{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8c1e61e7",
   "metadata": {},
   "source": [
    "# Assessing KSTAR Networks and Activity Predictions\n",
    "\n",
    "In addition to the dotplot, we have provided a few additional functions for assessing your KSTAR results, as well as the underlying networks that gave rise to them. Here, still using the example of TCR activation, we have provided some examples on how to use these functions.\n",
    "\n",
    "If there are any functions/analysis that are not discussed here that you would like to see in the future, please add a feature request at [KSTAR's github repository](https://github.com/NaegleLab/KSTAR)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c00b059",
   "metadata": {},
   "source": [
    "## Load results and other necessary data (networks, binary evidence, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8751accb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import necessary packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from kstar import helpers, config, calculate\n",
    "import pickle\n",
    "\n",
    "odir = './example'\n",
    "logName = 'example_run'\n",
    "phosphomod = 'Y'\n",
    "logName_new = f\"{logName}_{phosphomod}\"\n",
    "#get activity log\n",
    "activity_log = helpers.get_logger(f\"activity_{logName}\", f\"{odir}/RESULTS/activity_{logName}.log\")\n",
    "\n",
    "#Load kstar_slim save\n",
    "kinact_dict = calculate.from_kstar_slim(logName, odir, activity_log)\n",
    "\n",
    "#load networks used for prediction\n",
    "phospho_types = ['Y'] #running on this type of kinase/substrate network\n",
    "\n",
    "#Load the pickles containing the 50 pruned networks for tyrosine kinases\n",
    "networks = pickle.load(open(config.NETWORK_Y_PICKLE, \"rb\" ) )\n",
    "\n",
    "#load binarized experiment\n",
    "binary_evidence = kinact_dict['Y'].evidence_binary.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0491d70c",
   "metadata": {},
   "source": [
    "## Identify relevant phosphorylation sites for a kinase of interest\n",
    "\n",
    "In some cases, you may want to know what phosphorylation sites an active kinase is interacting with to get a clearer picture of the specific kinase-substrate interactions taking place. Using the analysis.interactions module, you can identify the phosphorylation sites that are contributing most to the activity prediction (based on the number of networks for which the observed site is connected to the kinase of interest). For our TCR activation example, we observed that LCK is highly activated upon activation. We would like to know what substrates LCK is mostly interacting with.\n",
    "\n",
    "*Important note to keep in mind: KSTAR networks contain predictions and are not guaranteed to be true interactions. Just because it appears in one of the networks does not mean it is a true interaction*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4abb2b33",
   "metadata": {},
   "outputs": [],
   "source": [
    "from kstar.analysis import interactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "baaecd6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_influence = interactions.getSubstrateInfluence_inExperiment(networks, binary_evidence, 'LCK')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b5a4b0a",
   "metadata": {},
   "source": [
    "The above function produces a dictionary with each data column/sample as a key. For each sample, a pandas series object is generated which indicates potential substrates of the kinase of interest that were identified as evidence for the given sample, as well as the number of different networks that kinase-substrate interaction is found in. The more networks an interaction is found, the higher likelihood of it being a true interaction and the stronger its influence on the final predictions. Only sites found in the given sample AND have at least one interaction across the 50 networks will appear in these results. Known substrates will be found across most, if not all, networks (to check if a site is a known substrate, we often go to [PhosphoSitePlus](https://www.phosphosite.org/homeAction.action))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "66e72aaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Q08881_Y240     50.0\n",
       "Q08881_Y273     50.0\n",
       "P43403_Y198     50.0\n",
       "P43403_Y204     50.0\n",
       "P43403_Y597     50.0\n",
       "P15498_Y791     50.0\n",
       "P09693_Y171     48.0\n",
       "P09693_Y160     46.0\n",
       "Q00610_Y1237    40.0\n",
       "Q8IZP0_Y484     39.0\n",
       "Name: Number of Networks connected to LCK, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment_influence['data:time:15'].iloc[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4f5dab1",
   "metadata": {},
   "source": [
    "We can see that LCK is likely responsible for phosphorylating sites on ITK (Q08881), ZAP70 (P43403), and VAV (P15498), among others. This makes sense based on what is known about TCR signaling."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec910a0b",
   "metadata": {},
   "source": [
    "## Exploring Network and Experiment Coverage of Phosphorylation Sites\n",
    "\n",
    "In addition to digging deeper into the KSTAR results, you may want to better understand the networks that went into prediction (and how they might compare to other kinase-substrate networks). The below approaches are designed for such analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da004f2b",
   "metadata": {},
   "source": [
    "### Number of unique phosphorylation sites in network(s)\n",
    "\n",
    "You may be interested to know how many phosphorylation sites are actually contained within a network. To check, run the following function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4dcd9115",
   "metadata": {},
