README.md
In sanofi-pi/veni: Generates QC plots, performs ambient RNA removal, and enhanced differential from single cell RNA sequencing data

Get the most out of your single cell data. Explore the docs » View Demo · Report Bug · Request Feature

What is veni?
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Getting Started
Installation
Usage
Roadmap
Contributing
License
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Acknowledgements

Although Seurat is useful for performing basic visualization and quantification of single cell RNA-seq data, it does not perform statistical analyses that are crucial for clinical applications. We wrote veni to help solve that problem. Here are the benefits of veni:

Veni implements regression-based ambient RNA correction, which corrects for a known confounding factor in droplet-based technologies.
Often, a single sample in a dataset contributes >10-fold more cells than any other sample in the dataset. This one sample can dominate standard downstream analysis steps such as clustering, differential expression, cell type classification, differential abundance and gene co-expression network analysis. veni implements jackknifing to solve this problem, which removes single-sample artifacts from differential gene expression analysis.
Unlike Seurat, veni is memory optimized to handle >100 datasets in R simultaneously. veni utilizes sparse hdf5 files to load individual genes and cells from datasets rather than the entire expression matrix.

You may suggest changes by forking this repo and creating a pull request or opening an issue.

To install veni in R, simply do:

devtools::install_github("mathewchamberlain/veni")

Running veni is simple. Here is an example vignette for processing a 1:1 mixture of human and mouse cells hosted on 10X. More to come.

See the open issues for a list of proposed features (and known issues).

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.