extractVarPart: Extract variance statistics

In variancePartition: Quantify and interpret divers of variation in multilevel gene expression experiments

Description

Extract variance statistics from list of models fit with lm() or lmer()

Usage

extractVarPart(modelList, showWarnings = TRUE, ...)

Arguments

modelList	list of lmer() model fits
showWarnings	show warnings about model fit (default TRUE)
...	other arguments

Value

data.frame of fraction of variance explained by each variable, after correcting for all others.

Examples

# library(variancePartition)

# Intialize parallel backend with 4 cores
library(BiocParallel)
register(SnowParam(4))

# load simulated data:
# geneExpr: matrix of gene expression values
# info: information/metadata about each sample
data(varPartData)

# Specify variables to consider
# Age is continuous so we model it as a fixed effect
# Individual and Tissue are both categorical, so we model them as random effects
form <- ~ Age + (1|Individual) + (1|Tissue) 

# Step 1: fit linear mixed model on gene expresson
# If categoritical variables are specified, a linear mixed model is used
# If all variables are modeled as continuous, a linear model is used
# each entry in results is a regression model fit on a single gene
# Step 2: extract variance fractions from each model fit
# for each gene, returns fraction of variation attributable to each variable 
# Interpretation: the variance explained by each variable
# after correction for all other variables
varPart <- fitExtractVarPartModel( geneExpr, form, info )
 
# violin plot of contribution of each variable to total variance
plotVarPart( sortCols( varPart ) )

# Advanced: 
# Fit model and extract variance in two separate steps
# Step 1: fit model for each gene, store model fit for each gene in a list
results <- fitVarPartModel( geneExpr, form, info )

# Step 2: extract variance fractions
varPart <- extractVarPart( results )

variancePartition documentation built on Nov. 8, 2020, 5:18 p.m.