{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sub-sampling the data of a `toffee` file to just include standard peptides\n", "\n", "In order to build a set of test files with known properties that can be used within a testing framework, we wish to take a full experimental file and extract out a subset of the data. In this example, we are going to take a HEK293 control that has a collection of spike-in retention time standards, extract the data just for those peptides such that this file can be used in regression tests." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import os\n", "import pandas as pd\n", "import numpy as np\n", "import scipy\n", "import scipy.sparse\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from tqdm import tqdm\n", "\n", "import toffee\n", "from toffee import util\n", "from toffee.manipulation import Subsampler\n", "\n", "sns.set()\n", "sns.set_color_codes()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'0.12.18'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "toffee.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sub-sample using the Spectral Reference Library (SRL)\n", "\n", "Open the transition library for the iRT peptides" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "base_dir = os.environ.get('DIA_TEST_DATA_REPO', None)\n", "assert base_dir is not None\n", "srl_fname = base_dir + '/ProCan90/srl/hek_srl.OpenSwath.iRT.tsv'\n", "srl = pd.read_csv(srl_fname, sep='\\t')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and then create the object for sub-sampling a `toffee` file using the peptides in teh reference library." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "tof_fname = base_dir + '/ProCan90/tof/ProCan90-M03-01.tof'\n", "subsampler = Subsampler(\n", " tof_fname=tof_fname,\n", " precursor_and_product_df=srl,\n", " filter_ms2_windows=['ms2-025', 'ms2-041'],\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Running the sub-sampling is as simple as passing in the output file name." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2/2 [00:25<00:00, 11.43s/it]\n" ] } ], "source": [ "test_tof_fname = os.path.basename(tof_fname).replace('.tof', '.test.tof')\n", "subsampler.run(test_tof_fname)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Properties of the `toffee` HDF5 file\n", "\n", "Using standard HDF tools we can interrogate the new `toffee` file for things such as compression, dataset and attribute names, or even the Intrinsic Mass Spacing parameters." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-rw-r--r-- 1 btully 470603294 867K 15 Apr 20:43 ProCan90-M03-01.test.tof\r\n" ] } ], "source": [ "!ls -lhF $test_tof_fname" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Opened \"ProCan90-M03-01.test.tof\" with sec2 driver.\r\n", "ms1 Group\r\n", " Attribute: IMSAlpha scalar\r\n", " Type: native double\r\n", " Data: 7.01548e-05\r\n", " Attribute: IMSBeta scalar\r\n", " Type: native double\r\n", " Data: -5.92871e-05\r\n", " Attribute: IMSGamma scalar\r\n", " Type: native int\r\n", " Data: 266664\r\n", " Attribute: firstScanRetentionTimeOffset scalar\r\n", " Type: native double\r\n", " Data: 0.17\r\n", " Attribute: precursorCenter scalar\r\n", " Type: native double\r\n", " Data: -1\r\n", " Attribute: precursorLower scalar\r\n", " Type: native double\r\n", " Data: -1\r\n", " Attribute: precursorUpper scalar\r\n", " Type: native double\r\n", " Data: -1\r\n", " Attribute: scanCycleTime scalar\r\n", " Type: native double\r\n", " Data: 3.32\r\n", " Location: 1:405261\r\n", " Links: 1\r\n" ] } ], "source": [ "!h5ls -vg $test_tof_fname/ms1" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Opened \"ProCan90-M03-01.test.tof\" with sec2 driver.