makeHeatmap {DExMA}R Documentation

Visualization of the meta-analysis results

Description

It allows to see how the different significant genes are expressed in the different samples. Missing genes appear in gray

Usage

makeHeatmap(
    objectMA,
    resMA,
    typeMethod = c("FEM", "REM", "maxP", "minP", "Fisher", "Stouffer"),
    scaling = c("zscor", "rscale", "swr", "none"),
    regulation = c("all", "up", "down"),
    breaks = c(-2, 2),
    fdrSig = 0.05,
    numSig = 50
)

Arguments

objectMA

A list of list. Each list contains two elements. The first element is the expression matrix (genes in rows and sample in columns) and the second element is a vector of zeros and ones that represents the state of the different samples of the expression matrix. 0 represents one group (controls) and 1 represents the other group (cases). The result of the CreateobjectMA can be used too.

resMA

Output generated by the differents functions that performs meta-analysis (metaES, metaPvalue, metaRank or metaAnalysisDE)

typeMethod

A character that indicates the method that have been used to perform meta-analysis. See metaAnalysisDE function for more information.

scaling

Character variable to choose between different scaling approaches. See "Details" for more information.

regulation

Character variable that indicates whether we want the heatmap to show all significant genes ("all"), only the up-regulated genes ("up") or only the down-regulated genes("down")

breaks

Numeric vector of length 2 that contains the extreme values (minimum and maximum) of the range of values in which the heatmap color scale will be distributed. Default a vector By default a vector of -2 and 2 as extreme values.

fdrSig

Adjusted p-value from which a gene is considered significant. Default 0.05

numSig

The number of most significant genes to be represented

Details

Scaling approaches that can be used are:

Value

'NULL'

Author(s)

Juan Antonio Villatoro Garcia, juanantoniovillatorogarcia@gmail.com

References

Hadley Wickham and Dana Seidel (2020). scales: Scale Functions for Visualization. R package version 1.1.1. https://CRAN.R-project.org/package=scales

Lazar, C, Meganck, S, Taminau, J, and et al. 2013. “Batch Effect Removal Methods for Microarray Gene Expression Data Integration: A Survey,” 469–90.

Raivo Kolde 2019. pheatmap: Pretty Heatmaps. R package version 1.0.12. https://CRAN.R-project.org/package=pheatmap

See Also

createObjectMA, metaAnalysisDE

Examples


data(DExMAExampleData)

resultsMA <- metaAnalysisDE(maObject, typeMethod="REM")
makeHeatmap(objectMA=maObject, resMA=resultsMA, typeMethod="REM", 
scaling = "zscor", regulation = "all",breaks=c(-2,2), 
fdrSig = 0.05,numSig=40)


[Package DExMA version 1.1.2 Index]