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  • xiefanfang@gmail.com
    Junior Member
    • Sep 2012
    • 8

    Using log2 read counts for RNA-seq heatmap

    I want to plot a heatmap for my deferentially expressed genes. I used partek genomics to get read counts for each gene and used EdgeR to identify significant genes. After log2-transformation of the read counts with or without quantile normalization, I plot a heatmap in R with heatmap2 function. However, the red and green color are not that distinctively different between treatment groups (ideally, I want to have red for one panel and green for the other). I think the reason is that some genes are highly expressed whereas some have lower expression (like 8000 reads vs. 10 reads/a million counts for different genes). How can I adjust the data for a nicer heatmap (attached)? Also, I found that some papers used raw z-score for plotting. May I ask what raw z-score is and how to calculate that? Thanks a bunch!!
    Attached Files
  • TiborNagy
    Senior Member
    • Mar 2010
    • 329

    #2
    I have usually set the parameter scale='none'.
    The standard normal distribution (mean = 0, standard deviation = 1) known as z-distribution. So the scale of this distribution is called z-score.
    Coverting raw scores to z-score is very easy:
    z-score = (x - mean) / sd, where x is the raw score, mean is the mean of x and sd is the standard deviation of x.

    Comment

    • jcrow06
      Junior Member
      • Oct 2013
      • 4

      #3
      Hey thanks for this post.
      Did this work for you?

      I have a similar situation and like Tibor I have used the scale="none".
      I am trying to examine my RNAseq samples with RNAseq data from TCGA.
      I have their count table and mine and have performed the DE analysis using DESeq. I have generated nice results of significantly differentially expressed genes and would now like to compare the two on a heat map for genes that are considered differentially expressed in both experiments.

      In doing so obviously the scales are different when I use the "getVarianceStabilizedData(myData)" option in DESeq. I am thinking that z-score normalizing would be the best approach and would love to know if this can be confirmed.

      Cheers

      Comment

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