Jinzhuang Dou

Monopogen-Indel calls germline indels from single-cell and single-nucleus RNA data at 73.0% (retina) and ~87% (ovarian cancer) WGS-validated precision, though no single-cell somatic indel matched a WGS somatic indel.

This study evaluates somatic variant calling in normal tissues using single-cell RNA and ATAC sequencing, showing Monopogen outperforms other tools in accuracy and sensitivity.

This study compares imaging-based spatial transcriptomics platforms using FFPE tumor samples, finding differences in performance and highlighting factors affecting data quality.

The paper introduces NSC-seq, a CRISPR-based molecular clock to track temporal and clonal events in development and precancer, revealing polyancestral initiation in colonic precancers.

Monopogen calls single nucleotide variants from single-cell sequencing data, achieving high accuracy in germline variant detection and enabling ancestry inference and variant association analysis.

The paper models scRNA-seq data from developmental processes using SDEs to identify transition cells that explain heterogeneity and predict cell fate in early tumorigenesis.

This study systematically identifies sequence determinants of CRISPR/Cas9 specificity by analyzing off-target effects of 1,902 gRNAs on 13,314 targets, revealing three key factors and developing a predictive tool.

Sensei determines required sample sizes for detecting cell type abundance changes in single-cell studies using a beta-binomial model and t-test expansion.

SCMER identifies non-redundant molecular features that delineate both common cell lineages and rare cellular states in single-cell data.

The paper introduces MEDALT and LSA to reconstruct copy number lineage trees from single-cell data and identify fitness-associated genes, showing improved accuracy over phylogenetics methods in cancer datasets.

This paper introduces CytoSpill, a statistical method to correct spillover effects in CyTOF data without single-stained controls, improving clustering accuracy and enabling novel subpopulation discovery.

bindSC integrates single-cell multi-omics data accurately by aligning rows and columns without approximations, outperforming existing methods in various biological contexts.

This paper introduces Batch-Corrected Distance (BCD) to improve clustering and visualization of single-cell gene expression data by mitigating batch effects, showing better performance than existing methods on longitudinal datasets.

This study presents whole-genome sequencing data from 4,810 Singaporeans across three ethnic groups, revealing genetic structure, admixture, and improved imputation for Asian and Oceanian populations.