{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Quantitative comparison of experimental data sets with NetworkUnit\n", "Supplementary notebook for *Gutzen, R., von Papen, M., Trensch, G., Quaglio, P., Grün, S., and Denker, M. (2018) Reproducible neural network simulations: statistical methods for model validation on the level of network activity data*." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of Contents\n", "* [Introduction](#intro) \n", "* [Setup](#set) \n", "* [NetworkUnit classes](#cla) \n", "* [Results](#res) \n", " * [Firing rates](#fir) \n", " * [Inter spike intervals](#int) \n", " * [Local coefficient of variation](#loc) \n", "* [Conclusions](#con) \n", "* [References](#ref) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction\n", "This notebook showcases the basic workflow of [NetworkUnit](https://github.com/INM-6/NetworkUnit) applied to experimental data and provides an example of how to use [NetworkUnit](https://github.com/INM-6/NetworkUnit) to quantify the statistical difference between data sets (instead of model validation). **The quantified difference between experimental datasets can be used to guide the definiton of the acceptable agreement for a model** that aims to explain these datasets. The quantification of the statistical difference between the underlying experimental data is therefore an important first step in the general validation process. However, care must be taken in interpreting these results due to the large number of factors contributing to the observed variability. Such factors include amongst others a slightly different electrode location within the motor cortex for the two monkeys and differences in the signal-to-noise ratio of the measurements which affects spike sorting and can result in different response profiles.\n", "\n", "The experimental data for this example is taken from [*Brochier et al.* (2018)](#brochier2018). The provided datasets were recorded in the motor cortex of two macaque monkeys using Utah multi-electrode arrays. The underlying task was an instructed delayed reach-to-grasp task, the details of which are described in [*Riehle et al.* (2013)](#riehle2013) and [*Brochier et al.* (2018)](#brochier2018)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Setup \n", "## Requirements\n", "* This notebook downloads the experimental data from the gin repository [multielectrode_grasp](https://web.gin.g-node.org/doi/multielectrode_grasp). For that matter you need the [*gin-client*](https://web.gin.g-node.org/G-Node/Info/wiki/GinCli#quickstart). Alternatively, you can download the data repository manually from its homepage.\n", "* This notebook works with Python 2.7 and Python 3. The required modules are listed in `requirements.txt` within the gin repository [**network_validation**](https://web.gin.g-node.org/INM-6/network_validation)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Clone NetworkUnit from GitHub\n", "Clone the newest version of the repository NetworkUnit to current directory and adds the corresponding path to `sys`." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:22:55.962414Z", "start_time": "2018-10-24T10:22:52.610372Z" } }, "outputs": [], "source": [ "%%capture\n", "import sys, os\n", "\n", "# Clone repository and pull commit at time of paper submission\n", "!git clone https://github.com/INM-6/NetworkUnit.git\n", "!cd NetworkUnit/; git checkout 'f9d5c6deabe56c2419b9698d285a62af254cfc56'; git fetch; git pull\n", "\n", "# add to path\n", "sys.path.insert(0, './NetworkUnit')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Clone experimental data repository\n", "Clone the newest version of the repository multielectrode_grasp to current directory. This will not download any large data files. These will be downloaded using `gin` (or alternatively using `git-annex`). If you experience problems with `git-annex` or `gin`, you can also download the complete data repository from its [homepage](https://web.gin.g-node.org/doi/multielectrode_grasp)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:22:58.254941Z", "start_time": "2018-10-24T10:22:55.970446Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading repository doi/multielectrode_grasp \u001b[32mOK\u001b[0m \n", "Initialising local storage \u001b[32mOK\u001b[0m\n" ] } ], "source": [ "!gin get doi/multielectrode_grasp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now download the required contents and add the corresponding path to `sys`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:24:26.671464Z", "start_time": "2018-10-24T10:22:58.263256Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading datasets/l101210-001.odml \u001b[32mOK\u001b[0m \n", "Downloading datasets/l101210-001.ccf \u001b[32mOK\u001b[0m \n", "Downloading datasets/l101210-001.nev \u001b[32mOK\u001b[0m \n", "Downloading datasets/l101210-001-02.nev \u001b[32mOK\u001b[0m \n", "Downloading datasets/i140703-001.odml \u001b[32mOK\u001b[0m \n", "Downloading datasets/i140703-001.ccf \u001b[32mOK\u001b[0m \n", "Downloading datasets/i140703-001.nev \u001b[32mOK\u001b[0m \n", "Downloading datasets/i140703-001-03.nev \u001b[32mOK\u001b[0m \n" ] } ], "source": [ "# download large data files needed for this example\n", "os.chdir('./multielectrode_grasp')\n", "!gin get-content ./datasets/l101210-001.odml # metadata for monkey L\n", "!gin get-content ./datasets/l101210-001.ccf\n", "!gin get-content ./datasets/l101210-001.nev # unsorted spike times for monkey L\n", "!gin get-content ./datasets/l101210-001-02.nev # sorted spike times for monkey L\n", "!gin get-content ./datasets/i140703-001.odml # metadata for monkey I\n", "!gin get-content ./datasets/i140703-001.ccf\n", "!gin get-content ./datasets/i140703-001.nev # unsorted spike times for monkey I\n", "!gin get-content ./datasets/i140703-001-03.nev # sorted spike times for monkey I\n", "os.chdir('..')\n", "\n", "# add to path\n", "sys.path.insert(0, './multielectrode_grasp/code/python-neo')\n", "sys.path.insert(0, './multielectrode_grasp/code/python-odml')\n", "sys.path.insert(0, './multielectrode_grasp/code/reachgraspio')\n", "sys.path.insert(0, './multielectrode_grasp/code')\n", "\n", "# set data path\n", "data_path = './multielectrode_grasp/datasets/'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:24:27.986194Z", "start_time": "2018-10-24T10:24:26.679945Z" } }, "outputs": [], "source": [ "%matplotlib inline\n", "from __future__ import print_function\n", "import matplotlib.pyplot as plt\n", "import sciunit\n", "import neo\n", "import numpy as np\n", "from neo_utils import add_epoch, cut_segment_by_epoch, get_events\n", "from reachgraspio import reachgraspio as rgio\n", "import seaborn as sns\n", "from networkunit import tests, scores, models\n", "from networkunit.capabilities.cap_ProducesSpikeTrains import ProducesSpikeTrains\n", "from matplotlib.lines import Line2D" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Functions" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:24:28.002908Z", "start_time": "2018-10-24T10:24:27.989987Z" } }, "outputs": [], "source": [ "def plot_stats(test_instance, DATA, xlabel='', logx=False, logy=False, title='', **kwargs):\n", " \n", " # Calculate effect size\n", " effect_size = test_instance.judge([DATA[0], DATA[1]]).iloc[0,1]\n", " \n", " # Plot probability density distributions\n", " h = test_instance.visualize_samples(DATA[0], DATA[1], var_name=xlabel, lw=0.5, **kwargs)\n", " if logx:\n", " plt.xscale('log')\n", " if logy:\n", " plt.yscale('log')\n", " sns.despine()\n", " \n", " # Plt legend and effect size annotation\n", " legend_elements = [Line2D([0], [0], color=d.params['color'], lw=2, label=d.name) for d in DATA]\n", " plt.legend(handles=legend_elements, loc=1)\n", " ax = plt.gca()\n", " plt.text(0.68, 0.76, 'effect size = {:.2f}'.format(effect_size.score), \n", " transform = ax.transAxes, fontsize=12)\n", " plt.title(title) \n", " \n", " return h" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Define NetworkUnit classes\n", "A general experimental data class is defined, which is the basis to load the two data sets