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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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DAESC+: High-performance, integrated software for single-cell allele-specific expression data.

Tengfei Cui, Guanghao Qi

Biorxiv : the Preprint Server for Biology

|September 18, 2025

Summary

This summary is machine-generated.

We developed DAESC+, a new software package for analyzing single-cell allele-specific expression (ASE). DAESC+ accurately processes scRNA-seq data and efficiently identifies differential gene regulation in immune cells.

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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

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Published on: October 25, 2018

Area of Science:

Genomics
Bioinformatics
Immunology

Background:

Single-cell allele-specific expression (ASE) offers insights into gene regulation.
Existing computational tools for single-cell ASE analysis are limited.
Accurate and scalable tools are needed for processing and analyzing multiplexed scRNA-seq data.

Purpose of the Study:

To introduce DAESC+, a comprehensive software package for single-cell ASE analysis.
To provide a user-friendly preprocessing pipeline (DAESC-P) for obtaining ASE counts.
To develop a scalable, GPU-powered tool (DAESC-GPU) for differential ASE analysis.

Main Methods:

DAESC-P: A bioinformatics pipeline for processing multiplexed scRNA-seq data to generate ASE counts.
DAESC-GPU: A graphics processing unit-accelerated tool for scalable differential ASE analysis.
Application to OneK1K cohort data for identifying differential gene regulation in immune cells.

Main Results:

DAESC-P demonstrates higher accuracy compared to the SALSA pipeline.
DAESC-GPU achieves significant speed improvements and scalability to over a million cells.
Identification of 15 genes with differential regulatory patterns in CD4+ T cells and 2 in B cells.

Conclusions:

DAESC+ provides an accurate, efficient, and scalable solution for single-cell ASE analysis.
The software facilitates the discovery of gene regulatory differences in immune cell populations.
DAESC+ advances the utility of single-cell ASE for understanding gene regulation.