makeHeatmap: Visualization of the meta-analysis results

In Juananvg/DExMA: Differential Expression Meta-Analysis

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,
    scaling=c("zscor","rscale","swr","none"),
    regulation=c("all", "up","down"),
    breaks=c(-2,2),
    fdrSig = 0.05,
    logFCSig = 1.5,
    numSig = "all",
    color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100),
    na_col = "darkgrey",
    legend = TRUE, 
    cluster_cols = FALSE, 
    cluster_rows = FALSE,
    show_rownames = TRUE,
    show_colnames = FALSE)

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)
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
logFCSig	In absolute value. Log Fold Change threshold from which genes are considered in the heatmap.
numSig	The number of most significant genes to be represented. If numSig = "all", all significant genes that meet the selected parameters will be represented.
color	Vector of colors used in heatmap
na_col	Color of the NA cell in the heatmap
legend	Logical to determine if legend should be drawn or not
cluster_cols	boolean values determining if columns should be clustered.
cluster_rows	boolean values determining if rows should be clustered.
show_rownames	boolean specifying if row names are be shown.
show_colnames	boolean specifying if column names are be shown.

Details

Scaling approaches that can be used are:

• "rscale": it applies rescale function of scales package. Values will be between -1 and 1)

• "zscor": It calculates a z-score value for each gene, that is, the mean gene expression from each gene is subtracted from each gene expression value and then it is divided by the standard deviation

• "swr": it applys scaling relative to reference dataset approach

• "none": any scaling approach it is applied.

Value

The matrix represented in the heatmap

Author(s)

Juan Antonio Villatoro Garcia, [e-mail]

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, 
scaling = "zscor", regulation = "all",breaks=c(-2,2), 
fdrSig = 0.05, logFCSig = 1.5, numSig=40)

Juananvg/DExMA documentation built on Dec. 5, 2023, 1:12 p.m.