{"repo":"aehrc/VariantSpark","free":true,"listed":false,"github":"https://github.com/aehrc/VariantSpark","clone":"git clone https://github.com/aehrc/VariantSpark.git","description":"machine learning for genomic variants","language":"JavaScript","stars":147,"topics":["variant-spark","gwas","random-forest","genome","association-studies","vcf","variantspark","notebook","databricks","bioinformatics"],"license":null,"category":"machine-learning","readme_excerpt":"Variant Spark variant-spark is a scalable toolkit for genome-wide association studies optimized for GWAS-like datasets. Machine learning methods and, in particular, random forests (RFs) are promising alternatives to standard single SNP analyses in genome-wide association studies (GWAS). RFs provide variable importance measures to rank SNPs according to their predictive power. Although there are several existing random forest implementations available, some even parallel or distributed such as Random Jungle, ranger, or SparkML, most of them are not optimized to deal with GWAS datasets, which usually come with thousands of samples and millions of variables. variant-spark currently provides the basic functionality of building a random forest model and estimating variable importance with the mean decrease gini method. The tool can operate on VCF and CSV files. Future extensions will include support for other importance measures, variable selection methods, and data formats. variant-spark utilizes a novel approach of building random forests from data in transposed representation, which allows it to efficiently deal with even extremely wide GWAS datasets. Moreover, since the most common genomics variant calls file format, i.e. VCF, which uses the transposed representation, variant-spark can work directly with the VCF data, without the costly pre-processing required by other tools. variant-spark is built on top of Apache Spark – a modern distributed framework for big data processing","default_branch":null,"files":null,"tree":[],"storefront":"/r/aehrc","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/aehrc/VariantSpark/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}