Measuring DNA methylation and understanding role in expression regulation in solid tumors

Large blocks of hypo-methylation (sometimes Mbps long) in colon cancer

Measuring DNA methylation and understanding role in expression regulation in solid tumors

Hyper-variable genes are enriched within these blocks.

Measuring DNA methylation and understanding role in expression regulation in solid tumors

Consistently hyper-variable genes are tissue-specific.

Measuring DNA methylation and understanding role in expression regulation in solid tumors

Blocks can be detected using Illumina bead arrays.

Measuring DNA methylation and understanding role in expression regulation in solid tumors

Hyper-variability is enriched within hypo-methylation blocks

• State-of-the-art computational and statistical analysis platform
• We develop and apply methods for these analyses in this platform
• Our collaborators take part in analysis in this platform

• Data transformation and modeling: data smoothing, region finding (R/Bioconductor: Bsmooth, minfi)
• Genome browsing: search by gene, search by overlap
• Region analysis: overlap with other data (our own, other labs, UCSC, ensembl)
• Regulation: expression data (Gene Expression Barcode)

• Funding calls to (strictly) analyze project data
  • Epigenomics roadmap, Encode, TCGA, ...
• Journals calling for (strictly) analysis papers (e.g., Nature Methods)
• We have unprecedented ability to measure
• and lots of publicly available data to contextualize it

• Funding calls to (strictly) analyze project data
  • Epigenomics roadmap, Encode, TCGA, ...
• Journals calling for (strictly) analysis papers (e.g., Nature Methods)
• We have unprecedented ability to measure
• and lots of publicly available data to contextualize it

Integrative, visual and computational exploratory analysis of genomic data

• Browser-based
• Interactive
• Integration of data
• Reproducible dissemination
• Communication with R/Bioc: epivizr package

I want to use a genome browser track as a display device in R!!

Plug-in data from R with epivizr package

Workspaces and filtering

Data transformations and customization

Navigate and annotate

Transformations and Aggregation

Add new visualizations

Statistically informed visual exploration

Reproduce, disseminate and collaborate

Using the epivizr package

• Setup up an epivizr session

mgr <- startEpiviz(workspace="qyOTB6vVnff")

• Add a device with GRanges data

blocks_dev <- mgr$addDevice(colon_blocks, "450k blocks")

• Subset ranges by width

keep <- width(colon_blocks) > 250000
mgr$updateDevice(blocks_dev, colon_blocks[keep,])

Using the epivizr package: browse by regions of interest.

• What's around the widest blocks?

o <- order(-width(colon_blocks))
slideShowRegions <- colon_blocks[o[1:5],]
slideShowRegions <- slideShowRegions + 1e5
mgr$slideshow(slideShowRegions)

epivizr uses WebSockets for connection, same as shiny. Big, big, big thanks to the @rstudio folks for working on this infrastructure.

Our architecture is dynamically extensible. We can easily integrate new data types and add new visualizations.

Example: adding a new visualization

see: https://gist.github.com/11017650

• Based on "three-table" design in Bioconductor infrastructure

• Standalone version (javascript code bundled in epivizr)
• Browse your favorite genome:

library(epivizr)
library(Mus.musculus)

mgr <- startStandalone(geneInfo=Mus.musculus, geneInfoName="mm10",
                          keepSeqlevels=paste0("chr",c(1:19,"X","Y")))

One interpretation of Big Data is Many relevant sources of contextual data

• Easily access/integrate contextual data
• Driven by exploratory analysis of immediate data

One interpretation of Big Data is Many relevant sources of contextual data

• Iterative process
• Visual and computational exploration go hand in hand

We are building a software system to support creative exploratory analysis of epigenome-wide datasets...

• Context
  • Integrate and align multiple data sources; navigate; search
  • Connect: brushing
  • Encode: map visualization properties to data on the fly
  • Reconfigure: multiple views of the same data

• Data
  • Select and filter: tight-knit integration with R/Bioconductor;
  • (future) filters on visualization propagate to data environment
• Model
  • New 'measurements' the result of modeling; perhaps suggested by data context

Florin Chelaru, UMD

• CBCB@UMD
• JHU/Harvard: K. Hansen, W. Timp, R. Irizarry, A. Feinberg
• Genentech: Michael Lawrence
• Rstudio: Joe Cheng, et al.
• Funding: NIH, Genentech

• http://epiviz.github.io
• http://epiviz.cbcb.umd.edu
• http://github.com/epiviz

Nature Methods 2014
Follow us: @epiviz

These slides available: http://hcorrada.github.io/bioit_world2015