{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Designing HybSeq Probes from a large sequence alignment\n",
    "\n",
    "\n",
    "One of the most important considerations when designing probes for targeted sequencing is how related the the source sequences are to the potential samples that will be enriched. In phylogenetic studies of non-model organisms, there may not be prior sequences available in the target taxa, but minimizing sequence divergence is still important.\n",
    "\n",
    "One solution is to use any existing sequence data to design probes from multiple ortholgous sources per gene. This effectively increases probe tiling and should also broaden the use of the probe set to more divergent taxa. Given a sequence alignment, we can choose sequences that are representative of specific clades, but this may be biased. \n",
    "\n",
    "Instead, we can let the data tell us what the most representative sequences should be. In this notebook we will generate pairwise distance matrices from DNA sequence alignments. The distances will be clustered using one or more multivariate statistics techniques (such as k-means clustering or discrimant analysis) to explore the optimal number of clusters for the alignment, and we will select representative sequences from each cluster.\n",
    "\n",
    "We will use Python implementations of distance matrices and visualizations taken from the Introduction to Applied Bioinformatics: http://readiab.org/book/latest/2/3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from skbio import TabularMSA, DNA, DistanceMatrix\n",
    "from skbio.sequence.distance import hamming, kmer_distance\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "gene = \"7653\"\n",
    "fasta_filename = \"/Users/mjohnson/Desktop/Projects/AngiospermHybSeq/genes/{}/FNA2AA-upp-masked.fasta\".format(gene)\n",
    "angiosperm_id_fn = \"/Users/mjohnson/Desktop/Projects/AngiospermHybSeq/1kp_angio_codes.txt\"\n",
    "angio_1kp_ids = set([x.rstrip() for x in open(angiosperm_id_fn)])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading the data\n",
    "\n",
    "The MSA has a multiple sequence alignemnt of one gene from 1KP. We keep only the sequences from Angiosperms, including genome sequence.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Shape(sequence=603, position=4956)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msa = TabularMSA.read(fasta_filename, constructor=DNA)\n",
    "seqs_to_keep = []\n",
    "for seq in msa:\n",
    "    if seq.metadata[\"id\"] in angio_1kp_ids:\n",
    "        seqs_to_keep.append(seq)\n",
    "        \n",
    "angio_msa = TabularMSA(seqs_to_keep)        \n",
    "angio_msa.reassign_index(minter=\"id\")\n",
    "angio_msa.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the alignment contains only angiosperms, remove the positions that are more than 95% gaps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Shape(sequence=603, position=1824)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "angio_msa_dict = angio_msa.to_dict()\n",
    "angio_msa_df = pd.DataFrame(angio_msa_dict)\n",
    "\n",
    "#This might throw an error if there are ever any positions without gaps. Seems unlikely for this dataset...\n",
    "\n",
    "def gap_dectector(sequence_column):\n",
    "    '''Returns the number of gap characters in a column of a sequence matrix'''\n",
    "    try:\n",
    "        return sequence_column.value_counts()[b\"-\"]\n",
    "    except KeyError:\n",
    "        return 0\n",
    "\n",
    "gapped_columns = angio_msa_df.apply(gap_dectector ,axis=1)\n",
    "#This could be modified to remove columns that have 90% gaps, etc.\n",
    "angio_msa_df_nogaps = angio_msa_df[gapped_columns < len(angio_msa_df.columns) * 0.95]\n",
    "\n",
    "#In skbio, DNA sequences are stored as bytecode, (b'A') so need to convert back to strings\n",
    "\n",
    "nogap_seqs = [DNA(angio_msa_df_nogaps[i].str.decode(\"utf-8\").str.cat(), metadata = {\"id\":i}) for i in angio_msa_df_nogaps]\n",
    "angio_msa_nogap = TabularMSA(nogap_seqs)\n",
