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An R package to robustly identify subpopulations in single-cell RNASeq data.

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Discovery of cell subpopulations using single cell sequencing data and ReSET

What is ReSET (Robust Subpopulation dEcision Trees)?

ReSET is A tool that leverages the knowledge of cell subpopulation hierarchy to discover robust populations defined from single-cell RNA-Seq experiments. ReSET creates a hierarchical model that fits two independent single cell RNA-Seq datasets.

Overview

Single-cell RNA sequencing enables unbiased analysis of expression patterns. However, researchers don’t have the tools for appropriate decision making during the analysis. Our general aim is to introduce a data-driven strategy to identify the appropriate number of robust subpopulations, their discriminatory defining markers and the relationships between populations.

Workflow diagram

pipeline-diagram

Getting started

To run ReSET on your experimental data, describe your samples in a CSV file sample_sheet.csv, provide a settings.yaml to override the defaults defaults, and select the pipeline.

To generate a settings file template for any pipeline:

ReSET [pipeline] --init=settings

To generate a sample sheet template for any pipeline:

ReSET [pipeline] --init=sample-sheet

Here's a simple example to run the RNAseq pipeline:\

ReSET rnaseq my-sample-sheet.csv --settings my-settings.yaml

To see all available options run ReSET --help.

Install

A pre-built package is available in this repository.

First, you need to install the devtools package which is available from CRAN. Invoke R and then type

install.packages("devtools")

Load the devtools package.

library(devtools)

Install ReSET

install_github("NCBI-Hackathons/robustSingleCell")

Example Data Sources

10X Genomics, 4k Pan T Cells from a Healthy Donor

10X Genomics, 3k Pan T Cells from a Healthy Donor

Dependencies

python = 2.7

R >= 3.5

Project Team (Alphabetical Order)

Main team

Contributors

Presentations

License

NCBI-Hackathons/robustSingleCell is licensed under the MIT License. See LICENSE for further details.

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An R package to robustly identify subpopulations in single-cell RNASeq data.

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