diff --git a/DESCRIPTION b/DESCRIPTION
index 1b5c2843..f6fae51b 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: SCP
 Type: Package
 Title: Single Cell Pipeline
-Version: 0.5.2
+Version: 0.5.3
 Author: Hao Zhang
 Maintainer: Hao Zhang <haozhang@njmu.edu.cn>
 Description: An end-to-end Single-Cell Pipeline designed to facilitate comprehensive analysis and exploration of single-cell data.
diff --git a/R/SCP-analysis.R b/R/SCP-analysis.R
index 9cc13f2a..c6aabf5e 100644
--- a/R/SCP-analysis.R
+++ b/R/SCP-analysis.R
@@ -1814,7 +1814,7 @@ ListDB <- function(species = "Homo_sapiens",
 #' Default is c("GO", "GO_BP", "GO_CC", "GO_MF", "KEGG", "WikiPathway", "Reactome",
 #' "CORUM", "MP", "DO", "HPO", "PFAM", "CSPA", "Surfaceome", "SPRomeDB", "VerSeDa",
 #' "TFLink", "hTFtarget", "TRRUST", "JASPAR", "ENCODE", "MSigDB",
-#' "CellChat", "Chromosome", "GeneType", "Enzyme", "TF").
+#' "CellTalk", "CellChat", "Chromosome", "GeneType", "Enzyme", "TF").
 #' @param db_IDtypes A character vector specifying the desired ID types to be used for gene identifiers in the gene annotation databases. Default is c("symbol", "entrez_id", "ensembl_id").
 #' @param db_version A character vector specifying the version of the gene annotation databases to be retrieved. Default is "latest".
 #' @param db_update A logical value indicating whether the gene annotation databases should be forcefully updated. If set to FALSE, the function will attempt to load the cached databases instead. Default is FALSE.
@@ -1901,7 +1901,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
                         "CORUM", "MP", "DO", "HPO", "PFAM",
                         "CSPA", "Surfaceome", "SPRomeDB", "VerSeDa",
                         "TFLink", "hTFtarget", "TRRUST", "JASPAR", "ENCODE",
-                        "MSigDB", "CellChat",
+                        "MSigDB", "CellTalk", "CellChat",
                         "Chromosome", "GeneType", "Enzyme", "TF"
                       ),
                       db_IDtypes = c("symbol", "entrez_id", "ensembl_id"),
@@ -1913,15 +1913,14 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
   db_list <- list()
   for (sps in species) {
     message("Species: ", sps)
-    default_IDtypes <- c(
+    default_IDtypes <- list(
       "GO" = "entrez_id", "GO_BP" = "entrez_id", "GO_CC" = "entrez_id", "GO_MF" = "entrez_id", "KEGG" = "entrez_id",
       "WikiPathway" = "entrez_id", "Reactome" = "entrez_id", "CORUM" = "symbol",
       "MP" = "symbol", "DO" = "symbol", "HPO" = "symbol", "PFAM" = "entrez_id", "Chromosome" = "entrez_id",
       "GeneType" = "entrez_id", "Enzyme" = "entrez_id", "TF" = "symbol",
       "CSPA" = "symbol", "Surfaceome" = "symbol", "SPRomeDB" = "entrez_id", "VerSeDa" = "symbol",
       "TFLink" = "symbol", "hTFtarget" = "symbol", "TRRUST" = "symbol", "JASPAR" = "symbol", "ENCODE" = "symbol",
-      "MSigDB" = "symbol", "CellChat" = "symbol"
-      # "CellTalk" = "symbol", "DGI" = "entrez_id"
+      "MSigDB" = c("symbol", "ensembl_id"), "CellTalk" = "symbol", "CellChat" = "symbol"
     )
     if (!is.null(custom_TERM2GENE)) {
       if (length(db) > 1) {
@@ -1931,7 +1930,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
         stop("When building a custom database, 'custom_IDtype', 'custom_species' and 'custom_version' must be provided.")
       }
       custom_IDtype <- match.arg(custom_IDtype, choices = c("symbol", "entrez_id", "ensembl_id"))
-      default_IDtypes[db] <- custom_IDtype
+      default_IDtypes[[db]] <- custom_IDtype
     }
     if (isFALSE(db_update) && is.null(custom_TERM2GENE)) {
       for (term in db) {
@@ -2022,7 +2021,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
             if (subterm == "GO") {
               TERM2GENE <- bg[, c("GOALL", org_key)]
               TERM2NAME <- bg[, c("GOALL", "TERM")]
-              colnames(TERM2GENE) <- c("Term", default_IDtypes[subterm])
+              colnames(TERM2GENE) <- c("Term", default_IDtypes[[subterm]])
               colnames(TERM2NAME) <- c("Term", "Name")
               TERM2NAME[["ONTOLOGY"]] <- bg[["ONTOLOGYALL"]]
               semData <- NULL
@@ -2030,7 +2029,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
               simpleterm <- unlist(strsplit(subterm, split = "_"))[2]
               TERM2GENE <- bg[which(bg[["ONTOLOGYALL"]] %in% simpleterm), c("GOALL", org_key)]
               TERM2NAME <- bg[which(bg[["ONTOLOGYALL"]] %in% simpleterm), c("GOALL", "TERM")]
-              colnames(TERM2GENE) <- c("Term", default_IDtypes[subterm])
+              colnames(TERM2GENE) <- c("Term", default_IDtypes[[subterm]])
               colnames(TERM2NAME) <- c("Term", "Name")
               TERM2NAME[["ONTOLOGY"]] <- simpleterm
               semData <- suppressMessages(GOSemSim::godata(orgdb, ont = simpleterm))
@@ -2088,7 +2087,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
           TERM2NAME[, "Name"] <- gsub(pattern = paste0(" - ", paste0(unlist(strsplit(db_species["KEGG"], split = "_")), collapse = " "), ".*$"), replacement = "", x = TERM2NAME[, "Name"])
           TERM2NAME <- TERM2NAME[TERM2NAME[, "Pathway"] %in% TERM2GENE[, "Pathway"], , drop = FALSE]
 
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["KEGG"])
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["KEGG"]])
           colnames(TERM2NAME) <- c("Term", "Name")
           TERM2GENE <- na.omit(unique(TERM2GENE))
           TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2126,7 +2125,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
               warning("Use the human annotation to create the WikiPathway database for ", sps, immediate. = TRUE)
               db_species["WikiPathway"] <- "Homo_sapiens"
               wiki_sp <- "Homo_sapiens"
-              gmtfile <- gmtfiles[grep(wiki_sp, gmtfile, fixed = TRUE)]
+              gmtfile <- gmtfiles[grep(wiki_sp, gmtfiles, fixed = TRUE)]
             } else {
               stop("Stop the preparation.")
             }
@@ -2144,7 +2143,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
           bg <- do.call(rbind.data.frame, wiki_gmt)
           TERM2GENE <- bg[, c(1, 2)]
           TERM2NAME <- bg[, c(1, 3)]
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["WikiPathway"])
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["WikiPathway"]])
           colnames(TERM2NAME) <- c("Term", "Name")
           TERM2GENE <- na.omit(unique(TERM2GENE))
           TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2182,7 +2181,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
           df$PATHNAME <- gsub(x = df$PATHNAME, pattern = paste0("^", reactome_sp, ": "), replacement = "", perl = TRUE)
           TERM2GENE <- df[, c(2, 1)]
           TERM2NAME <- df[, c(2, 3)]
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["Reactome"])
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["Reactome"]])
           colnames(TERM2NAME) <- c("Term", "Name")
           TERM2GENE <- na.omit(unique(TERM2GENE))
           TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2217,7 +2216,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
           TERM2GENE <- read.gmt(gsub(".gz", "", temp))
           version <- "Harmonizome 3.0"
           TERM2NAME <- TERM2GENE[, c(1, 1)]
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["CORUM"])
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["CORUM"]])
           colnames(TERM2NAME) <- c("Term", "Name")
           TERM2GENE <- na.omit(unique(TERM2GENE))
           TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2314,7 +2313,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
           TERM2GENE <- mp_gene[, c("V5", "symbol")]
           TERM2NAME <- mp_gene[, c("V5", "MP")]
 
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["MP"])
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["MP"]])
           colnames(TERM2NAME) <- c("Term", "Name")
           TERM2GENE <- na.omit(unique(TERM2GENE))
           TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2352,7 +2351,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
           }
           TERM2GENE <- do_df[, c("DOID", "DBObjectSymbol")]
           TERM2NAME <- do_df[, c("DOID", "DOtermName")]
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["DO"])
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["DO"]])
           colnames(TERM2NAME) <- c("Term", "Name")
           TERM2GENE <- na.omit(unique(TERM2GENE))
           TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2390,7 +2389,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
 