into the NetworkUnit framework. For the sake of simplicity, we load all the single unit activity data within a fixed time window of 800ms ranging from `'TS_ON'` to `'CUE_ON'`. `'TS_ON'` defines the start of the trial and 800ms later the grip type is signaled by `'CUE_ON'`. This means that there is no trial type information in the given time window. The data is handed over to NetworkUnit as a list of spike trains with `n_units * n_trial` elements. Spike trains are discarded if their signal to noise ration is small (`SNR`$<2.5$)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:24:28.058751Z", "start_time": "2018-10-24T10:24:28.006288Z" } }, "outputs": [], "source": [ "class exp_data(models.experimental_data, ProducesSpikeTrains):\n", " \"\"\"\n", " A model class to load data from reach2grasp experiment. Spike times from the\n", " complete recording sessions are loaded.\n", " \"\"\"\n", "\n", " def load(self, datfile, **kwargs):\n", " \"\"\"\n", " Loads data from datfile.\n", " \"\"\" \n", " # set paths\n", " fpath = data_path + datfile\n", " print('loading data from ' +fpath +'.nev')\n", " print('this may take a minute..')\n", " # load session and get event times\n", " session = rgio.ReachGraspIO(fpath, odml_directory=data_path)\n", " block = session.read_block(nsx_to_load='all', units='all', load_events=True, scaling='voltage')\n", " data_segment = block.segments[0]\n", " start_event = get_events(data_segment, properties={\n", " 'trial_event_labels': self.params['start_trigger'],\n", " 'performance_in_trial': session.performance_codes['correct_trial']})[0]\n", " stop_event = get_events(data_segment, properties={\n", " 'trial_event_labels': self.params['stop_trigger'],\n", " 'performance_in_trial': session.performance_codes['correct_trial']})[0]\n", " # select segments and get spike trains\n", " selected_trial_segments = self.select_trial_segments(data_segment, start_event, stop_event)\n", " spiketrains = self.get_spiketrains(selected_trial_segments)\n", " return spiketrains\n", " \n", " \n", " def select_trial_segments(self, data_segment, start_event, stop_event, **kwargs):\n", " '''\n", " Filters out trials from given data_segment\n", " ''' \n", " epoch = add_epoch(data_segment, event1=start_event, event2=stop_event, attach_result=False) \n", " cut_trial_block = neo.Block(name=\"Cut_Trials\")\n", " cut_trial_block.segments = cut_segment_by_epoch(data_segment, epoch, reset_time=True)\n", " selected_trial_segments = []\n", " for tr_typ in self.params['trial_types']:\n", " selected_trial_segments.extend(cut_trial_block.filter(targdict={'belongs_to_trialtype': tr_typ}, \n", " objects=neo.Segment))\n", " self.n_trials = len(selected_trial_segments)\n", " self.n_units = len(selected_trial_segments[0].filter({'sua': True}))\n", " return selected_trial_segments\n", " \n", " \n", " def get_spiketrains(self, selected_trial_segments, **kwargs):\n", " '''\n", " Appends all spike trains in selected_trial_segments to a list\n", " of spike trains. Spike trains with SNR<2.5 are sorted out.\n", " '''\n", " spiketrains = []\n", " for seg_id, seg in enumerate(selected_trial_segments):\n", " # Discarding non-valid trials\n", " if seg.annotations['trial_id'] == -1:\n", " continue\n", " # Selecting only the SUAs\n", " for st in seg.filter({'sua': True}):\n", " # Check the SNR, only use units with SNR>2.5 (see Brochier et al, 2018)\n", " if st.annotations['SNR'] > 2.5:\n", " st.annotations['trial_id'] = seg.annotations['trial_id']\n", " st.annotations['trial_type'] = seg.annotations['belongs_to_trialtype']\n", " st.annotate(trial_id_trialtype=seg_id)\n", " spiketrains.append(st)\n", " return spiketrains" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Monkey L" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:24:28.072772Z", "start_time": "2018-10-24T10:24:28.063645Z" } }, "outputs": [], "source": [ "class exp_class_L(exp_data):\n", " datfile = 'l101210-001'\n", " params = {'start_trigger': 'TS-ON',\n", " 'stop_trigger': 'CUE-ON',\n", " 'trial_types': ['PGLF', 'PGHF', 'SGLF', 'SGHF'],\n", " 'color': sns.color_palette('Set1')[0]\n", " }" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:26:07.603350Z", "start_time": "2018-10-24T10:24:28.077722Z" }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loading data from ./multielectrode_grasp/datasets/l101210-001.nev\n", "this may take a minute..