Bibliography

I am an Assistant Professor at the Department of Statistical Sciences "Paolo Fortunati" at the University of Bologna (Italy).
Previously, I was a Postdoc at the Robinson lab at the Institute of Molecular Life Sciences at the University of Zurich (Switzerland), where I worked on  the development of statistical methods in bioinformatics. Before that, during my PhD at the Department of Statistics at the University of Warwick (UK),  I investigated, via Bayesian hierarchical approaches with latent variables, single-cell stochastic models in systems biology, based on flow cytometry data.
In general terms, my interests are broad and lie in  the development and application of statistical methods in computational biology, particularly in bioinformatics and systems biology.

Scientific Insterests

Statistical methodology: Bayesian hierarchical models, MCMC, missing data, latent variables, hidden Markov model, stochastic models, stochastic differential equations (SDE), mixed effects models, non-parametric permutation approaches, differential testing, statistical software development.

Computational biology: bulk and single-cell RNA sequencing (RNA-seq), spatial transcriptomics, mass spectrometry, flow and mass cytometry, fluorescence in situ hybridization, differential expression, differential regulation, alternative splicing.

Research positions

• 2022 - present: Assistant Professor. The Universify of Bologna (Italy), Department of Statistical Sciences "Paolo Fortunati".

• 2016 - 2022: Postdoc. The University of Zurich (Switzerland), Institute of Molecular Life Sciences, Robinson lab.

Education

• 2013 - 2017:      PhD in Statistics.  The University of Warwick (UK), Department of Statistics.

Thesis: "Bayesian Hierarchical Stochastic Inference on Multiple, Single Cell, Latent States from both Longitudinal and Stationary Data".

Supervisor: Prof Barbel Finkenstadt.

• 2010 - 2012:  Master's degree in Statistics. The University of Padua (Italy), Department of Statistical Sciences.

Dissertation: "A composite likelihood approach to predict the babies sex".

Supervisor: Prof. Bruno Scarpa. Co-supervisor: Prof. Nicola Sartori. 

• 2007 - 2010:       Bachelor degree in Statistics. Sapienza University of Rome (Italy), Department of Statistical Sciences.

Dissertation: "A logistic analysis to predict the axillary lymph node status, in patients affected by breast cancer and with positive sentinel lymph node". 

Supervisor: Prof. Maria Grazia Pittau.

Pre-prints

1. Simone Tiberi, Joël Meili*, Peiying Cai*, Charlotte Soneson*, Dongze He, Hirak Sarkar, Alejandra Avalos-Pacheco, Rob Patro, and Mark D Robinson.
   DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes.
   biorXiv (2023). *joint second authorship.

Publications

1. Peiying Cai, Mark D Robinson, and Simone Tiberi.
   DESpace: spatially variable gene detection via differential expression testing of spatial clusters.
   Bioinformatics (2024).

2. Simone Tiberi, Helena L Crowell, Pantelis Samartsidis, Lukas M Weber and Mark D Robinson.
   distinct: a novel approach to differential distribution analyses.
   The Annals of Applied Statistics (2023). 

3. Rachel M. Miller, Ben T. Jordan, Madison M. Mehlferber, Erin D. Jeffery, Christina Chatzipantsiou, Simi Kaur, Robert J. Millikin, Yunxiang Dai, Simone Tiberi, Peter J. Castaldi, Michael R. Shortreed, Chance John Luckey, Ana Conesa, Lloyd M. Smith, Anne Deslattes Mays, Gloria M. Sheynkman.
   Enhanced protein isoform characterization through long-read proteogenomics.
   Genome Biology (2022).

4. Francesco Pantano, Francesca Zalfa, Michele Iuliani, Sonia Simonetti, Paolo Manca, Andrea Napolitano, Simone Tiberi, Marco Russano, Fabrizio Citarella, Simone Foderaro, Elisabetta Vulpis, Alessandra Zingoni, Laura Masuelli, Roberto Bei, Giulia Ribelli; Marzia Del Re, Romano Danesi, Bruno Vincenzi; Giuseppe Perrone, Giuseppe Tonini, Daniele Santini.
   Large-scale profiling of extracellular vesicles identified miR-625-5p as a novel biomarker of im- munotherapy response in advanced non-small cell lung cancer patients.
   Cancers (2022).

