update shiny app

R/18-report_functional_module.R

@@ -54,6 +54,7 @@ report_functional_module <-
            interpretation_result,
            path = ".",
            type = c("html", "pdf", "word", "md", "all")) {
+    # browser()
     if (missing(object)) {
       stop("object is missing")
     }
@@ -75,7 +76,7 @@ report_functional_module <-
         as.numeric(stringr::str_extract(
           grep(pattern = "Report", dir(path), value = TRUE),
           "[0-9]{1,10}"
-        ))
+        )), na.rm = TRUE
       )
 
       if(is.na(idx)){
@@ -265,6 +266,7 @@ report_functional_module <-
       interpretation_result <- "> No interpretation result is provided."
     }
 
+    # browser()
     ##transform rmd to HTML or pdf
     if (type == "html" | type == "all") {
       rmarkdown::render(

inst/shinyapp/files/introduction.Rmd

@@ -6,20 +6,34 @@ output:
     toc_float:
       collapsed: false
       smooth_scroll: true
-    number_sections: true
+    number_sections: false
 date: "2023-12-25"
 ---
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
-# R Markdown
+# Module Annotation for Pathway Analysis
 
-This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.
+The R package "MAPA" represents a significant advancement in the field of bioinformatics, particularly in the analysis of RNA-seq and proteomics data. Developed by Dr. Xiaotao Shen, this tool is available on GitHub at [https://github.com/jaspershen/mapa](https://github.com/jaspershen/mapa).
 
-When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
+MAPA is designed to perform pathway enrichment analysis, a crucial step in understanding the biological significance of large-scale experimental data. By analyzing RNA-seq and proteomics datasets, MAPA helps identify enriched biological pathways, providing insights into the molecular mechanisms underlying specific diseases or phenotypic expressions.
 
-```{r cars}
-summary(cars)
-```
+One of the standout features of MAPA is its ability to reduce redundancy in biological information. It achieves this by merging enriched pathways into coherent modules and functional modules This not only simplifies the interpretation of complex datasets but also provides a clearer understanding of the biological processes at play.
+
+Additionally, MAPA leverages the capabilities of large language models like ChatGPT to interpret and contextualize the biological results. This integration allows for a more nuanced and comprehensive analysis, aligning the computational findings with biological relevance to specific diseases or phenotypes.
+
+Overall, MAPA serves as an essential tool for researchers and scientists in the field of bioinformatics, offering a sophisticated yet user-friendly approach to pathway analysis and interpretation. Its ability to distill complex datasets into meaningful biological insights is invaluable for advancing our understanding of various biological processes and disease mechanisms.
+
+# Contacts
+
+If you have any questions about MAPA, please contact Dr. Xiaotao Shen.
+
+<i class="fa fa-weixin"></i> [shenzutao1990](https://www.shenxt.info/files/wechat_QR.jpg)
+
+<i class="fa fa-envelope"></i> [email protected]
+
+<i class="fa fa-twitter"></i> [Twitter](https://twitter.com/JasperShen1990)
+
+<i class="fa fa-map-marker-alt"></i> [M339, Alway building, Cooper Lane, Palo Alto, CA 94304](https://www.google.com/maps/place/Alway+Building/@37.4322345,-122.1770883,17z/data=!3m1!4b1!4m5!3m4!1s0x808fa4d335c3be37:0x9057931f3b312c29!8m2!3d37.4322345!4d-122.1748996)

inst/shinyapp/files/introduction.html

@@ -353,23 +353,44 @@
 </div>
 
 
-<div id="r-markdown" class="section level1" number="1">
-<h1><span class="header-section-number">1</span> R Markdown</h1>
-<p>This is an R Markdown document. Markdown is a simple formatting
-syntax for authoring HTML, PDF, and MS Word documents. For more details
-on using R Markdown see <a href="http://rmarkdown.rstudio.com" class="uri">http://rmarkdown.rstudio.com</a>.</p>
-<p>When you click the <strong>Knit</strong> button a document will be
-generated that includes both content as well as the output of any
-embedded R code chunks within the document. You can embed an R code
-chunk like this:</p>
-<pre class="r"><code>summary(cars)</code></pre>
-<pre><code>##      speed           dist       
-##  Min.   : 4.0   Min.   :  2.00  
-##  1st Qu.:12.0   1st Qu.: 26.00  
-##  Median :15.0   Median : 36.00  
-##  Mean   :15.4   Mean   : 42.98  
-##  3rd Qu.:19.0   3rd Qu.: 56.00  
-##  Max.   :25.0   Max.   :120.00</code></pre>
+<div id="module-annotation-for-pathway-analysis" class="section level1">
+<h1>Module Annotation for Pathway Analysis</h1>
+<p>The R package “MAPA” represents a significant advancement in the
+field of bioinformatics, particularly in the analysis of RNA-seq and
+proteomics data. Developed by Dr. Xiaotao Shen, this tool is available
+on GitHub at <a href="https://github.com/jaspershen/mapa">https://github.com/jaspershen/mapa</a>.</p>
+<p>MAPA is designed to perform pathway enrichment analysis, a crucial
+step in understanding the biological significance of large-scale
+experimental data. By analyzing RNA-seq and proteomics datasets, MAPA
+helps identify enriched biological pathways, providing insights into the
+molecular mechanisms underlying specific diseases or phenotypic
+expressions.</p>
+<p>One of the standout features of MAPA is its ability to reduce
+redundancy in biological information. It achieves this by merging
+enriched pathways into coherent modules and functional modules This not
+only simplifies the interpretation of complex datasets but also provides
+a clearer understanding of the biological processes at play.</p>
+<p>Additionally, MAPA leverages the capabilities of large language
+models like ChatGPT to interpret and contextualize the biological
+results. This integration allows for a more nuanced and comprehensive
+analysis, aligning the computational findings with biological relevance
+to specific diseases or phenotypes.</p>
+<p>Overall, MAPA serves as an essential tool for researchers and
+scientists in the field of bioinformatics, offering a sophisticated yet
+user-friendly approach to pathway analysis and interpretation. Its
+ability to distill complex datasets into meaningful biological insights
+is invaluable for advancing our understanding of various biological
+processes and disease mechanisms.</p>
+</div>
+<div id="contacts" class="section level1">
+<h1>Contacts</h1>
+<p>If you have any questions about MAPA, please contact Dr. Xiaotao
+Shen.</p>
+<p><i class="fa fa-weixin"></i> <a href="https://www.shenxt.info/files/wechat_QR.jpg">shenzutao1990</a></p>
+<p><i class="fa fa-envelope"></i> <a href="mailto:[email protected]" class="email">[email protected]</a></p>
+<p><i class="fa fa-twitter"></i> <a href="https://twitter.com/JasperShen1990">Twitter</a></p>
+<p><i class="fa fa-map-marker-alt"></i> <a href="https://www.google.com/maps/place/Alway+Building/@37.4322345,-122.1770883,17z/data=!3m1!4b1!4m5!3m4!1s0x808fa4d335c3be37:0x9057931f3b312c29!8m2!3d37.4322345!4d-122.1748996">M339,
+Alway building, Cooper Lane, Palo Alto, CA 94304</a></p>
 </div>
 