           TERM2GENE <- hpo[, c("hpo_id", "gene_symbol")]
           TERM2NAME <- hpo[, c("hpo_id", "hpo_name")]
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["HPO"])
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["HPO"]])
           colnames(TERM2NAME) <- c("Term", "Name")
           TERM2GENE <- na.omit(unique(TERM2GENE))
           TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2419,7 +2418,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
             bg[is.na(bg[["PFAM_name"]]), "PFAM_name"] <- bg[is.na(bg[["PFAM_name"]]), "PFAM"]
             TERM2GENE <- bg[, c("PFAM", org_key)]
             TERM2NAME <- bg[, c("PFAM", "PFAM_name")]
-            colnames(TERM2GENE) <- c("Term", default_IDtypes["PFAM"])
+            colnames(TERM2GENE) <- c("Term", default_IDtypes[["PFAM"]])
             colnames(TERM2NAME) <- c("Term", "Name")
             TERM2GENE <- na.omit(unique(TERM2GENE))
             TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2444,7 +2443,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
           chr[, 2] <- paste0("chr", chr[, 2])
           TERM2GENE <- chr[, c(2, 1)]
           TERM2NAME <- chr[, c(2, 2)]
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["Chromosome"])
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["Chromosome"]])
           colnames(TERM2NAME) <- c("Term", "Name")
           TERM2GENE <- na.omit(unique(TERM2GENE))
           TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2469,7 +2468,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
             bg <- suppressMessages(select(orgdb, keys = keys(orgdb), columns = c("GENETYPE", org_key)))
             TERM2GENE <- bg[, c("GENETYPE", org_key)]
             TERM2NAME <- bg[, c("GENETYPE", "GENETYPE")]
-            colnames(TERM2GENE) <- c("Term", default_IDtypes["GeneType"])
+            colnames(TERM2GENE) <- c("Term", default_IDtypes[["GeneType"]])
             colnames(TERM2NAME) <- c("Term", "Name")
             TERM2GENE <- na.omit(unique(TERM2GENE))
             TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2516,7 +2515,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
             bg[, "Name"] <- enzyme[bg[, "ENZYME"], 2]
             TERM2GENE <- bg[, c("ENZYME", org_key)]
             TERM2NAME <- bg[, c("ENZYME", "Name")]
-            colnames(TERM2GENE) <- c("Term", default_IDtypes["Enzyme"])
+            colnames(TERM2GENE) <- c("Term", default_IDtypes[["Enzyme"]])
             colnames(TERM2NAME) <- c("Term", "Name")
             TERM2GENE <- na.omit(unique(TERM2GENE))
             TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2596,7 +2595,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
 
           TERM2GENE <- rbind(data.frame("Term" = "TF", "symbol" = tf[["Symbol"]]), data.frame("Term" = "TF cofactor", "symbol" = tfco[["Symbol"]]))
           TERM2NAME <- data.frame("Term" = c("TF", "TF cofactor"), "Name" = c("TF", "TF cofactor"))
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["TF"])
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["TF"]])
           colnames(TERM2NAME) <- c("Term", "Name")
           TERM2GENE <- na.omit(unique(TERM2GENE))
           TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2635,7 +2634,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
           ), , drop = FALSE]
           TERM2GENE <- data.frame("Term" = "SurfaceProtein", "symbol" = surfacepro[["ENTREZ.gene.symbol"]])
           TERM2NAME <- data.frame("Term" = "SurfaceProtein", "Name" = "SurfaceProtein")
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["CSPA"])
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["CSPA"]])
           colnames(TERM2NAME) <- c("Term", "Name")
           TERM2GENE <- na.omit(unique(TERM2GENE))
           TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2671,7 +2670,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
           unlink(temp)
           TERM2GENE <- data.frame("Term" = "SurfaceProtein", "symbol" = surfaceome[["UniProt.gene"]])
           TERM2NAME <- data.frame("Term" = "SurfaceProtein", "Name" = "SurfaceProtein")
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["Surfaceome"])
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["Surfaceome"]])
           colnames(TERM2NAME) <- c("Term", "Name")
           TERM2GENE <- na.omit(unique(TERM2GENE))
           TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2706,7 +2705,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
           unlink(temp)
           TERM2GENE <- data.frame("Term" = "SecretoryProtein", "entrez_id" = unlist(strsplit(spromedb$Gene_ID, ";")))
           TERM2NAME <- data.frame("Term" = "SecretoryProtein", "Name" = "SecretoryProtein")
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["SPRomeDB"])
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["SPRomeDB"]])
           colnames(TERM2NAME) <- c("Term", "Name")
           TERM2GENE <- na.omit(unique(TERM2GENE))
           TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2747,11 +2746,11 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
           close(con)
           unlink(temp)
           verseda <- verseda[grep("^>", verseda)]
-          verseda <- gsub("^>", "", verseda)
-          verseda_id <- GeneConvert(geneID = verseda, geneID_from_IDtype = "ensembl_peptide_id", geneID_to_IDtype = "symbol", species_from = db_species["VerSeDa"])
+          verseda <- gsub("^>|\\.\\d+", "", verseda)
+          verseda_id <- GeneConvert(geneID = verseda, geneID_from_IDtype = c("ensembl_peptide_id", "refseq_peptide", "refseq_peptide_predicted", "uniprot_isoform", "uniprotswissprot", "uniprotsptrembl"), geneID_to_IDtype = "symbol", species_from = db_species["VerSeDa"])
           TERM2GENE <- data.frame("Term" = "SecretoryProtein", "symbol" = unique(verseda_id$geneID_expand$symbol))
           TERM2NAME <- data.frame("Term" = "SecretoryProtein", "Name" = "SecretoryProtein")
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["VerSeDa"])
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["VerSeDa"]])
           colnames(TERM2NAME) <- c("Term", "Name")
           TERM2GENE <- na.omit(unique(TERM2GENE))
           TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2786,7 +2785,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
           TERM2GENE <- read.gmt(temp)
           version <- "v1.0"
           TERM2NAME <- TERM2GENE[, c(1, 1)]
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["TFLink"])
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["TFLink"]])
           colnames(TERM2NAME) <- c("Term", "Name")
           TERM2GENE <- na.omit(unique(TERM2GENE))
           TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2819,7 +2818,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
           TERM2GENE <- read.table(temp, header = TRUE, fill = T, sep = "\t")
           version <- "v1.0"
           TERM2NAME <- TERM2GENE[, c(1, 1)]
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["hTFtarget"])
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["hTFtarget"]])
           colnames(TERM2NAME) <- c("Term", "Name")
           TERM2GENE <- na.omit(unique(TERM2GENE))
           TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2838,8 +2837,8 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
         if (any(db == "TRRUST") && (!"TRRUST" %in% names(db_list[[sps]]))) {
           if (!sps %in% c("Homo_sapiens", "Mus_musculus")) {
             if (isTRUE(convert_species)) {
-              warning("Use the human annotation to create the hTFtarget database for ", sps, immediate. = TRUE)
-              db_species["hTFtarget"] <- "Homo_sapiens"
+              warning("Use the human annotation to create the TRRUST database for ", sps, immediate. = TRUE)
+              db_species["TRRUST"] <- "Homo_sapiens"
             } else {
               warning("hTFtarget database only support Homo_sapiens and Mus_musculus. Consider using convert_species=TRUE", immediate. = TRUE)
               stop("Stop the preparation.")
@@ -2855,7 +2854,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
           TERM2GENE <- read.table(temp, header = FALSE, fill = T, sep = "\t")[, 1:2]
           version <- "v2.0"
           TERM2NAME <- TERM2GENE[, c(1, 1)]
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["TRRUST"])
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["TRRUST"]])
           colnames(TERM2NAME) <- c("Term", "Name")
           TERM2GENE <- na.omit(unique(TERM2GENE))
           TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2889,7 +2888,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
           TERM2GENE <- read.gmt(gsub(".gz", "", temp))
           version <- "Harmonizome 3.0"
           TERM2NAME <- TERM2GENE[, c(1, 1)]
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["JASPAR"])
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["JASPAR"]])
           colnames(TERM2NAME) <- c("Term", "Name")
           TERM2GENE <- na.omit(unique(TERM2GENE))
           TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2923,7 +2922,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
           TERM2GENE <- read.gmt(gsub(".gz", "", temp))
           version <- "Harmonizome 3.0"
           TERM2NAME <- TERM2GENE[, c(1, 1)]
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["ENCODE"])
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["ENCODE"]])
           colnames(TERM2NAME) <- c("Term", "Name")
           TERM2GENE <- na.omit(unique(TERM2GENE))
           TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -2961,22 +2960,21 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
           ), version)]
           version <- version[length(version)]
           version <- regmatches(version, m = regexpr("(?<=href\\=\")\\S+(?=/\"\\>)", version, perl = TRUE))
+          # version="2023.1.Hs" "2023.2.Hs"
 
