Question: A weird PCA result using a local Galaxy
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7 months ago
Gary • 450
Taiwan/Taichung/China Medical University Hospital

Hi,

We use a local Galaxy to run PCA (principal component analysis) based on six mouse RNA-Seq data. However, our result is weird: (1) PC1 can explain nearly all variation (94.5%); (2) All six samples on the 0.4 of PC1. Could you help us? Many thanks.

Best,

Gary

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ADD COMMENTlink 7 months ago Gary • 450 • updated 7 months ago Devon Ryan 90k
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If almost all the variance is explained by the first PC, it means that the variables are collinear, i.e. they can all be expressed as a linear transformation of one of them. If this is not what you expect, check that the data is really what it should be.

ADD REPLYlink 7 months ago
Jean-Karim Heriche
19k
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This is from plotPCA in deepTools, which unfortunately defaults to not transposing the matrix before computing the PCA (I assume it was done this way originally since the PCA() function in matplotlib doesn't accept matrices with more columns than rows). So in this case the results just indicate that "genes are quite variable, but similar between samples", which is OK for basic QC but usually not what people actually care to look at in a PCA.

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Devon Ryan
90k
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Many thanks to your super professional answer.

ADD REPLYlink 7 months ago
Gary
• 450
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7 months ago
Devon Ryan 90k
Freiburg, Germany

Make sure to set Transpose Matrix to Yes (it's under Show advanced options). The resulting plot will be much more useful (I wish I'd just made that the default).

ADD COMMENTlink 7 months ago Devon Ryan 90k

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