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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Variant Call Adjacency Matrix\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the modern era of Genomics and Bioinformatics, tools are being created to interpret large scale genomic data. One such common comparison between two individual's is to compute a person's allele frequency through identifying their genomic variants. This notebook demonstrates an alternative approach to interpreting genomic variants through data visualization. \n",
+ "\n",
+ "The GA4GH variant service is used to produce an interconnected matrix of variants from data in 1000 genomes.\n",
+ "\n",
+ "Further analysis of specific populations in the 1000 genomes dataset is performed. \n",
+ "\n",
+ "The machine learning algorithm, K-means clustering on the variant matrix is performed. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# GA4GH Server Connection is Established"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A client object is used to communicate with the GA4GH server and helpful modules are imported. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from __future__ import print_function\n",
+ "import ga4gh.client.client as client\n",
+ "client = client.HttpClient(\"http://1kgenomes.ga4gh.org\")\n",
+ "\n",
+ "import collections\n",
+ "%matplotlib inline\n",
+ "import matplotlib\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.mlab as mlab\n",
+ "from mpl_toolkits.axes_grid1 import ImageGrid\n",
+ "from numpy.random import RandomState\n",
+ "from sklearn.cluster import KMeans\n",
+ "from mpl_toolkits.mplot3d import Axes3D\n",
+ "import unirest\n",
+ "import requests \n",
+ "import json\n",
+ "from multiprocessing import Pool\n",
+ "from finch import *\n",
+ "import operator\n",
+ "import random "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# The 1000 Genomes Dataset is Chosen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "datasets = list(client.search_datasets())\n",
+ "\n",
+ "# get 1000 gneomes dataset \n",
+ "dataset = client.get_dataset(datasets[0].id)\n",
+ "\n",
+ "release = None\n",
+ "functional = None\n",
+ "for variant_set in client.search_variant_sets(dataset_id=dataset.id):\n",
+ " if variant_set.name == \"phase3-release\":\n",
+ " release = variant_set\n",
+ " else:\n",
+ " functional = variant_set\n",
+ "\n",
+ "callsi = list(client.search_call_sets(release.id))\n",
+ "\n",
+ "variant_sets = list(client.search_variant_sets(dataset.id))\n",
+ "variant_set_id = variant_sets[0].id\n",
+ "\n",
+ "call_set_ids = []\n",
+ "\n",
+ "for csi in callsi: \n",
+ " call_set_ids.append(csi.id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Population Groups are Mapped to Respective Call Set Ids "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def map_call_set_ids_to_population_group():\n",
+ " \"\"\"\n",
+ " This allows for the individuals being analyzed to be identified based on 1000 genomes population group. \n",
+ " \"\"\"\n",
+ " population_map = {}\n",
+ " \n",
+ " for call_set in client.search_call_sets(release.id):\n",
+ " \n",
+ " bio_sample = client.get_bio_sample(call_set.bio_sample_id)\n",
+ " population_map[call_set.id] = bio_sample.info['Population'].values[0].string_value \n",
+ " \n",
+ " \n",
+ " return population_map"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# generate_dictionary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def generate_dictionary(num_people, num_chrom, begin, stop):\n",
+ " \"\"\"\n",
+ " This function generates a dictionary of call set id keys and values for the index \n",
+ " that the call set id appeared in throughout a list of variants within a given range on the human genome.\n",
+ " \n",
+ " num_people - the number of people to analyze\n",
+ " num_chrom - the chromosome number\n",
+ " begin - the start position on the genome\n",
+ " stop - the end position on the genome \n",
+ " \"\"\"\n",
+ " dictionary = {}\n",
+ " \n",
+ " variants = client.search_variants(variant_set_id, call_set_ids= call_set_ids[0:num_people],\n",
+ " start=begin, end=stop, reference_name = num_chrom )\n",
+ " \n",
+ " i = 0\n",
+ " for v in variants:\n",
+ " for call_inner in v.calls:\n",
+ " i += 1\n",
