README.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DeepInsight\n",
    "\n",
    "This repository contains the original MatLab code for DeepInsight as described in \n",
    "the paper [DeepInsight: A methodology to transform a non-image data to an image \n",
    "for convolution neural network architecture][1].\n",
    "\n",
    "# pyDeepInsight\n",
    "\n",
    "This package provides a python version of the image transformation procedure of \n",
    "DeepInsight. This is not guaranteed to give the same results as the published\n",
    "MatLab code and should be considered experimental.\n",
    "\n",
    "## Installation\n",
    "    python3 -m pip -q install git+git://github.com/alok-ai-lab/DeepInsight.git#egg=DeepInsight\n",
    "    \n",
    "[1]: https://doi.org/10.1038/s41598-019-47765-6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Usage\n",
    "\n",
    "The following is a walkthrough of standard usage of the ImageTransformer class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyDeepInsight import ImageTransformer, LogScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load example TCGA data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480, 5000)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expr_file = r\"./examples/data/tcga.rnaseq_fpkm_uq.example.txt.gz\"\n",
    "expr = pd.read_csv(expr_file, sep=\"\\t\")\n",
    "y = expr['project'].values\n",
    "X = expr.iloc[:, 1:].values\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=23, stratify=y)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize data to values between 0 and 1. The following normalization \n",
    "procedure is described in the \n",
    "[DeepInsight paper supplementary information](\n",
    "https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-019-47765-6/MediaObjects/41598_2019_47765_MOESM1_ESM.pdf)\n",
    "as norm-2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ln = LogScaler()\n",
    "X_train_norm = ln.fit_transform(X_train)\n",
    "X_test_norm = ln.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize image transformer. There are three built-in feature extraction options, 'tsne', 'pca', and 'kpca' to align with the original MatLab implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "it = ImageTransformer(feature_extractor='tsne', \n",
    "                      pixels=50, random_state=1701, \n",
    "                      n_jobs=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, any class instance with method `.fit_transform()` that returns a 2-dimensional array of extracted features can also be provided to the ImageTransformer class. This allows for customization of the feature extraction procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "tsne = TSNE(n_components=2, perplexity=30, metric='cosine',\n",
    "            random_state=1701, n_jobs=-1)\n",
    "\n",
    "it = ImageTransformer(feature_extractor=tsne, pixels=50)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train image transformer on training data. Setting plot=True results in at \n",
    "a plot showing the reduced features (blue points), convex full (red), and \n",
    "minimum bounding rectagle (green) prior to rotation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "_ = it.fit(X_train_norm, plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The feature density matrix can be extracted from the trained transformer in order to view overall feature overlap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fdm = it.feature_density_matrix()\n",
    "fdm[fdm == 0] = np.nan\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "ax = sns.heatmap(fdm, cmap=\"viridis\", linewidths=0.01, \n",
    "                 linecolor=\"lightgrey\", square=True)\n",
    "ax.xaxis.set_major_locator(ticker.MultipleLocator(5))\n",
    "ax.yaxis.set_major_locator(ticker.MultipleLocator(5))\n",
    "for _, spine in ax.spines.items():\n",
    "    spine.set_visible(True)\n",
    "_ = plt.title(\"Genes per pixel\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to update the pixel size without retraining."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "px_sizes = [25, (25, 50), 50, 100]\n",
    "\n",
    "fig, ax = plt.subplots(1, len(px_sizes), figsize=(25, 7))\n",
    "for ix, px in enumerate(px_sizes):\n",
    "    it.pixels = px\n",
    "    fdm = it.feature_density_matrix()\n",
    "    fdm[fdm == 0] = np.nan\n",
    "    cax = sns.heatmap(fdm, cmap=\"viridis\", linewidth=0.01, \n",
    "                      linecolor=\"lightgrey\", square=True, \n",
    "                      ax=ax[ix], cbar=False)\n",
    "    cax.set_title('Dim {} x {}'.format(*it.pixels))\n",
    "    for _, spine in cax.spines.items():\n",
    "        spine.set_visible(True)\n",
    "    cax.xaxis.set_major_locator(ticker.MultipleLocator(5))\n",
    "    cax.yaxis.set_major_locator(ticker.MultipleLocator(5))\n",
    "plt.tight_layout()    \n",
    "    \n",
    "it.pixels = 50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The trained transformer can then be used to transform sample data to image \n",
    "matricies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_img = it.transform(X_train_norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit and transform can be done in a single step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_img = it.fit_transform(X_train_norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the image matrices first four samples \n",
    "of the training set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 4, figsize=(25, 7))\n",
    "for i in range(0,4):\n",
    "    ax[i].imshow(X_train_img[i])\n",
    "    ax[i].title.set_text(\"Train[{}] - class '{}'\".format(i, y_train[i]))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transforming the testing data is done the same as transforming the \n",
    "training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test_img = it.transform(X_test_norm)\n",
    "\n",
    "fig, ax = plt.subplots(1, 4, figsize=(25, 7))\n",
    "for i in range(0,4):\n",
    "    ax[i].imshow(X_test_img[i])\n",
    "    ax[i].title.set_text(\"Test[{}] - class '{}'\".format(i, y_test[i]))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The image matrices can then be used as input for the CNN model."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:DITest]",
   "language": "python",
   "name": "conda-env-DITest-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}