           url <- paste0("https://data.broadinstitute.org/gsea-msigdb/msigdb/release/", version, "/msigdb.v", version, ".json")
           download(url = url, destfile = temp)
-          lines <- readLines(temp, warn = FALSE)
+          lines <- paste0(readLines(temp, warn = FALSE), collapse = "")
           lines <- gsub("\"", "", lines)
-          genesets <- strsplit(lines, "\\{|\\}")
+          lines <- gsub("^\\{|\\}$", "", lines)
+          terms <- strsplit(lines, "\\},")[[1]]
           term_list <- list()
-          for (idx in seq_along(genesets)) {
-            term_content <- genesets[[idx]]
-            if (length(term_content) == 1) {
-              next
-            }
-            term_name <- trimws(gsub(":|_", " ", term_content[[1]]))
-            term_id <- trimws(regmatches(term_content[[2]], m = regexpr("(?<=systematicName:)\\S+(?=,pmid)", term_content[[2]], perl = TRUE)))
-            term_gene <- trimws(regmatches(term_content[[2]], m = regexpr(pattern = "(?<=geneSymbols:\\[)\\S+(?=\\],msigdbURL)", term_content[[2]], perl = TRUE)))
-            term_collection <- trimws(gsub(".*collection:", "", term_content[[2]]))
+          for (idx in seq_along(terms)) {
+            term_content <- terms[[idx]]
+            term_name <- trimws(gsub(":|_|\\{.*", " ", term_content))
+            term_id <- trimws(regmatches(term_content, m = regexpr("(?<=systematicName:)\\S+?(?=,)", term_content, perl = TRUE)))
+            term_gene <- trimws(regmatches(term_content, m = regexpr(pattern = "(?<=geneSymbols:\\[)\\S+?(?=\\],)", term_content, perl = TRUE)))
+            term_collection <- trimws(regmatches(term_content, m = regexpr(pattern = "(?<=collection:)\\S+?(?=\\,)", term_content, perl = TRUE)))
             term_collection <- gsub(":.*", "", term_collection)
             term_list[[idx]] <- c(id = term_id, "name" = term_name, "gene" = term_gene, "collection" = term_collection)
           }
@@ -2987,7 +2985,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
           TERM2NAME <- df[, c(1, 2, 4)]
           TERM2GENE <- df[, c(1, 3)]
           colnames(TERM2NAME) <- c("Term", "Name", "Collection")
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["MSigDB"])
+          colnames(TERM2GENE) <- c("Term", paste0(default_IDtypes[["MSigDB"]], collapse = "."))
           TERM2NAME <- na.omit(unique(TERM2NAME))
           TERM2GENE <- na.omit(unique(TERM2GENE))
 
@@ -3003,6 +3001,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
 
           for (collection in unique(TERM2NAME[["Collection"]])) {
             db_species[paste0("MSigDB_", collection)] <- db_species["MSigDB"]
+            default_IDtypes[[paste0("MSigDB_", collection)]] <- default_IDtypes[["MSigDB"]]
             TERM2NAME_sub <- TERM2NAME[TERM2NAME[["Collection"]] == collection, , drop = FALSE]
             TERM2GENE_sub <- TERM2GENE[TERM2GENE[["Term"]] %in% TERM2NAME_sub[["Term"]], , drop = FALSE]
             db_list[[db_species["MSigDB"]]][[paste0("MSigDB_", collection)]][["TERM2GENE"]] <- TERM2GENE_sub
@@ -3018,48 +3017,48 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
         }
 
         ## CellTalk ---------------------------------------------------------------------------
-        # if (any(db == "CellTalk") && (!"CellTalk" %in% names(db_list[[sps]]))) {
-        #   if (!sps %in% c("Homo_sapiens", "Mus_musculus")) {
-        #     if (isTRUE(convert_species)) {
-        #       warning("Use the human annotation to create the CellTalk database for ", sps, immediate. = TRUE)
-        #       db_species["CellTalk"] <- "Homo_sapiens"
-        #     } else {
-        #       warning("CellTalk database only support Homo_sapiens and Mus_musculus. Consider using convert_species=TRUE", immediate. = TRUE)
-        #       stop("Stop the preparation.")
-        #     }
-        #   }
-        #   message("Preparing database: CellTalk")
-        #   url <- switch(db_species["CellTalk"],
-        #     "Homo_sapiens" = "https://raw.githubusercontent.com/ZJUFanLab/CellTalkDB/master/database/human_lr_pair.rds",
-        #     "Mus_musculus" = "https://raw.githubusercontent.com/ZJUFanLab/CellTalkDB/master/database/mouse_lr_pair.rds"
-        #   )
-        #
-        #   temp <- tempfile()
-        #   download(url = url, destfile = temp)
-        #   lr <- readRDS(temp)
-        #   version <- "v1.0"
-        #
-        #   lr[["ligand_gene_symbol2"]] <- paste0("ligand_", lr[["ligand_gene_symbol"]])
-        #   lr[["receptor_gene_symbol2"]] <- paste0("receptor_", lr[["receptor_gene_symbol"]])
-        #   TERM2GENE <- rbind(
-        #     data.frame("Term" = lr[["ligand_gene_symbol2"]], "symbol" = lr[["receptor_gene_symbol"]]),
-        #     data.frame("Term" = lr[["receptor_gene_symbol2"]], "symbol" = lr[["ligand_gene_symbol"]])
-        #   )
-        #   TERM2NAME <- TERM2GENE[, c(1, 1)]
-        #   colnames(TERM2GENE) <- c("Term", default_IDtypes["CellTalk"])
-        #   colnames(TERM2NAME) <- c("Term", "Name")
-        #   TERM2GENE <- na.omit(unique(TERM2GENE))
-        #   TERM2NAME <- na.omit(unique(TERM2NAME))
-        #   db_list[[db_species["CellTalk"]]][["CellTalk"]][["TERM2GENE"]] <- TERM2GENE
-        #   db_list[[db_species["CellTalk"]]][["CellTalk"]][["TERM2NAME"]] <- TERM2NAME
-        #   db_list[[db_species["CellTalk"]]][["CellTalk"]][["version"]] <- version
-        #   if (sps == db_species["CellTalk"]) {
-        #     saveCache(db_list[[db_species["CellTalk"]]][["CellTalk"]],
-        #       key = list(version, as.character(db_species["CellTalk"]), "CellTalk"),
-        #       comment = paste0(version, " nterm:", length(TERM2NAME[[1]]), "|", db_species["CellTalk"], "-CellTalk")
-        #     )
-        #   }
-        # }
+        if (any(db == "CellTalk") && (!"CellTalk" %in% names(db_list[[sps]]))) {
+          if (!sps %in% c("Homo_sapiens", "Mus_musculus")) {
+            if (isTRUE(convert_species)) {
+              warning("Use the human annotation to create the CellTalk database for ", sps, immediate. = TRUE)
+              db_species["CellTalk"] <- "Homo_sapiens"
+            } else {
+              warning("CellTalk database only support Homo_sapiens and Mus_musculus. Consider using convert_species=TRUE", immediate. = TRUE)
+              stop("Stop the preparation.")
+            }
+          }
+          message("Preparing database: CellTalk")
+          url <- switch(db_species["CellTalk"],
+            "Homo_sapiens" = "https://raw.githubusercontent.com/ZJUFanLab/CellTalkDB/master/database/human_lr_pair.rds",
+            "Mus_musculus" = "https://raw.githubusercontent.com/ZJUFanLab/CellTalkDB/master/database/mouse_lr_pair.rds"
+          )
+
+          temp <- tempfile()
+          download(url = url, destfile = temp)
+          lr <- readRDS(temp)
+          version <- "v1.0"
+
+          lr[["ligand_gene_symbol2"]] <- paste0("ligand_", lr[["ligand_gene_symbol"]])
+          lr[["receptor_gene_symbol2"]] <- paste0("receptor_", lr[["receptor_gene_symbol"]])
+          TERM2GENE <- rbind(
+            data.frame("Term" = lr[["ligand_gene_symbol2"]], "symbol" = lr[["receptor_gene_symbol"]]),
+            data.frame("Term" = lr[["receptor_gene_symbol2"]], "symbol" = lr[["ligand_gene_symbol"]])
+          )
+          TERM2NAME <- TERM2GENE[, c(1, 1)]
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["CellTalk"]])
+          colnames(TERM2NAME) <- c("Term", "Name")
+          TERM2GENE <- na.omit(unique(TERM2GENE))
+          TERM2NAME <- na.omit(unique(TERM2NAME))
+          db_list[[db_species["CellTalk"]]][["CellTalk"]][["TERM2GENE"]] <- TERM2GENE
+          db_list[[db_species["CellTalk"]]][["CellTalk"]][["TERM2NAME"]] <- TERM2NAME
+          db_list[[db_species["CellTalk"]]][["CellTalk"]][["version"]] <- version
+          if (sps == db_species["CellTalk"]) {
+            saveCache(db_list[[db_species["CellTalk"]]][["CellTalk"]],
+              key = list(version, as.character(db_species["CellTalk"]), "CellTalk"),
+              comment = paste0(version, " nterm:", length(TERM2NAME[[1]]), "|", db_species["CellTalk"], "-CellTalk")
+            )
+          }
+        }
 
