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Robert Gentleman
Researcher at Genentech
Publications - 140
Citations - 53506
Robert Gentleman is an academic researcher from Genentech. The author has contributed to research in topics: Bioconductor & Gene expression profiling. The author has an hindex of 52, co-authored 139 publications receiving 48510 citations. Previous affiliations of Robert Gentleman include Harvard University & Brigham and Women's Hospital.
Papers
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Journal ArticleDOI
Design and Analysis of DNA Microarray Investigations
TL;DR: The book’s impressive breadth and depth make it an essential reference for any researcher interested in understanding the state-of-the-art methods and potential applications in latent multilevel, longitudinal, and structural equation modeling.
Journal ArticleDOI
iFlow: A Graphical User Interface for Flow Cytometry Tools in Bioconductor
TL;DR: An open source, extensible graphical user interface (GUI) iFlow, which sits on top of the Bioconductor backbone, enabling basic analyses by means of convenient graphical menus and wizards, and is envisioned to be easily extensible in order to quickly integrate novel methodological developments.
Book ChapterDOI
Case Studies Using Graphs on Biological Data
TL;DR: This chapter considers four specific data-analytic and inferential problems that can be addressed using graphs and shows how one can investigate relationships between gene expression and protein-protein interaction data, how GO annotations can be used to analyze gene sets, and how literature citations can be related to experimental data.
Journal ArticleDOI
VariantTools: an extensible framework for developing and testing variant callers.
TL;DR: VariantTools, an extensible framework for developing and testing variant callers that is extensible, modular and flexible, so that they are tunable to particular use cases, and they interoperate with existing analysis software so they can be embedded in established work flows.
On the synthesis of microarray experiments
TL;DR: The synthesis of different microarray data sets using a random effects paradigm is considered and it is demonstrated how relatively standard statistical approaches yield good results.