[vebaev]

Yes Simon,
the =Inf are coming from rows where one of the groups are 0 and other is some reads (sometimes 5 simetimes 500)

I also found your post in other topic about these =Inf that if the last 2 columns the values are too big or close to zero I should discard these rows from further analysis?

[Simon Anders]

What about these genes that are 0 reads in one group and non-zero in the other group? I got =+Inf ot =-Inf? Should I took tham or discard them from the most diff.altered table?
Thanks

If you have 0 counts in one condition and hundreds of counts in other, this is most likely a valid signal, and DESeq should indicate this with a small p value. Of course, the fold change estimate is not to useful but that is a general problem if one of conditions has very low counts

[NicoBxl]

NicoBxl,

I have tun on my 4 samples (2 controles and 2 treatment) deseq with binomial test and I got list.

What about these genes that are 0 reads in one group and non-zero in the other group? I got =+Inf ot =-Inf? Should I took tham or discard them from the most diff.altered table?

Thanks

That's a tough question. Maybe check the p-value. What's the read count for the "other" group ?

[vebaev]

NicoBxl,

I have tun on my 4 samples (2 controles and 2 treatment) deseq with binomial test and I got list.

What about these genes that are 0 reads in one group and non-zero in the other group? I got =+Inf ot =-Inf? Should I took tham or discard them from the most diff.altered table?

Thanks

[vebaev]

Yes but I wanted to take in consideration of how much a seq maps to a genome and deseq is not taking this into account?
If I have seq with read counts 10 that maps in 2 location perfectly, and I want to see if location 1 is changes in samples, it does not mean that all 10 reads are coming from location 1, isn,t it?

Or I'm somwhere wrong in my logic

Anyway, I will run this with deseq to see the result!

[NicoBxl]

My data are not pure read counts, but rather a value that came from read counts normalized by the total library reads and how muuch times a read maps to a genome.

So you should use raw reads with DESeq or edgeR. It seems the best solution to your problem.

[vebaev]

My data are not pure read counts, but rather a value that came from read counts normalized by the total library reads and how muuch times a read maps to a genome.

[NicoBxl]

If your data are HTS reads, they don't follow a normal distribution. You've to use a non-parametric test like the Wilcoxon Test or the Kruskal-Wallis Test.

[vebaev]

Thanks a lot!

[chenyao]

see wiki:
http://en.wikipedia.org/wiki/Normal_distribution.

for simple, you can plot your data to see if it is symmetric and bell-shaped.

but I doult it, most data didn't obey it. But it's good to see the distribution of your data at first.

[vebaev]

Sorry for the stupid question but what it is mean by "normal distribution"

[chenyao]

It's very simple, much more than deseq.

you can use "sam" or "dchip" module.

It depends on the distribution of your data. If it's normal distribution, the simplest method is t-test.

[vebaev]

Poorly since I was using only deseq...

But if there is no other way I will try to look for this, and try to find what R module can do this for me

[chenyao]

Do you know "R"?

It has many tools to use.

[vebaev]

Thanks,
do you know what sotfware tool to use? since I'm not good in statistics for this moderated t-test?