This function generates a split barplot. This is a plot where the top 10 Go terms are shown, sorted based on a column ('col_split'). Two conditions are shown at the same time. One condition is shown in the positive axis, while the other in the negative one. The condition to be shown as positive is set with 'pos_col'.
The GO terms will be shown inside the bars, if the term is too long, using 'cutoff', you can control the maximum number of characters per line.
Pre-filter of the dataframe to contain significant Terms is recommended
Usage
DO.SplitBarGSEA(
df_GSEA,
term_col,
col_split,
cond_col,
pos_cond,
cutoff = 40,
log10_transform = TRUE,
figsize = c(12, 8),
topN = 10,
colors_pairs = c("sandybrown", "royalblue"),
alpha_colors = 0.3,
path = NULL,
spacing = 5,
txt_size = 12,
filename = "SplitBar.svg",
title = "Top 10 GO Terms in each Condition: ",
showP = FALSE,
celltype = "all"
)Arguments
- df_GSEA
dataframe with the results of a gene set enrichment analysis
- term_col
column in the dataframe that contains the terms
- col_split
column in the dataframe that will be used to sort and split the plot
- cond_col
column in the dataframe that contains the condition information
- pos_cond
condition that will be shown in the positive side of the plot
- cutoff
maximum number of characters per line
- log10_transform
if col_split contains values between 0 and 1, assume they are pvals and apply a -log10 transformation
- figsize
figure size
- topN
how many terms are shown
- colors_pairs
colors for each condition (1st color –> negative axis; 2nd color –> positive axis)
- alpha_colors
alpha value for the colors of the bars
- path
path to save the plot
- spacing
space to add between bars and origin. It is a percentage value, indicating that the bars start at 5 % of the maximum X axis value.
- txt_size
size of the go terms text
- filename
filename for the plot
- title
title of the plot
- showP
if False, the axis is return
- celltype
vector with cell types you want to subset for, use "all" for all celltypes contained in the dataframe column "celltype"
Examples
library(enrichR)
#> Welcome to enrichR
#> Checking connections ...
#> Enrichr ...
#> Connection is Live!
#> FlyEnrichr ...
#> Connection is Live!
#> WormEnrichr ...
#> Connection is Live!
#> YeastEnrichr ...
#> Connection is Live!
#> FishEnrichr ...
#> Connection is Live!
#> OxEnrichr ...
#> Connection is Live!
sce_data <- readRDS(system.file("extdata", "sce_data.rds", package = "DOtools"))
DGE_result <- DO.MultiDGE(sce_data,
sample_col = "orig.ident",
method_sc = "wilcox",
annotation_col = "annotation",
ident_ctrl = "healthy")
#> Centering and scaling data matrix
#> 2025-07-26 09:22:41 - Corrected annotation names in pseudo-bulk object by replacing '-' with '_'.
#> 2025-07-26 09:22:41 - Starting DGE single cell method analysis
#> 2025-07-26 09:22:41 - Comparing disease with healthy in: Monocytes
#> 2025-07-26 09:22:41 - Skipping Monocytes since one comparison has fewer than 3 cells!
#> 2025-07-26 09:22:41 - Comparing disease with healthy in: CD4_T_cells
#> 2025-07-26 09:22:41 - Comparing disease with healthy in: NK
#> 2025-07-26 09:22:41 - Comparing disease with healthy in: CD8_T_cells
#> 2025-07-26 09:22:41 - Skipping CD8_T_cells since one comparison has fewer than 3 cells!
#> 2025-07-26 09:22:41 - Comparing disease with healthy in: B_cells
#> 2025-07-26 09:22:41 - Comparing disease with healthy in: pDC
#> 2025-07-26 09:22:41 - Finished DGE single cell method analysis
#> 2025-07-26 09:22:41 - Starting DGE pseudo bulk method analysis
#> 2025-07-26 09:22:41 - Comparing disease with healthy in: B_cells
#> 2025-07-26 09:22:41 - Skipping B_cells since one comparison has fewer than 3 cells!
#> 2025-07-26 09:22:41 - Comparing disease with healthy in: CD4_T_cells
#> 2025-07-26 09:22:41 - Skipping CD4_T_cells since one comparison has fewer than 3 cells!
#> 2025-07-26 09:22:41 - Comparing disease with healthy in: Monocytes
#> 2025-07-26 09:22:41 - Skipping Monocytes since one comparison has fewer than 3 cells!
#> 2025-07-26 09:22:41 - Comparing disease with healthy in: NK
#> 2025-07-26 09:22:41 - Skipping NK since one comparison has fewer than 3 cells!
#> 2025-07-26 09:22:41 - Comparing disease with healthy in: pDC
#> 2025-07-26 09:22:41 - Skipping pDC since one comparison has fewer than 3 cells!
#> 2025-07-26 09:22:41 - Comparing disease with healthy in: CD8_T_cells
#> 2025-07-26 09:22:41 - Skipping CD8_T_cells since one comparison has fewer than 3 cells!
#> 2025-07-26 09:22:41 - Finished DGE pseudo bulk method analysis
#> 2025-07-26 09:22:41 - DGE pseudo bulk result is empty...
DGE_result <- DGE_result[DGE_result$celltype == "CD4_T_cells",]
result_GO <- DO.enrichR(df_DGE = DGE_result,
gene_column = "gene",
pval_column = "p_val_SC_wilcox",
log2fc_column = "avg_log2FC_SC_wilcox",
pval_cutoff = 0.05,
log2fc_cutoff = 0.25,
path = NULL,
filename = "",
species = "Human",
go_catgs = "GO_Biological_Process_2023")
#> Connection changed to https://maayanlab.cloud/Enrichr/
#> Connection is Live!
#> Uploading data to Enrichr... Done.
#> Querying GO_Biological_Process_2023... Done.
#> Parsing results... Done.
#> Uploading data to Enrichr... Done.
#> Querying GO_Biological_Process_2023... Done.
#> Parsing results... Done.
result_GO$celltype <- "CM1"
#Run SplitBarGSEA visualisation
DO.SplitBarGSEA(df_GSEA = result_GO,
term_col = "Term",
col_split = "Combined.Score",
cond_col = "State",
pos_cond = "enriched",
cutoff = 40,
log10_transform = TRUE,
figsize = c(12, 8),
topN = 10,
colors_pairs = c("sandybrown", "royalblue"),
alpha_colors = 0.3,
path = NULL,
spacing = 5,
txt_size = 12,
filename = "SplitBar.svg",
title = "Top 10 GO Terms in each Condition: ",
showP = FALSE,
celltype = "all")