         ## CellChat ---------------------------------------------------------------------------
         if (any(db == "CellChat") && (!"CellChat" %in% names(db_list[[sps]]))) {
@@ -3111,7 +3110,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
             TERM2GENE[["symbol"]] <- tolower(TERM2GENE[["symbol"]])
           }
           TERM2NAME <- TERM2GENE[, c(1, 1)]
-          colnames(TERM2GENE) <- c("Term", default_IDtypes["CellChat"])
+          colnames(TERM2GENE) <- c("Term", default_IDtypes[["CellChat"]])
           colnames(TERM2NAME) <- c("Term", "Name")
           TERM2GENE <- na.omit(unique(TERM2GENE))
           TERM2NAME <- na.omit(unique(TERM2NAME))
@@ -3158,7 +3157,7 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
         }
       }
 
-      # ## MeSH ---------------------------------------------------------------------------
+      ## MeSH ---------------------------------------------------------------------------
       # if (any(db == "MeSH") && (!"MeSH" %in% names(db_list[[sps]]))) {
       #   message("Preparing database: MeSH")
       #   # dir.create("~/.cache/R/AnnotationHub",recursive = TRUE,showWarnings = FALSE)
@@ -3232,35 +3231,42 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
         if (is.na(default_IDtypes[term])) {
           IDtype <- colnames(TERM2GENE)[2]
         } else {
-          IDtype <- default_IDtypes[term]
+          IDtype <- default_IDtypes[[term]]
         }
-        res <- GeneConvert(
-          geneID = as.character(unique(TERM2GENE[, IDtype])),
-          geneID_from_IDtype = IDtype,
-          geneID_to_IDtype = "ensembl_id",
-          species_from = sp_from,
-          species_to = sps,
-          Ensembl_version = Ensembl_version,
-          mirror = mirror,
-          biomart = biomart,
-          max_tries = max_tries
-        )
-        if (is.null(res$geneID_res)) {
-          warning("Failed to convert species for the database: ", term, immediate. = TRUE)
-          next
+        if (grepl("MSigDB_", term)) {
+          TERM2GENE_map <- db_list[[sps]][["MSigDB"]][["TERM2GENE"]]
+          TERM2GENE <- TERM2GENE_map[TERM2GENE_map[["Term"]] %in% TERM2GENE[["Term"]], , drop = FALSE]
+          TERM2NAME <- TERM2NAME[TERM2NAME[["Term"]] %in% TERM2GENE[["Term"]], , drop = FALSE]
+        } else {
+          res <- GeneConvert(
+            geneID = as.character(unique(TERM2GENE[, 2])),
+            geneID_from_IDtype = IDtype,
+            geneID_to_IDtype = "ensembl_id",
+            species_from = sp_from,
+            species_to = sps,
+            Ensembl_version = Ensembl_version,
+            mirror = mirror,
+            biomart = biomart,
+            max_tries = max_tries
+          )
+          if (is.null(res$geneID_res)) {
+            warning("Failed to convert species for the database: ", term, immediate. = TRUE)
+            next
+          }
+          map <- res$geneID_collapse
+          TERM2GENE[["ensembl_id-converted"]] <- map[as.character(TERM2GENE[, 2]), "ensembl_id"]
+          TERM2GENE <- unnest(TERM2GENE, cols = "ensembl_id-converted", keep_empty = FALSE)
+          TERM2GENE <- TERM2GENE[, c("Term", "ensembl_id-converted")]
+          colnames(TERM2GENE) <- c("Term", "ensembl_id")
+          TERM2NAME <- TERM2NAME[TERM2NAME[["Term"]] %in% TERM2GENE[["Term"]], , drop = FALSE]
         }
-        map <- res$geneID_collapse
-        TERM2GENE[["ensembl_id-converted"]] <- map[as.character(TERM2GENE[, IDtype]), "ensembl_id"]
-        TERM2GENE <- unnest(TERM2GENE, cols = "ensembl_id-converted", keep_empty = FALSE)
-        TERM2GENE <- TERM2GENE[, c("Term", "ensembl_id-converted")]
-        colnames(TERM2GENE) <- c("Term", "ensembl_id")
-        TERM2NAME <- TERM2NAME[TERM2NAME[["Term"]] %in% TERM2GENE[["Term"]], , drop = FALSE]
+
         db_info[["TERM2GENE"]] <- unique(TERM2GENE)
         db_info[["TERM2NAME"]] <- unique(TERM2NAME)
         version <- paste0(db_info[["version"]], "(converted from ", sp_from, ")")
         db_info[["version"]] <- version
         db_list[[sps]][[term]] <- db_info
-        default_IDtypes[term] <- "ensembl_id"
+        default_IDtypes[[term]] <- "ensembl_id"
         ### save cache
         saveCache(db_list[[sps]][[term]],
           key = list(version, sps, term),
@@ -3279,26 +3285,32 @@ PrepareDB <- function(species = c("Homo_sapiens", "Mus_musculus"),
         if (is.na(default_IDtypes[term])) {
           IDtype <- colnames(TERM2GENE)[2]
         } else {
-          IDtype <- default_IDtypes[term]
+          IDtype <- default_IDtypes[[term]]
         }
-        res <- GeneConvert(
-          geneID = as.character(unique(TERM2GENE[, IDtype])),
-          geneID_from_IDtype = IDtype,
-          geneID_to_IDtype = IDtypes,
-          species_from = sps,
-          species_to = sps,
-          Ensembl_version = Ensembl_version,
-          mirror = mirror,
-          biomart = biomart,
-          max_tries = max_tries
-        )
-        if (is.null(res$geneID_res)) {
-          warning("Failed to convert species for the database: ", term, immediate. = TRUE)
-          next
+        if (grepl("MSigDB_", term)) {
+          map <- db_list[[sps]][["MSigDB"]][["TERM2GENE"]][, -1, drop = FALSE]
+          map <- aggregate(map, by = list(map[[1]]), FUN = function(x) list(unique(x)))
+          rownames(map) <- map[, 1]
+        } else {
+          res <- GeneConvert(
+            geneID = as.character(unique(TERM2GENE[, 2])),
+            geneID_from_IDtype = IDtype,
+            geneID_to_IDtype = IDtypes,
+            species_from = sps,
+            species_to = sps,
+            Ensembl_version = Ensembl_version,
+            mirror = mirror,
+            biomart = biomart,
+            max_tries = max_tries
+          )
+          if (is.null(res$geneID_res)) {
+            warning("Failed to convert species for the database: ", term, immediate. = TRUE)
+            next
+          }
+          map <- res$geneID_collapse
         }
-        map <- res$geneID_collapse
         for (type in IDtypes) {
-          TERM2GENE[[type]] <- map[as.character(TERM2GENE[, IDtype]), type]
+          TERM2GENE[[type]] <- map[as.character(TERM2GENE[, 2]), type]
           TERM2GENE <- unnest(TERM2GENE, cols = type, keep_empty = TRUE)
         }
         db_list[[sps]][[term]][["TERM2GENE"]] <- TERM2GENE
@@ -3614,7 +3626,7 @@ RunEnrichment <- function(srt = NULL, group_by = NULL, test.use = "wilcox", DE_t
 #'
 #' @inheritParams RunEnrichment
 #' @param geneScore A numeric vector that specifies the gene scores, for example, the log2(fold change) values of gene expression.
-#' @param scoreType This parameter defines the GSEA score type. Possible options are ("std", "pos", "neg"). By default ("std") the enrichment score is computed as in the original GSEA. The "pos" and "neg" score types are intended to be used for one-tailed tests (i.e. when one is interested only in positive ("pos") or negateive ("neg") enrichment).
+#' @param scoreType This parameter defines the GSEA score type. Possible options are "std", "pos", "neg". By default ("std") the enrichment score is computed as in the original GSEA. The "pos" and "neg" score types are intended to be used for one-tailed tests (i.e. when one is interested only in positive ("pos") or negateive ("neg") enrichment).
 #' @returns
 #' If input is a Seurat object, returns the modified Seurat object with the enrichment result stored in the tools slot.
 #'
diff --git a/R/SCP-plot.R b/R/SCP-plot.R
index 989abf5e..14abec00 100644
--- a/R/SCP-plot.R
+++ b/R/SCP-plot.R
@@ -1327,7 +1327,7 @@ CellDimPlot <- function(srt, group.by, reduction = NULL, dims = c(1, 2), split.b
   set.seed(seed)
 
