Joshua W. K. Ho

The paper uses spatial and single-cell transcriptomics with mitochondrial lineage tracing to uncover fate switching and premetastatic niche formation in osteosarcoma.

CLEAR-ST corrects mRNA lateral diffusion in spatial transcriptomics by modeling contamination with a physics-informed probabilistic framework, improving spatial domain recovery and downstream analysis accuracy.

vOMIX-MEGA processes terabyte-scale viral metagenomic data faster and more efficiently than existing pipelines, achieving faster runtime and higher accuracy.

This study uses mitochondrial variants as barcodes to track clonal dynamics and spatial organization in human hematopoietic organoids.

This study examines the association between gut microbiome diversity and 24-hour blood pressure variability, identifying sex-specific bacterial species linked to systolic and diastolic blood pressure fluctuations.

This paper benchmarks three LLMs for extracting gene-disease-phenotype relationships from biomedical texts, finding GPT-4o achieves 88.8% accuracy using zero-shot prompting.

AtlasAgent uses a vision-language model to rapidly evaluate large-scale single-cell data integration, outperforming traditional methods in speed and efficiency.

This paper introduces inDecay, a few-shot learning system for predicting CRISPR editing outcomes in cell lines and zygotes, achieving high accuracy with minimal data.

StaVia integrates spatial and temporal data with higher-order random walks to improve cell lineage analysis and visualization in developmental atlases.

This paper introduces a framework that enhances LLMs with gene-related tasks using retrieval-augmented generation and MMR algorithm, showing improved performance in gene question answering, cell type annotation, and gene interaction prediction.

The paper identifies coverage bias at transcription start sites in metagenomic data that correlates with gene expression, independent of GC bias and sequencing artifacts.

scDecouple separates true cellular response from gRNA infection bias in scCRISPR-seq data, improving gene perturbation analysis in heterogeneous samples.

MetaQuad identifies shared polymorphic variants in metagenomic samples using density-based clustering, reducing false positives while maintaining true-positive rates.

Vulture is a scalable cloud-based pipeline for microbial calling in scRNA-seq data, outperforming existing tools in speed and cost while discovering virus-enriched cell types in cancer and COVID-19 datasets.

The paper introduces TRIAGE-Cluster and TRIAGE-ParseR to define cell diversity in scRNA-seq data using epigenetic data as a biological anchor.

This paper develops MTtrans, a multi-task learning model that identifies robust, generalizable cis-regulatory motifs in 5' UTRs by integrating diverse datasets, showing improved accuracy and cross-species transferability.

Scope+ provides an open-source architecture for accessing and analyzing large single-cell atlases at the cell level, demonstrated through the Covidscope portal for COVID-19 data.

The paper creates an iPSC differentiation atlas and identifies TMEM88 as a regulator of mesendodermal development and blood pressure.

DCATS performs differential composition analysis for complex single-cell experiments, maintaining high sensitivity and specificity compared to existing methods.

MQuad identifies clonally informative mitochondrial variants from single-cell sequencing data with high sensitivity and specificity, outperforming existing methods.

VIA is a scalable trajectory inference method that accurately reconstructs complex cellular trajectories in large single-cell datasets, including cyclic and disconnected structures.

starmapVR provides an immersive visualization tool for single-cell and spatial omic data using VR, supporting 3D and 2.5D views with multiple interaction methods.

The paper develops a high-throughput assay to study Tcf7l2 binding and finds that nearby transcription factor motifs influence its binding in addition to DNA sequence and chromatin accessibility.

Sierra identifies differential transcript usage from polyA-captured scRNA-seq data, validated against bulk RNA-seq and showing 3UTR shortening in cardiac fibroblasts.

PARC is a scalable graph-based clustering algorithm for large single-cell datasets, outperforming existing methods in speed and rare population detection.

Scavenger recovers unaligned RNA-seq reads using aligned reads' information, improving gene expression analysis and revealing pseudogene expression impacts.

Ularcirc detects and analyzes circular RNAs using a novel method to filter false positives and provides visualization tools for circRNA characterization.

FlowGrid is an ultrafast clustering algorithm for large single-cell flow cytometry data, achieving faster performance than existing methods on large datasets.

GEOracle identifies perturbation gene targets in GEO by mining free text metadata and performing differential expression analysis.

CardiacProfileR extracts and visualizes heart rate profiles from wearable fitness trackers to assess cardiovascular health.