5. Christian Sailer, Simone Tiberi, Bernhard Schmid, Juerg Stoecklin and Ueli Grossniklaus.
   Apomixis and genetic background affect distinct traits in Hieracium pilosella L. grown under competition.
   BMC Biology (2021).

6. Massimo Cavallaro, Mark Walsh, Matt Jones, James Teahan, Simone Tiberi, Bärbel Finkenstädt and Daniel Hebenstreit.
   3’-5’ crosstalk contributes to transcriptional bursting.
   Genome Biology (2021).

7. Simone Tiberi and Mark D Robinson.
   BANDITS: Bayesian differential splicing accounting for sample-to-sample variability and mapping uncertainty.
   Genome Biology (2020).

8. Koen Van Den Berge​*, Katharina Hembach​*, Charlotte Soneson​*, Simone Tiberi​*, Lieven Clement, Michael I Love, Rob Patro, Mark Robinson​.
   RNA sequencing data: hitchhiker's guide to expression analysis.
   Annual Review of Biomedical Data Science (2019). *joint first authorship.

9. Anthony Lee, Simone Tiberi and Giacomo Zanella.
   Unbiased approximations of products of expectations.
   Biometrika (2019).

10. Simone Tiberi, Mark Walsh, Massimo Cavallaro, Daniel Hebenstreit and Bärbel Finkenstädt.
    Bayesian inference on stochastic gene transcription from flow cytometry data.
    Bioinformatics (2018).

11. Simone Tiberi, Bruno Scarpa and Nicola Sartori.
    A composite likelihood approach to predict the sex of the baby.
    Statistical Methods in Medical Research (2018).

12. Augusto Lombardi, Stefano Maggi, Marzia Lo Russo, Francesco Scopinaro, Domenica Di Stefano, Maria Grazia Pittau, Simone Tiberi and Claudio Amanti.
    Non-sentinel lymph node metastases in breast cancer patients with a positive sentinel lymph node: validation of five nomograms and development of a new predictive model.
    Tumori (2011).

Book Chapters

1. Philipp Weiler, Koen Van den Berge, Kelly Street*, Simone Tiberi*.
   A guide to trajectory inference and RNA velocity.
   Part of the book Single Cell Transcriptomics: Methods and Protocols (2022).  *joint last authorship.
   Also available on biorxiv: https://doi.org/10.1101/2021.12.22.473434.

Theses

• PhD Thesis: Bayesian hierarchical stochastic inference on multiple, single cell, latent states from both longitudinal and stationary data.

• MSc Thesis: A composite likelihood approach to predict the babies sex (in Italian).

Software

Bioconductor R packages

• IsoBayes: Isoform-level Bayesian proteogenomics inference.
  IsoBayes infers the presence/absence of protein isoforms (via its posterior probability), and also estimates their abundance (and a posterior credible interval). IsoBayes inputs liquid cromatography mass spectrometry (MS) data, and can work with both PSM counts, and intensities. When available, trascript isoform abundances (i.e., TPMs) are also incorporated: TPMs are used to formulate an informative prior for the respective protein isoform relative abundance.
  IsoBayes is available on GitHub (https://bioconductor.org/packages/IsoBayes).

• DESpace: an intuitive framework for identifying spatially variable genes (SVGs) from spatial transcriptomics data.
  DESpace inputs pre-annotated spatial clusters, models gene expression using a negative binomial model (via edgeR), with spatial clusters as covariates. SVGs are then identified by testing the significance of spatial clusters.
  DESpace is available on Bioconductor (https://bioconductor.org/packages/DESpace).

• DifferentialRegulation: a method for detecting differentially regulated genes.
  DifferentialRegulation targets differences in the balance of spliced and unspliced mRNA abundances, obtained from single-cell RNA-sequencing (scRNA-seq) data. DifferentialRegulation accounts for the sample-to-sample variability (via a Bayesian hierarchical model), and for the mapping uncertainty of scRNA-seq reads (via latent states).
  DifferentialRegulation is available on Bioconductor (https://bioconductor.org/packages/DifferentialRegulation).

