Jean Yee Hwa Yang

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

scREMOTE predicts regulatory gene relationships and builds a computational model for cell reprogramming using multimodal single-cell data, showing improved long-term prediction of transcription factor effects.

SurvBenchmark evaluates survival analysis methods on clinical and omics data, showing model performance varies with metrics and datasets.

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

The paper introduces treekoR, a framework that recaptures hierarchical structures in cytometry data to better associate cell proportions with disease.

This study benchmarks 12 scRNA-seq simulation methods using 35 experimental datasets, identifying performance differences and limitations in capturing biological signals and heterogeneity.

The paper introduces hRUV, a hierarchical method to remove unwanted variation in large-scale metabolomics data, showing improved preservation of biological signals.

The paper identifies diet-responsive genes in Drosophila by analyzing genetic variation across diets and confirms their role in nutrient storage and utilization.

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

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

This paper introduces a method to improve fMRI network classification by integrating transcriptional data, enhancing prediction accuracy for schizophrenia and enabling biological interpretation of functional connectivity features.

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

The paper introduces an autoencoder-based cluster ensemble framework for scRNA-seq data to improve cell type-specific clustering.

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.

The paper maps multi-omic changes during mouse pluripotency transition, showing phosphoproteome dynamics precede epigenome and transcriptome changes.

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

bcGST corrects bias in gene set tests for boutique arrays by adjusting Fisher's Exact Test to account for incomplete genome coverage.

DCARS identifies gene pairs with significant changes in correlation across survival stages, outperforming existing methods in cancer datasets.