Mo Chao Huang

Dimension reduction and denoising of single-cell RNA sequencing data in the presence of observed confounding variables

SAVERCAT improves batch correction and denoising in single-cell RNA sequencing by adjusting for observed confounding variables, outperforming existing methods in high-dimensional data.

Trantrace: a reliable, traceable, and collaborative solution to streamline the translation of emerging biomedical resources

Trantrace streamlines large-scale biomedical translation with collaboration tools, version control, and task management, enhancing efficiency for multilingual knowledge sharing.

Data Denoising with transfer learning in single-cell transcriptomics

This paper improves scRNA-seq data quality using transfer learning by combining a deep autoencoder with a Bayesian model to extract gene-gene relationships across diverse datasets.

Gene Expression Distribution Deconvolution in Single Cell RNA Sequencing

The paper introduces DESCEND, a method to deconvolve true gene expression distributions from noisy scRNA-seq data, improving estimates of dispersion and burstiness while adjusting for covariates.

SAVER: Gene expression recovery for UMI-based single cell RNA sequencing

SAVER improves gene expression estimates in scRNA-seq by imputing zeros and enhancing low/moderate expression genes, improving downstream analysis accuracy.