Yingxin Lin

Spatial mitochondrial lineage tracing uncovers a premetastatic niche and microenvironment programmed fate switching in osteosarcoma

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

Differential developmental blueprints of organ-intrinsic nervous systems

The paper compares the developmental strategies of the enteric nervous system and intrinsic cardiac nervous system to reveal how organ-specific specialization occurs through distinct molecular and cellular mechanisms.

A Unified Framework for Selecting and Evaluating Cell-Type-Specific Gene Co-expressions in Single-Cell Data

This study compares p-value and correlation strength for selecting gene pairs in single-cell data, finding p-values more robust for co-expression network estimation and benchmarking.

INSPIRE: interpretable, flexible and spatially-aware integration of multiple spatial transcriptomics datasets from diverse sources

INSPIRE integrates and interprets multiple spatial transcriptomics datasets, revealing tissue structures and biological processes across diverse samples and technologies.

Leveraging cell-type specificity and similarity improves single-cell eQTL fine-mapping

The paper introduces CASE, a Bayesian framework for cell-type-specific and shared eQTL fine-mapping, showing improved performance over existing methods in simulations and real data.

Identifying genetic regulations on immune cell type proportions and their impacts on autoimmune diseases

This study identifies genetic loci influencing immune cell proportions and their associations with autoimmune diseases using GWAS and cWAS.

VISTA Uncovers Missing Gene Expression and Spatial-induced Information for Spatial Transcriptomic Data Analysis

VISTA improves spatial transcriptomic data by imputing missing gene expression and enables downstream analyses like ligand-receptor discovery and spatial velocity inference.

Improving polygenic risk prediction performance through integrating electronic health records by phenotype embedding

This paper introduces EEPRS, a framework that improves polygenic risk prediction by integrating electronic health records through phenotype embeddings, showing significant performance gains over traditional methods.

UNICORN: Towards Universal Cellular Expression Prediction with a Multi-Task Learning Framework

UNICORN improves gene expression and multi-omic phenotype prediction using multi-task learning with foundation model embeddings and custom loss functions.

The current landscape and emerging challenges of benchmarking single-cell methods

This paper benchmarks single-cell methods by analyzing 282 papers, highlighting challenges in combining benchmarking studies and preventing fatigue.

scMODAL: A general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links

scMODAL is a deep learning framework for aligning single-cell multi-omics data using feature links, effectively removing unwanted variation and identifying cell subpopulations.

Spatial dynamics of mammalian brain development and neuroinflammation by multimodal tri-omics mapping

Spatial tri-omic sequencing (DBiT ARP-seq and DBiT CTRP-seq) and CODEX imaging map chromatin, RNA, and protein dynamics across mammalian brain development and lysolecithin-induced neuroinflammation.

scTIE: data integration and inference of gene regulation using single-cell temporal multimodal data

scTIE integrates temporal multimodal data and infers gene regulatory relationships predictive of cellular state changes, outperforming existing methods in data integration and biological signal preservation.

Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2

scMerge2 integrates large-scale multi-sample multi-condition single-cell data across cohorts, improving disease progression discrimination through cell-type expression signatures.

Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data

BIDCell improves subcellular spatial transcriptomics segmentation by combining self-supervised learning with biologically informed loss functions, outperforming existing methods across multiple tissue types.

Scope+: An open source generalizable architecture for single-cell atlases at sample and cell levels

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.

Characterization of cell-cell communication in COVID-19 patients

The paper characterizes cell-cell communication patterns in COVID-19 patients to identify differences between moderate and severe disease outcomes using single-cell RNA-seq data.

scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction

scFeatures creates interpretable molecular representations of single-cell and spatial data to predict disease outcomes using diverse feature types.

scJoint: transfer learning for data integration of atlas-scale single-cell RNA-seq and ATAC-seq

scJoint integrates atlas-scale scRNA-seq and scATAC-seq data using transfer learning, achieving higher cell type accuracy and joint visualizations than existing methods.

Cepo uncovers cell identity through differential stability

Cepo identifies cell identity by analyzing differentially stable genes from scRNA-seq data, improving cell-type characterization and spatial mapping.

Single-cell and single-variant resolution analysis of clonal evolution in human liver cancer

The paper analyzes clonal evolution in liver cancer at single-cell and single-variant resolution, revealing genetic and phenotypic heterogeneity and their evolutionary relationships.

Investigating higher order interactions in single cell data with scHOT

The paper introduces scHOT, a method to detect changes in higher order gene interactions in single-cell data, demonstrating its application in liver development and spatial gene correlation patterns.

scClassify: hierarchical classification of cells

scClassify improves cell type classification in scRNA-seq data by using ensemble learning, achieving more accurate and fine-grained results than original publications.

CiteFuse enables multi-modal analysis of CITE-seq data

CiteFuse is a tool for analyzing CITE-seq data, integrating RNA and protein data, and offering clustering, differential expression, and visualization features.

Evaluating stably expressed genes in single cells

This study evaluates stably expressed genes (SEGs) in single cells using a computational framework and finds they are more stable than traditional housekeeping genes across species.

scMerge: Integration of multiple single-cell transcriptomics datasets leveraging stable expression and pseudo-replication

scMerge integrates multiple scRNA-Seq datasets by removing unwanted variation through stably expressed genes and pseudo-replicates, enhancing biological discovery.