\r\n", "IMSAlphaPerScan Dataset {1627/1627}\r\n", " Location: 1:413217\r\n", " Links: 1\r\n", " Chunks: {1627} 13016 bytes\r\n", " Storage: 13016 logical bytes, 6975 allocated bytes, 186.61% utilization\r\n", " Filter-0: deflate-1 OPT {6}\r\n", " Type: native double\r\n", "IMSBetaPerScan Dataset {1627/1627}\r\n", " Location: 1:422560\r\n", " Links: 1\r\n", " Chunks: {1627} 13016 bytes\r\n", " Storage: 13016 logical bytes, 7470 allocated bytes, 174.24% utilization\r\n", " Filter-0: deflate-1 OPT {6}\r\n", " Type: native double\r\n", "imsCoord Dataset {257400/257400}\r\n", " Location: 1:432398\r\n", " Links: 1\r\n", " Chunks: {257400} 1029600 bytes\r\n", " Storage: 1029600 logical bytes, 198728 allocated bytes, 518.10% utilization\r\n", " Filter-0: deflate-1 OPT {6}\r\n", " Type: native unsigned int\r\n", "intensity Dataset {257400/257400}\r\n", " Location: 1:633670\r\n", " Links: 1\r\n", " Chunks: {257400} 1029600 bytes\r\n", " Storage: 1029600 logical bytes, 251727 allocated bytes, 409.01% utilization\r\n", " Filter-0: deflate-1 OPT {6}\r\n", " Type: native unsigned int\r\n", "retentionTimeIdx Dataset {1627/1627}\r\n", " Location: 1:406565\r\n", " Links: 1\r\n", " Chunks: {1627} 6508 bytes\r\n", " Storage: 6508 logical bytes, 3956 allocated bytes, 164.51% utilization\r\n", " Filter-0: deflate-1 OPT {6}\r\n", " Type: native unsigned int\r\n" ] } ], "source": [ "!h5ls -v $test_tof_fname/ms1" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Opened \"ProCan90-M03-01.test.tof\" with sec2 driver.\r\n", "ms2-041 Group\r\n", " Attribute: IMSAlpha scalar\r\n", " Type: native double\r\n", " Data: 7.01547e-05\r\n", " Attribute: IMSBeta scalar\r\n", " Type: native double\r\n", " Data: -3.32516e-05\r\n", " Attribute: IMSGamma scalar\r\n", " Type: native int\r\n", " Data: 142548\r\n", " Attribute: firstScanRetentionTimeOffset scalar\r\n", " Type: native double\r\n", " Data: 1.467\r\n", " Attribute: precursorCenter scalar\r\n", " Type: native double\r\n", " Data: 611.5\r\n", " Attribute: precursorLower scalar\r\n", " Type: native double\r\n", " Data: 608.5\r\n", " Attribute: precursorUpper scalar\r\n", " Type: native double\r\n", " Data: 614.5\r\n", " Attribute: scanCycleTime scalar\r\n", " Type: native double\r\n", " Data: 3.32\r\n", " Location: 1:174499\r\n", " Links: 1\r\n" ] } ], "source": [ "!h5ls -vg $test_tof_fname/ms2-041" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Opened \"ProCan90-M03-01.test.tof\" with sec2 driver.\r\n", "IMSAlphaPerScan Dataset {1627/1627}\r\n", " Location: 1:181206\r\n", " Links: 1\r\n", " Chunks: {1627} 13016 bytes\r\n", " Storage: 13016 logical bytes, 8244 allocated bytes, 157.88% utilization\r\n", " Filter-0: deflate-1 OPT {6}\r\n", " Type: native double\r\n", "IMSBetaPerScan Dataset {1627/1627}\r\n", " Location: 1:191818\r\n", " Links: 1\r\n", " Chunks: {1627} 13016 bytes\r\n", " Storage: 13016 logical bytes, 4755 allocated bytes, 273.73% utilization\r\n", " Filter-0: deflate-1 OPT {6}\r\n", " Type: native double\r\n", "imsCoord Dataset {96051/96051}\r\n", " Location: 1:198941\r\n", " Links: 1\r\n", " Chunks: {96051} 384204 bytes\r\n", " Storage: 384204 logical bytes, 172435 allocated bytes, 222.81% utilization\r\n", " Filter-0: deflate-1 OPT {6}\r\n", " Type: native unsigned int\r\n", "intensity Dataset {96051/96051}\r\n", " Location: 1:373920\r\n", " Links: 1\r\n", " Chunks: {96051} 384204 bytes\r\n", " Storage: 384204 logical bytes, 28973 allocated bytes, 1326.08% utilization\r\n", " Filter-0: deflate-1 OPT {6}\r\n", " Type: native unsigned int\r\n", "retentionTimeIdx Dataset {1627/1627}\r\n", " Location: 1:175803\r\n", " Links: 1\r\n", " Chunks: {1627} 6508 bytes\r\n", " Storage: 6508 logical bytes, 2707 allocated bytes, 240.41% utilization\r\n", " Filter-0: deflate-1 OPT {6}\r\n", " Type: native unsigned int\r\n" ] } ], "source": [ "!h5ls -v $test_tof_fname/ms2-041" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare to the original data\n", "\n", "By opening up the file we just created, we can ensure that the data it contains matches exactly the same data as the original file. In doing so, we can prove that the data used in testing later is indistiguisable regardless of which file is being used.\n", "\n", "First off, we need a series of functions that check data inside numpy and scipy matrices." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def sparse_assert_allclose(A, B, atol=1e-8):\n", " \"\"\"\n", " Test to see if two sparse matrices compare equal.