See the pkgdown website!
Singlet is in active development right now. Do not expect stable functionality yet. Coming soon!
Singlet brings fast Non-negative Matrix Factorization (NMF) with automatic rank determination to the Seurat package for single-cell analysis.
First install the development version of RcppML, note that the CRAN RcppML version will not work:
devtools::install_github("zdebruine/RcppML")
Then install required dependencies, including limma and fgsea:
BiocManager::install("fgsea")
BiocManager::install("limma")
Now install singlet:
devtools::install_github("zdebruine/singlet")
Analyze your single-cell assay with NMF:
library(singlet)
library(Seurat)
library(dplyr)
library(cowplot)
set.seed(123) # for reproducible NMF models
get_pbmc3k_data() %>% NormalizeData %>% RunNMF -> pbmc3k
pbmc3k <- RunUMAP(pbmc3k, reduction = "nmf", dims = 1:ncol(pbmc3k@reductions$nmf))
plot_grid(
RankPlot(pbmc3k) + NoLegend(),
DimPlot(pbmc3k) + NoLegend(),
ncol = 2)
NMF can do almost anything that PCA can do, but also imputes missing signal, always has an optimal rank (for variance-stabilized data), uses all the information in your assay (incl. "non-variable" genes), is robust across experiments, learns signatures of transcriptional activity, and is colinear and non-negative (interpretable) rather than orthogonal and signed (not interpretable)
Singlet internally provides the fastest implementation of NMF. Cross-validation can take a few minutes for datasets with a few ten thousand cells, but is extremely scalable and runs excellently on HPC nodes and average laptops alike.