# DBDIpy (Version 1.2.2)
DBDIpy is an open-source Python library for the curation and interpretation of dielectric barrier discharge ionisation mass spectrometric datasets.
# Introduction
Mass spectrometric data from direct injection analysis is hard to interpret as missing chromatographic separation complicates identification of fragments and adducts generated during the ionization process.
Here we present an *in-silico* approach to putatively identify multiple ion species arising from one analyte compound specially tailored for time-resolved datasets from plasma ionization techniques. These are rapidly gaining popularity in applications as breath analysis, process control or food research.
DBDIpy's core functionality relys on putative identification of in-source fragments (eg. [M-H<sub>2</sub>O+H]<sup>+</sup>) and in-source generated adducts (eg. [M+nO+H]<sup>+</sup>).
Custom adduct species can be defined by the user and passed to this open-search algorithm. The identification is performed in a three-step procedure (from V > 2.* on, in preparation):
- calculation of pointwise correlation identifies features with matching temporal intensity profiles through the experiment.
- (exact) mass differences are used to refine the nature of potential candidates.
- calculation of MS2 spectral similarity score by ...
DBDIpy further comes along with functions optimized for preprocessing of experimental data and visualization of identified adducts. The library is integrated into the matchms ecosystem to assimilate DBDIpy's functionalities into existing workflows.
For details, we invite you to read the [tutorial](#tutorial) or to try out the functions with our [demonstrational dataset](https://doi.org/10.5281/zenodo.7221089) or your own data!
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| `Supporting Data` | [](https://doi.org/10.5281/zenodo.7221089)|
| `Articel (open access)` | [](https://doi.org/10.1093/bioinformatics/btad088)|
Latest Changes (since V 1.2.1)
------------
minor fixes
Currently under development:
------------
- major implementation for V2: modification of the former two-step search algorithm towards refinement by MS2 spectral similarity scoring.
- addition of utility functions, e.g. calculation of consensus spectra.
- improved spectral alignment.
- introduction of an open search option to propose potential adducts / in-source fragments.
- runtime optimization.
User guide
============
## Installation
Prerequisites:
- Anaconda (recommended)
- Python 3.7, 3.8, 3.9 or 3.10
DBDIpy can be installed from PyPI
with:
```python
# we recommend installing DBDIpy in a new virtual environment
conda create --name DBDIpy python=3.9
conda activate DBDIpy
pip install DBDIpy
```
Known installation issues:
Apple M1 chip users might encounter issues with automatic installation of matchms.
Manual installation of the dependency as described on the libraries [official site](https://github.com/matchms/matchms) helps solving the issue.
## Tutorial
The following tutorial showcases an ordinary data analysis workflow by going through all functions of DBDIpy from loading data until visualization of correlation results. Therefore, we supplied a demo dataset which is publicly available [here](https://doi.org/10.5281/zenodo.7221089).
The demo data is from an experiments where wheat bread was roasted for 20 min and monitored by DBDI coupled to FT-ICR-MS. It consists of 500 randomly selected features.
<p align = "center">
Fig.1 - Schematic DBDIpy workflow for in-source adduct and fragment detection: imported MS1 data are aligned, imputed and parsed to combined correlation and mass difference analysis.
</p>
### 1. Importing MS data
DBDIpy core functions utilize 2D tabular data. Raw mass spectra containing *m/z*-intensity-pairs first will need to be aligned to a DataFrame of features. We build features by using the ``align_spectra()`` function. ``align_spectra()`` is the interface to load data from open file formats such as .mgf, .mzML or .mzXML files via ``matchms.importing``.
If your data already is formatted accordingly, you can skip this step.
```python
##loading libraries for the tutorial
import os
import feather
import numpy as np
import pandas as pd
import DBDIpy as dbdi
from matchms.importing import load_from_mgf
from matchms.exporting import save_as_mgf
##importing the downloaded .mgf files from demo data by matchms
demo_path = "" #enter path to demo dataset
demo_mgf = os.path.join(demo_path, "example_dataset.mgf")
spectrums = list(load_from_mgf(demo_mgf))
##align the listed Spectra
specs_aligned = dbdi.align_spectra(spec = spectrums, ppm_window = 2)
```
We first imported the demo MS1 data into a list of ``matchms.Spectra`` objects. At this place you can run your personal ``matchms`` preprocessing pipelines or manually apply filters like noise reduction.
