Movember Foundation, Prostate Cancer Canada, Ontario Institute for Cancer Research, Canadian Institute for Health Research, NIHR Cambridge Biomedical Research Centre, The University of Cambridge, Cancer Research UK, Cambridge Cancer Charity, Prostate Cancer UK, Hutchison Whampoa Limited, Terry Fox Research Institute, Princess Margaret Cancer Centre Foundation, PMH-Radiation Medicine Program Academic Enrichment Fund, Motorcycle Ride for Dad (Durham), Canadian Cancer Society.
BackgroundWe introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment.ResultsThis open-source package includes multiple methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it suitable for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for integration with computational pipelines.ConclusionBPG provides a new approach for linking interactive and scripted data visualization and is available at http://labs.oicr.on.ca/boutros-lab/software/bpg or via CRAN at https://cran.r-project.org/web/packages/BoutrosLab.plotting.generalElectronic supplementary materialThe online version of this article (10.1186/s12859-019-2610-2) contains supplementary material, which is available to authorized users.
Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS (Subnetwork Integration for Multi-Modal Signatures): an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery.
We introduce BPG, an easy-to-use framework for generating publication-quality, highly-customizable plots in the R statistical environment. This open-source package includes novel methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it ideal for complex datasets. A webbased interactive tool allows online figure customization, from which R code can be downloaded for seamless integration with computational pipelines. BPG is available at http://labs.oicr.on.ca/boutros-lab/software/bpg . CC-BY 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/156067 doi: bioRxiv preprint first posted online Jun. 26, 2017; BPG P'ng et al. Page 3 of 8Biological experiments are increasingly generating large, multifaceted datasets. Exploring such data and communicating observations is, in turn, growing more difficult and the need for robust scientific datavisualization is growing rapidly 1,2,3,4 . Myriad data visualization tools exist, particularly as web-based interfaces and non-R-based local software packages. Unfortunately these do not integrate easily into R-based statistical pipelines such as the widely used Bioconductor 5 . Within R, visualization packages exist, including base graphics 6 , ggplot2 7 , lattice 8 , Sushi 9 , circlize 10 , multiDimBio 11 , NetBioV 12 , GenomeGraphs 13 and ggbio 14 . These lack publication-quality defaults, contain limited plot types, provide limited scope for automatic generation of multi-panel figures, are constrained to specific data-types and do not allow interactive visualization.Good visualization software must create a wide variety of chart-types in order to match the diversity of datatypes available. It should provide flexible parametrization for highly customized figures and allow for multiple output formats while employing reasonable, publication-appropriate default settings, such as producing high resolution output. In addition, it should integrate seamlessly with existing computational pipelines while also providing an easily intuitive, interactive mode. There should be an ability to transition between pipeline and interactive mode, allowing cyclical development. Finally, good design principles should be encouraged, such as suggesting appropriate color choices and layouts for specific use-cases. To help users quickly gain proficiency, detailed examples, tutorials and an application programming interface (API) are required. To date, no existing visualization suite fills these needs.To address this gap, we have created the BPG library, which is implemented in R using the grid graphics system and lattice framework. It generates a broad suite of chart-types, ranging from common plots such as bar charts and box plots to more specialized plots, such as Manhattan plots (Figure 1; code is in Supplementary File 1).
Acute Myeloid Leukemia (AML) develops due to the acquisition of mutations from multiple functional classes. Here, we demonstrate that activating mutations in the granulocyte colony stimulating factor receptor (CSF3R), cooperate with loss of function mutations in the transcription factor CEBPA to promote acute leukemia development. This finding of mutation-synergy is broadly applicable other mutations that activate the JAK/STAT pathway or disrupt CEBPA function (i.e. activating mutations in JAK3 and Core Binding Factor translocations). The interaction between these distinct classes of mutations occurs at the level of myeloid lineage enhancers where mutant CEBPA prevents activation of subset of differentiation associated enhancers. To confirm this enhancer-dependent mechanism, we demonstrate that CEBPA mutations must occur as the initial event in AML initiation, confirming predictions from clinical sequencing data. This improved mechanistic understanding will facilitate therapeutic development targeting the intersection of oncogene cooperativity. B.J.D.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.