Number of dimensions: 50 Number of cells: 2696 Projected dimensional reduction calculated: FALSE Jackstraw run: FALSE Computed using assay: SCT $umap A dimensional reduction object with key umap_ Number of dimensions: 2 Number of cells: 2696 Projected dimensional reduction calculated: FALSE Jackstraw ...
(A4) 细胞个数 n.cells <- nrow(x = object) #如果细胞数 小于 k,则警告,并设置k为细胞数-1 if (n.cells < k.param) { warning( "k.param set larger than number of cells. Setting k.param to number of cells - 1.", call. = FALSE ) k.param <- n.cells - 1 } #(A5) 如果 l...
# but are incompatible with some feature pre-filtering options We therefore suggest initially # limiting the number of cells used for testing head(FindMarkers(pbmc, ident.1 = "CD14+ Mono", ident.2 = "FCGR3A+ Mono", test.use = "DESeq2", max.cells.per.ident = 50)) converting counts ...
#如果是从GEO等网站中下载的单细胞表达矩阵,可以直接读入表达矩阵:pbmc.data <- read.table("FPKM.txt",header = TRUE) pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200) # 创建seurat对象(10X数据和表达矩阵都可以通过这条命令创建对象), # m...
ggtitle("Gene Expression Distribution") + xlab("Log-transformed expression values") + ylab("Number of cells") ``` 请注意,上述代码示例仅为了演示`VlnPlot`的基本使用方法。在实际应用中,您需要根据您的数据和分析需求来调整参数和设置。©2022 Baidu |由 百度智能云 提供计算服务 | 使用百度前必读 | ...
pbmc <- FilterCells(pbmc, subset.names = "nFeature_RNA", low.thresholds = 200, high.thresholds = Inf) # Filter genes based on number of cells expressed pbmc <- FilterGenes(pbmc, subset.names = "nCells_RNA", low.thresholds = 3, high.thresholds = Inf) # Normalize data pbmc <- Norma...
pbmc<-CreateSeuratObject(counts=pbmc.data,project="pbmc3k",min.cells=3,# 基因至少在多少个细胞中有表达 min.features=200# 至少表达多少基因)pbmc ## An objectofclassSeurat##13714features across2700samples within1assay ## Active assay:RNA(13714features,0variable features) ...
plot of chunk unnamed-chunk-14 上面是找conserved gene,下面才是常见的找差异基因。 # 先展示下某些基因在两组中的表达量library(ggplot2)library(cowplot)theme_set(theme_cowplot())# 选择CD4 Naive T这个亚群继续探索t.cells <- subset(immune.combined, idents = "CD4 Naive T")Idents(t.cells) <- "...
将cells设置为一个数字,可以绘制光谱两端的“极端”细胞,这极大地加快了绘制大型数据集的速度。虽然这显然是一个监督分析,但我们发现这是一个有价值的工具,用于探索相关的特征集。 为了克服scRNA-seq数据中单个特征中大量的技术噪声,Seurat根据他们的PCA评分将细胞分组,每个PC实质上代表一个“元特征”,它将跨相关...
RNA-based clustering is driven by confounding sources of variation 在这里,我们依照标准的 Seurat 工作流程,根据基因表达谱对细胞进行聚类。 我们希望得到出现特定现象的细胞类群,但我们发现类群主要由细胞周期阶段和复制 ID 影响的。 我们只观察到一个包含细胞表达 IFNgamma 通路 gRNA 的扰动特异性簇。