bump to version 0.7.0

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: EnrichGT
 Title: EnrichGT - all in one enrichment analysis soluction
-Version: 0.6.0.1
+Version: 0.7.0
 Author: Zhiming Ye
 Maintainer: Zhiming Ye <[email protected]>
 Description: Do biological enrichment analysis and parsing and clustering

NAMESPACE

@@ -16,7 +16,7 @@ export(database_progeny_mouse)
 export(egt_compare_groups)
 export(egt_enrichment_analysis)
 export(egt_gsea_analysis)
-export(egt_infer)
+export(egt_infer_act)
 export(egt_plot_results)
 export(egt_plot_umap)
 export(egt_recluster_analysis)

R/cp_helpers.R

@@ -240,6 +240,7 @@ egt_gsea_analysis_internal <- function(genes,database,p_val_cut_off=0.5,min_gene
     db0 <- database[,c(1,2)]
     database <- database[,c(2,3)]
   }else{
+    colnames(database)[1] <- "term"
     db0 <- data.frame(ID = database$term, term = database$term)
   }
   db0 <- db0 |> dplyr::mutate(CheckDup = paste0(ID,term)) |> dplyr::filter(!duplicated(CheckDup)) |> dplyr::select(-CheckDup) |> dplyr::rename(pathway = term) # Because of output is pathway

R/infAct.R

@@ -5,6 +5,8 @@
 #' [PROGENy](https://saezlab.github.io/progeny/) is a comprehensive resource containing a curated collection of pathways and their target genes, with weights for each interaction.
 #'
 #'[CollecTRI](https://github.com/saezlab/CollecTRI) is a comprehensive resource containing a curated collection of TFs and their transcriptional targets compiled from 12 different resources. This collection provides an increased coverage of transcription factors and a superior performance in identifying perturbed TFs compared to our previous.
+#' 
+#' If when doing re-enrichment, you select a high number of clusters, that may cause low gene number in each meta-gene module, and then can't be infered sucessfully. So if result is empty, please increase the number of re-clustering when doing it. 
 #'
 #' @param x an EnrichGT_obj object.
 #' @param DB can be "progeny" (the Pathway activity database), or "collectri" (TF activity database)
@@ -15,10 +17,11 @@
 #' @importFrom stringr str_to_title
 #' @author Zhiming Ye, Saez-Rodriguez Lab (The decoupleR package, https://saezlab.github.io/decoupleR/)
 #'
-egt_infer <- function(x,DB="collectri",species="human"){
+egt_infer_act <- function(x,DB="collectri",species="human"){
   genelist <- tryCatch(x@gene_modules,error=function(e){
     cli::cli_abort("NOT an enrichGT object. ")
   })
-  InferedCpres <- doEnrich(genelist,p_val_cut_off = 1 ,min_geneset_size=2,max_geneset_size=1000,database=(dbParser(DB,species)))
+  cli::cli_alert_warning("If when doing re-enrichment, you select a high number of clusters, that may cause low gene number in each meta-gene module, and then can't be infered sucessfully. So if result is empty, please increase the number of re-clustering when doing it. ")
+  InferedCpres <- egt_enrichment_analysis(genelist,p_val_cut_off = 1 ,min_geneset_size=2,max_geneset_size=1000,database=(dbParser(DB,species)))
   return(InferedCpres)
 }

R/kegg.R

@@ -1,10 +1,3 @@
-#' @export
-#'
-#' @rdname KEGGhelp
-database_kegg_show_organism <- function(){
-  x <- read.delim("https://rest.kegg.jp/list/organism",quote = "\t",header = F)
-  return(x)
-}
 
 keggModuleList <- function(orgkegg){
   k0<-glue::glue("https://rest.kegg.jp/list/pathway/{orgkegg}")
@@ -107,3 +100,12 @@ database_kegg <- function(kegg_organism="hsa",OrgDB = org.Hs.eg.db,kegg_modules=
 
 }
 
+
+
+#' @export
+#'
+#' @rdname KEGGhelp
+database_kegg_show_organism <- function(){
+  x <- read.delim("https://rest.kegg.jp/list/organism",quote = "\t",header = F)
+  return(x)
+}

R/ora.R

@@ -23,6 +23,7 @@ doEnrich_Internal <- function(genes,database,p_adj_methods,p_val_cut_off,backgro
     db0 <- database[,c(1,2)]
     database <- database[,c(2,3)]
   }else{
+    colnames(database)[1] <- "term"
     db0 <- data.frame(ID = database$term, term = database$term)
   }
   db0 <- db0 |> dplyr::mutate(CheckDup = paste0(ID,term)) |> dplyr::filter(!duplicated(CheckDup)) |> dplyr::select(-CheckDup) |> dplyr::rename(TERMs = term)