\n", " Modified from: https://stackoverflow.com/a/47771340/758811\n", " \"\"\"\n", " np.testing.assert_array_equal(A.shape, B.shape)\n", " r1, c1, v1 = scipy.sparse.find(A)\n", " r2, c2, v2 = scipy.sparse.find(B)\n", " np.testing.assert_array_equal(r1, r2)\n", " np.testing.assert_array_equal(c1, c2)\n", " np.testing.assert_allclose(v1, v2, atol=atol)\n", " \n", "def chromatogram_assert_allclose(A, B):\n", " np.testing.assert_allclose(\n", " A.retentionTime, \n", " B.retentionTime\n", " )\n", " np.testing.assert_allclose(\n", " A.massOverCharge, \n", " B.massOverCharge\n", " )\n", " sparse_assert_allclose(\n", " A.intensities, \n", " B.intensities\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For further checking, we can plot the raw data of each file alongside one another" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def plot_raw(original_chrom, test_chrom, mz_value, window):\n", " def _plot_raw_impl(chrom, ax1, ax2, title, label_y=True):\n", " xlim = [chrom.retentionTime[0] / 60.0, chrom.retentionTime[-1] / 60.0]\n", " dense_intensities = chrom.intensities.toarray()\n", "\n", " intensities = np.log10(1 + dense_intensities.T)\n", " ax1.matshow(intensities, extent=xlim + [0, 6.0], cmap=plt.cm.viridis)\n", " ax1.set_xlim(xlim)\n", " ax1.set_ylabel('m/z (Da)')\n", " ax1.yaxis.set_visible(False)\n", " ax1.set_title(title, y=0.9)\n", "\n", " ax2.plot(chrom.retentionTime / 60.0, np.sum(dense_intensities, axis=1))\n", " ax2.set_xlim(xlim)\n", " ax2.set_xlabel('Retention Time (min)')\n", " if label_y:\n", " ax2.set_ylabel('Intensity')\n", " \n", " fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(15, 3), sharex=True, sharey='row')\n", " _plot_raw_impl(original_chrom, axes[0, 0], axes[1, 0], 'Original Data')\n", " _plot_raw_impl(test_chrom, axes[0, 1], axes[1, 1], 'Test Data', label_y=False)\n", " fig.suptitle('{} Data | Mass Over Charge = {:.2F}'.format(window, mz_value))\n", " fig.tight_layout()\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, loading the MS1 data is more expensive than others, so we only want to do it once" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "test_swath_run = toffee.SwathRun(test_tof_fname)\n", "test_ms1_swath_map = test_swath_run.loadSwathMap(toffee.ToffeeWriter.MS1_NAME)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, let's loop through the precursors and products that were used by the sub-samplerand compare the new file to the existing data" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\r", " 0%| | 0/2 [00:00" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2/2 [00:03<00:00, 1.82s/it]\n" ] } ], "source": [ "ppm_width = 100\n", "for k, ms2_name in enumerate(tqdm(sorted(subsampler.precursors.ms2Name.unique().tolist()))):\n", " # filter the library\n", " try:\n", " precursors = subsampler.precursors[subsampler.precursors.ms2Name == ms2_name]\n", " products = subsampler.products[subsampler.products.ms2Name == ms2_name]\n", " except KeyError:\n", " # this will happen if the window is not in the library\n", " continue\n", "\n", " # load the MS2 swath map\n", " ms2_swath_map = subsampler.swath_run.loadSwathMap(ms2_name)\n", " test_ms2_swath_map = test_swath_run.loadSwathMap(ms2_name)\n", " \n", " # collect the MS1 data\n", " for i, (_, row) in enumerate(precursors.iterrows()):\n", " mz = row.IsotopeMz\n", " mz_range = toffee.MassOverChargeRangeWithPPMFullWidth(mz, ppm_width)\n", " ms1_xic = subsampler.ms1_swath_map.extractedIonChromatogramSparse(mz_range)\n", " test_ms1_xic = test_ms1_swath_map.extractedIonChromatogramSparse(mz_range)\n", " if i == 0 and k < 4:\n", " plot_raw(ms1_xic, test_ms1_xic, mz, window='MS1')\n", " try:\n", " chromatogram_assert_allclose(ms1_xic, test_ms1_xic)\n", " except AssertionError:\n", " plot_raw(ms1_xic, test_ms1_xic, mz, window=f'Error! MS1')\n", "\n", " # collect the MS2 data\n", " for i, (_, row) in enumerate(products.iterrows()):\n", " mz = row.IsotopeMz\n", " mz_range = toffee.MassOverChargeRangeWithPPMFullWidth(mz, ppm_width)\n", " ms2_xic = ms2_swath_map.extractedIonChromatogramSparse(mz_range)\n", " test_ms2_xic = test_ms2_swath_map.extractedIonChromatogramSparse(mz_range)\n", " if i == 0 and k < 4:\n", " plot_raw(ms2_xic, test_ms2_xic, mz, window=ms2_name)\n", " try:\n", " chromatogram_assert_allclose(ms2_xic, test_ms2_xic)\n", " except AssertionError:\n", " plot_raw(ms2_xic, test_ms2_xic, mz, window=f'Error! {ms2_name}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now, clean up" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "if os.path.isfile(test_tof_fname):\n", " os.remove(test_tof_fname)" ] } ], "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": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }