Ethnicity exploration.ipynb

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   "source": [
    "### 1\n",
    "# Import and load the packages we will need. \n",
    "# You will probably need to install ipywidgets and bokeh\n",
    "# conda install -c https://conda.anaconda.org/anaconda ipywidgets\n",
    "# conda install -c https://conda.anaconda.org/anaconda bokeh\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import collections\n",
    "import gzip\n",
    "import numpy as np\n",
    "import os\n",
    "import phate\n",
    "import time\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from ipywidgets import interact\n",
    "import bokeh\n",
    "import bokeh.io\n",
    "from bokeh.io import push_notebook\n",
    "from bokeh.plotting import figure, show, save, output_notebook, output_file\n",
    "from bokeh.palettes import Category20b\n",
    "\n",
    "from collections import defaultdict\n",
    "import itertools\n",
    "\n",
    "# To install sklearn: conda install scikit-learn\n",
    "import sklearn\n",
    "from sklearn import decomposition\n",
    "from sklearn.decomposition import PCA as PCA # We'll use this to check our implementation\n",
    "from sklearn.manifold import TSNE\n",
    "from MulticoreTSNE import MulticoreTSNE as mTSNE\n",
    "\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.manifold import MDS\n",
    "\n",
    "import umap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "parent_dir = '/Users/alex/Documents/Ethnicity'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "source": [
    "### 16\n",
    "from collections import Counter # We'll use this built-in function to count genotypes.\n",
    "\n",
    "class snp(object):\n",
    "\n",
    "    def __init__(self, line, select=False, autosome_only =True):\n",
    "        \"\"\"The initialization method takes in a line from the vcf file, as a string, \n",
    "        and records the relevant information. \n",
    "        line: a string from a vcf file\n",
    "        select: a list of positions of individuals to be analyzed, where positions run from 0 to \n",
    "        nInd-1, the number of individuals\n",
    "        \"\"\" \n",
    "        \n",
    "        split_line = line.split()  #  First break down the line into a list of each field\n",
    "        \n",
    "        self.failed = False  # A label that we will set to True if something goes wrong.\n",
    "        \n",
    "        if line.startswith('#'):\n",
    "            self.failed = True\n",
    "            self.failure_cause = \"line was a header line, not a snp\"\n",
    "            return\n",
    "        \n",
    "        if len(split_line)<=5:\n",
    "            self.failed = True\n",
    "            self.failure_cause = \"incorrectly formatted line, should have at least 5 fields \" + line\n",
    "            return\n",
    "          \n",
    "        self.chrom = split_line[0]\n",
    "        if autosome_only:\n",
    "            if self.chrom not in [\"%d\" % (i,) for i in range(1,23)]:\n",
    "                self.failed = True\n",
    "                self.failure_cause = \"not recognized as an autosome while autosome_only set to True\"\n",
    "                return\n",
    "        \n",
    "        self.chrom = int(split_line[0]) # Chromosome (numbered)\n",
    "        self.position = int(split_line[1])  # The coordinates of the snp\n",
    "        self.rid = split_line[2] # Name/Record ID\n",
    "        self.ref_allele = split_line[3]\n",
    "        self.alt_allele = split_line[4] # The alterate allele according to the vcf; also a string \n",
    "        # Only accept snps in ACGT. \n",
    "        if self.ref_allele not in [\"A\",\"C\",\"G\",\"T\"] or self.alt_allele not in [\"A\",\"C\",\"G\",\"T\"]:\n",
    "            self.failed = True\n",
    "            self.failure_cause = \"ref or alt not in ACGT\"\n",
    "            return\n",
    "        self.filter = split_line[6]  # See vcf format specifications for the interpretation of \n",
    "                                    # the filter field\n",
    "        if self.filter not in ['PASS', '.'] :  # PASS indicates a SNP that passed all QC filters.\n",
    "            self.failed = True\n",
    "            self.failure_cause = self.filter\n",
    "            return\n",
    "        \n",
    "        \n",
    "        self.genotype_strings = split_line[9:]\n",
    "\n",
    "\n",
    "        # Prepare a list that will contain the transformed genotypes. \n",
    "        # Since we already know how long the list will be, it makes sense \n",
    "        # to create an array of zeros of the same length as self.gtypes, \n",
    "        \n",
    "        self.genotype_array = np.zeros(len(self.genotype_strings), dtype = np.int8)             \n",
    "\n",
    "        # Count the number of each genotype. \n",
    "        # There may be different strings giving the same genotype so we increment the \n",
    "        # counts found so far for the genotype by the number of times the  \n",
    "        # For example, \"0/0\" and \"0\\0\" give homref, and \"0|1\" and \"1|0\" give het\n",
    "        \n",
    "        n_missing = 0\n",
    "        for index,genotype_string in enumerate(self.genotype_strings):\n",
    "            if genotype_string == './.':\n",
    "                n_missing +=1 \n",
    "                self.genotype_array[index]=-1\n",
    "                continue # missing data will be left as 0\n",
    "            allele_0 = genotype_string[0] # Get the first allele (as a string)\n",
    "            allele_1 = genotype_string[2]\n",
    "            if (allele_0=='1' and allele_1=='1'): # Use rstrip because windows machines will occasionally have extra \\n\n",
    "                self.genotype_array[index]=2\n",
    "            elif ((allele_0=='0' and allele_1=='1') or (allele_0=='1' and allele_1=='0')):\n",
    "                self.genotype_array[index]=1   \n",
    "            elif (allele_0=='0' and allele_1=='0'):\n",
    "                # The array was initialized to zero, so nothing to do here!\n",
    "                continue\n",
    "            else:\n",
    "                print((\"unknown genotype\", genotype_string))\n",
    "                self.failed=True\n",
    "                self.failedreason=\"unknown genotype\"\n",
    "                return"
   ]
  },
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   "source": [
    "### 17\n",
    "# the vcf file can be found at \n",
    "#http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/hd_genotype_chip/\n",
    " \n",
    "vcf_file_name = 'ALL.wgs.nhgri_coriell_affy_6.20140825.genotypes_has_ped.vcf.gz'\n",
    "#vcf_file = os.path.join(parent_dir, vcf_file_name)\n",
    "vcf_file = '/Volumes/Stockage/alex/1000G/ALL.wgs.nhgri_coriell_affy_6.20140825.genotypes_has_ped.vcf.gz'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed: not recognized as an autosome while autosome_only set to True\n",
      "Failed: not recognized as an autosome while autosome_only set to True\n",
      "Failed: not recognized as an autosome while autosome_only set to True\n",
      "Failed: not recognized as an autosome while autosome_only set to True\n",
      "Failed: not recognized as an autosome while autosome_only set to True\n",
      "Failed: not recognized as an autosome while autosome_only set to True\n",
      "Failed: not recognized as an autosome while autosome_only set to True\n",
      "Failed: not recognized as an autosome while autosome_only set to True\n",
      "Failed: not recognized as an autosome while autosome_only set to True\n",
      "Failed: not recognized as an autosome while autosome_only set to True\n",
      "905886 3715\n",
      "86864\n",
      "271.67923283576965\n"
     ]
    }
   ],
   "source": [
    "### 18\n",
    "start_time = time.time()\n",
    "number_of_lines_to_skip = 10 # Number_of_lines_to_skip each time we process a line. This allows us to get \n",
    "                              # a genome-wide sample without having to store all the lines in memory.\n",
    "\n",
    "genotype_matrix = []  # Will contain our numerical genotype matrix. \n",
    "genotype_positions = []\n",
    "genotype_names = []\n",
    "x = 0\n",
    "error_count = 0\n",
    "\n",
    "with gzip.open(vcf_file,'rt') as f:\n",
    "    count = 0\n",
    "    for line in f:\n",
    "        count+=1\n",
    "        if count % number_of_lines_to_skip == 0:\n",
    "            if line.startswith(\"#\") or snp(line).failed:\n",
    "                if snp(line).failure_cause != \"line was a header line, not a snp\":\n",
    "                    error_count += 1\n",
    "                    if x < 10:\n",
    "                        print('Failed: ' + snp(line).failure_cause)\n",
    "                        x+=1\n",
    "                continue\n",
    "            \n",
    "            return_snp = snp(line)\n",
    "            genotype_matrix.append(return_snp.genotype_array)\n",
    "            genotype_names.append(return_snp.rid)\n",
    "            genotype_positions.append([return_snp.chrom, return_snp.position])\n",
    "\n",
    "end_time = time.time()\n",
    "            \n",
    "print(count, error_count)\n",
    "print(len(genotype_matrix))\n",
    "print(str(end_time - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  2  0 ...  2  0  0]\n",
      " [-1  2  0 ...  0  0  0]\n",
      " [ 0  2  0 ...  2  0  1]\n",
      " ...\n",
      " [ 0  2  0 ...  1  0  1]\n",
      " [ 0  2  0 ...  0  1  0]\n",
      " [ 0  2  0 ...  2  1  0]] (3450, 86864)\n"
     ]
    }
   ],
   "source": [
    "transposed_genotype_matrix = np.array(genotype_matrix).transpose()\n",
    "print(transposed_genotype_matrix, transposed_genotype_matrix.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['GBR', 'FIN', 'CHS', 'PUR', 'CDX', 'CLM', 'IBS', 'PEL', 'PJL', 'KHV', 'ACB', 'GWD', 'ESN', 'BEB', 'MSL', 'STU', 'ITU', 'CEU', 'YRI', 'CHB', 'JPT', 'LWK', 'ASW', 'MXL', 'TSI', 'GIH']\n"
     ]
    }
   ],
   "source": [
    "population_file_name = 'affy_samples.20141118.panel' # This file is available at \n",
    "#http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/hd_genotype_chip/\n",
    "\n",
    "population_file = os.path.join(parent_dir, population_file_name)\n",
    "\n",
    "population_by_individual = defaultdict(int)\n",
    "individuals_by_population = defaultdict(list)  # A dictionary containing all the individuals in a given population\n",
    "\n",
    "for line in open(population_file,'r'):\n",
    "    split_line = line.split()\n",
    "    if split_line[0] == 'sample':  # header line\n",
    "        continue\n",
    "\n",
    "    sample_name = split_line[0]\n",
    "    population_name = split_line[1]\n",
