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devtools::install_github("digitalcytometry/cytotrace2", subdir = "cytotrace2_r") library(CytoTRACE2)# 出现报错Using github PAT from envvar GITHUB_TOKENDownloading GitHub repo digitalcytometry/cytotrace2@HEADError in utils::download.file(url, path, method = method, quiet = quiet, : download from &#... 阅7 转0 评0 公众公开 24-04-26 09:12 |
scRNA|使用scMetabolism完成单细胞代谢激活分数估计。#可以计算celltype均值，然后绘制df <- countexp.Seurat@meta.data#19列开始是代谢通路的得分，按照celltype计算均值avg_df = aggregate(df[,19:ncol(df)], list(df$celltype), mean)#热图需要转为矩阵avg_df <- avg_df %>% select(1:20) %>% #展示前20个 column_to_rownames(... 阅3 转0 评0 公众公开 24-04-24 09:12 |
riskScore_cli2$pathologic_T2 <- riskScore_cli2$pathologic_T# str_replaceriskScore_cli2$pathologic_T2 <- str_replace(riskScore_cli2$pathologic_T2, "T1[a-d-c-d]?", "T1")#str_detectriskScore_cli2$pathologic_T2 <- ifelse(str_detect(riskScore_cli2$pathologic_T2, "^T1"), "T1"... 阅5 转0 评0 公众公开 24-04-19 09:06 |
# 备份原seurat_clusters结果sce.T$seurat_clusters2 <- sce.T$seurat_clusters# 将注释结果赋值给 seurat_clusterssce.T$seurat_clusters <- sce.T$celltype3.2 图形美化。T$seurat_clusters <- factor(sce.T$seurat_clusters , levels = c("CD4+ Effector" , "CD8A+ NK-like" , "CD8A+ Tissue-resident&qu... 阅9 转0 评0 公众公开 24-04-10 09:09 |
RNAseq-ML | SuperPC 算法构建预后模型 并预测。cv.fit <- superpc.cv(fit,data, n.threshold = 20,#default ,Number of thresholds to consider n.fold = 5, #Number of cross-validation folds n.components=3, min.features=5,#default max.features=nrow(data$x), compute.fullcv= TRUE, compute.preval=TRUE)superpc.plotcv(cv.fit) 阅12 转1 评0 公众公开 24-03-26 09:09 |
Seurat_V5|单细胞转录组 + 蛋白，WNN方法分析单细胞多模态数据。by = ''''''''celltype.l2'''''''', label = TRUE, repel = TRUE, label.size = 2.5) + NoLegend() bm <- RunUMAP(bm, reduction = ''''''''apca'''''... 阅13 转0 评0 公众公开 24-03-21 09:09 |
p <- ggplot() + geom_half_violin(data = exprs[exprs$group == ''''''''MET'''''''',], aes(x = Proj, y = BNIP3, fill = group), color = ''''''''black'''''''', scale = '''''... 阅31 转0 评0 公众公开 24-03-04 09:12 |
#查看数据brain.mat <- open_matrix_dir(dir = "brain_counts")brain.mat.# 默认对象切换到sketch（内存中的2w细胞）DefaultAssay(brain) <- "sketch"brain <- FindVariableFeatures(brain)brain <- ScaleData(brain)brain <- RunPCA(brain)brain <- FindNeighbors(brain, dims = 1:50)brain <- Find... 阅148 转2 评0 公众公开 23-12-26 09:09 |
资源贴|送你singleR的7个内置注释数据集。前面在单细胞工具箱|singleR-单细胞类型自动注释（含数据版）中介绍了singleR进行自动化注释的方法以及2个人的内置数据集，这次补充另外的5个注释数据集。Rdata'''''''')ref <- BlueprintEncodeData() save(ref,file = ''''''''Bl... 阅196 转5 评0 公众公开 23-12-20 09:09 |
by = c("Method", "CellType", "cca_clusters"), combine = FALSE, label.size = 2)#####RPCA######obj <- FindNeighbors(obj, reduction = "integrated.rpca", dims = 1:30)obj <- FindClusters(obj, resolution = 2, cluster.name = "rpca_clusters")obj <- RunUMAP(obj, reduct... 阅473 转3 评0 公众公开 23-12-19 09:12 |