By aplication of ``align_spectra()``, we transformed the list of spectra objects to a two-dimensional ``pandas.DataFrame``. Now you have a column for each mass spectrometric scan and features are aligned to rows. The first column shows the mean *m/z* of a feature.
If a signal was not detected in a scan, the according field will be set to an instance of ``np.nan``.
Remember to set the ``ppm_window`` parameter according to the resolution of you mass spectrometric system.
We now can inspect the aligned data, e.g. by running:
```python
specs_aligned.describe()
specs_aligned.info()
```
Several metabolomics data processing steps can be applied here if not already performed in ``matchms``. These might include application of noise-cutoffs, feature selection based on missing values, normalization or many others.
``specs_aligned.isnull().values.any()`` will give us an idea if there are missing values in the data. These cannot be handled by successive DBDIpy functions and most machine learning algorithms, so we need to impute them.
### 2. Imputation of missing values
``impute_intensities()`` will assure that after imputation we will have a set of uniform length extracted ion chromatograms (XIC) in our DataFrame. This is an important prerequisite for pointwise correlation calculation and for many tools handling time series data.
Missing values in our feature table will be imputed by a two-stage imputation algorithm.
- First, missing values within the detected signal region are interpolated in between.
- Second, a noisy baseline is generated for all XIC to be of uniform length which the length of the longest XIC in the dataset.
The function lets the user decide which imputation method to use. Default mode is ``linear``, however several others are available.
```python
feature_mz = specs_aligned["mean"]
specs_aligned = specs_aligned.drop("mean", axis = 1)
##impute the dataset
specs_imputed = dbdi.impute_intensities(df = specs_aligned, method = "linear")
```
Now ``specs_imputed`` does not contain any missing values anymore and is ready for adduct and in-source fragment detection.
```python
##check if NaN are present in DataFrame
specs_imputed.isnull().values.any()
Out[]: False
```
### 3. Detection of adducts and in-source fragments: MS1 data only
Based on the ``specs_imputed``, we compute pointwise correlation of XIC traces to identify in-source adducts or in-source fragments generated during the plasma ionization process. The identification is performed in a two-step procedure:
- First, calculation of pointwise intensity correlation identifies feature groups with matching temporal intensity profiles through the experiment.
- Second, (exact) mass differences are used to refine the nature of potential candidates.
By default, ``identify_adducts()`` searches for [M-H<sub>2</sub>O+H]<sup>+</sup>, [M+1O+H]<sup>+</sup> and [M+2O+H]<sup>+</sup>.
For demonstrational purposes we also want to search for [M+3O+H]<sup>+</sup> in this example.
Note that ``identify_adducts()`` has a variety of other parameters which allow high user customization. See the help file of the functions for details.
```python
##prepare a DataFrame to search for O3-adducts
adduct_rule = pd.DataFrame({'deltamz': [47.984744],'motive': ["O3"]})
##identify in-source fragments and adducts
search_res = dbdi.identify_adducts(df = specs_imputed, masses = feature_mz, custom_adducts = adduct_rule,
method = "pearson", threshold = 0.9, mass_error = 2)
```
The function will return a dictionary holding one DataFrame for each adduct type that was defined. A typical output looks like the following:
```python
##output search results
search_res
Out[24]:
{'O': base_mz base_index match_mz match_index mzdiff corr
19 215.11789 24 231.11280 ID40 15.99491 0.963228
310 224.10699 33 240.10191 ID51 15.99492 0.939139
605 231.11280 39 215.11789 ID25 15.99491 0.963228
1413 240.10191 50 224.10699 ID34 15.99492 0.939139
1668 244.13321 55 260.12812 ID67 15.99491 0.976541,
...
