Quan Hoang Nguyen

The paper introduces TRIAGE-Cluster and TRIAGE-ParseR to define cell diversity in scRNA-seq data using epigenetic data as a biological anchor.

The paper uses spatial transcriptomics to analyze how CDK4/6 inhibition affects tumor response in SHH medulloblastoma by mapping cellular states and spatial organization.

i-stLearn provides an interactive web platform for analyzing spatial transcriptomics data, enabling cell clustering, interaction analysis, and trajectory inference.

The paper creates an iPSC differentiation atlas and identifies TMEM88 as a regulator of mesendodermal development and blood pressure.

LmTag improves tag SNP selection by prioritizing functional SNPs while enhancing imputation accuracy for population-specific genotyping arrays.

This paper evaluates imputation and polygenic score performance of 23 human genotyping arrays across 6 ancestry groups using public and in-house datasets, finding that array selection significantly impacts results.

The paper identifies HNF1B dysfunction as a cause of Mayer-Rokitansky-Küster-Hauser syndrome using genomic analysis and mouse models.

This study identifies HOPX as a molecular switch regulating cardiomyocyte progenitor and maturation gene programs, linking it to cardiac function and genetic variation.

This paper introduces HEMnet, a deep learning model that transfers molecular labels from immunohistochemistry to H&E images to identify cancer cells without manual annotation.

The paper identifies cell type-specific genetic control of gene expression in fibroblasts and iPSCs using single-cell RNA sequencing and eQTL mapping.

This study uses spatial transcriptomics to analyze human and mouse kidney tissues, identifying gene expression patterns, nephron segments, cell interactions, and genetic variants while preserving spatial context.

This study compares the performance of BGI and Illumina sequencing technologies for single-cell RNA-sequencing, finding that BGI outperforms Illumina in cell and gene detection but similar in gRNA detection.

SpaCell integrates spatial gene expression and tissue morphology to improve cell type identification and image prediction.

scSplit demultiplexes pooled single-cell RNA-Seq samples using genetic differences inferred from the data, showing high accuracy and consistency with known truths.

The paper generates a human neural retina transcriptome atlas using single-cell RNA sequencing, identifying 18 cell types and revealing a novel role for MALAT1 in rod degeneration.

ascend provides an R package for streamlined single-cell RNA-seq analysis, offering functions for filtering, normalization, clustering, and visualization.

scPred is a method for predicting cell types from single-cell RNA sequencing data, achieving high accuracy in classifying cell states and subtypes across multiple tissues and disease contexts.

The paper identifies four transcriptionally distinct subpopulations in human induced pluripotent stem cells using single-cell RNA sequencing and develops a machine learning method to classify and predict transitions between these states.

The paper maps cardiac differentiation at single-cell resolution, identifies HOPX dysregulation, and shows hypertrophic signaling is required for HOPX transcription in hPSC-derived cardiomyocytes.

The paper develops a computational method to predict regulatory regions in mammals using human genomics data, and applies it to cattle to prioritize variants and identify genome editing targets.

The paper characterizes the transcriptome of human stem cell-derived retinal ganglion cells using single-cell RNA sequencing, identifying three subpopulations with varying maturity levels.

The paper reconstructs cell fate trajectories in hiPSC-derived cardiomyocytes using single-cell RNA-seq and identifies genes with dynamic roles in cardiovascular disease risk.