Momeneh Foroutan

ClustoCell identifies cell states and markers from single-cell transcriptomes by analyzing transcriptional architecture, improving accuracy and stability over existing methods.

The paper introduces RUVprps, a software package for normalizing RNA-seq data by removing unwanted variation, offering improved performance and features for analysis.

This paper identifies major sources of unwanted variation in cancer RNA-seq data and proposes a novel method to remove them using pseudo-replicates of pseudo-samples.

The paper introduces a single-sample scoring method for gene set analysis that is stable across varying sample compositions and demonstrates improved computational efficiency compared to existing methods.

The paper develops an NK cell gene signature to predict survival in metastatic cutaneous melanoma patients using TCGA RNA-seq data.

The paper shows that multiple microRNAs work together to control EMT by reducing noise and enhancing gene expression changes, more accurately reflecting endogenous regulation.