KEGG pathway enrichment

Xiaotao Shen PhD (https://www.shenxt.info/)

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Created on 2020-03-28 and updated on 2021-03-13

Source: vignettes/kegg_pathway_enrichment.Rmd 
library(metPath)
#> Warning in fun(libname, pkgname): mzR has been built against a different Rcpp version (1.0.5)
#> than is installed on your system (1.0.6). This might lead to errors
#> when loading mzR. If you encounter such issues, please send a report,
#> including the output of sessionInfo() to the Bioc support forum at 
#> https://support.bioconductor.org/. For details see also
#> https://github.com/sneumann/mzR/wiki/mzR-Rcpp-compiler-linker-issue.
#> ✓ metID 0.9.0
#> metPath,
#> More information can be found at https://jaspershen.github.io/metPath/
#> Authors: Xiaotao Shen (shenxt@stanford.edu)
#> Maintainer: Xiaotao Shen.
#> Version 0.0.9 (20200406)

Load packages

Load KEGG pathway human database 

data("kegg_hsa_pathway", package = "metPath")
kegg_hsa_pathway
#> ---------Pathway source&version---------
#> KEGG & 2021-03-02 
#> -----------Pathway information------------
#> 342 pathways 
#> 332 pathways haves genes 
#> 0 pathways haves proteins 
#> 280 pathways haves compounds 
#> Pathway class: Metabolism; Carbohydrate metabolism;Metabolism; Lipid metabolism;Metabolism; Metabolism of cofactors and vitamins;Metabolism; Energy metabolism;Metabolism; Amino acid metabolism;Metabolism; Nucleotide metabolism;Metabolism; Biosynthesis of other secondary metabolites;Metabolism; Metabolism of other amino acids;Metabolism; Glycan biosynthesis and metabolism;Metabolism; Metabolism of terpenoids and polyketides;Genetic Information Processing; Translation;Metabolism; Xenobiotics biodegradation and metabolism;Human Diseases; Drug resistance: antineoplastic;Environmental Information Processing; Membrane transport;Genetic Information Processing; Folding, sorting and degradation;Genetic Information Processing; Transcription;Genetic Information Processing; Replication and repair;Organismal Systems; Endocrine system;Environmental Information Processing; Signal transduction;Environmental Information Processing; Signaling molecules and interaction;Organismal Systems; Immune system;Cellular Processes; Cell growth and death;Cellular Processes; Transport and catabolism;Organismal Systems; Aging;Organismal Systems; Circulatory system;Organismal Systems; Development and regeneration;Cellular Processes; Cellular community - eukaryotes;Organismal Systems; Environmental adaptation;Organismal Systems; Nervous system;Organismal Systems; Sensory system;Cellular Processes; Cell motility;Human Diseases; Endocrine and metabolic disease;Organismal Systems; Excretory system;Organismal Systems; Digestive system;Human Diseases; Neurodegenerative disease;Human Diseases; Substance dependence;Human Diseases; Infectious disease: bacterial;Human Diseases; Infectious disease: parasitic;Human Diseases; Infectious disease: viral;Human Diseases; Cancer: overview;Human Diseases; Cancer: specific types;Human Diseases; Immune disease;Human Diseases; Cardiovascular disease
get_pathway_class(kegg_hsa_pathway)
#> # A tibble: 43 x 2
#>    class                                                                       n
#>  * <chr>                                                                   <int>
#>  1 Cellular Processes; Cell growth and death                                   8
#>  2 Cellular Processes; Cell motility                                           1
#>  3 Cellular Processes; Cellular community - eukaryotes                         5
#>  4 Cellular Processes; Transport and catabolism                                7
#>  5 Environmental Information Processing; Membrane transport                    1
#>  6 Environmental Information Processing; Signal transduction                  26
#>  7 Environmental Information Processing; Signaling molecules and interact…     5
#>  8 Genetic Information Processing; Folding, sorting and degradation            7
#>  9 Genetic Information Processing; Replication and repair                      7
#> 10 Genetic Information Processing; Transcription                               3
#> # … with 33 more rows

Pathway enrichment

We use the demo compound list from metPath.

data("query_id_kegg", package = "metPath")
query_id_kegg
#>  [1] "C00164" "C00099" "C00300" "C01026" "C00122" "C00037" "C05330" "C00097"
#>  [9] "C00079" "C00065" "C00188" "C00082" "C00183" "C00166" "C00163" "C00022"
#> [17] "C00213"

Remove the disease pathways:

remain_idx =
  kegg_hsa_pathway@pathway_class %>%
  unlist() %>%
  stringr::str_detect("Disease") %>%
  `!`() %>%
  which()

pathway_database =
  filter_pathway(object = kegg_hsa_pathway, remain_idx = remain_idx)
result = 
enrich_kegg(query_id = query_id_kegg, 
            query_type = "compound", 
            id_type = "KEGG",
            pathway_database = pathway_database, 
            p_cutoff = 0.05, 
            p_adjust_method = "BH", 
            threads = 3)

Check the result:

result
#> ---------Pathway database&version---------
#> KEGG & 2021-03-02 
#> -----------Enrichment result------------
#> 195 pathways are enriched 
#> 28 pathways p-values < 0.05 
#> Glycolysis / Gluconeogenesis;Citrate cycle (TCA cycle);Pentose phosphate pathway;Pentose and glucuronate interconversions;Fructose and mannose metabolism ... (only top 5 shows)

Plot to show Pathway enrichment 

enrich_bar_plot(object = result)

enrich_scatter_plot(object = result)

enrich_network(object = result)