Package 'tima'

Title: Taxonomically Informed Metabolite Annotation
Description: This package provides the infrastructure to perform Taxonomically Informed Metabolite Annotation.
Authors: Adriano Rutz [aut, cre] , Pierre-Marie Allard [ctb]
Maintainer: Adriano Rutz <[email protected]>
License: GPL (>= 3)
Version: 2.11.0
Built: 2024-09-18 15:26:34 UTC
Source: https://github.com/taxonomicallyinformedannotation/tima

Help Index

Annotate masses

Description

This function annotates a feature table based on exact mass match. It requires a structural library, its metadata, and lists of adducts, clusters, and neutral losses to be considered. The polarity has to be pos or neg and retention time and mass tolerances should be given. The feature table is expected to be pre-formatted.

Usage

annotate_masses(
  features = get_params(step = "annotate_masses")$files$features$prepared,
  output_annotations = get_params(step =
    "annotate_masses")$files$annotations$prepared$structural$ms1,
  output_edges = get_params(step = "annotate_masses")$files$networks$spectral$edges$raw,
  name_source = get_params(step = "annotate_masses")$names$source,
  name_target = get_params(step = "annotate_masses")$names$target,
  library = get_params(step = "annotate_masses")$files$libraries$sop$merged$keys,
  str_stereo = get_params(step =
    "annotate_masses")$files$libraries$sop$merged$structures$stereo,
  str_met = get_params(step =
    "annotate_masses")$files$libraries$sop$merged$structures$metadata,
  str_nam = get_params(step =
    "annotate_masses")$files$libraries$sop$merged$structures$names,
  str_tax_cla = get_params(step =
    "annotate_masses")$files$libraries$sop$merged$structures$taxonomies$cla,
  str_tax_npc = get_params(step =
    "annotate_masses")$files$libraries$sop$merged$structures$taxonomies$npc,
  adducts_list = get_params(step = "annotate_masses")$ms$adducts,
  clusters_list = get_params(step = "annotate_masses")$ms$clusters,
  neutral_losses_list = get_params(step = "annotate_masses")$ms$neutral_losses,
  ms_mode = get_params(step = "annotate_masses")$ms$polarity,
  tolerance_ppm = get_params(step = "annotate_masses")$ms$tolerances$mass$ppm$ms1,
  tolerance_rt = get_params(step = "annotate_masses")$ms$tolerances$rt$adducts
)

Arguments

features

Table containing your previous annotation to complement
output_annotations

Output for mass based structural annotations
output_edges

Output for mass based edges
name_source

Name of the source features column
name_target

Name of the target features column
library

Library containing the keys
str_stereo

File containing structures stereo
str_met

File containing structures metadata
str_nam

File containing structures names
str_tax_cla

File containing Classyfire taxonomy
str_tax_npc

File containing NPClassifier taxonomy
adducts_list

List of adducts to be used
clusters_list

List of clusters to be used
neutral_losses_list

List of neutral losses to be used
ms_mode

Ionization mode. Must be 'pos' or 'neg'
tolerance_ppm

Tolerance to perform annotation. Should be <= 20 ppm
tolerance_rt

Tolerance to group adducts. Should be <= 0.05 minutes

Value

The path to the files containing MS1 annotations and edges

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
github <- "https://raw.githubusercontent.com/"
repo <- "taxonomicallyinformedannotation/tima-example-files/main/"
data_interim <- "data/interim/"
dir <- paste0(github, repo)
dir <- paste0(dir, data_interim)
annotate_masses(
  features = paste0(dir, "features/example_features.tsv"),
  library = paste0(dir, "libraries/sop/merged/keys.tsv"),
  str_stereo = paste0(dir, "libraries/sop/merged/structures/stereo.tsv"),
  str_met = paste0(dir, "libraries/sop/merged/structures/metadata.tsv"),
  str_nam = paste0(dir, "libraries/sop/merged/structures/names.tsv"),
  str_tax_cla = paste0(dir, "libraries/sop/merged/structures/taxonomies/classyfire.tsv"),
  str_tax_npc = paste0(dir, "libraries/sop/merged/structures/taxonomies/npc.tsv")
)
unlink("data", recursive = TRUE)

## End(Not run)

Annotate spectra

Description

This function annotates spectra

Usage

annotate_spectra(
  input = get_params(step = "annotate_spectra")$files$spectral$raw,
  library = get_params(step = "annotate_spectra")$files$libraries$spectral,
  polarity = get_params(step = "annotate_spectra")$ms$polarity,
  output = get_params(step = "annotate_spectra")$files$annotations$raw$spectral$spectral,
  threshold = get_params(step =
    "annotate_spectra")$annotations$thresholds$ms2$similarity$annotation,
  ppm = get_params(step = "annotate_spectra")$ms$tolerances$mass$ppm$ms2,
  dalton = get_params(step = "annotate_spectra")$ms$tolerances$mass$dalton$ms2,
  qutoff = get_params(step = "annotate_spectra")$ms$thresholds$ms2$intensity,
  approx = get_params(step = "annotate_spectra")$annotations$ms2approx
)

Arguments

input

Query file containing spectra. Currently an '.mgf' file
library

Library containing spectra to match against. Can be '.mgf' or '.sqlite' (Spectra formatted)
polarity

MS polarity. Must be 'pos' or 'neg'.
output

Output file.
threshold

Minimal similarity to report
ppm

Relative ppm tolerance to be used
dalton

Absolute Dalton tolerance to be used
qutoff

Intensity under which ms2 fragments will be removed.
approx

Perform matching without precursor match

Details

It takes two files as input. A query file that will be matched against a library file.

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
get_file(
  url = get_default_paths()$urls$examples$spectra_mini,
  export = get_params(step = "annotate_spectra")$files$spectral$raw
)
get_file(
  url = get_default_paths()$urls$examples$spectral_lib_mini$with_rt,
  export = get_default_paths()$data$source$libraries$spectra$exp$with_rt
)
annotate_spectra(
  library = get_default_paths()$data$source$libraries$spectra$exp$with_rt
)
unlink("data", recursive = TRUE)

## End(Not run)

Benchmark taxize spectra

Description

This function adds taxa to the benchmark

Usage

benchmark_taxize_spectra(input, keys, org_tax_ott, output)

Arguments

input

Initial features
keys

SOP keys
org_tax_ott

Taxonomy
output

Prepared features

Details

Because they are still quite dirty

Value

The path to the taxed benchmark

Examples

NULL

Calculate entropy

Description

This function calculates entropy similarity between two spectra

Usage

calculate_entropy(
  index,
  target,
  frags,
  ms2_tolerance,
  ppm_tolerance,
  threshold = 0.1
)

Arguments

index

Index of the first spectrum
target

Index of the second spectrum
frags

List of fragments
ms2_tolerance

MS2 tolerance
ppm_tolerance

ppm tolerance
threshold

Threshold value for the score

Value

A list containing the calculated entropy similarity values or NULL if the score is below the threshold

Examples

NULL

Calculate mass of M

Description

This function calculates the mass of M

Usage

calculate_mass_of_m(adduct_string, mz, electron_mass = 0.0005485799)

Arguments

adduct_string

Adduct to be parsed
mz

mz
electron_mass

Electron mass

Value

A mass

Examples

calculate_mass_of_m(mz = 123.4567, adduct_string = "[M+H]+")
calculate_mass_of_m(mz = 123.4567, adduct_string = "[M+Na]+")
calculate_mass_of_m(mz = 123.456, adduct_string = "[2M1-C6H12O6 (hexose)+NaCl+H]2+")

Clean bio

Description

This function cleans the results obtained after biological weighting

Usage

clean_bio(
  annot_table_wei_bio = get("annot_table_wei_bio", envir = parent.frame()),
  edges_table = get("edges_table", envir = parent.frame()),
  minimal_consistency = get("minimal_consistency", envir = parent.frame())
)

Arguments

annot_table_wei_bio

Table containing your biologically weighted annotation
edges_table

Table containing the edges between features
minimal_consistency

Minimal consistency score for a class. FLOAT

Value

A table containing the biologically weighted annotation where only a given number of initial candidates are kept

See Also

weight_bio

Examples

NULL

Clean chemo

Description

This function cleans the results obtained after chemical weighting

Usage

clean_chemo(
  annot_table_wei_chemo = get("annot_table_wei_chemo", envir = parent.frame()),
  components_table = get("components_table", envir = parent.frame()),
  features_table = get("features_table", envir = parent.frame()),
  structure_organism_pairs_table = get("structure_organism_pairs_table", envir =
    parent.frame()),
  candidates_final = get("candidates_final", envir = parent.frame()),
  minimal_ms1_bio = get("minimal_ms1_bio", envir = parent.frame()),
  minimal_ms1_chemo = get("minimal_ms1_chemo", envir = parent.frame()),
  minimal_ms1_condition = get("minimal_ms1_condition", envir = parent.frame()),
  high_confidence = get("high_confidence", envir = parent.frame()),
  remove_ties = get("remove_ties", envir = parent.frame()),
  summarise = get("summarise", envir = parent.frame())
)