    "#angio_msa_nogap.write(\"/Users/mjohnson/Desktop/Projects/AngiospermHybSeq/{}.onlyangios.fasta\".format(gene))\n",
    "\n",
    "angio_msa_nogap.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also want to remove the sequences that have > 50% gaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Shape(sequence=307, position=1824)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seqs_to_keep = []\n",
    "for seq in angio_msa_nogap:\n",
    "    num_gaps = len([x for x in seq.gaps() if x])\n",
    "    if num_gaps < angio_msa_nogap.shape[1] * 0.5:\n",
    "        seqs_to_keep.append(seq)\n",
    "        \n",
    "angio_msa_nogap_noshort = TabularMSA(seqs_to_keep)\n",
    "angio_msa_nogap_noshort.reassign_index(minter=\"id\")\n",
    "\n",
    "angio_msa_nogap_noshort.write(\"/Users/mjohnson/Desktop/Projects/AngiospermHybSeq/onekp_only_angios_degapped/{}.onlyangios.noshort.fasta\".format(gene))\n",
    "angio_msa_nogap_noshort.shape\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distance Matrix\n",
    "\n",
    "We calculate the \"Hamming distance\" as described here: http://readiab.org/book/latest/2/4#6.3\n",
    "\n",
    "The Hamming distance between two equal-length sequences is the proportion of differing characters.\n",
    "\n",
    "We make a small adjustment to only calculate the Hamming distance between sites with no gaps (equivalent to the p-distance calculated by PAUP\\*)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance between Amborella and Rice:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.46052631578947367"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def p_distance(seq1,seq2):\n",
    "    from skbio.sequence import Sequence\n",
    "    from numpy import isnan\n",
    "    myseq1 = str(seq1)\n",
    "    myseq2 = str(seq2)\n",
    "    \n",
    "    degapped1 = []\n",
    "    degapped2 = []\n",
    "    \n",
    "    for i in range(len(myseq1)):\n",
    "        if myseq1[i] != \"-\":\n",
    "            if myseq2[i] != \"-\":\n",
    "                degapped1.append(myseq1[i])\n",
    "                degapped2.append(myseq2[i])\n",
    "    degapped1 = \"\".join(degapped1)\n",
    "    degapped2 = \"\".join(degapped2)\n",
    "    \n",
    "    #print(degapped1)\n",
    "    #print(degapped2)\n",
    "    \n",
    "    hamming_dist = hamming(Sequence(degapped1),Sequence(degapped2))\n",
    "    #print(hamming_dist)\n",
    "    if isnan(hamming_dist):\n",
    "        #print(seq1.metadata[\"id\"], seq2.metadata[\"id\"])\n",
    "        return 0.0\n",
    "    else:\n",
    "        return hamming_dist\n",
    "\n",
    "\n",
    "p_dm = DistanceMatrix.from_iterable(angio_msa_nogap_noshort, metric=hamming, key='id')\n",
    "print(\"Distance between Amborella and Rice:\")\n",
    "p_dm[\"Ambtr_v1.0.27\",\"Orysa_v7.0\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The square pairwise distance matrix is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QVR+mfyxHU22YoKFx9vDTOA2rCVg6b1zfwndOT1KqqG1mpaEDJJZex53L6nh4II0AogGd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21vHtl8O8/+pVPDMQY39XBWNRiaH5NE2VfqIZiXt21jOVlKj0W3OJR84s0VjppzFgcQpL3XZW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IoWA/M51jYJAud1bX6WsmX8TpGPGJzP4TZVvA7tbCFsxK1yYjqD01DJFiu4TY2wWyeZF4wh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\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LoaYgIOASHaSKDT735j382hcvMtjmZyFdwzAtLsWrGJaFy+EgXVdYyCnMpSqUqwqGYdO2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1079a9d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = p_dm.plot(cmap='Blues', title='Pairwise Dissimilarity between sequences, gene {}'.format(gene))\n",
    "p_dm_df = p_dm.to_data_frame()\n",
    "p_dm_df.to_csv(\"/Users/mjohnson/Desktop/Projects/AngiospermHybSeq/onekp_only_angios_pdistance/{}_angio_p_dm.csv\".format(gene))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding Representative Sequences -- Manual Selection\n",
    "\n",