    "    population_by_individual[sample_name] = population_name\n",
    "    individuals_by_population[population_name].append(sample_name) \n",
    "\n",
    "populations = list(individuals_by_population.keys())\n",
    "print(populations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "continent groupings\n",
      "\n",
      "\n",
      "EAS ['CDX', 'CHB', 'JPT', 'KHV', 'CHS']\n",
      "SAS ['BEB', 'GIH', 'ITU', 'PJL', 'STU']\n",
      "AFR ['ASW', 'ACB', 'ESN', 'GWD', 'LWK', 'MSL', 'YRI']\n",
      "EUR ['GBR', 'FIN', 'IBS', 'TSI', 'CEU']\n",
      "AMR ['CLM', 'MXL', 'PEL', 'PUR']\n",
      "\n",
      "\n",
      "Population labels\n",
      "\n",
      "\n",
      "CDX Chinese Dai in Xishuangbanna, China\n",
      "CHB Han Chinese in Bejing, China\n",
      "JPT Japanese in Tokyo, Japan\n",
      "KHV Kinh in Ho Chi Minh City, Vietnam\n",
      "CHS Southern Han Chinese, China\n",
      "BEB Bengali in Bangladesh\n",
      "GIH Gujarati Indian in Houston,TX\n",
      "ITU Indian Telugu in the UK\n",
      "PJL Punjabi in Lahore,Pakistan\n",
      "STU Sri Lankan Tamil in the UK\n",
      "ASW African Ancestry in Southwest US\n",
      "ACB African Caribbean in Barbados\n",
      "ESN Esan in Nigeria\n",
      "GWD Gambian in Western Division\n",
      "LWK Luhya in Webuye, Kenya\n",
      "MSL Mende in Sierra Leone\n",
      "YRI Yoruba in Ibadan, Nigeria\n",
      "GBR British in England and Scotland\n",
      "FIN Finnish in Finland\n",
      "IBS Iberian populations in Spain\n",
      "TSI Toscani in Italy\n",
      "CEU Utah residents with N and W European ancestry\n",
      "CLM Colombian in Medellin, Colombia\n",
      "MXL Mexican Ancestry in LA, California\n",
      "PEL Peruvian in Lima, Peru\n",
      "PUR Puerto Rican in Puerto Rico\n"
     ]
    }
   ],
   "source": [
    "# The path to the place where you put the population name file.\n",
    "population_description_file = os.path.join(parent_dir, \"20131219.populations.tsv\")\n",
    "\n",
    "name_by_code = {}  # A dictionary giving the full name of each population code\n",
    "pop_by_continent = {}  # A dictionary giving the code of each population within a continent  \n",
    "continent_by_population = {}  # A dictionary giving the continent for each population code\n",
    "for line in open(population_description_file,'r'):\n",
    "    split_line = line.split('\\t')\n",
    "    if split_line[0] in ['Population Description','Total','']:  # header or footer\n",
    "        continue\n",
    "    name_by_code[split_line[1]] = split_line[0]\n",
    "    continent_by_population[split_line[1]] = split_line[2]\n",
    "    try: \n",
    "        pop_by_continent[split_line[2]].append(split_line[1])\n",
    "    except KeyError:\n",
    "        pop_by_continent[split_line[2]] = [split_line[1]]\n",
    "\n",
    "continents = list(pop_by_continent.keys()) \n",
    "\n",
    "print(\"\\n\\ncontinent groupings\\n\\n\")\n",
    "for continent in continents:\n",
    "    print(continent, pop_by_continent[continent])\n",
    "    \n",
    "    \n",
    "# Finally, we want a list of populations listed by continent. \n",
    "pops=[]\n",
    "for continent in continents:\n",
    "    pops.extend(pop_by_continent[continent])\n",
    "print(\"\\n\\nPopulation labels\\n\\n\")\n",
    "\n",
    "for pop in pops:\n",
    "    print(pop, name_by_code[pop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from bokeh.palettes import Category20b\n",
    "from bokeh.palettes import Purples\n",
    "from bokeh.palettes import Greens\n",
    "from bokeh.palettes import YlOrBr\n",
    "from bokeh.palettes import YlOrRd\n",
    "from bokeh.palettes import PuOr\n",
    "from bokeh.palettes import RdGy\n",
    "\n",
    "color_dict = {}\n",
    "for i, cont in enumerate(continents): \n",
    "    for j, pop in enumerate(pop_by_continent[cont]):\n",
    "        color_dict[pop] = Category20b[20][4*i+j%4]\n",
    "\n",
    "# Issue: shared colours between:\n",
    "# CDX and CHS (purple)\n",
    "# GIH and STU (green)\n",
    "# ASW and LWK (brown/yellow)\n",
    "# ACB and MSL (brown/yellow)\n",
    "# ESN and YRI (goldenrod/canary/saffron/paella)\n",
    "# GBR and CEU (dark red)\n",
    "\n",
    "# Manually assign colours to CHS, STU, LWK, MSL, CEU\n",
    "color_dict['CHS'] = Purples[9][4]# purple\n",
    "color_dict['STU'] = Greens[9][6] # green\n",
    "color_dict['LWK'] = PuOr[11][-1] # brown\n",
    "color_dict['MSL'] = PuOr[11][-2] # rusty brown\n",
    "color_dict['YRI'] = PuOr[11][-3] # cappucino w/ extra milk (stirred)\n",
    "color_dict['CEU'] = RdGy[11][-3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# For troubleshooting purposes (checking individual values of colours)\n",
    "\n",
    "#for i, cont in enumerate(continents):\n",
    "#    for j, pop in enumerate(pop_by_continent[cont]):\n",
    "#        print(str(i), str(j), cont, color_dict[pop], pop, name_by_code[pop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### 27\n",
    "# Get the path to the file containing the vcf header. \n",
    "# That should be the same vcf you used above, but if your filtered file\n",
    "# is somehow missing the header, you can aways look at the initial file that still has the header.\n",
    "x=0\n",
    "\n",
    "vcf_file = '/Volumes/Stockage/alex/1000G/ALL.wgs.nhgri_coriell_affy_6.20140825.genotypes_has_ped.vcf.gz'\n",
    "for line in gzip.open(vcf_file,'rt'):\n",
    "    if line.startswith(\"#\"):\n",
    "        if not line.startswith(\"##\"):\n",
    "            # Extract the individuals for the population, as a list of strings\n",
    "            # Make sure you don't have trailing \\n if you're running windows!\n",
    "            individuals = line.split()[9:]\n",
    "            # Once we've extracted the individuals, we can exit the loops. \n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### 28\n",
    "# Build a list of populations for each indiviudal in the vcf file\n",
    "lspop = []\n",
    "for ind in individuals:\n",
    "    pop = population_by_individual[ind]\n",
    "    if pop == 0:\n",
    "        #print(0)\n",
    "        lspop.append(\"missing\")\n",
    "    else:\n",
    "        lspop.append(pop)\n",
    "\n",
    "        \n",
    "indices_of_population_members = defaultdict(list)\n",
    "\n",
    "for index,individual in enumerate(individuals):\n",
    "    try:\n",
    "        indices_of_population_members[population_by_individual[individual]].append(index)\n",
    "    except KeyError: # We do not have population info for this individual\n",
    "        continue\n",
    "\n",
    "#indices_of_population_members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'continents' (list)\n",
      "Stored 'pop_by_continent' (dict)\n",
      "Stored 'pop' (str)\n",
      "Stored 'indices_of_population_members' (defaultdict)\n",
      "Stored 'name_by_code' (dict)\n",
      "Stored 'continent_by_population' (dict)\n",
      "Stored 'individuals' (list)\n",
      "Stored 'population_by_individual' (defaultdict)\n",
      "Stored 'individuals_by_population' (defaultdict)\n",
      "Stored 'populations' (list)\n",
      "Stored 'color_dict' (dict)\n"
     ]
    }
   ],
   "source": [
    "%store continents\n",
    "%store pop_by_continent\n",
    "%store pop\n",
    "%store indices_of_population_members\n",
    "%store name_by_code\n",
    "%store continent_by_population\n",
    "%store individuals\n",
    "%store population_by_individual\n",
    "%store individuals_by_population\n",
    "%store populations\n",
    "%store color_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Principal component generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate the PCs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca_full = PCA().fit(transposed_genotype_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca_full.components_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.savetxt(pca_full.components_, '/Users/alex/Documents/Ethnicity/1kgp_components')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'transposed_genotype_matrix' (ndarray)\n"
     ]
    }
   ],
   "source": [
    "#%store pca_full\n",
    "%store transposed_genotype_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pca_full' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-52-ac5aaf93b2e0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# Have to subset the components and use that.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mcomps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpca_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'pca_full' is not defined"
     ]
    }
   ],
   "source": [
    "# Calculate the PCs manually. We want a subset of k PCs\n",
    "# Have to subset the components and use that.\n",
    "k = 100\n",
    "comps = pca_full.components_[:k,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Mean-center the SNP data and then transform it (projecting to k dimensions)\n",
    "projections_to_PCs = np.dot((transposed_genotype_matrix - pca_full.mean_), comps.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.savetxt('/Volumes/Stockage/alex/1000G/projections/pca_1000g_100', projections_to_PCs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a0da4c7f0>]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eegXZBmOFX91yotToKcaxEzWxtXVGWd/ezfHePgBOnO5n7/GPidmGay+9kHMnlWCAZQ1V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\nCon/DxM6DqCFSQkVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0c27c048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(projections_to_PCs[:,1], projections_to_PCs[:,2], '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca_100 = np.loadtxt('/Volumes/Stockage/alex/1000G/projections/pca_1000g_100')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Secondary dimension reduction methods\n",
    "\n",
    "Test a variety of other secondary reduction measures and see what sticks.\n",
    "\n",
    "Conclusion: MDS has some potential but still doesn't do as well as TSNE. It did split Japan into two subclusters though...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create an MDS projection for PCs 11 through 30.\n",
    "mds_proj_list = list()\n",
    "\n",
    "for pc_proj in pc_proj_list:\n",
    "    if 10<=pc_proj.shape[1]<=30:\n",
    "        temp_proj = MDS(n_components=2).fit_transform(pc_proj)\n",
    "        np.savetxt('1000G_MDS_PC_' + str(pc_proj.shape[1]), temp_proj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Try t-SNE (2 dimensions)\n",
    "genotype_tsne = TSNE(n_components=2).fit_transform(projections_to_PCs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bulding kNN graph and diffusion operator...\n",
      "Built graph and diffusion operator in 1520.77 seconds.\n",
      "Calculating diffusion potential...\n",
