2.C: miEEA & rbioapi

Moosa Rezwani

2023-09-30


0.1 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.


0.2 First, find enrichment categories

Before Performing enrichment analysis on a 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")

0.3 Submit Enrichment analysis request to miEAA

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

0.3.1 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 perform enrichment analysis on our miRNA set without limiting the analysis to any categories
mieaa_all <- rba_mieaa_enrich(test_set = mirs,
                             mirna_type = "mature",
                             test_type = "ORA",
                             species = 9606)
#>  -- Step 1/3: Submitting Enrichment analysis request:
#> No categories were supplied, Requesting enrichment using all of the 32 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 analysis's status every 5 seconds.
#>     Your submitted job ID is: 14d01a80-c933-4f5c-aa73-3577f34f33e9
#> .....
#> 
#>  -- Step 3/3: Retrieving the results.
#> Retrieving results of submitted enrichment request with ID: 14d01a80-c933-4f5c-aa73-3577f34f33e9
## 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 = "KEGG_mature"
                             )
#>  -- Step 1/3: Submitting Enrichment analysis request:
#> Submitting ORA enrichment request for 17 miRNA IDs of species Homo sapiens to miEAA servers.
#> 
#>  -- Step 2/3: Checking for Submitted enrichment analysis's status every 5 seconds.
#>     Your submitted job ID is: d9607447-b95c-439e-9da1-b692c4919c91
#> .
#> 
#>  -- Step 3/3: Retrieving the results.
#> Retrieving results of submitted enrichment request with ID: d9607447-b95c-439e-9da1-b692c4919c91

0.3.2 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)

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 (link to the documentation site) for an in-depth review.


0.4 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().


0.5 How to Cite?

To cite miEAA (Please see https://ccb-compute2.cs.uni-saarland.de/mieaa2/):

To cite rbioapi:


0.7 Session info

#> R version 4.3.1 (2023-06-16 ucrt)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 11 x64 (build 22621)
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#> time zone: Europe/Brussels
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#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
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#> other attached packages:
#> [1] rbioapi_0.8.0
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#> loaded via a namespace (and not attached):
#>  [1] digest_0.6.33     R6_2.5.1          fastmap_1.1.1     xfun_0.40        
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#>  [9] rmarkdown_2.25    DT_0.29           cli_3.6.1         sass_0.4.7       
#> [13] jquerylib_0.1.4   compiler_4.3.1    httr_1.4.7        rstudioapi_0.15.0
#> [17] tools_4.3.1       curl_5.0.2        ellipsis_0.3.2    evaluate_0.21    
#> [21] bslib_0.5.1       yaml_2.3.7        htmlwidgets_1.6.2 rlang_1.1.1      
#> [25] jsonlite_1.8.7    crosstalk_1.2.0