Visualization prototype for identifying co-occurring observations regarding events in a sequence.
Switch branches/tags
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
img
lib
preprocess
shaders
.gitignore
.hgignore
.htaccess
LICENSE
README.md
binarytojson.js
bitmap.js
colorbrewer.js
d3viewer.html
definedData.json
index.html
matrixviewer.js
read_windows.png
style.css
test_depth.png

README.md

CooccurViewer

This project contains the prototype implementations of the co-occurrence system presented in our Computer Graphics Forum paper "Visualizing Co-occurrence of Events in Populations of Viral Genome Sequences." This work (will be|was) presented at the EuroVis 2016 conference on 8 June 2016 in Groningen, NL.

The D3-based Co-occurrence viewer for the SIV dataset

The project seeks to expose correlation of observations made between any pair of events in a data sequence. In this project, we present two methods for identifying interesting co-occurrences (see the manuscript for a detailed discussion).

The first is a matrix-based approach (index.html in the root folder). This is a WebGL-based approach for showing all correlations in the full pairwise correlation space, and is presented as a negative example in the manuscript. Please note that your current configuration must support WebGL for this implementation to work; see WebGL Report for details.

The second is a more guided, explicit approach (d3viewer.html in the root folder), presented as the 'CooccurViewer' application in the manuscript. This is a D3-based approach that uses thresholds of particular criteria to filter the data down to managable size.

A demo is availble of these two approaches through the project website.

Documentation

In order to generate the data for the application, one must parse a SAM file into binary file for consumption by the visualization. A Java program within the preprocess/ directory contains this program, as well as methods for building (compile.sh) and executing the program (runMetric.sh). The program has built-in parameter checking and a help screen, copied below:

usage: CoOccurLibrary [-d </path/to/outputDir/>] -f <FILE.sam> [-h] -n
	   <reads> -p <positions> [-r <ref.fa>] [-w <window>]

Parses a given SAM file into a metric that can be used by the MatrixViewer
visualization. See more information at <URL>
 -d,--outputDir </path/to/outputDir/>   Directory to dump output files
 -f,--inputSAM <FILE.sam>               The SAM file to process
 -h,--help                              Prints this help sheet
 -n,--numReads <reads>                  The number of reads to expect (run
										`wc -l <FILE.sam>` to estimate;
										necessary for memory allocation)
 -p,--numPositions <positions>          The number of positions to expect
										(overestimate by reading number of
										lines in FILE.sam)
 -r,--inputReference <ref.fa>           Sets the reference to the sequence
										found in the given file.
 -w,--windowSize <window>               The number of positions around
										every positions to check for
										correlation (default 300)

Please direct any questions to Alper Sarikaya ([email]).

Once the output data directory is generated, copy the directory and its contents to the data/ directory in the visualization. To let the vis know that additional data is available, ammend the definedData.json file in the root to point to the relevant data files. Define a named top-level object with the name of the data directory (e.g. SIV), and then the required data as below, at minimum.

"SIV": {
	"attenuation": "readBreadth.dat",
	"metrics": [ "conjProbDiff.dat" ],
	"fullcounts": "fullCounts.dat",
	"refdata": "reference.dat",
	"annotations", "sivmac239_proteins.json"
}

The annotations file is optional. The annotation file should be a list of anonymous JavaScript objects with the following fields defined at a minimum: gene (the name of the annotation) and locations (the starting and ending positions of the annotated region, e.g. 9333-10124). See the SIV annotation file for an example.

Start a local webserver (e.g. python -m SimpleHTTPServer), navigate to the appropriate visualization (e.g. 127.0.0.1:8000/d3viewer.html), and select the desired dataset from the blue dropdown at the top.

Libraries used

These implementations use a multitude of libraries to help it go. Below is a list of the libraries used, their licenses, and how they are used in the system.

  • Bootstrap (MIT) -- Used to style and organize UI components on the page, including modal windows.
  • Bootstrap-submenu (MIT) -- Used to enable submenus for Bootstrap 3.0 (for dataset hierarchies)
  • jQuery (MIT) -- Used to support Bootstrap and provide event listeners for mouse
  • jQuery UI (MIT) -- Supports the operation of sliders
  • jquery-mousewheel (MIT) -- Adds normalized support for mousewheel events (zooming on canvas)
  • Hashable.js (none?) -- Adds support for parsing/updating the URL hash to save current viewing state
  • lightgl.js (MIT?) -- Provides a nice abstraction layer for doing low-level WebGL commands (e.g. drawing to texture, managing shaders, binding textures)

Contact

Please contact Alper Sarikaya with any comments or questions, or feel free to open an issue or pull request.