embeddr: Laplacian eigenmaps and principal curves for pseudotemporal ordering and clustering of single-cell RNA-seq data
Embeddr creates a reduced dimensional representation of the gene space using a high-variance gene correlation graph and laplacian eigenmaps. It then fits a smooth pseudotime trajectory using principal curves.
First install embeddr and scater:
library(devtools)
install_github('davismcc/scater')
install_github('kieranrcampbell/embeddr')If you have a gene-by-cell data.frame X of single-cell RNA-seq measurements and a cell-by-feature data.frame PD of cell descriptors
then you can create an SCESet via
pd <- new('AnnotatedDataFrame', data=PD)
sce <- newSCESet(cellData = X, phenoData = pd)library(embeddr)
## Create a cell-cell correlation graph and use it for the reduced embedding:
sce <- embeddr(sce)
## Plot a reduced-dimension embedding
plot_embedding(sce)
## Optionally cluster the embedding. Cluster assignments are stored in pData(sce)$cluster.
## If no number of clusters is designated, the number is chosen using the BIC from package mclust
sce <- cluster_embedding(sce)
## Fit pseudotime using principal curves
sce <- fit_pseudotime(sce)
## Plot genes 1:10 in pseudotime:
plot_in_pseudotime(sce[1:10,])
## Fit differential expression pseudotime model. This will report gene name, p-val and q-val
diff_gene_test <- pseudotime_test(sce)
Fully worked examples using the Monocle dataset and the distal lung epithelium dataset (Quake et al.) can be found in the vignettes/ directory.