mev slides
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WebMeV – Multi-Experiment Viewer on Web
Yaoyu WangCenter for Cancer Computational
Biology, DFCI
Aims and Design Principles
Program Aims:• Free, open-source software for the analysis of high-dimensional genomic data• As an interface to the wide array of tools available in Bioconductor and through
other open-source projects • Natively integrate public genomic databases• Support analysis of data emerging from Next Generation sequencing technologies.• Collaborative tool for result sharing
Design Principles:• Adapt solely on open-source software technology• Adapt elastic computing for variable data analysis complexity• Application portability• Use cases:
– How my favorite genes vary in the dataset from this paper?– Map phenotypes to genotype– How other people I trust interpret this data I analyzed?
MeV Infrastructure Overview
MeV Infrastructure Overview
Private and public domain data catalog
MeV Infrastructure Overview
Clinical/Phenotype cohort refinement
MeV Infrastructure Overview
Interactive Data Visualization and analysis
MeV Infrastructure OverviewSource code downloadable from: https://github.com/dfci-cccb/mev
Beta web version accessible from: http://mev.tm4.org
Data Loading Interface
Loading Options:
- Local files- Files on Cloud (via Google
Drive)- Preloaded TCGA data
searchable by:- Disease type- Data level- Platform- Keywords
- Searchable GEO database- Keywords- GEO series ID (GSE
ID)
GEO Database search by keywords and GSE number to Import
Refine Clinical/Phenotype data to construct customized cohort
View Details- View cohort details- View aggregate statistics- View value distribution
Actions:- Filter data to analyze for
selected cohort - Search by self define
facets- Build composite
phenotypes- Build cohort sets
Refine Clinical/Phenotype data to construct customized cohort
Interactive cohort filter and facet
View Details- View cohort details- View aggregate statistics- View value distribution
Actions:- Filter data to analyze for
selected cohort - Search by self define
facets- Build composite
phenotypes- Build cohort sets
Cohort visualization after data import
View Details- Full data set heatmap- Selectable Cohort
clinical/Phenotype details- Accordion section of
clinical/phenotype summary viewer
Main Analysis Control
View Details- Full data set heatmap- Selectable cohort
clinical/Phenotype details- Clinical/phenotype summary
viewer- Accordion style analysis
results display- Sample selection set manager- Gene selection set manger- Expandable result section
Actions:- Assign samples to sets- Assign genes to sets- Apply and filter analysis
results to visualize on primary data
- Create new dataset for analysis based on analysis results
- R/Bioconductor compatible for rapid analysis incorporation (‘Plug-in’)
Analysis Result PanelSet Manager- Self-selected sample and
gene sets- Set operations (merge,
difference, intersect, export)
Analysis Result Viewer- Accordion style
organization for large number of analysis
- Multi-tab result presentation
- Result summary with tailored data visualization
Next Steps and Future Directions
• Continue development of analysis tools, visualization tools, clinical/phenotype cohort selector
• Enable data and result sharing and discussion through Cloud storage (i.e. Google drive) for collaboration
• Incorporate more public domain tools from Bioconductor, GenePattern, and Cytoscope
• Expanding functionalities to allow more directly use of NGS sequence-based data