{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example - Burst Variance Analysis\n", "\n", "*This notebook is part of smFRET burst analysis software [FRETBursts](http://tritemio.github.io/FRETBursts/).*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> This notebook shows how to implement Burst Variance Analysis (BVA) ([Torella 2011](http://dx.doi.org/10.1016/j.bpj.2011.01.066)) using FRETBursts.\n", "\n", "> For a complete tutorial on burst analysis see \n", "> [FRETBursts - us-ALEX smFRET burst analysis](FRETBursts - us-ALEX smFRET burst analysis.ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Loading the software" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start loading the **`FRETBursts`** software:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Optimized (cython) burst search loaded.\n", " - Optimized (cython) photon counting loaded.\n", "--------------------------------------------------------------\n", " You are running FRETBursts (version 0.6.4).\n", "\n", " If you use this software please cite the following paper:\n", "\n", " FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET\n", " Ingargiola et al. (2016). http://dx.doi.org/10.1371/journal.pone.0160716 \n", "\n", "--------------------------------------------------------------\n" ] } ], "source": [ "from fretbursts import *\n", "sns = init_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data file" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "URL: http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\n", "File: 0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\n", " \n", "File already on disk: /Users/anto/src/FRETBursts/notebooks/data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 \n", "Delete it to re-download.\n" ] } ], "source": [ "url = 'http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5'\n", "download_file(url, save_dir='./data')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'./data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "file_name = \"0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n", "\n", "# Here the folder is the subfolder \"data\" of current notebook folder\n", "folder_name = './data/'\n", "full_fname = folder_name + file_name\n", "full_fname" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Perfect, I found the file!\n" ] } ], "source": [ "import os\n", "if os.path.isfile(full_fname):\n", " print (\"Perfect, I found the file!\")\n", "else:\n", " print (\"Sorry, I can't find the file:\\n\", full_fname)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Load the selected file" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "d = loader.photon_hdf5(full_fname)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# us-ALEX parameters\n", "\n", "At this point, in `d`, we only have the timestamps (`ph_times_t`) and the detector numbers (`det_t`):" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "data_0023uLRpitc_NTP_20dT_0.5GndCl G1.000" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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oOKrM3y+i+qDKwSQ3NxcJCQmYOHEiHBwcAACFhYWws3v0x9nd3R1qtRpdu3YFAKjVarRr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\nqWkl21OboaSqy6/tNhER0V/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d.add(det_donor_accept=(0, 1), \n", " alex_period=4000, \n", " D_ON=(2100, 3900), \n", " A_ON=(150, 1900),\n", " offset=700)\n", "bpl.plot_alternation_hist (d)\n", "loader.usalex_apply_period(d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Burst Search and Selection" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we perform a standard burst search using the donor-excitation photon stream \n", "(`Ph_sel(Dex='DAem')`) as required for BVA.\n", "Then, we apply a few selection filters to discard D-only and A-only bursts." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Calculating BG rates ... " ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] } ], "source": [ "d.calc_bg(bg.exp_fit, time_s=50.1, tail_min_us='auto', F_bg=1.7)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Performing burst search (verbose=False) ..." ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Recomputing background limits for Dex ... " ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Recomputing background limits for all ... " ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Fixing burst data to refer to ph_times_m ... " ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Calculating burst periods ..