The original DA-seq can only work with Seurat v3 and v4, so we made some modification to adapt to Seurat v5
## Introduction
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](https://www.biorxiv.org/content/10.1101/711929v3). 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.
