MPI-LeipzigDataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examining the MPI-Leipzig Mind-Brain-Body Dataset\n",
    "\n",
    "The MRI data are available at https://openfmri.org/dataset/ds000221/. The behavioral data are available via NITRC: https://www.nitrc.org/projects/mpilmbb/. Note I was required to edit one file in the NITRC data (phenotype/BDI.json); it was missing a few `\"` marks required to be valid json format.\n",
    "\n",
    "### Behavoiral Data available\n",
    "\n",
    "Each `.json` file describes the headers of the correspondingly name `.tsv` file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACS.json\n",
      "ACS.tsv\n",
      "AMAS.json\n",
      "AMAS.tsv\n",
      "ASR.json\n",
      "ASR.tsv\n",
      "AUT.json\n",
      "AUT.tsv\n",
      "BCQ.json\n",
      "BCQ.tsv\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "ls MPI-Leipzig/behavioral_data_MPILMBB/phenotype | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can investigate what keys are available in any `.tsv` header by examining the corresponding `.json` file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"MeasurementToolMetadata\": {\n",
      "        \"Description\": \"Beck's Depression Inventory II\",\n",
      "        \"TermURL\": \"\"\n",
      "    },\n",
      "    \"BDI_sum_1\": {\n",
      "      \"Description\": \"BDI Item 1\",\n",
      "      \"Derivative\": true\n",
      "    },\n",
      "    \"BDI_sum_2\": {\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "\n",
    "cat MPI-Leipzig/behavioral_data_MPILMBB/phenotype/BDI.json | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can investigate what values are associated with those behavioral keys by examining the `.tsv` files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "participant_id\tBDI_sum_1\tBDI_sum_2\tBDI_sum_3\tBDI_sum_4\tBDI_sum_5\tBDI_sum_6\tBDI_sum_7\tBDI_sum_8\tBDI_sum_9\tBDI_sum_10\tBDI_sum_11\tBDI_sum_12\tBDI_sum_13\tBDI_sum_14\tBDI_sum_15\tBDI_sum_16\tBDI_sum_17\tBDI_sum_18\tBDI_sum_19\tBDI_sum_20\tBDI_sum_21\tBDI_summary_sum\n",
      "sub-010001\t0.0\t0.0\t0.0\t0.0\t1.0\t0.0\t0.0\t3.0\t0.0\t3.0\t1.0\t0.0\t0.0\t2.0\t0.0\t1.0\t1.0\t0.0\t0.0\t1.0\t3.0\t16.0\n",
      "sub-010002\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t1.0\t0.0\t0.0\t0.0\t1.0\t2.0\n",
      "sub-010004\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t1.0\t0.0\t0.0\t2.0\t1.0\t0.0\t0.0\t0.0\t1.0\t5.0\n",
      "sub-010005\t0.0\t0.0\t1.0\t0.0\t0.0\t0.0\t1.0\t0.0\t0.0\t0.0\t0.0\t0.0\t1.0\t0.0\t0.0\t1.0\t1.0\t0.0\t0.0\t0.0\t0.0\t5.0\n",
      "sub-010006\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t1.0\t0.0\t0.0\t0.0\t0.0\t0.0\t1.0\t0.0\t0.0\t0.0\t0.0\t2.0\n",
      "sub-010007\t1.0\t0.0\t0.0\t1.0\t0.0\t0.0\t0.0\t0.0\t0.0\t1.0\t0.0\t0.0\t2.0\t0.0\t0.0\t1.0\t0.0\t0.0\t1.0\t0.0\t2.0\t9.0\n",
      "sub-010008\t0.0\t0.0\t0.0\t0.0\t1.0\t0.0\t1.0\t0.0\t0.0\t0.0\t1.0\t1.0\t0.0\t1.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t5.0\n",
      "sub-010009\t0.0\t0.0\t0.0\t0.0\t0.0\t1.0\t1.0\t0.0\t0.0\t0.0\t0.0\t0.0\t1.0\t1.0\t1.0\t0.0\t1.0\t0.0\t0.0\t0.0\t0.0\t6.0\n",
      "sub-010011\t0.0\t0.0\t0.0\t0.0\t0.0\t1.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t1.0\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "\n",
    "head MPI-Leipzig/behavioral_data_MPILMBB/phenotype/BDI.tsv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigating the range of data available\n",
    "\n",
    "Provided in this repository is the script `find_subjects_behavior_data.py` which takes as arguments a list of `.tsv` files and a list of corresponding behavior keys to examine for each file. It pulls out the data associated with the behavior key given (ex: `NEO_N`) for each subject in the corresponding `.tsv` file. It provides the function `get_data`, which returns:\n",
    " - `subjects`: a dictionary with the subject names as keys. The values are themselves dictionaries keyed by behavior key name. For example:\n",
    " ```\n",
    " subjects['sub-000021'] = {\n",
    "                             'BDI_summary_sum':1.0,\n",
    "                             'HADS-D_summary_sum':2.0,\n",
    "                             'NEO_N':63.0\n",
    "                          }\n",
    " ```\n",
    " Note the sub-dictionary values will always be floats. If the behavior test was not recorded for that subject, the behavior key will not be present in that subject's dictionary.\n",
    " - `complete_subjects`: a dictionary structured as `subjects`. Only includes subjects that have values for all behavior keys given.\n",
