annotatePC: Annotate top PCs from the dataset

In shbrief/GenomicSuperSignature: Interpretation of RNA-seq experiments through robust, efficient comparison to public databases

Annotate top PCs from the dataset

Description

This function finds the RAV with the highest validation score (including RAVs with negative silhouette width) for specified PC of the dataset and returns the top enriched pathways.

Usage

annotatePC(
  PCnum,
  val_all,
  RAVmodel,
  n = 5,
  scoreCutoff = 0.5,
  nesCutoff = NULL,
  simplify = TRUE,
  abs = FALSE,
  trimed_pathway_len = 45
)

Arguments

PCnum	A numeric vector. PC number of your dataset to retrieve annotation results for. The vector can contain any integer number among 1:8.
val_all	The output from validate
RAVmodel	The RAVmodel used to generate the input for the argument, val_all.
n	An integer. Default is 5. The number of the top enriched pathways to print out. If there are fewer than n pathways passed the cutoff, it will print out NA.
scoreCutoff	A numeric value for the minimum correlation between loadings of the dataset principal component and the RAV. Default is 0.5.
nesCutoff	A numeric value for the minimum Normalized Enrichment Score (NES) for the enrichment analysis. Default is NULL The suggested value is 3.
simplify	A logical. Under default (TRUE), the output will be a data frame with the number of column same as the length of PCnum argument, and the number of row same as the n argument. If it is set to FALSE, the output will be a list with the length of PCnum argument, where each element is a data frame containing detailed GSEA output of enriched pathways.
abs	Default is FALSE. If it's set to TRUE, the enriched pathways will be listed based on absolute value of the Normalized Enrichment Score (NES).
trimed_pathway_len	Positive integer values, which is the display width of pathway names. Default is 45.

Value

A data frame of a list based on the simplify argument. Check the output detail above.

Examples

data(miniRAVmodel)
library(bcellViper)
data(bcellViper)
val_all <- validate(dset, miniRAVmodel)
annotatePC(2, val_all, miniRAVmodel)

shbrief/GenomicSuperSignature documentation built on May 3, 2023, 10:07 p.m.