Shili Lin

ssJSD: A fusion of sparsity and spatial information for HiC single-cell clustering

ssJSD improves scHiC clustering by integrating spatial interaction patterns and sparsity patterns to better distinguish cell types.

Can Random Walking on a Hi-C Contact Matrix Lead to Data Quality Improvement? An Assessment

This study evaluates random walk methods for improving Hi-C data quality, finding no significant benefit for TAD identification despite parameter tuning challenges.

Novel Spatial-Structural-Zero-Aware Dissimilarity Measures for Subtype Discovery Using Single Cell Hi-C Data

This study introduces szKendall, a dissimilarity measure for scHi-C data that accounts for structural zeros, improving cell clustering performance.

scHiCSRS: A Self-Representation Smoothing Method with Gaussian Mixture Model for Imputing single-cell Hi-C Data

scHiCSRS improves scHi-C data quality by distinguishing structural zeros from dropouts, leading to more accurate clustering in downstream analysis.

Integration of scHi-C and scRNA-seq data defines distinct 3D-regulated and biological-context dependent cell subpopulations

The paper integrates scHi-C and scRNA-seq data to identify 3D-regulated cell subpopulations in breast cancer, revealing new chromatin structures and biological contexts.

HiCImpute: A Bayesian Hierarchical Model for Identifying Structural Zeros and Enhancing Single Cell Hi-C Data.

HiCImpute identifies structural zeros in scHi-C data and improves imputation, leading to more accurate clustering and subtype discovery in neuronal cells.

Human alveolar macrophage response to Mycobacterium tuberculosis: immune characteristics underlying large inter-individual variability

The paper examines inter-individual variability in human alveolar macrophage responses to Mycobacterium tuberculosis by analyzing gene and protein expression over time.

Bayestrat: Population Stratification Correction Using Bayesian Shrinkage Prior for Genetic Association Studies

Bayestrat improves population stratification correction in genetic association studies by using Bayesian shrinkage priors, reducing type I error while increasing power in simulations and real datasets.