0aW9uKCl7cmV0dXJuIHRoaXMuZXEoMCl9LGxhc3Q6ZnVuY3Rpb24oKXtyZXR1cm4gdGhpcy5lcSgtMSl9LGVxOmZ1bmN0aW9uKGEpe3ZhciBiPXRoaXMubGVuZ3RoLGM9K2ErKDA+YT9iOjApO3JldHVybiB0aGlzLnB1c2hTdGFjayhjPj0wJiZiPmM/W3RoaXNbY11dOltdKX0sZW5kOmZ1bmN0aW9uKCl7cmV0dXJuIHRoaXMucHJldk9iamVjdHx8dGhpcy5jb25zdHJ1Y3RvcihudWxsKX0scHVzaDpmLHNvcnQ6Yy5zb3J0LHNwbGljZTpjLnNwbGljZX0sbS5leHRlbmQ9bS5mbi5leHRlbmQ9ZnVuY3Rpb24oKXt2YXIgYSxiLGMsZCxlLGYsZz1hcmd1bWVudHNbMF18fHt9LGg9MSxpPWFyZ3VtZW50cy5sZW5ndGgsaj0hMTtmb3IoImJvb2xlYW4iPT10eXBlb2YgZyYmKGo9ZyxnPWFyZ3VtZW50c1toXXx8e30saCsrKSwib2JqZWN0Ij09dHlwZW9mIGd8fG0uaXNGdW5jdGlvbihnKXx8KGc9e30pLGg9PT1pJiYoZz10aGlzLGgtLSk7aT5oO2grKylpZihudWxsIT0oZT1hcmd1bWVudHNbaF0pKWZvcihkIGluIGUpYT1nW2RdLGM9ZVtkXSxnIT09YyYmKGomJmMmJihtLmlzUGxhaW5PYmplY3QoYyl8fChiPW0uaXNBcnJheShjKSkpPyhiPyhiPSExLGY9YSYmbS5pc0FycmF5KGEpP2E6W10pOmY9YSYmbS5pc1BsYWluT2JqZWN0KGEpP2E6e30sZ1tkXT1tLmV4dGVuZChqLGYsYykpOnZvaWQgMCE9PWMmJihnW2RdPWMpKTtyZXR1cm4gZ30sbS5leHRlbmQoe2V4cGFuZG86ImpRdWVyeSIrKGwrTWF0aC5yYW5kb20oKSkucmVwbGFjZSgvXEQvZywiIiksaXNSZWFkeTohMCxlcnJvcjpmdW5jdGlvbihhKXt0aHJvdyBuZXcgRXJyb3IoYSl9LG5vb3A6ZnVuY3Rpb24oKXt9LGlzRnVuY3Rpb246ZnVuY3Rpb24oYSl7cmV0dXJuImZ1bmN0aW9uIj09PW0udHlwZShhKX0saXNBcnJheTpBcnJheS5pc0FycmF5fHxmdW5jdGlvbihhKXtyZXR1cm4iYXJyYXkiPT09bS50eXBlKGEpfSxpc1dpbmRvdzpmdW5jdGlvbihhKXtyZXR1cm4gbnVsbCE9YSYmYT09YS53aW5kb3d9LGlzTnVtZXJpYzpmdW5jdGlvbihhKXtyZXR1cm4hbS5pc0FycmF5KGEpJiZhLXBhcnNlRmxvYXQoYSkrMT49MH0saXNFbXB0eU9iamVjdDpmdW5jdGlvbihhKXt2YXIgYjtmb3IoYiBpbiBhKXJldHVybiExO3JldHVybiEwfSxpc1BsYWluT2JqZWN0OmZ1bmN0aW9uKGEpe3ZhciBiO2lmKCFhfHwib2JqZWN0IiE9PW0udHlwZShhKXx8YS5ub2RlVHlwZXx8bS5pc1dpbmRvdyhhKSlyZXR1cm4hMTt0cnl7aWYoYS5jb25zdHJ1Y3RvciYmIWouY2FsbChhLCJjb25zdHJ1Y3RvciIpJiYhai5jYWxsKGEuY29uc3RydWN0b3IucHJvdG90eXBlLCJpc1Byb3RvdHlwZU9mIikpcmV0dXJuITF9Y2F0Y2goYyl7cmV0dXJuITF9aWYoay5vd25MYXN0KWZvcihiIGluIGEpcmV0dXJuIGouY2FsbChhLGIpO2ZvcihiIGluIGEpO3JldHVybiB2b2lkIDA9PT1ifHxqLmNhbGwoYSxiKX0sdHlwZTpmdW5jdGlvbihhKXtyZXR1cm4gbnVsbD09YT9hKyIiOiJvYmplY3QiPT10eXBlb2YgYXx8ImZ1bmN0aW9uIj09dHlwZW9mIGE/aFtpLmNhbGwoYSldfHwib2JqZWN0Ijp0eXBlb2YgYX0sZ2xvYmFsRXZhbDpmdW5jdGlvbihiKXtiJiZtLnRyaW0oYikmJihhLmV4ZWNTY3JpcHR8fGZ1bmN0aW9uKGIpe2EuZXZhbC5jYWxsKGEsYil9KShiKX0sY2FtZWxDYXNlOmZ1bmN0aW9uKGEpe3JldHVybiBhLnJlcGxhY2UobywibXMtIikucmVwbGFjZShwLHEpfSxub2RlTmFtZTpmdW5jdGlvbihhLGIpe3JldHVybiBhLm5vZGVOYW1lJiZhLm5vZGVOYW1lLnRvTG93ZXJDYXNlKCk9PT1iLnRvTG93ZXJDYXNlKCl9LGVhY2g6ZnVuY3Rpb24oYSxiLGMpe3ZhciBkLGU9MCxmPWEubGVuZ3RoLGc9cihhKTtpZihjKXtpZihnKXtmb3IoO2Y+ZTtlKyspaWYoZD1iLmFwcGx5KGFbZV0sYyksZD09PSExKWJyZWFrfWVsc2UgZm9yKGUgaW4gYSlpZihkPWIuYXBwbHkoYVtlXSxjKSxkPT09ITEpYnJlYWt9ZWxzZSBpZ
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<meta name="viewport" content="width=device-width, initial-scale=1" />
