Junil Kim

Identifying Context-Specific Cell-Cell Interaction Genes Without Ligand-Receptor Databases from Spatial Transcriptomics

CellNeighborEX v2 identifies context-specific cell-cell interaction genes from spatial transcriptomics data without relying on ligand-receptor databases.

Inference of Causal Interaction Networks of Gut Microbiota Using Transfer Entropy

This study uses transfer entropy to construct causal interaction networks of gut microbiota from time-series data, revealing power-law degree distribution and differential feedback loop enrichment between two subjects.

SC2Spa: a deep learning based approach to map transcriptome to spatial origins at cellular resolution

SC2Spa maps single-cell RNA-seq data to spatial coordinates using deep learning, improving resolution for low-resolution spatial transcriptomics data and identifying spatially variable genes.

Single-nucleus and spatial transcriptomic analysis identified molecular features of neuronal heterogeneity and distinct glial responses in Parkinson's disease.

This study uses single-nucleus and spatial transcriptomics to identify neuronal heterogeneity and glial responses in Korean Parkinson's disease patients.

Single-Cell RNA-Seq Analysis Reveals Four Stages of Replicative Senescence in Human Umbilical Vein Endothelial Cells

The paper analyzes scRNA-Seq data from HUVECs to identify four stages of replicative senescence and characterizes TF expression patterns across these stages.

CellNeighborEX: Deciphering Neighbor-Dependent Gene Expression from Spatial Transcriptomics Data

CellNeighborEX identifies gene expression influenced by neighboring cells in spatial transcriptomics data, revealing new insights into cell-cell interactions in mouse embryos, brain, and liver cancer.

Neighbor-specific gene expression revealed from physically interacting cells during mouse embryonic development

The paper introduces PIC-seq to study cell-cell contact-specific gene expression during mouse embryonic development and identifies novel gene expression signatures dependent on neighboring cell types.

scmTE: multivariate transfer entropy builds interpretable compact gene regulatory networks by reducing false predictions

scmTE infers gene regulatory networks from scRNA-seq data by reducing false predictions, identifying known and novel regulatory interactions in biological processes.

Decreased KATP channel activity contributes to the low glucose threshold for insulinsecretion in the early postnatal period

The paper shows that decreased KATP channel surface density in early postnatal rat beta-cells lowers the glucose threshold for insulin secretion, which normalizes by postnatal day 14.

VeTra: a tool for trajectory inference based on RNA velocity

VeTra infers cell trajectories using RNA velocity and identifies key regulators by grouping cells based on transition direction.

PDX1 directs a core developmentally and evolutionarily conserved gene program in the pancreatic islet

The paper identifies a core set of developmentally and evolutionarily conserved PDX1-bound genes in mouse and human pancreas development, showing stable PDX1 binding across ages with context-dependent regulatory roles.

TENET: Gene network reconstruction using transfer entropy reveals key regulatory factors from single cell transcriptomic data

TENET reconstructs gene regulatory networks from scRNA-seq data using transfer entropy, identifying key regulators with better performance than existing methods.

SHARP: Single-cell RNA-seq Hyper-fast and Accurate Processing via Ensemble Random Projection

SHARP processes large-scale scRNA-seq data efficiently, outperforming existing methods in speed and accuracy for datasets over 40,000 cells.