genesorteR: Feature Ranking in Clustered Single Cell Data
Mahmoud M. Ibrahim, Rafael Kramann
genesorteR ranks features in single cell data to identify marker genes more accurately and efficiently than differential expression analysis.
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RWTH Aachen UniversityORCID 0000-0001-6954-5314Unclaimed ORCID profile · follow to get notified of new preprints
3 papers
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Mahmoud M. Ibrahim, Rafael Kramann
genesorteR ranks features in single cell data to identify marker genes more accurately and efficiently than differential expression analysis.
Luz García‐Alonso, Christian H. Holland, Mahmoud M. Ibrahim…·Julio Sáez-Rodríguez
The paper benchmarks and integrates TF regulon resources to assess their impact on TF activity estimation in human gene expression data.
Mahmoud M. Ibrahim, Aslıhan Karabacak Calviello, Alexander Glahs…·Uwe Ohler
The paper investigates transcription initiation directionality in metazoans, finding species-specific sequence asymmetry and independent initiation events at promoters and enhancers.