• distinct: a method for differential analyses via hierarchical permutation tests.
  distinct is a statistical method to perform differential testing between two or more groups of distributions. Unlike most methods for differential expression, distinct identifies both, differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean (e.g., unimodal vs. bi-modal distributions with the same mean).
  distinct is available on Bioconductor (https://bioconductor.org/packages/distinct).

• BANDITS: Bayesian ANalysis of DIfferenTial Splicing.
  BANDITS is a Bayesian hierarchical model for detecting differential splicing, of both genes and transcripts, while accounting for sample-to-sample variability and mapping uncertainty.
  BANDITS is available on Bioconductor (https://bioconductor.org/packages/BANDITS).

Presentations

Oral presentations

• Seminar, The MRC Biostatistics Unit, University of Cambridge, 13 December 2022.
  Recording available here: https://www.youtube.com/watch?v=pEiQ7t2KfRo.

• European Bioconductor Conference 2022, Heidelburg, 14-16 September 2022.

• Bioconductor Conference 2022, Seattle, 27-29 July 2022.

• Seminar, Differential expression from single­-cell RNA­seq data. University of Pretoria,  November 3 2021.

• Bioconductor Conference 2021, virtual, 4-6 August 2021.

• European Young Statisticians Meetings 2021, virtual, 6-10 September 2021.

• BC2 Basel Computational Biology Conference 2021. Basel, 13-15 September 2021.

• European Bioconductor Meeting 2020, virtual, 14-18 December 2020. Slides available at: https://f1000research.com/slides/9-1450.

• ISMB/ECCB 2019, Basel, 21-25 July 2019. Slides available at: https://f1000research.com/slides/8-1223.

• IBS Channel Network Conference 2019, Rothamsted Research, 10-12 July 2019.

• BITS Bioinformatics Italian Society Meeting 2019, Palermo, 26-28 June 2019.

• ECCB EUROPEAN CONFERENCE ON COMPUTATIONAL BIOLOGY 2018. Athens, 8-12 September 2018.

• IBC International Biometric Conference 2018. Barcelona, 8-13 July 2018.

• BC2 Basel Computational Biology Conference 2017. Basel, 12-15 September 2017.

• IBS Channel Network Conference 2017. Hasselt University, 24-26 April 2017.

Teaching

• University of Bologna:

  • Academic year  2022/2023, Term 1: Statistics.

  • Academic year  2022/2023, Term 1: Big Data and Analytics.

• SIB course - Introduction to Bayesian statistics with R, Basel:

  • 30 November 2022 - Recordings available here: https://www.youtube.com/watch?v=teZRw1I9ujg and https://www.youtube.com/watch?v=3ur-ItPJPp0;

  • 24 May 2023 - Recording available here: https://www.youtube.com/watch?v=P_wXv1iFlSk.

• Whole transcriptome sequencing data analysis. University of Pretoria (South Africa), February 1-8 2019.

Material available at: https://github.com/markrobinsonuzh/pretoria_rnaseq_course_feb2019.

• Bioinformatics for Adaptation Genomics 2018, Weggis (Switzerland), 11-17 Feb 2018.

Theory and methods of RNA-seq studies: material available at: https://github.com/SimoneTiberi/BG4-2018.

• University of Zurich:

  • Academic year  2016/2017, Term 1: STA121 - Statistical Modelling.

• University of Warwick (tutorials):

  •  Academic year 2015/2016, Term 1: ST301/ST413 - Bayesian Statistics & Decision Theory

  • Academic year 2015/2016, Term 1: ST333/ST406 - Applied Stochastic Processes

  • Academic year 2014/2015, Term 2: ST219 - Mathematical Statistics Part B

  • Academic year 2014/2015, Term 1: ST218 - Mathematical Statistics Part A

  • Academic year 2013/2014, Term 2: ST115 - Introduction to Probability

  • Academic year 2013/2014, Term 1: ST116 - Mathematical Techniques