diff --git a/R/SCP-cellqc.R b/R/SCP-cellqc.R
index 53939fec..92857644 100644
--- a/R/SCP-cellqc.R
+++ b/R/SCP-cellqc.R
@@ -291,7 +291,7 @@ isOutlier <- function(x, nmads = 2.5, constant = 1.4826, type = c("both", "lower
 #'   plot_type = "upset", stat_level = "Fail"
 #' )
 #' table(ifnb_sub$CellQC)
-#' @importFrom Seurat Assays as.SingleCellExperiment PercentageFeatureSet WhichCells SplitObject
+#' @importFrom Seurat Assays as.SingleCellExperiment PercentageFeatureSet WhichCells SplitObject AddMetaData
 #' @importFrom stats loess predict aggregate
 #' @importFrom Matrix colSums t
 #' @export
@@ -337,6 +337,7 @@ RunCellQC <- function(srt, assay = "RNA", batch = NULL,
   if (!paste0("nFeature_", assay) %in% colnames(srt@meta.data)) {
     srt@meta.data[[paste0("nFeature_", assay)]] <- colSums(srt[[assay]]@counts > 0)
   }
+  srt_raw <- srt
   if (!is.null(batch)) {
     srtList <- SplitObject(srt, split.by = batch)
   } else {
@@ -475,9 +476,9 @@ RunCellQC <- function(srt, assay = "RNA", batch = NULL,
     }
     srtList[[i]] <- srt
   }
-  if (length(srtList) > 1) {
-    return(Reduce(merge, srtList))
-  } else {
-    return(srtList[[1]])
-  }
+  cells <- unlist(lapply(srtList, colnames))
+  srt_raw <- srt_raw[, cells]
+  meta.data <- do.call(rbind.data.frame, unname(lapply(srtList, function(x) x@meta.data)))
+  srt_raw <- AddMetaData(srt_raw, metadata = meta.data)
+  return(srt_raw)
 }
diff --git a/R/SCP-plot.R b/R/SCP-plot.R
index 4122b87e..8ddf1e4d 100644
--- a/R/SCP-plot.R
+++ b/R/SCP-plot.R
@@ -13121,8 +13121,8 @@ EnrichmentPlot <- function(srt, db = "GO_BP", group_by = NULL, group_use = NULL,
         guides(linewidth = guide_legend(override.aes = list(alpha = 1, color = "grey"), order = 2)) +
         scale_fill_manual(
           name = switch(enrichmap_label,
-            "term" = "Gene:",
-            "gene" = "Term:"
+            "term" = "Feature:",
+            "feature" = "Term:"
           ),
           values = palette_scp(levels(df_nodes[["clusters"]]), palette = palette, palcolor = palcolor),
           labels = if (enrichmap_label == "term") df_keyword2[levels(df_nodes[["clusters"]]), "label"] else df_keyword1[levels(df_nodes[["clusters"]]), "label"],