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  • Jamiou
    Junior Member
    • Jun 2015
    • 2

    #1

    Basic questions about fold change calculations

    Hello, SEQanswers! I'm a student of biotech and I'm currently doing my bachelor thesis somewhat related to bioinformatics, and a part of it is to look at changes in expression levels for genes from RNA-seq data. I know basically nothing of this field, but I'm trying to learn, so I'm sorry if my questions are way too basic and/or stupid. I feel that bioinformatics could possibly be something I want to do for a masters degree, so I really want to dive as deep into it as I can at this opportunity!

    First question, fold change. The way I understand it this is simply the abundance level (I read that RPKM is an abundance level, but I haven't read that much about it yet) for gene X in sample A divided by sample B, correct? But when I search for articles for fold change I mostly find various software for "differential expression" (which I assume is sort of the same as fold change?). I cannot find any articles that use fold change as sample A/sample B... Why is this? I assume that there is some reason that you can't or shouldn't do this calculation, but I don't understand what it is.

    Second question, (which I discovered when trying to find answers to the first question) is about genes with very low abundance. Regardless of how you calculate fold change, how do you account for genes that have a very low abundance level, i.e. close to the limit of detection? For example, if you have abundance levels of sample A and sample B that is (both) close to 0, but still yield some fold change you are interested in, can you really say that the gene has a different abundance level? I mean, if both abundance levels are so close to the limit of detection they could both possibly be false, right? How do you generally account for this kind of thing, or do I just misunderstand how RNA-seq detection limits work? I read that an RPKM of 1 is approximately equivalent to 1 RNA molecule per cell, so if you have RPKMs of (for example) 0.8 and 0.2 you will have a fold change of 4, but can you really trust that number?
  • dpryan
    Devon Ryan
    • Jul 2011
    • 3478

    #2
    Most (probably all) packages that are used to find differentially expressed genes will return either a fold-change or a log2 fold-change (this is typically computed on the log2 scale). You would normally compute the fold-change between groups, rather than between samples (since who cares if two samples differ if the groups that they're part of don't).

    You're second question relates to the first. Firstly one computes a p-value and then sort the significant results by fold-change, since low abundance genes/transcripts will show randomly high fold-changes. Secondly, one can compute the fold-change by incorporating a prior distribution. This is done in DESeq2, for example, where lowly expressed genes will have their fold-changes shrunken toward 0.

    There is no fixed correspondence between RPKM and molecules per cell. In fact, you would be wise to not use RPKM for any statistics, use either raw or estimated counts instead.

    Comment

    • Jamiou
      Junior Member
      • Jun 2015
      • 2

      #3
      Oh, okay. But where do you get the p-value from? That is some sort of hypothesis test, right? So if fold change is gene x in group A / group B, how do I get a p-value from that? And is it possible to get significant p-values ever for genes expressed close to zero (in the groups)?

      Why is RPKM bad for statistics? I think I read that some software use RPKM (Cufflinks?). Why is raw (what do you mean by that?) or estimated counts better?

      Comment

      • dpryan
        Devon Ryan
        • Jul 2011
        • 3478

        #4
        Yes, the p-value is derived from a hypothesis test. Popular programs for this include DESeq2, edgeR, limma/voom, and cuffdiff. It's typically not possible to get significant results from very lowly expressed genes, since they tend to lack enough alignments to lend statistical power.

        The conversion to RPKM loses all precision information, which makes it difficult to use for statistics. You can google for more.

        Comment

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