DA-seq is a method to detect cell subpopulations with differential abundance between single cell RNA-seq (scRNA-seq) datasets from different samples, described in the preprint, "Detection of differentially abundant cell subpopulations discriminates biological states in scRNA-seq data", available here. Given a low dimensional transformation, for example principal component analysis (PCA), of the merged gene expression matrices from different samples (biological states, conditions, etc.), DA-seq first computes a score vector for each cell to represent the DA behavior in the neighborhood to select cells in the most DA neighborhoods; then groups these cells into distinct DA cell subpopulations.

This repository contains the DA-seq package.

Required packages: RANN, glmnet, caret, Seurat, e1071, reticulate, ggplot2, cowplot, scales, ggrepel

Python 3 or above (Miniconda is recommended.)

Required modules: numpy, pandas, sklearn, tensorflow, keras

The DAseq package can be installed from this GitHub repository. Installation of just the DAseq package should not take longer than a minute or two.

devtools::install_github("KlugerLab/DAseq")

References of DAseq functions can be found here.

Please check DA-seq tutorial.

Data used in the tutorial is from Sade-Feldman, Moshe, et al. (Cell. 2018).

DAseq has been tested on MacOS (Catalina, 10.15) and Ubuntu 16.04.