How to filter genes inside a normalized count data file based on p-adjust value?
How to filter genes inside a normalized count data file based on p-adjust value?
I want to add a filter to only take the genes under specific p-adjust value?
Here are the normalization steps:
dds <- DESeqDataSetFromMatrix(countData = countdata,colData = coldata,design= ~ condition)
dds <- DESeq(dds)
count_data_normalized <- counts(dds, normalized=TRUE)
0 answers
No answers yet.
Log in to answer this question.
More posts like this
-
Error function vst in DESeq2
written by Larissa •
-
How can I normalize miRNA read count from TCGA
written by Fluke •
-
DESeq2: DESeqDataSetFromMatrix Error
written by Athena •
-
boxplot issue
written by kalyani •
-
When making a heat map with normalized data, should you still see the control data?
written by mgranada3 •
-
Filter out genes less than 10 count from DESeq2 does not work
written by synat.keam •
-
Exporting normalized counts data from DeSeq2
written by mohammedtoufiq91
-
Setting FDR at 0.05
written by ovariohisterectomia •
-
Understanding DESeq2 padj and Volcano Plot
written by juan.crescente •
-
heatmap using DESeq2
written by sumithrasank75
Based on which p-values ? I can't see any differential expression analysis here
Do I have to extract the results table from results function: results(dds), then I make filtration based on p-adjust value? But If I do that, then how can I extract again the genes under specific p-value?
If you want to select genes from a differential analysis results, yes you should use
results()but pay attention to your study design to extract the contrast you need. Then you can usedplyrand thefilterfunction to retain the genes which meet your specific criteriaSo, use results command to extract the p-adjust, then I need to select the filtered genes to pick them only from the count data normalized file, right? If so then how? by left_join? Thanks a lot @Basti
When you have the filtered genes, just select from counts the corresponding rownames
But the genes will gonna be several as the data already have 24,000 genes
Please clarify your problem, and explain the desired output