Install package

if (!require("BiocManager"))
    install.packages("BiocManager")
BiocManager::install("MicrobiomeProfiler")

Introduction

MicrobiomeProfiler is a functional enrichment tool for microbiome data based clusterProfiler. It is an R/shiny package with user-friendly interface.

As showed in the following figure, the sidebar panel was the input options and the main panel was designed to show output results (Also can be seen in other analysis).

KEGG enrichment analysis

Getting Started Quickly

Run the application:

library(MicrobiomeProfiler)
run_MicrobiomeProfiler()

Supported Analysis

Also, MicrobiomeProfiler provides several enrich functions for optional analysis.

• KEGG enrichment analysis
• COG enrichment analysis
• Microbe-Disease enrichment analysis
• Metabo-Pathway analysis

There are four reference gene catalogs collected from publications that can be used as universe for KEGG analysis in specific scenarios.

Case Study

Data input

To click the Example button, the example gene list would be showed in the input area. Also, more parameters can be set below the input area, for instance, p value cutoff. There is a customer_defined_universe choice for users to define the specific universe for enrichment analysis (Also for other enrichment analysis). After that, clicking the Submit button to process analysis. The Clean button was designed for cleaning the current results.

Customize the universe

Customize the universe

And then, the universe input box would be showed below.

Run example

Here we showed the case study of example: Comparative functional KEGG enrichment analysis between Lung Microbioe in IPF and Healthy Individuals. 295 significantly differential KEGG orthologs between Lung Microbioe in idiopathnic pulmonary fibrosis Patients (IPF) and healthy individuals were reported for KEGG enrichment analysis.

Case study

The default visualization results showed top 10 significant terms. In addition, users can click the interested terms on the table, and click the Update to get the results. Furthermore, there are some output settings to adjust the output figure.

To show interested tetms

Annotation database

MicrobiomeProfiler has utilized four database.

• KEGG database: Kyoto Encyclopedia of Genes and Genomes
• COG database: Clusters of Orthologous Groups of proteins
• Disbiome: Linking microbiome to disease
• SMPDB: The Small Molecule Pathway Database

Session Information

#> R version 4.4.2 (2024-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
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#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] prettydoc_0.4.1
#> 
#> loaded via a namespace (and not attached):
#>  [1] digest_0.6.37     R6_2.5.1          fastmap_1.2.0     xfun_0.49        
#>  [5] maketools_1.3.1   cachem_1.1.0      knitr_1.49        htmltools_0.5.8.1
#>  [9] png_0.1-8         rmarkdown_2.29    buildtools_1.0.0  lifecycle_1.0.4  
#> [13] cli_3.6.3         sass_0.4.9        jquerylib_0.1.4   compiler_4.4.2   
#> [17] sys_3.4.3         tools_4.4.2       evaluate_1.0.1    bslib_0.8.0      
#> [21] yaml_2.3.10       jsonlite_1.8.9    rlang_1.1.4