{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 5.2 Big Data in Genomics - Visualise\n",
"\n",
"This notebooks visualises the results of kMeans clustering of the genomics variants from chromosome 22 of the 1000 Genomes project dataset (phase3).\n",
"\n",
"We have reduced all the variants to 50 cluster centers, so that now each of the ~2500 individuals can be representation by a vector of size 50.\n",
"\n",
"The results are available in: `data/cluster-centers_chr22.csv.gz`.\n",
"\n",
"Now we will compute the average representation for each population averaging the vectors of the inviduals from this population and then use hierarchical clustering to see, which populations are similiar."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# import pandas and set display options\n",
"import pandas as pd\n",
"pd.set_option('display.max_rows', 5)\n",
"pd.set_option('display.max_columns', 8)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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" <th>48</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>Sample ID</th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>HG00096</th>\n",
" <td>1.763517</td>\n",
" <td>0.027793</td>\n",
" <td>0.000000</td>\n",
" <td>1.129517</td>\n",
" <td>...</td>\n",
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" <td>0.000000</td>\n",
" <td>0.621353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HG00097</th>\n",
" <td>1.788846</td>\n",
" <td>0.039244</td>\n",
" <td>0.000000</td>\n",
" <td>0.997455</td>\n",
" <td>...</td>\n",
" <td>0.000880</td>\n",
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" <td>0.557942</td>\n",
" </tr>\n",
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" <tr>\n",
" <th>NA21143</th>\n",
" <td>1.754749</td>\n",
" <td>0.028460</td>\n",
" <td>0.725055</td>\n",
" <td>0.796692</td>\n",
" <td>...</td>\n",
" <td>0.002933</td>\n",
" <td>0.000000</td>\n",
" <td>0.938338</td>\n",
" <td>0.628241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NA21144</th>\n",
" <td>1.781052</td>\n",
" <td>0.017232</td>\n",
" <td>0.000000</td>\n",
" <td>0.928753</td>\n",
" <td>...</td>\n",
" <td>0.010850</td>\n",
" <td>0.000000</td>\n",
" <td>0.919571</td>\n",
" <td>0.559157</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2535 rows × 50 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 ... 46 \\\n",
"Sample ID ... \n",
"HG00096 1.763517 0.027793 0.000000 1.129517 ... 0.004106 \n",
"HG00097 1.788846 0.039244 0.000000 0.997455 ... 0.000880 \n",
"... ... ... ... ... ... ... \n",
"NA21143 1.754749 0.028460 0.725055 0.796692 ... 0.002933 \n",
"NA21144 1.781052 0.017232 0.000000 0.928753 ... 0.010850 \n",
"\n",
" 47 48 49 \n",
"Sample ID \n",
"HG00096 1.012876 0.000000 0.621353 \n",
"HG00097 0.000000 1.179625 0.557942 \n",
"... ... ... ... \n",
"NA21143 0.000000 0.938338 0.628241 \n",
"NA21144 0.000000 0.919571 0.559157 \n",
"\n",
"[2535 rows x 50 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# read and display the representations\n",
"\n",
"representationPD = pd.read_csv('data/cluster-centers_chr22.csv.gz', index_col=0)\n",
"representationPD"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>Family ID</th>\n",
" <th>Paternal ID</th>\n",
" <th>Maternal ID</th>\n",
" <th>Gender</th>\n",
" <th>...</th>\n",
" <th>phase 3 genotypes</th>\n",
" <th>related genotypes</th>\n",
" <th>omni genotypes</th>\n",
" <th>affy_genotypes</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Individual ID</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
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" <tr>\n",
" <th>HG00096</th>\n",
" <td>HG00096</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>HG00097</th>\n",
" <td>HG00097</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>NA21143</th>\n",
" <td>NA21143</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
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" <tr>\n",
" <th>NA21144</th>\n",
" <td>NA21144</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3691 rows × 16 columns</p>\n",
"</div>"
],
"text/plain": [
" Family ID Paternal ID Maternal ID Gender ... \\\n",
"Individual ID ... \n",
"HG00096 HG00096 0 0 1 ... \n",
"HG00097 HG00097 0 0 2 ... \n",
"... ... ... ... ... ... \n",
"NA21143 NA21143 0 0 2 ... \n",
"NA21144 NA21144 0 0 2 ... \n",
"\n",
" phase 3 genotypes related genotypes omni genotypes \\\n",
"Individual ID \n",
"HG00096 1 0 1 \n",
"HG00097 1 0 1 \n",
"... ... ... ... \n",
"NA21143 1 0 1 \n",
"NA21144 1 0 1 \n",
"\n",
" affy_genotypes \n",
"Individual ID \n",
"HG00096 1 \n",
"HG00097 1 \n",
"... ... \n",
"NA21143 1 \n",
"NA21144 1 \n",
"\n",
"[3691 rows x 16 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# read the pedigree file that associates individuals with their populations\n",
"\n",
"pedPD = pd.read_csv('data/integrated_call_samples_v2.20130502.ALL.ped.bz2', sep='\\t', index_col=1)\n",
"pedPD"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>...</th>\n",
" <th>46</th>\n",
" <th>47</th>\n",
" <th>48</th>\n",
" <th>49</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Population</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ACB</th>\n",
" <td>1.805210</td>\n",
" <td>0.309657</td>\n",
" <td>0.182349</td>\n",
" <td>0.826776</td>\n",
" <td>...</td>\n",
" <td>0.111718</td>\n",
" <td>0.264843</td>\n",
" <td>0.026167</td>\n",
" <td>0.792401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ASW</th>\n",
" <td>1.802774</td>\n",
" <td>0.265552</td>\n",