+ " dictionary[call_inner.call_set_id] = i\n",
+ " \n",
+ " return (dictionary) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# normalize_indexes()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def normalize_indexes(dictionary):\n",
+ " \"\"\"\n",
+ " initializes the indexes of the variant dictionay to be {0,...,n} where n is the number of call set ids \n",
+ " \n",
+ " dictionary - call set id keys, call set id index values \n",
+ " \"\"\"\n",
+ " keys = []\n",
+ " keys = dictionary.keys()\n",
+ " \n",
+ " new_dictionary = {}\n",
+ " i = 0\n",
+ " j = 0\n",
+ " k = 0\n",
+ " for i in range(len(dictionary)): \n",
+ " l = keys[j]\n",
+ " dictionary[l] = k\n",
+ " j += 1\n",
+ " k += 1\n",
+ " \n",
+ " return dictionary"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# variant_matrix()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This function connects two identical sets of participant sample's callSetId's through the variants shared between them.\n",
+ "\n",
+ " M D (Mother/Father allele donated)\n",
+ " Where A or B = [1,0] : heterozygous for variant\n",
+ " [0,1] : heterozygous for variant\n",
+ " [1,1] : homozygous dominant for variant\n",
+ " [0,0] : homozygous recessive for variant\n",
+ " \n",
+ " | 1 | 0 |\n",
+ " 1 |[1,1] | [1,0] |\n",
+ " | | | \n",
+ " 0 |[1,0] | [0,0] |\n",
+ " \n",
+ " \n",
+ " True for the shared genotype:\n",
+ " \n",
+ " case 1: \n",
+ " A = [1,0]\n",
+ " B = [0,1]\n",
+ " case 2: \n",
+ " A = [0,0]\n",
+ " B = [0,0]\n",
+ " case 3: \n",
+ " A = [1,0]\n",
+ " B = [1,0]\n",
+ " case 4: \n",
+ " A = [1,1]\n",
+ " B = [0,1]\n",
+ " \n",
+ " False for the shared genotype:\n",
+ " \n",
+ " case 1: \n",
+ " A = [1,0]\n",
+ " B = [0,0]\n",
+ " case 2:\n",
+ " A = [0,1]\n",
+ " B = [0,0]\n",
+ " case 3: \n",
+ " A = [0,0]\n",
+ " B = [1,0]\n",
+ " case 4:\n",
+ " A = [0,0]\n",
+ " B = [0,1]\n",
+ " \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "def variant_matrix(num_people, num_chrom, begin, stop):\n",
+ " \"\"\"\n",
+ " num_people - the number of people to analyze\n",
+ " num_chrom - the chromosome number\n",
+ " begin - the start position on the genome\n",
+ " stop - the end position on the genome \n",
+ " \"\"\" \n",
+ " \n",
+ " # get dictionary of call set ids for the number of people \n",
+ " csid_dict = {} \n",
+ " csid_dict = generate_dictionary(num_people, num_chrom, begin, stop)\n",
+ " \n",
+ " normalize_indexes(csid_dict)\n",
+ " \n",
+ " h = len(csid_dict)\n",
+ " w = len(csid_dict)\n",
+ " dimension = h*w\n",
+ " \n",
+ " # initialize callMatrix with zeroes \n",
+ " callMatrix = {}\n",
+ " callMatrix = [[0 for x in range(w)] for y in range(h)] \n",
+ " np.zeros((h,w)) \n",
+ " \n",
+ " vs = client.search_variants(variant_set_id, call_set_ids= call_set_ids[0:num_people],\n",
+ " start=begin, end=stop, reference_name = num_chrom)\n",
+ " \n",
+ " # iterate through each persons call set id and increment cells in the matrix for shared genotypes \n",
+ " for v in vs:\n",
+ " for call_outer in v.calls: \n",
+ " for call_inner in v.calls:\n",
+ " outer_index = csid_dict[call_outer.call_set_id] \n",
+ " inner_index = csid_dict[call_inner.call_set_id] \n",
+ " # can be 0 or 1\n",
+ " A1 = call_outer.genotype[0] \n",
+ " A2 = call_outer.genotype[1]\n",
+ " B1 = call_inner.genotype[0]\n",
+ " B2 = call_inner.genotype[1] \n",
+ " # True for the shared genotype\n",
+ " if (((A1 + A2 ) > 0) and ((B1 + B2) > 0)):\n",
+ " callMatrix[outer_index][inner_index] += 1\n",
+ "\n",
+ " elif (((A1 + A2 ) == 0) and ((B1 + B2) == 0)):\n",
+ " callMatrix[outer_index][inner_index] += 1\n",
+ " \n",
+ " return (callMatrix)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# visualize_matrix functions "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def visualize_matrix_color(num_people, num_chrom, begin, stop):\n",
+ " \n",
+ " \"\"\"\n",
+ " visualizes the matrix in rgb color\n",
+ " \n",
+ " num_people - the number of people to analyze\n",
+ " num_chrom - the chromosome number\n",
+ " begin - the start position on the genome\n",
+ " stop - the end position on the genome \n",
+ " \"\"\"\n",
+ " \n",
+ " callMatrix = variant_matrix(num_people, num_chrom, begin, stop)\n",
+ " \n",
+ " fig = plt.figure(1,(10.,10.))\n",
