UMAP_TCGA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6359f175-8093-42d0-a925-9e1868a161b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import optimize\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import SpectralEmbedding\n",
    "from sklearn.metrics.pairwise import euclidean_distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7b831342-d20c-4db1-a39a-befa66483b12",
   "metadata": {},
   "outputs": [],
   "source": [
    "expr = pd.read_csv('./Data/TestData.tsv', sep='\\t', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "98248fa9-4d63-4456-920f-ea0cb169680b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EGFR</th>\n",
       "      <th>RB1</th>\n",
       "      <th>TP53</th>\n",
       "      <th>CDKN2A</th>\n",
       "      <th>MYC</th>\n",
       "      <th>RB2</th>\n",
       "      <th>TP54</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SampleID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sample_1</th>\n",
       "      <td>12.08</td>\n",
       "      <td>2.92</td>\n",
       "      <td>2.80</td>\n",
       "      <td>81.40</td>\n",
       "      <td>1.16</td>\n",
       "      <td>2.31</td>\n",
       "      <td>4165.196191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample_2</th>\n",
       "      <td>12.38</td>\n",
       "      <td>3.53</td>\n",
       "      <td>3.22</td>\n",
       "      <td>46.17</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.88</td>\n",
       "      <td>3561.146205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample_3</th>\n",
       "      <td>14.02</td>\n",
       "      <td>2.59</td>\n",
       "      <td>4.01</td>\n",
       "      <td>72.80</td>\n",
       "      <td>0.89</td>\n",
       "      <td>2.72</td>\n",
       "      <td>4284.348574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample_4</th>\n",
       "      <td>17.67</td>\n",
       "      <td>6.75</td>\n",
       "      <td>2.63</td>\n",
       "      <td>39.81</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.88</td>\n",
       "      <td>5098.680869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample_5</th>\n",
       "      <td>17.52</td>\n",
       "      <td>4.57</td>\n",
       "      <td>3.18</td>\n",
       "      <td>10.94</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.90</td>\n",
       "      <td>3406.132832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           EGFR   RB1  TP53  CDKN2A   MYC   RB2         TP54\n",
       "SampleID                                                    \n",
       "Sample_1  12.08  2.92  2.80   81.40  1.16  2.31  4165.196191\n",
       "Sample_2  12.38  3.53  3.22   46.17  0.89  1.88  3561.146205\n",
       "Sample_3  14.02  2.59  4.01   72.80  0.89  2.72  4284.348574\n",
       "Sample_4  17.67  6.75  2.63   39.81  1.08  1.88  5098.680869\n",
       "Sample_5  17.52  4.57  3.18   10.94  1.51  1.90  3406.132832"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e3694b44-50f3-444b-bdc6-145c1241f48b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           EGFR   RB1  TP53  CDKN2A\n",
      "SampleID                           \n",
      "Sample_1  12.08  2.92  2.80   81.40\n",
      "Sample_2  12.38  3.53  3.22   46.17\n",
      "Sample_3  14.02  2.59  4.01   72.80\n",
      "Sample_4  17.67  6.75  2.63   39.81\n",
      "\n",
      "This data set contains 200 samples\n",
      "\n",
      "Dimensions of the  data set: \n",
      "(200, 6) (200,)\n"
     ]
    }
   ],
   "source": [
    "print(expr.iloc[0:4,0:4])\n",
    "X_train = expr.values[:,0:(expr.shape[1]-1)]\n",
    "X_train = np.log(X_train + 1)\n",
    "n = X_train.shape[0]\n",
    "print(\"\\nThis data set contains \" + str(n) + \" samples\")\n",
    "y_train = expr.values[:,expr.shape[1]-1]\n",
    "print(\"\\nDimensions of the  data set: \")\n",
    "print(X_train.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d1c7266a-031d-4a77-b0ed-2c06b7c2896c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.38078923 0.14689306 1.10781041]\n",