" <td>0.179231</td>\n",
" <td>0.853655</td>\n",
" <td>...</td>\n",
" <td>0.080712</td>\n",
" <td>0.265509</td>\n",
" <td>0.042977</td>\n",
" <td>0.755019</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",
" </tr>\n",
" <tr>\n",
" <th>TSI</th>\n",
" <td>1.769293</td>\n",
" <td>0.030587</td>\n",
" <td>0.099593</td>\n",
" <td>1.038307</td>\n",
" <td>...</td>\n",
" <td>0.008914</td>\n",
" <td>0.200485</td>\n",
" <td>0.052477</td>\n",
" <td>0.535489</td>\n",
" </tr>\n",
" <tr>\n",
" <th>YRI</th>\n",
" <td>1.808162</td>\n",
" <td>0.361357</td>\n",
" <td>0.270144</td>\n",
" <td>0.791332</td>\n",
" <td>...</td>\n",
" <td>0.107181</td>\n",
" <td>0.282396</td>\n",
" <td>0.021718</td>\n",
" <td>0.836228</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>26 rows × 50 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 ... 46 \\\n",
"Population ... \n",
"ACB 1.805210 0.309657 0.182349 0.826776 ... 0.111718 \n",
"ASW 1.802774 0.265552 0.179231 0.853655 ... 0.080712 \n",
"... ... ... ... ... ... ... \n",
"TSI 1.769293 0.030587 0.099593 1.038307 ... 0.008914 \n",
"YRI 1.808162 0.361357 0.270144 0.791332 ... 0.107181 \n",
"\n",
" 47 48 49 \n",
"Population \n",
"ACB 0.264843 0.026167 0.792401 \n",
"ASW 0.265509 0.042977 0.755019 \n",
"... ... ... ... \n",
"TSI 0.200485 0.052477 0.535489 \n",
"YRI 0.282396 0.021718 0.836228 \n",
"\n",
"[26 rows x 50 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# compute the average representation per population\n",
"\n",
"populationRepresentationPD = representationPD.join(pedPD).groupby('Population').mean()[representationPD.columns]\n",
"populationRepresentationPD"
]
},
{
"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>Population Description</th>\n",
" <th>Super Population Code</th>\n",
" <th>Sequence Data Available</th>\n",
" <th>Alignment Data Available</th>\n",
" <th>Variant Data Available</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Population Code</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>CHB</th>\n",
" <td>Han Chinese in Bejing, China</td>\n",
" <td>EAS</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>JPT</th>\n",
" <td>Japanese in Tokyo, Japan</td>\n",
" <td>EAS</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STU</th>\n",
" <td>Sri Lankan Tamil from the UK</td>\n",
" <td>SAS</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ITU</th>\n",
" <td>Indian Telugu from the UK</td>\n",
" <td>SAS</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>26 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" Population Description Super Population Code \\\n",
"Population Code \n",
"CHB Han Chinese in Bejing, China EAS \n",
"JPT Japanese in Tokyo, Japan EAS \n",
"... ... ... \n",
"STU Sri Lankan Tamil from the UK SAS \n",
"ITU Indian Telugu from the UK SAS \n",
"\n",
" Sequence Data Available Alignment Data Available \\\n",
"Population Code \n",
"CHB 1 1 \n",
"JPT 1 1 \n",
"... ... ... \n",
"STU 1 1 \n",
"ITU 1 1 \n",
"\n",
" Variant Data Available \n",
"Population Code \n",
"CHB 1 \n",
"JPT 1 \n",
"... ... \n",
"STU 1 \n",
"ITU 1 \n",
"\n",
"[26 rows x 5 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# load population descriptions\n",
"populationDescPD = pd.read_csv('data/1000_gen_populations.txt', sep='\\t', index_col=0)\n",
"populationDescPD"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Population\n",
"ACB AFR African Caribbeans in Barbados\n",
"ASW AFR Americans of African Ancestry in SW USA\n",
" ... \n",
"TSI EUR Toscani in Italia\n",
"YRI AFR Yoruba in Ibadan, Nigeria\n",
"dtype: object"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create labels for the dendrogram `SuperPopulationCode` + `Population Description`\n",
"\n",
"populationLabels = populationDescPD \\\n",
" .loc[populationRepresentationPD.index][['Super Population Code', 'Population Description']].apply(\" \".join, axis=1)\n",
"populationLabels"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# compute pair-wise distances between population representations\n",
"# and run hierarchical clustering\n",
"\n",
"from scipy.spatial.distance import pdist\n",
"import scipy.cluster\n",
"\n",
"pairWisePopulationDistances = pdist(populationRepresentationPD)\n",
"populationLinkage = scipy.cluster.hierarchy.linkage(pairWisePopulationDistances, method='complete')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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vAu+a2S7AzqQA9Z29fjfgf81saz/PvJCfAAea2U7AnsAfc31sAVxhZtsB7wEH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\ns3nAy5J65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"text/plain": [
"<matplotlib.figure.Figure at 0x113f6bed0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# display the dendrogram\n",
"\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams[\"figure.figsize\"] = (5,6)\n",
"plt.close()\n",
"scipy.cluster.hierarchy.dendrogram(populationLinkage, orientation = 'left', \n",
" color_threshold = 0.4, labels = populationLabels, \n",
" leaf_font_size = 10)\n",
"plt.show()\n",
"display()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can clearly see from the chart above that the 50 cluster centers are indeed a reasonable representation of the entire chromosome.\n",
"\n",
"You can now play around modifying pieces of the code.\n",
"\n",
"When you are done and you are running off the local machine remember to *close the notebook* with `File/Close and Halt`."
]
}
],
"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
}