   "outputs": [],
   "source": [
    "from kstar.analysis import coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "079b71a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34986"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coverage.numUniqueSubstrates(networks['nkin0'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da5a21a5",
   "metadata": {},
   "source": [
    "The above value is for only a single pruned network. What about across all 50 pruned networks? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7cef65ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39268"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coverage.numUniqueSubstrates(networks)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fd84a4c",
   "metadata": {},
   "source": [
    "You'll see that the overall coverage of the phosphoproteome is higher across all 50 networks than when using only a single network, just one of the advantages of the ensemble approach used in KSTAR. We can also ask the average number of sites covered within each individual network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f60938d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Y': 35040.46, 'ST': 151171.56}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coverage.averageUniqueSubstrates_KSTAR()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cd0e06d",
   "metadata": {},
   "source": [
    "### Distribution of Study Bias in Phosphoproteome and in Experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f9aaa2f",
   "metadata": {},
   "source": [
    "One of the major challenges KSTAR was designed tackle is the prevalance of study bias towards certain phosphorylation sites within networks and experiments. You can see the distribution of study bias within your experiment, and compare it to the distribution seen across the entire phosphoproteome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c375d52e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from kstar.analysis import coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "efbc23f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'In Single Sample')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize = (4,4),nrows = 3, sharex = 'all')\n",
    "fig.subplots_adjust(hspace = 0.5)\n",
    "coverage.getStudyBiasDistribution_InPhosphoproteome(ax = axes[0])\n",
    "axes[0].set_title('Phosphoproteome')\n",
    "coverage.getStudyBiasDistribution_InExperiment(kinact_dict['Y'].evidence_binary, ax = axes[1])\n",
    "axes[1].set_title('In Experiment (All Samples)')\n",
    "coverage.getStudyBiasDistribution_InSample(kinact_dict['Y'].evidence_binary, data_column = 'data:time:30', ax = axes[2])\n",
    "axes[2].set_title('In Single Sample')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a70570a4",
   "metadata": {},
   "source": [
    "As described in the KSTAR publication, we see that the phosphoproteomic experiment contains a much higher proportion of well-studied sites than the overall phosphoproteome. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e455eb7",
   "metadata": {},
   "source": [
    "### Coverage of Experiment by Kinase-Substrate Network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfea4ed7",
   "metadata": {},
   "source": [
    "Another challenge of many kinase activity prediction approaches, particularly those relying on PhosphoSitePlus annotations, is that only a small percentage of sites can actually be used for activity prediction due to unknown kinase interactions. To get an idea of how many phosphorylation sites observed in an experiment will actually be used in prediction, you can use the below functions, either for a single network (passing a pandas dataframe) or an ensemble of networks (passing a dictionary of pandas dataframes):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9d2e552d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from kstar.analysis import coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "74ab53b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8483965014577259"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coverage.experimentCoverage(kinact_dict['Y'].evidence_binary, networks['nkin0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "105d78f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9883381924198251"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coverage.experimentCoverage(kinact_dict['Y'].evidence_binary, networks)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2852c35",
   "metadata": {},
   "source": [
    "We have almost complete coverage of our phosphoproteomic dataset when using all 50 pruned networks, meaning that almost every observed phosphorylation site is contributing to prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20a676cb",
   "metadata": {},
   "source": [
    "### Similarity between kinases in network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d2a8b4a",
   "metadata": {},
   "source": [
    "Lastly, kinase-substrate prediction networks often suffer from a high degree of similarity between kinases from the same family. We can look at the mutual information shared between kinases within a network (or ensemble of networks), or the fraction of shared substrates. We can then plot this as heatmap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d986b0ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "from kstar.analysis import kinase_MI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "54c37a40",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_shared_substrates, mutual_info, overlapping_substrates = kinase_MI.kinase_mutual_information(networks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "05998e23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,10))\n",
    "kinase_MI.plot_kinase_heatmap(mutual_info, annotate = False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}