      "Calculated diffusion potential in 3.41 seconds.\n",
      "Embedding data using classic MDS...\n",
      "Embedded data in 22.78 seconds.\n",
      "Finished PHATE embedding in 1546.96 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using classic MDS...\n",
      "Embedded data in 22.31 seconds.\n",
      "Finished PHATE embedding in 22.31 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using metric MDS...\n",
      "Embedded data in 929.52 seconds.\n",
      "Finished PHATE embedding in 929.52 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using metric MDS...\n",
      "Embedded data in 924.02 seconds.\n",
      "Finished PHATE embedding in 924.02 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1065.52 seconds.\n",
      "Finished PHATE embedding in 1065.52 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 54.26 seconds.\n",
      "Finished PHATE embedding in 54.26 seconds.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Try PHATE\n",
    "# Based off of:\n",
    "# https://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/test/phate_examples.ipynb?flush_cache=true\n",
    "?phate.PHATE\n",
    "\n",
    "# Create instance of PHATE operator (use default options)\n",
    "phate_operator = phate.PHATE()\n",
    "\n",
    "# Run PHATE with classic MDS on the projected data as well as the unprojected data\n",
    "Y_cmds_raw = phate_operator.fit_transform(transposed_genotype_matrix)\n",
    "Y_cmds_pcs = phate_operator.fit_transform(projections_to_PCs)\n",
    "\n",
    "# Run PHATE with metric MDS on each one\n",
    "phate_operator.reset_mds(mds=\"metric\")\n",
    "Y_mmds_raw = phate_operator.fit_transform(transposed_genotype_matrix)\n",
    "Y_mmds_pcs = phate_operator.fit_transform(projections_to_PCs)\n",
    "\n",
    "# Run PHATE with non-metric MDS on each one\n",
    "phate_operator.reset_mds(mds=\"nonmetric\")\n",
    "Y_nmmds_raw = phate_operator.fit_transform(transposed_genotype_matrix)\n",
    "Y_nmmds_pcs = phate_operator.fit_transform(projections_to_PCs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 53.97 seconds.\n",
      "Finished PHATE embedding in 53.97 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1028.05 seconds.\n",
      "Finished PHATE embedding in 1028.05 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 30480.76 seconds.\n",
      "Finished PHATE embedding in 30480.76 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 2569.61 seconds.\n",
      "Finished PHATE embedding in 2569.61 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 2353.35 seconds.\n",
      "Finished PHATE embedding in 2353.35 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1693.67 seconds.\n",
      "Finished PHATE embedding in 1693.67 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 949.11 seconds.\n",
      "Finished PHATE embedding in 949.11 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1004.41 seconds.\n",
      "Finished PHATE embedding in 1004.41 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1049.83 seconds.\n",
      "Finished PHATE embedding in 1049.83 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1091.85 seconds.\n",
      "Finished PHATE embedding in 1091.85 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 996.38 seconds.\n",
      "Finished PHATE embedding in 996.38 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1035.32 seconds.\n",
      "Finished PHATE embedding in 1035.32 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 74064.06 seconds.\n",
      "Finished PHATE embedding in 74064.06 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 2634.39 seconds.\n",
      "Finished PHATE embedding in 2634.39 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 2563.40 seconds.\n",
      "Finished PHATE embedding in 2563.40 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-49-20df39bb192e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mtemp_comps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpca_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mtemp_pc_projection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransposed_genotype_matrix\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mpca_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemp_comps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mtemp_projection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mphate_operator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_pc_projection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0mnmmds_pcs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_projection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/phate-0.1-py3.6.egg/phate/phate.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    242\u001b[0m         self.embedding, self.diff_op, self.diff_potential = embed_phate(X, n_components=self.ndim, a=self.a, k=self.k, t=self.t,\n\u001b[1;32m    243\u001b[0m                                                                         \u001b[0mmds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknn_dist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mknn_dist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmds_dist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmds_dist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnjobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnjobs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 244\u001b[0;31m                                                                         diff_op = self.diff_op, diff_potential = self.diff_potential, random_state=self.random_state)\n\u001b[0m\u001b[1;32m    245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/phate-0.1-py3.6.egg/phate/phate.py\u001b[0m in \u001b[0;36membed_phate\u001b[0;34m(data, n_components, a, k, t, mds, knn_dist, mds_dist, diff_op, diff_potential, njobs, random_state)\u001b[0m\n\u001b[1;32m    116\u001b[0m     \u001b[0mtic\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Embedding data using %s MDS...\"\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m     \u001b[0membedding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0membed_MDS\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdiff_potential\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_components\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistance_metric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmds_dist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnjobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnjobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Embedded data in %.2f seconds.\"\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Finished PHATE embedding in %.2f seconds.\\n\"\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/phate-0.1-py3.6.egg/phate/mds.py\u001b[0m in \u001b[0;36membed_MDS\u001b[0;34m(X, ndim, how, distance_metric, njobs, seed)\u001b[0m\n\u001b[1;32m    110\u001b[0m         Y = MDS(n_components=ndim, metric=False, max_iter=3000, eps=1e-12,\n\u001b[1;32m    111\u001b[0m                      \u001b[0mdissimilarity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"precomputed\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnjobs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m                      n_init=1).fit_transform(X_dist,init=Y_mmds)\n\u001b[0m\u001b[1;32m    113\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Allowable 'how' values for MDS: 'classic', 'metric', or 'nonmetric'. '%s' was passed.\"\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/manifold/mds.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, init)\u001b[0m\n\u001b[1;32m    427\u001b[0m             \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    428\u001b[0m             \u001b[0meps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 429\u001b[0;31m             return_n_iter=True)\n\u001b[0m\u001b[1;32m    430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    431\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/manifold/mds.py\u001b[0m in \u001b[0;36msmacof\u001b[0;34m(dissimilarities, metric, n_components, init, n_init, n_jobs, max_iter, verbose, eps, random_state, return_n_iter)\u001b[0m\n\u001b[1;32m    252\u001b[0m                 \u001b[0mn_components\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_components\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m                 \u001b[0mmax_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 254\u001b[0;31m                 eps=eps, random_state=random_state)\n\u001b[0m\u001b[1;32m    255\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbest_stress\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mstress\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mbest_stress\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m                 \u001b[0mbest_stress\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstress\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/manifold/mds.py\u001b[0m in \u001b[0;36m_smacof_single\u001b[0;34m(dissimilarities, metric, n_components, init, max_iter, verbose, eps, random_state)\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m             \u001b[0;31m# Compute the disparities using a monotonic regression\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m             \u001b[0mdisparities_flat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mir\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msim_flat_w\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdis_flat_w\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m             \u001b[0mdisparities\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdis_flat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m             \u001b[0mdisparities\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msim_flat\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdisparities_flat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    518\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    519\u001b[0m             \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 520\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    521\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/isotonic.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, T)\u001b[0m\n\u001b[1;32m    387\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mout_of_bounds\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"clip\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m             \u001b[0mT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_min_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_max_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/scipy/interpolate/polyint.