Hello all,
While I know that there is no "right" way to perform clustering, I am wondering whether the rlog normalization in DESeq shrinks the data enough to be used for heirarchical clustering and subsequent analysis of time series expression profiles using cutree().
I am wondering because with rlog, I still get a pretty wide range of values, and my clusters end up not being as "tight" as I want them to be. Would doing something like median centering help, or should I use a different normalization than rlog, like standardization so that all of my rows have mean = 0, sd = 1.
While I know that there is no "right" way to perform clustering, I am wondering whether the rlog normalization in DESeq shrinks the data enough to be used for heirarchical clustering and subsequent analysis of time series expression profiles using cutree().
I am wondering because with rlog, I still get a pretty wide range of values, and my clusters end up not being as "tight" as I want them to be. Would doing something like median centering help, or should I use a different normalization than rlog, like standardization so that all of my rows have mean = 0, sd = 1.
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