Normalize RiboTag samples to input before differential expression with DESeq2
0
0
Entering edit mode
11 months ago
lozzi • 0

Hello,

I am trying to perform differential expression analysis on a sample set that looks like this:

  DataFrame with 33 rows and 3 columns
                 ID   Status      Sex
        <character> <factor> <factor>
BP10           BP10  Control   male  
BP17           BP17  Control   female
BP18           BP18  Exp       male  
BP19           BP19  Control   female
BP1             BP1  Input     male  
...             ...      ...      ...
v2_BP5       v2_BP5  Input     female
v2_BP6       v2_BP6  Exp       female
v2_BP7       v2_BP7  Exp       female
v2_BP8       v2_BP8  Exp       female

I want to do differential expression analysis between Control and Experimental with DESeq2 (which I can do) but I want to essentially 'normalize' the samples to the input first, before doing DESeq. Is this possible? I haven't been successful thus far.

Here's what my DESeq code usually looks like if I was just going to compare experimental to control without taking input into consideration:

dds<-DESeqDataSet(se=se,design=~Status)
dds<-DESeq(object=dds)

dds<-estimateSizeFactors(dds)

(res<-results(object=dds,
              alpha=0.05,                      
              lfcThreshold=1.5,                  
              pAdjustMethod='BH',             
              contrast=c('Status','Exp','Control'))) 
summary(res)  

Thanks in advance for any advice!

RiboTag DESeq2 RNA-Seq Differential Expression Analysis • 359 views
ADD COMMENT
0
Entering edit mode

@lozzi Did you manage to normalize each Ip sample by its input before the differential analysis. I am trying to use the same approach with RiboTag data and DESeq2 as well. Thanks

ADD REPLY

Login before adding your answer.

Traffic: 1281 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6