consensus_trait_cor {BioNERO}R Documentation

Correlate set-specific modules and consensus modules to sample information

Description

Correlate set-specific modules and consensus modules to sample information

Usage

consensus_trait_cor(
  consensus,
  cor_method = "spearman",
  continuous_trait = FALSE,
  palette = "RdYlBu",
  cex.lab.x = 0.6,
  cex.lab.y = 0.6,
  cex.text = 0.6,
  transpose = FALSE
)

Arguments

consensus

Consensus network returned by consensus_modules.

cor_method

Correlation method to be used. One of 'spearman' or 'pearson'. Default is 'spearman'.

continuous_trait

Logical indicating if trait is a continuous variable. Default is FALSE.

palette

RColorBrewer's color palette to use. Default is "RdYlBu", a palette ranging from blue to red.

cex.lab.x

Font size for x axis labels. Default: 0.6.

cex.lab.y

Font size for y axis labels. Default: 0.6.

cex.text

Font size for numbers inside matrix. Default: 0.6.

transpose

Logical indicating whether to transpose the heatmap of not. Default is FALSE.

Details

Significance levels: 1 asterisk: significant at alpha = 0.05. 2 asterisks: significant at alpha = 0.01. 3 asterisks: significant at alpha = 0.001. no asterisk: not significant.

Value

Data frame of consensus module-trait correlations and p-values.

See Also

corPvalueFisher,labels2colors,labeledHeatmap,blueWhiteRed

Examples

set.seed(12)
data(zma.se)
filt.zma <- filter_by_variance(zma.se, n=500)
zma.set1 <- filt.zma[, sample(colnames(filt.zma), size=20, replace=FALSE)]
zma.set2 <- filt.zma[, sample(colnames(filt.zma), size=20, replace=FALSE)]
list.sets <- list(zma.set1, zma.set2)
# SFT power previously identified with consensus_SFT_fit()
consensus <- consensus_modules(list.sets, power = c(11, 13),
                               cor_method = "pearson")
consensus_trait <- consensus_trait_cor(consensus, cor_method = "pearson")

[Package BioNERO version 1.0.4 Index]