Merge pull request #189 from ansgarkorf/updare-workflows

upgrade workflows

docs/workflows/gcmsworkflow/gcms-workflow.md

@@ -0,0 +1,54 @@
+# Untargeted GC-MS Workflow
+The workflow proposed herein is intended as a general pipeline for untargeted GC/EI-MS data preprocessing. GC/CI-MS can be processed analog to [LC-MS](../../workflows/lcmsworkflow/lcms-workflow.md).
+The main goal is essentially to turn the highly-complex GC-MS raw data into a list of features, and corresponding signal intensity, detected across the analysed samples.
+Such feature lists can then be annotated and/or exported for further downstream analysis (e.g., identification, search against spectral libraries, statistical analysis, etc.).
+A schematic representation of the workflow is shown below:
+
+![workflow-image](mzmine_workflows_2_gc.png)
+
+## Raw data processing
+The raw data processing consists of essentially two steps: [Data import](../../module_docs/io/data-import.md#ms-data) and [Mass detection](../../module_docs/featdet_mass_detection/mass-detection.md)
+
+### Raw data import
+Either open (e.g. mzML) and native vendor (e.g. Thermo, Bruker) data formats can be imported in mzmine. All the supported formats can be found [here](../../module_docs/io/data-import.md#ms-data).
+
+### Mass detection
+This step produces a list (referred to as "mass list") of the m/z values found in each MS scan across the LC run that exceed a user-defined threshold (i.e. noise level). For more details see the [Mass detection](../../module_docs/featdet_mass_detection/mass-detection.md) module.
+
+## Feature detection
+The goal of the "Feature detection" is to obtain a list of all the detected features (characterized by a RT and m/z value) from the raw GC-MS data.
+
+### Chromatogram building
+The first step in the Feature detection is to build the extracted ion chromatograms (EICs) for each detected m/z (see [Mass detection](../../module_docs/featdet_mass_detection/mass-detection.md)).
+For this, use the [Chromatogram builder](../../module_docs/lc-ms_featdet/featdet_adap_chromatogram_builder/adap-chromatogram-builder.md) module.
+
+The "detected" features in each file are listed in the so-called "feature lists", which are then further processed and aligned to connect corresponding features across all samples.
+
+### Smoothing in retention time dimension (optional)
+Depending on the GC peak shape (i.e. data noisiness), the user can perform smoothing in retention time dimension. 
+For more details see the [Mass detection](../../module_docs/featdet_mass_detection/mass-detection.md) and [Smoothing](../../module_docs/featdet_smoothing/smoothing.md) modules.
+
+### Feature resolving
+Feature resolving step enables separation of co-eluting and overlapping chromatography peaks. It is one of the pivotal steps in data preprocessing. For more details on the algorithm used and parameters settings, see the [Local minimum resolver](../../module_docs/featdet_resolver_local_minimum/local-minimum-resolver.md) module.
+
+### Spectral deconvolution
+When using a hard ionization technique such as electron ionization (EI), multiple m/z values belong to the same compound. These m/z fragments can be grouped together based on their chromatographic behaviour (peak shape correlation).
+The grouping results in a cleaned up feature list as well as high quality deconvoluted GC/EI-MS spectra, perfect for spectral library matching. Find more info on [spectral deconvolution](../../module_docs/featdet_spectraldeconvolutiongc/spectraldeconvolutiongc.md) here.
+
+## Feature alignment 
+Feature alignment enables alignment of corresponding features across multiple samples. 
+
+### GC aligner
+This module aligns detected features in different samples through a match score. The score is calculated based on the retention time and spectral similarity of each feature. 
+For more information, see the [GC aligner](../../module_docs/align_gcei/align_gc_ei.md) module. 
+
+## Gap-filling 
+Absence of features in some samples can either reflect the truth - the metabolite is absent in the given sample, or it can be due to data preprocessing.To account for  this, gap filling is applied as the next step.
+
+## Annotation, Filtering, Statistics and Export
+Depending on the downstream analyses, there are several options which are accessible through the **Feature list methods** menu. Annotate compounds using [spectral library search](../../module_docs/id_spectral_library_search/spectral_library_search.md),
+apply various filtering criteria, explore the results using the statistics dashboard, or export the results.
+
+## Page Contributors
+
+{{ git_page_authors }}

