Fixed order of navigation in interface details. Added navigation with…

…in each page, moved video tutorial to the top and added link to showcases. Only pushing markdown files. Closes #359 (#382)

Co-authored-by: PaulJonasJost <jost.pauljonas@gmail.com>

docs/_data/navigation.yml

@@ -4,22 +4,24 @@ main:
   - title: "Interface Details"
     url: "/interface-details/"
     children:
+      - title: "Data Input"
+        url: "/interface-details/01-required-data-input/"
       - title: "Data selection"
-        url: "/interface-details/selection/"
+        url: "/interface-details/02-selection/"
       - title: "Pre-processing"
-        url: "/interface-details/pre-processing/"
+        url: "/interface-details/03-pre-processing/"
       - title: "Sample Correlation"
-        url: "/interface-details/sample-correlation/"
+        url: "/interface-details/04-sample-correlation/"
       - title: "Significance Analysis"
-        url: "/interface-details/significance-analysis/"
+        url: "/interface-details/05-significance-analysis/"
       - title: "PCA"
-        url: "/interface-details/pca/"
+        url: "/interface-details/06-pca/"
       - title: "Heatmap"
-        url: "/interface-details/heatmap/"
+        url: "/interface-details/07-heatmap/"
       - title: "Single Gene Visualisations"
-        url: "/interface-details/single-gene-visualisations/"
+        url: "/interface-details/08-single-gene-visualisations/"
       - title: "Enrichment Analysis"
-        url: "/interface-details/enrichment-analysis/"
+        url: "/interface-details/09-enrichment-analysis/"
   - title: "Local installation"
     url: "/installation/"
   - title: "Using provided R Code and Data"

docs/index.md

@@ -18,7 +18,7 @@ Have fun exploring! 🎉
 
 ### Quick Links to Get You Started:
 
--   📚 Want to know the required data and format? Visit [Interface Details](interface-details/required-data-input.md).
+-   📚 Want to know the required data and format? Visit [Interface Details](interface-details/01-required-data-input.md).
 -   🔧 Need to run the app locally? Check out the [Installation Guide](installation.md).
 -   💡 Looking for customization inspiration? Explore our [Customization Examples](code-and-data/examples.md).
--   📊 Want to know more about the significance analysis? Visit [Significance Analysis](interface-details/significance-analysis.md)
+-   📊 Want to know more about the significance analysis? Visit [Significance Analysis](interface-details/05-significance-analysis.md)

docs/interface-details.md

@@ -50,6 +50,3 @@ To get more information on the tabs, navigate to the respective documentation us
 5. **Get Underlying R Code and Data:** Upon button click, the R script and respective data to generate the shown plot will be available for download. The script includes data selection, preprocessing, and analysis. For more details, refer to [Code and Data](code-and-data.md).
 
 6. **Notes:** At the bottom of each tab, there is a Notes field where you can enter text that will be saved within the report. You can use [markdown syntax](https://www.markdownguide.org/cheat-sheet/) here.
-
-
-

docs/interface-details/required-data-input.md → docs/interface-details/01-required-data-input.md

@@ -5,7 +5,6 @@ parent: Interface Details
 nav_order: 1
 ---
 
-
 # The Very Start 🚀
 To start your own analysis, you need to pass the first hurdle - Uploading your data. (Note: If you want to check out what the app is capable of, you might want to use the Test data. For detailed instructions, check out [Interface Details](interface-details/selection.md)).
 
@@ -29,12 +28,12 @@ When you have only your omics data table (referred to as the Data Matrix), ensur
 ### Creating a Sample Table 🔧
 Having the data matrix, you need to create a fitting Sample table.🖇️ A fitting sample table has the column names of the data matrix in its row names. Each additional column in the sample table carries additional information about the samples (referred to as sample annotation). Examples are condition, height, or phenotype. You are not limited to the number of columns, hence to the number of information you can supply to your samples. 
 
-Within cOmicsART, this information is used, for example, to perform batch correction, data selection, or to visualize respective groups of samples. If this sounds confusing, don't worry - it becomes clearer throughout usage and this documentation. Make sure to check out the [data selection](selection.md) to see how your sample data is utilized in the data selection process.
+Within cOmicsART, this information is used, for example, to perform batch correction, data selection, or to visualize respective groups of samples. If this sounds confusing, don't worry - it becomes clearer throughout usage and this documentation. Make sure to check out the [data selection](02-selection.md) to see how your sample data is utilized in the data selection process.
 
