Shili Lin

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

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

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

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

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

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

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.