occdn.limour.top/source/_posts/scvelo准备3：从seurat输出标注和umap.md

---

title: scVelo准备3：从Seurat输出标注和UMAP tags: [] id: '1803' categories:

• - 拟时序

• • 生信

    date: 2022-05-08 01:21:21

    library(Seurat)
    library(tidyverse)
    library(stringr)
    sce <- readRDS("~/upload/zl_liu/data/pca.rds")
    

    f_scVelo_group_by <- function(df, groupN){
    res <- list()
    for(n in unique(as.character(df[[groupN]]))){
    res[[n]] <- df[df[[groupN]] == n,]
    }
    res
    }
    f_scVelo_get_reduction <- function(dfl, cell.embeddings){
    for(n in names(dfl)){
    dfl[[n]] <- cbind(dfl[[n]], cell.embeddings[rownames(dfl[[n]]),])
    }
    dfl
    }
    f_scVelo_str_extract_rowN <- function(dfl, grepP='(?=.{10})([AGCT]{16})(?=-1)'){
    for(n in names(dfl)){
    rownames(dfl[[n]]) <- str_extract(rownames(dfl[[n]]), grepP)
    }
    dfl
    }
    
    test <- f_scVelo_group_by(sce[[c('patient_id','cell_type_fig3')]], 'patient_id')
    test <- f_scVelo_get_reduction(test, sce@reductions$umap@cell.embeddings)
    test <- f_scVelo_get_reduction(test, sce@reductions$pca@cell.embeddings)
    test <- f_scVelo_str_extract_rowN(test)
    

    f_scVelo_label_reduction <- function(dfl, workdir, groupN, outDir){
    outDir = file.path(workdir, outDir, 'velocyto', 'metadata.csv')
    write.csv(dfl[[groupN]], outDir)
    }
    
    work='/home/jovyan/upload/zl_liu/data/data/res'
    f_scVelo_label_reduction(test, work, 'patient1', 'hPB003')
    f_scVelo_label_reduction(test, work, 'patient3', 'hPB004')
    f_scVelo_label_reduction(test, work, 'patient4', 'hPB006')
    f_scVelo_label_reduction(test, work, 'patient5', 'hPB007')