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Single-cell analysis with non-negative matrix factorization

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# singlet v.0.0.99

See the [pkgdown website](https://zdebruine.github.io/singlet/)!

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.

## Install

First install the development version of `RcppML`, note that the CRAN RcppML version will not work:

```{R}
devtools::install_github("zdebruine/RcppML")
```

Then install required dependencies, including `limma` and `fgsea`:

```{R}
BiocManager::install("fgsea")
BiocManager::install("limma")
```

Now install `singlet`:

```{R}
devtools::install_github("zdebruine/singlet")
```

## Introductory Vignette

[Guided clustering tutorial](https://zdebruine.github.io/singlet/articles/Guided_Clustering_with_NMF.html)

## Dimension Reduction with NMF

Analyze your single-cell assay with NMF:

```{R}
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.

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