Hello! I am actually trying to overlap my peaks from ChIp-seq to my differentially expressed genes obtained after RNA-seq analysis. I will try to be more clear about it... I have my ChiP-seq peaks for my protein of interest X. I used HOMER to annotate them. Let's say I obtained that 30% of my peaks are enriched at promoter regions. Then I have my RNA-seq data in conditions wt versus X-knockout. I used DESeq2 package from R to obtain a list of differentially expressed genes. My question is now to see whether my ChIp-seq peaks for promoters overlap with my list of differentially expressed genes, i.e. my protein X is effectively binding and regulating the expression of these genes. I would like to know if there is some tool able to allow this also at a statistical level. Of course, even a tool to directly overlap ChIp-seq data with RNA-seq would be great :)
Does anyone have a suggestion for that?
Thank you!
You can use a Fisher's test (fisher.test() in R) for the statistics.
Regarding "overlapping" data, it depends on what you mean. I would personally make a combined heatmap of the ChIP and RNAseq data (at least for the DE genes). You can use deepTools for this, though it'd be easiest if you used the develop branch from github, since the computeMatrixOperations command won't otherwise be available until the next release (ETA November 1). The general steps would be:
reference-point and a reasonable setting for -b--metagene option.computeMatrixOperations cbind with the output of 2 and 3This allows you to see the differences even in cases where there happened to not be a peak called.
Sorry for the naive question, but when doing computeMatrix on the ChIPseq file, what do you use for -R. I keep getting this error:
computeMatrix scale-regions: error: argument --regionsFileName/-R is required
I'm guessing it wants the bed file with the peaks - but isn't the point of this approach to not use the peaks as that is limiting?
Thanks for your help.
We usually use transcripts.
GTF or BED, yes
Hello Devon. I followed the steps from 1-3. Now, I'm stuck at step 4. Below is the command I ran :
computeMatrixOperations cbind -m peak_sorted_matrix rna_16hr_sorted_matrix -o output.mat.gz
Error :
Traceback (most recent call last):
File "/home/anupriya/.local/bin/computeMatrixOperations", line 11, in <module>
main(args)
File "/home/anupriya/.local/lib/python2.7/site-packages/deeptools/computeMatrixOperations.py", line 677, in main
cbindMatrices(hm, args)
File "/home/anupriya/.local/lib/python2.7/site-packages/deeptools/computeMatrixOperations.py", line 408, in cbindMatrices
hm.matrix.matrix = np.hstack((hm.matrix.matrix, np.empty(hm2.matrix.matrix.shape)))
File "/home/anupriya/miniconda2/lib/python2.7/site-packages/numpy/core/shape_base.py", line 288, in hstack
return _nx.concatenate(arrs, 1)
ValueError: all the input array dimensions except for the concatenation axis must match exactly
I used same '-a' and '-b' options in computeMatrix command for ChIP-seq and RNA-seq, still got this error. How can I fix this?
It appears you used a different GTF or BED file to produce the two matrices. Can you post the commands you used to create both?
Hi Devon , below are the commands and the bed files I used :
computeMatrix scale-regions -S rna_16hr_sorted.bw -R m.sme_exons.bed --metagene -a 500 -b 500 --outFileName rna_16hr_sorted_matrix
computeMatrix reference-point -S peak_sorted.bw -R m.sme_transcript.bed -a 500 -b 500 --outFileName peak_sorted_matrix
head -n 20 m.sme_transcript.bed
Chromosome 499 1692
Chromosome 1721 2614
Chromosome 2624 3778
Chromosome 3775 4359
Chromosome 4591 6618
Chromosome 6648 9176
Chromosome 9229 10011
Chromosome 10184 10276
Chromosome 10411 11211
Chromosome 11215 12246
Chromosome 12243 13301
Chromosome 13310 14140
Chromosome 14130 15089
Chromosome 15286 15522
Chromosome 15525 17252
Chromosome 17249 19018
Chromosome 19052 41623
Chromosome 41688 42635
Chromosome 42943 43353
Chromosome 43365 44687
head -n 20 m.sme_exons.bed
Chromosome 499 1692
Chromosome 1721 2614
Chromosome 2624 3778
Chromosome 3775 4359
Chromosome 4591 6618
Chromosome 6648 9176
Chromosome 9229 10011
Chromosome 10072 10148
Chromosome 10184 10276
Chromosome 10293 10368
Chromosome 10411 11211
Chromosome 11215 12246
Chromosome 12243 13301
Chromosome 13310 14140
Chromosome 14130 15089
Chromosome 15286 15522
Chromosome 15525 17252
Chromosome 17249 19018
Chromosome 19052 41623
Chromosome 41688 42635
You'll have to ensure that you do the following:
computeMatrixOperations is just merging them by rows, since it has no way to otherwise determine which rows belong together).computeMatrix is keeping the input file order (--sortRegions keep).
It's unclear what should be matched together if you have extra rows. In that case you must necessarily lose data (not that a few rows matter).
Hi Devon, I took common rows between exon.bed and transcripts.bed and ran remaining commands :
computeMatrixOperations cbind -m peaks_sorted_matrix1 rna_16hr_sorted_matrix1 -o output1.mat.gz
plotProfile --matrixFile output1.mat.gz --outFileName trial.pdf --samplesLabel peaks rna_16hrs --startLabel GS --endLabel GE --dpi 500 --perGroup --plotHeight 15
and got this plot. Why peaks are not covering the whole plot , did I miss something? https://www.dropbox.com/s/ls6742nyqblt8k9/trial.pdf?dl=0
They don't cover the whole plot because the two datasets are different size. In general using --perGroup with a dataset like that doesn't make sense, as the columns of data for each sample aren't comparable (only the rows are).
Or I'll remove --perGroup option and create the graph like this : https://www.dropbox.com/s/gw0rc9jgsq09e69/trial1.pdf?dl=0 and then it can be compared?
Yes, though it looks like you have an older version of deepTools, since I think I fixed the issue with the tick labels not being correct in more recent versions.
Have a look on BETA tool.
Target analysis by integration of transcriptome and ChIP-seq data with BETA
http://www.nature.com/nprot/journal/v8/n12/full/nprot.2013.150.html
ok thank you! I'll try them out! Cheers
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