getFeatureSpace: Get discriminant feature space

In powellgenomicslab/scPred: scPred - accurate supervised method for cell-type classification from single-cell RNA-seq data

Description

Given a prediction variable, finds a feature set of class-informative principal components that explain variance differences between cell types.

Usage

getFeatureSpace(object, pvar, correction = "fdr", sig = 1, reduction = "pca")

Arguments

object	A seurat object
pvar	Column in meta.data slot containing the cell-type labels of each single cell
correction	Multiple testing correction method used. Default: false discovery rate. See p.adjust function
sig	Significance level to determine principal components explaining class identity
reduction	Name of reduction in Seurat objet to be used to determine the feature space. Default: "pca"

Value

An Seurat object along with a scPred object stored in the @misc slot containing a data.frame of significant features with the following columns:

• PC: Principal component

• pValue: p-value obtained from Wilcoxon rank sum test

• pValueAdj: Adjusted p-value according to correction parameter

• expVar: Explained variance for each principal component

• cumExpVar: All principal components are ranked according to their significance and variance explained. This column contains the cumulative variance explained across the ranked principal components

Author(s)

Jose Alquicira Hernandez

Examples

library(scPred)
pbmc_small <- getFeatureSpace(pbmc_small, "RNA_snn_res.0.8")

powellgenomicslab/scPred documentation built on July 16, 2021, 12:14 a.m.