Rename-Sig-Ana

README.md

@@ -4,6 +4,10 @@
 Readme](docs/assets/images/cOmicsART_README.png)
 *Image generated using DALL-E by OpenAI. Adjusted by Lea Seep*
 
+The availability of bulk-omic data is steadily increasing, necessitating collaborative efforts between experimental and computational researchers. While software tools with graphical user interfaces (GUIs) enable rapid and interactive data assessment, they are limited to pre-implemented methods, often requiring transitions to custom code for further adjustments. 
+However, most available tools lack GUI-independent reproducibility such as direct integration with R, resulting in very limited support for transition. Therefore, we introduce the **customizable Omics Analysis and reporting tool – cOmicsArt**. cOmicsArt aims to enhance collaboration through seamless integration of GUI-based analysis with R. The GUI allows researchers to perform user-friendly exploratory and statistical analyses with interactive visualizations and automatic documentation. 
+Downloadable R scripts and results ensure reproducibility and smooth integration with R, supporting both novice and experienced programmers by enabling easy customizations and serving as a foundation for more advanced analyses. This versatility also allows for usage in educational settings guiding students from GUI-based analysis to R Code.
+
 Please refer to the following listed links for further information on
 the respective topics:
 
@@ -26,7 +30,7 @@ General Test:
 -   A screen recording of cOmicsArt is available at
     <https://www.youtube.com/watch?v=pTGjtIYQOak>
 
--   A snapshot upon publication can be found on Zenodo: very last thing;
+-   A snapshot upon publication can be found on Zenodo: <https://zenodo.org/records/13740904>
     Note, that you can find within the branches the Zenodo based
     branches.
 

docs/code-and-data/examples.md

@@ -23,7 +23,7 @@ To recreate this example **within** cOmicsArt, use the following steps:
 2. We want to use all the data, so we will not filter the data. Hence, directly click `"Start 
    the Journey"`
 3. Select `DESeq2` as the pre-processing method with `condition` as the main factor
-4. In the `Significance Analysis`, run the significance analysis for `trt:untrt`, Significance 
+4. In the `Differential Analysis`, run the differential analysis for `trt:untrt`, significance 
    level: `0.05` and test-correction: `Benjamini-Hochberg`
 5. Select now the `trt:untrt` tab, in that the `Volcano` tab
 6. Download the data and code by clicking on `Get underlying R code and data` under 
@@ -58,7 +58,7 @@ the steps to follow:
 var images = [
     {src: "/cOmicsArt/assets/images/Slideshow1.png", subtitle: "1. Select Testdata, 2.1 Choose all data, 2.2 Start the Journey"},
     {src: "/cOmicsArt/assets/images/Slideshow2.png", subtitle: "3.1 Select DESeq2 as pre-processing method, 3.2 Select condition as main factor, 3.3 Run the pre-processing"},
-    {src: "/cOmicsArt/assets/images/Slideshow3.png", subtitle: "4.1 Select trt:untrt 4.2 Run the significance analysis, 5. Select trt:untrt tab"},
+    {src: "/cOmicsArt/assets/images/Slideshow3.png", subtitle: "4.1 Select trt:untrt 4.2 Run the differential analysis, 5. Select trt:untrt tab"},
     {src: "/cOmicsArt/assets/images/Slideshow4.png", subtitle: "5.2 Select Volcano tab, 6. Download the data and code"},
 ];
 var currentIndex = 0;

docs/index.md

@@ -21,4 +21,4 @@ Have fun exploring! 🎉
 -   📚 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/05-significance-analysis.md)
+-   📊 Want to know more about the differential analysis? Visit [Differential Analysis](interface-details/05-significance-analysis.md)

docs/interface-details.md

@@ -41,7 +41,7 @@ To get more information on the tabs, navigate to the respective documentation us
 
 ![The design of the side panel](/cOmicsArt/assets/images/design_principleSidePanel.png)
 
-2. **Main Panel Structure:** Each main panel contains the visualization of the analysis results. Some panels are further subdivided to show multiple results, for example, the Significance Analysis tab.
+2. **Main Panel Structure:** Each main panel contains the visualization of the analysis results. Some panels are further subdivided to show multiple results, for example, the Differential Analysis tab.
 
 3. **Picture Download Options:** Users can download visualizations and results in common formats (e.g., PNG, TIFF, PDF). There are respective buttons to select the file format. Upon 'Save plot' the file is downloaded to the local machine.
 