\n" ] } ], "source": [ "dataL = exp_class_L(name='MONKEY L')\n", "_ = dataL.produce_spiketrains()\n", "for i in range(len(dataL.spiketrains)):\n", " dataL.spiketrains[i].name = dataL.name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Monkey I" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:26:07.616361Z", "start_time": "2018-10-24T10:26:07.608895Z" } }, "outputs": [], "source": [ "class exp_class_I(exp_data):\n", " datfile = 'i140703-001'\n", " params = {'start_trigger': 'TS-ON',\n", " 'stop_trigger': 'CUE-ON',\n", " 'trial_types': ['PGLF', 'PGHF', 'SGLF', 'SGHF'],\n", " 'color': sns.color_palette('Set1')[1]\n", " }" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:27:22.916995Z", "start_time": "2018-10-24T10:26:07.621533Z" }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loading data from ./multielectrode_grasp/datasets/i140703-001.nev\n", "this may take a minute..\n" ] } ], "source": [ "dataI = exp_class_I(name='MONKEY I')\n", "_ = dataI.produce_spiketrains()\n", "for i in range(len(dataI.spiketrains)):\n", " dataI.spiketrains[i].name = dataI.name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summarize data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:27:22.926496Z", "start_time": "2018-10-24T10:27:22.920404Z" } }, "outputs": [], "source": [ "# Put all data models into one list\n", "DATA = [dataL, dataI]\n", "data_names = [d.name for d in DATA]\n", "ndat = len(DATA)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:27:22.940822Z", "start_time": "2018-10-24T10:27:22.931552Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data for MONKEY L consists of 135 trials each containing activity of 93 single units.\n", "This results in a total of 12015 spike trains (540 spike trains filtered out due to low SNR).\n", "\n", "Data for MONKEY I consists of 142 trials each containing activity of 156 single units.\n", "This results in a total of 21300 spike trains (852 spike trains filtered out due to low SNR).\n", "\n" ] } ], "source": [ "for d in DATA:\n", " print('Data for {} consists of {} trials each containing activity of {} single units.'.format(\n", " d.name, d.n_trials, d.n_units))\n", " print('This results in a total of {} spike trains ({} spike trains filtered out due to low SNR).\\n'.format(\n", " len(d.spiketrains), d.n_units*d.n_trials-len(d.spiketrains)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Raster plots" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:27:43.226424Z", "start_time": "2018-10-24T10:27:22.945067Z" }, "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(nrows=ndat, ncols=1, figsize=(14,ndat*4), sharex=True)\n", "fig.subplots_adjust(hspace=0)\n", "\n", "# plot first 2000 spike trains as example\n", "nunits = 2000\n", "sts_list = [d.spiketrains[:nunits] for d in DATA]\n", "\n", "t_end = sts_list[0][0].t_stop.rescale('ms').magnitude\n", "for i in range(ndat):\n", " for j in range(len(sts_list[i])):\n", " sp_times = sts_list[i][j].rescale('ms').magnitude\n", " ax[i].plot(sp_times, np.zeros_like(sp_times)+j, '.k', ms=1.5)\n", " ax[i].set_ylabel(DATA[i].name +'\\n spike train #')\n", " ax[i].set_ylim([-0.05*nunits, 1.05*nunits])\n", "ax[-1].set_xlim([0, t_end])\n", "ax[-1].set_xlabel('time [ms]')\n", "ax[0].set_title('Exemplary raster plot of first {} spike trains'.format(nunits))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Firing rates\n", "### Define test classes" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:27:43.240116Z", "start_time": "2018-10-24T10:27:43.230534Z" } }, "outputs": [], "source": [ "class fr_effect_class(sciunit.TestM2M, tests.firing_rate_test):\n", " score_type = scores.effect_size\n", "fr_effect = fr_effect_class()\n", "\n", "class