View source: R/plotCorrStructure.R
plotCorrStructure | R Documentation |
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)
library(BiocParallel)
# 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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