Ahmed Essam Mahfouz

The paper investigates the spatio-temporal expression of MG-related genes in human tissues and the CNS using public datasets, identifying new tissues and brain regions with high expression.

This paper introduces scHPL, a hierarchical progressive learning method for single-cell data that continuously updates cell identity classifications across multiple datasets while preserving original annotations.

The paper introduces differential dropout analysis to detect biological variation in single-cell RNA sequencing data and shows it outperforms count-based methods.

The paper identifies brain-region specific splicing patterns at single-cell and spatial resolution in postnatal mice, revealing cell-type specific and region-specific isoform expression.

Cytosplore-Transcriptomics provides a scalable interactive framework for analyzing scRNA-seq data, enabling visualization and downstream analysis of large datasets.

SCHNEL is a scalable clustering tool for high-dimensional single-cell data that outperforms existing methods on cytometry datasets and handles large datasets efficiently.

SpaGE integrates spatial and scRNA-seq data to predict whole-transcriptome spatial gene expressions, outperforming existing methods and identifying new spatial gene patterns.

The paper identifies and removes confounding biological factors like the cell cycle from single-cell data to improve trajectory inference accuracy.

The paper identifies 201 differentially expressed genes in stress-sensitive brain regions linked to psychiatric disorders and potential drug targets.

The paper identifies transcriptomic signatures of brain regional vulnerability to Parkinson’s disease by analyzing gene expression patterns across Braak Lewy body stages in human brain datasets.

The paper identifies gene expression changes in brain regions with gray matter loss in Parkinson's disease and links them to cholinergic circuits and signaling pathways.

The paper benchmarks 20 automatic cell identification methods for single-cell RNA-sequencing data across eight datasets, finding that SVM performs best overall.

The paper compares cell types in human and mouse cortex using single nucleus RNA-sequencing, finding conserved architecture with species-specific differences.

The paper introduces a Linear Discriminant Analysis classifier for predicting cell types in mass cytometry data, showing comparable performance to state-of-the-art methods and scalability to large datasets.

The paper develops a structural equation model that integrates genetic variants and brain imaging features using spatial transcriptomics data to study Alzheimer's disease.

The paper introduces ScISOr-Seq, a method combining single-cell full-length cDNA amplification with short-read and long-read sequencing to identify isoforms in cerebellar cell types across thousands of cells.