Ken Chen

Monopogen-Indel calls germline indels from single-cell and single-nucleus RNA data at 73.0% (retina) and ~87% (ovarian cancer) WGS-validated precision, though no single-cell somatic indel matched a WGS somatic indel.

This paper introduces TCR-FramePose, a geometric framework for analyzing TCR-pMHC recognition, showing it captures novel structural features beyond conventional descriptors.

This study evaluates somatic variant calling in normal tissues using single-cell RNA and ATAC sequencing, showing Monopogen outperforms other tools in accuracy and sensitivity.

This paper introduces ABMACT, an agent-based model to simulate tumor-immune dynamics in adoptive cell therapy and identifies key factors for optimal efficacy.

The paper constructs an integrated atlas of tumor-infiltrating dendritic cells across species and immunotherapy conditions, revealing conserved activation states and spatial reorganization in response to therapy.

This study uses CyTOF to profile circulating tumor cells in SCLC patients, identifying subtype-specific markers and treatment response correlations.

This study identifies immune factor profiles in blood and CSF associated with LRRK2 mutations and Parkinson's disease, showing distinct changes in serum and CSF markers.

This study identifies Bhlhe40 as a key regulator of T cell effector differentiation, showing distinct roles in CD4 and CD8 T cells for anti-PD-1 and anti-CTLA-4 therapies.

This study compares imaging-based spatial transcriptomics platforms using FFPE tumor samples, finding differences in performance and highlighting factors affecting data quality.

The paper constructs an immune cell knowledge graph (ICKG) using AI to annotate immunological gene sets more accurately than existing methods.

cancerSimCraft simulates cancer genome data at clonal and single-cell resolution, enabling benchmarking of computational methods under varied conditions.

The paper introduces NSC-seq, a CRISPR-based molecular clock to track temporal and clonal events in development and precancer, revealing polyancestral initiation in colonic precancers.

This study compares neoantigen vaccines and immune checkpoint therapies, finding they use distinct and overlapping mechanisms, with combined use showing superior efficacy.

This study uses linked-read sequencing to phase somatic mutations in multiple myeloma, extending haplotype blocks and revealing phasing patterns in key mutated genes.

The paper constructs immune-specific gene sets using non-negative matrix factorization on bulk RNA-seq data to improve immune subtype analysis and ICB response prediction.

This paper introduces LSGI, a framework to identify interpretable spatial gradients in spatial transcriptomics data, revealing pathway gradients in glioblastoma and across 87 tumor datasets.

This study shows that the LRRK2 G2019S mutation impairs Th9 and Treg cell differentiation while enhancing Th2 differentiation through JAK/STAT3 signaling in T cells.

This study identifies four transcriptionally distinct venetoclax-resistant clusters in AML, revealing phenotypic, genetic, and drug response differences and their implications for treatment resistance.

GSDensity provides pathway-centric analysis of single-cell and spatial transcriptomics data without clustering, revealing novel cell-pathway associations and spatially relevant pathways in cancer and brain development.

This paper constructs a comprehensive spatially resolved single-cell genomic atlas of the adult human breast, identifying 11 major cell types and revealing diverse immune and epithelial cell populations.

The paper introduces epiNB, an interpretable machine learning model for predicting patient-specific HLA-I antigen presentation using Mass Spectrometry data, achieving state-of-the-art accuracy with fewer parameters and no hyperparameter tuning.

novoRNABreak detects novel splice junctions and fusion transcripts in cancer RNA-seq data with high sensitivity and efficiency.

Monopogen calls single nucleotide variants from single-cell sequencing data, achieving high accuracy in germline variant detection and enabling ancestry inference and variant association analysis.

The paper develops METAFlux to infer metabolic fluxes from bulk and single-cell RNA-seq data, showing improved accuracy over existing methods in cancer TME analysis.

The paper models scRNA-seq data from developmental processes using SDEs to identify transition cells that explain heterogeneity and predict cell fate in early tumorigenesis.

This paper introduces CellTrek, a computational method that integrates scRNA-seq and spatial transcriptomics data to map single cells in tissues, showing accurate spatial organization in mouse and human tissues.

Sensei determines required sample sizes for detecting cell type abundance changes in single-cell studies using a beta-binomial model and t-test expansion.

The paper introduces the Van Elteren test to improve identification of differentially expressed genes in single-cell data by addressing batch effects, showing higher sensitivity and specificity than existing methods.

SCMER identifies non-redundant molecular features that delineate both common cell lineages and rare cellular states in single-cell data.

The paper introduces HotPho, a tool that identifies 3D co-clustering of phosphosites and cancer mutations on protein structures, revealing 474 hybrid clusters with potential functional significance.