      " [0.38078923 0.         0.36275208 0.45216431]\n",
      " [0.14689306 0.36275208 0.         1.16898235]\n",
      " [1.10781041 0.45216431 1.16898235 0.        ]]\n",
      "\n",
      "\n",
      "[0.08003243174357877, 0.048622209992217336, 0.0778421998248575, 0.031672749591457716]\n"
     ]
    }
   ],
   "source": [
    "dist = np.square(euclidean_distances(X_train, X_train))\n",
    "rho = [sorted(dist[i])[1] for i in range(dist.shape[0])]\n",
    "print(dist[0:4, 0:4])\n",
    "print('\\n')\n",
    "print(rho[0:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ba565364-278a-4274-bcfd-798ec4c6e35c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For each row of Euclidean distance matrix (dist_row) compute probability in high dimensions (1D array)\n",
    "def prob_high_dim(sigma, dist_row):\n",
    "    d = dist[dist_row] - rho[dist_row]\n",
    "    d[d < 0] = 0\n",
    "    return np.exp(- d / sigma)\n",
    "\n",
    "# Compute n_neighbor = k (scalar) for each 1D array of high-dimensional probability\n",
    "def k(prob):\n",
    "    return np.power(2, np.sum(prob))\n",
    "\n",
    "#     Solve equation k_of_sigma(sigma) = fixed_k with respect to sigma by the binary search algorithm\n",
    "def sigma_binary_search(k_of_sigma, fixed_k):\n",
    "    sigma_lower_limit = 0\n",
    "    sigma_upper_limit = 1000\n",
    "    for i in range(20):\n",
    "        approx_sigma = (sigma_lower_limit + sigma_upper_limit) / 2\n",
    "        if k_of_sigma(approx_sigma) < fixed_k:\n",
    "            sigma_lower_limit = approx_sigma\n",
    "        else:\n",
    "            sigma_upper_limit = approx_sigma\n",
    "        if np.abs(fixed_k - k_of_sigma(approx_sigma)) <= 1e-5:\n",
    "            break\n",
    "    return approx_sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "44ede8af-057c-4756-ba3e-92995b07bba6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma binary search finished 100 of 200 cells\n",
      "Sigma binary search finished 200 of 200 cells\n",
      "\n",
      "Mean sigma = 0.03\n"
     ]
    }
   ],
   "source": [
    "N_NEIGHBOR = 15\n",
    "prob = np.zeros((n,n))\n",
    "sigma_array = []\n",
    "for dist_row in range(n):\n",
    "    func = lambda sigma: k(prob_high_dim(sigma, dist_row))\n",
    "    binary_search_result = sigma_binary_search(func, N_NEIGHBOR)\n",
    "    prob[dist_row] = prob_high_dim(binary_search_result, dist_row)\n",
    "    sigma_array.append(binary_search_result)\n",
    "    if (dist_row + 1) % 100 == 0:\n",
    "        print(\"Sigma binary search finished {0} of {1} cells\".format(dist_row + 1, n))\n",
    "print(\"\\nMean sigma = \" + str(round(np.mean(sigma_array),3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d19294a0-6e86-441a-9dfb-933142b0ea74",
   "metadata": {},
   "outputs": [],
   "source": [
    "#P = prob + np.transpose(prob) - np.multiply(prob, np.transpose(prob))\n",
    "P = (prob + np.transpose(prob)) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e794265e-0eac-4be5-9030-2bef9be8d428",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hyperparameters a = 1.121 and b = 1.057\n"
     ]
    }
   ],
   "source": [
    "MIN_DIST = 0.25\n",
    "\n",
    "x = np.linspace(0, 3, 300)\n",
    "\n",
    "def f(x, min_dist):\n",
    "    y = []\n",
    "    for i in range(len(x)):\n",
    "        if(x[i] <= min_dist):\n",
    "            y.append(1)\n",
    "        else:\n",
    "            y.append(np.exp(- x[i] + min_dist))\n",
    "    return y\n",
    "\n",
    "dist_low_dim = lambda x, a, b: 1 / (1 + a*x**(2*b))\n",
    "\n",
    "p , _ = optimize.curve_fit(dist_low_dim, x, f(x, MIN_DIST))\n",
    "\n",
    "a = p[0]\n",
    "b = p[1] \n",
    "print(\"Hyperparameters a = \" + str(round(a,3)) + \" and b = \" + str(round(b,3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "609d8358-f06e-4b5f-b3e8-86fe68c588b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 1\n",