+ " grid = ImageGrid(fig, 111,\n",
+ " nrows_ncols=(1,1),\n",
+ " axes_pad=0.1)\n",
+ " \n",
+ " ax = grid[0]\n",
+ " ax.set_title('Color matrix comparing the occurence of shared variants obtained from call set ids between individuals in the the genomic dataset\\n', fontsize=14, fontweight='bold')\n",
+ " ax.set_xlabel(\"Individual's call set ids\", fontsize=12)\n",
+ " ax.set_ylabel(\"Individual's call set ids\", fontsize=12) \n",
+ " ax.imshow(callMatrix, origin = \"lower\", interpolation=\"nearest\")\n",
+ " \n",
+ " plt.show()\n",
+ "\n",
+ "def visualize_matrix_grey(num_people, num_chrom, begin, stop):\n",
+ " \"\"\"\n",
+ " visualizes the matrix in greyscale color\n",
+ " \n",
+ " num_people - the number of people to analyze\n",
+ " num_chrom - the chromosome number\n",
+ " begin - the start position on the genome\n",
+ " stop - the end position on the genome \n",
+ " \"\"\"\n",
+ " \n",
+ " callMatrix = variant_matrix(num_people, num_chrom, begin, stop)\n",
+ " \n",
+ " fig = plt.figure(1,(10.,10.))\n",
+ " \n",
+ " grid = ImageGrid(fig, 111,\n",
+ " nrows_ncols=(1,1),\n",
+ " axes_pad=0.1)\n",
+ " ax = grid[0] \n",
+ " ax.set_title('Greyscale matrix comparing the occurence of shared variants obtained from call set ids between individuals in the the genomic dataset\\n', fontsize=14, fontweight='bold')\n",
+ " ax.set_xlabel(\"Individual's call set ids\", fontsize=12)\n",
+ " ax.set_ylabel(\"Individual's call set ids\", fontsize=12)\n",
+ " \n",
+ " ax.imshow(callMatrix, cmap= 'Greys', origin = \"lower\", interpolation=\"nearest\")\n",
+ " \n",
+ " \n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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GzaINLrTYz6H8LPEtlP1Hr++G9Y+3n01JFpxDWUfbU/q8D9HYFmdY3hOZw7nn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\nJEmSJEw6SPMSEW/NzPfW1+8aNV5mvr2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AKP4/ZJ4YFMdMyoEAAAAASUVORK5C\nYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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d8hmfc8lJhHeQTz3t9lXydrEd+SDkrFKfymllmrWlDvuRz9I8iXzm5u3k/n4m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\nq+ox01EgT53ud1ySf8rifB4PyGLZyhGrqm+vqtOr6v5ZhD5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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "visualize_matrix_color(20, \"1\", 10000, 15000)\n",
+ "visualize_matrix_grey(20, \"1\", 10000, 15000)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# k_means_clustering_on_variant_matrix()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def k_means_clustering_on_variant_matrix(num_people, num_chrom, begin, stop, clusters):\n",
+ " \"\"\"\n",
+ " returns a list of labels given by the k-means clustering algorithm which indicate which \n",
+ " individuals should be clustered together \n",
+ " \n",
+ " num_people - the number of people to analyze\n",
+ " num_chrom - the chromosome number\n",
+ " begin - the start position on the genome\n",
+ " stop - the end position on the genome \n",
+ " clusters - the number of clusters to form with this run on kmeans \n",
+ " \"\"\"\n",
+ " \n",
+ " # number of individuals ()\n",
+ " N = num_people\n",
+ " # randomization\n",
+ " rand = RandomState(1123581321) \n",
+ " # matrix generation \n",
+ " callMatrix = {}\n",
+ " callMatrix = variant_matrix(N, num_chrom, begin, stop)\n",
+ " # initialize KMeans clustering object\n",
+ " kmeans_obj = KMeans(n_clusters=clusters, n_init=10, \n",
+ " init='k-means++', precompute_distances=True,\n",
+ " tol=1e-4, random_state= rand)\n",
+ " # Compute clustering and transform matrix to cluster-distance space. \n",
+ " labels = kmeans_obj.fit_predict(callMatrix)\n",
+ " \n",
+ " label_list = []\n",
+ " for i in labels:\n",
+ " label_list.append(i)\n",
+ " \n",
+ " return label_list"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def assign_labels_to_indexes(num_people, num_chrom, begin, stop, clusters):\n",
+ " \n",
+ " \"\"\"\n",
+ " assigns the labels from the kmeans clustering to the individuals in a variant matrix \n",
+ " \n",
+ " num_people - the number of people to analyze\n",
+ " num_chrom - the chromosome number\n",
+ " begin - the start position on the genome\n",
+ " stop - the end position on the genome \n",