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     77\u001b[0m         \"\"\"\n\u001b[1;32m     78\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_x\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     80\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_finish_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/scipy/interpolate/interpolate.py\u001b[0m in \u001b[0;36m_evaluate\u001b[0;34m(self, x_new)\u001b[0m\n\u001b[1;32m    606\u001b[0m         \u001b[0;31m#    The behavior is set by the bounds_error variable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m         \u001b[0mx_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 608\u001b[0;31m         \u001b[0my_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    609\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extrapolate\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m             \u001b[0mbelow_bounds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabove_bounds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_bounds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/scipy/interpolate/interpolate.py\u001b[0m in \u001b[0;36m_call_linear_np\u001b[0;34m(self, x_new)\u001b[0m\n\u001b[1;32m    545\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_linear_np\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    546\u001b[0m         \u001b[0;31m# Note that out-of-bounds values are taken care of in self._evaluate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 547\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_new\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    548\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_linear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36minterp\u001b[0;34m(x, xp, fp, left, right, period)\u001b[0m\n\u001b[1;32m   2042\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0minterp_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2043\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2044\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0minterp_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2045\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2046\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mperiod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Try out PHATE on other PC embeddings.\n",
    "phate_operator.reset_mds(mds=\"nonmetric\")\n",
    "\n",
    "nmmds_pcs = []\n",
    "\n",
    "for i in range(1, 15):\n",
    "    temp_comps = pca_full.components_[:i*10,:]\n",
    "    temp_pc_projection = np.dot((transposed_genotype_matrix - pca_full.mean_), temp_comps.T)\n",
    "    temp_projection = phate_operator.fit_transform(temp_pc_projection)\n",
    "    nmmds_pcs.append(temp_projection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# MDS (skipping the PHATE bit)\n",
    "mds_raw = MDS().fit_transform(transposed_genotype_matrix)\n",
    "mds_pc30 = MDS().fit_transform(projections_to_PCs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished with MDS projection of 10 PCs\n",
      "Finished with MDS projection of 20 PCs\n",
      "Finished with MDS projection of 30 PCs\n",
      "Finished with MDS projection of 40 PCs\n",
      "Finished with MDS projection of 50 PCs\n",
      "Finished with MDS projection of 60 PCs\n",
      "Finished with MDS projection of 70 PCs\n",
      "Finished with MDS projection of 80 PCs\n",
      "Finished with MDS projection of 90 PCs\n",
      "Finished with MDS projection of 100 PCs\n",
      "Finished with MDS projection of 110 PCs\n",
      "Finished with MDS projection of 120 PCs\n",
      "Finished with MDS projection of 130 PCs\n",
      "Finished with MDS projection of 140 PCs\n",
      "Finished with MDS projection of 150 PCs\n",
      "Finished with MDS projection of 160 PCs\n",
      "Finished with MDS projection of 170 PCs\n",
      "Finished with MDS projection of 180 PCs\n",
      "Finished with MDS projection of 190 PCs\n",
      "Finished with MDS projection of 200 PCs\n",
      "Finished with MDS projection of 210 PCs\n",
      "Finished with MDS projection of 220 PCs\n",
      "Finished with MDS projection of 230 PCs\n",
      "Finished with MDS projection of 240 PCs\n",
      "Finished with MDS projection of 250 PCs\n"
     ]
    }
   ],
   "source": [
    "mds_pcs = []\n",
    "\n",
    "for p in pc_list:\n",
    "    #temp_comps = pca_full.components_[:i*10,:]\n",
    "    #temp_pc_projection = np.dot((transposed_genotype_matrix - pca_full.mean_), temp_comps.T)\n",
    "    temp_projection = MDS().fit_transform(p)\n",
    "    mds_pcs.append(temp_projection)\n",
    "    print(\"Finished with MDS projection of \" + str(p.shape[1]) + \" PCs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished creating interactive HTML for MDS with 10 PCs\n",
      "Finished creating interactive HTML for MDS with 20 PCs\n",
      "Finished creating interactive HTML for MDS with 30 PCs\n",
      "Finished creating interactive HTML for MDS with 40 PCs\n",
      "Finished creating interactive HTML for MDS with 50 PCs\n",
      "Finished creating interactive HTML for MDS with 60 PCs\n",
      "Finished creating interactive HTML for MDS with 70 PCs\n",
      "Finished creating interactive HTML for MDS with 80 PCs\n",
      "Finished creating interactive HTML for MDS with 90 PCs\n",
      "Finished creating interactive HTML for MDS with 100 PCs\n",
      "Finished creating interactive HTML for MDS with 110 PCs\n",
      "Finished creating interactive HTML for MDS with 120 PCs\n",
      "Finished creating interactive HTML for MDS with 130 PCs\n",
      "Finished creating interactive HTML for MDS with 140 PCs\n",
      "Finished creating interactive HTML for MDS with 150 PCs\n",
      "Finished creating interactive HTML for MDS with 160 PCs\n",
      "Finished creating interactive HTML for MDS with 170 PCs\n",
      "Finished creating interactive HTML for MDS with 180 PCs\n",
      "Finished creating interactive HTML for MDS with 190 PCs\n",
      "Finished creating interactive HTML for MDS with 200 PCs\n",
      "Finished creating interactive HTML for MDS with 210 PCs\n",
      "Finished creating interactive HTML for MDS with 220 PCs\n",
      "Finished creating interactive HTML for MDS with 230 PCs\n",
      "Finished creating interactive HTML for MDS with 240 PCs\n",
      "Finished creating interactive HTML for MDS with 250 PCs\n"
     ]
    }
   ],
   "source": [
    "for p in range(0, len(pc_list)):\n",
    "    plot_bokeh(mds_pcs[p], 'MDS on ' + str(pc_list[p].shape[1]) + 'PCs', 'MDS_' + str(pc_list[p].shape[1]) + 'PC',\n",
    "               'MDS with ' + str(pc_list[p].shape[1]) + 'PCs')\n",
    "    print(\"Finished creating interactive HTML for MDS with \" + str(pc_list[p].shape[1]) + \" PCs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished with MDS projection of1PCs\n",
      "Finished with MDS projection of2PCs\n",
      "Finished with MDS projection of3PCs\n",
      "Finished with MDS projection of4PCs\n",
      "Finished with MDS projection of5PCs\n",
      "Finished with MDS projection of6PCs\n",
      "Finished with MDS projection of7PCs\n",
      "Finished with MDS projection of8PCs\n",
      "Finished with MDS projection of9PCs\n",
      "Finished creating interactive HTML for MDS with 10 PCs\n",
      "Finished creating interactive HTML for MDS with 20 PCs\n",
      "Finished creating interactive HTML for MDS with 30 PCs\n",
      "Finished creating interactive HTML for MDS with 40 PCs\n",
      "Finished creating interactive HTML for MDS with 50 PCs\n",
      "Finished creating interactive HTML for MDS with 60 PCs\n",
      "Finished creating interactive HTML for MDS with 70 PCs\n",
      "Finished creating interactive HTML for MDS with 80 PCs\n",
      "Finished creating interactive HTML for MDS with 90 PCs\n"
     ]
    }
   ],
   "source": [
    "pc_list2 = []\n",
    "for i in range(1, 10):\n",
    "    comps = PCA(n_components=i).fit(transposed_genotype_matrix)\n",
    "    proj = comps.transform(transposed_genotype_matrix)\n",
    "    pc_list2.append(proj)\n",
    "\n",
    "mds_pcs2 = []\n",
    "\n",
    "for p in pc_list2:\n",
    "    temp_projection = MDS().fit_transform(p)\n",
    "    mds_pcs2.append(temp_projection)\n",
    "    print(\"Finished with MDS projection of\" + str(p.shape[1]) + \"PCs\")\n",
    "\n",
    "for p in range(0, len(pc_list2)):\n",
    "    plot_bokeh(mds_pcs2[p], 'MDS on ' + str(pc_list2[p].shape[1]) + 'PCs', 'MDS_' + str(pc_list2[p].shape[1]) + 'PC',\n",
    "               'MDS with ' + str(pc_list2[p].shape[1]) + 'PCs')\n",
    "    print(\"Finished creating interactive HTML for MDS with \" + str(pc_list[p].shape[1]) + \" PCs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pc_list_full = pc_list2 + pc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished t-SNE projection for 2 PCs.\n",
      "Finished t-SNE projection for 3 PCs.\n",
      "Finished t-SNE projection for 4 PCs.\n",
      "Finished t-SNE projection for 5 PCs.\n",
      "Finished t-SNE projection for 6 PCs.\n",
      "Finished t-SNE projection for 7 PCs.\n",
      "Finished t-SNE projection for 8 PCs.\n",
      "Finished t-SNE projection for 9 PCs.\n",
      "Finished t-SNE projection for 10 PCs.\n",
      "Finished t-SNE projection for 11 PCs.\n",
      "Finished t-SNE projection for 12 PCs.\n",
      "Finished t-SNE projection for 13 PCs.\n",
      "Finished t-SNE projection for 14 PCs.\n",
      "Finished t-SNE projection for 15 PCs.\n",
      "Finished t-SNE projection for 16 PCs.\n",
      "Finished t-SNE projection for 17 PCs.\n",
      "Finished t-SNE projection for 18 PCs.\n",
      "Finished t-SNE projection for 19 PCs.\n",
      "Finished t-SNE projection for 20 PCs.\n",
      "Finished t-SNE projection for 30 PCs.\n",
      "Finished t-SNE projection for 40 PCs.\n",
      "Finished t-SNE projection for 50 PCs.\n",
      "Finished t-SNE projection for 60 PCs.\n",
      "Finished t-SNE projection for 70 PCs.\n",
      "Finished t-SNE projection for 80 PCs.\n",
      "Finished t-SNE projection for 90 PCs.\n",
      "Finished t-SNE projection for 100 PCs.\n"
     ]
    }
   ],
   "source": [
    "# t-SNE transformations\n",
    "pc_list = [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,30,40,50,60,70,80,90,100]\n",
    "out_dir = '/Volumes/Stockage/alex/1000G/projections'\n",
    "\n",
    "for p in pc_list:\n",
    "    temp_projection = TSNE(n_components = 2).fit_transform(projections_to_PCs[:,0:p])\n",
    "    np.savetxt(os.path.join(out_dir,'1000G_TSNE_PC' + str(p)),temp_projection)\n",
    "    print(\"Finished t-SNE projection for \" + str(p) + \" PCs.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# t-SNE projection on non-transformed data\n",
    "tsne_raw = TSNE(n_components = 2).fit_transform(transposed_genotype_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1039.94 seconds.\n",
