Author

Maria Flavia Di Renzo

Candiolo Cancer InstituteORCID 0000-0002-5093-3373Unclaimed ORCID profile · follow to get notified of new preprints

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Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Approach:statistical model· 2 Assay:bulk RNA-seq· 1 Research Focus:clustering· 1 Approach:machine learning (classical)· 1 Assay:scRNA-seq· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Francesca CorderoiD· 2 shared Maddalena ArigoniiD· 2 shared Marco BeccutiiD· 2 shared Martina OliveroiD· 2 shared Raffaele CalogeroiD· 2 shared Alessandro WeisziD· 1 shared Anna SapinoiD· 1 shared Luca AlessandrìiD· 1 shared

Activity by Year

2018-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 39 mutual co-author links

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2 shared papersBioinformatics

Francesca CorderoORCID

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2 papers

Recent·Most cited

Sparsely-Connected Autoencoder (SCA) for single cell RNAseq data mining

Luca Alessandrì, Francesca Cordero, Marco Beccuti, Nicola Licheri, Maddalena Arigoni, Martina Olivero, Maria Flavia Di Renzo, Anna Sapino, Raffaele Calogero

The paper evaluates the sparsely-connected autoencoder (SCA) for functional mining of single-cell RNA-seq clusters, showing its ability to uncover hidden features and improve cluster reconstruction.

npj Systems Biology and Applications · Jan 5, 2021Peer-reviewedBioinformatics

Complementary information on single nucleotide variants, INDELs and functional translocations can be obtained with RNAseq using different library preparations

Riccardo Panero, Maddalena Arigoni, Martina Olivero, Francesca Cordero, Alessandro Weisz, Marco Beccuti, Maria Flavia Di Renzo, Raffaele Calogero

The paper compares two RNA-seq library preparation protocols, showing ACCESS detects more coding SNVs/INDELs but POLYA is better for UTRs, with no advantage for fusion transcripts.

bioRxiv · Apr 14, 2018 · PreprintGenomics