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<h1 class="title toc-ignore">Sample workflow DESeq2 for Tessa</h1>
|
||
|
|
<h4 class="author">Corneel Vermeulen</h4>
|
||
|
|
<h4 class="date">2019-12-12</h4>
|
||
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</div>
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<div id="gsk-asthma-remission-project" class="section level3">
|
||
|
|
<h3>GSK asthma remission project</h3>
|
||
|
|
<p>Hoi Tesssa, Dit is een aangepaste versie van het tutorial script dat ik voor Ilse maakte, vanwege een <a href="#%20https://support.bioconductor.org/p/95695/">verandering in DESeq2 VERSION 1.16.0</a>. Omdat de orginele count table erg groot is gebruikt het script een miniatuur versie gemaakt van 1000 willekeurige features.</p>
|
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</div>
|
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<div id="preliminaries" class="section level3">
|
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|
<h3>Preliminaries</h3>
|
||
|
|
<p>load all required packages</p>
|
||
|
|
<pre class="r"><code>library("DESeq2")
|
||
|
|
library("gplots")</code></pre>
|
||
|
|
</div>
|
||
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|
<div id="load-data" class="section level3">
|
||
|
|
<h3>load data</h3>
|
||
|
|
<p>Hier laad je de count table and de database respectievelijk. Je kunt hier evt. je werkmap instellen.</p>
|
||
|
|
<pre class="r"><code>#setwd("/path/to/your/wd")
|
||
|
|
CT<-read.delim("sample.htseq.txt.table", header = TRUE, sep = "\t",
|
||
|
|
quote = "", fill = TRUE, comment.char = "")
|
||
|
|
db <-read.csv("db.csv",header=T,sep=";",dec=",")</code></pre>
|
||
|
|
</div>
|
||
|
|
<div id="prepare-data" class="section level3">
|
||
|
|
<h3>Prepare data</h3>
|
||
|
|
<p>Dit is het lastigste stuk. De sample_IDs zijn gemangeld tijdens het verwerken van de samples en dat maakt het matchen aan de klinische data een tijdrovende klus.</p>
|
||
|
|
<pre class="r"><code>head(colnames(CT))</code></pre>
|
||
|
|
<pre><code>## [1] "probe" "X101_NORM.htseq.txt" "X102_NORM.htseq.txt"
|
||
|
|
## [4] "X104_NORM.htseq.txt" "X105_NORM_.htseq.txt" "X106_NORM.htseq.txt"</code></pre>
|
||
|
|
<pre class="r"><code>head(db$rnaseq.id)</code></pre>
|
||
|
|
<pre><code>## [1] X9_0_T06_90304 X613_0_T07_90064 X377_0_T06_90091 X535_0_T07_90052
|
||
|
|
## [5] X433_0_T06_90135 X25_0_T06_90258
|
||
|
|
## 232 Levels: X101_NORM_ X102_NORM_ X104_NORM_ X105_NORM_ ... X99_NORM_</code></pre>
|
||
|
|
