Meetup held July 30, 2019 viewing two keynote presentations given at the recent useR 2019 conference in Toulouse, France.
useR Conference: http://www.user2019.fr/
The Bioconductor project has had profound influence on the statistical analysis and comprehension of high-throughput genomic data, while contributing many innovations to the R language and community. Bioconductor started in 2002 and has grown to more than 1700 packages downloaded to ½ million unique IP addresses annually; Bioconductor has more than 30,000 citations in the scientific literature, and positively impacts many scientific careers. The desire for open, reproducible science contributes to many aspects of Bioconductor, including literate programming vignettes, multi-package workflows, teaching courses and online material, extended package checks, use of formal (S4) classes, reusable ‘infrastructure’ packages for robust and interoperable code, centralized version control and support, nightly cross-platform builds, and a distinctive release strategy that enables developer innovation while providing user stability. Contrasts between Bioconductor and R provide rich opportunities for reflection on establishing open source communities, how users translate software into science, and software development best practices. The ever-changing environment of scientific computing, especially the emergence of cloud-based computation and very large and heterogeneous public data resources, point to areas where Bioconductor, and R, will continue to innovate.
Slides: Keynote Slides
In many application settings, the data have missing features which make data analysis challenging. An abundant literature addresses missing data as well as more than 150 R packages. Julie has created an R-miss-tastic platform along with a dedicated task view (https://cran.r-project.org/web/views/MissingData.html) which aims at giving an overview of main references, contributors, tutorials to offer users keys to analyze their data. This plate form highlights that this is an active field of work and that as usual different problems requires designing dedicated methods. She starts with an start by the inferential framework, where the aim is to estimate at best the parameters and their variance in the presence of missing data. Last multiple imputation methods have focused on taking into account the heterogeneity of the data (multi-sources with variables of different natures, etc.). And finally, a discussion on recent results in a supervised-learning setting.