'O2': base_mz base_index match_mz match_index mzdiff corr
1437 240.10191 50 272.09174 ID77 31.98983 0.988866
1677 244.13321 55 276.12304 ID84 31.98983 0.972251
2362 260.12812 66 292.11795 ID100 31.98983 0.964096
3024 272.09174 76 240.10191 ID51 31.98983 0.988866
3354 276.12304 83 244.13321 ID56 31.98983 0.972251,
...
'H2O': base_mz base_index match_mz match_index mzdiff corr
621 231.11280 39 249.12337 ID60 18.01057 0.933640
1883 249.12337 59 231.11280 ID40 18.01057 0.933640
3263 275.13902 82 293.14958 ID102 18.01056 0.948774
4775 293.14958 101 275.13902 ID83 18.01056 0.948774
5573 300.08665 112 318.09722 ID140 18.01057 0.905907
...
'O3': base_mz base_index match_mz match_index mzdiff corr
320 224.10699 33 272.09174 ID77 47.98475 0.924362
1688 244.13321 55 292.11795 ID100 47.98474 0.964896
3013 272.09174 76 224.10699 ID34 47.98475 0.924362
4631 292.11795 99 244.13321 ID56 47.98474 0.964896
13597 438.28502 308 486.26976 ID356 47.98474 0.935359
...
````
The ``base_mz`` and ``base_index`` column give us the index of the features which correlates with a correlation partner specified in ``match_mz`` and ``match_index``.
The mass difference between both is given for validation purpose and the correlation coefficient between both features is listed.
Now we can for example search series of Oxygen adducts of a single analyte:
```python
##search for oxygenation series
two_adducts = np.intersect1d(search_res["O"]["base_index"], np.intersect1d(search_res["O"]["base_index"],search_res["O2"]["base_index"]))
three_adducts = np.intersect1d(two_adducts , search_res["O3"]["base_index"])
three_adducts
Out[33]: array([55, 99], dtype=int64)
```
This tells us that features 55 and 99 both putatively have [M+1-3O+H]<sup>+</sup> adduct ions with correlations of r > 0.9 in our dataset.
Let's visualize this finding!
### 4. Detection of adducts and in-source fragments: refined scoring by MS2 similarity matching
...
### 5. Visualization of correlation results
Now that we putatively identified some related ions of a single analyte, we want to check their temporal response during the baking experiment.
Therefore, we can use the ``plot_adducts()`` function to conveniently draw XICs.
The demo dataset even comes along with some annotated metadata for our features, so we can decorate the plot and check our previous results!
```python
##load annotation metadta
demo_path = "" #enter path to demo dataset
demo_meta = os.path.join(demo_path, "example_metadata.feather")
annotation_metadata = feather.read_dataframe(demo_meta)
##plot the XIC
dbdi.plot_adducts(IDs = [55,66,83,99], df = specs_imputed, metadata = annotation_metadata, transform = True)
```
<p align="center">
<img width="430" height="288" src="https://user-images.githubusercontent.com/81673643/200293545-6b58e887-09d1-4326-8d3b-bc52ea93231e.png">
</p>
<p align = "center">
Fig.2 - XIC plots for features 55, 66, 83 and 99 which have highly correlated intensity profile through the baking experiment.
</p>
We see that the XIC traces show a similar intensity profile through the experiment. The plot further tells us the correlation coefficients of the identified adducts.
From the metadata we can see that the detected mass signals were previously annotated as C<sub>15</sub>H<sub>17</sub>O<sub>2-5</sub>N which tells us that we most probably found an Oxgen-adduct series.
If MS2 data was recorded during the experiment we now can go on further and compare fragment spectra to reassure the identifications. You might find [ms2deepscore](https://github.com/matchms/ms2deepscore) to be a usefull library to do so in an automated way.
### 6. Exporting tabular MS data to match.Spectra objects
If you want to export your (imputed) tabular data to ``matchms.Spectra`` objects, you can do so by calling the ``export_to_spectra()`` function. We just need to re-add a column containing *m/z* values of the features.
