extracting highly variable genes finished (0:00:00) --> added 'highly_variable', boolean vector (adata.var) 'means', float vector (adata.var) 'dispersions', float vector (adata.var) 'dispersions_norm', float vector (adata.var) sc.pl.highly_variable_genes(adata) image-20240409143231562 In ...
highly variable genes > top10 <- head(VariableFeatures(pbmc), 10) > top10 [1] "PPBP" "LYZ" "S100A9" "IGLL5" "GNLY" "FTL" "PF4" "FTH1" "GNG11" "S100A8" # plot variable features with and without labels plot1 <- VariableFeaturePlot(pbmc) plot2 <- LabelPoints(plot = plot1,...
pbmc<-NormalizeData(pbmc) pbmc<- FindVariableFeatures(pbmc, selection.method ="vst", nfeatures =2000) # Identify the10most highly variable genes top10<- head(VariableFeatures(pbmc),10) # plot variable features with and without labels plot1<-VariableFeaturePlot(pbmc) plot2<- LabelPoints(plot ...
使用Seurat版本2 # V2pbmc<-FindVariableGenes(object=pbmc,mean.function=ExpMean,dispersion.function=LogVMR)length(pbmc@var.genes)#默认值是:x.low.cutoff=0.1,x.high.cutoff=8,y.cutoff=1,#就是说取log后的平均表达量(x轴)介于0.1-8之间的;分散程度(y轴,即标准差)至少为1的 V3计算mean.function和Fas...
# Identify the 10 most highly variable genes 确定 10 个变化最大的基因 top10 <- head(VariableFeatures(pbmc), 10) # plot variable features with and without labels 绘制有标签和无标签的变量特征 plot1 <- VariableFeaturePlot(pbmc) plot2 <- LabelPoints(plot = plot1, points = top10, repel =...
pbmc<-FindVariableFeatures(pbmc,selection.method="vst",nfeatures=2000)# Identify the10most highly variable genes top10<-head(VariableFeatures(pbmc),10)# plot variable featureswithand without labels plot1<-VariableFeaturePlot(pbmc)plot2<-LabelPoints(plot=plot1,points=top10,repel=TRUE)plot2 ...
pbmc<- FindVariableFeatures(pbmc, selection.method = 'vst', nfeatures = 2000)# Identify the 10 most highly variable genestop10<- head(VariableFeatures(pbmc), 10)# plot variable features with and without labelsplot1<- VariableFeaturePlot(pbmc)plot2<- LabelPoints(plot = plot1, points = top...
这样的基因叫做高度变化基因(highly variable genes ,HyGs),y降维和聚类分群也是常用的操作,它们都是依赖于基因表达量(例如综合细胞中各个基因的表达量,然后把表达模式相近的细胞聚在一起)。因此挑选哪些基因进行聚类分群和降维分析,这是非常关键的,挑选的基因一定要有代表性,尽可能多的包含生物信息而剔除其他技术噪音...
seurat <- FindVariableFeatures(object = seurat, = "vst", nfeatures = 2000) # Identify the 10 most highly variable genes top10 <- head(VariableFeatures(seurat), 10) seurat <- FindVariableFeatures(object = seurat, = "vst", nfeatures = 2000, ...
pbmc<-FindVariableFeatures(pbmc,selection.method="vst",nfeatures=2000)# Identify the 10 most highly variable genestop10<-head(VariableFeatures(pbmc),10)# plot variable features with and without labelsplot1<-VariableFeaturePlot(pbmc)plot2<-LabelPoints(plot=plot1,points=top10,repel=TRUE)plot1+plot...