RNAvelocity系列教程3：使用Seurat和velocyto估算RNA速率

此教程演示分析存储在 Seurat 对象中的 RNA 速率定量。参数基于RNA速率教程。如果您在工作中使用Seurat，请引用：

RNA velocity of single cells

Gioele La Manno, Ruslan Soldatov, Amit Zeisel, Emelie Braun, Hannah Hochgerner, Viktor Petukhov, Katja Lidschreiber, Maria E. Kastriti, Peter Lönnerberg, Alessandro Furlan, Jean Fan, Lars E. Borm, Zehua Liu, David van Bruggen, Jimin Guo, Xiaoling He, Roger Barker, Erik Sundström, Gonçalo Castelo-Branco, Patrick Cramer, Igor Adameyko, Sten Linnarsson & Peter V. Kharchenko

doi: 10.1038/s41586-018-0414-6

Website: https://velocyto.org

准备工作：提前安装好如下3个R包。

• Seurat
• velocyto.R
• SeuratWrappers

加载所需R包

library(Seurat)
library(velocyto.R)
library(SeuratWrappers)

下载所需示例数据

# 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')

转换为seurat对象

ldat <- ReadVelocity(file = "~/Downloads/SCG71.loom")
bm <- as.Seurat(x = ldat)

整合降维聚类

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)

速率分析及可视化

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)

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