+ " clusters - the number of clusters to form with this run on kmeans \n",
+ " \n",
+ " returns a dictionary of call set id keys, (label, matrix index( individual )) pairs values \n",
+ " \"\"\"\n",
+ " \n",
+ " label_list = k_means_clustering_on_variant_matrix(num_people, \n",
+ " num_chrom, begin, stop, clusters)\n",
+ " dictionary = generate_dictionary(num_people, num_chrom, begin, stop) \n",
+ " dictionary = normalize_indexes(dictionary)\n",
+ " \n",
+ " i = 0\n",
+ " j = 0\n",
+ " k = 0\n",
+ " label_index_dict = {}\n",
+ " label_index_pair_list = []\n",
+ " for i in label_list:\n",
+ " pair = (i,j)\n",
+ " label_index_pair_list.append(pair)\n",
+ " j += 1\n",
+ " \n",
+ " \n",
+ " label_index_pair_list.sort()\n",
+ "\n",
+ " i=0\n",
+ " j=0\n",
+ " k=0\n",
+ " for i,j in dictionary.items():\n",
+ " l = label_index_pair_list[k]\n",
+ " dictionary[i] =l\n",
+ " k += 1 \n",
+ " \n",
+ " return (dictionary)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 123,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def compare():\n",
+ " \n",
+ " \"\"\"\n",
+ " compares populations with the labels that they have been assigned to \n",
+ " \n",
+ " returns population occurences \n",
+ " \"\"\"\n",
+ " \n",
+ " matrix = {}\n",
+ " label_dictionary = {}\n",
+ " population_dictionary= {}\n",
+ " \n",
+ " label_dictionary = assign_labels_to_indexes(50, \"1\", 100000, 200000, 6)\n",
+ " population_dictionary = map_call_set_ids_to_population_group()\n",
+ " \n",
+ " population_occurence = []\n",
+ " \n",
+ " for ld_key, ld_val in label_dictionary.iteritems():\n",
+ " for pd_key, pd_val in population_dictionary.iteritems():\n",
+ " if ( ld_key == pd_key): \n",
+ " #print (\"Population: {} has cluster {}\".format(pd_val, ld_val[0]))\n",
+ " population_occurence.append((pd_val, ld_val[0]))\n",
+ " \n",
+ " \n",
+ " return population_occurence"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 124,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def map_populations():\n",
+ " \n",
+ " \"\"\"\n",
+ " maps populations to there clusters \n",
+ " returns a dictionary of population keys\n",
+ " \"\"\"\n",
+ " \n",
+ " population_cluster_list = []\n",
+ " population_cluster_list = compare()\n",
+ " \n",
+ " i = 0\n",
+ " j = 0\n",
+ " population_dict = {}\n",
+ " for i in population_cluster_list:\n",
+ " population_dict[i[0]] = 0\n",
+ " \n",
+ " \n",
+ " for i in population_cluster_list:\n",
+ " if ( i[0] in population_dict):\n",
+ " population_dict[i[0]] += 1\n",
+ " \n",
+ " return population_dict\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 125,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def map_clusters():\n",
+ " \n",
+ " \"\"\"\n",
+ " maps clusters to there populations \n",
+ " returns a dictionary of population keys \n",
+ " \"\"\"\n",
+ " \n",
+ " population_cluster_list = []\n",
+ " population_cluster_list = compare()\n",
+ " \n",
+ " i = 0\n",
+ " j = 0\n",
+ " population_dict = {}\n",
+ " for i in population_cluster_list:\n",
+ " population_dict[i[1]] = 0\n",
+ " \n",
+ " for i in population_cluster_list:\n",
+ " if ( i[1] == j):\n",
+ " population_dict[i[1]] += 1\n",
+ " else:\n",
+ " population_dict[i[1]] += 1\n",
+ " j += 1\n",
+ " return population_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 133,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def map_a_dictionary(dictionary):\n",
+ " \n",
+ " plt.bar(range(len(dictionary)), dictionary.values(), align='center')\n",
+ " plt.xticks(range(len(dictionary)), dictionary.keys())\n",
+ " \n",
+ " ax = fig.add_subplot(111)\n",
+ " ax.set_title('Distribution of shared variants among the genomic dataset (as compared to the reference genome)\\n', fontsize=14, fontweight='bold')\n",
+ " ax.set_xlabel('number of shared variants called within a range of bases on one chromosome')\n",
+ " ax.set_ylabel('percent occurence of the amount of shared variants')\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 134,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def graph_populations():\n",
+ " \n",
+ " population_dict = map_populations()\n",
+ " \n",
+ " fig = plt.figure(1,(10.,10.))\n",
+ "\n",
+ " plt.bar(range(len(population_dict)), population_dict.values(), align='center')\n",