      "Finished PHATE embedding in 1039.94 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1039.44 seconds.\n",
      "Finished PHATE embedding in 1039.44 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 16043.37 seconds.\n",
      "Finished PHATE embedding in 16043.37 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1050.03 seconds.\n",
      "Finished PHATE embedding in 1050.03 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1050.91 seconds.\n",
      "Finished PHATE embedding in 1050.91 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 987.75 seconds.\n",
      "Finished PHATE embedding in 987.75 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1044.74 seconds.\n",
      "Finished PHATE embedding in 1044.75 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1021.52 seconds.\n",
      "Finished PHATE embedding in 1021.52 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1022.42 seconds.\n",
      "Finished PHATE embedding in 1022.42 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 15528.17 seconds.\n",
      "Finished PHATE embedding in 15528.17 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1027.63 seconds.\n",
      "Finished PHATE embedding in 1027.63 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1044.05 seconds.\n",
      "Finished PHATE embedding in 1044.05 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1069.19 seconds.\n",
      "Finished PHATE embedding in 1069.19 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1053.19 seconds.\n",
      "Finished PHATE embedding in 1053.19 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1047.41 seconds.\n",
      "Finished PHATE embedding in 1047.41 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1023.25 seconds.\n",
      "Finished PHATE embedding in 1023.25 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1053.40 seconds.\n",
      "Finished PHATE embedding in 1053.40 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1021.18 seconds.\n",
      "Finished PHATE embedding in 1021.18 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1076.04 seconds.\n",
      "Finished PHATE embedding in 1076.04 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1073.84 seconds.\n",
      "Finished PHATE embedding in 1073.84 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1050.22 seconds.\n",
      "Finished PHATE embedding in 1050.22 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1067.01 seconds.\n",
      "Finished PHATE embedding in 1067.01 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1021.52 seconds.\n",
      "Finished PHATE embedding in 1021.52 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 16295.51 seconds.\n",
      "Finished PHATE embedding in 16295.51 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1030.89 seconds.\n",
      "Finished PHATE embedding in 1030.89 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1025.00 seconds.\n",
      "Finished PHATE embedding in 1025.00 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1050.86 seconds.\n",
      "Finished PHATE embedding in 1050.86 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1041.29 seconds.\n",
      "Finished PHATE embedding in 1041.29 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 994.65 seconds.\n",
      "Finished PHATE embedding in 994.65 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1019.90 seconds.\n",
      "Finished PHATE embedding in 1019.90 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1048.76 seconds.\n",
      "Finished PHATE embedding in 1048.76 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1102.85 seconds.\n",
      "Finished PHATE embedding in 1102.85 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1020.89 seconds.\n",
      "Finished PHATE embedding in 1020.89 seconds.\n",
      "\n",
      "Using precomputed diffusion operator...\n",
      "Using precomputed diffusion potential...\n",
      "Embedding data using nonmetric MDS...\n",
      "Embedded data in 1035.10 seconds.\n",
      "Finished PHATE embedding in 1035.10 seconds.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "phate_pcs = []\n",
    "phate_operator.reset_mds(mds=\"nonmetric\")\n",
    "\n",
    "for p in pc_list_full:\n",
    "    temp_projection = phate_operator.fit_transform(p)\n",
    "    phate_pcs.append(temp_projection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appended tSNE projection for 1 PCs with perplexity 5.\n",
      "Appended tSNE projection for 1 PCs with perplexity 30.\n",
      "Appended tSNE projection for 1 PCs with perplexity 50.\n",
      "Appended tSNE projection for 1 PCs with perplexity 100.\n",
      "Appended tSNE projection for 2 PCs with perplexity 5.\n",
      "Appended tSNE projection for 2 PCs with perplexity 30.\n",
      "Appended tSNE projection for 2 PCs with perplexity 50.\n",
      "Appended tSNE projection for 2 PCs with perplexity 100.\n",
      "Appended tSNE projection for 3 PCs with perplexity 5.\n",
      "Appended tSNE projection for 3 PCs with perplexity 30.\n",
      "Appended tSNE projection for 3 PCs with perplexity 50.\n",
      "Appended tSNE projection for 3 PCs with perplexity 100.\n",
      "Appended tSNE projection for 4 PCs with perplexity 5.\n",
      "Appended tSNE projection for 4 PCs with perplexity 30.\n",
      "Appended tSNE projection for 4 PCs with perplexity 50.\n",
      "Appended tSNE projection for 4 PCs with perplexity 100.\n",
      "Appended tSNE projection for 5 PCs with perplexity 5.\n",
      "Appended tSNE projection for 5 PCs with perplexity 30.\n",
      "Appended tSNE projection for 5 PCs with perplexity 50.\n",
      "Appended tSNE projection for 5 PCs with perplexity 100.\n",
      "Appended tSNE projection for 6 PCs with perplexity 5.\n",
      "Appended tSNE projection for 6 PCs with perplexity 30.\n",
      "Appended tSNE projection for 6 PCs with perplexity 50.\n",
      "Appended tSNE projection for 6 PCs with perplexity 100.\n",
      "Appended tSNE projection for 7 PCs with perplexity 5.\n",
      "Appended tSNE projection for 7 PCs with perplexity 30.\n",
      "Appended tSNE projection for 7 PCs with perplexity 50.\n",
      "Appended tSNE projection for 7 PCs with perplexity 100.\n",
      "Appended tSNE projection for 8 PCs with perplexity 5.\n",
      "Appended tSNE projection for 8 PCs with perplexity 30.\n",
      "Appended tSNE projection for 8 PCs with perplexity 50.\n",
      "Appended tSNE projection for 8 PCs with perplexity 100.\n",
      "Appended tSNE projection for 9 PCs with perplexity 5.\n",
      "Appended tSNE projection for 9 PCs with perplexity 30.\n",
      "Appended tSNE projection for 9 PCs with perplexity 50.\n",
      "Appended tSNE projection for 9 PCs with perplexity 100.\n",
      "Appended tSNE projection for 10 PCs with perplexity 5.\n",
      "Appended tSNE projection for 10 PCs with perplexity 30.\n",
      "Appended tSNE projection for 10 PCs with perplexity 50.\n",
      "Appended tSNE projection for 10 PCs with perplexity 100.\n",
      "Appended tSNE projection for 20 PCs with perplexity 5.\n",
      "Appended tSNE projection for 20 PCs with perplexity 30.\n",
      "Appended tSNE projection for 20 PCs with perplexity 50.\n",
      "Appended tSNE projection for 20 PCs with perplexity 100.\n",
      "Appended tSNE projection for 30 PCs with perplexity 5.\n",
      "Appended tSNE projection for 30 PCs with perplexity 30.\n",
      "Appended tSNE projection for 30 PCs with perplexity 50.\n",
      "Appended tSNE projection for 30 PCs with perplexity 100.\n",
      "Appended tSNE projection for 40 PCs with perplexity 5.\n",
      "Appended tSNE projection for 40 PCs with perplexity 30.\n",
      "Appended tSNE projection for 40 PCs with perplexity 50.\n",
      "Appended tSNE projection for 40 PCs with perplexity 100.\n",
      "Appended tSNE projection for 50 PCs with perplexity 5.\n",
      "Appended tSNE projection for 50 PCs with perplexity 30.\n",
      "Appended tSNE projection for 50 PCs with perplexity 50.\n",
      "Appended tSNE projection for 50 PCs with perplexity 100.\n",
      "Appended tSNE projection for 60 PCs with perplexity 5.\n",
      "Appended tSNE projection for 60 PCs with perplexity 30.\n",
      "Appended tSNE projection for 60 PCs with perplexity 50.\n",
      "Appended tSNE projection for 60 PCs with perplexity 100.\n",
      "Appended tSNE projection for 70 PCs with perplexity 5.\n",
      "Appended tSNE projection for 70 PCs with perplexity 30.\n",
      "Appended tSNE projection for 70 PCs with perplexity 50.\n",
      "Appended tSNE projection for 70 PCs with perplexity 100.\n",
      "Appended tSNE projection for 80 PCs with perplexity 5.\n",
      "Appended tSNE projection for 80 PCs with perplexity 30.\n",
      "Appended tSNE projection for 80 PCs with perplexity 50.\n",
      "Appended tSNE projection for 80 PCs with perplexity 100.\n",
      "Appended tSNE projection for 90 PCs with perplexity 5.\n",
      "Appended tSNE projection for 90 PCs with perplexity 30.\n",
      "Appended tSNE projection for 90 PCs with perplexity 50.\n",
      "Appended tSNE projection for 90 PCs with perplexity 100.\n",
      "Appended tSNE projection for 100 PCs with perplexity 5.\n",
      "Appended tSNE projection for 100 PCs with perplexity 30.\n",
      "Appended tSNE projection for 100 PCs with perplexity 50.\n",
      "Appended tSNE projection for 100 PCs with perplexity 100.\n",
      "Appended tSNE projection for 110 PCs with perplexity 5.\n",
      "Appended tSNE projection for 110 PCs with perplexity 30.\n",
      "Appended tSNE projection for 110 PCs with perplexity 50.\n",
      "Appended tSNE projection for 110 PCs with perplexity 100.\n",
      "Appended tSNE projection for 120 PCs with perplexity 5.\n",
      "Appended tSNE projection for 120 PCs with perplexity 30.\n",
      "Appended tSNE projection for 120 PCs with perplexity 50.\n",
      "Appended tSNE projection for 120 PCs with perplexity 100.\n",
      "Appended tSNE projection for 130 PCs with perplexity 5.\n",
      "Appended tSNE projection for 130 PCs with perplexity 30.\n",
      "Appended tSNE projection for 130 PCs with perplexity 50.\n",
      "Appended tSNE projection for 130 PCs with perplexity 100.\n",
      "Appended tSNE projection for 140 PCs with perplexity 5.\n",
      "Appended tSNE projection for 140 PCs with perplexity 30.\n",
      "Appended tSNE projection for 140 PCs with perplexity 50.\n",
      "Appended tSNE projection for 140 PCs with perplexity 100.\n",
      "Appended tSNE projection for 150 PCs with perplexity 5.\n",
      "Appended tSNE projection for 150 PCs with perplexity 30.\n",
      "Appended tSNE projection for 150 PCs with perplexity 50.\n",
      "Appended tSNE projection for 150 PCs with perplexity 100.\n",
