PCA

bincs can do principal component analysis (PCA) of three types of data.

Example Output

img/pca/fibroblast.png

Targets

Bincs can create two types of PCA for your raw data, and one for the normalized data that is input to limma.

pca_chip_vs_merged_input

Create a PCA of all the ChIP samples log2-divided by the merged input from the samples’ respective groups.

The output of this target is {prefix}/data/plot_pca/pca_chip_vs_merged_input.pdf.

pca_individual

Create a PCA of all the ChIP-samples and all Input-samples in the same plot. The data are RPKM-normalized.

The output of this target is {prefix}/data/plot_pca/pca_individual.pdf.

pca_limma

Create a PCA of the data that is input to limma for differential expression. This data has gone through several rounds of normalization.

The output of this target is {prefix}/data/plot_pca/pca_{caller}.pdf.