Bulk add multiple libraries for lipid matching

Bulk add multiple libraries for lipid matching

add_lib_collection(rule_file, path = ".")

Arguments

rule_file	CSV file containing the library list and rules. Use the template provided in the examples below.
path	Folder path containing a list of CSV files for libraries. All csv files listed in rule_file must be present. See examples below.

Value

A tibble with all added libraries. The created libraries are also added to the full list of available libraries.

Examples

# File cotanining list of libraries and their matching rules
rule_file <- system.file("extdata", "library_info.csv", package = "lipID")
readr::read_csv(rule_file)
#> Parsed with column specification:
#> cols(
#>   file = col_character(),
#>   and_cols = col_character(),
#>   or_cols = col_character(),
#>   dda = col_logical(),
#>   aif = col_logical(),
#>   class_only = col_logical(),
#>   mode = col_character(),
#>   adduct = col_character(),
#>   class_name = col_character()
#> )
#> # A tibble: 1 x 9
#>   file    and_cols or_cols dda   aif   class_only mode  adduct class_name       
#>   <chr>   <chr>    <chr>   <lgl> <lgl> <lgl>      <chr> <chr>  <chr>            
#> 1 PC_H.c… 2;12     8;9     TRUE  TRUE  FALSE      Pos   [M+H]+ Custom Phosphati…

# Folder containing csv files listed in rule_file
lib_dir <- system.file("extdata", package = "lipID")

mylibs <- add_lib_collection(rule_file, path = lib_dir)
#> Parsed with column specification:
#> cols(
#>   file = col_character(),
#>   and_cols = col_character(),
#>   or_cols = col_character(),
#>   dda = col_logical(),
#>   aif = col_logical(),
#>   class_only = col_logical(),
#>   mode = col_character(),
#>   adduct = col_character(),
#>   class_name = col_character()
#> )
#> Parsed with column specification:
#> cols(
#>   PC = col_character(),
#>   `[M+H]+` = col_double(),
#>   `MH-PC` = col_double(),
#>   `MH-tail1&TrimethylAmine` = col_double(),
#>   `MH-tail2&TrimethylAmine` = col_double(),
#>   `MH-Tail1` = col_double(),
#>   `MH-Tail2` = col_double(),
#>   LPC_T1_H = col_double(),
#>   LPC_T2_H = col_double(),
#>   `RCOO+58_T1` = col_double(),
#>   `RCOO+58_T2` = col_double(),
#>   `PC Head` = col_double()
#> )
mylibs
#> # A tibble: 219 x 13
#>    `basename(file)` and_cols or_cols dda   aif   class_only mode  adduct
#>    <chr>            <list>   <list>  <lgl> <lgl> <lgl>      <chr> <chr> 
#>  1 PC_H.csv         <chr [2… <chr [… TRUE  TRUE  FALSE      Pos   [M+H]+
#>  2 <NA>             <chr [1… <chr [… TRUE  FALSE FALSE      Neg   [M-H]-
#>  3 <NA>             <chr [1… <chr [… TRUE  FALSE FALSE      Neg   [M-H]-
#>  4 <NA>             <chr [3… <chr [… TRUE  FALSE FALSE      Neg   [M-H]-
#>  5 <NA>             <chr [4… <chr [… TRUE  FALSE FALSE      Neg   [M-H]-
#>  6 <NA>             <chr [2… <chr [… TRUE  TRUE  FALSE      Pos   [M+H]+
#>  7 <NA>             <chr [1… <chr [… TRUE  TRUE  FALSE      Neg   [M-H]-
#>  8 <NA>             <chr [2… <chr [… TRUE  TRUE  FALSE      Neg   [M-H]-
#>  9 <NA>             <chr [1… <chr [… TRUE  TRUE  FALSE      Neg   [M-H]-
#> 10 <NA>             <chr [2… <chr [… TRUE  TRUE  FALSE      Pos   [M+NH…
#> # … with 209 more rows, and 5 more variables: class_name <chr>, classkmd <dbl>,
#> #   ions <named list>, user_defined <lgl>, file <chr>

# Get only the added libraries
mylibs %>% dplyr::filter(user_defined)
#> # A tibble: 1 x 13
#>   `basename(file)` and_cols or_cols dda   aif   class_only mode  adduct
#>   <chr>            <list>   <list>  <lgl> <lgl> <lgl>      <chr> <chr> 
#> 1 PC_H.csv         <chr [2… <chr [… TRUE  TRUE  FALSE      Pos   [M+H]+
#> # … with 5 more variables: class_name <chr>, classkmd <dbl>, ions <named list>,
#> #   user_defined <lgl>, file <chr>

