missRows-package: Handling Missing Individuals in Multi-Omics Data Integration

In GonzalezIgnacio/HandlingMissRows: Handling Missing Individuals in Multi-Omics Data Integration

Description

The missRows package implements the MI-MFA method to deal with missing individuals ('biological units') in multi-omics data integration. The MI-MFA method generates multiple imputed datasets from a Multiple Factor Analysis model, then the yield results are combined in a single consensus solution. The package provides functions for estimating coordinates of individuals and variables, imputing missing individuals, and various diagnostic plots to inspect the pattern of missingness and visualize the uncertainty due to missing values.

Details

Package:	missRows
Type:	Package
Version:	1.0
Date:	2018-03-19
License:	Artistic-2.0
Depends:	R (>= 3.4)
Imports:	methods, gtools, plyr, ggplot2, stats, grDevices,
	S4Vectors, MultiAssayExperiment

Author(s)

Ignacio Gonz?lez and Valentin Voillet

Maintainer: Ignacio Gonz?lez <[e-mail]>

References

Voillet V., Besse P., Liaubet L., San Cristobal M., Gonz?lez I. (2016). Handling missing rows in multi-omics data integration: Multiple Imputation in Multiple Factor Analysis framework. BMC Bioinformatics, 17(40).

Examples

## A typical MI-MFA session might look like the following. 
## Here we assume there are two data tables with missing rows, 
## "table1" and "table2", and the stratum for each individual 
## is stored in a data frame "df".

## Not run: 

#-- Data preparation
midt <- newMIDTList(table1, table2, colData=df)

#-- Performing MI
midt <- MIMFA(midt, ncomp=2, M=30)

#-- Analysis of the results
plotInd(midt)
plotVar(midt)

## End(Not run)

GonzalezIgnacio/HandlingMissRows documentation built on Jan. 17, 2020, 6:29 p.m.