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