draw.GSEA: GSEA (Gene Set Enrichment Analysis) Plot for one Gene Set or...

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

GSEA (Gene Set Enrichment Analysis) Plot for one Gene Set or one Driver

Description

draw.GSEA draws a GSEA plot to analyze one gene set (with gene list annotated) or one driver (with list of target genes).

Usage

draw.GSEA(
  rank_profile = NULL,
  use_genes = NULL,
  use_direction = NULL,
  main = "",
  pdf_file = NULL,
  annotation = NULL,
  annotation_cex = 1.2,
  left_annotation = NULL,
  right_annotation = NULL
)

Arguments

rank_profile	a named vector of numerics, the differential values (DE or DA) calculated from a sample comparison (e.g. "G4 vs. Others"). Names of the vector must be gene names. For the DA, user could use 'processDriverProfile()' to convert the DA profile into gene-name based profile. The differential values can be "logFC" or "t-statistics".
use_genes	a vector of characters, a vector of genes to display. The genes can either be annotated genes in gene set or the targe genes from a specific driver. The gene names must be a subset of names(rank_profile).
use_direction	a vector of numeric 1s and -1s, 1 is positive regulation from driver, -1 is negative regulation from driver. Users can get this vector by converting the signs of "spearman". If NULL, no regulation direction will be displayed. Default is NULL.
main	character, an overall title for the plot. Default is "".
pdf_file	character, the file path to save as PDF file. If NULL, no PDF file will be saved. Default is NULL.
annotation	character, the annotation set by users for easier reference. Normally the annotation is the P-value or other statistics to show the significance of the interested gene set or driver. If NULL, will perform a Kolmogorov-Smirnov test to get the significance value. If want to get the statistics from GSEA test, could use 'funcEnrich.GSEA()' to get the statistics first. Default is NULL.
annotation_cex	numeric, giving the amount by which the text of annotation should be magnified relative to the default. Default is 1.2.
left_annotation	character, annotation displayed on the left of the figure, representing left condition of the rank_profile. Default is "".
right_annotation	character, annotation displayed on the right of the figure, representing right condition of the rank_profile. Default is "".

Value

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

Examples

gs.preload(use_spe='Homo sapiens',update=FALSE)
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

## draw for the most significant gene set
# by driver's DA profile
DA_profile <- processDriverProfile(Driver_name=ms_tab$gene_label,
                                    Driver_profile=ms_tab$logFC.G4.Vs.others_DA,
                                    choose_strategy='absmax',
                                    return_type ='gene_statistics')
res1 <- funcEnrich.GSEA(rank_profile=DA_profile,
                         use_gs=c('H'),
                         Pv_thre=0.1,Pv_adj = 'none')
top_gs <- res1[1,'#Name'] ## draw for the top 1
annot <- sprintf('NES: %s \nAdjusted P-value: %s',
          signif(res1[1,'NES'],2),
          signif(res1[1,'Adj_P'],2))
draw.GSEA(rank_profile=DA_profile,
          use_genes=all_gs2gene$H[[top_gs]],
          main=sprintf('GSEA plot for gene set %s',
          top_gs),
          annotation=annot,annotation_cex=1.2,
          left_annotation='high in G4',
          right_annotation='high in others')

## draw for the most significant driver
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)
driver_list <- rownames(sig_driver)
DE_profile <- analysis.par$DE[[1]]$`Z-statistics`;
names(DE_profile) <- rownames(analysis.par$DE[[1]])
use_driver <- driver_list[1]
use_target_genes <- analysis.par$merge.network$target_list[[use_driver]]$target
use_target_direction <- sign(analysis.par$merge.network$target_list[[use_driver]]$spearman) ## 1/-1
annot <- sprintf('P-value: %s',signif(ms_tab[use_driver,'P.Value.G4.Vs.others_DA'],2))

## draw for the driver
draw.GSEA(rank_profile=DE_profile,
          use_genes=use_target_genes,
          use_direction=use_target_direction,
          main=sprintf('GSEA plot for driver %s',
          ms_tab[use_driver,'gene_label']),
          annotation=annot,annotation_cex=1.2,
          left_annotation='high in G4',
          right_annotation='high in others')
draw.GSEA(rank_profile=DE_profile,
          use_genes=use_target_genes,
          use_direction=NULL,
          main=sprintf('GSEA plot for driver %s',
          ms_tab[use_driver,'gene_label']),
          annotation=NULL,annotation_cex=1.2,
          left_annotation='high in G4',
          right_annotation='high in others')

## draw for the gene set
gs.preload(use_spe='Homo sapiens',update=FALSE)
use_target_genes <- all_gs2gene[[1]][[1]]
draw.GSEA(rank_profile=DE_profile,
         use_genes=use_target_genes,
         main=sprintf('GSEA plot for %s',names(all_gs2gene[[1]][1])),
         left_annotation='high in G4',
         right_annotation='high in others')

## 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
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)
driver_list <- rownames(sig_driver)
DE_profile <- analysis.par$DE[[1]]$`Z-statistics`;
names(DE_profile) <- rownames(analysis.par$DE[[1]])
use_driver <- driver_list[1]
use_target_genes <- analysis.par$merge.network$target_list[[use_driver]]$target
use_target_direction <- sign(analysis.par$merge.network$target_list[[use_driver]]$spearman) ## 1/-1
annot <- sprintf('P-value: %s',signif(ms_tab[use_driver,'P.Value.G4.Vs.others_DA'],2))
analysis.par$out.dir.PLOT <- getwd() ## directory for saving the pdf files
draw.GSEA(rank_profile=DE_profile,use_genes=use_target_genes,
          use_direction=use_target_direction,
          main=sprintf('GSEA plot for driver %s',ms_tab[use_driver,'gene_label']),
          pdf_file = sprintf('%s/GSEA_driver.pdf',
          analysis.par$out.dir.PLOT),
          annotation=annot,annotation_cex=1.2,
          left_annotation='high in G4',
          right_annotation='high in others')

## draw for the gene set
gs.preload(use_spe='Homo sapiens',update=FALSE)
use_target_genes <- all_gs2gene[[1]][[1]]
draw.GSEA(rank_profile=DE_profile,
         use_genes=use_target_genes,
         main=sprintf('GSEA plot for %s',names(all_gs2gene[[1]][1])),
         pdf_file = sprintf('%s/GSEA_GS_each.pdf',analysis.par$out.dir.PLOT),
         left_annotation='high in G4',
         right_annotation='high in others')

## End(Not run)

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