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Originally posted by Simon Anders
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I deleted that one and wrote a new reply, because i thought I might got it wrong, then i asked the one who did the experiment, so the latest reply is the real case. -
Okay, I give up. You have to show a little cooperation if you want help.
I mean, you deleted the answer to my question about which steps of the protocol you replicated, because you were worried it might have been wrong. That's fine, but do I really need to tell you that you now have to find out the correct answer before we can proceed in the discussion?Comment
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So sorry about it! It seems you can't see the my new edited post. Here it is.Originally posted by Simon Anders View Post
This is the experiment design that I asked from the one who did the experiment:
24h after seeding A431 cell cultures in triplicates were treated with Gefitinib(2.5µM) or left untreated. Cells were harvested at 2h (treated and untreated), 6h and 24h after treatment by trypsination and washed in PBS.
Now, I only compare the untreated cells and cells harvested 24h after treatment.Comment
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Which steps were replicated?Comment
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Sorry, didn't see that you wrote "in triplicates". So, you have three independently grown cell cultures. According to the dispersion plot, they are extremely similar. Bet double check this with scatter plots comparing pairs of replicates.
If so, all the many differentially expressed genes are genuine difference between the treated and the control samples. This can be either because you inhibitor has an extremely dramatic effect on very many genes, or because you are confounded with something else (e.g., that the treatment of the two replicate groups differed in other aspects than just the application of the drug).Comment
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Originally posted by Simon Anders View Post
It is true that cell lines replicates are more similar to each other than those taken from different patients. See scatter plot H0-R1 vs H0-R2.
When I looked into the output file(resSig=res$padj<0.1) which contains 20000+ genes as I mentioned before, Actually those genes with all zeros are marked as NA, which should be ignored. But there are about 10000 left. Then filtered by log2foldchange greater than 1 or less than -1, padj <0.01, i manage to squeeze those to 3000 genes. I paste some typical values here:
id baseMean baseMeanA baseMeanB foldChange log2FoldChange pval padj
22137 13.23380504 0.40600685 26.06160322 64.19005799 6.004277959 4.89E-13 2.75E-12
19134 21.3334924 0.812013699 41.85497111 51.54466131 5.687751104 7.34E-05 0.000210824
5997 9.239158636 0.380818753 18.09749852 47.52260325 5.570541963 2.33E-09 1.02E-08
6426 8.856450011 0.40600685 17.30689317 42.62709653 5.413698887 6.06E-09 2.57E-08
18020 51.94853226 2.618403166 101.2786614 38.67955198 5.273499179 2.92E-39 5.36E-38
Should I trust those significantly differentially expressed genes with very low counts in both conditions?Comment
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A change from 1 to 30 reads is very unlikely to be due to shot noise, so this should be trustworthy.
Your scatter plot is not very useful, BTW. Try using logarithmic axis scaling.Comment
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