Dear all,
Could you please advice me what's the role of PCA for quality control of microarray gene expression data? What if the samples of two groups don't "separate" well?
Thank you very much.
PCA is a technique for determining the key variables in a multidimensional data set that explain the differences in the observations (variance). Variation across multiple dimensions is difficult to interpret. PCA can be used to simplify the analysis and visualization of multidimensional data sets. In case of microarrays, it allows to summarize the ways in which gene responses vary under different conditions to expression data (where the experimental conditions are the variables, and the gene expression measurements are the observations). Examination of the components also provides insight into the underlying factors that are measured in the experiments.
For a better understanding of PCA, see this explanation
What's the role of PCA for quality control of microarray gene expression data?
PCA allows to visualize relations between variables and to assess the existence of groups of individuals and groups of variables. In that regard, it can be used to see if the sample groups separate according to prior knowledge.
What if the samples of two groups don't "separate" well?
There can be different interpretations :
Are u aware of batch effects in your data ? Have you normalized and log-transformed before performing PCA ? Do you expect the different conditions to strongly impact the transcriptome ?
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Dear Yague,
Thank you for your answer.
I conducted normalization process as provided by affy package before performing PCA. Other quality control tests are acceptable. Then I think 'not well separated' in PCA is not a big problem. Please correct me if I am wrong.
It is not a problem by itself but you should try to understand why it does not separate well according to the conditions.
Yes, I will try to find out. Thank you for you suggestion.