{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 5.1 Big Data in Genomics - Clustering\n",
"\n",
"This notebooks demonstated how kMeans clustering can be used to create the reduced (in terms of dimensions) representation of genonomic variants. \n",
"\n",
"In this notebook we use a small sample of the data from '1000 Genomes Project', which provide whole genomes for ~2500 individuals from ~20 populatios in the world. (We use only about 1000 variants from chromosome 22).\n",
"\n",
"The data are available in the `VCF` (Variant Call Format), which essentially describe the positions, at which the genome of each of the individuals differs from the 'reference genome'.\n",
"\n",
"Let's have a quick look at the data first (`data/ALL.chr22.phase3_1000.vcf.bz2`):"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"##INFO=<ID=EX_TARGET,Number=0,Type=Flag,Description=\"indicates whether a variant is within the exon pull down target boundaries\">\n",
"##INFO=<ID=MULTI_ALLELIC,Number=0,Type=Flag,Description=\"indicates whether a site is multi-allelic\">\n",
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]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"bunzip2: I/O or other error, bailing out. Possible reason follows.\n",
"bunzip2: Broken pipe\n",
"\tInput file = data/ALL.chr22.phase3_1000.vcf.bz2, output file = (stdout)\n"
]
}
],
"source": [
"%%sh\n",
"\n",
"bunzip2 -c 'data/ALL.chr22.phase3_1000.vcf.bz2' | head -n 255 | tail -n 5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will preprocess the file and load it to a DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[u'#CHROM', u'POS', u'ID', u'REF', u'ALT', u'QUAL', u'FILTER', u'INFO', u'FORMAT', u'HG00096']\n",
"[u'HG00096', u'HG00097', u'HG00099', u'HG00100', u'HG00101', u'HG00102', u'HG00103', u'HG00105', u'HG00106', u'HG00107']\n"
]
}
],
"source": [
"# load the file as text RDD\n",
"chr22RDD = sc.textFile('data/ALL.chr22.phase3_1000.vcf.bz2', 4)\n",
"\n",
"# extract the header line (starting with '#CHROM`)\n",
"header = chr22RDD.filter(lambda line: line.startswith(\"#CHROM\")).map(lambda line:line.split()).first()\n",
"print(header[0:10])\n",
"columnNames = header[0:9]\n",
"sampleNames = header[9:]\n",
"print(sampleNames[0:10])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of variants: 1000\n"
]
}
],
"source": [
"from pyspark.ml.linalg import Vectors\n",
"\n",
"# encode the genotypes base on the amount of variation from the base allele\n",
"# 0|0 -> 0\n",
"# 0|1 or 1|0 -> 1\n",
"# 1|1 -> 2\n",
"encodeGenotype = lambda genotype: float(sum( allele!='0' for allele in genotype.split('|')))\n",
"encodeGenotypes = lambda genotypes: Vectors.dense(map(encodeGenotype, genotypes))\n",
"\n",
"def splitGenotypeLine(line):\n",
" columns = line.split()\n",
" return columns[0:9] + [ encodeGenotypes(columns[9:]) ]\n",
"\n",
"\n",
"# create a DataFrame from with the encoded genotypes\n",
"df = spark.createDataFrame(chr22RDD.filter(lambda line: not line.startswith(\"#\")).map(splitGenotypeLine), \n",
" schema = columnNames + ['encoded_genotypes'])\n",
"df.cache()\n",
"print(\"Number of variants: %s\" % df.count())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"root\n",
" |-- #CHROM: string (nullable = true)\n",
" |-- POS: string (nullable = true)\n",
" |-- ID: string (nullable = true)\n",
" |-- REF: string (nullable = true)\n",
" |-- ALT: string (nullable = true)\n",
" |-- QUAL: string (nullable = true)\n",
" |-- FILTER: string (nullable = true)\n",
" |-- INFO: string (nullable = true)\n",
" |-- FORMAT: string (nullable = true)\n",
" |-- encoded_genotypes: vector (nullable = true)\n",
"\n"
]
}
],
"source": [
"df.printSchema()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>#CHROM</th>\n",
" <th>POS</th>\n",
" <th>ID</th>\n",
" <th>REF</th>\n",
" <th>ALT</th>\n",
" <th>QUAL</th>\n",
" <th>FILTER</th>\n",
" <th>INFO</th>\n",
" <th>FORMAT</th>\n",
" <th>encoded_genotypes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>22</td>\n",
" <td>16050075</td>\n",
" <td>rs587697622</td>\n",
" <td>A</td>\n",
" <td>G</td>\n",
" <td>100</td>\n",
" <td>PASS</td>\n",
" <td>AC=1;AF=0.000199681;AN=5008;NS=2504;DP=8012;EA...</td>\n",
