examples/example_full_pipeline.ipynb

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   "source": [
    "<h1 id=\"tocheading\">Table of Contents</h1>\n",
    "<div id=\"toc\"></div>"
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    "%%javascript\n",
    "$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js')"
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   "outputs": [],
   "source": [
    "# Import some necessary modules\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import pandas as pd\n",
    "import time\n",
    "import scipy as sp\n",
    "import pickle\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import MuSiCal\n",
    "import musical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we outline all the steps in a full pipeline of mutational signature analysis with MuSiCal, except preprocessing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Input data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The input mutation count matrix $X$ should be of size (n_features, n_samples), where, for example, n_features = 96 for SBS signatures.\n",
    "\n",
    "Here we use a simulated dataset based on PCAWG skin melanomas as an example. The dataset contains 15 SBS signatures. You can replace `X` with your own dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.read_csv('./data/simulated_example.Skin.Melanoma.X.csv', index_col=0)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
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       "         SP124323  SP124281  SP124389  SP124362  SP124394  SP124380  SP124399  \\\n",
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       "Type                                                                         \n",
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   "source": [
    "X.head()"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *De novo* signature discovery "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*De novo* signature discovery can be performed with `musical.DenovoSig`. An example code is as follows:\n",
    "```\n",
    "model = musical.DenovoSig(X, \n",
    "                          min_n_components=1, # Minimum number of signatures to test\n",
    "                          max_n_components=20, # Maximum number of signatures to test\n",
    "                          init='random', # Initialization method\n",
    "                          method='mvnmf', # mvnmf or nmf\n",
    "                          n_replicates=20, # Number of mvnmf/nmf replicates to run per n_components\n",
    "                          ncpu=10, # Number of CPUs to use\n",
    "                          max_iter=100000, # Maximum number of iterations for each mvnmf/nmf run\n",
    "                          bootstrap=True, # Whether or not to bootstrap X for each run\n",
    "                          tol=1e-8, # Tolerance for claiming convergence of mvnmf/nmf\n",
    "                          verbose=1, # Verbosity of output\n",
    "                          normalize_X=False # Whether or not to L1 normalize each sample in X before mvnmf/nmf\n",
    "                         )\n",
    "model.fit()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the code above may take a while to finish. It is thus recommended to submit jobs to a cluster for the calculation. You can specify the number of CPUs to use through the `ncpu` parameter. Note that the same number of CPUs should be reserved via the job scheduler. For Slurm, `sbatch -n 10` should be used, for example, when `ncpu=10`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have provided the output of `DenovoSig` on this dataset in the `./data/` folder. Let's load it. The output is saved as a `pickle` file. To save an object using `pickle`, you can use\n",
    "```\n",
    "with open('/Output/path/and/filename.pkl', 'wb') as f:\n",
    "    pickle.dump(model, f, pickle.HIGHEST_PROTOCOL)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/simulated_example.Skin.Melanoma.model.pkl', 'rb') as f:\n",
    "    model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    }
   ],
   "source": [
    "# Number of discovered de novo signatures\n",
    "print(model.n_components)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can inspect the plots for selecting the number of signatures. In this example, 11 *de novo* signatures are discovered, compared to the underlying truth of 15 signatures. Some of the true signatures are too weak, i.e., only present in a few samples or contribute to a small number of mutations. Therefore they are not discovered. However, they can be potentially rescued during the downstream matching step, as *de novo* signatures could be mixtures of multiple true signatures.  \n",
    "\n",
