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?
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?
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