Arguments

annot_table_wei_chemo

Table containing your chemically weighted annotation
components_table

Prepared components file
features_table

Prepared features file
structure_organism_pairs_table

Table containing the structure - organism pairs
candidates_final

Number of final candidates to keep
minimal_ms1_bio

Minimal biological score to keep MS1 based annotation
minimal_ms1_chemo

Minimal chemical score to keep MS1 based annotation
minimal_ms1_condition

Condition to be used. Must be "OR" or "AND".
high_confidence

Report high confidence candidates only. BOOLEAN
remove_ties

Remove ties. BOOLEAN
summarise

Boolean. summarise results (1 row per feature)

Value

A table containing the chemically weighted annotation where only a given number of initial candidates are kept

See Also

weight_chemo

Examples

NULL

Clean collapse

Description

This function collapses a grouped dataframe and trims it

Usage

clean_collapse(grouped_df, cols = NA)

Arguments

grouped_df

Grouped dataframe
cols

Column(s) to apply collapse to

Value

Cleaned and collapsed dataframe

Examples

NULL

Columns model

Description

This function models columns

Usage

columns_model()

Value

The columns model

Examples

NULL

Complement metadata of structures

Description

This function complement structural metadata

Usage

complement_metadata_structures(
  df,
  str_stereo = get("str_stereo", envir = parent.frame()),
  str_met = get("str_met", envir = parent.frame()),
  str_nam = get("str_nam", envir = parent.frame()),
  str_tax_cla = get("str_tax_cla", envir = parent.frame()),
  str_tax_npc = get("str_tax_npc", envir = parent.frame())
)

Arguments

df

Data frame with structural metadata to be complemented
str_stereo

File containing structures stereo
str_met

File containing structures metadata
str_nam

File containing structures names
str_tax_cla

File containing Classyfire taxonomy
str_tax_npc

File containing NPClassifier taxonomy

Value

Data frame with complemented structural metadata

Examples

NULL

Copy backbone

Description

This function copies backbone

Usage

copy_backbone(cache_dir = fs::path_home(".tima"), package = "tima")

Arguments

cache_dir

Cache directory
package

Package

Examples

NULL

Create components

Description

This function create components from edges

Usage

create_components(
  input = get_params(step = "create_components")$files$networks$spectral$edges$prepared,
  output = get_params(step = "create_components")$files$networks$spectral$components$raw
)

Arguments

input

Input file(s) containing edges
output

Output file.

Value

The path to the created components

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
github <- "https://raw.githubusercontent.com/"
repo <- "taxonomicallyinformedannotation/tima-example-files/main/"
data_interim <- "data/interim/"
dir <- paste0(github, repo)
dir <- paste0(dir, data_interim)
get_file(
  url = paste0(dir, "features/example_edges.tsv"),
  export = get_params(step = "create_components")$files$networks$spectral$edges$prepared
)
create_components()
unlink("data", recursive = TRUE)

## End(Not run)

Create directory

Description

This function creates a directory at the specified path if it does not already exist.

Usage

create_dir(export)

Arguments

export

Path to the directory to be created

Value

Message indicating the status of directory creation

Examples

create_dir(export = "path/to/directory_of_file")
unlink("path", recursive = TRUE)

Create edges

Description

This function applies similarity calculation to a list of spectra to create edges

Usage

create_edges(
  index,
  frags,
  precs,
  nspecs,
  ms2_tolerance,
  ppm_tolerance,
  threshold
)

Arguments

index

Indices
frags

Fragments
precs

Precursors
nspecs

Number of spectra
ms2_tolerance

MS2 tolerance
ppm_tolerance

ppm tolerance
threshold

Threshold

Examples

NULL

Create edges spectra

Description

This function create edges based on fragmentation spectra similarity

Usage

create_edges_spectra(
  input = get_params(step = "create_edges_spectra")$files$spectral$raw,
  output = get_params(step = "create_edges_spectra")$files$networks$spectral$edges$raw,
  name_source = get_params(step = "create_edges_spectra")$names$source,
  name_target = get_params(step = "create_edges_spectra")$names$target,
  threshold = get_params(step =
    "create_edges_spectra")$annotations$thresholds$ms2$similarity$edges,
  ppm = get_params(step = "create_edges_spectra")$ms$tolerances$mass$ppm$ms2,
  dalton = get_params(step = "create_edges_spectra")$ms$tolerances$mass$dalton$ms2,
  qutoff = get_params(step = "create_edges_spectra")$ms$thresholds$ms2$intensity
)

Arguments

input

Query file containing spectra. Currently an '.mgf' file
output

Output file.
name_source

Name of the source features column
name_target

Name of the target features column
threshold

Minimal similarity to report
ppm

Relative ppm tolerance to be used
dalton

Absolute Dalton tolerance to be used
qutoff

Intensity under which ms2 fragments will be removed.

Value

The path to the created spectral edges

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
get_file(
  url = get_default_paths()$urls$examples$spectra_mini,
  export = get_params(step = "create_edges_spectra")$files$spectral$raw
)
create_edges_spectra()
unlink("data", recursive = TRUE)

## End(Not run)

Decorate bio

Description

This function outputs information about biological weighting

Usage

decorate_bio(
  annot_table_wei_bio = get("annot_table_wei_chemo", envir = parent.frame()),
  score_biological_kingdom = get("score_biological_kingdom", envir = parent.frame()),
  score_biological_phylum = get("score_biological_phylum", envir = parent.frame()),
  score_biological_class = get("score_biological_class", envir = parent.frame()),
  score_biological_order = get("score_biological_order", envir = parent.frame()),
  score_biological_family = get("score_biological_family", envir = parent.frame()),
  score_biological_tribe = get("score_biological_tribe", envir = parent.frame()),
  score_biological_genus = get("score_biological_genus", envir = parent.frame()),
  score_biological_species = get("score_biological_species", envir = parent.frame()),
  score_biological_variety = get("score_biological_variety", envir = parent.frame())
)

Arguments

annot_table_wei_bio

Table to decorate
score_biological_kingdom

Kingdom score
score_biological_phylum

Phylum score
score_biological_class

Class score
score_biological_order

Order score
score_biological_family

Family score
score_biological_tribe

Tribe score
score_biological_genus

Genus score
score_biological_species

Species score
score_biological_variety

Variety score

Value

Message indicating the number of annotations weighted at each biological level

Examples

NULL

Decorate chemo

Description

This function outputs information about chemical weighting

Usage

decorate_chemo(
  annot_table_wei_chemo = get("annot_table_wei_chemo", envir = parent.frame()),
  score_chemical_cla_kingdom = get("score_chemical_cla_kingdom", envir = parent.frame()),
  score_chemical_cla_superclass = get("score_chemical_cla_superclass", envir =
    parent.frame()),
  score_chemical_cla_class = get("score_chemical_cla_class", envir = parent.frame()),
  score_chemical_cla_parent = get("score_chemical_cla_parent", envir = parent.frame()),
  score_chemical_npc_pathway = get("score_chemical_npc_pathway", envir = parent.frame()),
  score_chemical_npc_superclass = get("score_chemical_npc_superclass", envir =
    parent.frame()),
  score_chemical_npc_class = get("score_chemical_npc_class", envir = parent.frame())
)

Arguments

annot_table_wei_chemo

Table to decorate
score_chemical_cla_kingdom

Classyfire kingdom score
score_chemical_cla_superclass

Classyfire superclass score
score_chemical_cla_class

Classyfire class score
score_chemical_cla_parent

Classyfire parent score
score_chemical_npc_pathway

NPC pathway score
score_chemical_npc_superclass

NPC superclass score
score_chemical_npc_class

NPC class score

Value

Message indicating the number of annotations weighted at each chemical level

Examples

NULL

Decorate masses

Description

This function outputs information about MS1 annotation

Usage

decorate_masses(
  annotation_table_ms1 = get("annotation_table_ms1", envir = parent.frame())
)

Arguments

annotation_table_ms1

Table to decorate

Value

Message indicating the number of annotations obtained by MS1

Examples

NULL

Distance between two elements in a distance matrix

Description

This function calculates the distance between two elements in a distance matrix

Usage

dist_get(d, idx1, idx2)

Arguments

d

Distance matrix
idx1

Index of the first element
idx2

Index of the second element

Details

Credit goes to usedist package

Value

Distance between the two elements

Examples

NULL

Dist groups

Description

This function gets distances per group

Usage

dist_groups(d, g)

Arguments

d

A distance object
g

A grouping vector for the distance object

Value

A data frame containing distance information between pairs of observations in the distance object, with columns for the names or indices of the observations, the group labels for each observation, and the distance between the observations. The label column indicates whether the distance is within a group or between groups.

Examples

NULL

Export output

Description

This function creates the output directory if it doesn't exist and exports the data frame to a tab-delimited file.

Usage

export_output(x, file = output)

Arguments

x

data frame to be exported
file

path to the output file

Value

The path of the exported file

Examples

export_output(x = data.frame(), file = "output/file.tsv")
unlink("output", recursive = TRUE)

Export parameters

Description

This function writes the parameters to a YAML file in the specified directory.