fr_mwu_class(sciunit.TestM2M, tests.firing_rate_test):\n", " score_type = scores.mwu_statistic\n", "fr_mwu = fr_mwu_class()\n", "\n", "class fr_ttest_class(sciunit.TestM2M, tests.firing_rate_test):\n", " score_type = scores.students_t\n", " params = {'equal_var': False} # True: Student's t-test; False: Welch's t-test\n", " def compute_score(self, prediction1, prediction2):\n", " score = self.score_type.compute(prediction1, prediction2, **self.params)\n", " return score\n", "fr_ttest = fr_ttest_class(equal_var=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot results" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:28:02.566817Z", "start_time": "2018-10-24T10:27:43.243732Z" }, "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_stats(fr_effect, DATA, xlabel='FR [Hz]', logy=False, title='Firing rate', bins=np.arange(0, 70, 2.5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Statistical quantification\n", "The **effect size** quantifies the difference between the average values of both distributions with respect to their pooled standard deviation (CI = confidence interval). \n", "\n", "The **statistical hypothesis tests** quantify the probability that the two observations come from the *same distribution (MWU)* or have *equal means (t-test)*. A small p-value indicates a significant statistical difference." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:28:38.890372Z", "start_time": "2018-10-24T10:28:02.573001Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\u001b[4mEffect Size\u001b[0m\n", "\tdatasize: 12015 \t 21300\n", "\tEffect Size = 0.516 \t CI = (0.494, 0.539)\n", "\n", "\n", "\n", "\u001b[4mMann-Whitney-U-Test\u001b[0m\n", "\tdatasize: 12015 \t 21300\n", "\tU = 88683860.500 \t p value = 0.0\n", "\n", "\n", "\u001b[4mStudent's t-test\u001b[0m\n", "\tdatasize: 12015 \t 21300\n", "\tt = 43.708 \t p value = 0.00\n", "\n", "\n" ] } ], "source": [ "print(fr_effect.judge([DATA[0], DATA[1]]).iloc[0,1])\n", "print(fr_mwu.judge([DATA[0], DATA[1]]).iloc[0,1])\n", "print(fr_ttest.judge([DATA[0], DATA[1]]).iloc[0,1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inter spike intervals\n", "### Define test classes" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:28:38.909566Z", "start_time": "2018-10-24T10:28:38.893567Z" } }, "outputs": [], "source": [ "class isi_effect_class(sciunit.TestM2M, tests.isi_variation_test):\n", " score_type = scores.effect_size\n", " params = {'variation_measure': 'isi'}\n", " def compute_score(self, prediction1, prediction2):\n", " score = self.score_type.compute(prediction1, prediction2, **self.params)\n", " return score\n", "isi_effect = isi_effect_class()\n", "\n", "class isi_mwu_class(sciunit.TestM2M, tests.isi_variation_test):\n", " score_type = scores.mwu_statistic\n", " params = {'variation_measure': 'isi'}\n", " def compute_score(self, prediction1, prediction2):\n", " score = self.score_type.compute(prediction1, prediction2, **self.params)\n", " return score\n", "isi_mwu = isi_mwu_class()\n", "\n", "class isi_ttest_class(sciunit.TestM2M, tests.isi_variation_test):\n", " score_type = scores.students_t\n", " params = {'variation_measure': 'isi'}\n", " def compute_score(self, prediction1, prediction2):\n", " score = self.score_type.compute(prediction1, prediction2, **self.params)\n", " return score\n", "isi_ttest = isi_ttest_class()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot results" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:28:43.179511Z", "start_time": "2018-10-24T10:28:38.916033Z" }, "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_stats(isi_effect, DATA, xlabel='ISI [ms]', logx=True, logy=True, title='Inter spike interval', bins=np.logspace(1.69, 5, 50))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Statistical quantification\n", "The **effect size** quantifies the difference between the average values of both distributions with respect to their pooled standard deviation (CI = confidence interval). \n", "\n", "The **statistical hypothesis tests** quantify the probability that the two observations come from the *same distribution (MWU)* or have *equal means (t-test)*. A small p-value indicates a significant statistical difference." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:28:50.602625Z", "start_time": "2018-10-24T10:28:43.183706Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\u001b[4mEffect Size\u001b[0m\n", "\tdatasize: 131896 \t 149327\n", "\tEffect Size = 0.178 \t CI = (0.170, 0.185)\n", "\n", "\n", "\n", "\u001b[4mMann-Whitney-U-Test\u001b[0m\n", "\tdatasize: 131896 \t 149327\n", "\tU = 8782255119.500 \t p value = 0.0\n", "\n", "\n", "\u001b[4mStudent's t-test\u001b[0m\n", "\tdatasize: 131896 \t 149327\n", "\tt = -47.084 \t p value = 0.00\n", "\n", "\n" ] } ], "source": [ "print(isi_effect.judge([DATA[0], DATA[1]]).iloc[0,1])\n", "print(isi_mwu.judge([DATA[0], DATA[1]]).iloc[0,1])\n", "print(isi_ttest.judge([DATA[0], DATA[1]]).iloc[0,1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Local coefficient of variation\n", "### Define test classes" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:28:50.620295Z", "start_time": "2018-10-24T10:28:50.606497Z" } }, "outputs": [], "source": [ "class lv_effect_class(sciunit.TestM2M, tests.isi_variation_test):\n", " score_type = scores.effect_size\n", " params = {'variation_measure': 'lv'}\n", " def compute_score(self, prediction1, prediction2):\n", " score = self.score_type.compute(prediction1, prediction2, **self.params)\n", " return score\n", "lv_effect = lv_effect_class()\n", "\n", "class lv_mwu_class(sciunit.TestM2M, tests.isi_variation_test):\n", " score_type = scores.mwu_statistic\n", " params = {'variation_measure': 'lv'}\n", " def compute_score(self, prediction1, prediction2):\n", " score = self.score_type.compute(prediction1, prediction2, **self.params)\n", " return score\n", "lv_mwu = lv_mwu_class()\n", "\n", "class lv_ttest_class(sciunit.TestM2M, tests.isi_variation_test):\n", " score_type = scores.students_t\n", " params = {'variation_measure': 'lv'}\n", " def compute_score(self, prediction1, prediction2):\n", " score = self.score_type.compute(prediction1, prediction2, **self.params)\n", " return score\n", "lv_ttest = lv_ttest_class()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot results" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:28:53.248127Z", "start_time": "2018-10-24T10:28:50.626661Z" }, "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_stats(lv_effect, DATA, xlabel='LV []', title='Local coefficient of variation', bins='scott')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Statistical quantification\n", "The **effect size** quantifies the difference between the average values of both distributions with respect to their pooled standard deviation (CI = confidence interval). \n", "\n", "The **statistical hypothesis tests** quantify the probability that the two observations come from the *same distribution (MWU)* or have *equal means (t-test)*. A small p-value indicates a significant statistical difference." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2018-10-24T10:28:58.960013Z", "start_time": "2018-10-24T10:28:53.255238Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\u001b[4mEffect Size\u001b[0m\n", "\tdatasize: 10108 \t 13997\n", "\tEffect Size = 0.078 \t CI = (0.052, 0.103)\n", "\n", "\n", "\n", "\u001b[4mMann-Whitney-U-Test\u001b[0m\n", "\tdatasize: 10108 \t 13997\n", "\tU = 65385623.000 \t p value = 9.7e-24\n", "\n", "\n", "\u001b[4mStudent's t-test\u001b[0m\n", "\tdatasize: 10108 \t 13997\n", "\tt = 5.946 \t p value = 2.79e-9\n", "\n", "\n" ] } ], "source": [ "print(lv_effect.judge([DATA[0], DATA[1]]).iloc[0,1])\n", "print(lv_mwu.judge([DATA[0], DATA[1]]).iloc[0,1])\n", "print(lv_ttest.judge([DATA[0], DATA[1]]).iloc[0,1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusion\n", "\n", "We here presented an example statistical quantification of the difference between two experimental data sets obtained in the motor cortex of two macaque monkeys (*Brochier et al.