    "Now that we have a distance matrix, the next step is to decide which \"representative\" sequences are best for designing target capture probes. From the figure above we can see that some sequences diverged up to 80%, which is well beyond the tolerated range of 15-25%.\n",
    "\n",
    "One solution is to manually choose sequences. For instance, we could choose only genomic sequences that we \"know\" to be relatively diverged from one another, and hope that they represent the spectrum of divergences for this gene. Let's try this by choosing: *Arabidopsis*, *Amborella*, *Oryza*, *Vitis*, *Mimulus*, and *Populus*.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Distances > 30%: 269\n"
     ]
    },
    {
     "data": {
      "image/png": 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PZevWX/WdR5Vx8jKTOkBNM6kDTLiZ1AGSUFFviFDQe3+RsnVr+Zc15eMM/J2L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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fa44f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "manual_centroids = [\"Arath_TAIR10\",\"Ambtr_v1.0.27\",\"Orysa_v7.0\",\"Vitvi_Genoscope.12X\",\"Mimgu_v2.0\",\"Poptr_v3.0\"]\n",
    "manual_centroid_dist = p_dm_df[manual_centroids].apply(min,1)\n",
    "manual_centroid_dist.hist(bins=40)\n",
    "plt.title(\"Minimum Distance to Centroid, Manual Centroids \\n Gene {}\".format(gene))\n",
    "print(\"Number of Distances > 30%: {}\".format(len(manual_centroid_dist[manual_centroid_dist > 0.3])))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are too many sequences that are diverged more than 25% from each of our manually chosen sequences. The same is true even if we select all of the genome sequences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Centroids:  ['Ambtr_v1.0.27', 'Aquco_v1.1', 'Arath_TAIR10', 'Eucgr_v1.1', 'Manes_v4.1', 'Mimgu_v2.0', 'Orysa_v7.0', 'Phavu_v1.0', 'Poptr_v3.0', 'Prupe_v1.0', 'Solly_iTAGv2.3', 'Sorbi_v2.1', 'Theca_v1.1', 'Vitvi_Genoscope.12X']\n",
      "\n",
      "Number of Distances > 30%: 247\n"
     ]
    },
    {
     "data": {
      "image/png": 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KFStaOX3ccccxMzPTmjyjzr/ttlPccMMc3SxbtpxTT13Zd/nr+pad04vi3niV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\nG214qjftReJUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fa441d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_genomes = [x for x in p_dm_df.index if len(x) > 4]\n",
    "all_genomes_centroid_dist = p_dm_df[all_genomes].apply(min,1)\n",
    "all_genomes_centroid_dist.hist(bins=40)\n",
    "print(\"Centroids: \",all_genomes)\n",
    "plt.title(\"Minimum Distance to Centroid, All Genome Centroids \\n Gene {}\".format(gene))\n",
    "print(\"\\nNumber of Distances > 30%: {}\".format(len(all_genomes_centroid_dist[all_genomes_centroid_dist > 0.3])))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-means clustering \n",
    "\n",
    "Instead, we could let the distances themselves tell us which sequences are best, by clustering the sequences by their pairwise dissimilarity. By pre-selecting a number of clusters, we can let the data tell us which sequences cluster together, and then choose a representative from each cluster.\n",
    "\n",
    "Based on example from: http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_digits.html#example-cluster-plot-kmeans-digits-py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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03NovxuYo1cjjU78qdROUmDDG8OGaTfRs1woRabZcKQy/bx7GmNAT0u5jRVFi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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11014eeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import scale\n",
    "\n",
    "n_digits = 6 #number of clusters\n",
    "pca = PCA().fit(p_dm_df)\n",
    "\n",
    "reduced_data = PCA(n_components=2).fit_transform(p_dm_df)\n",
    "\n",
    "kmeans = KMeans(init='k-means++', n_clusters=n_digits, n_init=10)\n",
    "kmeans.fit(reduced_data)\n",
    "\n",
    "# Step size of the mesh. Decrease to increase the quality of the VQ.\n",
    "h = .02     # point in the mesh [x_min, m_max]x[y_min, y_max].\n",
    "\n",
    "# Plot the decision boundary. For that, we will assign a color to each\n",