Usage

export_params(
  parameters = get("parameters", envir = parent.frame()),
  directory = get_default_paths()$data$interim$params$path,
  step
)

Arguments

parameters

list of parameters to be exported
directory

directory where the YAML file will be saved
step

step identifier to be included in the YAML file name

Examples

NULL

Export spectra RDS

Description

This function exports spectra in RDS

Usage

export_spectra_rds(file, spectra)

Arguments

file

File where spectra will be exported.
spectra

The spectra object where spectra are stored

Examples

NULL

Extract spectra from a Spectra object

Description

This function extracts spectra from a Spectraobject

Usage

extract_spectra(object)

Arguments

object

Object of class Spectra

Value

Data frame containing spectra data

Examples

NULL

Fake annotations columns

Description

This function fakes annotations columns

Usage

fake_annotations_columns()

Examples

NULL

Fake ECMDB

Description

This function fakes ECMDB in case the download failed

Usage

fake_ecmdb(export)

Arguments

export

Path to save the file to

Examples

NULL

Fake HMDB

Description

This function fakes HMDB in case the download failed

Usage

fake_hmdb(export)

Arguments

export

Path to save the file to

Examples

NULL

Fake LOTUS

Description

This function fakes LOTUS in case the download failed

Usage

fake_lotus(export)

Arguments

export

Path to save the file to

Examples

NULL

Fake SOP columns

Description

This function fakes sop columns

Usage

fake_sop_columns()

Examples

NULL

Filter annotations

Description

This function filters initial annotations.

Usage

filter_annotations(
  annotations = get_params(step =
    "filter_annotations")$files$annotations$prepared$structural,
  features = get_params(step = "filter_annotations")$files$features$prepared,
  rts = get_params(step = "filter_annotations")$files$libraries$temporal$prepared,
  output = get_params(step = "filter_annotations")$files$annotations$filtered,
  tolerance_rt = get_params(step = "filter_annotations")$ms$tolerances$rt$library
)

Arguments

annotations

Prepared annotations file
features

Prepared features file
rts

Prepared retention time library
output

Output file
tolerance_rt

Tolerance to filter retention time

Value

The path to the filtered annotations

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
github <- "https://raw.githubusercontent.com/"
repo <- "taxonomicallyinformedannotation/tima-example-files/main/"
dir <- paste0(github, repo)
annotations <- get_params(step = "filter_annotations")$files$annotations$prepared$structural[[2]] |>
  gsub(
    pattern = ".gz",
    replacement = "",
    fixed = TRUE
  )
features <- get_params(step = "filter_annotations")$files$features$prepared |>
  gsub(
    pattern = ".gz",
    replacement = "",
    fixed = TRUE
  )
rts <- get_params(step = "filter_annotations")$files$libraries$temporal$prepared |>
  gsub(
    pattern = ".gz",
    replacement = "",
    fixed = TRUE
  )
get_file(url = paste0(dir, annotations), export = annotations)
get_file(url = paste0(dir, features), export = features)
get_file(url = paste0(dir, rts), export = rts)
filter_annotations(
  annotations = annotations,
  features = features,
  rts = rts
)
unlink("data", recursive = TRUE)

## End(Not run)

Filter high confidence only [Experimental]

Description

This function filters highly confident annotations only.

Usage

filter_high_confidence_only(df, score_bio_min = 0.85, score_ini_min = 0.75)

Arguments

df

Dataframe
score_bio_min

Minimal biological score. Current default to 0.85.
score_ini_min

Minimal initial score. Current default to 0.75.

Value

A dataframe containing only high confidence annotations

Examples

NULL

Get default paths

Description

This function gets default paths

Usage

get_default_paths(yaml = system.file("paths.yaml", package = "tima"))

Arguments

yaml

The YAML file containing the paths (default is "paths.yaml")

Value

A list containing the paths specified in the YAML file

Examples

get_default_paths()

Get example files

Description

This function downloads example files

Usage

get_example_files(
  example = c("features", "metadata", "sirius", "spectra"),
  in_cache = TRUE
)

Arguments

example

The example(s) you want to download
in_cache

Flag to indicate if storing the files in cache

Value

Example files.

Examples

get_example_files(example = c("features"), in_cache = FALSE)
unlink("data", recursive = TRUE)

Get example sirius

Description

This function gets example SIRIUS annotations

Usage

get_example_sirius(
  url = get_default_paths()$urls$examples$sirius,
  export = get_default_paths()$data$interim$annotations$example_sirius
)

Arguments

url

URL where the example is accessible
export

Path where to save the example

Examples

NULL

Get file

Description

This function get files

Usage

get_file(url, export, limit = 3600)

Arguments

url

URL of the file to be downloaded
export

File path where the file should be saved
limit

Timeout limit (in seconds)

Value

The path to the file

Examples

git <- "https://github.com/"
org <- "taxonomicallyinformedannotation"
repo <- "tima-example-files"
branch <- "main"
file <- "example_metadata.tsv"
get_file(
  url = paste(git, org, repo, "raw", branch, file, sep = "/"),
  export = "data/source/example_metadata.tsv"
)
unlink("data", recursive = TRUE)

Get GNPS Tables

Description

This function gets GNPS tables from corresponding job ID.

Usage

get_gnps_tables(
  gnps_job_id,
  gnps_job_example = get_default_paths()$gnps$example,
  filename,
  workflow = "fbmn",
  path_features,
  path_metadata,
  path_spectra,
  path_source = get_default_paths()$data$source$path,
  path_interim_a = get_default_paths()$data$interim$annotations$path,
  path_interim_f = get_default_paths()$data$interim$features$path
)

Arguments

gnps_job_id

GNPS job ID
gnps_job_example

GNPS job example
filename

Name of the file
workflow

Character string indicating the type of workflow, either "fbmn" or "classical"
path_features

Path to features
path_metadata

Path to metadata
path_spectra

Path to spectra
path_source

Path to store the source files
path_interim_a

Path to store the interim annotations file
path_interim_f

Path to store the interim features files

Value

The downloaded GNPS tables

Examples

NULL

Get last version from Zenodo

Description

This function gets the last version of a file from a Zenodo record

Usage

get_last_version_from_zenodo(doi, pattern, path)

Arguments

doi

DOI of the Zenodo record
pattern

Pattern to identify the file to download
path

Path to save the file to

Details

Credit goes to partially to https://inbo.github.io/inborutils/

Value

The path to the file

Examples

get_last_version_from_zenodo(
  doi = "10.5281/zenodo.5794106",
  pattern = "frozen.csv.gz",
  path = "frozen.csv.gz"
)
unlink("frozen.csv.gz")

Get MassBank spectra

Description

This function gets MassBank spectra

Usage

get_massbank_spectra(
  output_dir = "data/source/libraries/spectra/exp",
  mb_file = get_default_paths()$urls$massbank$file,
  mb_url = get_default_paths()$urls$massbank$url,
  mb_version = get_default_paths()$urls$massbank$version
)

Arguments

output_dir

Output where to store the spectra
mb_file

MassBank file
mb_url

MassBank URL
mb_version

MassBank version

Value

The path to MassBank spectra

Examples

NULL

Get organism taxonomy (Open Tree of Life Taxonomy)

Description

This function retrieves taxonomy from the Open Tree of Life taxonomy

Usage

get_organism_taxonomy_ott(
  df,
  url = "https://api.opentreeoflife.org/v3/taxonomy/about",
  retry = TRUE
)

Arguments

df

Dataframe containing your organism(s) name(s)
url

url of the ott api (for testing purposes)
retry

Boolean. Retry with generic epithet

Value

The path to the obtained OTT taxonomy

Examples

df <- data.frame("organism" = "Homo sapiens")
get_organism_taxonomy_ott(df)
unlink("data", recursive = TRUE)

Get parameters

Description

This function gets the parameters for the job. Combination of cli and yaml parameters

Usage

get_params(step)

Arguments

step

Name of the step being performed

Value

The parameters

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
get_params("prepare_params")

## End(Not run)

Go to cache

Description

This function goes to cache

Usage

go_to_cache(dir = ".tima")

Arguments

dir

Directory

Value

Goes to cache

Examples

NULL

Harmonize adducts

Description

This function annotates masses

Usage

harmonize_adducts(df, adducts_colname = "adduct")

Arguments

df

Dataframe
adducts_colname

Adducts colname

Value

A table with harmonized adducts

Examples

NULL

Harmonize names sirius

Description

This function harmonizes the names of Sirius outputs to make them compatible

Usage

harmonize_names_sirius(x)

Arguments

x

Character string containing a name

Value

Character string with the name modified according to the rules specified in the function

Examples

harmonized_name <- tima:::harmonize_names_sirius("My_name")

