News

10/2024

Introducing MATES! A deep-learning approach that accurately allocates multi-mapping reads to specific loci of TEs, utilizing context from adjacent read alignments flanking the TE locus. This development facilitates the exploration of single-cell heterogeneity and gene regulation through the lens of TEs, offering an effective transposon quantification tool for the single-cell genomics community.

07/2024

Introducing scCross! A tool leveraging variational autoencoders, generative adversarial networks, and the mutual nearest neighbors (MNN) technique for modality alignment. By enabling single-cell cross-modal data generation, multi-omics data simulation, and in silico cellular perturbations, scCross enhances the utility of single-cell multi-omics studies.

07/2024

Introducing scSemiProfiler! Our new tool in Nature Communications uses AI to "semi-profile" single-cell data at 1/10 to 1/3 of the cost, with near-identical results to real-profiled data. We're also building a cloud service for easy access.

09/2023

Welcome all the new students that have joined this big lab family this year!

10/2021

Our paper entitled "Temporal modeling using single cell transcriptomics" was recently accepted at Nature Reviews Genetics

08/2021

Our paper entitled "Computational tools for analyzing single cell data in pluripotent cell differentiation studies" was recently accepted at Cell Reports Methods.

08/2021

Our paper entitled "A versatile model for single-cell data analysis" was recently published at Nature Computational Science.

08/2020

Ding Lab actively seeks talented and motivated Ph.D., Master, and Undergraduate students who share our passion for single genomics and machine learning applications in health science. Please check here for details.