This gives you access to the matchms suite and enables you to safe your mass spectrometric data to open file formats.
Hint: you can manually add some metadata after construction of the list of spectra.
```python
##export tabular MS data back to list of spectrums.
specs_imputed["mean"] = feature_mz
speclist = dbdi.export_to_spectra(df = specs_imputed, mzcol = 88)
##write processed data to .mgf file
save_as_mgf(speclist, "DBDIpy_processed_spectra.mgf")
```
We hope you liked this quick introduction into DBDIpy and will find its functions helpful and inspiring on your way to work through data from direct infusion mass spectrometry. Of course, the functions are applicable to all sort of ionisation mechanisms and you can modify the set of adducts to search in accordance to your source.
If you have open questions left about functions, their parameter or the algorithms we invite you to read through the built-in help files. If this does not clarify the issues, please do not hesitate to get in touch with us!
Contact
============
leopold.weidner@tum.de
Acknowledgements
============
We thank Erwin Kupczyk and [Nicolas Schmidt](https://github.com/nibosco) for testing the software and their feedback during development.
Raw data
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"description": "# DBDIpy (Version 1.2.2)\nDBDIpy is an open-source Python library for the curation and interpretation of dielectric barrier discharge ionisation mass spectrometric datasets.\n\n# Introduction\n\nMass spectrometric data from direct injection analysis is hard to interpret as missing chromatographic separation complicates identification of fragments and adducts generated during the ionization process.\n\nHere we present an *in-silico* approach to putatively identify multiple ion species arising from one analyte compound specially tailored for time-resolved datasets from plasma ionization techniques. These are rapidly gaining popularity in applications as breath analysis, process control or food research. \n\nDBDIpy's core functionality relys on putative identification of in-source fragments (eg. [M-H<sub>2</sub>O+H]<sup>+</sup>) and in-source generated adducts (eg. [M+nO+H]<sup>+</sup>). \nCustom adduct species can be defined by the user and passed to this open-search algorithm. The identification is performed in a three-step procedure (from V > 2.* on, in preparation): \n- calculation of pointwise correlation identifies features with matching temporal intensity profiles through the experiment.\n- (exact) mass differences are used to refine the nature of potential candidates. \n- calculation of MS2 spectral similarity score by ...\n \n\nDBDIpy further comes along with functions optimized for preprocessing of experimental data and visualization of identified adducts. The library is integrated into the matchms ecosystem to assimilate DBDIpy's functionalities into existing workflows.\n\nFor details, we invite you to read the [tutorial](#tutorial) or to try out the functions with our [demonstrational dataset](https://doi.org/10.5281/zenodo.7221089) or your own data!\n\n\n| | Badges |\n|:------------- |:-----------------------------------------------------------------------------------|\n| `License` | []([https://pypi.com/project/pip/](https://opensource.org/licenses/MIT/))|\n| `Version` | [](https://pypi.org/project/DBDIpy/)|\n| `Downloads` | [](https://pepy.tech/project/dbdipy)|\n| `Status` | [](https://GitHub.com/leopold-weidner/DBDIpy/graphs/commit-activity)|\n| `Updated` | |\n| `Language` | [](https://www.python.org/)|\n| `Version` | [](https://www.python.org/)|\n| `Operating Systems` |  |\n| `Documentation` | [](https://github.com/leopold-weidner/DBDIpy)|\n| `Supporting Data` | [](https://doi.org/10.5281/zenodo.7221089)|\n| `Articel (open access)` | [](https://doi.org/10.1093/bioinformatics/btad088)|\n\n\nLatest Changes (since V 1.2.1)\n------------\nminor fixes\n\n\nCurrently under development:\n------------\n- major implementation for V2: modification of the former two-step search algorithm towards refinement by MS2 spectral similarity scoring.