seurat_data <- newimport(SeuratObject) ###重新定义了import函数,称为newimport newimport <- function(otherCDS, import_all = FALSE) { if(class(otherCDS)[1] == 'Seurat') { requireNamespace("Seurat") data <- otherCDS@assays$RNA@counts if(class(data) == "data.frame") { data <- as...
Read10X()和CreateSeuratObject()函数就是根据输入矩阵/数据框,创建Seurat对象的。重要步骤是 设置 ident 和添加 meta.data。 dat<-Read10X(data.dir="your/work/path")organoids<-CreateSeuratObject(counts=dat,project="Organoids",min.cells=3,min.features=200) min.cells 表示一个基因至少要在3个细胞中被...
mypbmc<-CreateSeuratObject(Seurat2_obj@raw.data) 所以懂了这对象的数据结构后,就可以按照自己的意愿做数据分析了。 把seurat v2 对象转化为seurat V3对象 其实Seurat 团队考虑到了这一点,在3中提供了由2 到3转化的函数:-UpdateSeuratObject: FAQ >library(Seurat)# 我是V3>mm<-readRDS("Seurat.old/mmm.rd...
seurat_data<-newimport(SeuratObject)###重新定义了import函数,称为newimport newimport<-function(otherCDS,import_all=FALSE){if(class(otherCDS)[1]=='Seurat'){requireNamespace("Seurat")data<-otherCDS@assays$RNA@countsif(class(data)=="data.frame"){data<-as(as.matrix(data),"sparseMatrix")}pd...
pData <- data.frame(cell_id = colnames(data), row.names = colnames(data)) pd <- new("AnnotatedDataFrame", data = pData) message("This Seurat object doesn't provide any meta data"); pd }) # remove filtered cells from Seurat
An efficiently restructured Seurat object, with an emphasis on multi-modal data. We have carefully re-designed the structure of the Seurat object, with clearer documentation, and a flexible framework to easily switch between RNA,protein, cell hashing, batch-corrected / integrated, or imputed data....
error in as.data.frame.default(x[[i]], optional = true) : cannot coerce class ‘structure("seurat", package = "seuratobject")’ to a data.frame 文心快码BaiduComate 针对你提出的问题,我将分点进行回答,并提供相应的代码片段作为佐证。 理解错误信息: 错误信息表明,as.data.frame.default函数在...
rna_counts <- inputdata.10x$`Gene Expression` atac_counts <- inputdata.10x$Peaks # Create Seurat object pbmc <- CreateSeuratObject(counts = rna_counts) pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern ="^MT-") head(pbmc@meta.data) ...
If you want to make Seurat object from a matrix, data.frame, etc you simply need to provide an matrix, dataframe, etc with cell names/barcodes as columns and features/genes as rows. In this case the authors have included extra rows which you need to remove before creating the object. ...
rna_counts<-inputdata.10x$`Gene Expression`atac_counts<-inputdata.10x$Peaks # Create Seurat object pbmc<-CreateSeuratObject(counts=rna_counts)pbmc[["percent.mt"]]<-PercentageFeatureSet(pbmc,pattern="^MT-")# Now addintheATAC-seq data