Good time of the day,
Could anybody please help (explain).
I am using bamCompare to normalise reads for computeMatrix plotHeatmap (in Galaxy). I aim to select genes having the protein of interest bound around TSS.
I have three ChIPed replicates. I have two controls (just in case, I did not know which one would work better). When I normalize my replicates (setting are given below), one of the replicates fails:
ERROR: The median coverage computed is zero for sample(s) # Try selecting a larger sample size or a region with coverage
I found a solution here: https://github.com/deeptools/deepTools/issues/599
E.g. when I select Method to use for scaling the largest sample to the smallest: Signal extraction scaling (SES) I increased: Length in bases used to sample the genome and compute the size or scaling factors from default 100 to 1000
Now, there is a warning: The default is fine. Only change it if you know what you are doing. (--sampleLength)
Unfortunately I do not know what I am doing. Could anybody please explain how it affects the results?
Also, there is another warning, "Check with plotFingerprint before using it" My plotFingerprint shows that my replicates are very close to controls (weak signal) and the replica that fails is even closer (please plotFingerprint attached). plotFingerprint. 1 - input, 2, 3, 4 - replicas; 5 - IgG
Shall I use SES at all in my case? What would be the best normalisation method in this case?
Thank you :)
Bin size in bases 50
Method to use for scaling the largest sample to the smallest SES Length in bases used to sample the genome and compute the size or scaling factors. 100 Number of samplings taken from the genome to compute the scaling factors 100000
How to compare the two files log2
Pseudocount 1.0 Compute an exact scaling factor False
Coverage file format bigwig
Region of the genome to limit the operation to Empty.
Show advanced options no
Job Resource Parameters no