Generalized PCA for non-normally distributed data. If you find this useful please cite Feature Selection and Dimension Reduction based on a Multinomial Model. (doi:10.1186/s13059-019-1861-6)

A python implementation is also available.

The glmpca package is available from CRAN. To install the stable release (recommended):

```
install.packages("glmpca")
```

To install the development version:

```
remotes::install_github("willtownes/glmpca")
```

```
library(glmpca)
#create a simple dataset with two clusters
mu<-rep(c(.5,3),each=10)
mu<-matrix(exp(rnorm(100*20)),nrow=100)
mu[,1:10]<-mu[,1:10]*exp(rnorm(100))
clust<-rep(c("red","black"),each=10)
Y<-matrix(rpois(prod(dim(mu)),mu),nrow=nrow(mu))
#visualize the latent structure
res<-glmpca(Y, 2)
factors<-res$factors
plot(factors[,1],factors[,2],col=clust,pch=19)
```

For more details see the vignettes. For compatibility with Bioconductor, see scry. For compatibility with Seurat objects, see Seurat-wrappers.

Please use https://github.com/willtownes/glmpca/issues to submit issues, bug reports, and comments.

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