N. M. Prashant

The paper introduces dreamlet, an R package for efficient differential expression analysis of large-scale single-cell transcriptomics data, showing improved speed and memory usage compared to existing methods.

This study performs a single-nucleus transcriptome-wide association study to identify gene-trait associations in brain disorders, revealing cell-type-specific genetic regulation and ancestry-specific patterns.

The paper constructs a single-nucleus atlas of genetic regulation in the human brain, identifying cell-type-specific regulatory effects and disease-associated genes.

The paper profiles human myeloid cells across aging and Alzheimer's disease, identifies AD-associated changes, and links GPNMB to neuroprotection via TREM2.

The paper identifies three gene regulatory states associated with cognitive resilience and pathogenesis in Alzheimer's disease using single-cell data from the dorsolateral prefrontal cortex.

The paper constructs a single-cell transcriptomic atlas of the human dorsolateral prefrontal cortex across neurodegenerative and neuropsychiatric diseases, revealing shared and distinct molecular mechanisms.

This study creates a single-nucleus transcriptomic atlas of Parkinson's disease progression, identifying cell-type-specific gene expression changes and linking them to genetic risk factors and disease stages.

This study creates a single-nucleus transcriptomic atlas of the human DLPFC across the lifespan, revealing three transcriptomic phases and ten age-related trajectories linked to neurodevelopmental and neurodegenerative disease risk.

The paper identifies novel expressed SNVs in tumor and normal scRNA-seq data, revealing a significant number of shared and potentially clinically relevant variants.

SCExecute enables cell barcode-stratified analysis of scRNA-seq data by executing user commands on extracted scBAM files to detect cell-specific variants.

This study shows that analyzing SNVs from individual cell scRNA-seq alignments identifies more SNVs and reveals functional variants compared to pooled analysis.

SCReadCounts estimates cell-level SNVs from scRNA-seq data, enabling mutation analysis and downstream applications in cancer and other studies.

scReQTL correlates SNVs to gene expression in single-cell RNA-seq data by using variant allele fraction instead of population genotypes, enabling analysis of rare variants.

The paper estimates allele-specific expression from 10x Genomics scRNA-seq data and finds that most SNVs are bi-allelic at higher read thresholds, with intronic SNVs showing more monoallelic expression.

RsQTL identifies splicing quantitative trait loci by correlating variant allele fractions with splicing patterns in RNA-seq data from human tissues.