   if (is.null(split.by)) {
-    split.by <- "All_cells"
+    split.by <- "All.groups"
     srt@meta.data[[split.by]] <- factor("")
   }
   for (i in unique(c(group.by, split.by))) {
@@ -1432,7 +1432,7 @@ CellDimPlot <- function(srt, group.by, reduction = NULL, dims = c(1, 2), split.b
     lineages_layers <- lineages_layers[!names(lineages_layers) %in% c("lab_layer", "theme_layer")]
   }
   if (!is.null(paga)) {
-    if (split.by != "All_cells") {
+    if (split.by != "All.groups") {
       stop("paga can only plot on the non-split data")
     }
     paga_layers <- PAGAPlot(srt,
@@ -1449,7 +1449,7 @@ CellDimPlot <- function(srt, group.by, reduction = NULL, dims = c(1, 2), split.b
     paga_layers <- paga_layers[!names(paga_layers) %in% c("lab_layer", "theme_layer")]
   }
   if (!is.null(velocity)) {
-    if (split.by != "All_cells") {
+    if (split.by != "All.groups") {
       stop("velocity can only plot on the non-split data")
     }
     velocity_layers <- VelocityPlot(srt,
@@ -1582,7 +1582,7 @@ CellDimPlot <- function(srt, group.by, reduction = NULL, dims = c(1, 2), split.b
         legend.position = legend.position,
         legend.direction = legend.direction
       )
-    if (split.by != "All_cells") {
+    if (split.by != "All.groups") {
       p <- p + facet_grid(. ~ split.by)
     }
 
@@ -2001,7 +2001,7 @@ FeatureDimPlot <- function(srt, features, reduction = NULL, dims = c(1, 2), spli
 
   assay <- assay %||% DefaultAssay(srt)
   if (is.null(split.by)) {
-    split.by <- "All_cells"
+    split.by <- "All.groups"
     srt@meta.data[[split.by]] <- factor("")
   }
   for (i in c(split.by)) {
@@ -2054,7 +2054,7 @@ FeatureDimPlot <- function(srt, features, reduction = NULL, dims = c(1, 2), spli
   features_meta <- features[features %in% colnames(srt@meta.data)]
   features_embedding <- features[features %in% names(embeddings)]
   if (length(intersect(features_gene, features_meta)) > 0) {
-    warning("Features appear in both gene names and metadata names:", paste0(intersect(features_gene, features_meta), collapse = ","))
+    warning("Features appear in both gene names and metadata names: ", paste0(intersect(features_gene, features_meta), collapse = ","))
   }
 
   if (isTRUE(calculate_coexp) && length(features_gene) > 0) {
@@ -2156,7 +2156,7 @@ FeatureDimPlot <- function(srt, features, reduction = NULL, dims = c(1, 2), spli
     dat_all <- cbind(dat_use, dat_exp[row.names(dat_use), features, drop = FALSE])
     dat_split <- split.data.frame(dat_all, dat_all[[split.by]])
     plist <- lapply(levels(dat_sp[[split.by]]), function(s) {
-      dat <- dat_split[[ifelse(split.by == "All_cells", 1, s)]][, , drop = FALSE]
+      dat <- dat_split[[ifelse(split.by == "All.groups", 1, s)]][, , drop = FALSE]
       for (f in features) {
         dat[, f][dat[, f] <= bg_cutoff] <- NA
         if (any(is.infinite(dat[, f]))) {
@@ -2259,7 +2259,7 @@ FeatureDimPlot <- function(srt, features, reduction = NULL, dims = c(1, 2), spli
         labs(title = title, subtitle = s, x = xlab, y = ylab) +
         scale_x_continuous(limits = c(min(dat_use[, paste0(reduction_key, dims[1])], na.rm = TRUE), max(dat_use[, paste0(reduction_key, dims[1])], na.rm = TRUE))) +
         scale_y_continuous(limits = c(min(dat_use[, paste0(reduction_key, dims[2])], na.rm = TRUE), max(dat_use[, paste0(reduction_key, dims[2])], na.rm = TRUE)))
-      if (split.by == "All_cells") {
+      if (split.by == "All.groups") {
         p <- p + facet_grid(. ~ features)
       } else {
         p <- p + facet_grid(split.by ~ features)
@@ -2451,7 +2451,7 @@ FeatureDimPlot <- function(srt, features, reduction = NULL, dims = c(1, 2), spli
     plist <- lapply(setNames(rownames(comb), rownames(comb)), function(i) {
       f <- comb[i, "feature"]
       s <- comb[i, "split"]
-      dat <- dat_split[[ifelse(split.by == "All_cells", 1, s)]][, c(colnames(dat_use), f), drop = FALSE]
+      dat <- dat_split[[ifelse(split.by == "All.groups", 1, s)]][, c(colnames(dat_use), f), drop = FALSE]
       dat[, f][dat[, f] <= bg_cutoff] <- NA
       if (any(is.infinite(dat[, f]))) {
         dat[, f][dat[, f] == max(dat[, f], na.rm = TRUE)] <- max(dat[, f][is.finite(dat[, f])], na.rm = TRUE)
@@ -2623,7 +2623,7 @@ FeatureDimPlot <- function(srt, features, reduction = NULL, dims = c(1, 2), spli
         }
       }
       if (nrow(dat) > 0) {
-        if (split.by == "All_cells") {
+        if (split.by == "All.groups") {
           p <- p + facet_grid(. ~ features)
         } else {
           p <- p + facet_grid(formula(paste0(split.by, "~features")))
@@ -2974,8 +2974,8 @@ FeatureDimPlot3D <- function(srt, features = NULL, reduction = NULL, dims = c(1,
   }
   assay <- assay %||% DefaultAssay(srt)
   if (is.null(split.by)) {
-    split.by <- "All_cells"
-    srt@meta.data[[split.by]] <- factor("All_cells")
+    split.by <- "All.cells"
+    srt@meta.data[[split.by]] <- factor("All.cells")
   }
   for (i in split.by) {
     if (!i %in% colnames(srt@meta.data)) {
@@ -3034,6 +3034,9 @@ FeatureDimPlot3D <- function(srt, features = NULL, reduction = NULL, dims = c(1,
 
   features_gene <- features[features %in% rownames(srt@assays[[assay]])]
   features_meta <- features[features %in% colnames(srt@meta.data)]
+  if (length(intersect(features_gene, features_meta)) > 0) {
+    warning("Features appear in both gene names and metadata names: ", paste0(intersect(features_gene, features_meta), collapse = ","))
+  }
 
