WebFeb 28, 2024 · how to use Seurat to analyze spatially-resolved RNA-seq data? Herein, the tutorial will cover these tasks: Normalization Dimensional reduction and clustering Detecting spatially-variable features Interactive visualization Integration with single-cell RNA-seq data Working with multiple slices WebJul 27, 2016 · Hint: type "g" and then "r" to quickly open this menu. Log in to see your Favorites; Global
Seurat-methods function - RDocumentation
Websubset (x = pbmc, idents = 'B cells') subset (x = pbmc, idents = c ('CD4 T cells', 'CD8 T cells'), invert = TRUE) # Subset on the expression level of a gene/feature subset (x = pbmc, subset = MS4A1 > 3) # Subset on a combination of criteria subset (x = pbmc, subset = MS4A1 > 3 & PC1 > 5) subset (x = pbmc, subset = MS4A1 > 3, idents = 'B cells') WebMar 16, 2024 · # Subset Seurat object based on identity class, also see ?SubsetData subset(x = pbmc, idents = "B cells") subset(x = pbmc, idents = c("CD4 T cells", "CD8 T cells"), invert = TRUE) # Subset on the expression level of a gene/feature subset(x = pbmc, subset = MS4A1 > 3) # Subset on a combination of criteria subset(x = pbmc, subset = MS4A1 > 3 & … is dirty jobs on netflix or hulu
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WebDescription. Given a merged object with multiple SCT models, this function uses minimum of the median UMI (calculated using the raw UMI counts) of individual objects to reverse the individual SCT regression model using minimum of median UMI as the sequencing depth covariate. The counts slot of the SCT assay is replaced with recorrected counts ... WebAug 12, 2024 · 按照idents提取:subset (x = object, idents = c (1, 2)) 按照meta.data中设置过的stim信息提取:subset (x = object, stim == "Ctrl") 按照某一个resolution下的分群提取:subset (x = object, RNA_snn_res.2 == 2) 当然还可以根据某个基因的表达量来提取:subset (x = object, gene1 > 1),subset (x = object, gene1 > 1, slot = "counts") 1.3 提取降维之后 … Web2 Answers. Sorted by: 1. If you are going to use idents like that, make sure that you have told the software what your default ident category is. This works for me, with the metadata column being called "group", and "endo" being one possible group there. Idents (combined.all) <- "group" endo_subset <- subset (combined.all, idents = c ("endo")) rxmedworld.net