plotCorrStructure
Plot correlation structure of a gene based on random effects
plotCorrStructure(
fit,
varNames = names(coef(fit)),
reorder = TRUE,
pal = colorRampPalette(c("white", "red", "darkred")),
hclust.method = "complete"
)fit |
linear mixed model fit of a gene produced by lmer() or fitVarPartModel() |
varNames |
variables in the metadata for which the correlation structure should be shown. Variables must be random effects |
reorder |
how to reorder the rows/columns of the correlation matrix. reorder=FALSE gives no reorder. reorder=TRUE reorders based on hclust. reorder can also be an array of indices to reorder the samples manually |
pal |
color palette |
hclust.method |
clustering methods for hclust |
Image of correlation structure between each pair of experiments for a single gene
# load library # library(variancePartition) # Intialize parallel backend with 4 cores library(BiocParallel) register(SnowParam(4)) # load simulated data: data(varPartData) # specify formula form <- ~ Age + (1|Individual) + (1|Tissue) # fit and return linear mixed models for each gene fitList <- fitVarPartModel( geneExpr[1:10,], form, info ) # Focus on the first gene fit = fitList[[1]] # plot correlation sturcture based on Individual, reordering samples with hclust plotCorrStructure( fit, "Individual" ) # don't reorder plotCorrStructure( fit, "Individual", reorder=FALSE ) # plot correlation sturcture based on Tissue, reordering samples with hclust plotCorrStructure( fit, "Tissue" ) # don't reorder plotCorrStructure( fit, "Tissue", FALSE ) # plot correlation structure based on all random effects # reorder manually by Tissue and Individual idx = order(info$Tissue, info$Individual) plotCorrStructure( fit, reorder=idx ) # plot correlation structure based on all random effects # reorder manually by Individual, then Tissue idx = order(info$Individual, info$Tissue) plotCorrStructure( fit, reorder=idx )
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