Sijia Huang

A Measure of Transcriptional Dyscoordination for Quantifying Aging in Single Cells

This paper introduces a measure of transcriptional dyscoordination to quantify aging in single cells, showing it increases with age and is linked to cellular processes like senescence and genome instability.

Structural basis of substrate recognition and allosteric inhibition in human B0AT2

This study determines cryo-EM structures of B0AT2 to elucidate substrate recognition and allosteric inhibition mechanisms, revealing conserved intracellular cavities across SLC6 transporters.

Dissecting the coordinated progression of cell states in spatial transcriptomics with CoPro

CoPro identifies spatially coordinated gene expression programs across tissues, revealing distinct cellular states and their interactions in colon, brain, liver, and kidney.

Signal recovery in single cell batch integration

This paper introduces CellANOVA, a statistical model to recover biological signals lost during single-cell data integration by distinguishing batch effects from true biological variation.

Single cell and spatial alternative splicing analysis with long read sequencing

The paper develops Longcell, a statistical framework for accurate isoform quantification in single cell and spatial long-read sequencing data, revealing intra-cell splicing heterogeneity and validating regulatory targets.

Integration of spatial and single-cell data across modalities with weak linkage

The paper introduces MaxFuse, a method for integrating spatial and single-cell omics data with weak linkage, demonstrating its effectiveness in consolidating proteomic, transcriptomic, and epigenomic information.

METTL3 maintains epithelial homeostasis through m6A-dependent regulation of chromatin modifiers

This study shows that METTL3 regulates chromatin modifiers via m6A-dependent mRNA degradation, affecting epithelial homeostasis and causing cutaneous and oral phenotypes.

DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data

DeepProg predicts patient survival subtypes using multi-omics data, identifying two optimal subtypes and improving risk-stratification across multiple cancers.

Lilikoi: an R package for personalized pathway-based classification modeling using metabolomics data

Lilikoi is an R package that enables personalized pathway-based classification using metabolomics data through four modules for mapping, transformation, selection, and classification.

Multi-modal meta-analysis of 1494 hepatocellular carcinoma samples reveals vast impacts of consensus driver genes on phenotypes

This study identifies 10 consensus driver genes in HCC across six cohorts and shows their associations with mRNA transcriptomes, patient survival, and demographic factors.