draw.heatmap: Draw Heatmap Plot to Display the Expression Level or Activity...

In jyyulab/NetBID: Network-based Bayesian Inference of Drivers, version 2

Draw Heatmap Plot to Display the Expression Level or Activity Level of Genes and Drivers

Description

draw.heatmap plots the heatmap to see the expression level or activity level of genes and drivers across selected samples.

Usage

draw.heatmap(
  mat = NULL,
  use_genes = rownames(mat),
  use_gene_label = use_genes,
  use_samples = colnames(mat),
  use_sample_label = use_samples,
  phenotype_info = NULL,
  use_phe = NULL,
  main = "",
  scale = "none",
  pdf_file = NULL,
  cluster_rows = TRUE,
  cluster_columns = TRUE,
  show_row_names = TRUE,
  show_column_names = TRUE,
  clustering_distance_rows = "pearson",
  clustering_distance_columns = "pearson",
  use_color = NULL,
  pre_define = NULL,
  ...
)

Arguments

mat	numeric matrix, the expression/activity matrix. Rows are genes or drivers, columns are selected samples.
use_genes	a vector of characters, selected genes (e.g. "originID"). Default is row names of mat.
use_gene_label	a vector of characters, a vector of labels for use_genes (e.g. "geneSymbol" or "gene_label"). Default is use_genes.
use_samples	a vector of characters, selected samples. Default is column names of mat.
use_sample_label	a vector of characters, a vector of labels for use_samples. Default is use_samples.
phenotype_info	data.frame, phenotype of samples. Users can call Biobase::pData(eset) to create. The row names should match the column names in mat. Default is NULL.
use_phe	a list of characters, selected phenotype of samples. A subset of columns from phenotype_info. Default is NULL.
main	character, an overall title for the plot. Default is "".
scale	character, users can choose from "row", "column" and "none". Indicating if the values should be centered and scaled in either the row direction or the column direction, or none. Default is "none".
pdf_file	character, the file path to save plot as PDF file. If NULL, no PDF file will be saved. Default is NULL.
cluster_rows, cluster_columns	logical, the same parameters in Heatmap. Please check ?Heatmap for more details. Default is TRUE.
show_row_names, show_column_names	logical, the same parameters used in Heatmap. Please check ?Heatmap for more details. Default is TRUE.
clustering_distance_rows, clustering_distance_columns	character, the same parameters used in Heatmap. Please check ?Heatmap for more details. Default is "pearson".
use_color	a vector of color codes, colors to be assigned to each member of display label. Default is brewer.pal(9, 'Set1').
pre_define	a vector of characters, pre-defined color codes for a certain input (e.g. c("blue", "red") with names c("A", "B")). Default is NULL.
...	for more options, please check ?Heatmap for more details.

Value

Return a logical value. If TRUE, the plot has been created successfully.

Examples

analysis.par <- list()
analysis.par$out.dir.DATA <- system.file('demo1/driver/DATA/',package = "NetBID2")
NetBID.loadRData(analysis.par=analysis.par,step='ms-tab')
ms_tab <- analysis.par$final_ms_tab
exp_mat <- Biobase::exprs(analysis.par$cal.eset) ## rownames matches originalID
ac_mat <- Biobase::exprs(analysis.par$merge.ac.eset) ## rownames matches originalID_label
phe_info <- Biobase::pData(analysis.par$cal.eset) ## phenotype information
sig_driver <- draw.volcanoPlot(dat=ms_tab,label_col='gene_label',
                               logFC_col='logFC.G4.Vs.others_DA',
                               Pv_col='P.Value.G4.Vs.others_DA',
                               logFC_thre=0.4,
                               Pv_thre=1e-7,
                               main='Volcano Plot for G4.Vs.others_DA',
                               show_label=FALSE,
                               label_type = 'origin',
                               label_cex = 0.5)
sig_driver <- sig_driver[1:10,]
draw.heatmap(mat=exp_mat,use_genes=ms_tab[rownames(sig_driver),'originalID'],
            use_gene_label=ms_tab[rownames(sig_driver),'gene_label'],
            use_samples=colnames(exp_mat),
            use_sample_label=phe_info[colnames(exp_mat),'geo_accession'],
            phenotype_info=phe_info,use_phe=c('gender','pathology','subgroup'),
            main='Expression for Top drivers',scale='row',
            cluster_rows=TRUE,cluster_columns=TRUE,
            clustering_distance_rows='pearson',
            clustering_distance_columns='pearson',
            row_names_gp = gpar(fontsize = 12),
            pre_define=c('WNT'='blue','SHH'='red','G4'='green'))
draw.heatmap(mat=ac_mat,use_genes=ms_tab[rownames(sig_driver),'originalID_label'],
             use_gene_label=ms_tab[rownames(sig_driver),'gene_label'],
             use_samples=colnames(ac_mat),
             use_sample_label=phe_info[colnames(ac_mat),'geo_accession'],
             phenotype_info=phe_info,use_phe=c('gender','pathology','subgroup'),
             main='Activity for Top drivers',scale='row',
             cluster_rows=TRUE,cluster_columns=TRUE,
             clustering_distance_rows='pearson',
             clustering_distance_columns='pearson',
             row_names_gp = gpar(fontsize = 6),
             pre_define=c('WNT'='blue','SHH'='red','G4'='green'))

