Hi all
I have two Seurat object for scRNA-Seq and extract the raw count from the object:
#crating two matrix for raw counts
ccl4_count <- as.matrix(ccl4_list@assays[["RNA"]]@counts)
ctrl_count <- as.matrix(ctrl_list@assays[["RNA"]]@counts)
Then I use the DESeq2 package for normalization and DEG analysis:
#creating one expression matrix
mat.ctrl_ccl4 <- cbind(ctrl_count,ccl4_count)
#grouping
gr <- factor(c(rep("ctrl", ncol(ctrl_count)) , rep("ccl4", ncol(ccl4_count))))
coldata <- data.frame(group=gr)
dds <- DESeqDataSetFromMatrix( mat.ctrl_ccl4 , coldata, design= ~group)
#the script bellow takes a long time :|
dds <- DESeq(dds)
normalizedCount <- log2(1+counts(dds, normalized=TRUE))
#finding DEG
dif <- data.frame(results(dds, c("group", "ccl4" , "ctrl")))
That is all my script. Eventually in the dif table, I have about 2000 genes with 0 p-value and now I don't know how to deal with these genes. They make the volcano plot seem incorrect.
The other problem is that normalization of single-cell data by DESeq2 is really time-consuming because of the large number of cells.
Does anyone have any idea of how to efficiently perform DEG analysis for single-cell analysis? Thanks for any help.
First, scRNA-seq has techinical limitation and sparsity is one of the biggest challenge. scRNA raw count is a sparse matrix. If you use raw count from all cells (meaning you take every cell as an individual sample) as input for DESeq2, many genes in many samples may not have any expression. That's why you get a wierd volcano plot.
Second, DEG test in single cell data could be made by many other algorithms. DESeq2 is designed for bulk data.
The "weird" plot is based on pvalues that are near or identical to the machine limit of displaying numericals. You can get that with
> .Machine$double.xmin
[1] 2.225074e-308
Not much to do about that other than in any case using something more appropriate for scRNA-seq data. Either the Wilcox test or something like limma-trend based on the logCPMs. This here is a great read to get started:
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