    "b = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "aad457c0-eef4-485f-b7c8-206d82ea0a5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute matrix of probabilities q_ij in low-dimensional space\n",
    "def prob_low_dim(Y):\n",
    "    inv_distances = np.power(1 + a * np.square(euclidean_distances(Y, Y))**b, -1)\n",
    "    return inv_distances\n",
    "# Compute Cross-Entropy (CE) from matrix of high-dimensional probabilities and coordinates of low-dimensional embeddings\n",
    "def CE(P, Y):\n",
    "    Q = prob_low_dim(Y)\n",
    "    return - P * np.log(Q + 0.01) - (1 - P) * np.log(1 - Q + 0.01)\n",
    "\n",
    "# Compute the gradient of Cross-Entropy (CE)\n",
    "def CE_gradient(P, Y):\n",
    "    y_diff = np.expand_dims(Y, 1) - np.expand_dims(Y, 0)\n",
    "    inv_dist = np.power(1 + a * np.square(euclidean_distances(Y, Y))**b, -1)\n",
    "    Q = np.dot(1 - P, np.power(0.001 + np.square(euclidean_distances(Y, Y)), -1))\n",
    "    np.fill_diagonal(Q, 0)\n",
    "    Q = Q / np.sum(Q, axis = 1, keepdims = True)\n",
    "    fact = np.expand_dims(a * P * (1e-8 + np.square(euclidean_distances(Y, Y)))**(b-1) - Q, 2)\n",
    "    return 2 * b * np.sum(fact * y_diff * np.expand_dims(inv_dist, 2), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aaae0fc0-4c74-4214-ba6b-fd8fa4a1af10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Gradient Descent: \n",
      "\n",
      "Cross-Entropy = 1.714 after 0 iterations\n",
      "Cross-Entropy = 0.11 after 10 iterations\n",
      "Cross-Entropy = 0.067 after 20 iterations\n",
      "Cross-Entropy = 0.062 after 30 iterations\n",
      "Cross-Entropy = 0.058 after 40 iterations\n"
     ]
    }
   ],
   "source": [
    "N_LOW_DIMS = 2\n",
    "LEARNING_RATE = 1\n",
    "MAX_ITER = 50       # 200\n",
    "\n",
    "np.random.seed(12345)\n",
    "model = SpectralEmbedding(n_components = N_LOW_DIMS, n_neighbors = 50)\n",
    "y = model.fit_transform(np.log(X_train + 1))\n",
    "#y = np.random.normal(loc = 0, scale = 1, size = (n, N_LOW_DIMS))\n",
    "\n",
    "CE_array = []\n",
    "print(\"Running Gradient Descent: \\n\")\n",
    "for i in range(MAX_ITER):\n",
    "    y = y - LEARNING_RATE * CE_gradient(P, y)\n",
    "    \n",
    "    plt.figure(figsize=(20,15))\n",
    "    plt.scatter(y[:,0], y[:,1], c = y_train.astype(int), cmap = 'tab10', s = 50)\n",
    "    plt.title(\"UMAP on Cancer Associated Fibroblasts (CAFs): Programmed from Scratch\", fontsize = 20)\n",
    "    plt.xlabel(\"UMAP1\", fontsize = 20); plt.ylabel(\"UMAP2\", fontsize = 20)\n",
    "    plt.savefig('./UMAP_Plots/UMAP_iter_' + str(i) + '.png')\n",
    "    plt.close()\n",
    "    \n",
    "    CE_current = np.sum(CE(P, y)) / 1e+5\n",
    "    CE_array.append(CE_current)\n",
    "    if i % 10 == 0:\n",
    "        print(\"Cross-Entropy = \" + str(round(CE_current,3)) + \" after \" + str(i) + \" iterations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8e973f5e-9ac7-458b-8276-c4ddbd701337",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "plt.plot(CE_array)\n",
    "plt.title(\"Cross-Entropy\", fontsize = 20)\n",
    "plt.xlabel(\"ITERATION\", fontsize = 20); plt.ylabel(\"CROSS-ENTROPY\", fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1c05224b-856b-4708-bd62-0b47f5f85fa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "plt.scatter(y[:,0], y[:,1], c = y_train.astype(int), cmap = 'tab10', s = 100)\n",
    "plt.title(\"UMAP on Something Something (CAFs)\", fontsize = 20)\n",
    "plt.xlabel(\"UMAP1\", fontsize = 20); plt.ylabel(\"UMAP2\", fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "840ad1cc-bd80-44e1-a486-4e34fcebc785",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29855c31-02a7-474e-97e4-fb2ac5ca8aee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7400302-394b-461e-8a22-bea20ad18341",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44924773-1804-4f6f-973a-db76a118962f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77d6f26f-24d5-45b5-a591-4850932e71b0",
   "metadata": {},
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}