[BioC] DESeq on CCAT identified chipseq peaks

[QAMRA Aditi (GIS)]

Hi Dr. Rory,

I understand now. Thank you !

A last question (hopefully) - Can you explain a little more on how the use of a blocking factor works in the case of matched normal tumor pairs ? Does it mean that using the DBA_REPLICATE condition as a blocking factor in such a case adjusts (?) and removes any sort of batch effects between replicates ?

Thanks !
Aditi
________________________________________
From: Rory Stark [Rory.Stark at cruk.cam.ac.uk]
Sent: Friday, May 16, 2014 2:08 AM
To: QAMRA Aditi (GIS)
Cc: bioconductor at r-project.org
Subject: Re: [BioC] DESeq on CCAT identified chipseq peaks

Hello Aditi-

It is a bit more complicated to derive a consensus-of-consensus peakset, but it can be done in a few steps. Assuming you've read your data into h3k4me3_readin, you first have to create a new object with the two consensus peaksets (one for each condition):

> h3k4me3_consensus <- dba.peakset(h3k4me3_readin, consensus = DBA_CONDITION, minOverlap=0.6)

If you look at h3k4me3_consensus, it will have two new consensus peaksets added  (as sets 11 and 12). Now you want to make the final consensus peakset as the union of these:

> h3k4me3_consensus <- dba.peakset( h3k4me3_consensus, consensus=11:12, minOverlap=1)

Now you can retrieve the final peakset as a GRanges object:

> h3k4me3_peakset <- dba.peakset(h3k4me3_consensus, 13, bRetrieve=T)

And supply it to dba.count for counting:

> h3k4me3_counts <- dba.count(h3k4me3_readin, peaks=h3k4me3_peakset)

Hope this helps!
Cheers-
Rory

>> on Fri, 16 May 2014 01:40:30 +0800 Aditi [guest] guest at bioconductor.org wrote:
>>

>> Hi Dr. Rory,

>>

>> Thanks a lot for pointing this out.

>>

>> I wanted to confirm one thing while using diffbind - If my sample sheet looks like -

>>

>>
SampleID

Tissue

Factor

Condition

Treatment

Replicate

bamReads

bamControl

Peaks

PeakCaller

PeakFormat

ScoreCol

LowerBetter

1

T

h3k4me3

tumor

none

1

PATH

PATH

PATH

raw

raw

4

FALSE

2

N

h3k4me3

normal

none

1

PATH

PATH

PATH

raw

raw

4

FALSE

3

T

h3k4me3

tumor

none

2

PATH

PATH

PATH

raw

raw

4

FALSE

4

N

h3k4me3

normal

none

2

PATH

PATH

PATH

raw

raw

4

FALSE

5

T

h3k4me3

tumor

none

3

PATH

PATH

PATH

raw

raw

4

FALSE

6

N

h3k4me3

normal

none

3

PATH

PATH

PATH

raw

raw

4

FALSE

7

T

h3k4me4

tumor

none

4

PATH

PATH

PATH

raw

raw

5

FALSE

8

N

h3k4me5

normal

none

4

PATH

PATH

PATH

raw

raw

6

FALSE

9

T

h3k4me6

tumor

none

5

PATH

PATH

PATH

raw

raw

7

FALSE

10

N

h3k4me7

normal

none

5

PATH

PATH

PATH

raw

raw

8

FALSE




>> Then to create a consensus peakset from the union of peaks that appear in atleast 3 of 5 samples of each condition, the commandline would be –

>>

>> h3k4me3_peakset = dba.peakset(h3k4me3_readin,consensus = DBA_CONDITION, minOverlap=0.6)

>>

>> I am not too clear on how to use this command and thus wanted to confirm.

>>

>> Thanks !

>> Aditi




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