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

@@ -82,7 +82,7 @@ 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)
+- Perform differential analysis on your data? → Go to [Differential 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)

docs/interface-details/02-selection.md

@@ -112,7 +112,7 @@ 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)
+- Perform differential analysis on your data? → Go to [Differential 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)

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

@@ -212,7 +212,7 @@ 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)
+- Perform differential analysis on your data? → Go to [Differential 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)

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

@@ -49,7 +49,7 @@ 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)
+- Perform differential analysis on your data? → Go to [Differential 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)

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

@@ -1,32 +1,32 @@
 ---
-title: "Significance Analysis"
+title: "Differential Analysis"
 layout: default
 parent: Interface Details
 nav_order: 5
 ---
 
-# Significance Analysis
+# Differential Analysis
 
-The Significance Analysis tab is divided into two main sections: the side panel and the main panel.
+The Differential Analysis tab is divided into two main sections: the side panel and the main panel.
 
 ## Side Panel 📚
 
 In the side panel, you have the following options:
 
 ### 1. Choose Groups to Compare
-Select the groups from your data for which you want to perform significance analysis.
+Select the groups from your data for which you want to perform differential analysis.
 
 - For DESeq preprocessing, select from predefined factors.
 - For other preprocessing methods, choose from available sample annotation columns.
 
 ### 2. Choose Comparisons
-Select the specific pairings of groups for which you want to perform significance analysis.
+Select the specific pairings of groups for which you want to perform differential analysis.
 
 - Automatically generates possible pairings based on selected groups.
 - Notation is "Treatment:Control" and indicates the direction of the comparison.
 
 ### 3. Choose Test Method
-Select the statistical test method for significance analysis.
+Select the statistical test method for differential analysis.
 
 - For DESeq preprocessing, a Wald test statistic is used. For more information [read here](https://en.wikipedia.org/wiki/Wald_test) or [check out the original paper](http://www.jstor.org/stable/1990256).
 - For other preprocessing methods, choose from:

docs/interface-details/06-pca.md

@@ -58,7 +58,7 @@ 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)
+- Perform differential analysis on your data? → Go to [Differential 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)

docs/interface-details/07-heatmap.md

@@ -93,7 +93,7 @@ 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)
+- Perform differential analysis on your data? → Go to [Differential Analysis](05-significance-analysis.md)
 - Conduct Principal Component Analysis? → Go to [PCA](06-pca.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/08-single-gene-visualisations.md

@@ -31,7 +31,7 @@ In the side panel, you have the following options:
 The main panel displays the single gene visualisations. Here are some key points:
 
 - **Visualisation**: The visualisation provides a boxplot or dot plot based on the number of samples per group and the selected options. Note that you only see boxplots if you have more than 3 samples per group. If there are fewer than 4 samples, only dots will be displayed.
-- **Select your desired comparisons**: Here you select which comparisons you want to test and display in the plot. Note that each test is taken as an individual test, there is no multiple testing correction done \([Why it is important](https://www.nature.com/articles/nbt1209-1135)\) when choosing more than one test. For more advanced testing please go to the [Significance analysis tab](05-significance-analysis.md)
+- **Select your desired comparisons**: Here you select which comparisons you want to test and display in the plot. Note that each test is taken as an individual test, there is no multiple testing correction done \([Why it is important](https://www.nature.com/articles/nbt1209-1135)\) when choosing more than one test. For more advanced testing please go to the [Differential analysis tab](05-significance-analysis.md)
 
 
 - **Download Options**: The visualisation can be downloaded directly in common formats (e.g., PNG, TIFF, PDF) or sent to the report. You can also download the underlying R code and data. For more information, check out [Interface Details](../interface-details.md).
@@ -50,7 +50,7 @@ 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)
+- Perform differential analysis on your data? → Go to [Differential 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)
 - Perform enrichment analysis on your data? → Go to [Enrichment Analysis](09-enrichment-analysis.md)

docs/interface-details/09-enrichment-analysis.md

@@ -90,7 +90,7 @@ 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)
+- Perform differential analysis on your data? → Go to [Differential 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)

docs/screen_recording.md

@@ -24,7 +24,7 @@ We’ve created a detailed screen recording to guide you through the navigation
 -   [04:40](https://www.youtube.com/watch?v=pTGjtIYQOak&t=280s) Data Preprocessing
 -   [06:28](https://www.youtube.com/watch?v=pTGjtIYQOak&t=388s) Sample Correlation
 -   [08:21](https://www.youtube.com/watch?v=pTGjtIYQOak&t=501s) Principal Component Analysis (PCA)
--   [10:26](https://www.youtube.com/watch?v=pTGjtIYQOak&t=626s) Significance Analysis
+-   [10:26](https://www.youtube.com/watch?v=pTGjtIYQOak&t=626s) Differential Analysis (renamed from Significance Analysis)
 -   [12:48](https://www.youtube.com/watch?v=pTGjtIYQOak&t=768s) Single Gene Visualization
 -   [13:31](https://www.youtube.com/watch?v=pTGjtIYQOak&t=811s) Enrichment Analysis
 -   [14:38](https://www.youtube.com/watch?v=pTGjtIYQOak&t=878s) ️ Heatmap

docs/showcases/showcase-a.md

@@ -93,9 +93,9 @@ To statistically test the two genes identified by their high loadings on PC1 and
 
