Marmar R. Moussa

This paper introduces a PCA-based method for selecting highly variable genes in scRNA-Seq data, showing it is comparable or better than dispersion-based methods and faster.

The paper predicts gene mutations in colon cancer using a gene-conditioned LSTM model with attention on inferred mutation paths from a directed co-occurrence asymmetry graph.

This study benchmarks PCA and Random Projection methods for dimensionality reduction in scRNA-seq, showing RP's superior computational speed and comparable clustering quality.

The paper compares cellular diversity in the primary motor cortex across human, marmoset, and mouse using single-nucleus transcriptomics and epigenomics, finding conserved cell types and species-specific adaptations.

SC1 is a web-based tool for scRNA-Seq analysis that offers interactive workflows, gene selection via TF-IDF, clustering, differential expression, and visualization.

This paper introduces SC1CC, a tool for clustering and ordering single cell RNA-Seq data by cell cycle phase using a new metric called Gene Smoothness Score.

The paper evaluates existing scRNA-Seq imputation methods and introduces a novel iterative approach based on similar cells, assessing performance on real datasets with varying sequencing depth.