Hi Michael and Bogdan.
Excuse me for this little off topic.
I'm wondering if some one of you has already done, or have an idea, on
what should expected to be the correlation between two ChIP-seq
replicated runs.
I mean: if protein1 and protein2 are actually the same protein and we
use the windowing function to count how many reads per 1 kb unit we
have in the two runs I would expect a straight and narrow scatterplot.
The inclination of the fitting line should reflect the abundance of
reads in each run (the line will move toward the axis were we have
more reads).
But what function is better suited to calculate such correlation,
Pearson , Spearman ...?
In a replicated ChIP-Seq experiment, for which I had also a control
lane (i.e. both IP and INPUT), with a big 1Mb window (mm9 genome), I
had a Spearman correlation of 0.99 for IP/IP and 0.94/0.96 for the IP/
INPUT correlation.
The differences in correlation are smaller than what we expect,
especially when looking to scatterplots were the differences are clear
( straight and narrow vs curved and wide).
Changing the window (600K) doesn't change the results, but I never
tried 1 kb.
The questions are what statistical test is more appropriated and what
would you expect for a replicated ChIP-seq experiment?
Any Idea?
Thanks everybody.
Cheers
Gabriel
On Aug 26, 2009, at 7:27 AM, Michael Lawrence wrote:
Well, it would help if we had a few more details, but to calculate the
number of reads within each 1 kb window of the genome, it's
something like:
library(BSgenome.Hsapiens.UCSC.hg18) # assuming human
widths <- rep(1000, seqlengths(Hsapiens)[1]/1000)
# assuming 'cov' is a coverage Rle for chr1
windows <- successiveViews(cov, widths)
x <- viewSums(windows)/1000
That should get you the numbers you need for that scatterplot.
Michael
On Tue, Aug 25, 2009 at 7:11 PM, Bogdan Tanasa <[email protected]>
wrote:
Hi everyone,
please could you let me know if there is any way to represent in a
2D plot
the number of ChIP-seq tags / 1kb genome window for 2 experiments ?
eg :
on X axis : the number of tags / 1kb window for protein 1;
on Y axis : the number of tags / 1kb window for protein 2;
thanks very much.
-- bogdan
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Gabriele Bucci, PhD
Microarrays and NGS Bioinformatics
Consortium for Genomic Technologies
IFOM-IEO Campus
via Adamello 16, 20139 Milan
Italy
[email protected]
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Tel +39 0294375134
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