   if (isTRUE(calculate_coexp) && length(features_gene) > 0) {
     if (length(features_meta) > 0) {
@@ -3146,9 +3149,9 @@ FeatureDimPlot3D <- function(srt, features = NULL, reduction = NULL, dims = c(1,
   split_option <- list()
   genes_option <- list()
   for (i in 0:nlevels(dat_use[[split.by]])) {
-    sp <- ifelse(i == 0, "All_cells", levels(dat_use[[split.by]])[i])
+    sp <- ifelse(i == 0, "All.cells", levels(dat_use[[split.by]])[i])
     ncells <- ifelse(i == 0, nrow(dat_use), table(dat_use[[split.by]])[sp])
-    if (i != 0 && sp == "All_cells") {
+    if (i != 0 && sp == "All.cells") {
       next
     }
     x <- list(dat_use[[paste0(reduction_key, dims[1], sp)]])
@@ -3381,6 +3384,7 @@ FeatureDimPlot3D <- function(srt, features = NULL, reduction = NULL, dims = c(1,
 #'   "Ins1", "Gcg", "Sst", "Ghrl" # Beta, Alpha, Delta, Epsilon
 #' ), group.by = "SubCellType", plot.by = "feature", stack = TRUE)
 #'
+#' library(Matrix)
 #' data <- pancreas_sub@assays$RNA@data
 #' pancreas_sub@assays$RNA@scale.data <- as.matrix(data / rowMeans(data))
 #' FeatureStatPlot(pancreas_sub,
@@ -3414,6 +3418,12 @@ FeatureStatPlot <- function(srt, stat.by, group.by = NULL, split.by = NULL, bg.b
                             legend.position = "right", legend.direction = "vertical",
                             theme_use = "theme_scp", theme_args = list(),
                             combine = TRUE, nrow = NULL, ncol = NULL, byrow = TRUE, force = FALSE, seed = 11) {
+  if (is.null(group.by)) {
+    group.by <- "All.groups" # avoid having the same name with split.by. split.by will be All.groups by default
+    xlab <- ""
+    srt[[group.by]] <- factor("All_groups")
+  }
+
   meta.data <- srt@meta.data
   meta.data[["cells"]] <- rownames(meta.data)
   assay <- assay %||% DefaultAssay(srt)
@@ -3437,9 +3447,11 @@ FeatureStatPlot <- function(srt, stat.by, group.by = NULL, split.by = NULL, bg.b
     plist <- list()
     for (g in unique(meta.reshape[[group.by]])) {
       if (length(rownames(meta.reshape)[meta.reshape[[group.by]] == g]) > 0) {
-        meta.reshape[[g]] <- meta.reshape[["Stat.by"]]
+        meta.use <- meta.reshape
+        meta.use[[group.by]] <- NULL
+        colnames(meta.use)[colnames(meta.use) == "Stat.by"] <- g
         p <- ExpressionStatPlot(
-          exp.data = exp.data, meta.data = meta.reshape, stat.by = g, group.by = "Features", split.by = split.by, bg.by = NULL, plot.by = "group", fill.by = fill.by,
+          exp.data = exp.data, meta.data = meta.use, stat.by = g, group.by = "Features", split.by = split.by, bg.by = NULL, plot.by = "group", fill.by = fill.by,
           cells = rownames(meta.reshape)[meta.reshape[[group.by]] == g], keep_empty = keep_empty, individual = individual,
           plot_type = plot_type,
           palette = palette, palcolor = palcolor, alpha = alpha,
@@ -3460,7 +3472,6 @@ FeatureStatPlot <- function(srt, stat.by, group.by = NULL, split.by = NULL, bg.b
           theme_use = theme_use, theme_args = theme_args,
           force = force, seed = seed
         )
-        meta.reshape[[g]] <- NULL
         plist <- append(plist, p)
       }
     }
@@ -3637,14 +3648,17 @@ ExpressionStatPlot <- function(exp.data, meta.data, stat.by, group.by = NULL, sp
     stop("meta.data is empty.")
   }
   if (is.null(group.by)) {
-    group.by <- "No.group.by"
+    group.by <- "All.groups"
     xlab <- ""
-    meta.data[[group.by]] <- factor("All")
+    meta.data[[group.by]] <- factor("")
   }
   if (is.null(split.by)) {
-    split.by <- "All_cells"
+    split.by <- "All.groups"
     meta.data[[split.by]] <- factor("")
   }
+  if (group.by == split.by && group.by == "All.groups") {
+    legend.position <- "none"
+  }
   for (i in unique(c(group.by, split.by, bg.by))) {
     if (!i %in% colnames(meta.data)) {
       stop(paste0(i, " is not in the meta.data."))
@@ -3704,6 +3718,9 @@ ExpressionStatPlot <- function(exp.data, meta.data, stat.by, group.by = NULL, sp
 
   features_gene <- stat.by[stat.by %in% rownames(exp.data)]
   features_meta <- stat.by[stat.by %in% colnames(meta.data)]
+  if (length(intersect(features_gene, features_meta)) > 0) {
+    warning("Features appear in both gene names and metadata names: ", paste0(intersect(features_gene, features_meta), collapse = ","))
+  }
 
   if (isTRUE(calculate_coexp) && length(features_gene) > 1) {
     if (length(features_meta) > 0) {
@@ -3826,7 +3843,7 @@ ExpressionStatPlot <- function(exp.data, meta.data, stat.by, group.by = NULL, sp
       colors <- palette_scp(stat.by, palette = palette, palcolor = palcolor)
     }
     if (fill.by == "group") {
-      if (split.by != "All_cells") {
+      if (split.by != "All.groups") {
         colors <- palette_scp(levels(dat_use[[split.by]]), palette = palette, palcolor = palcolor)
       } else {
         colors <- palette_scp(levels(dat_use[[g]]), palette = palette, palcolor = palcolor)
@@ -3878,14 +3895,14 @@ ExpressionStatPlot <- function(exp.data, meta.data, stat.by, group.by = NULL, sp
       keynm <- "Features"
     }
     if (fill.by == "group") {
-      dat[, "fill.by"] <- if (split.by == "All_cells") dat[, "group.by"] else dat[, "split.by"]
-      keynm <- ifelse(split.by == "All_cells", g, split.by)
+      dat[, "fill.by"] <- if (split.by == "All.groups") dat[, "group.by"] else dat[, "split.by"]
+      keynm <- ifelse(split.by == "All.groups", g, split.by)
     }
     if (fill.by == "expression") {
       dat[, "fill.by"] <- median_values[paste0(dat[["group.by"]], "-", dat[["split.by"]]), f]
       keynm <- "Median expression"
     }
-    if (split.by != "All_cells") {
+    if (split.by != "All.groups") {
       levels_order <- levels(dat[["split.by"]])
     } else {
       levels_order <- levels(dat[["group.by"]])
@@ -4244,6 +4261,7 @@ ExpressionStatPlot <- function(exp.data, meta.data, stat.by, group.by = NULL, sp
 
   return(plist)
 }
+
 #' Statistical plot of cells
 #'
 #' @inheritParams StatPlot
@@ -4426,12 +4444,12 @@ StatPlot <- function(meta.data, stat.by, group.by = NULL, split.by = NULL, bg.by
     stop("meta.data is empty.")
   }
   if (is.null(group.by)) {
-    group.by <- "No.group.by"
+    group.by <- "All.groups"
     xlab <- ""
-    meta.data[[group.by]] <- factor("All")
+    meta.data[[group.by]] <- factor("")
   }
   if (is.null(split.by)) {
-    split.by <- "All_cells"
+    split.by <- "All.groups"
     meta.data[[split.by]] <- factor("")
   }
 
@@ -4517,9 +4535,13 @@ StatPlot <- function(meta.data, stat.by, group.by = NULL, split.by = NULL, bg.by
     aspect.ratio <- 1
   }
 
-  if (any(group.by != "No.group.by") && plot_type %in% c("sankey", "chord", "venn", "upset")) {
+  if (any(group.by != "All.groups") && plot_type %in% c("sankey", "chord", "venn", "upset")) {
     warning("group.by is not used when plot sankey, chord, venn or upset", immediate. = TRUE)
   }
+  if (stat_type == "percent" && plot_type %in% c("sankey", "chord", "venn", "upset")) {
+    warning("stat_type is forcibly set to 'count' when plot sankey, chord, venn or upset", immediate. = TRUE)
+    stat_type <- "count"
+  }
   dat_all <- meta.data[, unique(c(stat.by, group.by, split.by, bg.by)), drop = FALSE]
   nlev <- sapply(dat_all, nlevels)
   nlev <- nlev[nlev > 100]
@@ -4578,12 +4600,12 @@ StatPlot <- function(meta.data, stat.by, group.by = NULL, split.by = NULL, bg.by
       sp <- comb[i, "split_name"]
       g <- comb[i, "group_name"]
       single_group <- comb[[i, "group_element"]]
-      colors_use <- colors[names(colors) %in% dat_split[[ifelse(split.by == "All_cells", 1, sp)]][[stat.by]]]
-      if (any(is.na(dat_split[[ifelse(split.by == "All_cells", 1, sp)]][[stat.by]])) && isTRUE(NA_stat)) {
+      colors_use <- colors[names(colors) %in% dat_split[[ifelse(split.by == "All.groups", 1, sp)]][[stat.by]]]
+      if (any(is.na(dat_split[[ifelse(split.by == "All.groups", 1, sp)]][[stat.by]])) && isTRUE(NA_stat)) {
         colors_use <- c(colors_use, colors["NA"])
       }
       if (stat_type == "percent") {
-        dat_use <- dat_split[[ifelse(split.by == "All_cells", 1, sp)]] %>%
+        dat_use <- dat_split[[ifelse(split.by == "All.groups", 1, sp)]] %>%
           xtabs(formula = paste0("~", stat.by, "+", g), addNA = NA_stat) %>%
           as.data.frame() %>%
           group_by(across(all_of(g)), .drop = FALSE) %>%
@@ -4592,7 +4614,7 @@ StatPlot <- function(meta.data, stat.by, group.by = NULL, split.by = NULL, bg.by
           mutate(value = Freq / groupn) %>%
           as.data.frame()
       } else {
-        dat_use <- dat_split[[ifelse(split.by == "All_cells", 1, sp)]] %>%
+        dat_use <- dat_split[[ifelse(split.by == "All.groups", 1, sp)]] %>%
           xtabs(formula = paste0("~", stat.by, "+", g), addNA = NA_stat) %>%
           as.data.frame() %>%
           mutate(value = Freq)
@@ -4817,7 +4839,7 @@ StatPlot <- function(meta.data, stat.by, group.by = NULL, split.by = NULL, bg.by
       par(mfrow = c(nrow, ncol))
     }
     for (sp in levels(dat_all[[split.by]])) {
-      dat_use <- dat_split[[ifelse(split.by == "All_cells", 1, sp)]]
+      dat_use <- dat_split[[ifelse(split.by == "All.groups", 1, sp)]]
       if (plot_type == "venn") {
         check_R(c("ggVennDiagram", "sf"))
         dat_list <- as.list(dat_use[, stat.by])
@@ -5091,12 +5113,12 @@ FeatureCorPlot <- function(srt, features, group.by = NULL, split.by = NULL, cell
   }
   assay <- assay %||% DefaultAssay(srt)
   if (is.null(split.by)) {
-    split.by <- "All_cells"
+    split.by <- "All.groups"
     srt@meta.data[[split.by]] <- factor("")
   }
   if (is.null(group.by)) {
-    group.by <- "All_cells"
-    srt@meta.data[[group.by]] <- factor("All_cells")
+    group.by <- "All.groups"
+    srt@meta.data[[group.by]] <- factor("")
   }
   for (i in c(split.by, group.by)) {
     if (!i %in% colnames(srt@meta.data)) {
@@ -5127,6 +5149,9 @@ FeatureCorPlot <- function(srt, features, group.by = NULL, split.by = NULL, cell
 