      "Appended tSNE projection for 160 PCs with perplexity 5.\n",
      "Appended tSNE projection for 160 PCs with perplexity 30.\n",
      "Appended tSNE projection for 160 PCs with perplexity 50.\n",
      "Appended tSNE projection for 160 PCs with perplexity 100.\n",
      "Appended tSNE projection for 170 PCs with perplexity 5.\n",
      "Appended tSNE projection for 170 PCs with perplexity 30.\n",
      "Appended tSNE projection for 170 PCs with perplexity 50.\n",
      "Appended tSNE projection for 170 PCs with perplexity 100.\n",
      "Appended tSNE projection for 180 PCs with perplexity 5.\n",
      "Appended tSNE projection for 180 PCs with perplexity 30.\n",
      "Appended tSNE projection for 180 PCs with perplexity 50.\n",
      "Appended tSNE projection for 180 PCs with perplexity 100.\n",
      "Appended tSNE projection for 190 PCs with perplexity 5.\n",
      "Appended tSNE projection for 190 PCs with perplexity 30.\n",
      "Appended tSNE projection for 190 PCs with perplexity 50.\n",
      "Appended tSNE projection for 190 PCs with perplexity 100.\n",
      "Appended tSNE projection for 200 PCs with perplexity 5.\n",
      "Appended tSNE projection for 200 PCs with perplexity 30.\n",
      "Appended tSNE projection for 200 PCs with perplexity 50.\n",
      "Appended tSNE projection for 200 PCs with perplexity 100.\n",
      "Appended tSNE projection for 210 PCs with perplexity 5.\n",
      "Appended tSNE projection for 210 PCs with perplexity 30.\n",
      "Appended tSNE projection for 210 PCs with perplexity 50.\n",
      "Appended tSNE projection for 210 PCs with perplexity 100.\n",
      "Appended tSNE projection for 220 PCs with perplexity 5.\n",
      "Appended tSNE projection for 220 PCs with perplexity 30.\n",
      "Appended tSNE projection for 220 PCs with perplexity 50.\n",
      "Appended tSNE projection for 220 PCs with perplexity 100.\n",
      "Appended tSNE projection for 230 PCs with perplexity 5.\n",
      "Appended tSNE projection for 230 PCs with perplexity 30.\n",
      "Appended tSNE projection for 230 PCs with perplexity 50.\n",
      "Appended tSNE projection for 230 PCs with perplexity 100.\n",
      "Appended tSNE projection for 240 PCs with perplexity 5.\n",
      "Appended tSNE projection for 240 PCs with perplexity 30.\n",
      "Appended tSNE projection for 240 PCs with perplexity 50.\n",
      "Appended tSNE projection for 240 PCs with perplexity 100.\n",
      "Appended tSNE projection for 250 PCs with perplexity 5.\n",
      "Appended tSNE projection for 250 PCs with perplexity 30.\n",
      "Appended tSNE projection for 250 PCs with perplexity 50.\n",
      "Appended tSNE projection for 250 PCs with perplexity 100.\n"
     ]
    }
   ],
   "source": [
    "#t-SNE projections with different perplexities\n",
    "perplexities = [5, 30, 50, 100]\n",
    "tsne_pcs_pp = []\n",
    "\n",
    "for p in pc_list_full:\n",
    "    temp_list = []\n",
    "    for plex in perplexities:\n",
    "        temp_list.append(TSNE(n_components=2, perplexity=plex).fit_transform(p))\n",
    "        print(\"Appended tSNE projection for \" + str(p.shape[1]) + \" PCs with perplexity \" + str(plex) + \".\")\n",
    "    \n",
    "    tsne_pcs_pp.append(temp_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created HTML for tSNE on {PC,PLEX}1,5\n",
      "Created HTML for tSNE on {PC,PLEX}1,30\n",
      "Created HTML for tSNE on {PC,PLEX}1,50\n",
      "Created HTML for tSNE on {PC,PLEX}1,100\n",
      "Created HTML for tSNE on {PC,PLEX}2,5\n",
      "Created HTML for tSNE on {PC,PLEX}2,30\n",
      "Created HTML for tSNE on {PC,PLEX}2,50\n",
      "Created HTML for tSNE on {PC,PLEX}2,100\n",
      "Created HTML for tSNE on {PC,PLEX}3,5\n",
      "Created HTML for tSNE on {PC,PLEX}3,30\n",
      "Created HTML for tSNE on {PC,PLEX}3,50\n",
      "Created HTML for tSNE on {PC,PLEX}3,100\n",
      "Created HTML for tSNE on {PC,PLEX}4,5\n",
      "Created HTML for tSNE on {PC,PLEX}4,30\n",
      "Created HTML for tSNE on {PC,PLEX}4,50\n",
      "Created HTML for tSNE on {PC,PLEX}4,100\n",
      "Created HTML for tSNE on {PC,PLEX}5,5\n",
      "Created HTML for tSNE on {PC,PLEX}5,30\n",
      "Created HTML for tSNE on {PC,PLEX}5,50\n",
      "Created HTML for tSNE on {PC,PLEX}5,100\n",
      "Created HTML for tSNE on {PC,PLEX}6,5\n",
      "Created HTML for tSNE on {PC,PLEX}6,30\n",
      "Created HTML for tSNE on {PC,PLEX}6,50\n",
      "Created HTML for tSNE on {PC,PLEX}6,100\n",
      "Created HTML for tSNE on {PC,PLEX}7,5\n",
      "Created HTML for tSNE on {PC,PLEX}7,30\n",
      "Created HTML for tSNE on {PC,PLEX}7,50\n",
      "Created HTML for tSNE on {PC,PLEX}7,100\n",
      "Created HTML for tSNE on {PC,PLEX}8,5\n",
      "Created HTML for tSNE on {PC,PLEX}8,30\n",
      "Created HTML for tSNE on {PC,PLEX}8,50\n",
      "Created HTML for tSNE on {PC,PLEX}8,100\n",
      "Created HTML for tSNE on {PC,PLEX}9,5\n",
      "Created HTML for tSNE on {PC,PLEX}9,30\n",
      "Created HTML for tSNE on {PC,PLEX}9,50\n",
      "Created HTML for tSNE on {PC,PLEX}9,100\n",
      "Created HTML for tSNE on {PC,PLEX}10,5\n",
      "Created HTML for tSNE on {PC,PLEX}10,30\n",
      "Created HTML for tSNE on {PC,PLEX}10,50\n",
      "Created HTML for tSNE on {PC,PLEX}10,100\n",
      "Created HTML for tSNE on {PC,PLEX}20,5\n",
      "Created HTML for tSNE on {PC,PLEX}20,30\n",
      "Created HTML for tSNE on {PC,PLEX}20,50\n",
      "Created HTML for tSNE on {PC,PLEX}20,100\n",
      "Created HTML for tSNE on {PC,PLEX}30,5\n",
      "Created HTML for tSNE on {PC,PLEX}30,30\n",
      "Created HTML for tSNE on {PC,PLEX}30,50\n",
      "Created HTML for tSNE on {PC,PLEX}30,100\n",
      "Created HTML for tSNE on {PC,PLEX}40,5\n",
      "Created HTML for tSNE on {PC,PLEX}40,30\n",
      "Created HTML for tSNE on {PC,PLEX}40,50\n",
      "Created HTML for tSNE on {PC,PLEX}40,100\n",
      "Created HTML for tSNE on {PC,PLEX}50,5\n",
      "Created HTML for tSNE on {PC,PLEX}50,30\n",
      "Created HTML for tSNE on {PC,PLEX}50,50\n",
      "Created HTML for tSNE on {PC,PLEX}50,100\n",
      "Created HTML for tSNE on {PC,PLEX}60,5\n",
      "Created HTML for tSNE on {PC,PLEX}60,30\n",
      "Created HTML for tSNE on {PC,PLEX}60,50\n",
      "Created HTML for tSNE on {PC,PLEX}60,100\n",
      "Created HTML for tSNE on {PC,PLEX}70,5\n",
      "Created HTML for tSNE on {PC,PLEX}70,30\n",
      "Created HTML for tSNE on {PC,PLEX}70,50\n",
      "Created HTML for tSNE on {PC,PLEX}70,100\n",
      "Created HTML for tSNE on {PC,PLEX}80,5\n",
      "Created HTML for tSNE on {PC,PLEX}80,30\n",
      "Created HTML for tSNE on {PC,PLEX}80,50\n",
      "Created HTML for tSNE on {PC,PLEX}80,100\n",
      "Created HTML for tSNE on {PC,PLEX}90,5\n",
      "Created HTML for tSNE on {PC,PLEX}90,30\n",
      "Created HTML for tSNE on {PC,PLEX}90,50\n",
      "Created HTML for tSNE on {PC,PLEX}90,100\n",
      "Created HTML for tSNE on {PC,PLEX}100,5\n",
      "Created HTML for tSNE on {PC,PLEX}100,30\n",
      "Created HTML for tSNE on {PC,PLEX}100,50\n",
      "Created HTML for tSNE on {PC,PLEX}100,100\n",
      "Created HTML for tSNE on {PC,PLEX}110,5\n",
      "Created HTML for tSNE on {PC,PLEX}110,30\n",
      "Created HTML for tSNE on {PC,PLEX}110,50\n",
      "Created HTML for tSNE on {PC,PLEX}110,100\n",
      "Created HTML for tSNE on {PC,PLEX}120,5\n",
      "Created HTML for tSNE on {PC,PLEX}120,30\n",
      "Created HTML for tSNE on {PC,PLEX}120,50\n",
      "Created HTML for tSNE on {PC,PLEX}120,100\n",
      "Created HTML for tSNE on {PC,PLEX}130,5\n",
      "Created HTML for tSNE on {PC,PLEX}130,30\n",
      "Created HTML for tSNE on {PC,PLEX}130,50\n",
      "Created HTML for tSNE on {PC,PLEX}130,100\n",
      "Created HTML for tSNE on {PC,PLEX}140,5\n",
      "Created HTML for tSNE on {PC,PLEX}140,30\n",
      "Created HTML for tSNE on {PC,PLEX}140,50\n",
      "Created HTML for tSNE on {PC,PLEX}140,100\n",
      "Created HTML for tSNE on {PC,PLEX}150,5\n",
      "Created HTML for tSNE on {PC,PLEX}150,30\n",
      "Created HTML for tSNE on {PC,PLEX}150,50\n",
      "Created HTML for tSNE on {PC,PLEX}150,100\n",
      "Created HTML for tSNE on {PC,PLEX}160,5\n",
      "Created HTML for tSNE on {PC,PLEX}160,30\n",
      "Created HTML for tSNE on {PC,PLEX}160,50\n",
      "Created HTML for tSNE on {PC,PLEX}160,100\n",
      "Created HTML for tSNE on {PC,PLEX}170,5\n",
      "Created HTML for tSNE on {PC,PLEX}170,30\n",
      "Created HTML for tSNE on {PC,PLEX}170,50\n",
      "Created HTML for tSNE on {PC,PLEX}170,100\n",
      "Created HTML for tSNE on {PC,PLEX}180,5\n",
      "Created HTML for tSNE on {PC,PLEX}180,30\n",
      "Created HTML for tSNE on {PC,PLEX}180,50\n",
      "Created HTML for tSNE on {PC,PLEX}180,100\n",
      "Created HTML for tSNE on {PC,PLEX}190,5\n",
      "Created HTML for tSNE on {PC,PLEX}190,30\n",
      "Created HTML for tSNE on {PC,PLEX}190,50\n",
      "Created HTML for tSNE on {PC,PLEX}190,100\n",
      "Created HTML for tSNE on {PC,PLEX}200,5\n",
      "Created HTML for tSNE on {PC,PLEX}200,30\n",
      "Created HTML for tSNE on {PC,PLEX}200,50\n",
      "Created HTML for tSNE on {PC,PLEX}200,100\n",
      "Created HTML for tSNE on {PC,PLEX}210,5\n",
      "Created HTML for tSNE on {PC,PLEX}210,30\n",
      "Created HTML for tSNE on {PC,PLEX}210,50\n",
      "Created HTML for tSNE on {PC,PLEX}210,100\n",
      "Created HTML for tSNE on {PC,PLEX}220,5\n",
      "Created HTML for tSNE on {PC,PLEX}220,30\n",
      "Created HTML for tSNE on {PC,PLEX}220,50\n",
      "Created HTML for tSNE on {PC,PLEX}220,100\n",
      "Created HTML for tSNE on {PC,PLEX}230,5\n",
      "Created HTML for tSNE on {PC,PLEX}230,30\n",
      "Created HTML for tSNE on {PC,PLEX}230,50\n",
      "Created HTML for tSNE on {PC,PLEX}230,100\n",
      "Created HTML for tSNE on {PC,PLEX}240,5\n",
      "Created HTML for tSNE on {PC,PLEX}240,30\n",
      "Created HTML for tSNE on {PC,PLEX}240,50\n",
      "Created HTML for tSNE on {PC,PLEX}240,100\n",
      "Created HTML for tSNE on {PC,PLEX}250,5\n",
      "Created HTML for tSNE on {PC,PLEX}250,30\n",
      "Created HTML for tSNE on {PC,PLEX}250,50\n",
      "Created HTML for tSNE on {PC,PLEX}250,100\n"
     ]
    }
   ],
   "source": [
    "for p in range(0, len(pc_list_full)):\n",
    "    temp_num_pcs = pc_list_full[p].shape[1]\n",
    "    for pl in range(0, len(perplexities)):\n",
    "        perplex = perplexities[pl]\n",
    "        plot_bokeh(tsne_pcs_pp[p][pl],\n",
    "                   'TSNE on ' + str(temp_num_pcs) + ' PCs with perplexity ' + str(perplex),\n",
    "                   'TSNE_' + str(temp_num_pcs) + 'PC_PLEX' + str(perplex),\n",
    "                   'TSNE with PC' + str(temp_num_pcs) + ' PLEX' + str(perplex))\n",
