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  • sazz
    Member
    • Oct 2012
    • 28

    #1

    Variance - basic statistics

    Hello all,

    I'm sorry for my very naive and basic question, but I am trying to understand a couple of graphs: dispersion, M vs A etc, and I am a little confused about "variance" term.

    When you check the formula of variance it is the average of the squared differences from the mean. So can I say, genes with high FPKM values tend to have "high variance" and also they are more dispersed relative to low expressed genes? (But I guess, high variance in high FPKM is not a problem when you plot a negative binomial distribution graph to calculate the significance of differential expression) But this also sounds odd because without thinking the math part, I am tempted to say, low expressed genes generally are not significant in differential expression analyses due to the "variability" between FPKM values I guess, there is a misconception here (for me) as I think the variability in "percentage". Moreover, this variability defines the shape of the negative binomial distribution, if it will be more squeezed or spread, used for statistical testing, right? :/

    Sorry for asking about basic statistics. I would appreciate if one could explain briefly.

    Thanks!
  • csmatyi
    Member
    • Oct 2011
    • 25

    #2
    Hello everyone, I have a statistics question:

    I have a data set:

    1
    1
    1
    0
    1
    1
    0
    1
    0
    0
    0
    0
    0
    0
    1
    0
    0

    I want to measure how well the 1's accrue at the top of the list, that is, how well the 1's and 0's separate. The above list should have a high value compoared to a random one:

    1
    0
    1
    0
    1
    0
    1
    0
    ...

    What kind of test do I need for this?

    Thanks!

    Comment

    • TiborNagy
      Senior Member
      • Mar 2010
      • 329

      #3
      Hi sazz!

      Variance in gene expression is not depends on FPKM. It is just a statistical measure of the replicates.

      csmatyi:

      I think you need khi-square test.

      Comment

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