docs/workflows/imagingworkflow/imaging-workflow.md

@@ -1,6 +1,6 @@
 # Imaging Workflow
 
-This page describes the MZmine workflow for non targeted feature detection in imaging datasets. The
+This page describes the mzmine workflow for non targeted feature detection in imaging datasets. The
 workflow is designed for MS imaging data and tested with matrix-assisted laser
 desorption/ionisation (MALDI)-MS data.
 
@@ -26,13 +26,10 @@ available to refine the data.
 Raw data is imported by a simple drag-and-drop gesture to the MS data files tab in the main window (
 see [Raw data import](../../module_docs/io/data-import.md). After the data import, noise must be
 filtered from the raw data via
-the [Mass detection](../../module_docs/featdet_mass_detection/mass-detection.md) module. Please not
-that this heavily impacts the performance of the whole workflow, since imaging spectra are usually
+the [Mass detection](../../module_docs/featdet_mass_detection/mass-detection.md) module. Please note that this heavily impacts the performance of the whole workflow, since imaging spectra are usually
 richer in information than LC-MS spectra. If you experience performance (e.g., RAM issues), consider
-using a higher cutoff. IMS-MS imaging data sets require mass detection on
-the [Frame](../../terminology/ion-mobility-terminology.md#accumulations-mobility-scans-and-frames)
-and [mobility scan](../../terminology/ion-mobility-terminology.md#accumulations-mobility-scans-and-frames)
-level.
+using a higher cutoff. IMS-MS imaging data sets require mass detection on the [Frame](../../terminology/ion-mobility-terminology.md#accumulations-mobility-scans-and-frames)
+and [mobility scan](../../terminology/ion-mobility-terminology.md#accumulations-mobility-scans-and-frames) level.
 
 ## Feature detection
 
@@ -41,17 +38,30 @@ the [Image builder](../../module_docs/imaging_featdet/featdet_image_builder/imag
 
 When working with IMS-MS imaging datasets, the ion mobility dimension should be added by
 the [IMS expander](../../module_docs/lc-ims-ms_featdet/featdet_ims_expander/ims-expander.md)
-subsequent to the image detection. After expanding, the IMS dimension must
+after the image detection. After expanding, the IMS dimension must
 be [resolved](../../module_docs/featdet_resolver_local_minimum/local-minimum-resolver.md#resolving-the-ion-mobility-dimension).
 
+## Isotope pattern and annotation
+
+Isotope filtering, pattern finding, and feature annotation can be performed analog as described in the [LC-MS workflow](../lcmsworkflow/lcms-workflow.md). 
+!!! warning
+
+    Tools, such as spectral library matching require MS2 spectra. Make sure yoour imaging data was acquired with MS/MS experiments.
+
+## Co-localization
+
+Find co-located molecules using the [Image co-localization module](../../module_docs/group_imagecorrelate/image-colocalization.md).  
+
 ## LC-Image aligner
 
 <!-- markdown-link-check-disable -->
 If an LC-MS dataset was acquired for the imaging sample, the results can be aligned using
 the [LC-Image Aligner](../../module_docs/align_lc-image/align_lc-image.md). This allows integration
 of the two datasets and can be used for more confident identifications in imaging experiments. (
 see https://www.nature.com/articles/s41587-023-01690-2)
-<!-- markdown-link-check-ensable -->
+<!-- markdown-link-check-enable -->
+
+![workflow-image](mzmine_workflows_4_integrative_MALDI.png)
 
 ## Feature filtering
 
@@ -61,8 +71,5 @@ the [Rows filter](../../module_docs/feature_list_row_filter/feature_list_rows_fi
 filters are found in the  :material-menu-open: **Feature list methods → Feature filtering** menu.
 When using the deisotoping modules, consider that there is no chromatographic separation.
 