    "        print('Created HTML for tSNE on {PC,PLEX}' + str(temp_num_pcs) + ',' + str(perplex))\n",
    "\n",
    "# plot_bokeh(Y_cmds_pcs, 'PHATE on 30 PCs (10 skip) classic MDS', 'PHATE_30PC_CMDS', 'PHATE with 30PCs CMDS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mds_pc_to_3D=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished projecting PCs to MDS, dimensions: {3,3}\n",
      "Time to execute: 140.24294233322144\n",
      "Finished projecting PCs to MDS, dimensions: {4,3}\n",
      "Time to execute: 379.97635293006897\n",
      "Finished projecting PCs to MDS, dimensions: {5,3}\n",
      "Time to execute: 287.2018690109253\n",
      "Finished projecting PCs to MDS, dimensions: {6,3}\n",
      "Time to execute: 377.0853691101074\n",
      "Finished projecting PCs to MDS, dimensions: {7,3}\n",
      "Time to execute: 273.4500548839569\n",
      "Finished projecting PCs to MDS, dimensions: {8,3}\n",
      "Time to execute: 378.25805592536926\n",
      "Finished projecting PCs to MDS, dimensions: {9,3}\n",
      "Time to execute: 378.642459154129\n",
      "Finished projecting PCs to MDS, dimensions: {10,3}\n",
      "Time to execute: 378.22457814216614\n",
      "Finished projecting PCs to MDS, dimensions: {20,3}\n",
      "Time to execute: 377.415803194046\n",
      "Finished projecting PCs to MDS, dimensions: {30,3}\n",
      "Time to execute: 378.3750820159912\n",
      "Finished projecting PCs to MDS, dimensions: {40,3}\n",
      "Time to execute: 378.5118577480316\n",
      "Finished projecting PCs to MDS, dimensions: {50,3}\n",
      "Time to execute: 378.1436071395874\n",
      "Finished projecting PCs to MDS, dimensions: {60,3}\n",
      "Time to execute: 378.34148716926575\n",
      "Finished projecting PCs to MDS, dimensions: {70,3}\n",
      "Time to execute: 378.9649188518524\n",
      "Finished projecting PCs to MDS, dimensions: {80,3}\n",
      "Time to execute: 378.1155059337616\n",
      "Finished projecting PCs to MDS, dimensions: {90,3}\n",
      "Time to execute: 378.4877541065216\n",
      "Finished projecting PCs to MDS, dimensions: {100,3}\n",
      "Time to execute: 382.4460120201111\n",
      "Finished projecting PCs to MDS, dimensions: {110,3}\n",
      "Time to execute: 378.7521607875824\n",
      "Finished projecting PCs to MDS, dimensions: {120,3}\n",
      "Time to execute: 379.44758200645447\n",
      "Finished projecting PCs to MDS, dimensions: {130,3}\n",
      "Time to execute: 378.48938512802124\n",
      "Finished projecting PCs to MDS, dimensions: {140,3}\n",
      "Time to execute: 378.2891399860382\n",
      "Finished projecting PCs to MDS, dimensions: {150,3}\n",
      "Time to execute: 377.8898890018463\n",
      "Finished projecting PCs to MDS, dimensions: {160,3}\n",
      "Time to execute: 378.22310400009155\n",
      "Finished projecting PCs to MDS, dimensions: {170,3}\n",
      "Time to execute: 380.188481092453\n",
      "Finished projecting PCs to MDS, dimensions: {180,3}\n",
      "Time to execute: 381.00935316085815\n",
      "Finished projecting PCs to MDS, dimensions: {190,3}\n",
      "Time to execute: 381.0398452281952\n",
      "Finished projecting PCs to MDS, dimensions: {200,3}\n",
      "Time to execute: 384.8016917705536\n",
      "Finished projecting PCs to MDS, dimensions: {210,3}\n",
      "Time to execute: 381.13986992836\n",
      "Finished projecting PCs to MDS, dimensions: {220,3}\n",
      "Time to execute: 381.00007009506226\n",
      "Finished projecting PCs to MDS, dimensions: {230,3}\n",
      "Time to execute: 381.6928298473358\n",
      "Finished projecting PCs to MDS, dimensions: {240,3}\n",
      "Time to execute: 382.06392216682434\n",
      "Finished projecting PCs to MDS, dimensions: {250,3}\n",
      "Time to execute: 382.39731907844543\n"
     ]
    }
   ],
   "source": [
    "# Generate 3D MDS projections\n",
    "n_c = 3\n",
    "\n",
    "for p in range(0, len(pc_list_full)):\n",
    "    if pc_list_full[p].shape[1] > 2:\n",
    "        start_time = time.time()\n",
    "        temp_projection = MDS(n_components=n_c).fit_transform(pc_list_full[p])\n",
    "        mds_pc_to_3D.append(temp_projection)\n",
    "        end_time = time.time()\n",
    "        print(\"Finished projecting PCs to MDS, dimensions: {\" + str(pc_list_full[p].shape[1])+ \",\" + str(n_c) + \"}\")\n",
    "        print(\"Time to execute: \" + str(end_time - start_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate interactive HTML files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Write a plotting function\n",
    "def plot_bokeh(dset, ptitle, outf, legtitle):\n",
    "    # Plot the embeddings and see what happens.\n",
    "    component_1_id = 0\n",
    "    component_2_id = 1\n",
    "\n",
    "    p = figure(plot_width=1500, plot_height=800)\n",
    "    p.title.text = legtitle\n",
    "\n",
    "    for cont in continents: \n",
    "        for pop in pop_by_continent[cont]:\n",
    "            projections_within_population = dset[indices_of_population_members[pop]]\n",
    "            p.circle(projections_within_population[:,component_1_id], projections_within_population[:,component_2_id], \n",
    "                     legend=name_by_code[pop], color = color_dict[pop])\n",
    "\n",
    "    p.legend.location = \"top_left\"\n",
    "\n",
    "    p.legend.click_policy=\"hide\"\n",
    "\n",
    "    output_file(outf+\".html\", title=ptitle)\n",
    "\n",
    "    save(p)\n",
    "    #show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plot_bokeh(projections_to_PCs[:,0:1], 'PC1', outf, legtitle):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plot_bokeh(projections_to_PCs, 'PC1 vs PC2', 'PCA_GT', 'PCA on GT data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plot_bokeh(Y_nmmds_raw, 'PHATE on genotype data (10 skip) non-metric MDS', 'PHATE_GT_NMMDS', 'PHATE with GT data NMMDS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plot_bokeh(mds_pc30, 'MDS on 30 PCs (10 skip)', 'MDS_30PC', 'MDS with 30PCs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plot_bokeh(mds_raw, 'MDS on genotype data (10 skip)', 'MDS_GT', 'MDS with GT data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plot_bokeh(nmmds_pcs[0], 'PHATE on 10PCs (10 skip) non-metric MDS', 'PHATE_10PC_NMMDS', 'PHATE with PC10 data NMMDs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plot_bokeh(nmmds_pcs[1], 'PHATE on 20PCs (10 skip) non-metric MDS', 'PHATE_20PC_NNMDS', 'PHATE with PC20 data NMMDS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from bokeh.palettes import Category20b\n",
    "from bokeh.palettes import Purples\n",
    "from bokeh.palettes import Greens\n",
    "from bokeh.palettes import YlOrBr\n",
    "from bokeh.palettes import YlOrRd\n",
    "from bokeh.palettes import PuOr\n",
    "from bokeh.palettes import RdGy\n",
    "from bokeh.palettes import PiYG\n",
    "\n",
    "color_dict = {}\n",
    "for i, cont in enumerate(continents): \n",
    "    for j, pop in enumerate(pop_by_continent[cont]):\n",
    "        color_dict[pop] = Category20b[20][4*i+j%4]\n",
    "\n",
    "# Issue: shared colours between:\n",
    "# CDX and CHS (purple)\n",
    "# GIH and STU (green)\n",
    "# ASW and LWK (brown/yellow)\n",
    "# ACB and MSL (brown/yellow)\n",
    "# ESN and YRI (goldenrod/canary/saffron/paella)\n",
    "# GBR and CEU (dark red)\n",
    "\n",
    "# Manually assign colours to CHS, STU, LWK, MSL, CEU\n",
    "color_dict['CHS'] = Purples[9][4]# purple\n",
    "color_dict['STU'] = Greens[9][6] # green\n",
    "color_dict['LWK'] = PuOr[11][-1] # brown\n",
    "color_dict['MSL'] = PuOr[11][-2] # rusty brown\n",
    "color_dict['YRI'] = PuOr[11][-3] # cappucino w/ extra milk (stirred)\n",
    "color_dict['CEU'] = RdGy[11][-3]\n",
    "\n",
    "dset = np.loadtxt('/Volumes/Stockage/alex/1000G/projections/1000G_UMAP_PC15_NC2_NN15_MD0.5_2018629161925')\n",
    "\n",
    "component_1_id = 0\n",
    "component_2_id = 1\n",
    "\n",
    "p = figure(plot_width=1500, plot_height=800)\n",
    "p.title.text = 'test'\n",
    "\n",
    "for cont in continents: \n",
    "    for pop in pop_by_continent[cont]:\n",
    "        projections_within_population = dset[indices_of_population_members[pop]]\n",
    "        p.circle(projections_within_population[:,component_1_id], projections_within_population[:,component_2_id], \n",
    "                 legend=name_by_code[pop], color = color_dict[pop], alpha=.7)\n",
    "\n",
    "p.legend.location = \"top_left\"\n",
    "\n",
    "p.legend.click_policy=\"hide\"\n",
    "\n",
    "output_file(\"test_colour_palette.html\", title='test')\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting UMAP results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "proj_dir = '/Volumes/Stockage/alex/1000G/projections'\n",
    "html_dir = '/Volumes/Stockage/alex/1000G/html'\n",
    "\n",
    "for fname in os.listdir(proj_dir):\n",
    "    if os.path.isdir(os.path.join(proj_dir, fname)) or fname=='pca_1000g_100' or \\\n",
    "    os.path.exists(os.path.join(html_dir, fname+\".html\")):\n",
    "        continue\n",
    "    else:\n",
    "        dset = np.loadtxt(os.path.join(proj_dir, fname))\n",
    "\n",
    "        p = figure(plot_width=1500, plot_height=800)\n",
    "        p.title.text = fname\n",
    "\n",
    "        for cont in continents: \n",
    "            for pop in pop_by_continent[cont]:\n",
    "                projections_within_population = dset[indices_of_population_members[pop]]\n",
    "                p.circle(projections_within_population[:,0], projections_within_population[:,1], \n",
    "                         legend=name_by_code[pop], color = color_dict[pop])\n",
    "\n",
    "        p.legend.location = \"top_left\"\n",
    "\n",
    "        p.legend.click_policy=\"hide\"\n",
    "\n",
    "        output_file(os.path.join(html_dir, fname+\".html\"), title=fname)\n",
    "\n",
    "        save(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate images (not interactive HTML)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "proj_dir = '/Volumes/Stockage/alex/1000G/projections'\n",
    "out_dir = '/Volumes/Stockage/alex/1000G/images'\n",
    "\n",
    "x=0\n",
    "\n",
    "for fname in os.listdir(proj_dir):\n",
    "    if x > 1:\n",
    "        continue\n",
    "        \n",
    "    proj = np.loadtxt(os.path.join(proj_dir, fname))\n",
    "    \n",
    "    fig = plt.figure(figsize=(20,20))\n",
    "    ax = fig.add_subplot(111, aspect=1)\n",
    "    \n",
    "    for cont in continents: \n",
    "        for pop in pop_by_continent[cont]:\n",
    "            temp_proj = proj[indices_of_population_members[pop]]\n",
    "            ax.scatter(temp_proj[:,0], temp_proj[:,1], label=name_by_code[pop], alpha=0.6, color=color_dict[pop])\n",