 If we change the 'groups to show the data for' to ‘Simulation_Treatment’, we can observe for Ppbp that NSD_1 and NSD_2 and HSD_3 and HSD_5 behave differently from the other members of the group (Fig. D4B). This aligns with the PCA results. Note, that we cannot 'test' for a difference as we have a single data point for each.
 
-### Assessing all genes – Significance analysis:
+### Assessing all genes – Differential analysis:
 
-We switch to the Significance analysis tab to analyse all genes at once. We want to compare the treatment groups, specifically HSD vs. NSD, taking the latter as control. The order is important to interpret the direction of up- and down-regulation but does not change anything in terms of significance. As we have chosen the DESeq2 pipeline, cOmicsArt automatically selects the appropriate test. We obtain 47 genes (0.29% of the entire set) with significant changes between the conditions. The majority (31 genes) are significantly upregulated (16 downregulated) with a chosen significance level of 0.05 (after Benjamini-Hochberg multiple testing correction) (Table D1).
+We switch to the Differential analysis tab to analyse all genes at once. We want to compare the treatment groups, specifically HSD vs. NSD, taking the latter as control. The order is important to interpret the direction of up- and down-regulation but does not change anything in terms of significance. As we have chosen the DESeq2 pipeline, cOmicsArt automatically selects the appropriate test. We obtain 47 genes (0.29% of the entire set) with significant changes between the conditions. The majority (31 genes) are significantly upregulated (16 downregulated) with a chosen significance level of 0.05 (after Benjamini-Hochberg multiple testing correction) (Table D1).
 
 The most significant gene is ENSMUSG00000044786 (ZFP36). To get an overview of actual effect sizes (fold changes), we subselect within the shown table to show only the significant genes by clicking into the respective padj column in the table (where 'all' stands). Here we can adjust the sliding bar to select only genes with a padj value in the determined range. A quick check at the bottom of the table confirms we only selected the 49 entries. We then sort the log2Fold changes by clicking on the little grey up and down arrows. The Log2Fold change range goes from -0.34 to 1.23. Switching to the visual representation of the table, we go to the tab Volcano. Setting a Log FC threshold of 0.5, we can see that 10 genes remain as significant highlights (Fig. D5).
 
@@ -207,7 +207,7 @@ Considering the cell type (bone-marrow neutrophils), the studied cells seem to d
 
 We saw that on a global scale, we could not observe a clear pattern to distinguish between the treatments. This is, for example, globally visible when assessing sample correlation, as the correlation is overall at a high level. When looking at the PCA, we can see a rather high spread of samples belonging to NSD, whereas the HSD samples are less spread within the dimension reduction plot. Additionally, the statistical analysis returns only a small set of differentially expressed genes, indicating that the treatment effect affects a smaller portion of the entire data set. When performing an overrepresentation analysis of the DE-genes, we obtain a clear signal for TNFa signaling via NFkB. When performing gene set enrichment analysis on the LFC-ranked genes among the most enriched terms oxidative phosphorylation stands out. This together suggests that the effect of the HSD treatment alters the cellular metabolism in a directed fashion to an inflammatory state.
 
-For further analysis, one might be interested in subselecting the data to focus on the potentially relevant aspects. For this, one can add the information from the statistical analysis to the gene annotation. This information is within the results table and can be obtained from the significance analysis tab. Moreover, one could add information to the entities indicating whether they are associated with the term Oxidative phosphorylation (using GO as a resource) to be able to visualize that specific subset within the heatmap panel. Additionally, while it may not be appropriate in this context, one could consider adding information to the samples, such as marking potential outliers and then redoing the analysis.
+For further analysis, one might be interested in subselecting the data to focus on the potentially relevant aspects. For this, one can add the information from the statistical analysis to the gene annotation. This information is within the results table and can be obtained from the differential analysis tab. Moreover, one could add information to the entities indicating whether they are associated with the term Oxidative phosphorylation (using GO as a resource) to be able to visualize that specific subset within the heatmap panel. Additionally, while it may not be appropriate in this context, one could consider adding information to the samples, such as marking potential outliers and then redoing the analysis.
 
 ## Final Documentation