2.C: miEEA & rbioapi

Moosa Rezwani

2021-06-21


1 Introduction

The miRNA Enrichment Analysis and Annotation Tool (miEAA) is a service provided by the Chair for Clinical Bioinformatics at Saarland University. Basically, miEAA is a multi-species microRNA enrichment analysis tool. For more information, see their website or published paper.


2 First, find enrichment categories

Before Enriching miRNA set, note that based on your input miRNA type (either all mature or precursor, not a mixture of both!) and the species, there will be different sets of supported enrichment categories.

Thus, it is recommended to retrieve a list of possible enrichment categories that you may use:

## A list of available enrichment categories for:
## mature human miRNA:
rba_mieaa_cats(mirna_type = "mature", species = 9606)
## precursor human miRNA
rba_mieaa_cats(mirna_type = "precursor", species = 9606)
## precursor zebrafish miRNA
rba_mieaa_cats(mirna_type = "mature", species = "Danio rerio")

3 Submit Enrichment analysis request to miEAA

There are two approaches to do this, we will start with the simpler one.

3.1 Approach 1: Using the Wrapper function

Just fill the arguments of rba_mieaa_enrich() according to the function’s manual; As you can see in the function’s arguments, you have a lot of controls over your enrichment request, but you need to provide test_set, mirna_type, test_type, and species:

## 1 We create a variable with our miRNAs' mature IDs
mirs <- c("hsa-miR-20b-5p", "hsa-miR-144-5p", "hsa-miR-17-5p", "hsa-miR-20a-5p",
         "hsa-miR-222-3p", "hsa-miR-106a-5p", "hsa-miR-93-5p", "hsa-miR-126-3p",
         "hsa-miR-363-3p", "hsa-miR-302c-3p", "hsa-miR-374b-5p", "hsa-miR-18a-5p",
         "hsa-miR-548d-3p", "hsa-miR-135a-3p", "hsa-miR-558", "hsa-miR-130b-5p",
         "hsa-miR-148a-3p")
## 2a We can enrich our miRNA set without limiting the enrichment to any categories
mieaa_all <- rba_mieaa_enrich(test_set = mirs,
                             mirna_type = "mature",
                             test_type = "ORA",
                             species = 9606)
#>  -- Step 1/3: Submitting Enrichment request:
#> No categories were supplied, Requesting enrichment using all of the 28 available categories for species 'Homo sapiens'.
#> Submitting ORA enrichment request for 17 miRNA IDs of species Homo sapiens to miEAA servers.
#> 
#>  -- Step 2/3: Checking for Submitted enrichment job's status every 5 seconds.
#>     Your submitted job ID is: 5da939d0-c679-449c-8d66-7d5afd72880c
#> ....
#> 
#>  -- Step 3/3: Retrieving the results of the finished enrichment job.
#> Retrieving results of submitted enrichment request with ID: 5da939d0-c679-449c-8d66-7d5afd72880c
## 2b Or, We can limit the enrichment to certain datasets (enrichment categories)
mieaa_kegg <- rba_mieaa_enrich(test_set = mirs,
                              mirna_type = "mature",
                              test_type = "ORA",
                              species = 9606,
                              categories = c("miRWalk_Diseases_mature",
                                            "miRWalk_Organs_mature")
                             )
#>  -- Step 1/3: Submitting Enrichment request:
#> Submitting ORA enrichment request for 17 miRNA IDs of species Homo sapiens to miEAA servers.
#> 
#>  -- Step 2/3: Checking for Submitted enrichment job's status every 5 seconds.
#>     Your submitted job ID is: 70ab0ce1-fa7e-49e2-ac9e-a76410f020e8
#> .
#> 
#>  -- Step 3/3: Retrieving the results of the finished enrichment job.
#> Retrieving results of submitted enrichment request with ID: 70ab0ce1-fa7e-49e2-ac9e-a76410f020e8

3.2 Approach 2: Going step-by-step

As stated before, rba_mieaa_enrich() is a wrapper function, meaning that it executes the following sequence of functions:

## 1 Submit enrichment request to miEAA
request <- rba_mieaa_enrich_submit(test_set = mirs,
                                  mirna_type = "mature",
                                  test_type = "ORA",
                                  species = 9606,
                                  categories = c("miRWalk_Diseases_mature",
                                                 "miRWalk_Organs_mature")
                                  )
## 2 check for job's running status
rba_mieaa_enrich_status(job_id = request$job_id)

## 3 If the job has completed, retrieve the results
results <- rba_mieaa_enrich_results(job_id = request$job_id)

4 Convert miRNA accessions

miEAA only recognizes miRBASE version 22 accessions. You can use rba_mieaa_convert_version() to convert miRNA accession between different miRBASE versions. Also, as stated before, miEAA differentiate between precursor and mature miRNA accessions, to convert between these 2 accession types, use rba_mieaa_convert_type().


6 Session info

#> R version 4.1.0 (2021-05-18)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 19043)
#> 
#> Matrix products: default
#> 
#> locale:
#> [1] LC_COLLATE=C                          
#> [2] LC_CTYPE=English_United States.1252   
#> [3] LC_MONETARY=English_United States.1252
#> [4] LC_NUMERIC=C                          
#> [5] LC_TIME=English_United States.1252    
#> system code page: 1256
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] rbioapi_0.7.4
#> 
#> loaded via a namespace (and not attached):
#>  [1] digest_0.6.27     R6_2.5.0          jsonlite_1.7.2    magrittr_2.0.1   
#>  [5] evaluate_0.14     httr_1.4.2        rlang_0.4.11      stringi_1.6.1    
#>  [9] curl_4.3.1        jquerylib_0.1.4   DT_0.18           bslib_0.2.5.1    
#> [13] rmarkdown_2.9     tools_4.1.0       stringr_1.4.0     htmlwidgets_1.5.3
#> [17] crosstalk_1.1.1   xfun_0.23         yaml_2.2.1        compiler_4.1.0   
#> [21] htmltools_0.5.1.1 knitr_1.33        sass_0.4.0