用Seurat做RNA Velocity

生信编程日常

发布于 2020-04-09 14:44:43

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发布于 2020-04-09 14:44:43

文章被收录于专栏：生物信息学、python、R、linux生物信息学、python、R、linux

安装加载相应的包

devtools::install_github('satijalab/seurat-wrappers')
library(Seurat)
library(velocyto.R)
library(SeuratWrappers)

If you don't have velocyto's example mouse bone marrow dataset, download with the CURL command curl::curl_download(url = 'http://pklab.med.harvard.edu/velocyto/mouseBM/SCG71.loom', destfile = '~/Downloads/SCG71.loom')

读入loom文件,并与分析基因表达矩阵一样的流程分析spliced文件

ldat <- ReadVelocity(file = "~/Downloads/SCG71.loom")
bm <- as.Seurat(x = ldat)
bm <- SCTransform(object = bm, assay = "spliced")
bm <- RunPCA(object = bm, verbose = FALSE)
bm <- FindNeighbors(object = bm, dims = 1:20)
bm <- FindClusters(object = bm)
bm <- RunUMAP(object = bm, dims = 1:20)

将velocity变化显示在分群数据上

bm <- RunVelocity(object = bm, deltaT = 1, kCells = 25, fit.quantile = 0.02)
ident.colors <- (scales::hue_pal())(n = length(x = levels(x = bm)))
names(x = ident.colors) <- levels(x = bm)
cell.colors <- ident.colors[Idents(object = bm)]
names(x = cell.colors) <- colnames(x = bm)
show.velocity.on.embedding.cor(emb = Embeddings(object = bm, reduction = "umap"), vel = Tool(object = bm, 
    slot = "RunVelocity"), n = 200, scale = "sqrt", cell.colors = ac(x = cell.colors, alpha = 0.5), 
    cex = 0.8, arrow.scale = 3, show.grid.flow = TRUE, min.grid.cell.mass = 0.5, grid.n = 40, arrow.lwd = 1, 
    do.par = FALSE, cell.border.alpha = 0.1)