Helpfiles (#156)

* Helpfile for RowSelection * Updated Helpfile for RowSelection * Updated Helpfile for DataSelection_Reset * Updated Helpfile for FileInput * Updated Helpfile for Upload via precompiled data. * Updated Helpfile for Metadata upload * Updated Helpfiles for Upload inspection and data download. * Helpfiles for preprocessing. Maybe Todo: add links? * Updated Helpfiles for EA * Updated Helpfiles for Heatmap * Updated Helpfiles for PCA * Updated Helpfiles for Sample Correlation * Updated Helpfiles for Sig Ana * Updated Helpfiles for Single Gene vis * Removed some unneccessary helpfiles * Update program/shinyApp/helpfiles/DataSelection_DataUploadFileInput.md Co-authored-by: Lea Seep <[email protected]> * Apply suggestions from code review Co-authored-by: Lea Seep <[email protected]> * Apply code suggestions. * Apply code suggestions. --------- Co-authored-by: Lea Seep <[email protected]>
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diff --git a/program/shinyApp/helpfiles/DataSelection_MetaData.md b/program/shinyApp/helpfiles/DataSelection_MetaData.md
@@ -2,7 +2,7 @@
 
 ***
 
-Here you can upload you `Metasatasheet` to easily upload all data at once in 
+Here you can upload you `Meta Data Sheet` to easily upload all data at once in 
 addition to a very detailed metadata information on the samples. This not only eases 
 the upload but also allows you to investigate the data in much more detail.
 

diff --git a/program/shinyApp/helpfiles/DataSelection_RowSelection.md b/program/shinyApp/helpfiles/DataSelection_RowSelection.md
@@ -5,7 +5,8 @@
 With `Row selection` you can choose the entities (e.g. genes) you want to include in the 
 analysis.
 
-With the first slider you can choose a category (e.g. gene names or gene types), by 
+With the first drop-down menu you can choose a category (e.g. gene names or gene types),
+by 
 which you want to filter the entities. The second slider allows you to choose the data 
 to include in the analysis concretly. The categories and data available depend on the 
 entitie annotation you have chosen in the `Data upload` section.

diff --git a/program/shinyApp/helpfiles/DataSelection_UploadInspection.md b/program/shinyApp/helpfiles/DataSelection_UploadInspection.md
@@ -9,10 +9,10 @@ via file input` tab and does not work for precompiled data.
 
 **Step 1: Upload Matrices and Tables**
 
-- If you haven't uploaded the necessary data, such as the main matrix (`data_matrix1`),
-  the sample annotation or metadata (`data_sample_anno1` or `metadataInput`), and the 
-  entity annotation (`data_row_anno1`), an error message will guide you. Make sure to 
-  upload all three matrices beforehand for a comprehensive inspection.
+- If you haven't uploaded the necessary data, such as the main matrix,
+  the sample annotation or metadata, and the entity annotation, an 
+  error message will be shown in the first entry. Make sure to upload all three matrices 
+  beforehand for a comprehensive inspection.
 
 **Step 2: Visualize Uploaded Data**
 
@@ -29,12 +29,5 @@ via file input` tab and does not work for precompiled data.
   - It ensures that the column names of the matrix match the row names of the sample table.
   - Checks are conducted for the presence of missing values in the matrices and tables.
 
-**Step 4: Display Overall Checks**
-
-- The results of the checks are displayed in a formatted text section, summarizing the overall data quality. Each check is marked as "Yes" (passed) or "No" (failed), and additional information is provided if any issues are detected.
-
-**Note:**
-- The application dynamically adjusts based on the uploaded data and guides you through the visual inspection process. If any checks fail, helpful suggestions or information are presented to assist you in resolving potential issues.
-
 ---
 
diff --git a/program/shinyApp/helpfiles/PCA_Choices.md b/program/shinyApp/helpfiles/PCA_Choices.md
@@ -1,6 +1,12 @@
 ## Principal Component Analysis (PCA) Options
 
 ***
+
+A principal component analysis (PCA) is a linear dimensionality reduction technique to 
+visualize clusters within your data. For a small introduction on PCAs, please refer to 
+the CRan package [LearnPCA](https://cran.r-project.org/web/packages/LearnPCA/) or to 
+the wikipedia article on [PCA](https://en.wikipedia.org/wiki/Principal_component_analysis). 
+
 **1. Coloring Options:**
 
 - **Description:**

diff --git a/program/shinyApp/helpfiles/PreProcessing_Procedures.md b/program/shinyApp/helpfiles/PreProcessing_Procedures.md
@@ -7,7 +7,8 @@
 
 **Step 2: Data Filtering (Optional)**
 
-- If the selected preprocessing procedure is "filterOnly," additional filtering steps are applied based on the data type (Transcriptomics or Metabolomics).
+- If the selected preprocessing procedure is not `None`, additional filtering steps are 
+  applied based on the data type (Transcriptomics or Metabolomics).
 - Low-abundance entities are filtered out based on specified criteria.
 
