Description Usage Arguments Value Author(s) Examples
View source: R/multiOmicsViz.R
Calculate the spearman correlation between the source omics data and other target omics data, identify the significant correlations and plot the significant correlations on the heat map in which the x-axis and y-axis are ordered by the chromosomal location.
1 2 | multiOmicsViz(sourceOmics,sourceOmicsName,chrome_sourceOmics,targetOmicsList,
targetOmicsName,chrome_targetOmics,fdrThr,outputfile,nThreads=NULL,legend=TRUE)
|
sourceOmics |
A R matrix, data.frame or SummarizedExperiment object containing the omics data. The data should contain the row names representing the genes and column names representing the samples. |
sourceOmicsName |
The name of the source omics data (e.g. CNA). |
chrome_sourceOmics |
The |
targetOmicsList |
A R list object containing at most 5 target omics data. Each omics data in the list should be a R matrix, data.frame or SummarizedExperiment object and contain the row names representing the genes and column names representing the samples. There should have multiple overlapping genes among all target omics data and at least 6 overlapping sample between source omics data and each target omics data. |
targetOmicsName |
A R vector object containing the name of all target omics data stored in
the |
chrome_targetOmics |
The |
fdrThr |
The FDR threshold for identifying the significant correlations. |
outputfile |
The output file name. |
nThreads |
If |
legend |
If |
If the targetOmicsList
contains one target omics data, the multiOmicsViz
function will plot a heat map in which x-axis represents the genes in the source omics
data, y-axis represents the genes in the target omics data, x-axis and y-axis are
ordered by chromosomal location, each point represents a significant correlation, red
color represents the significant positive correlation and blue color represents the
significant negative correlation.
If the targetOmicsList
contains multiple target omics data, the
multiOmicsViz
function will not only plot multiple heat maps for each target
omics data but also plot mutliple bar charts in which blue bars represent the number
of specific significant correlations for the target omics data and black bars
represents the number of common significant correlations among all target omics data.
Jing Wang
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | sourceOmics <- system.file("extdata","sourceOmics.txt",package="multiOmicsViz")
sourceOmics <- read.table(sourceOmics,header=TRUE,sep="\t",stringsAsFactors=FALSE,
check.names=FALSE)
targetOmics1 <- system.file("extdata","targetOmics.txt",package="multiOmicsViz")
targetOmics1 <- read.table(targetOmics1,header=TRUE,sep="\t",stringsAsFactors=FALSE,
check.names=FALSE)
targetOmicsList <- list()
targetOmicsList[[1]] <- targetOmics1
outputfile <- paste(tempdir(),"/heatmap",sep="")
multiOmicsViz(sourceOmics,"CNA","20",targetOmicsList,
"mRNA","All",0.001,outputfile)
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