    "    \n",
    "    ax.legend(ncol=5,loc='lower center', bbox_to_anchor=(0.5,-0.15), fontsize=12,markerscale=3)\n",
    "    fig.savefig(os.path.join(out_dir, fname + '.jpeg'),format='jpeg')\n",
    "    plt.close()\n",
    "    \n",
    "    #x+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Generate images without legends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "proj_dir = '/Volumes/Stockage/alex/1000G/projections'\n",
    "out_dir = '/Volumes/Stockage/alex/1000G/images/no_legend'\n",
    "\n",
    "x=0\n",
    "\n",
    "for fname in os.listdir(proj_dir):\n",
    "    if x > 1:\n",
    "        continue\n",
    "        \n",
    "    proj = np.loadtxt(os.path.join(proj_dir, fname))\n",
    "    \n",
    "    fig = plt.figure(figsize=(20,20))\n",
    "    ax = fig.add_subplot(111, aspect=1)\n",
    "    \n",
    "    for cont in continents: \n",
    "        for pop in pop_by_continent[cont]:\n",
    "            temp_proj = proj[indices_of_population_members[pop]]\n",
    "            ax.scatter(temp_proj[:,0], temp_proj[:,1], label=name_by_code[pop], alpha=0.6, color=color_dict[pop])\n",
    "    \n",
    "    fig.savefig(os.path.join(out_dir, fname + '.jpeg'),format='jpeg')\n",
    "    plt.close()\n",
    "    \n",
    "    #x+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'proj_dir' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-ea2345bbf2b1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfnames\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mproj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadtxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproj_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'proj_dir' is not defined"
     ]
    }
   ],
   "source": [
    "# One-off to print labels instead of points\n",
    "out_dir = '/Volumes/Stockage/alex/1000G/images/labels'\n",
    "fnames = ['1000G_UMAP_PC15_NC2_NN15_MD0.5_20184421291','1000G_TSNE_PLEX30_GT','1000G_UMAP_NN15_MD0.5_GT','pca_1000g_100']\n",
    "\n",
    "for fname in fnames:\n",
    "    proj = np.loadtxt(os.path.join(proj_dir, fname))\n",
    "\n",
    "    fig = plt.figure(figsize=(20,20))\n",
    "    ax = fig.add_subplot(111, aspect=1)\n",
    "\n",
    "    for cont in continents: \n",
    "        for pop in pop_by_continent[cont]:\n",
    "            temp_proj = proj[indices_of_population_members[pop]]\n",
    "            ax.scatter(temp_proj[:,0], temp_proj[:,1], label=name_by_code[pop], alpha=0, color=color_dict[pop])\n",
    "\n",
    "            for i,j in zip(temp_proj[:,0], temp_proj[:,1]):\n",
    "                ax.annotate(pop,xy=(i,j),color=color_dict[pop],size=7)\n",
    "\n",
    "    fig.savefig(os.path.join(out_dir, fname + '_labels.jpeg'),format='jpeg')\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sandbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "proj = projections_to_PCs\n",
    "out_dir = '/Volumes/Stockage/alex/1000G/images/pc_grid'\n",
    "\n",
    "gridsize = 20\n",
    "f, axarr = plt.subplots(gridsize,gridsize,figsize=(60,60))\n",
    "\n",
    "fname = '1000G_PC_grid_' + str(gridsize)\n",
    "\n",
    "for i in range(0,gridsize):\n",
    "    for j in range(0,gridsize):\n",
    "        if i<=j:\n",
    "            for cont in continents: \n",
    "                for pop in pop_by_continent[cont]:\n",
    "                    temp_proj = proj[indices_of_population_members[pop]]\n",
    "                    axarr[i,j].scatter(temp_proj[:,i], temp_proj[:,j+1], label=name_by_code[pop], \n",
    "                                       alpha=0.6, color=color_dict[pop], s=6)\n",
    "                    axarr[i,j].set_title('PC' + str(j+2) + ' vs PC' + str(i+1))\n",
    "\n",
    "plt.legend(ncol=5,loc='lower center', bbox_to_anchor=(-3,-1), fontsize=12,markerscale=3)\n",
    "#plt.show()\n",
    "f.savefig(os.path.join(out_dir, fname + '.jpeg'),format='jpeg')\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xeYKJKphyHRtVIIhwSWcwlG3pxS+z+B8/WwTlYRkSSzbfJhw/RPPb/5p44v0EQ7vJW5eK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cf2cd9nfmvQ1K2xfJo0bqG9HPnK9nVd2Sd3H2Lp0t+RJB2Ml8HhYW0LZ0Fp1pbAa3AW5xTWpizP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\nuNXGy2ZQW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k0xGEYPHMCr1mh8669RUYRwXap79uCWSpz+0peZ/bmfZf5vv71tgzrLsizr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RBCCHEF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\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XQghxTVlZMW8eOYuOPLTf6XetfIV10DqxdImjXNjs4Skm/vYYRZbjDGhPEfXFVDf1kc/U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3p7C6EAk2DjNr4YkGWvvGkYf7NO8yIispuCuYjILeRae6pdFHPvkR+ezCAf+86rXwTA76yawagf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\nkqQS/T/zRmPj1C70SQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#out_dir = '/Volumes/Stockage/alex/1000G/images/labels'\n",
    "#fnames = ['1000G_UMAP_PC15_NC2_NN15_MD0.5_20184421291','1000G_TSNE_PLEX30_GT','1000G_UMAP_NN15_MD0.5_GT','pca_1000g_100']\n",
    "\n",
    "proj_dir = \"/Volumes/Stockage/alex/1000G/projections\"\n",
    "\n",
    "fname = os.path.join(proj_dir, \"1000G_TSNE_UMAP_SEED_PC10_ITER1000_201812420234\")\n",
    "\n",
    "proj = np.loadtxt(os.path.join(proj_dir, fname))\n",
    "\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "ax = fig.add_subplot(111, aspect=1)\n",
    "\n",
    "for cont in continents: \n",
    "    for pop in pop_by_continent[cont]:\n",
    "        temp_proj = proj[indices_of_population_members[pop]]\n",
    "        ax.scatter(temp_proj[:,0], temp_proj[:,1], label=name_by_code[pop], alpha=0.6, color=color_dict[pop])\n",
    "\n",
    "        #for i,j in zip(temp_proj[:,0], temp_proj[:,1]):\n",
    "        #    ax.annotate(pop,xy=(i,j),color=color_dict[pop],size=7)\n",
    "\n",
    "    #fig.savefig(os.path.join(out_dir, fname + '_labels.jpeg'),format='jpeg')\n",
    "    #plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xuHoc7wqKwQqYsFxTn3bIOsuYsIGxETaoQGUBQ1oGTj7Dhw2CuElQaVHZ+ynS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m21rtTycT9PtBCkksHVwUBcdGxRHu5DbmG0uES4vYAg51c6JCESElCMHUhz7M3b/+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H0Vx2imiVksUJqcpPLifiLXRbMsynuueNtNMHutItGFjpu9EziilJrp7NS9wMeAc4pEb0de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\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S2LBF7GpmCdVfWM/vU2u0VsOf5lxsSdspWacfajkgyX+L7jJHd7lz1dcQQojLaS8dwya9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\ngP8PO/w9cE3lp/gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#out_dir = '/Volumes/Stockage/alex/1000G/images/labels'\n",
    "#fnames = ['1000G_UMAP_PC15_NC2_NN15_MD0.5_20184421291','1000G_TSNE_PLEX30_GT','1000G_UMAP_NN15_MD0.5_GT','pca_1000g_100']\n",
    "\n",
    "proj_dir = \"/Volumes/Stockage/alex/1000G/projections\"\n",
    "\n",
    "fname = os.path.join(proj_dir, \"1000G_TSNE_UMAP_SEED_PC10_ITER1000_2018124202451\")\n",
    "\n",
    "proj = np.loadtxt(os.path.join(proj_dir, fname))\n",
    "\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "ax = fig.add_subplot(111, aspect=1)\n",
    "\n",
    "for cont in continents: \n",
    "    for pop in pop_by_continent[cont]:\n",
    "        temp_proj = proj[indices_of_population_members[pop]]\n",
    "        ax.scatter(temp_proj[:,0], temp_proj[:,1], label=name_by_code[pop], alpha=0.6, color=color_dict[pop])\n",
    "\n",
    "        #for i,j in zip(temp_proj[:,0], temp_proj[:,1]):\n",
    "        #    ax.annotate(pop,xy=(i,j),color=color_dict[pop],size=7)\n",
    "\n",
    "    #fig.savefig(os.path.join(out_dir, fname + '_labels.jpeg'),format='jpeg')\n",
    "    #plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Compare the following three:\n",
    "# UMAP on 10 PCs\n",
    "# t-SNE on 10 PCs (default, 5000 iterations)\n",
    "# t-SNE on 10 PCs (seeded by UMAP, 5000 iterations)\n",
    "\n",
    "tg_dir = \"/Volumes/Stockage/alex/1000G/projections\"\n",
    "fname_umap = \"1000G_UMAP_PC10_NC2_NN15_MD0.5_20184421291\"\n",
    "fname_tsne_default = \"1000G_TSNE_PCs_SEED_PC10_ITER5000_2018124231521\"\n",
    "fname_tsne_umap = \"1000G_TSNE_UMAP_SEED_PC10_ITER5000_2018124231830\"\n",
    "\n",
    "out_dir = \"/Users/alex/Documents/Ethnicity/ms/images\"\n",
    "\n",
    "umap_proj = np.loadtxt(os.path.join(tg_dir, fname_umap))\n",
    "tsne_proj = np.loadtxt(os.path.join(tg_dir, fname_tsne_default))\n",
    "tsne_umap_proj = np.loadtxt(os.path.join(tg_dir, fname_tsne_umap))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JoXiQoOQXwYTAelAcJL/7fvxydrpvSUREREREROQ1RyVsckyy1lLO95Pd9SD4BWymAxub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CFAq1oby3FEx9FEK6jzKlFOFYFXas6iP1If+jLBC2aOxvIb2cLq90lSZ21hdX3/UR96UJpUv9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V0sN47RUyqCVfSUthEpn5ZNsUkN1+siyjurxZWdCqlN6PPKrCRLWCy9hREW4jk8AlqmxbVX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QTB2hK0gvJGgGCEXwt//nX7Dj7vdx46duxbVdpJTzGUVzhbXbOrMs37qSU3tOsHTzCto6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JqbSbs9Jj/8F4THJkArTLcLlTLhwcPYbhe3WiFttEibTXSxCM+aZW3+83cTHz5Kacd2ht90u/wf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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,20))\n",
    "ax = fig.add_subplot(221, aspect=1)\n",
    "\n",
    "proj = umap_proj\n",
    "\n",
    "for cont in continents: \n",
    "    for pop in pop_by_continent[cont]:\n",
    "        temp_proj = proj[indices_of_population_members[pop]]\n",
    "        ax.scatter(temp_proj[:,0], temp_proj[:,1], label=name_by_code[pop], alpha=0.6, color=color_dict[pop], s=5)\n",
    "\n",
    "ax.set_title(\"UMAP projection of top 10 PCs, 1KGP\")\n",
    "        \n",
    "ax = fig.add_subplot(223, aspect=1)\n",
    "proj = tsne_proj\n",
    "\n",
    "for cont in continents: \n",
    "    for pop in pop_by_continent[cont]:\n",
    "        temp_proj = proj[indices_of_population_members[pop]]\n",
    "        ax.scatter(temp_proj[:,0], temp_proj[:,1], label=name_by_code[pop], alpha=0.6, color=color_dict[pop], s=5)\n",
    "\n",
    "ax.set_title(\"t-SNE projection of top 10 PCs, 1KGP, 5000 iterations\")\n",
    "        \n",
    "ax = fig.add_subplot(224, aspect=1)\n",
    "proj = tsne_umap_proj\n",
    "\n",
    "for cont in continents: \n",
    "    for pop in pop_by_continent[cont]:\n",
    "        temp_proj = proj[indices_of_population_members[pop]]\n",
    "        ax.scatter(temp_proj[:,0], temp_proj[:,1], label=name_by_code[pop], alpha=0.6, color=color_dict[pop], s=5)\n",
    "        \n",
    "ax.set_title(\"t-SNE projection of top 10 PCs, UMAP initialization, 1KGP, 5000 iterations\")\n",
    "\n",
    "fig.savefig(os.path.join(out_dir, \"1KGP_tsne_umap.jpeg\"),format=\"jpeg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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