DESeq2 with multiple factors
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Entering edit mode
14 months ago
Nelo ▴ 20

I have 18 samples. These samples have 2 different factors with one factor(Infestation) having 2 levels (as control vs affected) & another factor(timepoint) having 3 levels(as 24h, 48h,96h) . Also the last factor (timepoint) have 3 biological replicates.

While performing:

counts <- read.delim("counts.csv", header = TRUE, row.names =1, sep = ",") dim(counts) [1] 27868 18 colData <-read.delim("colData.csv", header =TRUE, sep = ",", row.names =1) dds <- DESeqDataSetFromMatrix(countData = counts, colData = colData, design = ~ Infestation+timepoint) dds <- DESeq(dds) res <- results(dds) de_genes <- rownames(res)[which(res$padj < 0.5 & abs(res$log2FoldChange) > 1)][1:50]

This piece of code is getting very less genes( less than 10) using padj & log2fold values above mentioned.

BUT, modifying my design formula to include only one factor(i.e., Infestation) while disregarding timepoint factors gives me my specified number of 50 genes.

DESeq2 • 522 views
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Entering edit mode

It is unclear what comparison you are trying to make with your differential expression analysis. This needs to be defined in order to setup your model design and your results function.

In the code you shared you did not specify your contrast when running results(dds):

If results is run without specifying contrast or name, it will return the comparison of the last level of the last variable in the design formula over the first level of this variable. For example, for a simple two-group comparison, this would return the log2 fold changes of the second group over the first group (the reference level). Please see examples below and in the vignette.

also if you are going to filter on fold change then you should apply lfcshrinkage.

I recommend reviewing the following http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html

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