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 a promising alternative 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 number of 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 random forest model and estimating variable importance with mean decrease gini method and can operate on VCF and CSV files. Future extensions will include support of other importance measures, variable selection methods and data formats.
variant-spark utilizes a novel approach of building random forest 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 VCF and 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, which gives variant-spark the ability to to scale horizontally on both bespoke cluster and public clouds.
The potential users include:
- Medical researchers seeking to perform GWAS-like analysis on large cohort data of genome wide sequencing data or imputed SNP array data.
- Medical researchers or clinicians seeking to perform clustering on genomic profiles to stratify large-cohort genomic data
- General researchers with classification or clustering needs of datasets with millions of features.
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To learn more watch this video from YOW! Brisbane 2017.
variant-spark requires java jdk 1.8+ and maven 3+
In order to build the binaries use:
mvn clean install
For python variant-spark requires python 2.7 with pip. The other packages required for development are listed in
dev/dev-requirements.txt and can be installed with:
pip install -r dev/dev-requirements.txt
The complete built including all check can be run with:
variant-spark requires an existing spark 2.1+ installation (either a local one or a cluster one).
To run variant-spark use:
./variant-spark [(--spark|--local) <spark-options>* --] [<command>] <command-options>*
In order to obtain the list of the available commands use:
In order to obtain help for a specific command (for example
./variant-spark importance -h
You can use
--spark marker before the command to pass
spark-submit options to variant-spark. The list of spark options needs to be terminated with
./variant-spark --spark --master yarn-client --num-executors 32 -- importance ....
Please, note that
--spark needs to be the first argument of
You can also run variant-spark in the
--local mode. In this mode variant-spark will ignore any Hadoop or Spark configuration files and run in the local mode for both Hadoop and Spark. In particular in this mode all file paths are interpreted as local file system paths. Also any parameters passed after
--local and before
-- are ignored. For example:
./variant-spark --local -- importance -if data/chr22_1000.vcf -ff data/chr22-labels.csv -fc 22_16051249 -v -rn 500 -rbs 20 -ro
The difference between running in
--local mode and in
local master is that in the latter case Spark uses the hadoop filesystem configuration and the input files need to be copied to this filesystem (e.g. HDFS) Also the output will be written to the location determined by the hadoop filesystem settings. In particular paths without schema e.g. 'output.csv' will be resolved with the hadoop default filesystem (usually HDFS) To change this behavior you can set the default filesystem in the command line using
spark.hadoop.fs.default.name option. For example to use local filesystem as the default use:
veriant-spaek --spark ... --conf "spark.hadoop.fs.default.name=file:///" ... -- importance ... -of output.csv
You can also use the full URI with the schema to address any filesystem for both input and output files e.g.:
veriant-spaek --spark ... --conf "spark.hadoop.fs.default.name=file:///" ... -- importance -if hdfs:///user/data/input.csv ... -of output.csv
There are multiple methods for running variant-spark examples
variant-spark comes with a few example scripts in the
scripts directory that demonstrate how to run its commands on sample data .
There is a few small data sets in the
data directory suitable for running on a single machine. For example
runs variable importance command on a small sample of the chromosome 22 vcf file (from 1000 Genomes Project)
The full size examples require a cluster environment (the scripts are configured to work with Spark on YARN).
The data required for the examples can be obtained from: https://bitbucket.csiro.au/projects/PBDAV/repos/variant-spark-data
This repository uses the git Large File Support extension, which needs to be installed first (see: https://git-lfs.github.com/)
variant-spark-data repository and then to install the test data into your hadoop filesystem use:
By default the sample data will installed into the
variant-spark-data\input sub directory of your HDFS home directory.
You can choose a different location by setting the
VS_DATA_DIR environment variable.
After the test data has been successfully copied to HDFS you can run examples scripts, e.g.:
Note: if you installed the data to a non default location the
VS_DATA_DIR needs to be set accordingly when running the examples
Databricks notebook examples
For convenience we have also provided a sample end-to-end variant-spark workflow in a Databricks (Jupyter-style) notebook for Spark 2.2. The examples, using a synthetic phenotype (Hipster-index) can be found in the
notebook-examples folder of this repository.
To use an example:
- Create a free, community Databricks account
- Download the
VariantSpark_HipsterIndex_Spark2.2.scalafile. Databricks notebook is for Spark 2.2 (scala 2.11)
- Import the notebook file into your Databricks instance. Read the instructions in the notebook on how to import a new library to use the
- Start a cluster (be sure to select the version of Spark and Scala specified in the notebook). Wait up to 5 minutes for the cluster to be ready.
- Attach the notebook to the cluster
- Run the sample notebook