 

inst/shinyapp/files/tutorials.Rmd

@@ -2,31 +2,87 @@
 title: ""
 output: 
   html_document:
-    toc: true
+    toc: false
     toc_float:
       collapsed: false
       smooth_scroll: true
-    number_sections: true
+    number_sections: false
 date: "2023-12-25"
 ---
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
-# Upload data
+# Introduction
 
-## Data preparation
+MAPA is a Shiny application designed for analyzing and visualizing biological data, especially in the context of pathways and networks. This tutorial will walk you through the steps of using MAPA for data upload, enrichment, merging, visualization, interpretation, and reporting.
 
-## Data format
+This tutorial provides a step-by-step guide to using the MAPA Shiny app for biological data analysis. Each tab in the app is dedicated to a specific aspect of the analysis, ensuring a structured and comprehensive approach to data processing and visualization. Remember to explore various options and parameters to tailor the analysis to your specific needs.
 
-# Enrich pathways
+# Step 1: Data Upload and ID Mapping
 
-# Merge pathways
+1. **Upload Data**:
+   - Navigate to the "Upload Data" tab.
+   - Use `fileInput` to upload your data in CSV or Excel format. You can also check "Use example" to work with preloaded data.
+   - Select the type of ID your data contains (ENSEMBL, UniProt, or EntrezID).
 
-# Merge Modules
+2. **ID Mapping**:
+   - Click "Submit" to map IDs using the `clusterProfiler` and `org.Hs.eg.db` packages.
+   - The mapped data will appear in a table. You can download it by clicking "Download".
 
-# Data visualization
+# Step 2: Enrich Pathways
+
+1. **Select Databases and Parameters**:
+   - In the "Enrich Pathways" tab, choose the databases (GO, KEGG, Reactome) for pathway enrichment.
+   - Set parameters like organism, P-value cutoff, adjustment method, and gene set size.
+
+2. **Run Enrichment**:
+   - Click "Submit" to start the enrichment process.
+   - Once completed, results for each selected database will appear in separate tabs. These can be downloaded.
+
+# Step 3: Merge Pathways
+
+1. **Set Merging Criteria**:
+   - Go to the "Merge Pathways" tab.
+   - Adjust parameters like P-adjust cutoff, gene count cutoff, similarity cutoff, and similarity method for each database.
+
+2. **Merge and Visualize**:
+   - Click "Submit" to merge pathways.
+   - View the merged pathways in the result table and download if needed.
+   - You can also visualize the pathways by clicking on the “Generate plot” buttons under each database tab.
+
+# Step 4: Merge Modules
+
+1. **Merge Functional Modules**:
+   - In the "Merge Modules" tab, set the similarity cutoff and method.
+   - Click "Submit" to merge.
+   - View and download the merged module results.
+
+2. **Visualize Merged Modules**:
+   - Generate a plot for the merged modules using provided controls for customization.
+
+# Step 5: Data Visualization
+
+1. **Customized Plots**:
+   - The "Data Visualization" tab offers various plotting options (Barplot, Module Similarity Network, etc.).
+   - Customize your plot using the available options and generate it.
+   - Use the download button to save your plot.
+
+# Step 6: LLM Interpretation
+
+1. **Generate Interpretations**:
+   - Navigate to "LLM Interpretation".
+   - Provide an OpenAI Key for GPT-3 access, select your disease or phenotype, and set the interpretation parameters.
+   - Click "Submit" to get an interpretation of your data using GPT-3.
+
+# Step 7: Results and Reporting
+
+1. **Generate Report**:
+   - In the "Results and Report" tab, click on "Generate report" to compile your analysis results.
+   - View the report in the app or download it.
+
+2. **Code Access**:
+   - Throughout the process, you can view the R code for each step by clicking the "Code" button. This is useful for understanding the underlying computations or for advanced customization.
 
-# Results and report