Harmonize spectra

Description

This function harmonizes spectra headers

Usage

harmonize_spectra(
  spectra,
  metad = get("metad", envir = parent.frame()),
  mode,
  col_ad = get("col_ad", envir = parent.frame()),
  col_ce = get("col_ce", envir = parent.frame()),
  col_ci = get("col_ci", envir = parent.frame()),
  col_em = get("col_em", envir = parent.frame()),
  col_in = get("col_in", envir = parent.frame()),
  col_io = get("col_io", envir = parent.frame()),
  col_ik = get("col_ik", envir = parent.frame()),
  col_il = get("col_il", envir = parent.frame()),
  col_mf = get("col_mf", envir = parent.frame()),
  col_na = get("col_na", envir = parent.frame()),
  col_po = get("col_po", envir = parent.frame()),
  col_sm = get("col_sm", envir = parent.frame()),
  col_sn = get("col_sn", envir = parent.frame()),
  col_si = get("col_si", envir = parent.frame()),
  col_sp = get("col_sp", envir = parent.frame()),
  col_sy = get("col_sy", envir = parent.frame()),
  col_xl = get("col_xl", envir = parent.frame())
)

Arguments

spectra

Spectra object to be harmonized
metad

Metadata to identify the library
mode

MS ionization mode. Must contain 'pos' or 'neg'
col_ad

Name of the adduct in mgf
col_ce

Name of the collision energy in mgf
col_ci

Name of the compound id in mgf
col_em

Name of the exact mass in mgf
col_in

Name of the InChI in mgf
col_io

Name of the InChI without stereo in mgf
col_ik

Name of the InChIKey in mgf
col_il

Name of the InChIKey without stereo in mgf
col_mf

Name of the molecular formula in mgf
col_na

Name of the name in mgf
col_po

Name of the polarity in mgf
col_sm

Name of the SMILES in mgf
col_sn

Name of the SMILES without stereo in mgf
col_si

Name of the spectrum id in mgf
col_sp

Name of the SPLASH in mgf
col_sy

Name of the synonyms in mgf
col_xl

Name of the xlogp in mgf

Value

The harmonized spectra

Examples

NULL

Import spectra

Description

This function imports spectra from a file (.mgf or .sqlite)

Usage

import_spectra(
  file,
  cutoff = 0,
  dalton = 0.01,
  polarity = NA,
  ppm = 10,
  sanitize = TRUE
)

Arguments

file

File path of the spectrum file to be imported
cutoff

Absolute minimal intensity
dalton

Dalton tolerance
polarity

Polarity
ppm

PPM tolerance
sanitize

Flag indicating whether to sanitize. Default TRUE

Value

Spectra object containing the imported spectra

Examples

get_file(
  url = get_default_paths()$urls$examples$spectra_mini,
  export = get_default_paths()$data$source$spectra
)
import_spectra(file = get_default_paths()$data$source$spectra)
import_spectra(
  file = get_default_paths()$data$source$spectra,
  sanitize = FALSE
)

Install

Description

This function runs some required install

Usage

install(
  package = "tima",
  repos = c("https://taxonomicallyinformedannotation.r-universe.dev",
    "https://bioc.r-universe.dev", "https://cloud.r-project.org"),
  dependencies = TRUE,
  test = FALSE
)

Arguments

package

Package
repos

Repos
dependencies

Flag for dependencies
test

Flag for tests

Examples

NULL

Load yaml files

Description

This function load yaml files

Usage

load_yaml_files()

Value

A list of loaded yaml files

Examples

NULL

Log debug

Description

Simple helper for debugging

Usage

log_debug(...)

Arguments

...

one or more values to be logged

Value

Message for debugging

Examples

log_debug("This is a debug message")

Log pipe

Description

Simple helper for debugging between pipes

Usage

log_pipe(x, ...)

Arguments

x

value for the pipe
...

one or more values to be logged

Value

Message for debugging

Examples

NULL

Parse adduct

Description

This function parses adducts

Usage

parse_adduct(
  adduct_string,
  regex = "\\[(\\d*)M(?![a-z])(\\d*)([+-][\\w\\d].*)?.*\\](\\d*)([+-])?"
)

Arguments

adduct_string

Adduct to be parsed
regex

Regex used for parsing

Value

Parsed elements from adduct

Examples

parse_adduct("[M+H]+")
parse_adduct("[2M1-C6H12O6 (hexose)+NaCl+H]2+")

Parse CLI parameters

Description

This function parses command line parameters

Usage

parse_cli_params(arguments, parameters)

Arguments

arguments

CLI arguments
parameters

Parameters

Value

Parameters coming from the CLI

Examples

NULL

Parse YAML parameters

Description

This function parses YAML parameters

Usage

parse_yaml_params(
  def = get("default_path", envir = parent.frame()),
  usr = get("user_path", envir = parent.frame())
)

Arguments

def

Default path
usr

User path

Value

A list containing the parameters specified in the YAML files

Examples

NULL

Pre harmonize names sirius

Description

This function pre harmonizes Sirius names to make them compatible

Usage

pre_harmonize_names_sirius(x)

Arguments

x

Character string containing a name

Value

Character string with the name modified according to the rules specified in the function

Examples

prepared_name <- tima:::pre_harmonize_names_sirius("My name/suffix")

Prepare annotations GNPS

Description

This function prepares GNPS obtained annotations

Usage

prepare_annotations_gnps(
  input = get_params(step =
    "prepare_annotations_gnps")$files$annotations$raw$spectral$gnps,
  output = get_params(step =
    "prepare_annotations_gnps")$files$annotations$prepared$structural$gnps,
  str_stereo = get_params(step =
    "prepare_annotations_gnps")$files$libraries$sop$merged$structures$stereo,
  str_met = get_params(step =
    "prepare_annotations_gnps")$files$libraries$sop$merged$structures$metadata,
  str_nam = get_params(step =
    "prepare_annotations_gnps")$files$libraries$sop$merged$structures$names,
  str_tax_cla = get_params(step =
    "prepare_annotations_gnps")$files$libraries$sop$merged$structures$taxonomies$cla,
  str_tax_npc = get_params(step =
    "prepare_annotations_gnps")$files$libraries$sop$merged$structures$taxonomies$npc
)

Arguments

input

Input file
output

Output file
str_stereo

File containing structures stereo
str_met

File containing structures metadata
str_nam

File containing structures names
str_tax_cla

File containing Classyfire taxonomy
str_tax_npc

File containing NPClassifier taxonomy

Value

The path to the prepared GNPS annotations

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
prepare_annotations_gnps()
unlink("data", recursive = TRUE)

## End(Not run)

Prepare annotations SIRIUS

Description

This function prepares Sirius results to make them compatible

Usage

prepare_annotations_sirius(
  input_directory = get_params(step =
    "prepare_annotations_sirius")$files$annotations$raw$sirius,
  output_ann = get_params(step =
    "prepare_annotations_sirius")$files$annotations$prepared$structural$sirius,
  output_can = get_params(step =
    "prepare_annotations_sirius")$files$annotations$prepared$canopus,
  output_for = get_params(step =
    "prepare_annotations_sirius")$files$annotations$prepared$formula,
  sirius_version = get_params(step = "prepare_annotations_sirius")$tools$sirius$version,
  str_stereo = get_params(step =
    "prepare_annotations_sirius")$files$libraries$sop$merged$structures$stereo,
  str_met = get_params(step =
    "prepare_annotations_sirius")$files$libraries$sop$merged$structures$metadata,
  str_nam = get_params(step =
    "prepare_annotations_sirius")$files$libraries$sop$merged$structures$names,
  str_tax_cla = get_params(step =
    "prepare_annotations_sirius")$files$libraries$sop$merged$structures$taxonomies$cla,
  str_tax_npc = get_params(step =
    "prepare_annotations_sirius")$files$libraries$sop$merged$structures$taxonomies$npc
)

Arguments

input_directory

Directory containing the Sirius results
output_ann

Output where to save prepared annotation results
output_can

Output where to save prepared canopus results
output_for

Output where to save prepared formula results
sirius_version

Sirius version
str_stereo

File containing structures stereo
str_met

File containing structures metadata
str_nam

File containing structures names
str_tax_cla

File containing Classyfire taxonomy
str_tax_npc

File containing NPClassifier taxonomy

Value

The path to the prepared SIRIUS annotations

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
prepare_annotations_sirius()
unlink("data", recursive = TRUE)

## End(Not run)

Prepare annotations MS2

Description

This function prepares the spectral matches obtained previously to make them compatible

Usage

prepare_annotations_spectra(
  input = get_params(step =
    "prepare_annotations_spectra")$files$annotations$raw$spectral$spectral,
  output = get_params(step =
    "prepare_annotations_spectra")$files$annotations$prepared$structural$spectral,
  str_stereo = get_params(step =
    "prepare_annotations_spectra")$files$libraries$sop$merged$structures$stereo,
  str_met = get_params(step =
    "prepare_annotations_spectra")$files$libraries$sop$merged$structures$metadata,
  str_nam = get_params(step =
    "prepare_annotations_spectra")$files$libraries$sop$merged$structures$names,
  str_tax_cla = get_params(step =
    "prepare_annotations_spectra")$files$libraries$sop$merged$structures$taxonomies$cla,
  str_tax_npc = get_params(step =
    "prepare_annotations_spectra")$files$libraries$sop$merged$structures$taxonomies$npc
)