<p>Ik heb al wat werk verzet door in de db overal een “X” voor te plakken en alle streepjes te vervangen door underscores. Nu moeten we nog de “.htseq.txt” kwijtraken. Zie ook dat sommige samples “NORM” en anderen “NORM_” genoemd zijn. Verder zijn er meerdere controle samples. Veel lelijke code hier, en waarschijnlijk niet bruikbaar voor jou:</p>
|
||
|
|
<pre class="r"><code>colnames(CT)[1] <- ""
|
||
|
|
cn.CT <- colnames(CT)
|
||
|
|
cn.CT <- cn.CT[2:length(cn.CT)] #drop the empty first cell
|
||
|
|
cn.CT <- gsub(".htseq.txt", "", cn.CT) # remove the suffix
|
||
|
|
cn.CT <- gsub("NORM.{0,}", "NORM_", cn.CT) # add one trailing underscore
|
||
|
|
cn.all <- as.character(db$rnaseq.id)
|
||
|
|
idx <- match(cn.CT,cn.all) #match the clinical data to the correct samples
|
||
|
|
idx[which(is.na(idx))] <- which(cn.all == "X263_110_T04_90226") # multiple ref samples
|
||
|
|
coldata <- db[idx,]
|
||
|
|
rownames(coldata) <- cn.CT
|
||
|
|
countdata <- CT[,c(2:ncol(CT))]
|
||
|
|
rownames(countdata) <- CT[,1]
|
||
|
|
coldata$batch.extract[which(coldata$rnaseq.id == "X263_110_T04_90226")] <- seq(1:6)
|
||
|
|
coldata$batch.extract<-as.factor(coldata$batch.extract) #batch nrs are factor levels</code></pre>
|
||
|
|
</div>
|
||
|
|
<div id="qc-filter" class="section level3">
|
||
|
|
<h3>QC-filter</h3>
|
||
|
|
<p>We gebruiken alleen samples die de QC doorstaan en de beste reference sample (#2).</p>
|
||
|
|
<pre class="r"><code>qc.pass <- scan("rnaseqids_qc4_pass_plusref2.txt", what = "character")
|
||
|
|
idx.qc.pass <- which(coldata$rnaseq.id %in% qc.pass)
|
||
|
|
idx.ref2 <- which(coldata$rnaseq.id == "X263_110_T04_90226" & coldata$batch.extract == 2)
|
||
|
|
idx.qc.pass <- c(idx.qc.pass,idx.ref2)
|
||
|
|
coldata <- coldata[idx.qc.pass,]
|
||
|
|
countdata <- countdata[,idx.qc.pass]
|
||
|
|
colnames(countdata) <- NULL # fixes error</code></pre>
|
||
|
|
</div>
|
||
|
|
<div id="exploratory-analysis" class="section level3">
|
||
|
|
<h3>Exploratory analysis</h3>
|
||
|
|
<p>Nu we onze data klaar hebben, wordt alles veel eenvoudiger: We converteren onze count table naar een DESeqDataSet. We geven nog geen model op.</p>
|
||
|
|
<pre class="r"><code>dds <- DESeqDataSetFromMatrix(countData = countdata,
|
||
|
|
colData = coldata,
|
||
|
|
design = ~ 1)</code></pre>
|
||
|
|
<p>Dan kijken we in een PCA of onze dataset zich gedraagt. Het is noodzakelijk de ruwe count data te transformeren daarvoor. Omdat we teveel samples hebben om een rlog transformatie te doen gebruiken we een <a href="https://support.bioconductor.org/p/77122/">trucje</a>.</p>
|
||
|
|
<pre class="r"><code>dds <- estimateSizeFactors(dds)
|
||
|
|
baseMean <- rowMeans(counts(dds, normalized=TRUE))
|
||
|
|
idx.sub <- sample(which(baseMean > 5), 100)