*, 2018). For the sake of simplicity we used the complete data set including all trial types and did not separate between spike trains observed within one trial and spike trains observed in different trials. Therefore, we restrict our analysis to mono-variate measures such as the firing rate, inter spike intervals and the local coefficient of variation.\n", "\n", "The example is intended to clarify the following points:\n", "\n", "### NetworkUnit can be used to quantify the statistical difference between experimental data sets\n", "NetworkUnit is originally designed for the validation of models. However, the validation workflow is the same as the workflow for the quantitative comparison of any two data sets. Therefore, the measures provided by NetworkUnit can be applied to any set of simulated and/or experimental data. As shown in this example, such **a quantitative comparison is an important first step to assess the acceptable agreement** of a given model that aims to explain the experimental data.\n", "\n", "### The neural activity measured in monkey L and monkey I is significantly different\n", "Statistical hypothesis tests of firing rates, inter spike intervals and local coefficients of variation between the two monkeys reveal a significant statistical difference of the distributions and a significant statistical difference of the means. This is estimated using the Mann-Whitney-U test and Welch's t-test, respectively. Accordingly, **for the given data set a non-significant difference in statistical hypothesis tests should not be used as an acceptable agreement** between a given model and the experimental data. Such a definition of the acceptable agreement would be too strict and a given model could only be validated against a single experimental data set, but not against both experimental data sets.\n", "\n", "### The effect size can be used to define an acceptable agreement\n", "Effect sizes of average statistical quantities with respect to pooled standard deviation are smaller than $0.52$ for the firing rates, smaller than $0.18$ for the inter spike intervals, and smaller than $0.08$ for the local coefficient of variation. This indicates a reasonable agreement between the two experimental data sets. Accordingly, these values of can be used to define an acceptable agreement. Note, however, that the effect size does only measure the difference between average values. Also, it is based on the assumption of a Gaussian distribution, which may be violated for some of the statistical measures such as the inter spike interval." ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2018-08-07T09:58:30.763687Z", "start_time": "2018-08-07T09:58:30.756376Z" } }, "source": [ "# References\n", "\n", "Brochier et al. (2018) [Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task](https://www.nature.com/articles/sdata201855), Scientific Data, 5, pp. 1–23. doi: 10.1038/sdata.2018.55. \n", "Gutzen et al. (2018) Reproducible neural network simulations: model validation on the level of network activity data, Frontiers in Neuroinformatics \n", "Riehle et al. (2013) [Mapping the spatio-temporal structure of motor cortical LFP and spiking activities during reach-to-grasp movements](https://www.frontiersin.org/articles/10.3389/fncir.2013.00048/full), Frontiers in Neural Circuits, 7(48). doi: 10.3389/fncir.2013.00048. " ] }, { "cell_type": "code", "execution_count": null, "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": "3.6.6" }, "toc": { "colors": { "hover_highlight": "#DAA520", "navigate_num": "#000000", "navigate_text": "#333333", "running_highlight": "#FF0000", "selected_highlight": "#FFD700", "sidebar_border": "#EEEEEE", "wrapper_background": "#FFFFFF" }, "moveMenuLeft": true, "nav_menu": { "height": "344px", "width": "252px" }, "navigate_menu": true, "number_sections": false, "sideBar": true, "threshold": "3", "toc_cell": false, "toc_position": { "height": "773.133px", "left": "0px", "right": "1363px", "top": "134.867px", "width": "212px" }, "toc_section_display": "block", "toc_window_display": true, "widenNotebook": false } }, "nbformat": 4, "nbformat_minor": 2 }