genorec-engine
v1.1.1
Published
This is the recommendation engine for genomics visualization recommendation tool.
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Genorec-Engine
Genorec-Engine is a JavaScript library for recommending genomics visualizations.
NPM
https://www.npmjs.com/package/genorec-engine
Prerequisites
Install Node version v 12 or higher.
Installation
npm install genorec-engine
If you have a package.json file created in your project, you can also save the library to your package.json file.
npm install --save genorec-engine
How to use the library
- Import the library in your script file
var genorec = require("genorec-engine")
- To execute the code and get recommendation spec, you will need to the getRecommendation method and pass it an 'input spec' file.
var recommendationSpec = genorec.getRecommendation(inputSpec)
Input Spec
Currently, genorec-engine is designed to work with genorec web application. We recommend you check out the web application for genorec-engine.
https://github.com/aditeyapandey/Genorec-Client
To use genorec-engine independently, you can ues a sample input spec file.
{
"sequences": [
{
"sequenceId":"sequence_0",
"sequenceName":"XYZ",
"interFeatureTasks":{"compare":[],"correlate":[]},
"features":
[
{
"featureId":"feature_0",
"featureGranularity":"segment",
"featureDensity":"sparse",
"featureLabel": "Random",
"featureInterconnection": true,
"denseInterconnection": false,
"intraFeatureTasks":[],
"interactivity":false,
"attr":
[
{
"attrId":"attribute_0",
"dataType":"categorical",
"intraAttrTask":["identify"]
},
{
"attrId":"attribute_1",
"dataType":"quantitative",
"intraAttrTask":["identify","compare"]
},
{
"attrId":"attribute_2",
"dataType":"quantitative",
"intraAttrTask":["identify","compare"]
}
]
}
]
}],
"intraSequenceTask": {"connectedNodes":[],"sequenceConservation":[],"edgeValues":[]},
"denseConnection": false,
"sparseConnection": false}
Output Spec
The output recommendation spec of genore-engine contains information about the recommended visualization.
{
"recommendation_0": {
"recommendationStage": 5,
"arrangement": "circularStacked",
"predictionScore": 0.6666666666666667,
"visDetails": {
"Sequence_0": {
"recommendationStage": 4,
"trackAlignment": "stacked",
"visDetails": {
"TrackGroup_0": {
"recommendationStage": 3,
"layout": "circular",
"predictionScore": 0.16666666666666663,
"visDetails": {
"Track_0": {
"recommendationStage": 2,
"groupingTechnique": "combined",
"visDetails": {
"Attribute_0": {
"recommendationStage": 1,
"encoding": "intervalBarchartCN",
"predictionScore": 0.6
},
"Attribute_1": {
"recommendationStage": 1,
"encoding": "intervalBarchart",
"predictionScore": 0.8
}
}
},
"Track_1": {
"recommendationStage": 2,
"groupingTechnique": "none",
"visDetails": {
"Attribute_0": {
"recommendationStage": 1,
"encoding": "intervalBarchart",
"predictionScore": 0.8
}
}
}
},
"interconnection": true,
"granularity": "segment",
"availability": "sparse"
}
}
}
},
"sequenceInterconnection": false,
"connectionType": "sparse"
},
"tasks": ["explore"]
}