Shyam Prabhakar

HARP integrates PBMC data from multiple studies to create a reference atlas with consensus annotations and identifies age, sex, and disease-related cellular modules.

The paper introduces SACCELERATOR, a framework for spatially aware clustering that improves benchmarking by incorporating expert feedback and consensus analysis across diverse datasets.

The paper introduces scAtlasTb, a framework for building and optimizing single-cell reference atlases, demonstrating its application on three Human Cell Atlas projects.

This paper introduces HOMES-seq, a spatial transcriptomics workflow for detecting microbes and host transcripts in colorectal tumours, improving sensitivity and reducing costs.

The paper presents the first single-cell immune atlas of a Mainland Southeast Asian population, showing genetic ancestry shapes innate immune profiles and highlights sex-by-ancestry interactions in immune activation.

This paper introduces Spatial Perturb-Seq, a CRISPR-based method to screen genes in intact tissues, revealing gene effects on neuronal communication in mouse brains.

This study identifies PU.1 as a key transcription factor regulating epigenetic responses to Mtb, showing its role in supporting infection and potential as a therapeutic target.

The paper develops flow cytometry tests to predict blast crisis transformation in chronic myeloid leukemia patients using single-cell transcriptomics insights.

The paper presents AIDA, a diverse single-cell RNA-seq atlas of immune cells, revealing sub-continental diversity impacts on immune cell properties and identifying functional genetic variants.

ctQC improves biological inferences by applying cell-type-specific quality control to single cell and spatial transcriptomics data, enhancing clustering and reducing artifacts.

Demuxafy improves droplet assignment and doublet removal in scRNA-seq by combining multiple demultiplexing and doublet detection methods.

FISHnCHIPs enables highly sensitive spatial transcriptomics by imaging multiple co-expressed genes, revealing spatial gene expression patterns in tissues with improved sensitivity and coverage.

BANKSY integrates cell type clustering and tissue domain segmentation using spatial transcriptomic data, identifying novel niche-dependent cell states in mouse brain regions.

The paper introduces scLinaX, a method to quantify X chromosome inactivation escape using scRNA-seq, revealing heterogeneity across cell types and tissues.

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.

DUBStepR improves single-cell clustering by selecting features using gene-gene correlations and a Density Index, outperforming existing methods and detecting rare cell populations.

scConsensus combines supervised and unsupervised clustering to improve cell type identification in single-cell RNA sequencing data by integrating results and refining clusters with differentially expressed genes.

The paper introduces RCA2, a supervised clustering method for scRNA-seq data that is robust to batch effects and supports large-scale analysis.

This study uses long-read RNA sequencing to analyze isoform diversity in human and mouse cerebral cortex, revealing widespread alternative splicing and novel transcripts.

The paper identifies a convergent molecular subtype in ASD by integrating epigenomic and transcriptomic data, revealing shared dysregulation and links to immune processes and genetic risk variants.

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.

This study shows that loss of CTCF in mouse cardiomyocytes disrupts gene neighbourhoods, leading to ectopic enhancer-promoter interactions and heart failure.

The paper identifies genetic variants associated with heart failure by analyzing histone acetylation QTLs in human heart tissue.

FORKS improves pseudo-temporal ordering of single-cell and bulk data by combining k-means and Steiner trees, showing higher accuracy and robustness than existing methods.