|
||
|
|
dds.sub <- dds[idx.sub, ]
|
||
|
|
dds.sub <- estimateDispersions(dds.sub)
|
||
|
|
dispersionFunction(dds) <- dispersionFunction(dds.sub)
|
||
|
|
vsd <- varianceStabilizingTransformation(dds, blind=FALSE)
|
||
|
|
plotPCA(vsd, intgroup = "gender")</code></pre>
|
||
|
|
<p><img src="data:image/png;base64,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
|
||
|
|
</div>
|
||
|
|
<div id="differential-expression" class="section level3">
|
||
|
|
<h3>Differential expression</h3>
|
||
|
|
<p>Tenslotte het echte werk. We voeren ons model in en creeren een nieuwe DESeqDataSet (de oude wordt daarbij overschreven). Dan testen we of er een overall verschil is tussen de levels van asthma. Vervolgens kijken we naar een specifiek contrast.</p>
|
||
|
|
<pre class="r"><code>dds <- DESeqDataSetFromMatrix(countData = countdata,
|
||
|
|
colData = coldata,
|
||
|
|
design = ~ gender + asthma.ics)
|
||
|
|
# Overall test (takes long time to run)
|
||
|
|
dds <- DESeq(dds, test="LRT", full=~ gender + asthma.ics, reduced=~ gender)
|
||
|
|
res <- results(dds)
|
||
|
|
res$symbol <- mcols(dds)$symbol
|
||
|
|
head(res[order(res$pvalue),],4)</code></pre>
|
||
|
|
<pre><code>## log2 fold change (MLE): asthma.ics PersA no ICS vs ClinR
|
||
|
|
## LRT p-value: '~ gender + asthma.ics' vs '~ gender'
|
||
|
|
## DataFrame with 4 rows and 6 columns
|
||
|
|
## baseMean log2FoldChange lfcSE
|
||
|
|
## <numeric> <numeric> <numeric>
|
||
|
|
## ENSG00000130779 1789.38912546738 0.0478949207887351 0.0654612878978894
|
||
|
|
## ENSG00000119535 215.440807373427 0.206590053580329 0.465871144277395
|
||
|
|
## ENSG00000182957 562.782379494973 0.097333381899491 0.100818494331977
|
||
|
|
## ENSG00000100604 16.9398710766147 -0.5828701888104 0.239816945028832
|
||
|
|
## stat pvalue padj
|
||
|
|
## <numeric> <numeric> <numeric>
|
||
|
|
## ENSG00000130779 58.7554612212198 5.29625676133409e-12 3.49023320571917e-09
|
||
|
|
## ENSG00000119535 52.7191486758725 9.75644624431816e-11 3.21474903750283e-08
|
||
|
|
## ENSG00000182957 48.8988210026882 6.1296215354578e-10 1.34647353062223e-07
|
||
|
|
## ENSG00000100604 43.5464068704941 7.96970085754099e-09 1.31300821627988e-06</code></pre>
|
||
|
|
<pre class="r"><code>summary(res)</code></pre>
|
||
|
|
<pre><code>##
|
||
|
|
## out of 983 with nonzero total read count
|
||
|
|
## adjusted p-value < 0.1
|
||
|
|
## LFC > 0 (up) : 75, 7.6%
|
||
|
|
## LFC < 0 (down) : 63, 6.4%
|
||
|
|
## outliers [1] : 0, 0%
|
||
|
|
## low counts [2] : 326, 33%
|
||
|
|
## (mean count < 1)
|
||
|
|
## [1] see 'cooksCutoff' argument of ?results
|
||
|
|
