add workflow figures to docs

docs/workflows/imagingworkflow/imaging-workflow.md

@@ -19,6 +19,8 @@ removed ([raw data processing](#raw-data-import-and-processing)) prior
 to [feature detection](#feature-detection). Afterwards, multiple [filters](#feature-filtering) are
 available to refine the data.
 
+![workflow-image](../../../reference_media/workflows/mzmine_workflows_3_imaging.png)
+
 ## Raw data import and processing
 
 Raw data is imported by a simple drag-and-drop gesture to the MS data files tab in the main window (
@@ -42,12 +44,19 @@ the [IMS expander](../../module_docs/lc-ims-ms_featdet/featdet_ims_expander/ims-
 subsequent to 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).
 
+## LC-Image aligner
+
+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)
+
 ## Feature filtering
 
 After feature detection, the ion image features can be filtered to refine the results, for example
 by the [Feature filter](../../module_docs/feature_filter/feature_filter.md) or
 the [Rows filter](../../module_docs/feature_list_row_filter/feature_list_rows_filter.md). Additional
 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. 
+When using the deisotoping modules, consider that there is no chromatographic separation.
 
 {{ git_page_authors }}

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

@@ -84,6 +84,10 @@ the [Ims expander](../../module_docs/lc-ims-ms_featdet/featdet_ims_expander/ims-
 The resulting ion mobility traces (**f**), have to be resolved in mobility dimension afterwards, to
 create individual IMS features.
 
+
+This figure shows the workflow in a more detailed manner, with additional optional steps.
+![workflow-image](../../../reference_media/workflows/mzmine_workflows_1_lc.png)
+
 ### LC-IMS-MS workflow
 
 The LC-IMS-MS workflow will directly

docs/workflows/lcmsworkflow/lcms-workflow.md

@@ -1,7 +1,7 @@
 # Untargeted LC-MS Workflow
 The workflow proposed herein is intended as a general pipeline for untargeted LC-MS (or LC−MS/MS) data preprocessing. The main goal is essentially to turn the highly-complex LC-MS raw data into a list of features, and corresponding signal intensity, detected across the analysed samples. Such feature lists can then be 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](workflow-image.png)
+![workflow-image](../../../reference_media/workflows/mzmine_workflows_1_lc.png)
 
 
 ## Raw data processing
@@ -16,19 +16,16 @@ This step produces a list (referred to as "mass list") of the m/z values found i
 ## Feature processing
 The goal of the "Feature processing" is to obtain a list of all the detected features (characterized by a RT and m/z value) from the raw LC-MS data.
 
-
 ### 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).
 
 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 LC 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 and as such is one of the pivotal steps in data preprocessing. For more detalis on the algorithm used and parameters settings, see the [Local minimum resolver](../../module_docs/featdet_resolver_local_minimum/local-minimum-resolver.md) module.
 
@@ -37,7 +34,6 @@ In order to remove redundant features, such as the ones generated due to the pre
 [^13^C isotope filter (isotope grouper)](../../module_docs/filter_isotope_filter/isotope_filter.md) removes ^13^C isotope features from the feature list.
 Use the isotope finder for more sensitive detection of possible isotope signals.  
 
-
 ### Isotope pattern finder 
 Isotope pattern finder searches for the isotope signals of selected chemical elements in the mass list of each feature.
 The isotope pattern detected by the **isotope finder** module has priority over the one detected by the **isotope filter (grouper)** module, if both are available.