 ### Creating an Annotation Table 🔧
 Having the data matrix, you need to create a fitting annotation table.🖇️ A fitting annotation table has the row names of the data matrix in its row names. Each additional column in the annotation table carries additional information about the entities (referred to as row annotation). Examples are gene type, lipid class, or other IDs. You are not limited to the number of columns, hence to the number of information you can supply to your entities.
 
-Within cOmicsART, this information can be used within the data selection or to be visualized instead of the row index. For some examples of the data selection, go to [data selection](selection.md).
+Within cOmicsART, this information can be used within the data selection or to be visualized instead of the row index. For some examples of the data selection, go to [data selection](02-selection.md).
 
 ### All in Short 🎁
 The files must be in the following format:<br>
@@ -67,8 +66,24 @@ The Metadatasheet enables biomedical researchers to organize their data and meta
 ### Creating an Annotation Table 🔧
 Having the data matrix, you need to create a fitting annotation table. A fitting annotation table has the row names of the data matrix in its row names. Each additional column in the annotation table carries additional information about the entities (referred to as row annotation). Examples are gene type, lipid class, or other IDs. You are not limited to the number of columns, hence to the number of information you can supply to your entities.
 
-Within cOmicsArt, this information can be used within the data selection or to be visualized instead of the row index. For some examples of the data selection, go to [data selection](selection.md).
+Within cOmicsArt, this information can be used within the data selection or to be visualized instead of the row index. For some examples of the data selection, go to [data selection](02-selection.md).
 
 ## Starting with an RDS Object 
-Once you have uploaded your three data tables to the app, you can click on 'save as RDS Object'. This is an R-specific object that saves all three files in one object. Hence, for any new analysis, you can just upload this single file and not multiple to save yourself some time. Note that the RDS object is in general not a standardardized format, which means that you cannot upload any RDS object. 
+Once you have uploaded your three data tables to the app, you can click on 'save as RDS Object'. This is an R-specific object that saves all three files in one object. Hence, for any new analysis, you can just upload this single file and not multiple to save yourself some time. Note that the RDS object is in general not a standardardized format, which means that you cannot upload any RDS object.
+
+---
+
+## Further Navigation
 
+Do you want to...
+
+- Understand how to select and filter your data? → Go to [Data selection](02-selection.md)
+- Discover the pre-processing options available? → Go to [Pre-processing](03-pre-processing.md)
+- Explore how to correlate your samples? → Go to [Sample Correlation](04-sample-correlation.md)
+- Perform significance analysis on your data? → Go to [Significance Analysis](05-significance-analysis.md)
+- Conduct Principal Component Analysis? → Go to [PCA](06-pca.md)
+- Visualize your data with heatmaps? → Go to [Heatmap](07-heatmap.md)
+- Visualize individual genes? → Go to [Single Gene Visualisations](08-single-gene-visualisations.md)
+- Perform enrichment analysis on your data? → Go to [Enrichment Analysis](09-enrichment-analysis.md)
+
+---

docs/interface-details/selection.md → docs/interface-details/02-selection.md

@@ -2,7 +2,7 @@
 title: "Data Selection"
 layout: default
 parent: Interface Details
-nav_order: 1
+nav_order: 2
 ---
 
 # Data Selection
@@ -12,7 +12,7 @@ The Data Selection tab is the initial step to upload and manage your data in cOm
 ## Side Panel 📚
 
 The side panel offers several tabs for different data input methods. The data Upload 
-and possible structure are detailed in the [Data Input](required-data-input.md). 
+and possible structure are detailed in the [Data Input](01-required-data-input.md). 
 Nonetheless, here is a short overview:
 
 ### File Input 🛠️
@@ -95,8 +95,25 @@ The main panel provides options for detailed data selection:
 ### Start the Journey 🚀
 
 - **Start the Journey**: Click this button to proceed with the analysis. This will 
-  automatically bring you to the [Pre-processing tab](pre-processing.md).
+  automatically bring you to the [Pre-processing tab](03-pre-processing.md).
 
