Qianhui Huang

SPACE enables spatial CRISPR screening with whole-transcriptome and protein readouts at subcellular resolution, revealing novel regulatory mechanisms in tumor microenvironments.

Originator identifies and removes blood contamination in scRNA-seq data using genetic and contextual information, improving accuracy in downstream analysis.

Explain-seq is a pipeline that trains and interprets sequence-based deep learning models to predict enhancer activities and identify transcription factor motifs in genomic data.

This study benchmarks integration methods for spatial transcriptomics data, showing that combining highly variable and spatially variable genes improves clustering performance.

GranatumX provides a web-based platform for single-cell RNA sequencing analysis, enabling biologists to use bioinformatics tools through a graphical interface and allowing developers to integrate their own tools into customizable pipelines.

Lilikoi V2.0 is a new R package that uses deep learning and machine learning for disease classification and prognosis prediction using metabolomics data.

The paper evaluates ten cell-type annotation methods on scRNA-seq data, finding Seurat performs best for major cell types but struggles with rare populations and similar cell types.

BAMM-SC clusters droplet-based scRNA-seq data from multiple individuals by accounting for batch effects and heterogeneity, improving accuracy over existing methods.