使用Seurat和Velocyto估算RNA速率
分享是一种态度
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[1]。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)
参考资料
[1]
RNA速率教程: http://pklab.med.harvard.edu/velocyto/notebooks/R/SCG71.nb.html
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原始发表：2021-09-01，如有侵权请联系 cloudcommunity@tencent.com 删除
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