Question: DEseq results -- foldchange is 0/inf
0
gravatar for Nan
11 days ago by
Nan0
Nan0 wrote:

So after feature counts of RNA-seq bam file, I have an count file. I input the count file into DEseq, and got results which contain foldchange values such as 0/inf/NA, so how can I deal with these values when I want to use foldchange to filter out most up-/down- regulated genes?

So for foldchange == NA, I think this case can directly dropped. But what about foldchange ==0/inf?

Thank you.

id          baseMean  baseMeanA       baseMeanB  foldChange  log2FoldChange  pval      padj
SOCS4       1834      2321            1348       0.580       -0.7844         0.00038   0.844
NPIPA3      34.1155   68.23175774754  0          0           Inf             7.51E-09  7.71E-05
AL627309.5  2.0225    0               4.045      Inf         Inf             0.434     1
AP002833.2  0         0               0          NA          NA              NA        NA
rna-seq deseq • 148 views
ADD COMMENTlink modified 11 days ago by Chirag Parsania1.4k • written 11 days ago by Nan0

Please use the formatting bar (especially the code option) to present your post better. You can use backticks for inline code (`text` becomes text), or select a chunk of text and use the highlighted button to format it as a code block. I've done it for you this time.
code_formatting

P.S.: You can also pretty print tabular text using column, as shown here.

ADD REPLYlink modified 11 days ago • written 11 days ago by RamRS22k
1
gravatar for Chirag Parsania
11 days ago by
Chirag Parsania1.4k
University of Macau
Chirag Parsania1.4k wrote:

Hi,

Inf and NA occurred due to 0 in one of the sample (baseMeanA or baseMeanB). The common practice to deal with this problem is to add small number (e.g. 0.1 ) to normalised expression value (e.g basemean/FPKM/RPKM) and recalculate the fold change.

ADD COMMENTlink written 11 days ago by Chirag Parsania1.4k

Yes, this is a way to avoid 0/inf values. But I was worried if it is biologically reasonable?

ADD REPLYlink written 10 days ago by Nan0

It should be ok as you are adding small constant value to all the genes. Therefore, it nullifies any possibility of bias.

ADD REPLYlink written 10 days ago by Chirag Parsania1.4k
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