Differential Expression with DESeq2
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- Sharleen Robbins
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1 Differential Expression with DESeq2 Drosophila melanogaster This document and the data used in this example can be found at: or on the cluster at: /n/ngsdata/workshops/2015_march 1. Setup First, install DESeq2 ( source(' bioclite('deseq2') Then load the libraries we ll need into R: library('deseq2') library('rcolorbrewer') 2. Read gene counts into a data frame Read sample gene counts a tab-delimited file. The rows of the data frame are genes while the columns are samples. samplenames <- c('dmel_unf1', 'dmel_unf2', 'dmel_unf3', 'dmel_inf1', 'dmel_inf2', 'dmel_inf3') filepath = ' llsamples.tsv' countdata = read.table(file = filepath, header = TRUE, row.names = 1, sep = '\t') dim(countdata) #view number of rows and columns ## [1] Now create a second data frame for sample information, such as the experimental condition that each sample belongs to: condition <- c('control', 'Control', 'Control', 'Infected', 'Infected', 'Infected') #vector of co lumn names for the data frame coldata <- data.frame(row.names=colnames(countdata), condition=factor(condition, levels=c('control ','Infected'))) coldata ## condition ## dmel_unf1 Control ## dmel_unf2 Control ## dmel_unf3 Control ## dmel_inf1 Infected ## dmel_inf2 Infected ## dmel_inf3 Infected 1 of 5 3/31/15, 10:25 AM
2 3. Run DESeq2 First, create a DESeqDataSet by specifying the gene counts data frame, the sample information data frame and a design model: dataset <- DESeqDataSetFromMatrix(countData = countdata, coldata = coldata, design = ~condition) dataset ## class: DESeqDataSet ## dim: ## exptdata(0): ## assays(1): counts ## rownames(17321): FBgn FBgn FBgn ## FBgn ## rowdata metadata column names(0): ## colnames(6): dmel_unf1 dmel_unf2... dmel_inf2 dmel_inf3 ## coldata names(1): condition Then run the DESeq2 algorithm and extract results for our two-class comparison: dds <- DESeq(dataset) ## estimating size factors ## estimating dispersions ## gene-wise dispersion estimates ## mean-dispersion relationship ## final dispersion estimates ## fitting model and testing result <- results(dds, contrast=c('condition','infected','control')) result <- result[complete.cases(result),] #remove any rows with NA head(result) ## log2 fold change (MAP): condition Infected vs Control ## Wald test p-value: condition Infected vs Control ## DataFrame with 6 rows and 6 columns ## basemean log2foldchange lfcse stat pvalue ## <numeric> <numeric> <numeric> <numeric> <numeric> ## FBgn ## FBgn ## FBgn ## FBgn ## FBgn ## FBgn ## padj ## <numeric> ## FBgn ## FBgn ## FBgn ## FBgn ## FBgn ## FBgn of 5 3/31/15, 10:25 AM
3 4. View results View a summary of DESeq2 results: summary(result) ## ## out of with nonzero total read count ## adjusted p-value < 0.1 ## LFC > 0 (up) : 199, 1.9% ## LFC < 0 (down) : 202, 2% ## outliers [1] : 0, 0% ## low counts [2] : 0, 0% ## (mean count < 16.2) ## [1] see 'cookscutoff' argument of?results ## [2] see 'independentfiltering' argument of?results The top 50 up-regulated and down-regulated genes by p-value: n = 50 resordered <- result[order(result$padj),] topresults <- rbind( resordered[ resordered[,'log2foldchange'] > 0, ][1:n,], resordered[ resordered[,'log2foldchange'] < 0, ][n:1,] ) topresults[c(1:5,(2*n-4):(2*n)), c('basemean','log2foldchange','padj')] ## DataFrame with 10 rows and 3 columns ## basemean log2foldchange padj ## <numeric> <numeric> <numeric> ## FBgn e-168 ## FBgn e-116 ## FBgn e-107 ## FBgn e-98 ## FBgn e-97 ## FBgn e-08 ## FBgn e-08 ## FBgn e-08 ## FBgn e-11 ## FBgn e-16 Plot log fold change vs. mean expression for all genes, with genes where p < 0.1 colored red: plotma(result, main='deseq2: D. melanogaster Control vs. Infected', ylim=c(-2,2)) 3 of 5 3/31/15, 10:25 AM
4 PCA plot for all genes: rld <- rlogtransformation(dds, blind=true) plotpca(rld) Plot counts for a single gene. Below is the plot for the gene with the lowest p-value: plotcounts(dds, gene=which.min(result$padj), intgroup='condition', pch = 19) 4 of 5 3/31/15, 10:25 AM
5 Display top genes normalized counts in a heatmap: hmcol <- brewer.pal(11,'rdbu') ncounts <- counts(dds, normalized=true) heatmap(as.matrix(ncounts[ row.names(topresults), ]), Rowv = NA, col = hmcol, mar = c(8,2)) 5 of 5 3/31/15, 10:25 AM
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