plot_cellheatmap: cell-rPLR outlier identification

In walachja/cellrPLR: Cellwise Outlier Diagnostics Using Robust Pairwise Log-Ratios (cell-rPLR)

cell-rPLR outlier identification

Description

Plot function of algorithm for cellwise outlier diagnostics using robust pairwise log-ratios.

Usage

plot_cellheatmap(data, type = "biweight", g1, g2, mainGroup = "max", 
             plotly = FALSE, grid=FALSE,title=NULL)

Arguments

data	dataset, either 'matrix' or 'data.frame'
type	type of weighting function, possible values are 'biweight', 'huber', 'hampel'
g1	vector with positions of samples from group 1
g2	vector with positions of samples from group 2
mainGroup	integer or character: group which is chosen as based. Possible values are: 1,'1' - group 1, 2, '2' - group2, 'all' - all samples, 'max' - the bigger group
plotly	logical, should interactive plotly be used.
grid	logical, should grid be added to the plot.
title	title of plot

Value

matrix with cellwise outlier information, in range <-1,1>.

Author(s)

Jan Walach <[e-mail]>

References

'Cellwise outlier detection and biomarker identification in metabolomics based on pairwise log-ratios', Walach J., Filzmoser P., Kouril S., submitted

Examples

set.seed(741)
data <- gendata1_c()$X
colnames(data) <- LETTERS[1:9]
plot_cellheatmap(data, type = "biweight", g1 = 1:20 , g2 = 21:40, mainGroup = "max",grid = TRUE,plotly = TRUE,title = 'Simulated example')

walachja/cellrPLR documentation built on Nov. 7, 2024, 7:25 a.m.