    "x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1\n",
    "y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "\n",
    "# Obtain labels for each point in mesh. Use last trained model.\n",
    "Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(1)\n",
    "plt.clf()\n",
    "plt.imshow(Z, interpolation='nearest',\n",
    "           extent=(xx.min(), xx.max(), yy.min(), yy.max()),\n",
    "           cmap=plt.cm.Paired,\n",
    "           aspect='auto', origin='lower')\n",
    "\n",
    "plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)\n",
    "# Plot the centroids as a white X\n",
    "centroids = kmeans.cluster_centers_\n",
    "plt.scatter(centroids[:, 0], centroids[:, 1],\n",
    "            marker='x', s=169, linewidths=3,\n",
    "            color='w', zorder=10)\n",
    "plt.title('K-means clustering on the DNA sequence dataset \\n(PCA-reduced distance matrix)\\n'\n",
    "          'Centroids are marked with white cross')\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(y_min, y_max)\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure plots the PCA transformation of the distance matrix-- the axes correspond to PCA1 and PCA2, and each point represents a sequence in the alignment.\n",
    "\n",
    "The polygons are drawn to estimate the cluster boundaries in two dimensions.\n",
    "\n",
    "The white X represents the \"centroid\" of each cluster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding representative sequences -- cluster centroids\n",
    "\n",
    "Now that we have predicted clusters, are these clusters sufficient to have all sequences within the cluster be no more than 30% divergent?\n",
    "\n",
    "For each cluster, we figure out which of the real sequences in each cluster is closest to the centroid (Euclidean distance). Then we figure out the maximum pairwise distance any sequence and the centroid sequences. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Centroids:  ['LQJY', 'AYIY', 'PUDI', 'QZXQ', 'KJAA', 'EQDA']\n",
      "\n",
      "Number of Distances > 30%: 240\n"
     ]
    },
    {
     "data": {
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Qc57pV9432+wx5dkHm+rlF+CHS6PuACrWqDuAijXqDmAouajbjL/yvn69Ot7n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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1101e8518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Group the distance matrix by kmeans clusters\n",
    "from scipy.spatial import distance\n",
    "\n",
    "grouped = p_dm_df.groupby(kmeans.labels_)\n",
    "centroids = []\n",
    "for name,group in grouped:\n",
    "    #print(\"Group number: {}\".format(name))\n",
    "    \n",
    "    #Find the sample that is closest to the centroid. This is a pd Dataframe row index.\n",
    "    closest_to_centroid = pd.DataFrame(reduced_data).groupby(kmeans.labels_).get_group(name).apply(\n",
    "        lambda x: distance.euclidean(x,kmeans.cluster_centers_[name]), axis=1).sort_values().index[0]\n",
    "    #print(\"Number of sequences in group: {}\".format(len(group)))\n",
    "    \n",
    "    #Reduce the distance matrix to be square within the group\n",
    "#    reduced_group = group[group.index]\n",
    "#    print(\"Max distance within group: {}\".format(max(reduced_group.apply(max))))\n",
    "    closest_id = p_dm_df.index[closest_to_centroid]\n",
    "    #print(\"ID closest to centroid (Euclidean): {}\".format(closest_id))\n",
    "    centroids.append(closest_id)\n",
    "    \n",
    "#    print(\"Furthest within-group P distance distances to centroids ID:\")\n",
    "#    print(reduced_group[closest_id].sort_values(ascending=False)[0:2])\n",
    "#    print()\n",
    "#centroids.append(\"Arath_TAIR10\")\n",
    "print(\"Centroids: \", centroids)\n",
    "centroid_dist = p_dm_df[centroids].apply(min,1)\n",
    "centroid_dist.hist(bins=40)\n",
    "plt.title(\"Minimum Distance to Centroid, {} Clusters\\n Gene {}\".format(n_digits,gene))\n",