Arguments

input

Input file
output

Output file
str_stereo

File containing structures stereo
str_met

File containing structures metadata
str_nam

File containing structures names
str_tax_cla

File containing Classyfire taxonomy
str_tax_npc

File containing NPClassifier taxonomy

Value

The path to the prepared spectral annotations

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
github <- "https://raw.githubusercontent.com/"
repo <- "taxonomicallyinformedannotation/tima-example-files/main/"
data_interim <- "data/interim/"
dir <- paste0(github, repo)
input <- get_params(step = "prepare_annotations_spectra")$files$annotations$raw$spectral$spectral |>
  gsub(
    pattern = ".tsv.gz",
    replacement = "_pos.tsv",
    fixed = TRUE
  )
get_file(url = paste0(dir, input), export = input)
dir <- paste0(dir, data_interim)
prepare_annotations_spectra(
  input = input,
  str_stereo = paste0(dir, "libraries/sop/merged/structures/stereo.tsv"),
  str_met = paste0(dir, "libraries/sop/merged/structures/metadata.tsv"),
  str_nam = paste0(dir, "libraries/sop/merged/structures/names.tsv"),
  str_tax_cla = paste0(dir, "libraries/sop/merged/structures/taxonomies/classyfire.tsv"),
  str_tax_npc = paste0(dir, "libraries/sop/merged/structures/taxonomies/npc.tsv")
)
unlink("data", recursive = TRUE)

## End(Not run)

Prepare features components

Description

This function prepares the components (clusters in molecular network) for further use

Usage

prepare_features_components(
  input = get_params(step =
    "prepare_features_components")$files$networks$spectral$components$raw,
  output = get_params(step =
    "prepare_features_components")$files$networks$spectral$components$prepared
)

Arguments

input

Input file
output

Output file

Value

The path to the prepared features' components

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
github <- "https://raw.githubusercontent.com/"
repo <- "taxonomicallyinformedannotation/tima-example-files/main/"
dir <- paste0(github, repo)
input <- get_params(step = "prepare_features_components")$files$networks$spectral$components$raw
get_file(url = paste0(dir, input), export = input)
prepare_features_components(
  input = input
)
unlink("data", recursive = TRUE)

## End(Not run)

Prepare features edges

Description

This function prepares edges for further use

Usage

prepare_features_edges(
  input = get_params(step = "prepare_features_edges")$files$networks$spectral$edges$raw,
  output = get_params(step =
    "prepare_features_edges")$files$networks$spectral$edges$prepared,
  name_source = get_params(step = "prepare_features_edges")$names$source,
  name_target = get_params(step = "prepare_features_edges")$names$target
)

Arguments

input

Input file if 'manual'
output

Output file
name_source

Name of the source features column
name_target

Name of the target features column

Value

The path to the prepared edges

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
github <- "https://raw.githubusercontent.com/"
repo <- "taxonomicallyinformedannotation/tima-example-files/main/"
dir <- paste0(github, repo)
input_1 <- get_params(step = "prepare_features_edges")$files$networks$spectral$edges$raw$ms1
input_2 <- get_params(step = "prepare_features_edges")$files$networks$spectral$edges$raw$spectral
get_file(url = paste0(dir, input_1), export = input_1)
get_file(url = paste0(dir, input_2), export = input_2)
prepare_features_edges(
  input = list("ms1" = input_1, "spectral" = input_2)
)
unlink("data", recursive = TRUE)

## End(Not run)

Prepare features table

Description

This function prepares features

Usage

prepare_features_tables(
  features = get_params(step = "prepare_features_tables")$files$features$raw,
  output = get_params(step = "prepare_features_tables")$files$features$prepared,
  name_adduct = get_params(step = "prepare_features_tables")$names$adduct,
  name_features = get_params(step = "prepare_features_tables")$names$features,
  name_rt = get_params(step = "prepare_features_tables")$names$rt$features,
  name_mz = get_params(step = "prepare_features_tables")$names$precursor
)

Arguments

features

Path to the file containing the features data
output

Path to the file to export the merged data to
name_adduct

Name of the adduct column in the features data
name_features

Name of the features column in the features data
name_rt

Name of the retention time column in the features data
name_mz

Name of the m/z column in the features data

Value

The path to the prepared feature table

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
get_file(
  url = get_default_paths()$urls$examples$features,
  export = get_params(step = "prepare_features_tables")$files$features$raw
)
prepare_features_tables()
unlink("data", recursive = TRUE)

## End(Not run)

Prepare libraries of retention times

Description

This function prepares retention times libraries to be used for later

Usage

prepare_libraries_rt(
  mgf_exp = get_params(step = "prepare_libraries_rt")$files$libraries$temporal$exp$mgf,
  mgf_is = get_params(step = "prepare_libraries_rt")$files$libraries$temporal$is$mgf,
  temp_exp = get_params(step = "prepare_libraries_rt")$files$libraries$temporal$exp$csv,
  temp_is = get_params(step = "prepare_libraries_rt")$files$libraries$temporal$is$csv,
  output_rt = get_params(step = "prepare_libraries_rt")$files$libraries$temporal$prepared,
  output_sop = get_params(step = "prepare_libraries_rt")$files$libraries$sop$prepared$rt,
  col_ik = get_params(step = "prepare_libraries_rt")$names$mgf$inchikey,
  col_rt = get_params(step = "prepare_libraries_rt")$names$mgf$retention_time,
  col_sm = get_params(step = "prepare_libraries_rt")$names$mgf$smiles,
  name_inchikey = get_params(step = "prepare_libraries_rt")$names$inchikey,
  name_rt = get_params(step = "prepare_libraries_rt")$names$rt$library,
  name_smiles = get_params(step = "prepare_libraries_rt")$names$smiles,
  unit_rt = get_params(step = "prepare_libraries_rt")$units$rt
)

Arguments

mgf_exp

MGF containing experimental retention times
mgf_is

MGF containing in silico predicted retention times
temp_exp

File containing experimental retention times
temp_is

File containing in silico predicted retention times
output_rt

Output retention time file
output_sop

Output pseudo sop file
col_ik

Name of the InChIKey in mgf
col_rt

Name of the retention time in mgf
col_sm

Name of the SMILES in mgf
name_inchikey

Name of the InChIKey in file
name_rt

Name of the retention time in file
name_smiles

Name of the SMILES in file
unit_rt

Unit of the retention time. Must be "seconds" or "minutes"

Value

The path to the prepared retention time library

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
prepare_libraries_rt()
unlink("data", recursive = TRUE)

## End(Not run)

Prepare libraries of structure organism pairs CLOSED

Description

Prepare libraries of structure organism pairs CLOSED

Usage

prepare_libraries_sop_closed(
  input = get_params(step =
    "prepare_libraries_sop_closed")$files$libraries$sop$raw$closed,
  output = get_params(step =
    "prepare_libraries_sop_closed")$files$libraries$sop$prepared$closed
)

Arguments

input

Input file
output

Output file

Value

The path to the prepared structure-organism pairs library CLOSED

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
prepare_libraries_sop_closed()
unlink("data", recursive = TRUE)

## End(Not run)

Prepare libraries of structure organism pairs ECMDB

Description

Prepare libraries of structure organism pairs ECMDB

Usage

prepare_libraries_sop_ecmdb(
  input = get_params(step = "prepare_libraries_sop_ecmdb")$files$libraries$sop$raw$ecmdb,
  output = get_params(step =
    "prepare_libraries_sop_ecmdb")$files$libraries$sop$prepared$ecmdb
)

Arguments

input

Input file
output

Output file

Value

The path to the prepared structure-organism pairs library ECMDB

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
prepare_libraries_sop_ecmdb()
unlink("data", recursive = TRUE)

## End(Not run)

Prepare libraries of structure organism pairs HMDB

Description

This function prepares the HMDB structure-organism pairs

Usage

prepare_libraries_sop_hmdb(
  input = get_params(step = "prepare_libraries_sop_hmdb")$files$libraries$sop$raw$hmdb,
  output = get_params(step =
    "prepare_libraries_sop_hmdb")$files$libraries$sop$prepared$hmdb
)

Arguments

input

Input file
output

Output file

Value

The path to the prepared structure-organism pairs library HMDB

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
prepare_libraries_sop_hmdb()
unlink("data", recursive = TRUE)

## End(Not run)

Prepare libraries of structure organism pairs LOTUS

Description

This function prepares the LOTUS structure-organism pairs

Usage

prepare_libraries_sop_lotus(
  input = get_params(step = "prepare_libraries_sop_lotus")$files$libraries$sop$raw$lotus,
  output = get_params(step =
    "prepare_libraries_sop_lotus")$files$libraries$sop$prepared$lotus
)

Arguments

input

Input file
output

Output file

Value

The path to the prepared structure-organism pairs library LOTUS

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
prepare_libraries_sop_lotus()
unlink("data", recursive = TRUE)

## End(Not run)

Prepare merged structure organism pairs libraries

Description

This function prepares the libraries made of all sub-libraries containing structure-organism pairs

