A bit of a silly question (I suspect). I am using MATLAB to preprocess some bulk RNAseq data. I am calling PCA on my normalized counts just as an initial way to identify outliers I didn’t automatically reject. There are indeed some clear outliers but I am not sure how to use the score matrix output by pca to filter my original data because it is now in descending order of explained variance. How can I use the pca scores to return to the indicies of problematic samples. Thank you so much! Info below:
[coeff,score,latent,tsquared,explained] = pca(normCounts);

size(normCounts) 136 samples with 24427 measured genes

ans =

136 24427A bit of a silly question (I suspect). I am using MATLAB to preprocess some bulk RNAseq data. I am calling PCA on my normalized counts just as an initial way to identify outliers I didn’t automatically reject. There are indeed some clear outliers but I am not sure how to use the score matrix output by pca to filter my original data because it is now in descending order of explained variance. How can I use the pca scores to return to the indicies of problematic samples. Thank you so much! Info below:
[coeff,score,latent,tsquared,explained] = pca(normCounts);

size(normCounts) 136 samples with 24427 measured genes

ans =

136 24427 A bit of a silly question (I suspect). I am using MATLAB to preprocess some bulk RNAseq data. I am calling PCA on my normalized counts just as an initial way to identify outliers I didn’t automatically reject. There are indeed some clear outliers but I am not sure how to use the score matrix output by pca to filter my original data because it is now in descending order of explained variance. How can I use the pca scores to return to the indicies of problematic samples. Thank you so much! Info below:
[coeff,score,latent,tsquared,explained] = pca(normCounts);

size(normCounts) 136 samples with 24427 measured genes

ans =

136 24427 pca, rnaseq MATLAB Answers — New Questions

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