Update STRING vignette

- Add rba_string_enrichment_image() - use KEGG for enrichment example. Update do_enrich vignette - Add rba_string_enrichment_image()
moosa-r · Dec 24, 2024 · dd80e4f · dd80e4f
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diff --git a/vignettes/rbioapi_do_enrich.Rmd b/vignettes/rbioapi_do_enrich.Rmd
@@ -377,6 +377,28 @@ if (utils::hasName(string_annot, "PMID") && is.data.frame(string_annot$PMID)) {
 }
 ```
 
+
+In addition to a data frame, you can also get a plot summarizing the enrichment results. This API endpoint supports extensive customization of the plot; please refer to the `rba_string_enrichment_image()` function's manual for detailed instructions. Here we perform an enrichment analysis against the KEGG database, and retrieve a plot of the results.
+
+
+```{r string_enrichment_plot, fig.show='hide'}
+graph_3 <- rba_string_enrichment_image(ids = covid_critical,
+                                       species = 9606,
+                                       category = "KEGG",
+                                       image_format = "image",
+                                       save_image = FALSE,
+                                       group_by_similarity = 0.6)
+```
+
+```{r string_enrichment_plot_image, echo=FALSE, fig.cap="Visualization of enrichment analysis results", fig.align='center', fig.width=7}
+if (is.array(graph_3)) {
+  grid::grid.raster(graph_3, just = "center")
+} else {
+  print("Vignette building failed. It is probably because the web service was down during the building.")
+}
+```
+
+
 ## miEAA {#mieaa .heading2}
 
 The miRNA Enrichment Analysis and Annotation Tool ([miEAA](https://ccb-compute2.cs.uni-saarland.de/mieaa2/ "https://ccb-compute2.cs.uni-saarland.de/mieaa2")) is a service provided by the [Chair for Clinical Bioinformatics at Saarland University](https://www.ccb.uni-saarland.de/). What makes miEAA unique among other services presented here is that miEAA curates miRNA sets. Hence you can directly perform miRNA enrichment analysis across various species with the services miEAA provides.

diff --git a/vignettes/rbioapi_string.Rmd b/vignettes/rbioapi_string.Rmd
@@ -143,14 +143,14 @@ Let's go back to the interaction network. As you must have seen in the STRING we
 ```{r rba_string_network_image_ex1, fig.show='hide'}
 ## Example 1:
 graph_1 <- rba_string_network_image(ids = proteins_mapped,
-                                   image_format = "image",
-                                   species = 9606,
-                                   save_image = FALSE,
-                                   required_score = 500,
-                                   network_flavor = "confidence")
+                                    image_format = "image",
+                                    species = 9606,
+                                    save_image = FALSE,
+                                    required_score = 500,
+                                    network_flavor = "confidence")
 ```
 
-```{r rba_string_network_image_ex1_image, echo=FALSE, fig.cap="Network images - Example 1", fig.align='center'}
+```{r rba_string_network_image_ex1_image, echo=FALSE, fig.cap="Network images - Example 1", fig.align='center', fig.width=7}
 if (is.array(graph_1)) {
   grid::grid.raster(graph_1, just = "center")
 } else {
@@ -167,10 +167,10 @@ graph_2 <- rba_string_network_image(ids = proteins_mapped,
                                     required_score = 500,
                                     add_color_nodes = 5,
                                     add_white_nodes = 5,
-                                   network_flavor = "actions")
+                                    network_flavor = "actions")
 ```
 
-```{r rba_string_network_image_ex2_image, echo=FALSE, fig.cap="Network images - Example 2", fig.align='center'}
+```{r rba_string_network_image_ex2_image, echo=FALSE, fig.cap="Network images - Example 2", fig.align='center', fig.width=7}
 if (is.array(graph_2)) {
   grid::grid.raster(graph_2, just = "center")
 } else {
@@ -190,7 +190,7 @@ The first type is the conventional type, which statistically tests your supplied
 
 ```{r rba_string_enrichment}
 enriched <- rba_string_enrichment(ids = proteins_mapped,
-                                  species = 9606, )
+                                  species = 9606)
 ```
 
 As usual, we inspect the output using the `str()` function. As you can see below, the enrichment results of each category can be found as the returned list's elements.
@@ -199,11 +199,11 @@ As usual, we inspect the output using the `str()` function. As you can see below
 str(enriched, max.level = 1)
 ```
 
-Let us see the "DISEASES" results as an example. Below, we can see which terms of the [Human Disease Ontology](https://disease-ontology.org/ "Human Disease Ontology") were over-represented:
+Let us see the "KEGG" results as an example. Below, we can see which terms of the [KEGG pathways database](https://www.genome.jp/kegg/pathway.html) were over-represented:
 
 ```{r rba_string_enrichment_restults, echo=FALSE}
-if (utils::hasName(enriched, "DISEASES") && is.data.frame(enriched$DISEASES)) {
-  DT::datatable(data = enriched$DISEASES,
+if (utils::hasName(enriched, "KEGG") && is.data.frame(enriched$KEGG)) {
+  DT::datatable(data = enriched$KEGG,
               options = list(scrollX = TRUE, 
                              paging = TRUE,
                              fixedHeader = TRUE,
@@ -216,6 +216,28 @@ if (utils::hasName(enriched, "DISEASES") && is.data.frame(enriched$DISEASES)) {
 
 **Please Note:** Other services supported by rbioapi also provide Over-representation analysis tools. Please see the vignette article [Do with rbioapi: Over-Representation (Enrichment) Analysis in R](rbioapi_do_enrich.html) ([link to the documentation site](https://rbioapi.moosa-r.com/articles/rbioapi_do_enrich.html)) for an in-depth review.
 
+### Functional enrichment Plot {#functional-enrichment-plot .heading3}
+
+In addition to a data frame, you can also get a plot summarizing the enrichment results. This API endpoint supports extensive customization of the plot; please refer to the `rba_string_enrichment_image()` function's manual for detailed instructions. Here we perform the exact enrichment analysis done above, and retrieve a plot of the results.
+
+
+```{r rba_string_enrichment_plot, fig.show='hide'}
+graph_3 <- rba_string_enrichment_image(ids = proteins_mapped,
+                                       species = 9606,
+                                       category = "KEGG",
+                                       image_format = "image",
+                                       save_image = FALSE,
+                                       group_by_similarity = 0.6)
+```
+
+```{r rba_string_enrichment_plot_image, echo=FALSE, fig.cap="Visualization of enrichment analysis results", fig.align='center', fig.width=7}
+if (is.array(graph_3)) {
+  grid::grid.raster(graph_3, just = "center")
+} else {
+  print("Vignette building failed. It is probably because the web service was down during the building.")
+}
+```
+
 ### Protein-protein interaction enrichment {#protein-protein-interaction-enrichment .heading3}
 
 Even without incorporating annotation data, STRING can calculate if your proteins are functionally related. Briefly, STRING accomplishes this by comparing the interactions' distribution in your protein-set to the interactions' distribution in the proteome. Read [STRING's paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3531103/#__sec4title "STRING v9.1: protein-protein interaction networks, with increased coverage and integration") for more information.