Usage

prepare_libraries_sop_merged(
  files = get_params(step = "prepare_libraries_sop_merged")$files$libraries$sop$prepared,
  filter = get_params(step = "prepare_libraries_sop_merged")$organisms$filter$mode,
  level = get_params(step = "prepare_libraries_sop_merged")$organisms$filter$level,
  value = get_params(step = "prepare_libraries_sop_merged")$organisms$filter$value,
  output_key = get_params(step =
    "prepare_libraries_sop_merged")$files$libraries$sop$merged$keys,
  output_org_tax_ott = get_params(step =
    "prepare_libraries_sop_merged")$files$libraries$sop$merged$organisms$taxonomies$ott,
  output_str_stereo = get_params(step =
    "prepare_libraries_sop_merged")$files$libraries$sop$merged$structures$stereo,
  output_str_met = get_params(step =
    "prepare_libraries_sop_merged")$files$libraries$sop$merged$structures$metadata,
  output_str_nam = get_params(step =
    "prepare_libraries_sop_merged")$files$libraries$sop$merged$structures$names,
  output_str_tax_cla = get_params(step =
    "prepare_libraries_sop_merged")$files$libraries$sop$merged$structures$taxonomies$cla,
  output_str_tax_npc = get_params(step =
    "prepare_libraries_sop_merged")$files$libraries$sop$merged$structures$taxonomies$npc
)

Arguments

files

List of libraries to be merged
filter

Boolean. TRUE or FALSE if you want to filter the library
level

Biological rank to be filtered. Kingdom, phylum, family, genus, ...
value

Name of the taxon or taxa to be kept, e.g. 'Gentianaceae|Apocynaceae'
output_key

Output file for keys
output_org_tax_ott

Output file for organisms taxonomy (OTT)
output_str_stereo

Output file for structures stereo
output_str_met

Output file for structures metadata
output_str_nam

Output file for structures names
output_str_tax_cla

Output file for structures taxonomy (Classyfire)
output_str_tax_npc

Output file for structures taxonomy (NPC)

Details

It can be restricted to specific taxa to have more biologically meaningful annotation.

Value

The path to the prepared structure-organism pairs library MERGED

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
github <- "https://raw.githubusercontent.com/"
repo <- "taxonomicallyinformedannotation/tima-example-files/main/"
dir <- paste0(github, repo)
files <- get_params(step = "prepare_libraries_sop_merged")$files$libraries$sop$prepared$lotus |>
  gsub(
    pattern = ".gz",
    replacement = "",
    fixed = TRUE
  )
get_file(url = paste0(dir, files), export = files)
prepare_libraries_sop_merged(files = files)
unlink("data", recursive = TRUE)

## End(Not run)

Prepare libraries of spectra

Description

This function prepares spectra to be used for spectral matching

Usage

prepare_libraries_spectra(
  input = get_params(step = "prepare_libraries_spectra")$files$libraries$spectral$raw,
  nam_lib = get_params(step = "prepare_libraries_spectra")$names$libraries,
  col_ad = get_params(step = "prepare_libraries_spectra")$names$mgf$adduct,
  col_ce = get_params(step = "prepare_libraries_spectra")$names$mgf$collision_energy,
  col_ci = get_params(step = "prepare_libraries_spectra")$names$mgf$compound_id,
  col_em = get_params(step = "prepare_libraries_spectra")$names$mgf$exact_mass,
  col_in = get_params(step = "prepare_libraries_spectra")$names$mgf$inchi,
  col_io = get_params(step = "prepare_libraries_spectra")$names$mgf$inchi_no_stereo,
  col_ik = get_params(step = "prepare_libraries_spectra")$names$mgf$inchikey,
  col_il = get_params(step = "prepare_libraries_spectra")$names$mgf$inchikey_no_stereo,
  col_mf = get_params(step = "prepare_libraries_spectra")$names$mgf$molecular_formula,
  col_na = get_params(step = "prepare_libraries_spectra")$names$mgf$name,
  col_po = get_params(step = "prepare_libraries_spectra")$names$mgf$polarity,
  col_sm = get_params(step = "prepare_libraries_spectra")$names$mgf$smiles,
  col_sn = get_params(step = "prepare_libraries_spectra")$names$mgf$smiles_no_stereo,
  col_si = get_params(step = "prepare_libraries_spectra")$names$mgf$spectrum_id,
  col_sp = get_params(step = "prepare_libraries_spectra")$names$mgf$splash,
  col_sy = get_params(step = "prepare_libraries_spectra")$names$mgf$synonyms,
  col_xl = get_params(step = "prepare_libraries_spectra")$names$mgf$xlogp
)

Arguments

input

File containing spectra
nam_lib

Metadata to identify the library
col_ad

Name of the adduct in mgf
col_ce

Name of the collision energy in mgf
col_ci

Name of the compound id in mgf
col_em

Name of the exact mass in mgf
col_in

Name of the InChI in mgf
col_io

Name of the InChI without stereo in mgf
col_ik

Name of the InChIKey in mgf
col_il

Name of the InChIKey without stereo in mgf
col_mf

Name of the molecular formula in mgf
col_na

Name of the name in mgf
col_po

Name of the polarity in mgf
col_sm

Name of the SMILES in mgf
col_sn

Name of the SMILES without stereo in mgf
col_si

Name of the spectrum id in mgf
col_sp

Name of the SPLASH in mgf
col_sy

Name of the synonyms in mgf
col_xl

Name of the xlogp in mgf
polarity

MS polarity

Value

The path to the prepared spectral library

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
prepare_libraries_spectra()
unlink("data", recursive = TRUE)

## End(Not run)

Prepare params

Description

This function prepares main parameters

Usage

prepare_params(
  params_small = get_params(step = "prepare_params"),
  params_advanced = get_params(step = "prepare_params_advanced"),
  step = NA
)

Arguments

params_small

params_small
params_advanced

params_advanced
step

Step

Value

The path to the yaml files containing prepared parameters

Examples

NULL

Prepare taxa

Description

This function performs taxon name preparation to match the Open Tree of Life taxonomy

Usage

prepare_taxa(
  input = get_params(step = "prepare_taxa")$files$features$raw,
  extension = get_params(step = "prepare_taxa")$names$extension,
  name_features = get_params(step = "prepare_taxa")$names$features,
  name_filename = get_params(step = "prepare_taxa")$names$filename,
  colname = get_params(step = "prepare_taxa")$names$taxon,
  metadata = get_params(step = "prepare_taxa")$files$metadata$raw,
  top_k = get_params(step = "prepare_taxa")$organisms$candidates,
  org_tax_ott = get_params(step =
    "prepare_taxa")$files$libraries$sop$merged$organisms$taxonomies$ott,
  output = get_params(step = "prepare_taxa")$files$metadata$prepared,
  taxon = get_params(step = "prepare_taxa")$organisms$taxon
)

Arguments

input

File containing your features intensities
extension

Does your column names contain the file extension? (MZmine mainly)
name_features

Name of the features column in the features file
name_filename

Name of the file name column in the metadata file
colname

Name of the column containing biological source information
metadata

File containing your metadata including biological source
top_k

Number of organisms to be retained per feature top intensities
org_tax_ott

File containing Open Tree of Life Taxonomy
output

Output file
taxon

If you want to enforce all features to a given taxon, put its name here.

Details

Depending if the features are aligned between samples originating from various organisms or not, It can either attribute all features to a single organism, or attribute them to multiple ones, according to their relative intensities among the samples.

Value

The path to the prepared taxa

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
github <- "https://raw.githubusercontent.com/"
repo <- "taxonomicallyinformedannotation/tima-example-files/main/"
dir <- paste0(github, repo)
org_tax_ott <- get_params(step = "prepare_taxa")$files$libraries$sop$merged$organisms$taxonomies$ott |>
  gsub(
    pattern = ".gz",
    replacement = "",
    fixed = TRUE
  )
get_file(url = paste0(dir, org_tax_ott), export = org_tax_ott)
get_file(
  url = get_default_paths()$urls$examples$features,
  export = get_params(step = "prepare_taxa")$files$features$raw
)
prepare_taxa(
  taxon = "Homo sapiens",
  org_tax_ott = org_tax_ott
)
unlink("data", recursive = TRUE)

## End(Not run)

Read from SIRIUS zip

Description

This function reads files from Sirius compressed workspace

Usage

read_from_sirius_zip(sirius_zip, file)

Arguments

sirius_zip

Compressed directory containing the Sirius results
file

File to be read

Examples

NULL

Replace ID in file paths

Description

This function replaces the default ID in the example by a user-specified one

Usage

replace_id(
  x,
  user_filename = get_params(step = "prepare_params")$files$pattern,
  user_gnps = get_params(step = "prepare_params")$gnps$id,
  example_gnps = get_default_paths()$gnps$example
)

Arguments

x

a character string containing the default ID
user_filename

a user-specified value for a file name job ID
user_gnps

a user-specified value for a GNPS job ID
example_gnps

an example value for a GNPS job ID

Value

Character string with the GNPS job ID modified according to the rules specified in the function