-## Additional tools
-
-Find co-located molecules using the [Image co-localization module](../../module_docs/group_imagecorrelate/image-colocalization.md).  
 
 {{ git_page_authors }}

docs/workflows/imsworkflow/ion-mobility-data-processing-workflow.md

@@ -20,7 +20,7 @@ provided [here](../../terminology/ion-mobility-terminology.md).
 
 ## Feature detection workflows
 
-Ion mobility data can be processed in MZmine 3 in two ways. The first few steps are different for
+Ion mobility data can be processed in mzmine in two ways. The first few steps are different for
 the two workflows (see below).
 
 1. [LC-IMS-MS workflow via **ADAP Chromatogram builder and IMS expander** **(

docs/workflows/lcmsworkflow/lcms-workflow.md

@@ -9,7 +9,7 @@ The magenta steps only refer to ion mobility data processing and are omitted her
 The raw data processing consists of essentially two steps: [Data import](../../module_docs/io/data-import.md#ms-data) and [Mass detection](../../module_docs/featdet_mass_detection/mass-detection.md)
 
 ### Raw data import
-Either open (e.g. mzML) and native vendor (e.g. Thermo, Bruker) data formats can be imported in MZmine 3. All the supported formats are listed here (LINK to Doc). For more details see the [Data import](../../module_docs/io/data-import.md#ms-data) module.
+Either open (e.g. mzML) and native vendor (e.g. Thermo, Bruker) data formats can be imported in mzmine. All the supported formats can be found [here](../../module_docs/io/data-import.md#ms-data).
 
 ### Mass detection
 This step produces a list (referred to as "mass list") of the m/z values found in each MS scan across the LC run that exceed a user-defined threshold (i.e. noise level). For more details see the [Mass detection](../../module_docs/featdet_mass_detection/mass-detection.md) module.
@@ -19,7 +19,7 @@ The goal of the "Feature processing" is to obtain a list of all the detected fea
 
 ### Chromatogram building
 The first step in the "Feature processing" is to build the so-called extracted ion chromatograms (EICs) for each detected mass (see "Mass detection").
-There are two modules in MZmine 3 that can fulfil this task: [ADAP chromatogram builder](../../module_docs/lc-ms_featdet/featdet_adap_chromatogram_builder/adap-chromatogram-builder.md) (widely used) and **Grid mass** (create docs).
+For this, use the [Chromatogram builder](../../module_docs/lc-ms_featdet/featdet_adap_chromatogram_builder/adap-chromatogram-builder.md) module.
 
 The "detected" features in each file are listed in the so-called "feature lists", which are then further processed and aligned to connect corresponding features across all samples.
 
@@ -57,7 +57,7 @@ Gap-filling can be performed on the aligned feature lists to cope with missing f
 ## Export
 Depending on the downstream analyses, there are several export options which are accessible through **Feature list methods** → **Export feature list**.
 
-For GNPS-Feature based molecular networking, see [GNPS-FBMN](../../module_docs/io/data-exchange-with-other-software.md#gnps-fbmniimn-export) or apply molecular networking directly in mzmine [molecular_networking.md](../../module_docs/group_spectral_net/molecular_networking.md)
+For GNPS-Feature based molecular networking, see [GNPS-FBMN](../../module_docs/io/data-exchange-with-other-software.md#gnps-fbmniimn-export) or apply Interactive Molecular Networking directly in mzmine [molecular_networking.md](../../module_docs/group_spectral_net/molecular_networking.md)
 
 ## References
 

mkdocs.yml

@@ -138,6 +138,7 @@ nav:
   - Workflows:
       - Untargeted LC-MS workflow: workflows/lcmsworkflow/lcms-workflow.md
       - Untargeted LC-IMS-MS workflow: workflows/imsworkflow/ion-mobility-data-processing-workflow.md
+      - Untargeted GC-MS workflow: workflows/gcmsworkflow/gcms-workflow.md
       - Untargeted MS imaging workflow: workflows/imagingworkflow/imaging-workflow.md
       - Batch processing: workflows/batch_processing/batch-processing.md
       - Library generation: workflows/librarygeneration/library_generation.md