+ " plt.xticks(range(len(population_dict)), population_dict.keys())\n",
+ " \n",
+ " ax = fig.add_subplot(111)\n",
+ " ax.set_title('Distribution of populations in dataset\\n', fontsize=14, fontweight='bold')\n",
+ " ax.set_xlabel('Population')\n",
+ " ax.set_ylabel('Occurence count')\n",
+ " plt.show()\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 135,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def graph_clusters():\n",
+ " \n",
+ " cluster_dict = map_clusters()\n",
+ " \n",
+ " fig = plt.figure(1,(10.,10.))\n",
+ "\n",
+ " plt.bar(range(len(cluster_dict)), cluster_dict.values(), align='center', color='r')\n",
+ " plt.xticks(range(len(cluster_dict)), cluster_dict.keys())\n",
+ " \n",
+ " ax = fig.add_subplot(111)\n",
+ " ax.set_title('Distribution of clusters in the kmeans quantification\\n', fontsize=14, fontweight='bold')\n",
+ " ax.set_xlabel('Clusters')\n",
+ " ax.set_ylabel('Occurence count')\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 136,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def cluster_occurence():\n",
+ " \n",
+ " population_cluster_list = []\n",
+ " population_cluster_list = compare()\n",
+ " \n",
+ " population_occurence_dict = {} \n",
+ " for i in population_cluster_list:\n",
+ " population_occurence_dict[i] = 0\n",
+ " \n",
+ " i = 0\n",
+ " j = 0\n",
+ " for i in (population_cluster_list):\n",
+ " if ( i[1] == j and i in population_occurence_dict):\n",
+ " population_occurence_dict[i] += 1\n",
+ " else:\n",
+ " population_occurence_dict[i] += 1\n",
+ " j += 1\n",
+ " \n",
+ " return population_occurence_dict\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 137,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def normalize_cluster_occurence(pop_size):\n",
+ " \n",
+ " population_occurence_dict = cluster_occurence()\n",
+ " \n",
+ " poercent = 0.0\n",
+ " for i ,j in population_occurence_dict.iteritems():\n",
+ " percent = (float(j)/float(pop_size))*100.0\n",
+ " population_occurence_dict[i] = percent\n",
+ " \n",
+ " return population_occurence_dict\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 138,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def graph_cluster_occurence():\n",
+ " \n",
+ " \"\"\"\n",
+ " graphs the occurences of the clusters in each population \n",
+ " \"\"\"\n",
+ " \n",
+ " population_occurence_dict = normalize_cluster_occurence(50)\n",
+ " \n",
+ " fig = plt.figure(1,(38.,8.))\n",
+ "\n",
+ " plt.bar(range(len(population_occurence_dict)), population_occurence_dict.values(), align='center', color='g')\n",
+ " plt.xticks(range(len(population_occurence_dict)), population_occurence_dict.keys())\n",
+ " \n",
+ " ax = fig.add_subplot(111)\n",
+ " ax.set_title('Distribution of clusters in the kmeans quantification\\n', fontsize=14, fontweight='bold')\n",
+ " ax.set_xlabel('Population and its associated cluster')\n",
+ " ax.set_ylabel('Occurence count')\n",
+ " plt.show()\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def graph_population_map():\n",
+ " \n",
+ " population_map = {}\n",
+ " population_map = map_call_set_ids_to_population_group() \n",
+ " avg_populatin_map = {} \n",
+ " for i,j in population_map.iteritems():\n",
+ " avg_populatin_map[j] = 0\n",
+ "\n",
+ " count = 0\n",
+ " for i in avg_populatin_map:\n",
+ " for j,k in population_map.iteritems():\n",
+ " if ( i == k ):\n",
+ " avg_populatin_map[i] += 1\n",
+ " count += 1\n",
+ " print ( avg_populatin_map) \n",
+ " print (count)\n",
+ " for i,j in avg_populatin_map.iteritems():\n",
+ " avg_populatin_map[i] /= 2504.0\n",
+ " avg_populatin_map[i] *= 100.0\n",
+ " \n",
+ " total = 0\n",
+ " for i,j in avg_populatin_map.iteritems():\n",
+ " total += j\n",
+ " \n",
+ " avg = total/len(avg_populatin_map)\n",
+ " print (len(avg_populatin_map))\n",
+ " print (avg)\n",
+ " \n",
+ " avg_populatin_map = sorted(avg_populatin_map.items(), key=operator.itemgetter(1)) \n",
+ " avg_populatin_map = dict(avg_populatin_map)\n",
+ " \n",
+ " fig = plt.figure(1,(15.,10.))\n",
+ " plt.bar(range(len(avg_populatin_map)), avg_populatin_map.values(), align='center', color='y')\n",
+ " plt.xticks(range(len(avg_populatin_map)), avg_populatin_map.keys())\n",
+ " ax = fig.add_subplot(111)\n",
