Interactive and exploratory visualization of epigenome-wide data

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.

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

• 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
• Data integration from multiple sources
• 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

More transformations and aggregation

Add new visualizations

Using the epivizr package

• Setup up an epivizr session

mgr <- startEpiviz(workspace="qyOTB6vVnff")

• Calculate a statistic of interest

# Get tumor methylation base-pair data
m <- assay(se)[,"tumor"]

# Compute regions with highest variability across cpgs
region_stat <- calcWindowStat(m, step=25, window=80, stat=rowSds)
s <- region_stat[,"stat"]

Using the epivizr package: browse by regions of interest.

• What's around the regions with highest across CpG variability

# get locations in decreasing order
o <- order(s, decreasing=TRUE)
indices <- region_stat[o, "indices"]
slideShowRegions <- rowRanges(se)[indices] + 1250000L
mgr$slideshow(slideShowRegions)

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

Statistically informed visual exploration

• Standalone version (JS code bundled in epivizr BioC package)
• Browse your favorite genome:

library(epivizr)
library(Mus.musculus)

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

• EpivizWidgets: using epiviz visualizations within, e.g., Rmarkdown
• VisualCollaboration: collaborative annotation of workspaces, datasets, visualizations, etc.

Coordinates:

Samples:

Hierachically organized features

Built with epivizr and metagenomeSeq

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...

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

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

Nature Methods 2014
Follow us: @epiviz

These slides available: http://hcorrada.github.io/ismb2015
Me: @hcorrada, hcorrada@umiacs.umd.edu
www.cbcb.umd.edu/~hcorrada