Hi Syed,

Low count genes are filtered automatically, see the vignette section on
independent filtering.

You can also filter the gene list by effect size (I.e. absolute value of
log fold change). In the next release (v1.4 released on April 14) new
arguments to results() allow for testing for log fold changes above a
threshold which can help narrow the list of genes on effect size.

Mike
Hello ,

I am new to Deseq2 package and ,I am interested in differential gene
expression calculations. I have two conditions Condition a (T) and
Condition b (P),
>From the Deseq-2 results after using the below mentioned commands, I get a
long list of differentially expressed gene, more than 1000 unregulated and
more than thousand down regulated.

countsTable <- read.delim ("counts.txt", header=TRUE, row.names=1)
pdata = data.frame(condition = factor(c( "T", "T", "T", "P", "P", "P",
"P"))) # Three total vs four pure samples
library (DESeq2)
dds <- DESeqDataSetFromMatrix(countData=countsTable, colData = pdata,
design=~condition)
colData(dds)$condition <- relevel(colData(dds)$condition, "T")
dds <- DESeq(dds)
results <- results(dds)
results <- as.data.frame(results)
sig.up.results <- results[which(results$padj < 0.05 &
results$log2FoldChange > 0),]
sig.down.results <- results[which(results$padj < 0.05 &
results$log2FoldChange < 0),]
sig.results <- results[which(results$padj < 0.05),]
sig.results <- sig.results[order(sig.results$log2FoldChange,
decreasing=TRUE),]
plotMA(dds, pvalCutoff=0.05)
write.table (sig.results, file= "DESeq2_Sigresultsfiltered_48_96.txt", sep=
"\t ", row.names=TRUE, col.names=TRUE)
write.table (results, file= "DESeq2_allresults_filtered_48_96.txt", sep=
"\t ", col.names=TRUE)


Do you have any suggestions about my experiment or commands I am using to
narrow down
 my differential gene list. Do I ned to filter low count genes or highly
expressed house keeping genes, I appreciate your time.

best regards
Syed
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