    "The left panel displays the stability of solutions and the reconstruction errors at different numbers of signatures. The middle panel displays the stability of each discovered signature. The right panel shows our preferred method for selecting n_components, i.e., by comparing n_components with the optimal number of clusters as determined by the gap statistic. Please see our paper for more details. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 1512x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot_selection()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The obtained *de novo* signatures can be visualized. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1584 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = musical.sigplot_bar(model.W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matching and refitting -- signature assignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following *de novo* signature discovery, we match *de novo* signatures to the catalog and refit the data against matched signatures to obtain exposures. Matching and refitting are jointly called \"signature assignment\". \n",
    "\n",
    "For the purpose of downstream parameter optimization with data-driven simulations, here we perform matching and refitting on a 2-dimensional grid of parameters. The two parameters correspond to the two likelihood thresholds for likelihood-based sparse NNLS used in matching and refitting, respectively. \n",
    "\n",
    "The following grid will be used for both matching and refitting thresholds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh_grid = np.array([\n",
    "    0.0001, 0.0002, 0.0005,\n",
    "    0.001, 0.002, 0.005,\n",
    "    0.01, 0.02, 0.05,\n",
    "    0.1, 0.2, 0.5,\n",
    "    1., 2., 5.\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then load the signature catalog to be used. \n",
    "\n",
    "For better performance, you can restrict to signatures known to be present in the corresponding tumor type (in this case, Skin.Melanoma), and remove mismatch repair deficiency (MMRD)- and/or polymerase proofreading deficiency (PPD)- related signatures (since in this case, we know that our dataset does not contain MMRD/PPD samples). See example_refitting.ipynb for more details. \n",
    "\n",
    "Here, we do not perform this restriction and simply match to the entire SBS signature catalog, to demonstrate the sensitivity and specificity of our algorithm. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "84\n"
     ]
    }
   ],
   "source": [
    "catalog = musical.load_catalog('COSMIC-MuSiCal_v3p2_SBS_WGS')\n",
    "W_catalog = catalog.W\n",
    "print(W_catalog.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matching and refitting on the 2-dimensional grid can then be performed with:\n",
    "```\n",
    "model.assign_grid(W_catalog, \n",
    "                  method_assign='likelihood_bidirectional', # Method for performing matching and refitting\n",
    "                  thresh_match_grid=thresh_grid, # Grid of threshold for matchinng\n",
    "                  thresh_refit_grid=thresh_grid, # Grid of threshold for refitting\n",
    "                  thresh_new_sig=0.0, # De novo signatures with reconstructed cosine similarity below this threshold will be considered novel\n",
    "                  connected_sigs=False, # Whether or not to force connected signatures to co-occur\n",
    "                  clean_W_s=False # An optional intermediate step to avoid overfitting to small backgrounds in de novo signatures for 96-channel SBS signatures\n",
    "                 )\n",
    "```\n",
    "\n",
    "Again we recommend running the code above on a cluster, although the `assign_grid()` step is relatively fast (several minutes). In `model` that we loaded above, this assignment step is already performed. So we can look at the results.\n",
    "\n",
    "The signatures and exposures after assignment are called `W_s` and `H_s`, respectively, where `s` stands for sparse.\n",
    "\n",
    "First, let's look at the result with relatively small thresholds. Here, the key of the dictionary `W_s_grid` and `H_s_grid` is `(thresh_match, thresh_refit)`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(96, 53)\n",
      "(53, 107)\n"
     ]
    }
   ],
   "source": [
    "print(model.W_s_grid[(0.0001, 0.0001)].shape)\n",
    "print(model.H_s_grid[(0.0001, 0.0001)].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that, with very small thresholds, too many (53) signatures are assigned. \n",
    "\n",
    "Next, let's look at the result with relatively large thresholds. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(96, 5)\n",
      "(5, 107)\n"
     ]
    }
   ],
   "source": [
    "print(model.W_s_grid[(1.0, 1.0)].shape)\n",
    "print(model.H_s_grid[(1.0, 1.0)].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, too few (5) signatures are assigned. \n",
    "\n",
    "Therefore, it is important to choose the appropriate thresholds for matching and refitting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameter optimization with *in silico* validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MuSiCal utilizes the *in silico* validation module to optimize the matching and refitting thresholds using a grid search. This step can be performed with:\n",
    "```\n",
    "model.validate_grid(validate_n_replicates=1, # Number of simulation replicates to perform for each grid point\n",
    "                    grid_selection_method='pvalue', # Method for selecting the best grid point\n",
    "                    grid_selection_pvalue_thresh=0.05 # Threshold used for selecting the best grid point\n",
    "                   )\n",
    "```\n",
    "Again, we skip the actual calculation (which will take some time and should be done on a cluster) here and look at the results directly. \n",
    "\n",
    "The selected best grid point can be obtained as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.02, 0.001)\n",
      "0.02\n",
      "0.001\n"
     ]
    }
   ],
   "source": [
    "print(model.best_grid_point)\n",
    "print(model.thresh_match)\n",
    "print(model.thresh_refit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final matched signatures and signature assignments are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "W_s = model.W_s\n",
    "H_s = model.H_s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to evaluate the final result, let's load the true signatures and exposures from which this dataset is simulated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "W_true = pd.read_csv('./data/simulated_example.Skin.Melanoma.W_true.csv', index_col=0)\n",
    "H_true = pd.read_csv('./data/simulated_example.Skin.Melanoma.H_true.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that we have found the correct 15 signatures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['SBS1', 'SBS2', 'SBS3', 'SBS5', 'SBS7a', 'SBS7b', 'SBS7c', 'SBS7d', 'SBS13', 'SBS17a', 'SBS17b', 'SBS18', 'SBS37', 'SBS38', 'SBS55']\n",
      "['SBS1', 'SBS2', 'SBS3', 'SBS5', 'SBS7a', 'SBS7b', 'SBS7c', 'SBS7d', 'SBS13', 'SBS17a', 'SBS17b', 'SBS18', 'SBS37', 'SBS38', 'SBS55']\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print(W_s.columns.tolist())\n",
    "print(W_true.columns.tolist())\n",
    "print(W_s.columns.tolist() == W_true.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also compare `H_true` with `H_s` to evaluate the signature assignment result. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "H_true_reindexed = H_true.reindex(H_s.index).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "423 3 1178 1 424 1181\n"
     ]
    }
   ],
   "source": [
    "TP = np.logical_and(H_true_reindexed > 0, H_s > 0).sum().sum()\n",
    "FP = np.logical_and(H_true_reindexed == 0, H_s > 0).sum().sum()\n",
    "TN = np.logical_and(H_true_reindexed == 0, H_s == 0).sum().sum()\n",
    "FN = np.logical_and(H_true_reindexed > 0, H_s == 0).sum().sum()\n",
    "P = TP + FN\n",
    "N = TN + FP\n",
    "print(TP, FP, TN, FN, P, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity = 0.998\n",
      "False positive rate = 0.00254\n"
     ]
    }
   ],
   "source": [
    "print('Sensitivity = %.3g' % (TP/P))\n",
    "print('False positive rate = %.3g' % (FP/N))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have achieved good sensitivity while having low false positive rate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comments "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. *In silico* validation does not need to be performed on a grid. To perform a single validation, you can use `model.validate(W_s=W_s, H_s=H_s)`, where `model` is an object of the class `DenovoSig`, and a single signature assignment result to be validated (i.e., `W_s` and `H_s`) should be provided. We used this functionality to validate the signature assignments obtained by the PCAWG consortium in the paper. \n",
    "\n",
    "2. Similarly, there is a function `model.assign()` for performing a single signature assignment at some fixed matching and refitting thresholds. \n",
    "\n",
    "3. We have described the approach of parameter optimization through a joint two-dimensional grid search above. Alternatively, you can perform two stepwise one-dimensional grid searches for matching and refitting thresholds, respectively. \n",
    "```\n",
    "# Optimizing matching threshold\n",
    "model.assign_grid(\n",
    "    W_catalog, \n",
    "    method_assign='likelihood_bidirectional',\n",
    "    thresh_match_grid=thresh_grid,\n",
    "    thresh_refit_grid=np.array([0.0001]),\n",
    "    thresh_new_sig=0.0\n",
    ")\n",
    "model.validate_grid()\n",
    "best_thresh_match = model.thresh_match\n",
    "# Optimizing refitting threshold\n",
    "model.assign_grid(\n",
    "    W_catalog, \n",
    "    method_assign='likelihood_bidirectional',\n",
    "    thresh_match_grid=np.array([best_thresh_match]),\n",
    "    thresh_refit_grid=thresh_grid,\n",
    "    thresh_new_sig=0.0\n",
    ")\n",
    "model.validate_grid()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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