consensus_trait_cor {BioNERO} | R Documentation |
Correlate set-specific modules and consensus modules to sample information
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 )
consensus |
Consensus network returned by |
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. |
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.
Data frame of consensus module-trait correlations and p-values.
corPvalueFisher
,labels2colors
,labeledHeatmap
,blueWhiteRed
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")