 ### Other Notes 📌
 
 - **Question Marks**: The displayed question marks provide quick and immediate help. They offer guidance and additional information about the options available.
+
+---
+
+## Further Navigation
+
+Do you want to...
+
+- Learn how to upload your data? → Go to [Data Input](01-required-data-input.md)
+- Discover the pre-processing options available? → Go to [Pre-processing](03-pre-processing.md)
+- Explore how to correlate your samples? → Go to [Sample Correlation](04-sample-correlation.md)
+- Perform significance analysis on your data? → Go to [Significance Analysis](05-significance-analysis.md)
+- Conduct Principal Component Analysis? → Go to [PCA](06-pca.md)
+- Visualize your data with heatmaps? → Go to [Heatmap](07-heatmap.md)
+- Visualize individual genes? → Go to [Single Gene Visualisations](08-single-gene-visualisations.md)
+- Perform enrichment analysis on your data? → Go to [Enrichment Analysis](09-enrichment-analysis.md)
+
+---

docs/interface-details/pre-processing.md → docs/interface-details/03-pre-processing.md

@@ -2,7 +2,7 @@
 title: "Pre-processing"
 layout: default
 parent: "Interface Details"
-nav_order: 4
+nav_order: 3
 ---
 
 # Pre-processing
@@ -18,7 +18,11 @@ In the side panel, you have the following options:
   - Options: none, filterOnly, vst_DESeq, simpleCenterScaling, Scaling_0_1, log10, log2, pareto_scaling, ln
   - vst_DESeq also requires the selection of a design formula.
 
--   **Select Batch Effect Column**: Choose a batch effect if applicable. Possible choices are taken from the sample annotation columns.
+-   **Select Batch Effect Column**: Choose a batch effect if applicable. Possible 
+    choices are taken from the sample annotation columns. **Important**: This step is 
+    optional, as sometimes no batches need to be accounted for. Additionally, in the 
+    very first run, batch effects might not be know, thus using no batch effects and 
+    looking at correlations and PCA plots can help to identify them.
 
 -   **Get Pre-Processing**: Clicking this button will apply the selected pre-processing procedure to the data.
 
@@ -40,3 +44,20 @@ The main panel displays the results of the pre-processing. Here are some key poi
 
 -   **Question Marks**: The displayed question marks provide quick and immediate help. However, since you are reading this documentation, you found the extensive version. Hope it helped!
 -   **Pre-processing Interpretation**: Observing the pre-processed data can provide insights into how different pre-processing procedures affect the data. Adjusting the pre-processing parameters can help in optimizing the data for downstream analyses.
+
+---
+
+## Further Navigation
+
+Do you want to...
+
+- Learn how to upload your data? → Go to [Data Input](01-required-data-input.md)
+- Understand how to select and filter your data? → Go to [Data selection](02-selection.md)
+- Explore how to correlate your samples? → Go to [Sample Correlation](04-sample-correlation.md)
+- Perform significance analysis on your data? → Go to [Significance Analysis](05-significance-analysis.md)
+- Conduct Principal Component Analysis? → Go to [PCA](06-pca.md)
+- Visualize your data with heatmaps? → Go to [Heatmap](07-heatmap.md)
+- Visualize individual genes? → Go to [Single Gene Visualisations](08-single-gene-visualisations.md)
+- Perform enrichment analysis on your data? → Go to [Enrichment Analysis](09-enrichment-analysis.md)
+
+---

docs/interface-details/sample-correlation.md → docs/interface-details/04-sample-correlation.md

@@ -2,7 +2,7 @@
 title: "Sample Correlation"
 layout: default
 parent: Interface Details
-nav_order: 3
+nav_order: 4
 ---
 
 # Sample Correlation
@@ -24,7 +24,7 @@ In the side panel, you have the following options:
 
 - **Choose the color annotation for the samples**: Below the horizontal line, you can choose different options to color the rows. These options come from the sample annotations provided initially. This can help to potentially explain clustering behavior observed in the heatmap.
 
-  More details on these options can be found under [Required Data Input](required-data-input.md) or [Showcase A](../showcases/showcase-a.md).
+  More details on these options can be found under [Required Data Input](01-required-data-input.md) or [Showcase A](../showcases/showcase-a.md).
 
 ## Main Panel 💡
 
@@ -40,3 +40,20 @@ The main panel displays the correlation heatmap. Here are some key points:
 - **Question Marks**: The displayed question marks provide quick and immediate help. However, since you are reading this documentation, you found the extensive version. Hope it helped!
 - **Clustering Behavior**: Observing the clustering in the heatmap can give insights into the relationships between different samples based on the selected annotation.
 