## [2] see 'independentFiltering' argument of ?results</code></pre>
|
||
|
|
<pre class="r"><code>sum(res$padj < 0.05, na.rm=T)</code></pre>
|
||
|
|
<pre><code>## [1] 94</code></pre>
|
||
|
|
<pre class="r"><code>topGene <- rownames(res)[which.min(res$padj)]
|
||
|
|
plotCounts(dds, gene=topGene, intgroup=c("asthma.ics"))</code></pre>
|
||
|
|
<p><img src="data:image/png;base64,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
|
||
|
|
<p>Het feature met de meeste variatie tussen de groepen is ENSG00000130779, maar waarschijnlijk ben je geinteresseerd in een specifiek contrast, bijvoorbeeld asthma versus healthy:</p>
|
||
|
|
<pre class="r"><code>dds.wald <- DESeq(dds, test="Wald", betaPrior = TRUE)</code></pre>
|
||
|
|
<pre><code>## using pre-existing size factors</code></pre>
|
||
|
|
<pre><code>## estimating dispersions</code></pre>
|
||
|
|
<pre><code>## found already estimated dispersions, replacing these</code></pre>
|
||
|
|
<pre><code>## gene-wise dispersion estimates</code></pre>
|
||
|
|
<pre><code>## mean-dispersion relationship</code></pre>
|
||
|
|
<pre><code>## final dispersion estimates</code></pre>
|
||
|
|
<pre><code>## fitting model and testing</code></pre>
|
||
|
|
<pre><code>## -- replacing outliers and refitting for 33 genes
|
||
|
|
## -- DESeq argument 'minReplicatesForReplace' = 7
|
||
|
|
## -- original counts are preserved in counts(dds)</code></pre>
|
||
|
|
<pre><code>## estimating dispersions</code></pre>
|
||
|
|
<pre><code>## fitting model and testing</code></pre>
|
||
|
|
<pre class="r"><code>res.c <- results(dds.wald, contrast = c("asthma.ics","PersA_no_ICS","H"))
|
||
|
|
topGene.c <- rownames(res.c)[which.min(res.c$padj)]</code></pre>
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|
<p>Het feature dat het meest verschillend tot expressie komt in dit contrast is ENSG00000119535. Hierna zul je zelf je weg moeten vinden. Meer informatie vind je <a href="http://www.bioconductor.org/help/workflows/rnaseqGene/">hier</a> en <a href="https://www.bioconductor.org/help/course-materials/2015/LearnBioconductorFeb2015/B02.1.1_RNASeqLab.html">hier</a>.</p>
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</div>
|
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|
|
<div id="session-info" class="section level2">
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||
|
|
<h2>session info</h2>
|
||
|
|
<p>Packages used</p>
|
||
|
|
<pre class="r"><code>sessionInfo()</code></pre>
|
||
|
|
<pre><code>## R version 3.6.1 (2019-07-05)
|
||
|
|
## Platform: x86_64-pc-linux-gnu (64-bit)
|
||
|
|
## Running under: Ubuntu 18.04.3 LTS
|
||
|
|
##
|
||
|
|
## Matrix products: default
|
||
|
|
