diff --git a/docs/source/notebooks/Preparing_raw_data_for_PeakPerformance.ipynb b/docs/source/notebooks/Preparing_raw_data_for_PeakPerformance.ipynb index 7393632..c6eb5be 100644 --- a/docs/source/notebooks/Preparing_raw_data_for_PeakPerformance.ipynb +++ b/docs/source/notebooks/Preparing_raw_data_for_PeakPerformance.ipynb @@ -12,14 +12,17 @@ "metadata": {}, "source": [ "This example briefly shows how to prepare raw for `PeakPerformance`. \n", + " \n", "Extracted ion chromatograms from LC-MS/MS analyses are essentially time series with time in the first and signal intensity in the second dimension.\n", "This is represented by a NumPy array of shape `(2, ?)` (see also the example data in the repository).\n", - "Both time and intensity should also be NumPy arrays." + "Both time and intensity should also be NumPy arrays. \n", + " \n", + "The final section describes and demonstrates with an example __how to connect `PeakPerformance` to regular LC-MS/MS raw data files__ in the vendor-specific format." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -170,16 +173,189 @@ "np.save(\"Sample A1_90_43.9_44.1.npy\", timeseries_example)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting PeakPerformance to raw data in proprietary data formats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we used a Sciex TripleTOF6600 QqTOF device, the raw data was provided as `*.wiff` and `*.wiff.scan` files which are proprietary, binary file types.\n", + "Most vendors have similarly opaque data formats which can not be opened except by using the vendor software.\n", + "These files have to be converted to a free data format like `*.mzML` using the open-source software `ProteoWizard` which you can find [here](https://proteowizard.sourceforge.io/).\n", + "Follow the instructions in their documentation to convert your data files. \n", + "The data file for this example is located under `./paper raw data/exemplary results raw data` and since the `*.mzML` version of the file was too large for our GitHub storage, you can download it from [release v0.7.1](https://github.com/JuBiotech/peak-performance/releases/edit/v0.7.1) to which it was attached.\n", + "Alternatively, you can easily convert the original file using `ProteoWizard`. \n", + " \n", + "When you have completed this, you will find an example of how to open an `*.mzML` using the Python package `pyteomics` and obtaining an extracted ion chromatogram.\n", + "Since `pyteomics` is not usually needed to use `PeakPerformance`, it is not installed with the package.\n", + "Hence, install it in your environment following the instructions [in their documentation](https://pyteomics.readthedocs.io/en/latest/installation.html)." + ] + }, { "cell_type": "code", "execution_count": 1, "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "from pyteomics import mzml\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# load the exemplary mzML file\n", + "with mzml.MzML(r\"./paper raw data/connection to traditional data formats/A1_t1_1_Part2.mzML\") as reader:\n", + " # just to be able to see what is inside the file, pack everything into the data list\n", + " data = [spectrum for spectrum in reader]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# choose your experiment number\n", + "# in Sciex .wiff files, an experiment number pertains to one specific product ion scan\n", + "exp = \"experiment=14\"\n", + "\n", + "# filter the total data based on the experiment number to obtain the TOF (MS2) data for this product ion scan\n", + "filtered_data = [spectrum for spectrum in data if exp in spectrum[\"id\"] and spectrum[\"id\"][-1] == exp[-1] and spectrum[\"id\"][-2] == exp[-2]]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Define your TOF m/z range\n", + "mz_min = 43.9\n", + "mz_max = 44.1\n", + "\n", + "# Extract the TOF m/z range from the filtered data\n", + "extracted_data = []\n", + "\n", + "for spectrum in filtered_data:\n", + " mz_array = spectrum['m/z array']\n", + " intensity_array = spectrum['intensity array']\n", + " within_range = [(mz, intensity) for mz, intensity in zip(mz_array, intensity_array) if mz_min <= mz <= mz_max]\n", + " \n", + " if within_range:\n", + " extracted_data.append({'within_range': within_range, 'scan_time': spectrum['scanList']['scan'][0]['scan start time']})\n", + "\n", + "# Extract the time and intensity values\n", + "time_values = [spectrum[\"scan_time\"] for spectrum in extracted_data]\n", + "time_values = np.array(time_values)\n", + "intensity_values = [spectrum['within_range'] for spectrum in extracted_data]\n", + "intensity_sums = np.array([sum(intensity for _, intensity in spectrum) for spectrum in intensity_values])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(time_values, intensity_sums)\n", + "plt.xlabel(\"time / min\")\n", + "plt.ylabel(\"intensity / cps\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As stated in the chapter of the `PeakPerformance` documentation detailing its workflow, it is necessary to reduce the time window before using `PeakPerformance`. \n", + "A time frame of 3 - 5 times the peak width is a good rule of thumb." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(7.024266666667, 70.0), (7.094983333333, 106.0), (7.1657, 204.0), (7.236416666667, 108.0), (7.307116666667, 84.0), (7.377833333333, 96.0), (7.44855, 106.0), (7.519266666667, 156.0), (7.589983333333, 132.0), (7.6607, 72.0), (7.731416666667, 132.0), (7.802116666667, 144.0), (7.872833333333, 72.0), (7.94355, 130.0), (8.014266666667, 118.0), (8.084983333333, 156.0), (8.1557, 96.0), (8.226416666667, 142.0), (8.297116666667, 357.0), (8.367833333333, 731.0), (8.43855, 1655.0), (8.509266666667, 3735.0), (8.579983333333, 5745.0), (8.6507, 4673.0), (8.721416666667, 3007.0), (8.792116666667, 1055.0), (8.862833333333, 646.0), (8.93355, 291.0), (9.004266666667, 202.0), (9.074983333333, 216.0), (9.1457, 214.0), (9.216416666667, 178.0), (9.287116666667, 168.0), (9.357833333333, 96.0), (9.42855, 228.0), (9.499266666667, 108.0), (9.569983333333, 168.0), (9.6407, 154.0), (9.711416666667, 96.0), (9.782116666667, 108.0), (9.852833333333, 108.0), (9.92355, 130.0), (9.994266666667, 120.0), (10.064983333333, 116.0), (10.1357, 84.0), (10.206416666667, 120.0), (10.277116666667, 84.0), (10.347833333333, 84.0), (10.41855, 144.0), (10.489266666667, 214.0)]\n" + ] + } + ], + "source": [ + "data_dict = tuple(zip(time_values, intensity_sums))\n", + "data_selection = [t for t in data_dict if 7 <= t[0] <= 10.5]\n", + "print(data_selection)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'intensity / cps')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "time_selected = [x[0] for x in data_selection]\n", + "intensity_selected = [x[1] for x in data_selection]\n", + "plt.plot(time_selected, intensity_selected)\n", + "plt.xlabel(\"time / min\")\n", + "plt.ylabel(\"intensity / cps\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Last updated: 2024-10-25T18:54:22.467550+02:00\n", + "Last updated: 2024-11-08T19:56:01.696601+01:00\n", "\n" ] } @@ -199,7 +375,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pp_env", + "display_name": "nutpie_env", "language": "python", "name": "python3" }, @@ -213,7 +389,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.15" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/docs/source/notebooks/paper raw data/connection to traditional data formats/A1_t1_1_Part2.wiff b/docs/source/notebooks/paper raw data/connection to traditional data formats/A1_t1_1_Part2.wiff new file mode 100644 index 0000000..02d87c6 Binary files /dev/null and b/docs/source/notebooks/paper raw data/connection to traditional data formats/A1_t1_1_Part2.wiff differ diff --git a/docs/source/notebooks/paper raw data/connection to traditional data formats/A1_t1_1_Part2.wiff.scan b/docs/source/notebooks/paper raw data/connection to traditional data formats/A1_t1_1_Part2.wiff.scan new file mode 100644 index 0000000..8c55368 Binary files /dev/null and b/docs/source/notebooks/paper raw data/connection to traditional data formats/A1_t1_1_Part2.wiff.scan differ