prince.plot: Heatmap of the associations between principal components and...

In swamp: Visualization, Analysis and Adjustment of High-Dimensional Data in Respect to Sample Annotations

Description

This function uses the calculations performed by prince() and plots the log10 of p-values obtained by the linear models.

Usage

prince.plot(prince, label = colnames(prince$o), smallest = -20, note = F, 
            notecol = "black", notecex = 1, 
            breaks = seq(-20,0,length.out=100), col = heat.colors(99), 
            margins = c(5, 7), key = T, cexRow = 1, cexCol = 1, 
            xlab = "Principal Components (Variation)", colsep = NULL, 
            rowsep = NULL, sepcolor = "black", sepwidth = c(0.05,0.05),
            Rsquared = F, breaksRsquared = seq(0,1,length.out=100) )

Arguments

prince	an object generated by the function prince()
label	vector containing names of the sample annotation.
smallest	a numeric value. log10(p-values) less than smallest are set to smallest for plotting. default = -20. e.g. a log10 p-value of -37 will be set to -20. Smallest has to be less than 0.
note	set to TRUE to print the p-values in the cells of the plot.
notecol	to determine the color of the notes
notecex	to determine the font size of the notes
breaks	either a number (default=100) or a numeric vector (default would be seq(-20,0,length.out=100)) of breaks for the colors
col	a vector of colors with a length of breaks-1. default=heat.colors(99).
margins	a vector with the margins for columns and rows. default=c(5,7).
key	whether the color key should be printed, default=TRUE.
cexRow	font size of label. default=1.
cexCol	font size of column labeling. default=1.
xlab	an additional character vector to print at the bottom.
colsep	same as in heatmap.2 function.
rowsep	same as in heatmap.2 function.
sepcolor	same as in heatmap.2 function.
sepwidth	same as in heatmap.2 function.
Rsquared	set to TRUE to print Rsquared values instead of log10 p-values. Missing values in object o will flaw the comparability of p-values.
breaksRsquared	same format as argument breaks. will be used when Rsquared=TRUE

Details

This plot indicates batch effects, and shows the influence of the biological annotations on the overall variation. The input has to be an object generated by the prince() function. The plot is based on the heatmap.2 function from the gplots package. Colors, breaks, font size and margins can be changed and cell notes can be added. The log10 of p-values from linear model are plotted. If Rsquared is TRUE the R-squared values from linear model are plotted instead.

Note

requires the package gplots

Author(s)

Martin Lauss

Examples

## data as a matrix
set.seed(100)
g<-matrix(nrow=1000,ncol=50,rnorm(1000*50),dimnames=list(paste("Feature",1:1000),
          paste("Sample",1:50)))
g[1:100,26:50]<-g[1:100,26:50]+1 # the first 100 features show
# higher values in the samples 26:50
## patient annotations as a data.frame, annotations should be numbers and factors
# but not characters.
## rownames have to be the same as colnames of the data matrix 
set.seed(200)
o<-data.frame(Factor1=factor(c(rep("A",25),rep("B",25))),
              Factor2=factor(rep(c("A","B"),25)),
              Numeric1=rnorm(50),row.names=colnames(g))
              
## calculate principal components and use linear models to calculate their 
## dependence on sample annotations
res1<-prince(g,o,top=10)
## plot p-values as heatmap
prince.plot(prince=res1)

Example output

Loading required package: impute
Loading required package: amap
Loading required package: gplots

Attaching package: 'gplots'

The following object is masked from 'package:stats':

    lowess

Loading required package: MASS

swamp documentation built on Dec. 6, 2019, 5:09 p.m.