Hy Vuong

Benchmarking AlphaSC: A Leap in Single-Cell Data Processing

The paper benchmarks AlphaSC, a GPU-accelerated single-cell analytics package, showing it is much faster and more memory-efficient than Seurat, Scanpy, and RAPIDS without sacrificing quality.

IC-VAE: A Novel Deep Learning Framework for Interpreting Multiplexed Tissue Imaging Data.

The paper introduces IC-VAE, a deep learning framework that improves the analysis of multiplexed tissue imaging data by controlling shared information among latent subspaces, leading to better downstream analysis.

An Entropy Approach for Choosing Gene Expression Cutoff

The paper introduces an entropy-based method to determine gene expression cutoffs for cell type annotation in single-cell transcriptome data.

BBrowser: Making single-cell data easily accessible

A downloadable application pairs a curated single-cell database with a big-data analytics layer and a visualisation module for exploring single-cell omics data.

Venice: A new algorithm for finding marker genes in single-cell transcriptomic data

Venice identifies marker genes in single-cell data by distinguishing cell populations using a new metric, outperforming existing methods in speed and accuracy.

Hera-T: an efficient and accurate approach for quantifying gene abundances from 10X-Chromium data with high rates of non-exonic reads.

Hera-T improves gene abundance quantification in 10X-Chromium data by reducing runtime and memory use while maintaining accuracy compared to Cell Ranger.

A revisit of RSEM generative model and its EM algorithm for quantifying transcript abundances.

This paper revises the RSEM EM algorithm to improve quantification speed while maintaining accuracy, introducing optimizations and parameter convergence strategies.