+---
+
+## Further Navigation
+
+Do you want to...
+
+- Learn how to upload your data? → Go to [Data Input](01-required-data-input.md)
+- Understand how to select and filter your data? → Go to [Data selection](02-selection.md)
+- Discover the pre-processing options available? → Go to [Pre-processing](03-pre-processing.md)
+- Perform significance analysis on your data? → Go to [Significance Analysis](05-significance-analysis.md)
+- Conduct Principal Component Analysis? → Go to [PCA](06-pca.md)
+- Visualize your data with heatmaps? → Go to [Heatmap](07-heatmap.md)
+- Visualize individual genes? → Go to [Single Gene Visualisations](08-single-gene-visualisations.md)
+- Perform enrichment analysis on your data? → Go to [Enrichment Analysis](09-enrichment-analysis.md)
+
+---
+

docs/interface-details/significance-analysis.md → docs/interface-details/05-significance-analysis.md

@@ -2,7 +2,7 @@
 title: "Significance Analysis"
 layout: default
 parent: Interface Details
-nav_order: 4
+nav_order: 5
 ---
 
 # Significance Analysis
@@ -20,7 +20,7 @@ In the side panel, you have the following options:
 - **Test method**: You can select the statistical test method to use. You can choose 
   between [T-test](https://en.wikipedia.org/wiki/Student%27s_t-test), [Wilcoxon Rank 
   Sum Test](https://en.wikipedia.org/wiki/Mann–Whitney_U_test), and [Welch's T-test](https://en.wikipedia.org/wiki/Welch%27s_t-test). 
-  If you used `vst_DESeq` as the [preprocessing method](pre-processing.md), DESeq2 
+  If you used `vst_DESeq` as the [preprocessing method](03-pre-processing.md), DESeq2 
   will use the [Wald test](https://en.wikipedia.org/wiki/Wald_test).
 
 - **Significance level**: This slider allows you to set the significance level for the analysis, ranging from 0.005 to 0.1.
@@ -72,3 +72,20 @@ The main panel displays the results of the significance analysis. The main panel
   changes between the compared groups. Adjusting the significance level and thresholds 
   can help in identifying the most relevant results. Checking the intersections can 
   point towards commonalities between the groups.
+
+---
+
+## Further Navigation
+
+Do you want to...
+
+- Learn how to upload your data? → Go to [Data Input](01-required-data-input.md)
+- Understand how to select and filter your data? → Go to [Data selection](02-selection.md)
+- Discover the pre-processing options available? → Go to [Pre-processing](03-pre-processing.md)
+- Explore how to correlate your samples? → Go to [Sample Correlation](04-sample-correlation.md)
+- Conduct Principal Component Analysis? → Go to [PCA](06-pca.md)
+- Visualize your data with heatmaps? → Go to [Heatmap](07-heatmap.md)
+- Visualize individual genes? → Go to [Single Gene Visualisations](08-single-gene-visualisations.md)
+- Perform enrichment analysis on your data? → Go to [Enrichment Analysis](09-enrichment-analysis.md)
+
+---

docs/interface-details/pca.md → docs/interface-details/06-pca.md

@@ -2,7 +2,7 @@
 title: "PCA"
 layout: default
 parent: "Interface Details"
-nav_order: 5
+nav_order: 6
 ---
 
 # PCA
@@ -48,3 +48,20 @@ The main panel displays the PCA results. Here are some key points:
 - **Question Marks**: The displayed question marks provide quick and immediate help. However, since you are reading this documentation, you found the extensive version. Hope it helped!
 - **PCA Interpretation**: Observing the PCA plot can give insights into the relationships between different samples based on the selected annotation and the principal components chosen for the axes. For more information on PCA interpretation, check out [this video on PCA interpretation](https://www.youtube.com/watch?v=FgakZw6K1QQ).
 
+---
+
+## Further Navigation
+
+Do you want to...
+
+- Learn how to upload your data? → Go to [Data Input](01-required-data-input.md)
+- Understand how to select and filter your data? → Go to [Data selection](02-selection.md)
+- Discover the pre-processing options available? → Go to [Pre-processing](03-pre-processing.md)
+- Explore how to correlate your samples? → Go to [Sample Correlation](04-sample-correlation.md)
+- Perform significance analysis on your data? → Go to [Significance Analysis](05-significance-analysis.md)
+- Visualize your data with heatmaps? → Go to [Heatmap](07-heatmap.md)
+- Visualize individual genes? → Go to [Single Gene Visualisations](08-single-gene-visualisations.md)
+- Perform enrichment analysis on your data? → Go to [Enrichment Analysis](09-enrichment-analysis.md)
+
+---
+