# Simulating genes and counts for DESeq2 analysis

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Sometimes it is helpful to simulate gene expression data to test code or to see how your results look with simulated values from a particular probability distribution. Here I am going to show you how to simulate RNAseq expression data counts from a uniform distribution with a mininum = 0 and maximum = 1200.

# Get all human gene symbols from biomaRt library("biomaRt") mart <- useMart(biomart="ensembl", dataset = "hsapiens_gene_ensembl") my_results <- getBM(attributes = c("hgnc_symbol"), mart=mart) head(my_results) # Simulate 100 gene names to be used for our cnts matrix set.seed(32268) my_genes <- with(my_results, sample(hgnc_symbol, size=100, replace=FALSE)) head(my_genes) # Simulate a cnts matrix cnts = matrix(runif(600, min=0, max=1200), ncol=6) cnts = apply(cnts, c(1,2), as.integer) head(cnts) dim(cnts)

Now, say we run DESeq2 to look for differentially expressed genes between our two simulated groups.

# Running DESEQ2 based on https://bioconductor.org/packages/release/bioc/vignettes/gage/inst/doc/RNA-seqWorkflow.pdf library("DESeq2") grp.idx <- rep(c("KO", "WT"), each=3) coldat=DataFrame(grp=factor(grp.idx, levels=c("WT", "KO"))) # Add the column names and gene names colnames(cnts) <- paste(grp.idx, 1:6, sep="_") rownames(cnts) <- my_genes head(cnts) # Run DESeq2 analysis on the simulated counts dds <- DESeqDataSetFromMatrix(cnts, colData=coldat, design = ~ grp) dds <- DESeq(dds) deseq2.res <- results(dds) deseq2.fc=deseq2.res$log2FoldChange names(deseq2.fc)=rownames(deseq2.res) exp.fc=deseq2.fc head(exp.fc) # SDAD1 SVOPL SRGAP2C MTND1P2 CNN2P8 IL13 # -0.48840808 0.32122109 -0.55584857 0.00184246 -0.15371042 0.11555792

Now let’s see how many simulated genes had a log2 fold change greater than 1 by chance.

# Load the fold changes from DESeq2 analysis and order in decreasing order geneList = sort(exp.fc, decreasing = TRUE) # log FC is shown head(geneList) gene <- geneList[abs(geneList) >= 1] head(gene) # C1orf216 #-1.129836

Now it’s your turn! What other probability distributions could we simulate data from to perform a mock RNA seq experiment to determine how many genes could be different by chance? You can even use a bootstrap approach to calculate the p-value after running 1000 permutations of the code. Of course, to circumvent these problems we use adjusted p values but it is always nice to go back to basics and stress the importance of applying statistical methods when looking at differentially expressed genes. I encourage you all to leave your answers in the comment section below to inspire others.

Happy R programming!

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