Kok Siong Ang

Uni-TINT: Unifying T and Innate Lymphoid Cell Taxonomy with a high resolution, pan-disease and pan-tissue single-cell transcriptomic atlas

Uni-TINT creates a unified single-cell atlas of T and innate lymphoid cells across diseases and tissues, identifying new cell subtypes and functional associations.

Mosaic integration of spatial multi-omics with SpaMosaic

SpaMosaic integrates spatial multi-omics data using contrastive learning and graph neural networks, improving spatial domain identification and missing modality imputation across diverse datasets.

Multi-omics and functional analysis of a bioengineered vascularized pancreatic cancer model reveal an immunosuppressive and therapy-resistant niche

The paper develops a bioengineered vascularized pancreatic cancer model and uses multi-omics and functional assays to reveal an immunosuppressive and therapy-resistant tumor niche.

A Population-Scale Single-Cell Atlas of the Human Heart Reveals Cellular Remodeling and Cell-Cell Communication in Aging and Cardiac Disease

The paper constructs a large-scale heart atlas and identifies cellular changes and communication patterns in aging and disease using a new computational method.

Integrated analysis of spatial multi-omics with SpatialGlue

SpatialGlue integrates spatial multi-omics data using a graph neural network to improve spatial domain detection and cross-modality correlation analysis.

Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST

GraphST improves spatial transcriptomics analysis by integrating clustering, integration, and deconvolution through graph self-supervised contrastive learning, achieving higher accuracy than existing methods.

FastIntegration: a versatile R package for accessing and integrating large-scale single-cell RNA-seq data

FastIntegration integrates large-scale single-cell RNA-seq data efficiently and improves downstream analysis by providing batch-corrected gene expression values.

Unsupervised Spatial Embedded Deep Representation of Spatial Transcriptomics

SEDR couples a deep autoencoder over expression with a variational graph autoencoder over position, improving clustering in dorsolateral prefrontal cortex and splitting a visually uniform breast tumour region.