Selected Publications

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.
In BMC Bioinformatics, 2019

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.
In F1000Research, 2018

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/)
In **, 2017

Recent Publications

The full publications list is available on Google Scholar and on ORCID.

Here are some detailed information on a couple of my latest contributions:

Principal component analysis (PCA) is frequently used in genomics applications for quality assessment and exploratory analysis in …

Data exploration is critical to the comprehension of large biological data sets generated by high-throughput assays such as sequencing. …

RNA-sequencing is an ever increasingly popular tool for transcriptome profiling. A key point to make the best use of the available …

Projects

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Interactive and reproducible research

Developing tools enabling interactivity and reproducibility, for better analyses.

Plateletopedia

Understanding platelet transcriptomics, one dataset after the other, with different bioinformatics perspectives.

Software

🚧 WORK IN PROGRESS 🚧

R packages

I am the maintainer or co-developer of the following R packages:

Other resources

Teaching

🚧 WORK IN PROGRESS 🚧