https:\/\/cf.10xgenomics.com\/samples\/cell\/pbmc3k\/pbmc3k_filtered_gene_bc_matrices.tar.gz<\/a><\/p>\ntar -zxvf pbmc3k_filtered_gene_bc_matrices.tar.gz<\/p>\n
#\u5206\u6790\u6240\u9700\u8981\u7684\u6587\u4ef6\u4e09\u4e2a\u5728filtered_gene_bc_matrices\/hg19\/<\/p>\n
![\"Dingtalk_20230115121454\"](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/dingtalk_20230115121454.jpeg?resize=640%2C95\")
<\/p>\n
pbmc.data <- Read10X(data.dir = “filtered_gene_bc_matrices\/hg19\/”)<\/p>\n
# Initialize the Seurat object with the raw (non-normalized data).<\/p>\n
pbmc <- CreateSeuratObject(counts = pbmc.data, project = “pbmc3k”, min.cells = 3, min.features = 200)<\/p>\n
# Normalizing the data<\/p>\n
pbmc <- NormalizeData(pbmc, normalization.method = “LogNormalize”, scale.factor = 10000)<\/p>\n
#Identification of highly variable features<\/p>\n
pbmc <- FindVariableFeatures(pbmc, selection.method = “vst”, nfeatures = 2000)<\/p>\n
all.genes <- rownames(pbmc)<\/p>\n
pbmc <- ScaleData(pbmc, features = all.genes)<\/p>\n
#Run non-linear dimensional reduction (UMAP\/tSNE)<\/p>\n
pbmc <- RunUMAP(pbmc, dims = 1:10)<\/p>\n
#\u5f00\u59cb\u7ed8\u56fe<\/p>\n
colourCount = length(unique(pbmc@meta.data$celltype))<\/p>\n
getPalette = colorRampPalette(brewer.pal(9, “Set1”))<\/p>\n
celltype_colors <- getPalette(colourCount)<\/p>\n
#\u63d0\u53d6\u6837\u672c\u548c\u7ec6\u80de\u6570\u636e\uff0c\u5e76\u4e14\u8fdb\u884c\u957f\u5bbd\u6570\u636e\u8f6c\u6362<\/p>\n
plotC <- table(pbmc@meta.data$sample, pbmc@meta.data$celltype) %>% melt()<\/p>\n
colnames(plotC) <- c(“Sample”, “CellType”,”Number”)<\/p>\n
#\u7ed8\u5236\u6bcf\u4e2a\u7ec4\u7ec7\u4e2d\u7ec6\u80de\u6570\u76ee\u67f1\u72b6\u56fe<\/p>\n
pC1 <- ggplot(data = data_plotC, aes(x = Sample, y = Number, fill = CellType)) +<\/p>\n
geom_bar(stat = “identity”, width=0.8,aes(group=CellType),position=”stack”)+<\/p>\n
scale_fill_manual(values=celltype_colors) +<\/p>\n
theme_bw()+<\/p>\n
theme(panel.grid =element_blank()) +<\/p>\n
labs(x=””,y=”Average number”)+<\/p>\n
theme(axis.text = element_text(size=12, colour = “black”))+<\/p>\n
theme(axis.title.y = element_text(size=12, colour = “black”))+<\/p>\n
theme(panel.border = element_rect(size = 1, linetype = “solid”, colour = “black”))+<\/p>\n
theme(axis.text.x = element_text(angle = 45,hjust = 0.8, vjust = 0.6))<\/p>\n
#\u7ed8\u5236\u6bcf\u4e2a\u7ec4\u7ec7\u4e2d\u7ec6\u80de\u6bd4\u4f8b\u67f1\u72b6\u56fe<\/p>\n
pC2 <- ggplot(data = plotC, aes(x = Sample, y = Number, fill = CellType)) +<\/p>\n
geom_bar(stat = “identity”, width=0.8,aes(group=CellType),position=”fill”)+<\/p>\n
scale_fill_manual(values=celltype_colors) +<\/p>\n
theme_bw()+<\/p>\n
theme(panel.grid =element_blank()) +<\/p>\n
labs(x=””,y=”Cell proportion”)+<\/p>\n
scale_y_continuous(labels = percent)+ ####\u7528\u6765\u5c06y\u8f74\u79fb\u52a8\u4f4d\u7f6e<\/p>\n
theme(axis.text = element_text(size=12, colour = “black”))+<\/p>\n
theme(axis.title.y = element_text(size=12, colour = “black”))+<\/p>\n
theme(panel.border = element_rect(size = 1, linetype = “solid”, colour = “black”))+<\/p>\n
theme(axis.text.x = element_text(angle = 45,hjust = 0.8, vjust = 0.6))#\u8ba9\u6a2a\u8f74\u4e0a\u7684\u6807\u7b7e\u503e\u659c45\u5ea6<\/p>\n
#\u4e24\u4e2a\u56fe\u7247\u8fdb\u884c\u62fc\u56fe<\/p>\n
pC <- pC1 + pC2 + plot_layout(ncol = 2, widths = c(1,1),guides = ‘collect’)<\/p>\n
#\u4fdd\u5b58\u56fe\u7247<\/p>\n
ggsave(pC,file=”plotC.pdf”,width = 7, height = 5)<\/p>\n
![\"Dingtalk_20230115125005\"](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/dingtalk_20230115125005.jpeg?resize=640%2C460\")
<\/p>\n","protected":false},"excerpt":{"rendered":"
\u4eca\u5929\u7ed8\u5236\u4e00\u4e0b\u5355\u7ec6\u80de\u5206\u6790\u7ed3\u679c\u7ec4\u7ec7\u4e2d\u7ec6\u80de\u6570\u91cf\u548c\u7ec6\u80de\u6bd4\u4f8b\u67f1\u72b6\u56fe\uff0c\u4ee3\u7801\u5982\u4e0b\uff1a \u5b89\u88c5\u9700\u8981\u7684R\u5305 install.pack […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":""},"categories":[1],"tags":[],"jetpack_featured_media_url":"","_links":{"self":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/28656"}],"collection":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=28656"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/28656\/revisions"}],"predecessor-version":[{"id":28659,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/28656\/revisions\/28659"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=28656"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=28656"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=28656"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}