+ " ax.set_title('Distribution of populations in the 1kgenomes dataset\\n', fontsize=14, fontweight='bold')\n",
+ " ax.set_xlabel('Population')\n",
+ " ax.set_ylabel('Average amount this population has been sampled')\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{u'ACB': 96, u'CLM': 94, u'BEB': 86, u'PEL': 85, u'LWK': 99, u'MSL': 85, u'GBR': 91, u'IBS': 107, u'ASW': 61, u'TSI': 107, u'KHV': 99, u'CEU': 99, u'YRI': 108, u'CHB': 103, u'GWD': 113, u'STU': 102, u'CHS': 105, u'ESN': 99, u'FIN': 99, u'GIH': 103, u'PJL': 96, u'MXL': 64, u'ITU': 102, u'CDX': 93, u'JPT': 104, u'PUR': 104}\n",
+ "2504\n",
+ "26\n",
+ "3.84615384615\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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ZSe5QVS9M8qEkvzdqVQAAAAwaJfJNVXVOkh/smx7bWvvUuGUBAAAw5By2JDky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\nON2U/wNEdRm9/DqaewAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "graph_population_map()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{(u'CDX', 0): 3,\n",
+ " (u'CDX', 1): 1,\n",
+ " (u'CDX', 2): 5,\n",
+ " (u'CDX', 3): 6,\n",
+ " (u'CHS', 0): 32,\n",
+ " (u'CHS', 1): 7,\n",
+ " (u'CHS', 2): 29,\n",
+ " (u'CHS', 3): 28,\n",
+ " (u'CHS', 4): 9,\n",
+ " (u'CLM', 0): 28,\n",
+ " (u'CLM', 1): 3,\n",
+ " (u'CLM', 2): 32,\n",
+ " (u'CLM', 3): 16,\n",
+ " (u'CLM', 4): 11,\n",
+ " (u'FIN', 0): 24,\n",
+ " (u'FIN', 1): 9,\n",
+ " (u'FIN', 2): 30,\n",
+ " (u'FIN', 3): 19,\n",
+ " (u'FIN', 4): 16,\n",
+ " (u'FIN', 5): 1,\n",
+ " (u'GBR', 0): 24,\n",
+ " (u'GBR', 1): 3,\n",
+ " (u'GBR', 2): 29,\n",
+ " (u'GBR', 3): 25,\n",
+ " (u'GBR', 4): 6,\n",
+ " (u'PUR', 0): 32,\n",
+ " (u'PUR', 1): 4,\n",
+ " (u'PUR', 2): 34,\n",
+ " (u'PUR', 3): 21,\n",
+ " (u'PUR', 4): 13}"
+ ]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cluster_occurence()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# cluster_variant_matrix()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def cluster_variant_matrix(num_people, num_chrom, begin, stop, clusters):\n",
+ " \n",
+ " \"\"\"\n",
+ " performs similar operation to variant_matrix but with with k-means clustering data\n",
+ " \"\"\"\n",
+ " \n",
+ " csid_dict = {} \n",
+ " csid_dict = assign_labels_to_indexes(num_people, num_chrom, begin, stop, clusters)\n",
+ " \n",
+ " callMatrix = {}\n",
+ " callMatrix = np.array(callMatrix)\n",
+ " \n",
+ " h = len(csid_dict)\n",
+ " w = len(csid_dict)\n",
+ " dimension = h*w\n",
+ " \n",
+ " callMatrix = [[0 for x in range(w)] for y in range(h)] \n",
+ " np.zeros((h,w)) \n",
+ " #np.reshape(callMatrix, (h,w))\n",
+ " \n",
+ " vs = client.search_variants(variant_set_id, call_set_ids= call_set_ids[0:num_people],\n",
+ " start=begin, end=stop, reference_name = num_chrom)\n",
+ "\n",
+ " for v in vs:\n",
+ " for call_outer in v.calls: \n",
+ " for call_inner in v.calls: \n",
+ " outer_index = (csid_dict[call_outer.call_set_id])[1] \n",
+ " inner_index = (csid_dict[call_inner.call_set_id])[1] \n",
+ " # can be 0 or 1\n",
+ " A1 = call_outer.genotype[0] \n",
+ " A2 = call_outer.genotype[1]\n",
+ " B1 = call_inner.genotype[0]\n",
+ " B2 = call_inner.genotype[1] \n",
+ " # True for the shared genotype\n",
+ " if (((A1 + A2 ) > 0) and ((B1 + B2) > 0)):\n",
+ " callMatrix[outer_index][inner_index] += 1\n",
+ " elif (((A1 + A2 ) == 0) and ((B1 + B2) == 0)):\n",
+ " callMatrix[outer_index][inner_index] += 1 \n",
+ " \n",
+ " return (callMatrix)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 110,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def visualize_cluster_matrix_color(num_people, num_chrom, begin, stop, clusters):\n",
+ " \n",
+ " callMatrix = {}\n",
+ " callMatrix = cluster_variant_matrix(num_people, num_chrom, begin, stop, clusters)\n",
+ " \n",
+ " \n",
+ " fig = plt.figure(1,(10.,10.))\n",
+ " grid = ImageGrid(fig, 111,\n",
+ " nrows_ncols=(1,1),\n",
+ " axes_pad=0.1)\n",
+ " \n",
+ " ax = grid[0]\n",
+ " ax.set_title('Color matrix of variant clustering\\n', fontsize=14, fontweight='bold')\n",
+ " ax.set_xlabel(\"Individual's call set ids\", fontsize=12)\n",
+ " ax.set_ylabel(\"Individual's call set ids\", fontsize=12) \n",
+ " ax.imshow(callMatrix, origin = \"lower\", interpolation=\"nearest\")\n",