" <td>GT</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>22</td>\n",
" <td>16050115</td>\n",
" <td>rs587755077</td>\n",
" <td>G</td>\n",
" <td>A</td>\n",
" <td>100</td>\n",
" <td>PASS</td>\n",
" <td>AC=32;AF=0.00638978;AN=5008;NS=2504;DP=11468;E...</td>\n",
" <td>GT</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>22</td>\n",
" <td>16050213</td>\n",
" <td>rs587654921</td>\n",
" <td>C</td>\n",
" <td>T</td>\n",
" <td>100</td>\n",
" <td>PASS</td>\n",
" <td>AC=38;AF=0.00758786;AN=5008;NS=2504;DP=15092;E...</td>\n",
" <td>GT</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"DataFrame[#CHROM: string, POS: string, ID: string, REF: string, ALT: string, QUAL: string, FILTER: string, INFO: string, FORMAT: string, encoded_genotypes: vector]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.limit(3))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# train the kMeans model with 10 clusters\n",
"from pyspark.ml.clustering import KMeans\n",
"kMeans = KMeans(featuresCol='encoded_genotypes', k=10, initMode='random')\n",
"kMeansModel = kMeans.fit(df)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample ID</th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>HG00096</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.003282</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.5625</td>\n",
" <td>1.0</td>\n",
" <td>0.863636</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>HG00097</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.800000</td>\n",
" <td>0.006565</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.1250</td>\n",
" <td>0.0</td>\n",
" <td>1.409091</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2502</th>\n",
" <td>NA21143</td>\n",
" <td>0.000000</td>\n",
" <td>0.166667</td>\n",
" <td>1.533333</td>\n",
" <td>0.006565</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0000</td>\n",
" <td>0.0</td>\n",
" <td>1.863636</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2503</th>\n",
" <td>NA21144</td>\n",
" <td>1.222222</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.002188</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.7500</td>\n",
" <td>0.0</td>\n",
" <td>1.954545</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2504 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" Sample ID 0 1 2 3 4 5 6 7 \\\n",
"0 HG00096 0.000000 0.000000 0.000000 0.003282 0.0 0.0 0.5625 1.0 \n",
"1 HG00097 0.000000 0.083333 0.800000 0.006565 0.0 0.0 0.1250 0.0 \n",
"... ... ... ... ... ... ... ... ... ... \n",
"2502 NA21143 0.000000 0.166667 1.533333 0.006565 0.0 0.0 0.0000 0.0 \n",
"2503 NA21144 1.222222 0.000000 0.000000 0.002188 0.0 0.0 0.7500 0.0 \n",
"\n",
" 8 9 \n",
"0 0.863636 0.0 \n",
"1 1.409091 0.0 \n",
"... ... ... \n",
"2502 1.863636 0.0 \n",
"2503 1.954545 1.0 \n",
"\n",
"[2504 rows x 11 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# use pandas to transpose the cluster centers\n",
"import pandas as pd\n",
"pd.set_option('display.max_rows', 5)\n",
"clusterCentersPD = pd.DataFrame.from_records(kMeansModel.clusterCenters()).T\n",
"clusterCentersPD.insert(0, 'Sample ID', sampleNames)\n",
"clusterCentersPD"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"root\n",
" |-- Sample ID: string (nullable = true)\n",
" |-- 0: double (nullable = true)\n",
" |-- 1: double (nullable = true)\n",
" |-- 2: double (nullable = true)\n",
" |-- 3: double (nullable = true)\n",
" |-- 4: double (nullable = true)\n",
" |-- 5: double (nullable = true)\n",
" |-- 6: double (nullable = true)\n",
" |-- 7: double (nullable = true)\n",
" |-- 8: double (nullable = true)\n",
" |-- 9: double (nullable = true)\n",
"\n"
]
}
],
"source": [
"# create a Spark DataFrame from panads \n",
"clusterCentersDF = spark.createDataFrame(clusterCentersPD)\n",
"clusterCentersDF.printSchema()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# save the reduced representation to a csv file\n",
"clusterCentersDF.coalesce(1).write.csv(path='output/cluster_centers.csv', mode='overwrite', header=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "PySpark",
"language": "python",
"name": "pyspark"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 1
}