label=T to help label individual clusters TSNEPlot(object = pbmc) 这一步很耗时,可以保存该对象,便于重复,以及分享交流 代码语言:javascript 代码运行次数:0 运行 AI代码解释 save(pbmc, file = "pbmc3k.rData") Finding differentially expressed genes (cluster biomarkers) 差异分析在seurat包里面被封装成了...
if (length(x = cols) == 1) { plot <- plot + scale_color_brewer(palette = cols) # (D3)默认给出2个颜色 } else if (length(x = cols) > 1) { # 取基因列的uniq值 unique.feature.exp <- unique(data.plot[, feature]) # 如果只有1个值,可能表达相同,或者都是0 if (length(unique....
这两个会自动检测前面找到的默认2000个高变基因#所以这一步不运行也没关系adata=adata[:,adata.var.highly_variable]#不运行也没关系#Regress out effects of total counts per cell and the percentage of mitochondrial genes expressed. Scale the data to unit variance.#回归消除总表达数和线粒体对基因表达的...
AI代码解释 library(enrichplot) dotplot(gene_erich_results[[1]][["DGN"]], showCategory=30) 代码语言:javascript 代码运行次数:0 运行AI代码解释 ## categorySize can be scaled by 'pvalue or 'geneNum' p1<-cnetplot(gene_erichment_results[[1]][["DGN"]], categorySize="pvalue", fold...
Hi, I'm using Seurat v3 (dev version) and having issues with plotting a heatmap of my genes of interest. I am trying to input gene names that I have stored in a dataframe. There are no repeats of the gene symbols, and the DotPlot functio...
# Visualize QC metrics as a violin plot VlnPlot(pbmc, features = c("nFeature_RNA","nCount_RNA","percent.mt"), ncol =3) #nFeature_RNA:代表的是在该细胞中共检测到的表达量大于0的基因个数,nCount_RNA:代表的是该细胞中所有基因的表达量之和,percent.mt:代表的是线粒体基因表达量的百分比,通过...
Visualize QC metrics as a violin plotnCount RNA: 这些基因数目一共测到的count数目,也就是以前版本的UMI数目nFeature RNA: 每个细胞所检测到的基因数目,也就是以前版本的nGenepercent.mt: 每个细胞所检测到的线粒体基因VlnPlot(pbmc, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol ...
#Regress out effects of total counts per cell and the percentage of mitochondrial genes expressed. sc.pp.regress_out(adata, ["total_counts", "pct_counts_mt"]) sc.pp.scale(adata, max_value=10)sc.tl.pca(adata, svd_solver="arpack") ...
GenePlot(object = pbmc, gene1 = "nUMI", gene2 = "percent.mito") GenePlot(object = pbmc, gene1 = "nUMI", gene2 = "nGene") # We filter out cells that have unique gene counts over 2,500 or less than 200 # Note that low.thresholds and high.thresholds are used to define a 'gat...
plot1 + plot2 img 根据上述质控图,对数据中低质量的细胞进行过滤,过滤掉同时满足nFeature>2500,nFeature_RNA < 200,percent.mt > 5的细胞。 pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5) ...