   features_gene <- features[features %in% rownames(srt@assays[[assay]])]
   features_meta <- features[features %in% colnames(srt@meta.data)]
+  if (length(intersect(features_gene, features_meta)) > 0) {
+    warning("Features appear in both gene names and metadata names: ", paste0(intersect(features_gene, features_meta), collapse = ","))
+  }
 
   if (isTRUE(calculate_coexp) && length(features_gene) > 0) {
     if (length(features_meta) > 0) {
@@ -5513,7 +5538,7 @@ FeatureCorPlot <- function(srt, features, group.by = NULL, split.by = NULL, cell
 #' @importFrom patchwork wrap_plots
 #' @importFrom methods slot
 #' @export
-CellDensityPlot <- function(srt, features, group.by, split.by = NULL, assay = NULL, slot = "data",
+CellDensityPlot <- function(srt, features, group.by = NULL, split.by = NULL, assay = NULL, slot = "data",
                             flip = FALSE, reverse = FALSE, x_order = c("value", "rank"),
                             decreasing = NULL, palette = "Paired", palcolor = NULL,
                             cells = NULL, keep_empty = FALSE,
@@ -5531,10 +5556,17 @@ CellDensityPlot <- function(srt, features, group.by, split.by = NULL, assay = NU
   if (!inherits(features, "character")) {
     stop("'features' is not a character vectors")
   }
+  if (is.null(group.by)) {
+    group.by <- "All.groups"
+    srt@meta.data[[group.by]] <- factor("")
+  }
   if (is.null(split.by)) {
-    split.by <- "All_cells"
+    split.by <- "All.groups"
     srt@meta.data[[split.by]] <- factor("")
   }
+  if (group.by == split.by & group.by == "All.groups") {
+    legend.position <- "none"
+  }
   for (i in c(group.by, split.by)) {
     if (!i %in% colnames(srt@meta.data)) {
       stop(paste0(i, " is not in the meta.data of srt object."))
@@ -5554,6 +5586,9 @@ CellDensityPlot <- function(srt, features, group.by, split.by = NULL, assay = NU
 
   features_gene <- features[features %in% rownames(srt@assays[[assay]])]
   features_meta <- features[features %in% colnames(srt@meta.data)]
+  if (length(intersect(features_gene, features_meta)) > 0) {
+    warning("Features appear in both gene names and metadata names: ", paste0(intersect(features_gene, features_meta), collapse = ","))
+  }
 
   if (length(features_gene) > 0) {
     dat_gene <- t(slot(srt@assays[[assay]], slot)[features_gene, , drop = FALSE])
@@ -5652,7 +5687,7 @@ CellDensityPlot <- function(srt, features, group.by, split.by = NULL, assay = NU
             limits = limits, trans = y.trans, n.breaks = y.nbreaks,
             expand = c(0, 0)
           )
-        if (split.by != "All_cells") {
+        if (split.by != "All.groups") {
           p <- p + facet_grid(. ~ split.by)
         }
         p <- p + labs(title = title, subtitle = subtitle, x = f, y = g)
@@ -7813,8 +7848,8 @@ GroupHeatmap <- function(srt, features = NULL, group.by = NULL, split.by = NULL,
 
   assay <- assay %||% DefaultAssay(srt)
   if (is.null(group.by)) {
-    srt@meta.data[["No.group.by"]] <- factor("")
-    group.by <- "No.group.by"
+    srt@meta.data[["All.groups"]] <- factor("")
+    group.by <- "All.groups"
   }
   if (any(!group.by %in% colnames(srt@meta.data))) {
     stop(group.by[!group.by %in% colnames(srt@meta.data)], " is not in the meta data of the Seurat object.")
@@ -8126,7 +8161,7 @@ GroupHeatmap <- function(srt, features = NULL, group.by = NULL, split.by = NULL,
         column_split_list[[cell_group]] <- length(unique(column_split_list[[cell_group]]))
       }
     }
-    if (cell_group != "No.group.by") {
+    if (cell_group != "All.groups") {
       funbody <- paste0(
         "
         grid.rect(gp = gpar(fill = palette_scp(", paste0("c('", paste0(levels(srt@meta.data[[cell_group]]), collapse = "','"), "')"), ",palette = '", group_palette[i], "',palcolor=c(", paste0("'", paste0(group_palcolor[[i]], collapse = "','"), "'"), "))[nm]))
@@ -8177,7 +8212,7 @@ GroupHeatmap <- function(srt, features = NULL, group.by = NULL, split.by = NULL,
 
   for (i in seq_along(raw.group.by)) {
     cell_group <- raw.group.by[i]
-    if (cell_group != "No.group.by") {
+    if (cell_group != "All.groups") {
       lgd[[cell_group]] <- Legend(
         title = cell_group, labels = levels(srt@meta.data[[cell_group]]),
         legend_gp = gpar(fill = palette_scp(levels(srt@meta.data[[cell_group]]), palette = raw.group_palette[i], palcolor = group_palcolor[[i]])), border = TRUE
@@ -8731,9 +8766,9 @@ GroupHeatmap <- function(srt, features = NULL, group.by = NULL, split.by = NULL,
       matrix = if (flip) t(mat_list[[cell_group]]) else mat_list[[cell_group]],
       col = colors,
       layer_fun = getFunction("layer_fun", where = environment()),
-      row_title = row_title %||% if (flip) ifelse(cell_group != "No.group.by", cell_group, "") else character(0),
+      row_title = row_title %||% if (flip) ifelse(cell_group != "All.groups", cell_group, "") else character(0),
       row_title_side = row_title_side,
-      column_title = column_title %||% if (flip) character(0) else ifelse(cell_group != "No.group.by", cell_group, ""),
+      column_title = column_title %||% if (flip) character(0) else ifelse(cell_group != "All.groups", cell_group, ""),
       column_title_side = column_title_side,
       row_title_rot = row_title_rot,
       column_title_rot = column_title_rot,
@@ -8989,8 +9024,8 @@ FeatureHeatmap <- function(srt, features = NULL, cells = NULL, group.by = NULL,
     stop("feature_split must be the same length as features")
   }
   if (is.null(group.by)) {
-    srt@meta.data[["No.group.by"]] <- factor("")
-    group.by <- "No.group.by"
+    srt@meta.data[["All.groups"]] <- factor("")
+    group.by <- "All.groups"
   }
   if (any(!group.by %in% colnames(srt@meta.data))) {
     stop(group.by[!group.by %in% colnames(srt@meta.data)], " is not in the meta data of the Seurat object.")
@@ -9254,7 +9289,7 @@ FeatureHeatmap <- function(srt, features = NULL, cells = NULL, group.by = NULL,
         column_split_list[[cell_group]] <- length(unique(column_split_list[[cell_group]]))
       }
     }
-    if (cell_group != "No.group.by") {
+    if (cell_group != "All.groups") {
       funbody <- paste0(
         "
         grid.rect(gp = gpar(fill = palette_scp(", paste0("c('", paste0(levels(srt@meta.data[[cell_group]]), collapse = "','"), "')"), ",palette = '", group_palette[i], "',palcolor=c(", paste0("'", paste0(group_palcolor[[i]], collapse = "','"), "'"), "))[nm]))
@@ -9304,7 +9339,7 @@ FeatureHeatmap <- function(srt, features = NULL, cells = NULL, group.by = NULL,
   }
   for (i in seq_along(raw.group.by)) {
     cell_group <- raw.group.by[i]
-    if (cell_group != "No.group.by") {
+    if (cell_group != "All.groups") {
       lgd[[cell_group]] <- Legend(
         title = cell_group, labels = levels(srt@meta.data[[cell_group]]),
         legend_gp = gpar(fill = palette_scp(levels(srt@meta.data[[cell_group]]), palette = raw.group_palette[i], palcolor = group_palcolor[[i]])), border = TRUE
@@ -9664,9 +9699,9 @@ FeatureHeatmap <- function(srt, features = NULL, cells = NULL, group.by = NULL,
       name = cell_group,
       matrix = if (flip) t(mat_list[[cell_group]]) else mat_list[[cell_group]],
       col = colors,
-      row_title = row_title %||% if (flip) ifelse(cell_group != "No.group.by", cell_group, "") else character(0),
+      row_title = row_title %||% if (flip) ifelse(cell_group != "All.groups", cell_group, "") else character(0),
       row_title_side = row_title_side,
-      column_title = column_title %||% if (flip) character(0) else ifelse(cell_group != "No.group.by", cell_group, ""),
+      column_title = column_title %||% if (flip) character(0) else ifelse(cell_group != "All.groups", cell_group, ""),
       column_title_side = column_title_side,
       row_title_rot = row_title_rot,
       column_title_rot = column_title_rot,
@@ -10040,12 +10075,12 @@ CellCorHeatmap <- function(srt_query, srt_ref = NULL, bulk_ref = NULL,
 
