normalization of CHIP-seq samples (ChIP and INPUT)
1
0
Entering edit mode
Bogdan ▴ 670
@bogdan-2367
Last seen 14 months ago
Palo Alto, CA, USA

Dear all,

please could I have your advise on the following : shall we have multiple samples from ChIP experiments (where the proteins A, B, C were immuno-precipitated on the chromatin) and INPUT DNA (from the samples A, B, and C), would we normalize them all together (i.e. ChIP + INPUT) in edgeR, for example ?  Many thanks.

 

-- bogdan

 

edgeR deseq2 voom limma • 1.7k views
ADD COMMENT
0
Entering edit mode

If you haven't already, you should check out the csaw package, both the User's Guide and the associated publication.

ADD REPLY
0
Entering edit mode

Dear Ryan, thank you for your reply. Here the question is not very much about TMM normalization (implemented in csaw or edgeR); I would be interested to know  whether I could normalize in the same procedure both ChIP samples and INPUT_DNA samples. Sorry if I missed the discussion in the manual. Any suggestions are welcome.

ADD REPLY
0
Entering edit mode
Aaron Lun ★ 28k
@alun
Last seen 4 hours ago
The city by the bay

Well, it's fairly easy to normalize the ChIP sample to the input sample for each protein, see Section 4.2 of the csaw user's guide. However, I would question the sensibility of normalizing ChIP samples for different proteins, as this sounds like a precursor to some sort of comparison of binding intensities between different proteins. The scientific value of this analysis is questionable and the results seem hard to interpret. For example, what does differential binding mean if you have different proteins involved? How do you deal with differences in antibody efficiency?

ADD COMMENT
0
Entering edit mode

Dear Aaron, great to hear from you, and thank you for suggestions.

On a side note, thought I shall ask (following some old postings) : can we obtain the normalized counts by using also CPM function (below). Many thanks !

libSizes <- as.vector(colSums(x))  

y <- DGEList(counts=x, group=group, lib.size=libSizes)

## NORMALIZATION :

y <- calcNormFactors(y)

## OBTAINING THE NORMALIZED COUNTS with CPM function () :

counts.per.m <- cpm(y, normalized.lib.sizes=TRUE)
ADD REPLY
0
Entering edit mode

Thank you. just found the answer : edgeR TMM values

ADD REPLY

Login before adding your answer.

Traffic: 366 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