Federico Marini

Postdoctoral Research Fellow

CTH - Center for Thrombosis and Hemostasis

IMBEI - University Medical Center Mainz

About me

Hi! I am a Postdoctoral Virchow Fellow at the Center for Thrombosis and Hemostasis (CTH) and at the Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI) in Mainz, Germany.

My research interests involve the development of methods and software for interactive and reproducible research, bringing together the fields of bioinformatics, statistics, genomics, and biology. I can do this thanks to the joint supervision of Prof. Wolfram Ruf and Prof. Miguel Andrade.

Being a Virchow Fellow at the Center for Thrombosis and Hemostasis, I can sit at the interface with many other disciplines and focus on translational bioinformatics. With platelets transcriptomics as a use case, I am particularly interested to develop methodologies and implement methods (software packages, web applications, …) for interactive and reproducible research in RNA-seq analysis and other -omics data.

I love working in the programming language, and with software from the Bioconductor project, where I also contributed some packages. In the time when I am not sitting behing a computer screen, I like to get physically overwhelmed by my three wonderful kids and spend time with my family.

Interests

Bioinformatics
Biostatistics
Interactive & Reproducible Research
R, Bioconductor, Shiny
Transcriptomics & Genomics
Data Visualization

Education

PhD in Biostatistics/Bioinformatics, 2017

University Medical Center Mainz - IMBEI
MSc in Biomedical Engineering, 2008

Politecnico di Milano
BSc in Biomedical Engineering, 2005

Politecnico di Milano

Selected Publications

pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components

Principal component analysis (PCA) is frequently used in genomics applications for quality assessment and exploratory analysis in high-dimensional data, such as RNA sequencing (RNA-seq) gene expression assays. Despite the availability of many software packages developed for this purpose, an interactive and comprehensive interface for performing these operations is lacking. We developed the pcaExplorer software package to enhance commonly performed analysis steps with an interactive and user-friendly application, which provides state saving as well as the automated creation of reproducible reports. pcaExplorer is implemented in R using the Shiny framework and exploits data structures from the open-source Bioconductor project. Users can easily generate a wide variety of publication-ready graphs, while assessing the expression data in the different modules available, including a general overview, dimension reduction on samples and genes, as well as functional interpretation of the principal components. pcaExplorer is distributed as an R package in the Bioconductor project (http://bioconductor.org/packages/pcaExplorer/), and is designed to assist a broad range of researchers in the critical step of interactive data exploration.

Marini F, Binder H

In BMC Bioinformatics, 2019

Preprint PDF Project DOI Project website

iSEE: Interactive SummarizedExperiment Explorer

Data exploration is critical to the comprehension of large biological data sets generated by high-throughput assays such as sequencing. However, most existing tools for interactive visualisation are limited to specific assays or analyses. Here, we present the iSEE (Interactive SummarizedExperiment Explorer) software package, which provides a general visual interface for exploring data in a SummarizedExperiment object. iSEE is directly compatible with many existing R/Bioconductor packages for analysing high-throughput biological data, and provides useful features such as simultaneous examination of (meta)data and analysis results, dynamic linking between plots and code tracking for reproducibility. We demonstrate the utility and flexibility of iSEE by applying it to explore a range of real transcriptomics and proteomics data sets.

Rue-Albrecht K, Marini F, Soneson C, Lun ATL

In F1000Research, 2018

PDF Project DOI Give it a try! Project website

ideal: an R/Bioconductor package for Interactive Differential Expression Analysis

RNA-sequencing is an ever increasingly popular tool for transcriptome profiling. A key point to make the best use of the available datasets is to provide tools that are easy to use but still provide flexibility and transparency in the adopted methods. In the scope of differential expression analysis, we provide the R/Bioconductor package ideal, which serves as a web application to allow for interac- tive and reproducible analysis, while producing a wealth of effective visualizations to facilitate data interpretation. ideal is implemented in R, based on the Shiny framework, and distributed as a package on Github (https://github.com/federicomarini/ideal) as well as in the Bioconductor project (http://bioconductor.org/packages/ideal/)

Marini F

In **, 2017

Project Project website