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#!/bin/bash
# Job requirements
#Submit this script with: sbatch thefilename
#For more details about each parameter, please check SLURM sbatch documentation https://slurm.schedmd.com/sbatch.html

#SBATCH --time=8:00:00   # walltime
#SBATCH --ntasks=1   # number of tasks
#SBATCH --cpus-per-task=16   # number of CPUs Per Task i.e if your code is multi-threaded
#SBATCH --nodes=1   # number of nodes
#SBATCH -p datamover   # partition(s)

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"""Prototype of distance based clumping."""

from typing import TYPE_CHECKING

import numpy as np
import pyspark.ml.functions as fml
import pyspark.sql.functions as f
from pyspark.ml.linalg import DenseVector, Vectors, VectorUDT
from pyspark.sql import SparkSession

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"""Prototype of distance based clumping."""

import pyspark.sql.functions as f
from pyspark.sql import Column, SparkSession, Window

spark = SparkSession.builder.getOrCreate()

data = [
    ("s1", "chr1", 3, 2.0, False),
    ("s1", "chr1", 4, 3.0, False),

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"""
Compute all vs all Bayesian colocalisation analysis for all Genetics Portal

This script calculates posterior probabilities of different causal variants
configurations under the assumption of a single causal variant for each trait.

Logic reproduced from: https://github.com/chr1swallace/coloc/blob/main/R/claudia.R
"""

from functools import reduce

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from pyspark import SparkConf
from pyspark.sql import SparkSession
from pyspark.ml.regression import LinearRegression
from pyspark.ml.feature import VectorAssembler 
from pyspark.ml.linalg import VectorUDT, Vectors
import pyspark.sql.types as T
import pyspark.sql.functions as F

sparkConf = SparkConf()
sparkConf = sparkConf.set('spark.hadoop.fs.gs.requester.pays.mode', 'AUTO')

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import pyspark.sql.functions as F
from pyspark import SparkConf
from pyspark.sql import SparkSession
from functools import reduce 

sparkConf = SparkConf()
sparkConf = sparkConf.set('spark.hadoop.fs.gs.requester.pays.mode', 'AUTO')
sparkConf = sparkConf.set('spark.hadoop.fs.gs.requester.pays.project.id',
                          'open-targets-eu-dev')

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from os import sep
import pyspark.sql.functions as F
from pyspark import SparkConf
from pyspark.sql import SparkSession

sparkConf = SparkConf()
sparkConf = sparkConf.set('spark.hadoop.fs.gs.requester.pays.mode', 'AUTO')
sparkConf = sparkConf.set('spark.hadoop.fs.gs.requester.pays.project.id',
                          'open-targets-eu-dev')

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import pyspark.sql.functions as F
from pyspark import SparkConf
from pyspark.sql import SparkSession

sparkConf = SparkConf()
sparkConf = sparkConf.set('spark.hadoop.fs.gs.requester.pays.mode', 'AUTO')
sparkConf = sparkConf.set('spark.hadoop.fs.gs.requester.pays.project.id',
                          'open-targets-eu-dev')

# establish spark connection

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library("tidyverse")
library("sparklyr")
library("sparklyr.nested")
library("cowplot")
library("ggsci")

#Spark config
config <- spark_config()

# Allowing to GCP datasets access

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---
title: "Batch-query all platform evidence associated with a gene/target list (R)"
output:
  md_document:
    variant: markdown_github
---

How to batch-access information related to a list of targets from the Open Targets Platform is a recurrent question. Here, I provide an example on how to access all target-disease evidence for a set of IFN-gamma signalling related proteins. I will further reduce the evidence to focus on all the coding or non-coding variants clinically-associated with the gene list of interest. I used R and sparklyr, but a Python implementation would be very similar. The platform documentation and the community space have very similar examples.