+ " \n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "def visualize_cluster_matrix_grey(num_people, num_chrom, begin, stop, clusters):\n",
+ " \n",
+ " callMatrix = {}\n",
+ " callMatrix = cluster_variant_matrix(num_people, num_chrom, begin, stop, clusters)\n",
+ " \n",
+ " fig = plt.figure(1,(10.,10.))\n",
+ " \n",
+ " grid = ImageGrid(fig, 111,\n",
+ " nrows_ncols=(1,1),\n",
+ " axes_pad=0.1)\n",
+ " ax = grid[0] \n",
+ " ax.set_title('Greyscale matrix of variant clustering\\n', fontsize=14, fontweight='bold')\n",
+ " ax.set_xlabel(\"Individual's call set ids\", fontsize=12)\n",
+ " ax.set_ylabel(\"Individual's call set ids\", fontsize=12)\n",
+ " \n",
+ " ax.imshow(callMatrix, cmap= 'Greys', origin = \"lower\", interpolation=\"nearest\")\n",
+ " \n",
+ " \n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 120,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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AEhG+AAAAEhG+AAAAEhG+AAAAEg3PKGJ7tqTDJC2NiH3LuIsl7V1mGStpeUTsX+exCyQ9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\nkiRJDRm+JEmSGvoCfBkggsL+XI4AAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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p9WbNmpVaL0tPT09ard7e3rRafX19abUkaebMmWm1Pvaxj6XVyvzsOOWUU9JqZb5eUvdu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\nkiRJGsjwJUmSNJDhS5IkaSDDlyRJ0kC77BAu2aeNqLsAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "visualize_cluster_matrix_color(20, \"1\", 10000, 15000, 3)\n",
+ "visualize_cluster_matrix_grey(20, \"1\", 10000, 15000, 3)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# determine_matrix_range()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def determine_matrix_range(matrix, num_people, num_chrom, begin, stop):\n",
+ " \n",
+ " \"\"\"\n",
+ " finds the range of shared variants in a given variant matrix \n",
+ " \"\"\"\n",
+ " \n",
+ " matrix = {}\n",
+ " matrix = variant_matrix(matrix, num_people, num_chrom, begin, stop)\n",
+ " \n",
+ " i = 0\n",
+ " j = 0\n",
+ " cur = 0\n",
+ " lis = []\n",
+ " \n",
+ " for i in range(len(matrix)):\n",
+ " for j in range(len(matrix)):\n",
+ " cur = matrix[i][j]\n",
+ " lis.append(cur)\n",
+ " minimum = 0\n",
+ " maximum = 0 \n",
+ " if (len(lis) > 0): \n",
+ " minimum = min(lis)\n",
+ " maximum = max(lis)\n",
+ " #print (\"values in the matrix range from {} to {}\".\n",
+ " # format(minimum, maximum))\n",
+ " lis.sort()\n",
+ "\n",
+ " return lis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def find_percent_occurence(matrix, num_people, num_chrom, begin, stop):\n",
+ " \n",
+ " matrix_list = []\n",
+ " matrix = {}\n",
+ " matrix_list = determine_matrix_range(matrix, num_people, num_chrom, begin, stop) \n",
+ " i = 0\n",
+ " key_list = []\n",
+ " j = 0\n",
+ " for i in matrix_list:\n",
+ " variable = matrix_list[j]\n",
+ " if (variable == i):\n",
+ " key_list.append(variable)\n",
+ " j += 1\n",
+ " elif ( variable != i):\n",
+ " j += 1\n",
+ " \n",
+ " occur_dict = {}\n",
+ " occur_dict = occur_dict.fromkeys(key_list,0)\n",
+ " i = 0\n",
+ " j = 0 \n",
+ " total = 0.0\n",
+ " for i in matrix_list:\n",
+ " variable = matrix_list[j]\n",
+ " if ( i == variable):\n",
+ " occur_dict[i] += 1\n",
+ " j += 1\n",
+ " elif ( i != variable):\n",
+ " j += 1\n",
+ " total += 1\n",
+ " \n",
+ " percent = 0.0\n",
+ " for i in occur_dict:\n",
+ " percent = ((occur_dict[i]/total)*100.0)\n",
+ " occur_dict[i] = percent\n",
+ " \n",
+ " return occur_dict\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def graph_percent_occurence(matrix, num_people, num_chrom, begin, stop):\n",
+ " \n",
+ " fig = plt.figure(1,(9.,9.))\n",
+ " \n",
+ " matrix = {}\n",
+ " occur_dict = {}\n",
+ " occur_dict = find_percent_occurence(matrix, num_people, num_chrom, begin, stop)\n",
+ " \n",
+ " occur_dict = collections.OrderedDict(sorted(occur_dict.items()))\n",
+ " \n",
+ " plt.bar(range(len(occur_dict)), occur_dict.values(), align='center')\n",
+ " plt.xticks(range(len(occur_dict)), occur_dict.keys())\n",
+ " \n",
+ " ax = fig.add_subplot(111)\n",
+ " ax.set_title('Distribution of shared variants among the genomic dataset (as compared to the reference genome)\\n', fontsize=14, fontweight='bold')\n",
+ " ax.set_xlabel('number of shared variants called within a range of bases on one chromosome')\n",
