Cell type classification using an integrated reference

Question

[kayah]

I'm following the tutorial of scRNA-seq cell type annotation now(https://satijalab.org/seurat/articles/integration_mapping), but wondered that how Large Seurat file looks like. Because I want to anchor my data(which is analyzing nowdays..) to reference data but it didn't work well because of file format. for example I'm wondering that where is gene name. When I write a code like "View([e-mail])" then I can see barcode and nCount_RNA, nFeature_RNA, percent.mt, clusters but I can't find any gene name here. So my questions are how can I see gene name in my Seurat data set, and also what file format is need to run <<FindTransferAnchors>>??? this is how my code looks like. and my trouble site is last line, which try to look for anchors. Thank you!

library(Seurat)
library(SeuratObject)
library(dplyr)
library(tidyverse)
library(patchwork) 
WAT_M_Y <- Read10X(data.dir = "~/Desktop/GSE137869/WAT-M-Y/") 

colnames(WAT_M_Y) <- gsub("_", "-", colnames(WAT_M_Y))

WAT_M_Y <- CreateSeuratObject(counts = WAT_M_Y, 
                              project = "WAT_M_Y",
                              min.cells = 3,
                              min.features = 200)

WAT_M_Y [["percent.mt"]]<-PercentageFeatureSet(WAT_M_Y, pattern =  "^Mt-")


WAT_M_O <- Read10X(data.dir = 
                          "~/Desktop/GSE137869/WAT-M-O/") 
colnames(WAT_M_O) <- gsub("_", "-", colnames(WAT_M_O))
WAT_M_O <- CreateSeuratObject(counts = WAT_M_O, 
                              project = "WAT_M_O",
                              min.cells = 3,
                              min.features = 200)

WAT_M_O [["percent.mt"]]<-PercentageFeatureSet(WAT_M_O, pattern =  "^Mt-")
male_wat <- merge(WAT_M_Y, y = c(WAT_M_O), 
             add.cell.ids = c("WAT_M_Y", "WAT_M_O"),
             project = "male_wat")

[e-mail]$type <- c(rep("male_young", ncol(WAT_M_Y)),
                        rep("male_old", ncol(WAT_M_O)))

show(male_wat)
male_wat <- NormalizeData(male_wat)
male_wat <- FindVariableFeatures(male_wat)
male_wat <- ScaleData(male_wat)
male_wat <- RunPCA(male_wat)
male_wat <- FindNeighbors(male_wat, dims = 1:30, reduction = "pca")
male_wat <- FindClusters(male_wat, resolution = 0.6, cluster.name = "unintegrated_clusters")
male_wat <- RunUMAP(male_wat, dims = 1:30, reduction = "pca", reduction.name = "umap.unintegrated")
DimPlot(male_wat, reduction = "umap.unintegrated", group.by = c("type", "seurat_clusters"))


male_wat <- IntegrateLayers(object = male_wat, method = CCAIntegration, orig.reduction = "pca", new.reduction = "integrated.cca",
                        verbose = FALSE)
# re-join layers after integration
male_wat[["RNA"]] <- JoinLayers(male_wat[["RNA"]])

male_wat <- FindNeighbors(male_wat, reduction = "integrated.cca", dims = 1:30)
male_wat <- FindClusters(male_wat, resolution = 1)
male_wat <- RunUMAP(male_wat, dims = 1:30, reduction = "integrated.cca")
View([e-mail])
# Visualization
DimPlot(male_wat, reduction = "umap", group.by = c("type"))
wat_reference <- readRDS('~/Desktop/GSE137869/mouse_all_lite.rds')
wat_male.anchors <- FindTransferAnchors(reference = wat_reference, query = male_wat, dims = 1:30,
                                        reference.reduction = "cca")

[bk11]

how can I see gene name in my Seurat data set

To view genes names, you can run following code-

rownames(male_wat)