    "print(\"\\nNumber of Distances > 30%: {}\".format(len(centroid_dist[centroid_dist > 0.30])))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before, using sequences from all of the genomes left almost twice as many sequences with > 30% divergence. Ideally, we could pick the number of clusters that minimizes the number of sequences with > 30% divergence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10df71278>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xx8D33ycdjUjhUNWWtCju8JvfQJcu4Qp4kXynqi2RZmYGZWXwn//AQw8lHY1I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\niYg0talT4cADw98OHbKbxx2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110197a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "divergent_seqs = []\n",
    "pca = PCA().fit(p_dm_df)\n",
    "reduced_data = PCA(n_components=2).fit_transform(p_dm_df)\n",
    "\n",
    "for i in range(6,20):\n",
    "    n_digits = i #number of clusters\n",
    "\n",
    "    kmeans = KMeans(init='k-means++', n_clusters=n_digits, n_init=10)\n",
    "    kmeans.fit(reduced_data)\n",
    "    grouped = p_dm_df.groupby(kmeans.labels_)\n",
    "    centroids = []\n",
    "    for name,group in grouped:    \n",
    "        #Find the sample that is closest to the centroid. This is a pd Dataframe row index.\n",
    "        closest_to_centroid = pd.DataFrame(reduced_data).groupby(kmeans.labels_).get_group(name).apply(\n",
    "            lambda x: distance.euclidean(x,kmeans.cluster_centers_[name]), axis=1).sort_values().index[0]\n",
    "        closest_id = p_dm_df.index[closest_to_centroid]\n",
    "        centroids.append(closest_id)\n",
    "    #print(\"Centroids: \", centroids)\n",
    "    centroid_dist = p_dm_df[centroids].apply(min,1)\n",
    "    num_over_25 = len(centroid_dist[centroid_dist > 0.30])\n",
    "    divergent_seqs.append((i,num_over_25))\n",
    "    \n",
    "    #centroid_dist.hist(bins=40)\n",
    "    #plt.title(\"Minimum Distance to Centroid, {} Clusters\\n Gene {}\".format(n_digits,gene))\n",
    "    #print(\"\\n\\n Distances > 25%:\")\n",
    "    #print(\"\\nNumber of Distances > 25%: {}\".format(len(centroid_dist[centroid_dist > 0.25])))\n",
    "\n",
    "divergent_seqs_df = pd.DataFrame(divergent_seqs,columns=[\"NumClusters\",\"NumDivergent\"])\n",
    "plt.plot(divergent_seqs_df.NumClusters,divergent_seqs_df.NumDivergent,'-o')\n",
    "plt.xlabel(\"Number of clusters\")\n",
    "plt.title(\"Number of sequences with > 30% divergence from any centroid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The number of sequences with > 30% divergence may fluctuate as the number of clusters is increased because the clusters (and centroids) may be chosen differently if the kmeans fit is repeated. In this case, choosing 13 or more clusters will have the best effect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spectral Clustering\n",
    "\n",
    "Another option for assigning sequences to clusters is spectral clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/site-packages/sklearn/utils/validation.py:629: UserWarning: Array is not symmetric, and will be converted to symmetric by average with its transpose.\n",
      "  warnings.warn(\"Array is not symmetric, and will be converted \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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5YwavPPMMgxITOaPTMcHhYNWmTZQvX97Xt6AUIk6nk2LFyhAX9znwELAbTWvK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f897a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "from sklearn.cluster import spectral_clustering\n",
    "\n",
    "similarity = np.exp(-2 * p_dm_df / p_dm_df.std()).as_matrix()\n",
    "\n",
    "labels = spectral_clustering(similarity,n_clusters=6,assign_labels = 'discretize')\n",
    "colormap = np.array([\"r\",\"g\",\"b\",\"w\",\"purple\",\"orange\",\"brown\",\"lightblue\"])\n",
    "plt.scatter(reduced_data[:, 0], reduced_data[:, 1],c=colormap[labels])\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The assignment of sequences to clusters is less important than reliably definining a \"representative,\" so we may need to explore alternative ways of \"reducing\" the distance data besides PCA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## K Medoids\n",
