All of the statistical methods that I have developed so far are implemented using the R programming language. When time and funding allowed it, I have created R packages that make it easy to everyone to apply these methods; when this was not possible, I have uploaded the R scripts used to estimate a given model on my github page.

My involvement in the R community can be summarized as follows:

• I am the author and mantainer of the R packages neat and ptmixed;

• I contributed code to the R package EnrichmentBrowser;

• I was one of the organizers of the e-Rum2020 conference.

Below you can find more details about my contributions.

NEAT is an acronym that stands for Network Enrichment Analysis Test, a test that can be used to assess relations between sets of nodes (typically genes) in a network.

The method is described in Signorelli, Vinciotti and Wit (2016). NEAT: an efficient network enrichment analysis test. BMC Bioinformatics, 17:352, and it is implemented in the R package neat, available from CRAN.

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If you want to learn more about NEAT, you can: ​​

1. read the paper that describes the method;

2. read the vignette: An introduction to the R package neat;

3. visit the package page on CRAN;

4. check out the package manual.

 

News: since October 2020, the NEAT test can be computed also using the Bioconductor package EnrichmentBrowser.

ptmixed is an abbreviation that stands for Poisson-Tweedie generalized linear mixed model, a mixed-effects model that has been developed to flexibly model longitudinal count data that feature overdispersion, zero inflation and/or heavy tails. 

The model is described in Signorelli, M., Spitali, P., Tsonaka, R. (2020). Poisson-Tweedie mixed-effects model: a flexible approach for the analysis of longitudinal RNA-seq data. Statistical Modelling, and it is implemented in the R package ptmixed, available from CRAN.

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To find out more about ptmixed:

1. read the article that describes the methodology behind ptmixed;

2. read the vignette: An introduction to the R package ptmixed;

3. have a look at a short 5 minute talk about ptmixed that I presented at eRum2020;

4. visit the package page on CRAN;

5. check out the package manual.

I was one of the organizers of the third European R Users Meeting conference, e-Rum2020, both as member of the organizing committee and as responsible of the promotion of the conference. e-Rum2020 was originally planned as a physical event to be held in Milan in May 2020. However, due to the COVID19 pandemic we decided to turn the event into a free virtual conference - the very first fully virtual R conference ever!

Wanna find more about e-Rum2020?

1. check out the website of e-Rum2020;

2. have a look at my curated list of e-Rum2020 resources.​

R scripts and datasets associated to publications:

1. Signorelli, Spitali and Tsonaka (2020), Statistical Modelling

2. Signorelli and Wit (2020), Statistical Modelling

3. Signorelli and Wit (2018), JRSS-C

4. Signorelli, Vinciotti and Wit (2016), BMC Bioinformatics