\n- addition of utility functions, e.g. calculation of consensus spectra.\n- improved spectral alignment.\n- introduction of an open search option to propose potential adducts / in-source fragments.\n- runtime optimization.\n\n\n\nUser guide\n============\n\n## Installation\n\nPrerequisites: \n\n- Anaconda (recommended)\n- Python 3.7, 3.8, 3.9 or 3.10\n\nDBDIpy can be installed from PyPI \nwith:\n\n```python\n# we recommend installing DBDIpy in a new virtual environment\nconda create --name DBDIpy python=3.9\nconda activate DBDIpy\npip install DBDIpy\n```\n\nKnown installation issues:\nApple M1 chip users might encounter issues with automatic installation of matchms. \nManual installation of the dependency as described on the libraries [official site](https://github.com/matchms/matchms) helps solving the issue. \n \n\n## Tutorial\n\nThe following tutorial showcases an ordinary data analysis workflow by going through all functions of DBDIpy from loading data until visualization of correlation results. Therefore, we supplied a demo dataset which is publicly available [here](https://doi.org/10.5281/zenodo.7221089).\n\nThe demo data is from an experiments where wheat bread was roasted for 20 min and monitored by DBDI coupled to FT-ICR-MS. It consists of 500 randomly selected features. \n\n\n<p align = \"center\">\nFig.1 - Schematic DBDIpy workflow for in-source adduct and fragment detection: imported MS1 data are aligned, imputed and parsed to combined correlation and mass difference analysis.\n</p>\n\n### 1. Importing MS data\nDBDIpy core functions utilize 2D tabular data. Raw mass spectra containing *m/z*-intensity-pairs first will need to be aligned to a DataFrame of features. We build features by using the ``align_spectra()`` function. ``align_spectra()`` is the interface to load data from open file formats such as .mgf, .mzML or .mzXML files via ``matchms.importing``.\n\nIf your data already is formatted accordingly, you can skip this step.\n\n```python\n##loading libraries for the tutorial\nimport os\nimport feather\nimport numpy as np\nimport pandas as pd\nimport DBDIpy as dbdi\nfrom matchms.importing import load_from_mgf\nfrom matchms.exporting import save_as_mgf\n\n##importing the downloaded .mgf files from demo data by matchms\ndemo_path = \"\" #enter path to demo dataset\ndemo_mgf = os.path.join(demo_path, \"example_dataset.mgf\")\nspectrums = list(load_from_mgf(demo_mgf))\n\n##align the listed Spectra\nspecs_aligned = dbdi.align_spectra(spec = spectrums, ppm_window = 2) \n```\nWe first imported the demo MS1 data into a list of ``matchms.Spectra`` objects. At this place you can run your personal ``matchms`` preprocessing pipelines or manually apply filters like noise reduction.\nBy aplication of ``align_spectra()``, we transformed the list of spectra objects to a two-dimensional ``pandas.DataFrame``. Now you have a column for each mass spectrometric scan and features are aligned to rows. The first column shows the mean *m/z* of a feature.\nIf a signal was not detected in a scan, the according field will be set to an instance of ``np.nan``.\n\nRemember to set the ``ppm_window`` parameter according to the resolution of you mass spectrometric system. \n\nWe now can inspect the aligned data, e.g. by running: \n\n```python\nspecs_aligned.describe()\nspecs_aligned.info()\n```\n\nSeveral metabolomics data processing steps can be applied here if not already performed in ``matchms``. These might include application of noise-cutoffs, feature selection based on missing values, normalization or many others.\n\n``specs_aligned.isnull().values.any()`` will give us an idea if there are missing values in the data. These cannot be handled by successive DBDIpy functions and most machine learning algorithms, so we need to impute them.