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supporting code for the multinomial single cell RNA-Seq paper
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algs migrate GLM-PCA code to standalone R package Aug 18, 2019
real major update to all code based on latest version of manuscript. Jul 11, 2019
real_benchmarking major update to all code based on latest version of manuscript. Jul 11, 2019
simulations major update to all code based on latest version of manuscript. Jul 11, 2019
util major update to all code based on latest version of manuscript. Jul 11, 2019
.gitattributes adding all files for scrna paper reproducibility Mar 9, 2019
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README.md migrate GLM-PCA code to standalone R package Aug 18, 2019
scrna2019.Rproj initial commit to make gitignore and Rproj Mar 9, 2019

README.md

Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model

This repository contains supporting code to facilitate reproducible analysis. For details see the biorxiv preprint. If you find bugs please create a github issue.

GLM-PCA (dimension reduction for generalized linear model likelihoods) is now available as a standalone R package. This method is highlighted in the paper as being suitable for single cell RNA-Seq data.

Authors

Will Townes, Stephanie Hicks, Martin Aryee, and Rafa Irizarry

Description of Repository Contents

algs

Implementations of dimension reduction algorithms

  • existing.R - wrapper functions for PCA, tSNE, ZINB-WAVE, etc
  • glmpca.R - placeholder file that just loads the glmpca package.

real

Analysis of various real scRNA-Seq datasets. The Rmarkdown files can be used to produce figures in the manuscript

real_benchmarking

Systematic assessment of clustering performance of a variety of normalization, feature selection, and dimension reduction algorithms using ground-truth datasets.

Downloadable table of results from assessments

util

Utility functions.

  • clustering.R - wrappers for seurat clustering, model based clustering, and k-means
  • functions.R - Poisson and Binomial deviance and residuals functions, a function for loading 10x read counts from molecule information files.
  • functions_genefilter.R - convenience functions for gene filtering (feature selection) based on highly variable genes, highly expressed genes, and deviance.
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