## Not run: 
analysis.par <- list()
analysis.par$out.dir.DATA <- system.file('demo1/driver/DATA/',package = "NetBID2")
NetBID.loadRData(analysis.par=analysis.par,step='ms-tab')
ms_tab <- analysis.par$final_ms_tab
exp_mat <- Biobase::exprs(analysis.par$cal.eset) ## rownames matches originalID
ac_mat <- Biobase::exprs(analysis.par$merge.ac.eset) ## rownames matches originalID_label
phe_info <- Biobase::pData(analysis.par$cal.eset) ## phenotype information
analysis.par$out.dir.PLOT <- getwd() ## directory for saving the pdf files
sig_driver <- draw.volcanoPlot(dat=ms_tab,label_col='gene_label',
                               logFC_col='logFC.G4.Vs.others_DA',
                               Pv_col='P.Value.G4.Vs.others_DA',
                               logFC_thre=0.4,
                               Pv_thre=1e-7,
                               main='Volcano Plot for G4.Vs.others_DA',
                               show_label=FALSE,
                               label_type = 'origin',
                               label_cex = 0.5)
draw.heatmap(mat=exp_mat,use_genes=ms_tab[rownames(sig_driver),'originalID'],
            use_gene_label=ms_tab[rownames(sig_driver),'gene_label'],
            use_samples=colnames(exp_mat),
            use_sample_label=phe_info[colnames(exp_mat),'geo_accession'],
            phenotype_info=phe_info,
            use_phe=c('gender','pathology','subgroup'),
            main='Expression for Top drivers',scale='row',
            cluster_rows=TRUE,cluster_columns=TRUE,
            clustering_distance_rows='pearson',
            clustering_distance_columns='pearson',
            row_names_gp = gpar(fontsize = 12),
            pdf_file=sprintf('%s/heatmap_demo1.pdf',
            analysis.par$out.dir.PLOT),
            pre_define=c('WNT'='blue','SHH'='red','G4'='green'))
draw.heatmap(mat=ac_mat,use_genes=ms_tab[rownames(sig_driver),'originalID_label'],
             use_gene_label=ms_tab[rownames(sig_driver),'gene_label'],
             use_samples=colnames(ac_mat),
             use_sample_label=phe_info[colnames(ac_mat),'geo_accession'],
             phenotype_info=phe_info,
             use_phe=c('gender','pathology','subgroup'),
             main='Activity for Top drivers',scale='row',
             cluster_rows=TRUE,cluster_columns=TRUE,
             clustering_distance_rows='pearson',
             clustering_distance_columns='pearson',
             row_names_gp = gpar(fontsize = 6),
             pdf_file=sprintf('%s/heatmap_demo2.pdf',
             analysis.par$out.dir.PLOT),
             pre_define=c('WNT'='blue','SHH'='red','G4'='green'))

## End(Not run)

jyyulab/NetBID documentation built on Dec. 23, 2024, 6:34 a.m.