Person:

Gehlenborg, Nils

You finished your PhD, have been a postdoc for a while, and you start wondering, “What's next?” Suppose you come to the conclusion that you want to stay in academia, and move up the ladder to become a principal investigator (PI). How does one reach this goal given that academia is one of the most competitive environments out there? And suppose you do manage to snatch your dream position, how do you make sure you hit the ground running? Here we report on the workshop “P2P - From Postdoc To Principal Investigator” that we organized at ISMB 2012 in Long Beach, California. The workshop addressed some of the challenges that many postdocs and newly appointed PIs are facing. Three experienced PIs, Florian Markowetz (Group Leader, Cambridge Research Institute, Cancer Research UK), Gary Bader (Associate Professor, The Donnelly Centre, University of Toronto), and Philip Bourne (Professor, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego), provided insight into the transition from a trainee to PI and shared advice on how to make the best out it.

Rankings are a popular and universal approach to structuring otherwise unorganized collections of items by computing a rank for each item based on the value of one or more of its attributes. This allows us, for example, to prioritize tasks or to evaluate the performance of products relative to each other. While the visualization of a ranking itself is straightforward, its interpretation is not, because the rank of an item represents only a summary of a potentially complicated relationship between its attributes and those of the other items. It is also common that alternative rankings exist which need to be compared and analyzed to gain insight into how multiple heterogeneous attributes affect the rankings. Advanced visual exploration tools are needed to make this process efficient. In this paper we present a comprehensive analysis of requirements for the visualization of multi-attribute rankings. Based on these considerations, we propose LineUp - a novel and scalable visualization technique that uses bar charts. This interactive technique supports the ranking of items based on multiple heterogeneous attributes with different scales and semantics. It enables users to interactively combine attributes and flexibly refine parameters to explore the effect of changes in the attribute combination. This process can be employed to derive actionable insights as to which attributes of an item need to be modified in order for its rank to change. Additionally, through integration of slope graphs, LineUp can also be used to compare multiple alternative rankings on the same set of items, for example, over time or across different attribute combinations. We evaluate the effectiveness of the proposed multi-attribute visualization technique in a qualitative study. The study shows that users are able to successfully solve complex ranking tasks in a short period of time.

Comparisons of stem cell experiments at both molecular and semantic levels remain challenging due to inconsistencies in results, data formats, and descriptions among biomedical research discoveries. The Harvard Stem Cell Institute (HSCI) has created the Stem Cell Commons (stemcellcommons.org), an open, community-based approach to data sharing. Experimental information is integrated using the Investigation-Study-Assay tabular format (ISA-Tab) used by over 30 organizations (ISA Commons, isacommons.org). The early adoption of this format permitted the novel integration of three independent systems to facilitate stem cell data storage, exchange and analysis: the Blood Genomics Repository, the Stem Cell Discovery Engine, and the new Refinery platform that links the Galaxy analytical engine to data repositories.

Background: With ever-increasing amounts of data produced in biology research, scientists are in need of efficient data analysis methods. Cluster analysis, combined with visualization of the results, is one such method that can be used to make sense of large data volumes. At the same time, cluster analysis is known to be imperfect and depends on the choice of algorithms, parameters, and distance measures. Most clustering algorithms don’t properly account for ambiguity in the source data, as records are often assigned to discrete clusters, even if an assignment is unclear. While there are metrics and visualization techniques that allow analysts to compare clusterings or to judge cluster quality, there is no comprehensive method that allows analysts to evaluate, compare, and refine cluster assignments based on the source data, derived scores, and contextual data. Results: In this paper, we introduce a method that explicitly visualizes the quality of cluster assignments, allows comparisons of clustering results and enables analysts to manually curate and refine cluster assignments. Our methods are applicable to matrix data clustered with partitional, hierarchical, and fuzzy clustering algorithms. Furthermore, we enable analysts to explore clustering results in context of other data, for example, to observe whether a clustering of genomic data results in a meaningful differentiation in phenotypes. Conclusions: Our methods are integrated into Caleydo StratomeX, a popular, web-based, disease subtype analysis tool. We show in a usage scenario that our approach can reveal ambiguities in cluster assignments and produce improved clusterings that better differentiate genotypes and phenotypes. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1813-7) contains supplementary material, which is available to authorized users.

Abstract Motivation: Venn and Euler diagrams are a popular yet inadequate solution for quantitative visualization of set intersections. A scalable alternative to Venn and Euler diagrams for visualizing intersecting sets and their properties is needed. Results: We developed UpSetR, an open source R package that employs a scalable matrix-based visualization to show intersections of sets, their size, and other properties. Availability and implementation: UpSetR is available at https://github.com/hms-dbmi/UpSetR/ and released under the MIT License. A Shiny app is available at https://gehlenborglab.shinyapps.io/upsetr/. Contact: nils@hms.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online.