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