   cell_groups <- list()
   if (is.null(query_group)) {
-    srt_query@meta.data[["No.group.by"]] <- factor("")
-    query_group <- "No.group.by"
+    srt_query@meta.data[["All.groups"]] <- factor("")
+    query_group <- "All.groups"
   }
   if (is.null(ref_group)) {
-    srt_ref@meta.data[["No.group.by"]] <- factor("")
-    ref_group <- "No.group.by"
+    srt_ref@meta.data[["All.groups"]] <- factor("")
+    ref_group <- "All.groups"
   }
   if (!is.factor(srt_query[[query_group, drop = TRUE]])) {
     srt_query@meta.data[[query_group]] <- factor(srt_query[[query_group, drop = TRUE]], levels = unique(srt_query[[query_group, drop = TRUE]]))
@@ -10144,7 +10179,7 @@ CellCorHeatmap <- function(srt_query, srt_ref = NULL, bulk_ref = NULL,
   lgd[["ht"]] <- Legend(title = exp_name, col_fun = colors, border = TRUE)
 
   ha_query_list <- NULL
-  if (query_group != "No.group.by") {
+  if (query_group != "All.groups") {
     if (isFALSE(query_collapsing) && ((isFALSE(flip) & isTRUE(cluster_rows)) || (isTRUE(flip) & isTRUE(cluster_columns)))) {
       query_cell_annotation <- c(query_group, query_cell_annotation)
     } else {
@@ -10182,7 +10217,7 @@ CellCorHeatmap <- function(srt_query, srt_ref = NULL, bulk_ref = NULL,
   }
 
   ha_ref_list <- NULL
-  if (ref_group != "No.group.by") {
+  if (ref_group != "All.groups") {
     if (isFALSE(ref_collapsing) && ((isFALSE(flip) & isTRUE(cluster_columns)) || (isTRUE(flip) & isTRUE(cluster_rows)))) {
       ref_cell_annotation <- c(ref_group, ref_cell_annotation)
     } else {
diff --git a/inst/python/SCP_analysis.py b/inst/python/SCP_analysis.py
index a7763de8..56201f92 100644
--- a/inst/python/SCP_analysis.py
+++ b/inst/python/SCP_analysis.py
@@ -265,13 +265,18 @@ def SCVELO(adata=None, h5ad=None, group_by=None, palette=None,
           if m != "dynamical":
             scv.tl.rank_velocity_genes(adata, vkey=vkey, groupby=group_by)
             adata.var[vkey+"_score"]=adata.var["spearmans_score"]
-            df = scv.get_df(adata.uns["rank_velocity_genes"]["names"])
-            adata.uns["rank_"+vkey+"_genes"]=df
+            df1 = scv.get_df(adata.uns["rank_velocity_genes"]["names"])
+            adata.uns["rank_"+vkey+"_genenames"]=df1
+            df2 = scv.get_df(adata.uns["rank_velocity_genes"]["scores"])
+            adata.uns["rank_"+vkey+"_genescores"]=df2
+            del adata.uns["rank_velocity_genes"]
           else:
             scv.tl.rank_dynamical_genes(adata, groupby=group_by)
-            df = scv.get_df(adata.uns['rank_dynamical_genes']['names'])
-            adata.uns["rank_"+vkey+"_genes"]=df
-            
+            df1 = scv.get_df(adata.uns['rank_dynamical_genes']['names'])
+            adata.uns["rank_"+vkey+"_genenames"]=df1
+            df2 = scv.get_df(adata.uns['rank_dynamical_genes']['scores'])
+            adata.uns["rank_"+vkey+"_genescores"]=df2
+            del adata.uns["rank_dynamical_genes"]
 
           for cluster in df.columns:
             #df[0:1].values.ravel()[:12] ### by row
diff --git a/man/CellDensityPlot.Rd b/man/CellDensityPlot.Rd
index 4ad503a2..38733b4e 100644
--- a/man/CellDensityPlot.Rd
+++ b/man/CellDensityPlot.Rd
@@ -7,7 +7,7 @@
 CellDensityPlot(
   srt,
   features,
-  group.by,
+  group.by = NULL,
   split.by = NULL,
   assay = NULL,
   slot = "data",
diff --git a/man/FeatureStatPlot.Rd b/man/FeatureStatPlot.Rd
index be184270..66989ceb 100644
--- a/man/FeatureStatPlot.Rd
+++ b/man/FeatureStatPlot.Rd
@@ -281,6 +281,7 @@ FeatureStatPlot(pancreas_sub, stat.by = c(
   "Ins1", "Gcg", "Sst", "Ghrl" # Beta, Alpha, Delta, Epsilon
 ), group.by = "SubCellType", plot.by = "feature", stack = TRUE)
 
+library(Matrix)
 data <- pancreas_sub@assays$RNA@data
 pancreas_sub@assays$RNA@scale.data <- as.matrix(data / rowMeans(data))
 FeatureStatPlot(pancreas_sub,
diff --git a/man/PrepareDB.Rd b/man/PrepareDB.Rd
index d05e4862..7cb6c109 100644
--- a/man/PrepareDB.Rd
+++ b/man/PrepareDB.Rd
@@ -8,8 +8,8 @@ PrepareDB(
   species = c("Homo_sapiens", "Mus_musculus"),
   db = c("GO", "GO_BP", "GO_CC", "GO_MF", "KEGG", "WikiPathway", "Reactome", "CORUM",
     "MP", "DO", "HPO", "PFAM", "CSPA", "Surfaceome", "SPRomeDB", "VerSeDa", "TFLink",
-    "hTFtarget", "TRRUST", "JASPAR", "ENCODE", "MSigDB", "CellChat", "Chromosome",
-    "GeneType", "Enzyme", "TF"),
+    "hTFtarget", "TRRUST", "JASPAR", "ENCODE", "MSigDB", "CellTalk", "CellChat",
+    "Chromosome", "GeneType", "Enzyme", "TF"),
   db_IDtypes = c("symbol", "entrez_id", "ensembl_id"),
   db_version = "latest",
   db_update = FALSE,
@@ -32,7 +32,7 @@ PrepareDB(
 Default is c("GO", "GO_BP", "GO_CC", "GO_MF", "KEGG", "WikiPathway", "Reactome",
 "CORUM", "MP", "DO", "HPO", "PFAM", "CSPA", "Surfaceome", "SPRomeDB", "VerSeDa",
 "TFLink", "hTFtarget", "TRRUST", "JASPAR", "ENCODE", "MSigDB",
-"CellChat", "Chromosome", "GeneType", "Enzyme", "TF").}
+"CellTalk", "CellChat", "Chromosome", "GeneType", "Enzyme", "TF").}
 
 \item{db_IDtypes}{A character vector specifying the desired ID types to be used for gene identifiers in the gene annotation databases. Default is c("symbol", "entrez_id", "ensembl_id").}
 
diff --git a/man/RunGSEA.Rd b/man/RunGSEA.Rd
index 01f4406c..c790f650 100644
--- a/man/RunGSEA.Rd
+++ b/man/RunGSEA.Rd
@@ -48,7 +48,7 @@ If not specified, the \code{geneID} and \code{geneID_groups} arguments must be p
 
 \item{DE_threshold}{A character vector specifying the filter condition for differential expression analysis. This argument is only used if \code{srt} is specified.}
 
-\item{scoreType}{This parameter defines the GSEA score type. Possible options are ("std", "pos", "neg"). By default ("std") the enrichment score is computed as in the original GSEA. The "pos" and "neg" score types are intended to be used for one-tailed tests (i.e. when one is interested only in positive ("pos") or negateive ("neg") enrichment).}
+\item{scoreType}{This parameter defines the GSEA score type. Possible options are "std", "pos", "neg". By default ("std") the enrichment score is computed as in the original GSEA. The "pos" and "neg" score types are intended to be used for one-tailed tests (i.e. when one is interested only in positive ("pos") or negateive ("neg") enrichment).}
 
 \item{geneID}{A character vector specifying the gene IDs.}