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.
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.
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=settingsTo generate a sample sheet template for any pipeline:
ReSET [pipeline] --init=sample-sheetHere's a simple example to run the RNAseq pipeline:\
ReSET rnaseq my-sample-sheet.csv --settings my-settings.yamlTo see all available options run ReSET --help.
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")10X Genomics, 4k Pan T Cells from a Healthy Donor
10X Genomics, 3k Pan T Cells from a Healthy Donor
python = 2.7
R >= 3.5
- Assaf Magen (Team Lead)
- Mamie Wang
- Billy Kim
NCBI-Hackathons/robustSingleCell is licensed under the MIT License. See LICENSE for further details.