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Creating RNA-velocity informed 2D embeddings for single cell transcriptomics

View the Project on GitHub JEFworks-Lab/veloviz

VeloViz

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VeloViz creates RNA-velocity-informed low dimensional embeddings for single cell transcriptomics data.

The overall approach is detailed in the preprint.

Installation

To install VeloViz, we recommend using remotes:

require(remotes)
remotes::install_github('JEFworks-Lab/veloviz')

VeloViz is also available on Bioconductor:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("veloviz")

Example

Below is a short example showing how to create a VeloViz embedding on sc-RNAseq data. R objects containing the preprocessed data and velocity models used in this example can be downloaded from Zenodo.

# load packages
library(veloviz)
library(velocyto.R)

# get pancreas scRNA-seq data
download.file("https://zenodo.org/record/4632471/files/pancreas.rda?download=1", destfile = "pancreas.rda", method = "curl")
load("pancreas.rda")

spliced <- pancreas$spliced
unspliced <- pancreas$unspliced
clusters <- pancreas$clusters # cell type annotations
pcs <- pancreas$pcs # PCs used to make other embeddings (UMAP,tSNE..)

#choose colors based on clusters for plotting later
cell.cols <- rainbow(8)[as.numeric(clusters)]
names(cell.cols) <- names(clusters)

# compute velocity using velocyto.R
#cell distance in PC space
cell.dist <- as.dist(1-cor(t(pcs))) # cell distance in PC space

vel <- gene.relative.velocity.estimates(spliced,
                                       unspliced,
                                       kCells = 30,
                                       cell.dist = cell.dist,
                                       fit.quantile = 0.1)

#(or use precomputed velocity object)
# vel <- pancreas$vel

curr <- vel$current
proj <- vel$projected

# build VeloViz graph
veloviz <- buildVeloviz(
  curr = curr, proj = proj,
  normalize.depth = TRUE,
  use.ods.genes = TRUE,
  alpha = 0.05,
  pca = TRUE,
  nPCs = 20,
  center = TRUE,
  scale = TRUE,
  k = 5,
  similarity.threshold = 0.25,
  distance.weight = 1,
  distance.threshold = 0.5,
  weighted = TRUE,
  seed = 0,
  verbose = FALSE
)

# extract VeloViz embedding
emb.veloviz <- veloviz$fdg_coords

# compare to other embeddings

par(mfrow = c(2,2))
#PCA
emb.pca <- pcs[,1:2]
plotEmbedding(emb.pca, groups=clusters, main='PCA')

#tSNE
set.seed(0)
emb.tsne <- Rtsne::Rtsne(pcs, perplexity=30)$Y
rownames(emb.tsne) <- rownames(pcs)
plotEmbedding(emb.tsne, groups=clusters, main='tSNE',
              xlab = "t-SNE X", ylab = "t-SNE Y")

#UMAP
set.seed(0)
emb.umap <- uwot::umap(pcs, min_dist = 0.5)
rownames(emb.umap) <- rownames(pcs)
plotEmbedding(emb.umap, groups=clusters, main='UMAP',
              xlab = "UMAP X", ylab = "UMAP Y")

#VeloViz
plotEmbedding(emb.veloviz, groups=clusters[rownames(emb.veloviz)], main='veloviz')

Tutorials

scRNA-seq data preprocessing and visualization using VeloViz
MERFISH cell cycle visualization using VeloViz
Understanding VeloViz parameters
Visualizing the VeloViz graph using UMAP
VeloViz with dynamic velocity estimates from scVelo