Qin Ma

A Principled Statistical Framework for Analyzing Spatial Patterns in Spatially Resolved Multi-Omics

This paper introduces STORM, a statistical framework for analyzing spatial patterns in multi-omics data, demonstrating reliable performance in detecting spatial structures and supporting study design decisions.

Deep-learning-enabled multi-omics analyses for prediction of future metastasis in cancer

This paper introduces EmitGCL, a deep-learning framework that predicts future metastasis in cancer with high accuracy, identifies biomarkers, and validates therapeutic targets.

A cross model spatial and single-cell atlas reveals the conserved involvement of osteopontin in polycystic kidney disease

This study constructs a cross-model spatial and single-cell atlas of PKD, identifying Spp1 (osteopontin) as a conserved driver of disease progression through its signaling to Ly6clo monocytes.

SpaFlow depicts the dynamics of ligand-receptor interaction in spatial transcriptomics data

SpaFlow models ligand-receptor interactions in spatial transcriptomics data by simulating diffusion and binding processes, revealing spatially coherent signaling patterns in multiple disease contexts.

Single-Cell and Spatial Methods for Multimodal Functional Glycan Profiling in Tissues

The paper introduces two methods to profile functional glycan-lectin interactions in tissues using single-cell and spatial multiomic approaches, revealing stimulus-specific glycan remodeling and spatial glycan programs in immune cells and follicular lymphoma.

A multi-agent platform for assessment and improvement of bioinformatics software documentation

This paper introduces BioGuider, a multi-agent platform that evaluates and improves bioinformatics software documentation, showing a link between documentation quality and citation frequency.

Harnessing the Power of Single-Cell Large Language Models with Parameter Efficient Fine-Tuning using scPEFT

This paper introduces scPEFT, a parameter-efficient fine-tuning framework for scLLMs that improves task adaptation with reduced computational costs and enhances performance in disease-specific and cross-species analyses.

DAP12 deficiency alters microglia-oligodendrocyte communication and enhances resilience against tau toxicity

This study shows that DAP12 deficiency reduces tau-induced inflammation and myelin loss in mice, despite increased tau pathology, by altering microglia-oligodendrocyte interactions.

Epigenetic Modulation, Intra-tumoral Microbiome and Immunity in Early Onset Colorectal Cancer

This study examines epigenetic, microbiome, and immune differences in early-onset colorectal cancer (EOCRC) compared to average-onset CRC, finding accelerated epigenetic aging and stronger immune-microbe correlations in EOCRC.

Spinal Cord Injury Imprints a Glucocorticoid-Driven Failure State in Hematopoietic Stem Cells

This paper shows spinal cord injury causes hematopoietic stem cells to enter a repressed state due to glucocorticoid surges, leading to long-term hematopoietic failure, which can be reversed by early glucocorticoid receptor blockade.

scBSP: A fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data

MNase stratification reveals heterogeneous 5hmC in naive B cells

The paper uses MNase digestion to stratify 5hmC in naive B cells, revealing that most 5hmC is nucleosome-associated, with minor subsets showing distinct chromatin accessibility patterns.

TrimNN: Characterizing cellular community motifs for studying multicellular topological organization in complex tissues

TrimNN identifies spatial cell organization patterns from spatial transcriptomics and proteomics data to reveal associations between cell-type arrangements and phenotypes.

Systematic analysis of cellular crosstalk reveals a role for SEMA6D-TREM2 regulating microglial function in Alzheimer's disease

The paper investigates cell-to-cell communication in Alzheimer's disease to characterize disease biology and suggest new avenues for therapeutic intervention.

Characterizing Cellular Heterogeneity and Transcriptomic Features of Senotype Using Deep Graph Representation Learning

This paper introduces DeepSAS, a deep graph framework to identify senescent cells and their associated genes, revealing cell-type-specific senescence features in aging and disease.

SenSet, a novel human lung senescence cell gene signature, identifies cell-specific senescence mechanisms

The paper develops SenSet, a gene signature for human lung senescence, validated in 3D culture models and identifies cell-type specific senescence mechanisms in aging and disease.

Same-Slide Spatial Multi-Omics Integration Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment

This paper introduces IN-DEPTH and SGCC for spatial multi-omics analysis, revealing EBV-related immunomodulatory differences in DLBCL.

spaDesign: A Statistical Framework to Improve the Design of Sequencing-based Spatial Transcriptomics Experiments

spaDesign provides a statistical framework to optimize sequencing depth in spatial transcriptomics experiments by simulating data and estimating required sequencing depth for detecting spatial domains.

An explainable graph neural framework to identify cancer-associated intratumoral microbial communities

This paper introduces MICAH, a graph neural network framework that identifies cancer-associated microbial communities in intratumoral microbiome data across five cancer types.

Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data

STREAM infers enhancer-driven gene regulatory networks from single-cell RNA-seq and ATAC-seq data, showing improved performance in TF recovery and enhancer-gene discovery.

A Single-cell and Spatial RNA-seq Database for Alzheimer's Disease (ssREAD)

ssREAD is a database that compiles single-cell and spatial RNA-seq data for Alzheimer's Disease, offering analysis tools for integrative studies.

scBSP: A fast and accurate tool for identifying spatially variable genes from spatial transcriptomic data

scBSP identifies spatially variable genes from spatial transcriptomic data efficiently and accurately across different sequencing techniques and resolutions.

A Weighted Two-stage Sequence Alignment Framework to Identify DNA Motifs from ChIP-exo Data

TESA improves TFBS identification in ChIP-exo data by integrating nucleotide-resolution signal with a two-stage alignment framework, showing higher accuracy in E. coli datasets.

A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset

The paper introduces MEGA, a tool to identify cancer-associated microbes using RNA-seq data from the ORIEN dataset, leveraging graph attention networks and microbial community information.

MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer

MarsGT identifies rare cell populations in multi-omics single-cell data, outperforming existing tools and revealing novel biological insights in human and mouse datasets.

Exploring the Mosaic-like Tissue Architecture of Kidney Diseases Using Relation Equivariant Graph Neural Networks on Spatially Resolved Transcriptomics

This paper introduces REGNN, a graph deep learning framework for analyzing spatially resolved transcriptomics data, showing improved performance in identifying mosaic-like tissue architectures in kidney diseases.

Inference of disease-associated microbial gene modules based on metagenomic and metatranscriptomic data

This paper introduces IDAM, a method for inferring disease-associated microbial gene modules from metagenomic and metatranscriptomic data, showing superior performance and reproducibility in inflammatory bowel disease datasets.

Identification of HPV oncogene and host cell differentiation associated cellular heterogeneity in cervical cancer via single-cell transcriptomic analysis

LRT: T Cell Trajectory Inference by Integrative Analysis of Single Cell TCR-seq and RNA-seq data

LRT integrates scTCR-seq and scRNA-seq data to infer T cell trajectories by combining clonal relationships with transcriptomic profiles.

CEMIG: Prediction of the cis-regulatory motif using the De Bruijn graph from ATAC-seq

CEMIG predicts cis-regulatory motifs from ATAC-seq data, outperforming existing methods and identifying cell-type-specific and shared TF motifs in GM12878 and K562 cells.