ORA for differenatial test result

Enrich biological functions on significant candidate via a over representation analysis.

Usage

test_ORA(
  x,
  contrasts = NULL,
  type = c("GO", "KEGG", "REACTOME"),
  species = "Human",
  by_contrast = FALSE,
  pAdjustMethod = c("BH", "holm", "hochberg", "hommel", "bonferroni", "BY", "fdr",
    "none"),
  ...
)

Arguments

x: A SummarizedExperiment/DEGdata output from add_adjections or a charachter vector containing candidate identifier(SYMBOL, EntrezID, UniprotID or ENSEMBL).
contrasts: Character, analyse results in which contrasts.
type: Character, one of "GO","KEGG","REACTOME". The datasets for enrichment analysis.
species: The species name.
by_contrast: Logical(1). If true, draw enrichment on each contrast, else draw on the total significant candidates.
pAdjustMethod: Character, one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none".
...: Other parameters in enricher() except the cutoff setting

Value

A enrichResult or compareClusterResult object according by_contrast.

Examples

if (FALSE) {
# Load example
data(Silicosis_pg)
data <- Silicosis_pg
data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";")

# Make SummarizedExperiment
ecols <- grep("LFQ.", colnames(data_unique))
se <- make_se_parse(data_unique, ecols,mode = "delim")

# Filter and normalize
filt <- filter_se(se, thr = 0, fraction = 0.4, filter_formula = ~ Reverse != "+" & Potential.contaminant!="+")
norm <- normalize_vsn(filt)

# Impute missing values using different functions
imputed <- impute(norm, fun = "MinProb", q = 0.05)

# Test for differentially expressed proteins
diff <- test_diff(imputed, type = "control", control  = c("PBS"), fdr.type = "Storey's qvalue")
dep <- add_rejections(diff, alpha = 0.01,lfc = 2)

# GO enrichment
check_organismDB_depends(organism = "Mouse") # check annotation package of Mouse
res_ora <- test_ORA(dep, contrasts = "W4_vs_PBS", species = "Mouse",type = "GO")
enrichplot::dotplot(res_ora)
}