    " - `raw_data`: a dictionary keyed by behavior name. The value is a list of floats corresponding to all the entries in the `.tsv` file for that behavior key.\n",
    " - `complete_raw_data`: a dictionary keyed by behavior name. The value is a list of floats corresponding to the entries in the `.tsv` file for that behavior key that also have values for all other behavior keys. Note each behavior key's value will always be a subset of the behavior key's value in `raw_data`\n",
    "\n",
    "When run at the command-line, `find_subjects_behavior_data.py` will produce a set of box plots. Each box extends from the lower to upper quartile values of the data, with a line at the median. The whiskers extend from the box to show the range of the data. Flier points are those past the end of the whiskers. A dotted green line indicates the mean. The first column of box plots plot all available data for a given behavior key. The second column of box plots plot the data for a given behavior key such that the subjects who provide that data also have data for _every_ behavior key (taken from `complete_raw_data`). A row exists for each behavior key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "169 subjects have data in all given files\n"
     ]
    },
    {
     "data": {
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4HXXUUVq+fLmWLFlS9tCSwRZ9ixvukQet8J4LMBwbN27UzJkzd5s2c+ZMbdy4saQRpYct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\nQOIoegBIHEUPAImj6AEgcf8fezZyXrb/lR4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106a3ac18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Allow us to import python files in scripts\n",
    "import sys\n",
    "sys.path.append('./scripts')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import find_subjects_behavior_data as fsbd\n",
    "\n",
    "#Arguments that would normally be passed through the command line call\n",
    "behavior_files = [\n",
    "    \"MPI-Leipzig/behavioral_data_MPILMBB/phenotype/BDI.tsv\",\n",
    "    \"MPI-Leipzig/behavioral_data_MPILMBB/phenotype/HADS.tsv\",\n",
    "    \"MPI-Leipzig/behavioral_data_MPILMBB/phenotype/NEO.tsv\"\n",
    "]\n",
    "behavior_keys = [\n",
    "    \"BDI_summary_sum\",\n",
    "    \"HADS-D_summary_sum\",\n",
    "    \"NEO_N\"\n",
    "]\n",
    "\n",
    "#Get data using find_subject_data\n",
    "subjects, complete_subjects, raw_data, complete_raw_data = fsbd.get_data(behavior_files, behavior_keys)\n",
    "\n",
    "fsbd.draw_figure(behavior_keys, raw_data, complete_raw_data)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now we have an easy way to see the range of the behavioral data available. A non-static version of the above code is below.\n",
    "\n",
    "### MRI data available"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "If you'd like to interactively work with find_subjects_data, a non-static version of the code is below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "169 subjects have data in all given files\n"
     ]
    },
    {
     "data": {
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       "model_id": "d7ad4925936c47b6bf53e8ab54b30f79"
      }
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   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "#Allow us to import python files in scripts\n",
    "import sys\n",
    "sys.path.append('./scripts')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from ipywidgets import interactive \n",
    "\n",
    "import find_subjects_behavior_data as fsbd\n",
    "\n",
    "#Arguments that would normally be passed through the command line call\n",
    "behavior_files = [\n",
    "    \"MPI-Leipzig/behavioral_data_MPILMBB/phenotype/BDI.tsv\",\n",
    "    \"MPI-Leipzig/behavioral_data_MPILMBB/phenotype/HADS.tsv\",\n",
    "    \"MPI-Leipzig/behavioral_data_MPILMBB/phenotype/NEO.tsv\"\n",
    "]\n",
    "behavior_keys = [\n",
    "    \"BDI_summary_sum\",\n",
    "    \"HADS-D_summary_sum\",\n",
    "    \"NEO_N\"\n",
    "]\n",
    "\n",
    "#Get data using find_subject_data\n",
    "subjects, complete_subjects, raw_data, complete_raw_data = fsbd.get_data(behavior_files, behavior_keys)\n",
    "\n",
    "def draw_figure():\n",
    "    fsbd.draw_figure(behavior_keys, raw_data, complete_raw_data)\n",
    "    \n",
    "interactive_plot = interactive(draw_figure)\n",
    "interactive_plot\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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