inst/www/helpfiles/choose_cluster.md

PCA - Principal Component Analysis

This linear dimensionality reduction method is applied to the normalized count matrix. In this plot, each cell is a dot. The cells are placed according to their projection in the Component_1 & Component_2 (you are able to select any components). Cells close together have similar epigenomic traits. Coloring by 'total_counts' is a good way to ensure there is no direct correlation between the library size and the first components. Among other things, applying PCA to the large count matrix allows to:



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ChromSCape documentation built on Nov. 8, 2020, 6:57 p.m.