 **Step 3: Data Transformation**
@@ -39,7 +40,7 @@
     - Internally a `DESeq object` is created, which is used for the downstream 
       analysis. The vst transformation is used for visualizations such as the PCA.
     - The formulas supported are only simple ones for now. For a more complex 
-      analysis, we suggest to write your own script. For this, you can perform within the App your analysis (e.g. PCA) and download R Code and data. Within the provided script you then may change the DESeq formula at the appropriate position and rerun the entire code to update respective data and retrieve new analysis results.
+      analysis, we suggest to write your own script.
 
 - **Scaling 0-1:**
   - The data is scaled to fit within the range of 0 to 1.
@@ -51,17 +52,13 @@
 
 - **Logarithm Base 10 (log10):**
   - The base-10 logarithm of each data point is calculated.
-  - Special consideration is given to handling zero values to avoid undefined results.
-  - If any zero values are present, a small constant is added before applying the logarithm.
+  - Special consideration is given to handling zero values to avoid undefined 
+    results: If any zero values are present, +1 is added to all values before applying
+    the logarithm.
 
 - **Pareto Scaling:**
   - Pareto scaling emphasizes the importance of small values by dividing each data point by the square root of its standard deviation.
   - This method is suitable for datasets with a wide range of values.
 
-**Step 4: Additional Checks and Warnings**
-
-- Checks for the presence of NA values after preprocessing.
-- Display of warnings and additional information based on the preprocessing steps.
-
 ---
 
diff --git a/program/shinyApp/helpfiles/SampleCorr_Choices.md b/program/shinyApp/helpfiles/SampleCorr_Choices.md
@@ -4,7 +4,11 @@
 ***
 ### Correlation method
 
-Choose **one** of the following methods
+Choose **one** of the following correlation methods. These correlation methods provide 
+insights into different aspects of relationships between variables. Understanding 
+their characteristics helps in choosing the most appropriate method for a given 
+analysis. For a brief overview on the methods, see [this article on correlation 
+methods](https://en.wikipedia.org/wiki/Correlation_coefficient).
 
 **Pearson Correlation**
 

diff --git a/program/shinyApp/www/Report.md b/program/shinyApp/www/Report.md
@@ -1,7 +1,41 @@
-**DataInput** - Uploaded Omic Type: Lipidomics
+# ShinyOmics Report (18/12/2023)
+ **AppVersion: 0.1.3 (2022-12-02)** 
 
-**DataInput** - The used data was precompiled. Filename: 
-	SHINY_LipidObsesityWTonly_precompiled 2022-06-27.rds
+## DataInput {.tabset .tabset-fade}
 
-**DataInput** - The raw data dimensions are:502, 42
+### Info
+
+**DataInput** - Uploaded Omic Type: Transcriptomics
+
+**DataInput** - Test Data set used
+
+**DataInput** - All constant annotation entries for entities and samples are removed from the thin out the selection options!
+
+**DataInput** - The raw data dimensions are:33469, 8
+
+### Publication Snippet
+
+The Transcriptomics data was read into R (v. 4.2.1) (R Core Team (2022). _R: A Language and Environment for Statistical Computing_. R Foundationfor Statistical Computing, Vienna, Austria. <https://www.R-project.org/>.). The raw's data dimensions were: 33469, 8. All annotation that is constant over all samples is hidden within the Shiny-Application, as they do not provide any additional knowledge.This was done with the purrr package (v. 1.0.2)(Wickham H, Henry L (2023). _purrr: Functional Programming Tools_. R package version 1.0.2,<https://CRAN.R-project.org/package=purrr>.)
+
+<br>
+
+## Data Selection
+
+**DataSelection** - The following selection was conducted:
+
+**DataSelection** - Samples:
+	 DataSelection - based on: cell: all
+
+**DataSelection** - Entities:
+	 DataSelection - based on: geneName: all
+
+## Pre Processing
+
+**PreProcessing** - As general remove all entities which are constant over all samples (automatically)
+
+**PreProcessing** - Preprocessing procedure -standard (depending only on omics-type): Remove anything which row Count <= 10
+
+**PreProcessing** - Preprocessing procedure -specific (user-chosen): filterOnly
+
+**PreProcessing** - The resulting dimensions are: 22008, 8