Examples

tima:::replace_id(
  x = "example/123456_features.tsv",
  user_gnps = NULL,
  user_filename = "Foo"
)

Round reals

Description

This function rounds some reals in a dataframe

Usage

round_reals(df, dig = 5)

Arguments

df

Dataframe to use
dig

Number of digits

Examples

NULL

Run app

Description

This function runs the app

Usage

run_app(host = "127.0.0.1", port = 3838, browser = TRUE)

Arguments

host

Host. Default to 127.0.0.1
port

Port. Default to 3838
browser

Flag for browser use. Default to TRUE

Value

Opens the app

Examples

NULL

Sanitize spectra

Description

This function sanitizes spectra

Usage

sanitize_spectra(spectra, cutoff = 0, dalton = 0.01, polarity = NA, ppm = 10)

Arguments

spectra

Spectra object
cutoff

Absolute minimal intensity
dalton

Dalton tolerance
polarity

Polarity
ppm

PPM tolerance

Value

The sanitized spectra

Examples

data.frame(
  FEATURE_ID = c("FT001", "FT002", "FT003"),
  mz = c(list(123.4567, 234.5678, 345.6789))
) |>
  Spectra::Spectra() |>
  sanitize_spectra()

Select annotations columns

Description

This function selects annotations columns

Usage

select_annotations_columns(
  df,
  str_stereo = get("str_stereo", envir = parent.frame()),
  str_met = get("str_met", envir = parent.frame()),
  str_nam = get("str_nam", envir = parent.frame()),
  str_tax_cla = get("str_tax_cla", envir = parent.frame()),
  str_tax_npc = get("str_tax_npc", envir = parent.frame())
)

Arguments

df

Dataframe
str_stereo

File containing structures stereo
str_met

File containing structures metadata
str_nam

File containing structures names
str_tax_cla

File containing Classyfire taxonomy
str_tax_npc

File containing NPClassifier taxonomy

Value

The dataframe with annotation columns selected

Examples

NULL

Select sirius columns (canopus)

Description

This function selects sirius columns (canopus)

Usage

select_sirius_columns_canopus(df, sirius_version)

Arguments

df

Dataframe
sirius_version

Sirius version

Value

The dataframe with selected canopus columns

Examples

NULL

Select sirius columns (formulas)

Description

This function selects sirius columns (formulas)

Usage

select_sirius_columns_formulas(df, sirius_version)

Arguments

df

Dataframe
sirius_version

Sirius version

Value

The dataframe with selected sirius columns

Examples

NULL

Select sirius columns (structures)

Description

This function selects sirius columns (structures)

Usage

select_sirius_columns_structures(df, sirius_version)

Arguments

df

Dataframe
sirius_version

Sirius version

Value

The dataframe with selected structure columns

Examples

NULL

Select SOP columns

Description

This function selects sop columns

Usage

select_sop_columns(df)

Arguments

df

Dataframe

Value

The dataframe with selected structure organism pairs columns

Examples

NULL

Split Structure Organism Pairs table

Description

This function splits the structure organism table.

Usage

split_tables_sop(table)

Arguments

table

Table to split

Value

A list of tables from the structure organism pairs tables

Examples

NULL

Tima Full

Description

This function runs everything you need.

Usage

tima_full()

Value

Everything you need.

Examples

NULL

Transform score sirius CSI

Description

This function calculates the mass of M

Usage

transform_score_sirius_csi(csi_score, K = 50, scale = 10)

Arguments

csi_score

Original CSI score
K

Shift
scale

Scale

Value

A mass

Examples

NULL

Weight annotations

Description

This function weights annotations.

Usage

weight_annotations(
  library = get_params(step = "weight_annotations")$files$libraries$sop$merged$keys,
  org_tax_ott = get_params(step =
    "weight_annotations")$files$libraries$sop$merged$organisms$taxonomies$ott,
  str_stereo = get_params(step =
    "weight_annotations")$files$libraries$sop$merged$structures$stereo,
  annotations = get_params(step = "weight_annotations")$files$annotations$filtered,
  canopus = get_params(step = "weight_annotations")$files$annotations$prepared$canopus,
  formula = get_params(step = "weight_annotations")$files$annotations$prepared$formula,
  components = get_params(step =
    "weight_annotations")$files$networks$spectral$components$prepared,
  edges = get_params(step = "weight_annotations")$files$networks$spectral$edges$prepared,
  taxa = get_params(step = "weight_annotations")$files$metadata$prepared,
  output = get_params(step = "weight_annotations")$files$annotations$processed,
  candidates_final = get_params(step = "weight_annotations")$annotations$candidates$final,
  weight_spectral = get_params(step = "weight_annotations")$weights$global$spectral,
  weight_chemical = get_params(step = "weight_annotations")$weights$global$chemical,
  weight_biological = get_params(step = "weight_annotations")$weights$global$biological,
  score_biological_domain = get_params(step =
    "weight_annotations")$weights$biological$domain,
  score_biological_kingdom = get_params(step =
    "weight_annotations")$weights$biological$kingdom,
  score_biological_phylum = get_params(step =
    "weight_annotations")$weights$biological$phylum,
  score_biological_class = get_params(step =
    "weight_annotations")$weights$biological$class,
  score_biological_order = get_params(step =
    "weight_annotations")$weights$biological$order,
  score_biological_infraorder = get_params(step =
    "weight_annotations")$weights$biological$infraorder,
  score_biological_family = get_params(step =
    "weight_annotations")$weights$biological$family,
  score_biological_subfamily = get_params(step =
    "weight_annotations")$weights$biological$subfamily,
  score_biological_tribe = get_params(step =
    "weight_annotations")$weights$biological$tribe,
  score_biological_subtribe = get_params(step =
    "weight_annotations")$weights$biological$subtribe,
  score_biological_genus = get_params(step =
    "weight_annotations")$weights$biological$genus,
  score_biological_subgenus = get_params(step =
    "weight_annotations")$weights$biological$subgenus,
  score_biological_species = get_params(step =
    "weight_annotations")$weights$biological$species,
  score_biological_subspecies = get_params(step =
    "weight_annotations")$weights$biological$subspecies,
  score_biological_variety = get_params(step =
    "weight_annotations")$weights$biological$variety,
  score_chemical_cla_kingdom = get_params(step =
    "weight_annotations")$weights$chemical$cla$kingdom,
  score_chemical_cla_superclass = get_params(step =
    "weight_annotations")$weights$chemical$cla$superclass,
  score_chemical_cla_class = get_params(step =
    "weight_annotations")$weights$chemical$cla$class,
  score_chemical_cla_parent = get_params(step =
    "weight_annotations")$weights$chemical$cla$parent,
  score_chemical_npc_pathway = get_params(step =
    "weight_annotations")$weights$chemical$npc$pathway,
  score_chemical_npc_superclass = get_params(step =
    "weight_annotations")$weights$chemical$npc$superclass,
  score_chemical_npc_class = get_params(step =
    "weight_annotations")$weights$chemical$npc$class,
  minimal_consistency = get_params(step =
    "weight_annotations")$annotations$thresholds$consistency,
  minimal_ms1_bio = get_params(step =
    "weight_annotations")$annotations$thresholds$ms1$biological,
  minimal_ms1_chemo = get_params(step =
    "weight_annotations")$annotations$thresholds$ms1$chemical,
  minimal_ms1_condition = get_params(step =
    "weight_annotations")$annotations$thresholds$ms1$condition,
  ms1_only = get_params(step = "weight_annotations")$annotations$ms1only,
  compounds_names = get_params(step = "weight_annotations")$options$compounds_names,
  high_confidence = get_params(step = "weight_annotations")$options$high_confidence,
  remove_ties = get_params(step = "weight_annotations")$options$remove_ties,
  summarise = get_params(step = "weight_annotations")$options$summarise,
  pattern = get_params(step = "weight_annotations")$files$pattern,
  force = get_params(step = "weight_annotations")$options$force
)

Arguments

library

Library containing the keys
org_tax_ott

File containing organisms taxonomy (OTT)
str_stereo

File containing structures stereo
annotations

Prepared annotations file
canopus

Prepared canopus file
formula

Prepared formula file
components

Prepared components file
edges

Prepared edges file
taxa

Prepared taxed features file
output

Output file
candidates_final

Number of final candidates to keep
weight_spectral

Weight for the spectral score
weight_chemical

Weight for the biological score
weight_biological

Weight for the chemical consistency score
score_biological_domain

Score for a domain match (should be lower than kingdom)
score_biological_kingdom

Score for a kingdom match (should be lower than phylum)
score_biological_phylum

Score for a phylum match (should be lower than class)
score_biological_class

Score for a class match (should be lower than order)
score_biological_order

Score for a order match (should be lower than infraorder)
score_biological_infraorder

Score for a infraorder match (should be lower than order)
score_biological_family

Score for a family match (should be lower than subfamily)
score_biological_subfamily

Score for a subfamily match (should be lower than family)
score_biological_tribe

Score for a tribe match (should be lower than subtribe)
score_biological_subtribe

