Additional visualizations of variance structure

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

library("knitr")
opts_chunk$set(tidy=FALSE,dev="png",fig.show="show",
               # fig.width=7,fig.height=7, 
               echo=TRUE,
               message=FALSE, warning=FALSE)

# load library
library('variancePartition')

In the example dataset described in the main vignette, samples are correlated because they can come from the same individual or the same tissue. The function \Rfunction{plotCorrStructure} shows the correlation structure caused by each variable as well and the joint correlation structure. Figure \ref{fig:plotCorr}a,b shows the correlation between samples from the same individual where (a) shows the samples sorted based on clustering of the correlation matrix and (b) shows the original order. Figure \ref{fig:plotCorr}c,d shows the same type of plot except demonstrating the effect of tissue. The total correlation structure from summing individual and tissue correlation matricies is shown in \ref{fig:plotCorrAll}a,b. The code to generate these plots is shown below.

Plot variance structure

# Fit linear mixed model and examine correlation stucture
# for one gene
data(varPartData)

form <- ~ Age + (1|Individual) + (1|Tissue)

fitList <- fitVarPartModel( geneExpr[1:2,], form, info )

# focus on one gene
fit = fitList[[1]]

By Individual

Reorder samples

# Figure 1a
# correlation structure based on similarity within Individual
# reorder samples based on clustering
plotCorrStructure( fit, "Individual" )

Original order of samples

# Figure 1b
# use original order of samples
plotCorrStructure( fit, "Individual", reorder=FALSE )

By Tissue

Reorder samples

# Figure 1c
# correlation structure based on similarity within Tissue
# reorder samples based on clustering
plotCorrStructure( fit, "Tissue" )

Original order of samples

# Figure 1d
# use original order of samples
plotCorrStructure( fit, "Tissue", reorder=FALSE )

By Individual and Tissue

Reorder samples

# Figure 2a
# correlation structure based on similarity within 
# Individual *and* Tissue, reorder samples based on clustering
plotCorrStructure( fit )

Original order of samples

# Figure 2b
# use original order of samples
plotCorrStructure( fit, reorder=FALSE )

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