\n\n### 2. Imputation of missing values\n\n``impute_intensities()`` will assure that after imputation we will have a set of uniform length extracted ion chromatograms (XIC) in our DataFrame. This is an important prerequisite for pointwise correlation calculation and for many tools handling time series data. \n\nMissing values in our feature table will be imputed by a two-stage imputation algorithm. \n- First, missing values within the detected signal region are interpolated in between.\n- Second, a noisy baseline is generated for all XIC to be of uniform length which the length of the longest XIC in the dataset.\n\nThe function lets the user decide which imputation method to use. Default mode is ``linear``, however several others are available. \n\n```python\nfeature_mz = specs_aligned[\"mean\"]\nspecs_aligned = specs_aligned.drop(\"mean\", axis = 1)\n\n##impute the dataset\nspecs_imputed = dbdi.impute_intensities(df = specs_aligned, method = \"linear\")\n```\n\nNow ``specs_imputed`` does not contain any missing values anymore and is ready for adduct and in-source fragment detection.\n\n```python\n##check if NaN are present in DataFrame\nspecs_imputed.isnull().values.any()\nOut[]: False\n```\n\n\n### 3. Detection of adducts and in-source fragments: MS1 data only\n\nBased on the ``specs_imputed``, we compute pointwise correlation of XIC traces to identify in-source adducts or in-source fragments generated during the plasma ionization process. The identification is performed in a two-step procedure: \n- First, calculation of pointwise intensity correlation identifies feature groups with matching temporal intensity profiles through the experiment.\n- Second, (exact) mass differences are used to refine the nature of potential candidates. \n\nBy default, ``identify_adducts()`` searches for [M-H<sub>2</sub>O+H]<sup>+</sup>, [M+1O+H]<sup>+</sup> and [M+2O+H]<sup>+</sup>. \nFor demonstrational purposes we also want to search for [M+3O+H]<sup>+</sup> in this example.\nNote that ``identify_adducts()`` has a variety of other parameters which allow high user customization. See the help file of the functions for details.\n\n```python\n##prepare a DataFrame to search for O3-adducts\nadduct_rule = pd.DataFrame({'deltamz': [47.984744],'motive': [\"O3\"]})\n\n##identify in-source fragments and adducts\nsearch_res = dbdi.identify_adducts(df = specs_imputed, masses = feature_mz, custom_adducts = adduct_rule,\n method = \"pearson\", threshold = 0.9, mass_error = 2)\n```\n\nThe function will return a dictionary holding one DataFrame for each adduct type that was defined. A typical output looks like the following:\n\n```python\n##output search results\nsearch_res\nOut[24]: \n{'O': base_mz base_index match_mz match_index mzdiff corr\n 19 215.11789 24 231.11280 ID40 15.99491 0.963228\n 310 224.10699 33 240.10191 ID51 15.99492 0.939139\n 605 231.11280 39 215.11789 ID25 15.99491 0.963228\n 1413 240.10191 50 224.10699 ID34 15.99492 0.939139\n 1668 244.13321 55 260.12812 ID67 15.99491 0.976541,\n ...\n 'O2': base_mz base_index match_mz match_index mzdiff corr\n 1437 240.10191 50 272.09174 ID77 31.98983 0.988866\n 1677 244.13321 55 276.12304 ID84 31.98983 0.972251\n 2362 260.12812 66 292.11795 ID100 31.98983 0.964096\n 3024 272.09174 76 240.10191 ID51 31.98983 0.988866\n 3354 276.12304 83 244.13321 ID56 31.98983 0.972251,\n ...\n 'H2O': base_mz base_index match_mz match_index mzdiff corr\n 621 231.11280 39 249.12337 ID60 18.01057 0.933640\n 1883 249.12337 59 231.11280 ID40 18.01057 0.933640\n 3263 275.13902 82 293.14958 ID102 18.01056 0.948774\n 4775 293.14958 101 275.13902 ID83 18.01056 0.948774\n 5573 300.08665 112 318.09722 ID140 18.01057 0.905907\n ...\n 'O3': base_mz base_index match_mz match_index mzdiff corr\n 320 224.10699 33 272.09174 ID77 47.98475 0.924362\n 1688 244.13321 55 292.11795 ID100 47.98474 0.964896\n 3013 272.09174 76 224.10699 ID34 47.98475 0.924362\n 4631 292.11795 99 244.13321 ID56 47.98474 0.964896\n 13597 438.28502 308 486.26976 ID356 47.98474 0.935359\n ...\n````\nThe ``base_mz`` and ``base_index`` column give us the index of the features which correlates with a correlation partner specified in ``match_mz`` and ``match_index``.