Lin Xu

This study identifies tumor-intrinsic IFNγ signaling and niche adaptation as key drivers of early colonization in ovarian cancer metastasis using MetTag, a single-cell barcoding approach.

This study shows retinoid signaling specifies frontonasal identity and represses maxillary identity in cranial neural crest cells during facial development in mice.

This study identifies the REST-AKT-SOX2 axis as a key regulator of stem/progenitor identity and Midkine-mediated communication in SHH medulloblastoma, highlighting it as a potential therapeutic target.

SpaFun identifies domain-representative genes in spatial transcriptomics data using functional principal component analysis, outperforming existing methods in accuracy and efficiency.

The paper develops eTRex, a variational Bayesian model, to analyze 4,819 ATAC-seq datasets and create a pan-cancer atlas of transcriptional regulator profiles, revealing common and context-specific regulators.

This paper introduces Spa3D, a method that reconstructs 3D spatial structures from 2D spatial transcriptomics data, improving spatial domain identification, cell-cell communication analysis, and developmental trajectory modeling.

The paper shows that REST elevation in cerebellar granule cell progenitors increases chromatin accessibility, alters Grk6 transcript expression, and hyperactivates Cxcr4-Sdf1 signaling, leading to increased cell migration.

BIT identifies transcriptional regulators using a Bayesian hierarchical model, improving accuracy and interpretability by integrating epigenomic data with TR ChIP-seq libraries.

This study shows that actinomycin D and bortezomib disrupt protein homeostasis in Wilms tumor, sensitizing anaplastic cells to treatment and improving survival in xenograft models.

This paper introduces iMOSCATO, a Bayesian model that integrates single-cell RNA-seq and spatial transcriptomics data to decompose cell-type compositions and detect spatial domains.

This study introduces a Bayesian Dirichlet-multinomial regression model to integrate single-cell RNA sequencing data with clinical variables, uncovering cell-type-specific associations across three diseases.

BayeSMART improves multi-sample spatial clustering by integrating AI-reconstructed single-cell data with spatial context, outperforming existing methods in accuracy and efficiency.

The paper introduces a Bayesian clustering method with feature selection for spatially resolved transcriptomics data, improving accuracy through a zero-inflated negative binomial model and geospatial integration.

This paper reviews and benchmarks NGS-based computational methods for predicting transcriptional regulators using query gene sets, finding that BART, ChIP-Atlas, and Lisa perform best.

This paper introduces BayesDeep, a Bayesian model that reconstructs spatial transcriptomics data at single-cell resolution using histology images and SRT datasets.

This study establishes co-cultures of embryonic and extraembryonic stem cells to study lineage crosstalk during early mammalian development, revealing growth inhibition of pluripotent cells by extraembryonic endoderm cells.

The paper introduces iIMPACT, a method that integrates histology images and molecular data to improve clustering and domain detection in spatial transcriptomics.

The paper identifies a stress-specific erythroid progenitor population that links regenerative erythropoiesis to pathogenic erythrocytosis through Epo-IGF1R crosstalk.

MB-GAN generates synthetic microbiome data that mimic real datasets, preserving key statistical properties for method development.

The paper introduces the CSiN score to predict immunotherapy outcomes by assessing tumor neoantigen quality through clonality and immunogenicity, showing improved performance over existing methods.

The paper develops a deep learning tool to analyze pathology images and identify TME features predictive of survival in lung cancer.

The paper identifies GCNA as a conserved regulator of genome stability in germ cells across species, showing its role in preventing DNA damage and maintaining fertility.