# Introduction to MetID

#### Xuchen Wang

#### 2019-11-08

## 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.

##### Load MetID package first.

    library(MetID)

##### Load demo1 dataset.

    data("demo1")
    dim(demo1)
    #> [1] 20  6
    head(demo1)
    #>   Query.Mass
    #> 1   371.2283
    #> 2   371.2283
    #> 3   450.3221
    #> 4   450.3221
    #> 5   450.3221
    #> 6   450.3221
    #>                                                                                                             Name
    #> 1                                                                sn-Glycerol 3-phosphate bis(cyclohexylammonium)
    #> 2                                                                      1,4-Bis(chloromethyl)-2,5-diheptylbenzene
    #> 3 N-(3<U+FFFD>_<U+FFFD><U+FFFD>,12<U+FFFD>_<U+FFFD><U+FFFD>-dihydroxy-5<U+FFFD>_<U+FFFD>_-cholan-24-oyl)-glycine
    #> 4                                                                                              Glycodeoxycholate
    #> 5                                                                             Deoxycholic acid glycine conjugate
    #> 6                                                                        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

##### Check the form of demo1 dataset.

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

##### Change colnames of demo1.

Since the colnames do not meet our requirement, we need to change its colnames before we use get_scores_for_LC_MS function.

    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
    #> 1  371.2283
    #> 2  371.2283
    #> 3  450.3221
    #> 4  450.3221
    #> 5  450.3221
    #> 6  450.3221
    #>                                                                                                             name
    #> 1                                                                sn-Glycerol 3-phosphate bis(cyclohexylammonium)
    #> 2                                                                      1,4-Bis(chloromethyl)-2,5-diheptylbenzene
    #> 3 N-(3<U+FFFD>_<U+FFFD><U+FFFD>,12<U+FFFD>_<U+FFFD><U+FFFD>-dihydroxy-5<U+FFFD>_<U+FFFD>_-cholan-24-oyl)-glycine
    #> 4                                                                                              Glycodeoxycholate
    #> 5                                                                             Deoxycholic acid glycine conjugate
    #> 6                                                                        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 0.518
    #> 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.