get_go_emap_plot: Perform gene ontology analysis and visualization for DE...

In cparsania/parcutils: Utility Functions To Deal Day To Day Bioinformatics Tasks.

Perform gene ontology analysis and visualization for DE genesets in one go.

Description

Perform gene ontology analysis and visualization for DE genesets in one go.

Usage

get_go_emap_plot(
  x,
  org_db = org.Hs.eg.db::org.Hs.eg.db,
  universe = NULL,
  ont_type = "BP",
  p_adj_method = "BH",
  pval_cutoff = 0.05,
  qval_cutoff = 0.05,
  simplify = TRUE,
  min_geneset_size = 10,
  max_geneset_size = 500,
  go_similarity_cutoff = 0.8,
  show_n_terms = 30,
  color_terms_by = "p.adjust"
)

Arguments

x	an object of class 'parcutils' or 'parcutils_ir'.
org_db	an object of the class class OrgDB, default org.Hs.eg.db
universe	a character vector of genes, default NULL, to be used as background genes for GO enrichment analysis. Currently supports only ENSEMBL gene id - e.g. ENSMUSG00000030787. When set to NULL all genes from x will be used as background genes.
ont_type	a character string, default "BP", denoting ontology type. Values can be one of the "BP", "MF" , "CC"
p_adj_method	a character string, default "BH", denoting a method for p-adjustment. Values can be one of the "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"
pval_cutoff	a numeric, default 0.05 denoting p-value cutoff.
qval_cutoff	a numeric, default 0.05 denoting q-value cutoff.
min_geneset_size	a numeric, default 10, denoting minimal size of genes annotated by ontology term for testing.
max_geneset_size	a numeric, default 500, denoting maximal size of genes annotated by ontology term for testing.
go_similarity_cutoff	a numeric value, default 0.8, denoting gene ontology similarity cutoff.
show_n_terms	a numeric, default 30, denoting number of gene ontology terms to show in the plot.
color_terms_by	a character string, default "p.adjust", denoting a variable to color gene ontology terms.

Value

An EMAP plot.

Examples

## Not run: 
count_file <- system.file("extdata","toy_counts.txt" , package = "parcutils")
count_data <- readr::read_delim(count_file, delim = "\t", show_col_types = FALSE)

sample_info <- count_data %>% colnames() %>% .[-1]  %>%
  tibble::tibble(samples = . , groups = rep(c("control" ,"treatment1" , "treatment2"), each = 3) )


res <- parcutils::run_deseq_analysis(counts = count_data %>% dplyr::mutate(gene_id = stringr::str_replace(gene_id, ":.*","")),
                                     sample_info = sample_info,
                                     column_geneid = "gene_id" ,
                                     group_numerator = c("treatment1", "treatment2") ,
                                     group_denominator = c("control"))

go_out <- get_go_emap_plot(res)

# display plot
go_out$go_emap_plots

# display table
 go_out$go_enrichment_output %>% tibble::as_tibble()

## End(Not run)

cparsania/parcutils documentation built on Aug. 29, 2024, 4:36 p.m.