eigencorplot {PCAtools} | R Documentation |
Correlate principal components to continuous variable metadata and test significancies of these.
eigencorplot(pcaobj, components = getComponents(pcaobj, seq_len(10)), metavars, titleX = '', cexTitleX = 1.0, rotTitleX = 0, colTitleX = 'black', fontTitleX = 2, titleY = '', cexTitleY = 1.0, rotTitleY = 0, colTitleY = 'black', fontTitleY = 2, cexLabX = 1.0, rotLabX = 0, colLabX = 'black', fontLabX = 2, cexLabY = 1.0, rotLabY = 0, colLabY = 'black', fontLabY = 2, posLab = 'bottomleft', col = c('blue4', 'blue3', 'blue2', 'blue1', 'white', 'red1', 'red2', 'red3', 'red4'), posColKey = 'right', cexLabColKey = 1.0, cexCorval = 1.0, colCorval = 'black', fontCorval = 1, scale = TRUE, main = '', cexMain = 2, rotMain = 0, colMain = 'black', fontMain = 2, corFUN = 'pearson', corUSE = 'pairwise.complete.obs', signifSymbols = c('***', '**', '*', ''), signifCutpoints = c(0, 0.001, 0.01, 0.05, 1), colFrame = 'white', plotRsquared = FALSE, returnPlot = TRUE)
pcaobj |
Object of class 'pca' created by pca(). REQUIRED. |
components |
The principal components to be included in the plot. DEFAULT = getComponents(pcaobj, seq_len(10)). OPTIONAL. |
metavars |
A vector of column names in metadata representing continuos variables. REQUIRED. |
titleX |
X-axis title. DEFAULT = ”. OPTIONAL. |
cexTitleX |
X-axis title cex. DEFAULT = 1.0. OPTIONAL. |
rotTitleX |
X-axis title rotation in degrees. DEFAULT = 0. OPTIONAL. |
colTitleX |
X-axis title colour. DEFAULT = 'black'. OPTIONAL. |
fontTitleX |
X-axis title font style. 1, plain; 2, bold; 3, italic; 4, bold-italic. DEFAULT = 2. OPTIONAL. |
titleY |
Y-axis title. DEFAULT = ”. OPTIONAL. |
cexTitleY |
Y-axis title cex. DEFAULT = 1.0. OPTIONAL. |
rotTitleY |
Y-axis title rotation in degrees. DEFAULT = 0. OPTIONAL. |
colTitleY |
Y-axis title colour. DEFAULT = 'black'. OPTIONAL. |
fontTitleY |
Y-axis title font style. 1, plain; 2, bold; 3, italic; 4, bold-italic. DEFAULT = 2. OPTIONAL. |
cexLabX |
X-axis labels cex. DEFAULT = 1.0. OPTIONAL. |
rotLabX |
X-axis labels rotation in degrees. DEFAULT = 0. OPTIONAL. |
colLabX |
X-axis labels colour. DEFAULT = 'black'. OPTIONAL. |
fontLabX |
X-axis labels font style. 1, plain; 2, bold; 3, italic; 4, bold-italic. DEFAULT = 2. OPTIONAL. |
cexLabY |
Y-axis labels cex. DEFAULT = 1.0. OPTIONAL. |
rotLabY |
Y-axis labels rotation in degrees. DEFAULT = 0. OPTIONAL. |
colLabY |
Y-axis labels colour. DEFAULT = 'black'. OPTIONAL. |
fontLabY |
Y-axis labels font style. 1, plain; 2, bold; 3, italic; 4, bold-italic. DEFAULT = 2. OPTIONAL. |
posLab |
Positioning of the X- and Y-axis labels. 'bottomleft', bottom and left; 'topright', top and right; 'all', bottom / top and left /right; 'none', no labels. DEFAULT = 'bottomleft'. OPTIONAL. |
col |
Colour shade gradient for RColorBrewer. DEFAULT = c('blue4', 'blue3', 'blue2', 'blue1', 'white', 'red1', 'red2', 'red3', 'red4'). OPTIONAL. |
posColKey |
Position of colour key. 'bottom', 'left', 'top', 'right'. DEFAULT = 'right'. OPTIONAL. |
cexLabColKey |
Colour key labels cex. DEFAULT = 1.0. OPTIONAL. |
cexCorval |
Correlation values cex. DEFAULT = 1.0. OPTIONAL. |
colCorval |
Correlation values colour. DEFAULT = 'black'. OPTIONAL. |
fontCorval |
Correlation values font style. 1, plain; 2, bold; 3, italic; 4, bold-italic. DEFAULT = 1. OPTIONAL. |
scale |
Logical, indicating whether or not to scale the colour range to max and min cor values. DEFAULT = TRUE. OPTIONAL. |
main |
Plot title. DEFAULT = ”. OPTIONAL. |
cexMain |
Plot title cex. DEFAULT = 2. OPTIONAL. |
rotMain |
Plot title rotation in degrees. DEFAULT = 0. OPTIONAL. |
colMain |
Plot title colour. DEFAULT = 'black'. OPTIONAL. |
fontMain |
Plot title font style. 1, plain; 2, bold; 3, italic; 4, bold-italic. DEFAULT = 2. OPTIONAL. |
corFUN |
Correlation method: 'pearson', 'spearman', or 'kendall'. DEFAULT = 'pearson'. OPTIONAL. |
corUSE |
Method for handling missing values (see documentation for cor function via ?cor). 'everything', 'all.obs', 'complete.obs', 'na.or.complete', or 'pairwise.complete.obs'. DEFAULT = 'pairwise.complete.obs'. OPTIONAL. |
signifSymbols |
Statistical significance symbols to display beside correlation values. DEFAULT = c('***', '**', '*', ”). OPTIONAL. |
signifCutpoints |
Cut-points for statistical significance. DEFAULT = c(0, 0.001, 0.01, 0.05, 1). OPTIONAL. |
colFrame |
Frame colour. DEFAULT = 'white'. OPTIONAL. |
plotRsquared |
Logical, indicating whether or not to plot R-squared values. DEFAULT = FALSE. OPTIONAL. |
returnPlot |
Logical, indicating whether or not to return the plot object. DEFAULT = TRUE. OPTIONAL. |
A lattice
object.
Kevin Blighe <kevin@clinicalbioinformatics.co.uk>, Myles Lewis <myles.lewis@qmul.ac.uk>
options(scipen=10) options(digits=6) col <- 20 row <- 20000 mat1 <- matrix( rexp(col*row, rate = 0.1), ncol = col) rownames(mat1) <- paste0('gene', 1:nrow(mat1)) colnames(mat1) <- paste0('sample', 1:ncol(mat1)) mat2 <- matrix( rexp(col*row, rate = 0.1), ncol = col) rownames(mat2) <- paste0('gene', 1:nrow(mat2)) colnames(mat2) <- paste0('sample', (ncol(mat1)+1):(ncol(mat1)+ncol(mat2))) mat <- cbind(mat1, mat2) metadata <- data.frame(row.names = colnames(mat)) metadata$Group <- rep(NA, ncol(mat)) metadata$Group[seq(1,40,2)] <- 'A' metadata$Group[seq(2,40,2)] <- 'B' metadata$CRP <- sample.int(100, size=ncol(mat), replace=TRUE) metadata$ESR <- sample.int(100, size=ncol(mat), replace=TRUE) p <- pca(mat, metadata = metadata, removeVar = 0.1) eigencorplot(p, components = getComponents(p, 1:10), metavars = c('ESR', 'CRP'))