    "\n",
    "Another way to choose representative sequences is to use the k-medoid approach: https://en.wikipedia.org/wiki/K-medoids\n",
    "\n",
    "The principle is similar to k-means clustering, in that clusters are made by minimizing within-group distances. However, instead of centroids (which represent the \"mean\" of a cluster), the clusters are keyed around a specific point within the cluster (analagous to a median). As a result, there will be no need to calculate which point is closest to the centroid, instead one specific sequence will be chosen as the medoid of each cluster.\n",
    "\n",
    "Python medoid code is taken from here: https://github.com/letiantian/kmedoids\n",
    "The implementation of this method of calculating k-medoids in python is discussed here: https://www.researchgate.net/publication/272351873_NumPy_SciPy_Recipes_for_Data_Science_k-Medoids_Clustering\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "\n",
    "def kMedoids(D, k, tmax=1000):\n",
    "    # determine dimensions of distance matrix D\n",
    "    m, n = D.shape\n",
    "\n",
    "    # randomly initialize an array of k medoid indices\n",
    "    M = np.sort(np.random.choice(n, k))\n",
    "\n",
    "    # create a copy of the array of medoid indices\n",
    "    Mnew = np.copy(M)\n",
    "\n",
    "    # initialize a dictionary to represent clusters\n",
    "    C = {}\n",
    "    for t in range(tmax):\n",
    "        # determine clusters, i. e. arrays of data indices\n",
    "        J = np.argmin(D[:,M], axis=1)\n",
    "        for kappa in range(k):\n",
    "            C[kappa] = np.where(J==kappa)[0]\n",
    "        # update cluster medoids\n",
    "        for kappa in range(k):\n",
    "            J = np.mean(D[np.ix_(C[kappa],C[kappa])],axis=1)\n",
    "            j = np.argmin(J)\n",
    "            Mnew[kappa] = C[kappa][j]\n",
    "        np.sort(Mnew)\n",
    "        # check for convergence\n",
    "        if np.array_equal(M, Mnew):\n",
    "            break\n",
    "        M = np.copy(Mnew)\n",
    "    else:\n",
    "        # final update of cluster memberships\n",
    "        J = np.argmin(D[:,M], axis=1)\n",
    "        for kappa in range(k):\n",
    "            C[kappa] = np.where(J==kappa)[0]\n",
    "\n",
    "    # return results\n",
    "    return M, C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of Distances > 30%: 148\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1101845f8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e4109e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "medoids, membership =  kMedoids(p_dm,8)\n",
    "medoid_dist = p_dm_df[p_dm_df.ix[medoids].index].apply(min,1)\n",
    "print(\"\\nNumber of Distances > 30%: {}\".format(len(medoid_dist[medoid_dist > 0.30])))\n",
    "medoid_dist.hist(bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.\n",
      "  warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10e50e438>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+4e7vuPum7t7d3bsBHwM7u/unwKPAaQBmtjvwZaqEIZJLGmNDJP+yaXI7GngZ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\n26XNgAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e47fcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "divergent_seqs_medoids = []\n",
    "\n",
    "for k in range(6,20):\n",
    "    try:\n",
    "        medoids,membership = kMedoids(p_dm,k)\n",
    "        medoid_dist = p_dm_df[p_dm_df.ix[medoids].index].apply(min,1)\n",
    "        num_over_25 = len(medoid_dist[medoid_dist > 0.30])\n",
    "        divergent_seqs_medoids.append((k,num_over_25))\n",
    "    except ValueError:\n",
    "        divergent_seqs_medoids.append((k,np.nan))\n",
    "\n",
    "divergent_seqs_medoids_df = pd.DataFrame(divergent_seqs_medoids,columns=[\"NumClusters\",\"NumDivergent\"])\n",
    "plt.plot(divergent_seqs_df.NumClusters,divergent_seqs_medoids_df.NumDivergent,'-o')\n",
    "plt.xlabel(\"Number of clusters\")\n",
