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Reporting p.value for my PCA results

Hi I've done PCA for my gene expression data after DEG analysis, I can see that my case and control samples clustered distinctly but I want to report a p.value for this result, it worth to notice that I have the coordinate csv file that represents coordinates of samples from different phenotypes (case vs control).

Thanks in advance

pca gene expression
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updated by Kevin Blighe Kevin Blighe • written by Shamim Sarhadi Shamim Sarhadi

PCA is an exploratory data analysis method. It does not test a null hypothesis and generate a p-value.

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written by Nicolas Rosewick Nicolas Rosewick

ok. I'll try to explain a little bit more then

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written by Nicolas Rosewick Nicolas Rosewick

2 answers

What Nicolas says is true; however, there are indirect ways to derive a P value based on your PCA results.

Buid a regression model

You can indirectly derive P values in your situation by building a binary logistic regression model using the PC1 (or PC2, PC3, ..., PCX) values to predict case/control status. From this, you should see a strong P value based on what you say.

summary(glm(CaseControl ~ PC1))

Correlation

For other type of variables, like continuous variables, you can simply run a correlation test between the continuous variable and the PC1 values, and derive a P value from this too (in R, use cor.test())

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written by Kevin Blighe Kevin Blighe

PCA is an exploratory data analysis method. It does not test a null hypothesis and generate a p-value.

If you want to compute a p-value maybe you should try pvclust package in R. It didn't use PCA but a hierarchical clustering and report p-values for each sub-tree

http://stat.sys.i.kyoto-u.ac.jp/prog/pvclust/

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written by Nicolas Rosewick Nicolas Rosewick

PVclust is indeed great, and it bootstraps the clustering.

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written by Kevin Blighe Kevin Blighe

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