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  • Normalize Expression Matrix

    I have expression matrix where rows represent samples and columns represent time points. and values represent absolute counts of expression. I notice that some of the samples are highly expressed in all time points while some are less expressed in all time points. For examples:

    Sample1: 1000 6134 6245 3341 ..etc
    Sample2 2 3 4 2 ..etc

    So plotting heatmap for such matrix and/or finding differential expression will not be possible.

    So, I think I need to normalize the matrix and/or rescale the matrix to a common library (maybe to remove the sequencing bias!!). Am I correct?If yes, is there any known method(s) for doing that? Then, is there a library in R for doing such normalization?

  • #2
    There are a LOT of options for this. If this is RNAseq, then just use one of the standard packages. If not, many of the microarray packages (particularly limma and affy) will have a large variety of options.

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    • #3
      Thanks @dpryan.
      Actually, the counts are from metagenomic data from different annotation categories, taxonomy, kegg. So, I am wondering which normalization technique would be suitable?

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