\nThe mass difference between both is given for validation purpose and the correlation coefficient between both features is listed. \n\nNow we can for example search series of Oxygen adducts of a single analyte:\n\n```python\n##search for oxygenation series\ntwo_adducts = np.intersect1d(search_res[\"O\"][\"base_index\"], np.intersect1d(search_res[\"O\"][\"base_index\"],search_res[\"O2\"][\"base_index\"]))\nthree_adducts = np.intersect1d(two_adducts , search_res[\"O3\"][\"base_index\"])\n\nthree_adducts\nOut[33]: array([55, 99], dtype=int64)\n```\n\nThis tells us that features 55 and 99 both putatively have [M+1-3O+H]<sup>+</sup> adduct ions with correlations of r > 0.9 in our dataset.\nLet's visualize this finding!\n\n\n### 4. Detection of adducts and in-source fragments: refined scoring by MS2 similarity matching\n...\n\n\n### 5. Visualization of correlation results\n\nNow that we putatively identified some related ions of a single analyte, we want to check their temporal response during the baking experiment.\nTherefore, we can use the ``plot_adducts()`` function to conveniently draw XICs.\nThe demo dataset even comes along with some annotated metadata for our features, so we can decorate the plot and check our previous results!\n\n```python\n##load annotation metadta\ndemo_path = \"\" #enter path to demo dataset\ndemo_meta = os.path.join(demo_path, \"example_metadata.feather\")\nannotation_metadata = feather.read_dataframe(demo_meta)\n\n##plot the XIC\ndbdi.plot_adducts(IDs = [55,66,83,99], df = specs_imputed, metadata = annotation_metadata, transform = True)\n```\n\n\n<p align=\"center\">\n <img width=\"430\" height=\"288\" src=\"https://user-images.githubusercontent.com/81673643/200293545-6b58e887-09d1-4326-8d3b-bc52ea93231e.png\">\n</p>\n<p align = \"center\">\nFig.2 - XIC plots for features 55, 66, 83 and 99 which have highly correlated intensity profile through the baking experiment.\n</p>\n\nWe see that the XIC traces show a similar intensity profile through the experiment. The plot further tells us the correlation coefficients of the identified adducts.\nFrom the metadata we can see that the detected mass signals were previously annotated as C<sub>15</sub>H<sub>17</sub>O<sub>2-5</sub>N which tells us that we most probably found an Oxgen-adduct series. \n\nIf MS2 data was recorded during the experiment we now can go on further and compare fragment spectra to reassure the identifications. You might find [ms2deepscore](https://github.com/matchms/ms2deepscore) to be a usefull library to do so in an automated way. \n\n### 6. Exporting tabular MS data to match.Spectra objects\n\nIf you want to export your (imputed) tabular data to ``matchms.Spectra`` objects, you can do so by calling the ``export_to_spectra()`` function. We just need to re-add a column containing *m/z* values of the features.\nThis gives you access to the matchms suite and enables you to safe your mass spectrometric data to open file formats.\nHint: you can manually add some metadata after construction of the list of spectra. \n\n```python\n##export tabular MS data back to list of spectrums.\nspecs_imputed[\"mean\"] = feature_mz\n\nspeclist = dbdi.export_to_spectra(df = specs_imputed, mzcol = 88)\n\n##write processed data to .mgf file\nsave_as_mgf(speclist, \"DBDIpy_processed_spectra.mgf\")\n```\n\nWe hope you liked this quick introduction into DBDIpy and will find its functions helpful and inspiring on your way to work through data from direct infusion mass spectrometry. Of course, the functions are applicable to all sort of ionisation mechanisms and you can modify the set of adducts to search in accordance to your source. \n\nIf you have open questions left about functions, their parameter or the algorithms we invite you to read through the built-in help files. If this does not clarify the issues, please do not hesitate to get in touch with us!\n\nContact\n============\nleopold.weidner@tum.de\n\n\nAcknowledgements\n============\nWe thank Erwin Kupczyk and [Nicolas Schmidt](https://github.com/nibosco) for testing the software and their feedback during development.\n\n\n",
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