Quest about the input data for PCA analysis
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5 weeks ago
tujuchuanli ▴ 100

Hi,

I recently worked with the R package 'bluster' (https://bioconductor.org/packages/release/bioc/vignettes/bluster/inst/doc/clusterRows.html#graph-based-clustering). The manual taked a scRNA-seq data as example to demonstrate various clustering techniques. Initially, the authors perform PCA analysis using 'runPCA' from the scater package. I noted that the data input for 'runPCA' was log-transformed. My question is: Is log transformation necessary for analyzing all types of data, or is it specific to scRNA-seq data?

Thanks.

PCA • 522 views
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You probably always want to transform your data first to a log-like scale. On normal scale the variance between genes (observations, measurements...) is a linear function of the magnitude of counts. log-transformation corrects for that in large part. That is why such a transformation is standard to do. For RNA-seq/scRNA-seq that can be logcounts, logcpm, logtpm, something like that.

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Thank you for your response. I have just one more question. I had several gene signature lists, each containing 50-100 genes, amounting to more than 100 lists in total. I have scored each cell in a scRNA-seq dataset, resulting in a score matrix with rows representing cells and columns for signatures. I aim to perform PCA analysis on this score matrix. My question is: Should I log-transform the score matrix before proceeding with the PCA analysis?

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Thank you for pointing out my mistake; I am deeply embarrassed. This is my favorite forum, where I have found answers to many of my questions, which have been tremendously helpful. I would like to ask I should click the 'accepted' button after my questions have been answered. However, I can't find this button. Could you please tell me where it is?

Thank you and apology for my mistake again.

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this is the grey check button on the left of the correct answer.

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OK, Thank you to mention me. I have check the button~~.

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