Score for a subtribe match (should be lower than genus)
score_biological_genus

Score for a genus match (should be lower than subgenus)
score_biological_subgenus

Score for a subgenus match (should be lower than species)
score_biological_species

Score for a species match (should be lower than subspecies)
score_biological_subspecies

Score for a subspecies match (should be lower than variety)
score_biological_variety

Score for a variety match (should be the highest)
score_chemical_cla_kingdom

Score for a ⁠Classyfire kingdom⁠ match (should be lower than ⁠ Classyfire superclass⁠)
score_chemical_cla_superclass

Score for a ⁠Classyfire superclass⁠ match (should be lower than ⁠Classyfire class⁠)
score_chemical_cla_class

Score for a ⁠Classyfire class⁠ match (should be lower than ⁠Classyfire parent⁠)
score_chemical_cla_parent

Score for a ⁠Classyfire parent⁠ match (should be the highest)
score_chemical_npc_pathway

Score for a ⁠NPC pathway⁠ match (should be lower than ⁠ NPC superclass⁠)
score_chemical_npc_superclass

Score for a ⁠NPC superclass⁠ match (should be lower than ⁠NPC class⁠)
score_chemical_npc_class

Score for a ⁠NPC class⁠ match (should be the highest)
minimal_consistency

Minimal consistency score for a class. FLOAT
minimal_ms1_bio

Minimal biological score to keep MS1 based annotation
minimal_ms1_chemo

Minimal chemical score to keep MS1 based annotation
minimal_ms1_condition

Condition to be used. Must be "OR" or "AND".
ms1_only

Keep only MS1 annotations. BOOLEAN
compounds_names

Report compounds names. Can be very large. BOOLEAN
high_confidence

Report high confidence candidates only. BOOLEAN
remove_ties

Remove ties. BOOLEAN
summarise

Summarize results (1 row per feature). BOOLEAN
pattern

Pattern to identify your job. STRING
force

Force parameters. Use it at your own risk

Value

The path to the weighted annotations

See Also

annotate_masses weight_bio weight_chemo

Examples

## Not run: 
tima:::copy_backbone()
go_to_cache()
github <- "https://raw.githubusercontent.com/"
repo <- "taxonomicallyinformedannotation/tima-example-files/main/"
dir <- paste0(github, repo)
library <- get_params(step = "weight_annotations")$files$libraries$sop$merged$keys |>
  gsub(
    pattern = ".gz",
    replacement = "",
    fixed = TRUE
  )
org_tax_ott <- get_params(step = "weight_annotations")$files$libraries$sop$merged$organisms$taxonomies$ott |>
  gsub(
    pattern = ".gz",
    replacement = "",
    fixed = TRUE
  )
str_stereo <- get_params(step = "weight_annotations")$files$libraries$sop$merged$structures$stereo |>
  gsub(
    pattern = ".gz",
    replacement = "",
    fixed = TRUE
  )
annotations <- get_params(step = "weight_annotations")$files$annotations$filtered |>
  gsub(
    pattern = ".gz",
    replacement = "",
    fixed = TRUE
  )
canopus <- get_params(step = "weight_annotations")$files$annotations$prepared$canopus |>
  gsub(
    pattern = ".gz",
    replacement = "",
    fixed = TRUE
  )
formula <- get_params(step = "weight_annotations")$files$annotations$prepared$formula |>
  gsub(
    pattern = ".gz",
    replacement = "",
    fixed = TRUE
  )
components <- get_params(step = "weight_annotations")$files$networks$spectral$components$prepared |>
  gsub(
    pattern = ".gz",
    replacement = "",
    fixed = TRUE
  )
edges <- get_params(step = "weight_annotations")$files$networks$spectral$edges$prepared |>
  gsub(
    pattern = ".gz",
    replacement = "",
    fixed = TRUE
  )
taxa <- get_params(step = "weight_annotations")$files$metadata$prepared |>
  gsub(
    pattern = ".gz",
    replacement = "",
    fixed = TRUE
  )
get_file(url = paste0(dir, library), export = library)
get_file(url = paste0(dir, org_tax_ott), export = org_tax_ott)
get_file(url = paste0(dir, str_stereo), export = str_stereo)
get_file(url = paste0(dir, annotations), export = annotations)
get_file(url = paste0(dir, canopus), export = canopus)
get_file(url = paste0(dir, formula), export = formula)
get_file(url = paste0(dir, components), export = components)
get_file(url = paste0(dir, edges), export = edges)
get_file(url = paste0(dir, taxa), export = taxa)
weight_annotations(
  library = library,
  org_tax_ott = org_tax_ott,
  str_stereo = str_stereo,
  annotations = annotations,
  canopus = canopus,
  formula = formula,
  components = components,
  edges = edges,
  taxa = taxa
)
unlink("data", recursive = TRUE)

## End(Not run)

Weight bio

Description

This function weights the eventually MS1 complemented annotations according their biological source

Usage

weight_bio(
  annotation_table_taxed = get("annotation_table_taxed", envir = parent.frame()),
  structure_organism_pairs_table = get("structure_organism_pairs_table", envir =
    parent.frame()),
  weight_spectral = get("weight_spectral", envir = parent.frame()),
  weight_biological = get("weight_biological", envir = parent.frame()),
  score_biological_domain = get("score_biological_domain", envir = parent.frame()),
  score_biological_kingdom = get("score_biological_kingdom", envir = parent.frame()),
  score_biological_phylum = get("score_biological_phylum", envir = parent.frame()),
  score_biological_class = get("score_biological_class", envir = parent.frame()),
  score_biological_order = get("score_biological_order", envir = parent.frame()),
  score_biological_family = get("score_biological_family", envir = parent.frame()),
  score_biological_tribe = get("score_biological_tribe", envir = parent.frame()),
  score_biological_genus = get("score_biological_genus", envir = parent.frame()),
  score_biological_species = get("score_biological_species", envir = parent.frame()),
  score_biological_variety = get("score_biological_variety", envir = parent.frame())
)

Arguments

annotation_table_taxed

Table containing the initial annotation eventually complemented by additional MS1 annotations
structure_organism_pairs_table

Table containing the structure - organism pairs
weight_spectral

Weight for the spectral score
weight_biological

Weight for the biological score
score_biological_domain

Score for a domain match (should be lower than kingdom)
score_biological_kingdom

Score for a kingdom match (should be lower than phylum)
score_biological_phylum

Score for a phylum match (should be lower than class)
score_biological_class

Score for a class match (should be lower than order)
score_biological_order

Score for a order match (should be lower than family)
score_biological_family

Score for a family match (should be lower than tribe)
score_biological_tribe

Score for a tribe match (should be lower than genus)
score_biological_genus

Score for a genus match (should be lower than species)
score_biological_species

Score for a species match (should be lower than variety)
score_biological_variety

Score for a variety match (should be the highest)

Value

A table containing the biologically weighted annotation

Examples

NULL

Weight chemo

Description

This function weights the biologically weighted annotations according their chemical consistency

Usage

weight_chemo(
  annot_table_wei_bio_clean = get("annot_table_wei_bio_clean", envir = parent.frame()),
  weight_spectral = get("weight_spectral", envir = parent.frame()),
  weight_biological = get("weight_biological", envir = parent.frame()),
  weight_chemical = get("weight_chemical", envir = parent.frame()),
  score_chemical_cla_kingdom = get("score_chemical_cla_kingdom", envir = parent.frame()),
  score_chemical_cla_superclass = get("score_chemical_cla_superclass", envir =
    parent.frame()),
  score_chemical_cla_class = get("score_chemical_cla_class", envir = parent.frame()),
  score_chemical_cla_parent = get("score_chemical_cla_parent", envir = parent.frame()),
  score_chemical_npc_pathway = get("score_chemical_npc_pathway", envir = parent.frame()),
  score_chemical_npc_superclass = get("score_chemical_npc_superclass", envir =
    parent.frame()),
  score_chemical_npc_class = get("score_chemical_npc_class", envir = parent.frame())
)

Arguments

annot_table_wei_bio_clean

Table containing the biologically weighted annotation
weight_spectral

Weight for the spectral score
weight_biological

Weight for the biological score
weight_chemical

Weight for the chemical consistency score
score_chemical_cla_kingdom

Score for a ⁠Classyfire kingdom⁠ match (should be lower than ⁠ Classyfire superclass⁠)
score_chemical_cla_superclass

Score for a ⁠Classyfire superclass⁠ match (should be lower than ⁠Classyfire class⁠)
score_chemical_cla_class

Score for a ⁠Classyfire class⁠ match (should be lower than ⁠Classyfire parent⁠)
score_chemical_cla_parent

Score for a ⁠Classyfire parent⁠ match (should be the highest)
score_chemical_npc_pathway

Score for a pathway match (should be lower than superclass)
score_chemical_npc_superclass

Score for a superclass match (should be lower than class)
score_chemical_npc_class

Score for a class match (should be the highest)

Value

A table containing the chemically weighted annotation

Examples

NULL