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  • How to identify the Up and down Regulated genes from Cuff diff output

    Dear All,

    How to identify the Up and down regulated genes from the diff genes, I got diffgenes using Cummerbund codes.

    In my diff_gene.txt file could see most of the log2fold change values are +inf and -inf. From the list how to separate the up and down regulated genes??

    Cheers

    Sridhar
    Last edited by sridhar28; 10-23-2013, 08:00 AM. Reason: link not attached

  • #2
    Hey Sridhar,

    I am also getting similar results (+inf and -inf) on some of the log2fold change values. I believe this is happening because one of the conditions has a value of 0.

    I am reading through cuffdiff documentation to sort out whether this is due to normalization or processing issue, or whether I actually have zero reads for some transcripts. I doubt the latter is the case, based on my data of identical cell lines with different treatments, and high coverage of the ran-seq reads.

    Comment


    • #3
      Hello,

      In my view, ​​-inf and inf values, are effectively due to a division by 0 when calculating the fold-change.

      For example in a test_vs_control comparison, inf value for a gene means that there is many reads in test condition and 0 in the control. And conversely, -inf value for a gene means that there is 0 read for this gene in test condition and many in control condition.

      Do not ignore these genes, because if the p-value is significant, they can be considered deregulated.

      For me, there are two solutions, a "clean solution" and "less clean solution".

      - clean solution => replace the 0 values by a non-zero value (1 for example) and recalculate the fold-change for these genes.

      - less clean solution, but simpler => if the exact value of the fold-change is not important to you, and you only use it to filter the differentially expressed genes, just replace the inf & -inf values by a large value (-1000 and 1000 for example). Thus, you can select those genes that have a high fold-change (if the p-value is significant of course).

      Otherwise, to select genes differentially expressed, you can filter diff_gene.txt file according to two criteria, the log2-fold change and p-value. Generally a gene is considered significant if its p-value is less than 0.05, but it is up to you to set a threshold. In addition, you can consider that a gene is truly differentially expressed if its log2 fold-change is > 1 or < -1. But again, it's up to you to set that threshold.

      Thus, down-regulated genes have a fold-change <= -1 and a p-value <= 0.05 and up-regulated genes have a fold-change >= 1 and a p-value <= 0.05.

      I hope these ideas will help you.
      (PS: sorry if my english is bad, this language is not my native language)

      Comment

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