cell_shinyApp: Shiny app for cellwise outlier diagnostics

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

Description

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

Usage

cell_shinyApp(data, g1, g2, mainGroup = "max", mainGroup2 = "all", simul = FALSE)

Arguments

data	dataset, either 'matrix' or 'data.frame'
g1	vector with positions of samples from group 1
g2	vector with positions of samples from group 2
mainGroup	first plotly: 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
mainGroup2	second plotly: 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
simul	compute cellwise outlier diagnostics simultaneously, might be hard on memory (RAM)

Details

For an easier exploration and understanding of the data, a Shiny app is included. Shiny , an open source R package, is a web application and serves as an interactive tool for visualization. The Shiny app allows to interactively apply different weighting and aggregation functions for the cell-rPLR algorithm, and supports zooming into regions of the data matrix to see more details. Moreover, the variables can be interactively ordered based on their importance for the group discrimination.

Value

Shiny app is launched.

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]
cell_shinyApp(data, g1 = 1:20 , g2 = 21:40,simul = TRUE)

walachja/cellrPLR documentation built on May 22, 2019, 2:47 p.m.