+ " ax.set_ylabel('percent occurence of the amount of shared variants')\n",
+ " plt.show()\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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JTZiMSJIkSWrCZESSJElSEyYjkiRJkpowGZEkSZLUhMmIJEmSpCZMRiRJkiQ1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\nrkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "matrix = {}\n",
+ "graph_percent_occurence(matrix, 40, \"1\", 100000, 140000)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def mean_median_percent_occurence(matrix, num_people, num_chrom, begin, stop):\n",
+ " \n",
+ " matrix = {}\n",
+ " occur_dict = {}\n",
+ " occur_dict = find_percent_occurence(matrix, num_people, num_chrom, begin, stop)\n",
+ " \n",
+ " i = 0\n",
+ " mean = 0.0\n",
+ " median_index = 0\n",
+ " for i in occur_dict:\n",
+ " # print the dictionary of the percent occurence of shared variants \n",
+ " #print (i, occur_dict[i]) \n",
+ " mean += ((i*occur_dict[i])/100.0)\n",
+ " median_index += 1\n",
+ " \n",
+ " median = 0.0\n",
+ " if ((( median_index + 1) % 2) == 0 ):\n",
+ " median_index += 1\n",
+ " median_index /= 2 \n",
+ " iter = 0\n",
+ " i = 0\n",
+ " for i in occur_dict:\n",
+ " iter += 1\n",
+ " if (iter == median_index):\n",
+ " median = i\n",
+ " elif ((median_index % 2) == 0):\n",
+ " median_index /= 2\n",
+ " median_neighbor = median_index + 1\n",
+ " median_pair_index = (median_index, median_neighbor)\n",
+ " iter1 = 0\n",
+ " iter2 = 0\n",
+ " i = 0\n",
+ " for i in occur_dict:\n",
+ " iter1 += 1\n",
+ " iter2 += 1\n",
+ " if ( iter1 == median_pair_index[0]):\n",
+ " iter1 = i\n",
+ " iter2 += 1\n",
+ " if ( iter2 == median_pair_index[1]): \n",
+ " iter2 = i\n",
+ " median = ((iter1 + iter2)/2)\n",
+ " \n",
+ " mean_median_pair = (mean, median)\n",
+ " \n",
+ " #print (mean_median_pair)\n",
+ " return mean_median_pair"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def analyze_matrix_intervals():\n",
+ " callMatrix = {}\n",
+ " start = 100000\n",
+ " place = 105000\n",
+ " stop = 1000000\n",
+ " mean_median_pair_dict = {}\n",
+ " val = 0\n",
+ " while ( start != stop):\n",
+ " mean_median_pair_dict[val] = mean_median_percent_occurence(callMatrix, 40, \"1\", start, place ) \n",
+ " val += 1\n",
+ " place += 5000\n",
+ " start += 5000\n",
+ " \n",
+ " \n",
+ " fig = plt.figure(1,(20.,10.))\n",
+ " ylim(-1.0, 100.0)\n",
+ " \n",
+ " i = 0\n",
+ " j = 0\n",
+ " interval = 0\n",
+ " plt.ion()\n",
+ " for i,j in mean_median_pair_dict.values(): \n",
+ " if ( interval != val):\n",
+ " plt.scatter(interval, i, c= \"blue\", s=40) \n",
+ " plt.scatter(interval, j, c= \"red\", s=40) \n",
+ " plt.pause(0.05)\n",
+ " if ( interval + 1 == val):\n",
+ " plt.scatter(interval, i, c= \"blue\", s=40, label= \"mean\") \n",
+ " plt.scatter(interval, j, c= \"red\", s=40, label= \"median\") \n",
+ " plt.pause(0.05) \n",
+ " interval += 1 \n",
+ " \n",
+ " plt.plot(range(len(mean_median_pair_dict)), mean_median_pair_dict.values(), 'k')\n",
+ " xlim(0, interval)\n",
+ " plt.xticks(np.arange(interval))\n",
+ " \n",
+ " ax = fig.add_subplot(111)\n",
+ " ax.set_title('Conservation of shared variants throughout 5000bp intervals\\n', fontsize=14, fontweight='bold')\n",
+ " ax.set_xlabel('interval')\n",
+ " ax.set_ylabel('percent occurence of the amount of shared variants')\n",
+ " ax.legend()\n",
+ "\n",
+ "\n",
+ " plt.show()\n",
+ " \n",
+ " #return mean_median_pair_list"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 448,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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eVyxb+rtil1124eabb+ajH/0oO++8M7vvvjtLliyhXC7T3d3NVVddxamnnsqO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\nvvE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ZSB6+IsS1OMTnbe/jjTzFtFeG8M0h/CHqWZ7bgtw/aMTlaXdQbcFOtC/0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\nJEmv+f9jXrOXKg9OewAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "analyze_matrix_intervals()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/requirements.txt b/requirements.txt
index e8b23e8..3bcc752 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,4 +3,4 @@ requests==2.7.0
numpy>=1.1
matplotlib>=1.4.2
Flask==0.10.1
-ga4gh>=0.2.2
+ga4gh>=0.3.1