inst/www/helpfiles/most_contributing_features.md

In vallotlab/ChromSCape: Analysis of single-cell epigenomics datasets with a Shiny App

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Contribution to PCA

In this box, the contribution of features to the PCA are displayed with two graphs:

• In the barplot, you will get the top loci / closest genes associated to the top loci contributing to the Component of your choice. The top contributing features are taken by sorting the values of the features in the componenent of intereset (e.g. PC1, PC2...). If you observe a huge gap between the top 2-3 features and the rest, this means that very few features are driving most of the dimensionality reduction, and that other less contributing features might be hidden. This also gives you interesting features to look at in the Peak Calling & Coverage or Differential Analysis tabs.

• In the pie chart, the chromosome repartition of the top 100 most contributing features to the componenent of interest (e.g. PC1, PC2...) are displayed. This is used to see if there is any strong imbalance in the "chromosome contribution", e.g. if a chromosome is contributing more than it should be. Usually, if a single chromosome has > 30% representation, this might indicate that CNVs or genetic events are happening on this chromosome, and you might want to look into more details at these events, or exclude them from the analysis (see Filter & Normalize tab)

vallotlab/ChromSCape documentation built on Oct. 15, 2023, 1:47 p.m.