cisgenome: invalid negative binomial trend

[litd]

Hi,

I used cisgenome for most of our ChIP-Seq analysis. Recently we have Drosophila ChIP-Seq data, the computing FDR step using hts_windowsummaryv2 gave me all invalid neg binomal data as below.

# Window_Size=100 Poisson_Lambda=0.057520 Poisson_p=0.319700
NegBinom
ial_Alpha=0.029232 NegBinomial_Beta=-0.491803 NegBinomial_p=-nan
#No_of_reads/window No_of_window percentage poisson_expected
poisson_exp/obs negbinomial_expected negbinomial_exp/obs
0 509299 0.301829 0.301829 1.000000 -nan
-nan
1 29295 0.017361 0.017361 1.000000 -nan
-nan
2 29665 0.017581 0.000499 0.028401 -nan
-nan
3 31363 0.018587 0.000010 0.000515 -nan
-nan


We also have control data, so I ran 2-sample analysis, and came with the similar result.

#Window_Size=100 dP0_hat=0.556655
#Poisson_Lambda=0.034540 Poisson_p=0.303430
#NegBinomial_Alpha=0.019155 NegBinomial_Beta=-0.445431
NegBinomial_p=-nan
#No_of_reads/window No_of_window percentage poisson_expected
poisson_exp/obs negbinomial_expected negbinomial_exp/obs
0 494618 0.293129 0.293129 1.000000 -nan
-nan
1 17084 0.010125 0.010125 1.000000 -nan
-nan
2 15698 0.009303 0.000175 0.018795 -nan
-nan
3 15723 0.009318 0.000002 0.000216 -nan
-nan

Only when setting window_size beween 5-8, odd number shown on the last column.

#Forward+Reverse_Combined
# Window_Size=5 Poisson_Lambda=0.263733 Poisson_p=0.866340
NegBinomial_Alpha=0.373173 NegBinomial_Beta=0.414966
NegBinomial_p=1.000000
#No_of_reads/window No_of_window percentage poisson_expected
poisson_exp/obs negbinomial_expected negbinomial_exp/obs
0 22458969 0.665504 0.665504 1.000000
0.632701 0.950709
1 5923167 0.175515 0.175515 1.000000 0.166864
0.950709
2 2874109 0.085166 0.023145 0.271760 0.080968
0.950709
3 1351820 0.040057 0.002035 0.050794 0.045266
1.130041
4 621905 0.018428 0.000134 0.007280 0.026978
1.463935

I skipped the FDR cutoff choose, and directly ran peakdetectorv2, cisgenome gave some reasonable result. I just don't know how reliable they are. I want to use strand specific window scan peak detection.
If any experts can point me to the hints, really appreciate.

litd

[angelawu]

I also get weird outputs...

Hi,

I'm going to help bump up your post because I am also getting weird outputs from cisGenome. My bioinfo skills are negative, so please bear with any idiotic comments I might make...

I also tried to run a 2 sample analysis, using a ChIP-seq data set for human cell samples and a Input sample from the same source. I am using the Windows GUI version of the software. I chose the default window size of 100bp, although I have also tried window size 600bp, since my IP is with a Pol II like factor, so I thought a larger window size might be appropriate as well.

In both cases, I got something like this:


#Window_Size=100 dP0_hat=0.528540
#Poisson_Lambda=0.351238 Poisson_p=0.967322
#NegBinomial_Alpha=1.952113 NegBinomial_Beta=4.557810 NegBinomial_p=1.000000
#No_of_reads/window No_of_window percentage poisson_expected poisson_exp/obs negbinomial_expected negbinomial_exp/obs
0 20972032 0.680817 0.680817 1 0.678938 0.997241
1 7366170 0.239129 0.239129 1 0.238469 0.997241
2 1956325 0.063508 0.041996 0.66126 0.063333 0.997241
3 404028 0.013116 0.004917 0.374871 0.015012 1.144548
4 75878 0.002463 0.000432 0.175274 0.003344 1.357551
5 16213 0.000526 0.00003 0.057624 0.000716 1.360837
6 4927 0.00016 0.000002 0.0111 0.000149 0.933574
7 2207 0.000072 0 0.001243 0.000031 0.426
8 1256 0.000041 0 0.000096 0.000006 0.150714
9 848 0.000028 0 0.000006 0.000001 0.044414
10 574 0.000019 0 0 0 0.01293
11 449 0.000015 0 0 0 0.003232
12 323 0.00001 0 0 0 0.000872
13 289 0.000009 0 0 0 0.000188
14 195 0.000006 0 0 0 0.000054
15 204 0.000007 0 0 0 0.00001
16 197 0.000006 0 0 0 0.000002
17 125 0.000004 0 0 0 0.000001


I tried to go ahead with the 2-sample peak detection according to these values, but no output was given for any of my attempts (i.e. the black window flashed up briefly, and then nothing. Can anyone tell me what might be the problem??

Angela

[angelawu]

Oh, and one additional thing - it works fine if I do a 1-sample analysis...