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] } ], "source": [ "d.burst_search(m=10, computefret=False, ph_sel=Ph_sel(Dex='DAem'))\n", "d.calc_fret(count_ph=True, corrections=False)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "ds = d.select_bursts(select_bursts.naa, th1=30, computefret=False)\n", "ds1 = ds.select_bursts(select_bursts.size, th1=30, computefret=False)\n", "ds_FRET = ds1.select_bursts(select_bursts.S, S1=0.25, S2=0.85, computefret=False)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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WQgjhMZQqzzrcLT1pIYQQHkOprPqjItu3b2fkyJF06dKF/v37s2jRIgBycnIY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\n+xmrelOgv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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dx=ds_FRET\n", "alex_jointplot(dx)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Burst Variance Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define a function to compute $s_E$:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "def bva_sigma_E(n, bursts, DexAem_mask, out=None):\n", " \"\"\"\n", " Perform BVA analysis computing std.dev. of E for sub-bursts in each burst.\n", " \n", " Split each burst in n-photons chunks (sub-bursts), compute E for each sub-burst,\n", " then compute std.dev. of E across the sub-bursts.\n", "\n", " For details on BVA see:\n", "\n", " - Torella et al. (2011) Biophys. J. doi.org/10.1016/j.bpj.2011.01.066\n", " - Ingargiola et al. (2016) bioRxiv, doi.org/10.1101/039198\n", "\n", " Arguments:\n", " n (int): number of photons in each sub-burst\n", " bursts (Bursts object): burst-data object with indexes relative \n", " to the Dex photon stream.\n", " DexAem_mask (bool array): mask of A-emitted photons during D-excitation \n", " periods. It is a boolean array indexing the array of Dex timestamps \n", " (`Ph_sel(Dex='DAem')`).\n", " out (None or list): append the result to the passed list. If None,\n", " creates a new list. This is useful to accumulate data from\n", " different spots in a single list.\n", "\n", " Returns:\n", " E_sub_std (1D array): contains for each burst, the standard deviation of \n", " sub-bursts FRET efficiency. Same length of input argument `bursts`.\n", " \"\"\"\n", " E_sub_std = [] if out is None else out\n", " \n", " for burst in bursts:\n", " E_sub_bursts = []\n", " startlist = range(burst.istart, burst.istop + 2 - n, n)\n", " stoplist = [i + n for i in startlist]\n", " for start, stop in zip(startlist, stoplist):\n", " A_D = DexAem_mask[start:stop].sum()\n", " assert stop - start == n\n", " E = A_D / n\n", " E_sub_bursts.append(E)\n", " E_sub_std.append(np.std(E_sub_bursts))\n", " \n", " return E_sub_std" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we prepare the data for BVA:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "ph_d = ds_FRET.get_ph_times(ph_sel=Ph_sel(Dex='DAem'))\n", "bursts = ds_FRET.mburst[0]\n", "bursts_d = bursts.recompute_index_reduce(ph_d)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "Dex_mask = ds_FRET.get_ph_mask(ph_sel=Ph_sel(Dex='DAem')) \n", "DexAem_mask = ds_FRET.get_ph_mask(ph_sel=Ph_sel(Dex='Aem')) \n", "DexAem_mask_d = DexAem_mask[Dex_mask]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and call the `bva_sigma_E` function:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "n = 7\n", "E_sub_std = bva_sigma_E(n, bursts_d, DexAem_mask_d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we make a KDE plot of the 2D distribution `E_sub_std` versus the burst FRET efficiency:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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hNec6fY4xWINvR8WhcMzl2JWaiNiru64ExL6Nt7M8CR/A9QACnAshd1tBnbG/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\ny6X0DmfPxYcffojrr78et956K1QqFTIyMvDOO+9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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(6,6))\n", "x = np.arange(0,1.01,0.01)\n", "y = np.sqrt((x*(1-x))/n)\n", "plt.plot(x,y, lw=3, color='red')\n", "im = sns.kdeplot(ds_FRET.E[0], np.asfarray(E_sub_std), shade=True, cmap='viridis', shade_lowest=False)\n", "plt.xlim(0,1)\n", "plt.ylim(0,0.4)\n", "plt.xlabel('E', fontsize=14)\n", "plt.ylabel(r'$s_E$', fontsize=24);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "**Executed:** Tue Jul 11 21:51:51 2017\n", "\n", "**Duration:** 7 seconds.\n", "\n", "**Autogenerated from:** [Example - Burst Variance Analysis.ipynb](out/Example - Burst Variance Analysis.ipynb)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "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": "3.5.2" }, "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "138px", "width": "252px" }, "navigate_menu": true, "number_sections": false, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false, "widenNotebook": false } }, "nbformat": 4, "nbformat_minor": 1 }