I have a gene expression raw counts like below for 16 patients

         A  B    C      D   E   F
A2M     511 1623 665    208 553 469
AADAT   34  137 372     7   52  124
ABCB1   119 114 123    22   25  89
ABCB11  27  186 200    27   30  49
ABCC2   1   10  21     1    8   3

For each patients I also have some characteristics

Patients                          IC          TCs
A1                                3            3
B1                                2            0
C1                                2            0
D1                                0            0

I want to find genes differential expressed in these patients regarding these characteristics but I don't know how to do that

Any help please?


1 Answer 1


Look up a tutorial on DESeq2. To keep things simple for your first time, I'd reduce your phenotypes to "high" and "low" bins instead of trying to use the cell count numbers as they are. Then once you understand what the software is doing, you can get fancier.

  • $\begingroup$ Thank you I need some genes predicting for these traits, so can I simply do differentially expressed regarding each trait individually? But I am not sure if I need getting important genes for interaction of these traits? $\endgroup$
    – Exhausted
    Oct 20, 2019 at 17:34
  • $\begingroup$ Hi Angel, you need to be a bit more precise about what you are looking for. When you say "find genes differential expressed in these patients", it means genes that are significantly different between two groups. @swbarnes was correct in pointing you to defining binary phenotypes $\endgroup$
    – StupidWolf
    Oct 21, 2019 at 16:59
  • $\begingroup$ If you want "some genes predicting for these traits", what you are looking for is correlation between gene expression and phenotype. In this case I would do a spearman correlation between gene expression and IC for example. $\endgroup$
    – StupidWolf
    Oct 21, 2019 at 17:00
  • $\begingroup$ Correlation based on raw counts is probably not the best idea. $\endgroup$
    – swbarnes2
    Oct 21, 2019 at 21:05

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