Seurat to Veloctyo Code -- This is a general code chunk for running Velocyto on a Seurat object generated with 10x single-cell sequencing data.

Seurat_to_velocyto.R

library(velocyto.R)
library(pagoda2)
library(Seurat)

### ASSUMES Seurat object has already been computed -- in this case the object cells.combined is the result of seurat

# IMPORTANT!!! create a loom file using the velocyto command line tools first
ldat <- read.loom.matrices("filtered.loom")

# get the spliced and unspliced ratios from the loom file
emat <- ldat$spliced
nmat <- ldat$unspliced

#get the cellular embeddings from the Seurat tSNE object
wt.subset <- SubsetData(object = cells.combined, ident.use = c("WT"))
wt.emb <- wt.subset@[email protected]
wt.cell.dist <- as.dist(1-armaCor(t(wt.subset@[email protected])))

# Next major step, make sure your loom object and your seurat object use the same
# In our case, we had to append the sample name to the start of the emat and nmat (based on how we created the seurat object)

colnames(emat) <- paste("WT_",substring(colnames(emat),14,29),sep="")
colnames(nmat) <- paste("WT_",substring(colnames(nmat),14,29),sep="")

fit.quantile <- 0.02

wt_rvel.cd <- gene.relative.velocity.estimates(
  emat,nmat,deltaT=1,kCells=20,
  cell.dist=cell.dist,fit.quantile=fit.quantile,n.cores=24)
  
# Next important step -- in order to conserve tSNE coloring based on the cluster analysis
# This generates the TSNEPlot, and than extracts out the colors used for each cluster. Note the res.0.4 will change based on
# the resolution you ran the TSNE dimension reduction.

gg <- TSNEPlot(wt.subset,group.by="res.0.4")
ggplot_build(gg)$data
colors <- as.list(ggplot_build(gg)$data[[1]]$colour)
names(colors) <- rownames(wt.emb)

# Finally, create the plot
show.velocity.on.embedding.cor(wt.emb,wt_rvel.cd,n=100,scale='sqrt',
                                     cell.colors=ac(colors,alpha=0.5),
                                     cex=1.2,arrow.scale=20,show.grid.flow=T,
                                     min.grid.cell.mass=1.5,grid.n=40,arrow.lwd=1.5,
                                     do.par=F,cell.border.alpha = 0.1,
                                     n.cores=8,main="WT Cell Louvain")