    "plt.title(\"Number of sequences with > 30% divergence from any medoid\")    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some iterations return an error that I'm not quite sure how to fix...\n",
    "\n",
    "It appears that clusters can vary greatly based on individual runs of the k-medoids (or k-means) clustering. This problem is best illustrated with this YouTube video: https://www.youtube.com/watch?v=9nKfViAfajY\n",
    "\n",
    "It really doesn't matter for our purposes which cluster each sequence belongs to. Our task is a minimizaiton exercise, so we should repeat each value of K a number of times.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.\n",
      "  warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10f7ebf60>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hjtZzS9m2snrKtpXVU7atKhpWIiJyjogsFZF5BdrxIrJKRCZE274jIveISK+I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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e59c208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "divergent_seqs_medoids = []\n",
    "\n",
    "for k in range(6,50):\n",
    "    for i in range(10):\n",
    "        try:\n",
    "            medoids,membership = kMedoids(p_dm,k)\n",
    "            medoid_dist = p_dm_df[p_dm_df.ix[medoids].index].apply(min,1)\n",
    "            num_over_25 = len(medoid_dist[medoid_dist > 0.30])\n",
    "            divergent_seqs_medoids.append((k,num_over_25))\n",
    "        except ValueError:\n",
    "            divergent_seqs_medoids.append((k,np.nan))\n",
    "\n",
    "divergent_seqs_medoids_df = pd.DataFrame(divergent_seqs_medoids,columns=[\"NumClusters\",\"NumDivergent\"])\n",
    "divergent_seqs_medoids_df.boxplot(by=\"NumClusters\")\n",
    "plt.xlabel(\"Number of clusters\")\n",
    "plt.title(\"Number of sequences with > 30% divergence from any medoid\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This suggests that for this gene there *does* exist a set of just ten taxa that could represent 98% of all seqeunces!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Medoids:  Index(['WZFE', 'XHHU', 'XVRU', 'GNPX', 'BERS', 'DDRL', 'BYQM', 'CKDK', 'CWZU',\n",
      "       'EDIT', 'IHPC', 'Eucgr_v1.1', 'HQRJ', 'FFFY', 'GDKK', 'EYRD', 'DUQG',\n",
      "       'PEZP', 'HUSX', 'PPPZ', 'JNKW', 'EJBY', 'LAPO', 'CPKP', 'HOKG', 'NMGG',\n",
      "       'WOHL', 'FZQN', 'OSMU', 'MFIN', 'AXNH', 'BVOF', 'KEGA', 'VXKB', 'QOXT',\n",
      "       'TEZA', 'Ambtr_v1.0.27', 'UZXL', 'VGHH', 'HAEU', 'LELS', 'WBOD', 'NBMW',\n",
      "       'ZENX', 'XZME', 'MRKX', 'TIUZ', 'TJQY', 'ZCUA', 'DZLN'],\n",
      "      dtype='object')\n",
      "\n",
      "Number of Distances > 30%: 57\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.\n",
      "  warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10fba0048>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e469c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k=50\n",
    "best_run = len(p_dm_df)\n",
    "runs = {}\n",
    "for i in range(100):\n",
    "    try:\n",
    "        medoids,membership = kMedoids(p_dm,k)\n",
    "        medoid_dist = p_dm_df[p_dm_df.ix[medoids].index].apply(min,1)\n",
    "        num_over_25 = len(medoid_dist[medoid_dist > 0.25])\n",
    "        divergent_seqs_medoids.append((k,num_over_25))\n",
    "    except ValueError:\n",
    "        divergent_seqs_medoids.append((k,np.nan))\n",
    "        num_over_25 = np.nan\n",
    "    runs[i] = (medoids,membership,medoid_dist)\n",
    "    if num_over_25 < best_run:\n",
    "        best_run = num_over_25\n",
    "        best_run_idx = i\n",
    "        \n",
    "medoids,membership,medoid_dist = runs[best_run_idx]         \n",
    "print(\"Medoids: \", p_dm_df.ix[medoids].index)\n",
    "print(\"\\nNumber of Distances > 30%: {}\".format(len(medoid_dist[medoid_dist > 0.30])))\n",
    "medoid_dist.hist(bins=30)\n",
    "        \n",
    "        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The next step will be to write a dedicated script to systematically check K clusters until a minimum number of sequences is found to represent a maximum number of species."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}