Introduction to MetID

Introduction

Metabolomics offers the opportunity to characterize complex diseases. The use of both LC-MS and GC-MS increases the coverage of the metabolome by taking advantage of their complementary features. Although numerous ions are detected using these platforms, only a small subset of the metabolites corresponding to these ions can be identified. The vast majority of them are either unknowns or “known-unknowns”. So we propose an innovative network-based approach to enhance our ability to determine the identities of significant ions detected by LC-MS. Specifically, it uses a probabilistic framework to determine the identities of known-unknowns by prioritizing their putative metabolite IDs. This will be accomplished by exploiting the inter-dependent relationships between metabolites in biological organisms based on knowledge from pathways/biochemical networks. This is the R package MetID that implements the algorithm.

The main function in this package is get_scores_for_LC_MS. See ?get_scores_for_LC_MC for documentation. This function takes an input dataset and assigns scores for each putative identifications. When working with this function, you must:

• Have a data file with .csv or .txt extension. Otherwise, you need to read it in R as a ‘data.frame’ object first.

• Check if the colnames of your data meet requirements: columns named exactly as ‘metid’ (IDs for peaks), ‘query_m.z’ (query mass of peaks), ‘exact_m.z’ (exact mass of putative IDs), ‘kegg_id’ (IDs of putative IDs from KEGG Database), ‘pubchem_cid’ (CIDs of putative IDs from PubChem Database).

Example

This example shows the usage of function get_scores_for_LC_MS with a small dataset: demo1. This dataset only contains 3 compounds and is documented in ?demo1. Note: the scores are only meaningful when we have a dataset with a large number of compounds. So the result of demo1 dataset does not make sense.

library(MetID)
#> Registered S3 methods overwritten by 'ggplot2':
#>   method         from 
#>   [.quosures     rlang
#>   c.quosures     rlang
#>   print.quosures rlang

data("demo1")
dim(demo1)
#> [1] 20  6
head(demo1) 
#>   Query.Mass                                                   Name
#> 1   371.2283        sn-Glycerol 3-phosphate bis(cyclohexylammonium)
#> 2   371.2283              1,4-Bis(chloromethyl)-2,5-diheptylbenzene
#> 3   450.3221 N-(3�_��,12�_��-dihydroxy-5�_�_-cholan-24-oyl)-glycine
#> 4   450.3221                                      Glycodeoxycholate
#> 5   450.3221                     Deoxycholic acid glycine conjugate
#> 6   450.3221                Chenodeoxycholic acid glycine conjugate
#>       Formula Exact.Mass PubChem.CID KEGG.ID
#> 1 C15H35N2O6P   370.2233                    
#> 2   C22H36Cl2   370.2194                    
#> 3   C26H43NO5   449.3141                    
#> 4   C26H43NO5   449.3141                    
#> 5   C26H43NO5   449.3141    22833539        
#> 6   C26H43NO5   449.3141

names(demo1)
#> [1] "Query.Mass"  "Name"        "Formula"     "Exact.Mass"  "PubChem.CID"
#> [6] "KEGG.ID"

colnames(demo1) <- c('query_m.z','name','formula','exact_m.z','pubchem_cid','kegg_id')
out <- get_scores_for_LC_MS(demo1, type = 'data.frame', na='-', mode='POS')
#> Start building network: it may take several minutes......
#> <sparse>[ <logic> ] : .M.sub.i.logical() maybe inefficient
#> Start getting random samples: it may take several minutes......
#> Start writing scores......
#> Completed!
head(out)
#>   query_m.z                                                   name
#> 1  371.2283        sn-Glycerol 3-phosphate bis(cyclohexylammonium)
#> 2  371.2283              1,4-Bis(chloromethyl)-2,5-diheptylbenzene
#> 3  450.3221 N-(3�_��,12�_��-dihydroxy-5�_�_-cholan-24-oyl)-glycine
#> 4  450.3221                                      Glycodeoxycholate
#> 5  450.3221                     Deoxycholic acid glycine conjugate
#> 6  450.3221                Chenodeoxycholic acid glycine conjugate
#>       formula exact_m.z pubchem_cid kegg_id       inchikey metid score
#> 1 C15H35N2O6P  370.2233                               <NA>     1     -
#> 2   C22H36Cl2  370.2194                               <NA>     1     -
#> 3   C26H43NO5  449.3141                               <NA>     2     -
#> 4   C26H43NO5  449.3141                               <NA>     2     -
#> 5   C26H43NO5  449.3141    22833539         WVULKSPCQVQLCU     2  0.49
#> 6   C26H43NO5  449.3141                               <NA>     2     -

Other data sources

We also include a large dataset (demo2) which generates meaningful scores. As well as data frames, MetID works with data that is stored in other ways, like csv files and text files.