# Get built-in libraries as well as added ones for
# speific polarity / acquisition mode.
mylibs %>% dplyr::filter(mode == 'Pos', dda)  # Pos DDA libs
#> # A tibble: 84 x 13
#>    `basename(file)` and_cols or_cols dda   aif   class_only mode  adduct
#>    <chr>            <list>   <list>  <lgl> <lgl> <lgl>      <chr> <chr> 
#>  1 PC_H.csv         <chr [2… <chr [… TRUE  TRUE  FALSE      Pos   [M+H]+
#>  2 <NA>             <chr [2… <chr [… TRUE  TRUE  FALSE      Pos   [M+H]+
#>  3 <NA>             <chr [2… <chr [… TRUE  TRUE  FALSE      Pos   [M+NH…
#>  4 <NA>             <chr [2… <chr [… TRUE  FALSE FALSE      Pos   [M+H]+
#>  5 <NA>             <chr [4… <chr [… TRUE  TRUE  FALSE      Pos   [M+3N…
#>  6 <NA>             <chr [2… <chr [… TRUE  TRUE  FALSE      Pos   [M+NH…
#>  7 <NA>             <chr [1… <chr [… TRUE  TRUE  FALSE      Pos   [M+NH…
#>  8 <NA>             <chr [2… <chr [… TRUE  TRUE  FALSE      Pos   [M+NH…
#>  9 <NA>             <chr [1… <chr [… TRUE  FALSE FALSE      Pos   [M+NH…
#> 10 <NA>             <chr [1… <chr [… TRUE  FALSE TRUE       Pos   [M+NH…
#> # … with 74 more rows, and 5 more variables: class_name <chr>, classkmd <dbl>,
#> #   ions <named list>, user_defined <lgl>, file <chr>
mylibs %>% dplyr::filter(mode == 'Neg', aif)  # Neg AIF libs
#> # A tibble: 75 x 13
#>    `basename(file)` and_cols or_cols dda   aif   class_only mode  adduct
#>    <chr>            <list>   <list>  <lgl> <lgl> <lgl>      <chr> <chr> 
#>  1 <NA>             <chr [1… <chr [… TRUE  TRUE  FALSE      Neg   [M-H]-
#>  2 <NA>             <chr [2… <chr [… TRUE  TRUE  FALSE      Neg   [M-H]-
#>  3 <NA>             <chr [1… <chr [… TRUE  TRUE  FALSE      Neg   [M-H]-
#>  4 <NA>             <chr [4… <chr [… TRUE  TRUE  FALSE      Neg   [M-2H…
#>  5 <NA>             <chr [4… <chr [… TRUE  TRUE  FALSE      Neg   [M-H]-
#>  6 <NA>             <chr [2… <chr [… TRUE  TRUE  TRUE       Neg   [M-H]-
#>  7 <NA>             <chr [2… <chr [… TRUE  TRUE  FALSE      Neg   [M-H]-
#>  8 <NA>             <chr [3… <chr [… TRUE  TRUE  FALSE      Neg   [M-H]-
#>  9 <NA>             <chr [1… <chr [… FALSE TRUE  FALSE      Neg   [M+C2…
#> 10 <NA>             <chr [1… <chr [… TRUE  TRUE  FALSE      Neg   [M-H]-
#> # … with 65 more rows, and 5 more variables: class_name <chr>, classkmd <dbl>,
#> #   ions <named list>, user_defined <lgl>, file <chr>

# Filter your own libraries by Polarity / Acquisition mode
mylibs %>% dplyr::filter(user_defined, mode == 'Pos', dda)  # Only user_defined / Pos / DDA
#> # A tibble: 1 x 13
#>   `basename(file)` and_cols or_cols dda   aif   class_only mode  adduct
#>   <chr>            <list>   <list>  <lgl> <lgl> <lgl>      <chr> <chr> 
#> 1 PC_H.csv         <chr [2… <chr [… TRUE  TRUE  FALSE      Pos   [M+H]+
#> # … with 5 more variables: class_name <chr>, classkmd <dbl>, ions <named list>,
#> #   user_defined <lgl>, file <chr>