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
|
||
|
|
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
|
||
|
|
##
|
||
|
|
## locale:
|
||
|
|
## [1] LC_CTYPE=nl_NL.UTF-8 LC_NUMERIC=C
|
||
|
|
## [3] LC_TIME=nl_NL.UTF-8 LC_COLLATE=nl_NL.UTF-8
|
||
|
|
## [5] LC_MONETARY=nl_NL.UTF-8 LC_MESSAGES=nl_NL.UTF-8
|
||
|
|
## [7] LC_PAPER=nl_NL.UTF-8 LC_NAME=C
|
||
|
|
## [9] LC_ADDRESS=C LC_TELEPHONE=C
|
||
|
|
## [11] LC_MEASUREMENT=nl_NL.UTF-8 LC_IDENTIFICATION=C
|
||
|
|
##
|
||
|
|
## attached base packages:
|
||
|
|
## [1] parallel stats4 stats graphics grDevices utils datasets
|
||
|
|
## [8] methods base
|
||
|
|
##
|
||
|
|
## other attached packages:
|
||
|
|
## [1] gplots_3.0.1.1 DESeq2_1.24.0
|
||
|
|
## [3] SummarizedExperiment_1.14.0 DelayedArray_0.10.0
|
||
|
|
## [5] BiocParallel_1.18.0 matrixStats_0.54.0
|
||
|
|
## [7] Biobase_2.44.0 GenomicRanges_1.36.0
|
||
|
|
## [9] GenomeInfoDb_1.20.0 IRanges_2.18.0
|
||
|
|
## [11] S4Vectors_0.22.0 BiocGenerics_0.30.0
|
||
|
|
##
|
||
|
|
## loaded via a namespace (and not attached):
|
||
|
|
## [1] bit64_0.9-7 splines_3.6.1 gtools_3.8.1
|
||
|
|
## [4] Formula_1.2-3 assertthat_0.2.1 latticeExtra_0.6-28
|
||
|
|
## [7] blob_1.1.1 GenomeInfoDbData_1.2.1 yaml_2.2.0
|
||
|
|
## [10] pillar_1.4.0 RSQLite_2.1.1 backports_1.1.4
|
||
|
|
## [13] lattice_0.20-38 glue_1.3.1 digest_0.6.18
|
||
|
|
## [16] RColorBrewer_1.1-2 XVector_0.24.0 checkmate_1.9.3
|
||
|
|
## [19] colorspace_1.4-1 htmltools_0.3.6 Matrix_1.2-18
|
||
|
|
## [22] plyr_1.8.4 XML_3.98-1.19 pkgconfig_2.0.2
|
||
|
|
## [25] genefilter_1.66.0 zlibbioc_1.30.0 purrr_0.3.2
|
||
|
|
## [28] xtable_1.8-4 scales_1.0.0 gdata_2.18.0
|
||
|
|
## [31] htmlTable_1.13.1 tibble_2.1.1 annotate_1.62.0
|
||
|
|
## [34] ggplot2_3.1.1 nnet_7.3-12 lazyeval_0.2.2
|
||
|
|
## [37] survival_3.1-7 magrittr_1.5 crayon_1.3.4
|
||
|
|
## [40] memoise_1.1.0 evaluate_0.13 foreign_0.8-72
|
||
|
|
## [43] tools_3.6.1 data.table_1.12.2 stringr_1.4.0
|
||
|
|
## [46] locfit_1.5-9.1 munsell_0.5.0 cluster_2.1.0
|
||
|
|
## [49] AnnotationDbi_1.46.0 compiler_3.6.1 caTools_1.17.1.2
|
||
|
|
## [52] rlang_0.3.4 grid_3.6.1 RCurl_1.95-4.12
|
||
|
|
## [55] rstudioapi_0.10 htmlwidgets_1.3 labeling_0.3
|
||
|
|
## [58] bitops_1.0-6 base64enc_0.1-3 rmarkdown_1.12
|
||
|
|
## [61] gtable_0.3.0 DBI_1.0.0 R6_2.4.0
|
||
|
|
## [64] gridExtra_2.3 knitr_1.22 dplyr_0.8.1
|
||
|
|
## [67] bit_1.1-14 Hmisc_4.2-0 KernSmooth_2.23-16
|
||
|
|
## [70] stringi_1.4.3 Rcpp_1.0.1 geneplotter_1.62.0
|
||
|
|
## [73] rpart_4.1-15 acepack_1.4.1 tidyselect_0.2.5
|
||
|
|
## [76] xfun_0.7</code></pre>
|
||
|
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