Eric Ching-Pan Chu

Evaluation of analysis modes for RNA coexpression in single-cell and bulk tissue

This study evaluates coexpression analysis methods in single-cell and bulk data, finding that xCell provides more replicable and biologically interpretable results than other modes.

Identifying Reproducible Transcription Regulator Coexpression Patterns with Single Cell Transcriptomics

The paper identifies reproducible transcription regulator coexpression patterns using single-cell RNA-seq data across human and mouse, showing consistency with ChIP-seq results.

Limits to the inference of gene regulation from bulk tissue expression data

The paper develops a computational simulator to study how intracellular regulatory signals propagate from single cells to bulk tissue expression data, finding that such propagation is rare and conditions are highly specific.

Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets

The paper integrates ChIP-seq and TR perturbation data to rank human and mouse TR-target interactions and identify orthologous relationships.

Experiment level curation identifies high confidence transcriptional regulatory interactions in neurodevelopment

This study curates experimentally verified transcriptional regulatory interactions from literature to build a high-confidence resource for gene regulatory network development.