Type: Package
Title: Read and Analyze 'MetIDQ™' Software Output Files
Version: 1.1.0
Maintainer: Nils Mechtel <nils.mech@gmail.com>
Depends: R (≥ 4.0.0)
Imports: SummarizedExperiment, openxlsx, stringr, dplyr, tidyr, tibble, agricolae, ggplot2, ggrepel, utils, rlang, data.table, S4Vectors, viridis, viridisLite, plotly, limma
Description: The 'MetAlyzer' S4 object provides methods to read and reformat metabolomics data for convenient data handling, statistics and downstream analysis. The resulting format corresponds to input data of the Shiny app 'MetaboExtract' (https://www.metaboextract.shiny.dkfz.de/MetaboExtract/).
License: GPL-3
Encoding: UTF-8
Language: en-US
RoxygenNote: 7.3.2
Suggests: rmarkdown, knitr
VignetteBuilder: knitr
URL: https://github.com/nilsmechtel/MetAlyzer
BugReports: https://github.com/nilsmechtel/MetAlyzer/issues
NeedsCompilation: no
Packaged: 2024-12-06 11:18:54 UTC; luisherfurth
Author: Nils Mechtel ORCID iD [aut, cre], Luis Herfurth ORCID iD [aut], Carolin Andresen ORCID iD [aut], Daniel Huebschmann ORCID iD [aut]
Repository: CRAN
Date/Publication: 2024-12-06 14:00:02 UTC

Open file and read data

Description

This function creates a SummarizedExperiment (SE) from the given 'MetIDQ' output Excel sheet: metabolites (rowData), meta data (colData), concentration data (assay), quantification status(assay) The column "Sample Type" and the row "Class" are used as anchor cells in the Excel sheet and are therefore a requirement.

Usage

MetAlyzer_dataset(
  file_path,
  sheet = 1,
  status_list = list(Valid = c("#B9DE83", "#00CD66"), LOQ = c("#B2D1DC", "#7FB2C5",
    "#87CEEB"), LOD = c("#A28BA3", "#6A5ACD"), `ISTD Out of Range` = c("#FFF099",
    "#FFFF33"), Invalid = "#FFFFCC", Incomplete = c("#CBD2D7", "#FFCCCC")),
  silent = FALSE
)

Arguments

file_path

A character specifying the file path to the Excel file.

sheet

A numeric index specifying which sheet of the Excel file to use.

status_list

A list of HEX color codes for each quantification status.

silent

If TRUE, mute any print command.

Value

A Summarized Experiment object

Examples

metalyzer_se <- MetAlyzer_dataset(file_path = example_extraction_data())

Aggregate data

Description

This function reshapes conc_values, quant_status, metatabolites and sample IDs and combines them into a tibble data frame for filtering with dplyr and plotting with 'ggplot2'. "aggregated_data" is grouped by metabolites.

Usage

aggregate_data(metabolites, meta_data, conc_values, quant_status, status_vec)

Arguments

metabolites

metabolites MetAlyzer object

meta_data

Meta_data of the MetAlyzer object

conc_values

conc_values of a MetAlyzer object

quant_status

quant_status of a MetAlyzer object

status_vec

A vector of quantification status

Get Aggregated Data

Description

This function returns the tibble "aggregated_data".

Usage

aggregatedData(metalyzer_se)

Arguments

metalyzer_se

SummarizedExperiment

Examples

metalyzer_se <- MetAlyzer_dataset(file_path = example_extraction_data())

aggregatedData(metalyzer_se)

Perform an ANOVA

Description

This function filters based on the filter vector valid_vec, performs a one-way ANOVA and adds the column Group to aggregated_data with the results of a Tukey post-hoc test

Usage

calc_anova(c_vec, d_vec)

Arguments

c_vec

A character vector containing the categorical variables

d_vec

A numeric vector containing the dependent variables

One-way ANOVA

Description

This method performs a one-way ANOVA on the grouped aggregated_data (the categorical variable is removed from grouping first). The vector of the categorical variable needs to have at least two levels after removing NAs from the dependent variable vector. Otherwise a vector of NA is returned. A Tukey post-hoc test is then used to determine group names, starting with "A" followed by further letters. These group names are added to aggregated_data in the column ANOVA_Group. Thereby, metabolites can be identified which are significantly higher in one or more of the categorical variable compared to all other for each metabolite.

Usage

calculate_anova(
  metalyzer_se,
  categorical,
  groups = NULL,
  impute_perc_of_min = 0.2,
  impute_NA = TRUE
)

Arguments

metalyzer_se

A Metalyzer object

categorical

A column defining the categorical variable

groups

A vector of column names of aggregated_data to calculate the ANOVA group wise. If the column does not exists in aggregated_data it is automatically added from meta data. The default value is set to NULL, which uses the existing grouping of aggregated_data.

impute_perc_of_min

A numeric value below 1

impute_NA

Logical value whether to impute NA values

Value

A data frame containing the log2 fold change for each metabolite

Examples

metalyzer_se <- MetAlyzer_dataset(file_path = example_extraction_data())
metalyzer_se <- renameMetaData(
  metalyzer_se,
  Extraction_Method = "Sample Description"
)
# reduced to only 'Acylcarnitines' (first metabolic class) for simplicity
drop_vec = unique(metalyzer_se@elementMetadata$metabolic_classes)[2:24]
metalyzer_se <- filterMetabolites(
  metalyzer_se,
  drop_metabolites = drop_vec
)
metalyzer_se <- filterMetaData(
  metalyzer_se,
  Tissue == "Drosophila"
)
metalyzer_se <- calculate_anova(
  metalyzer_se,
  categorical = "Extraction_Method",
  groups = c("Metabolite"),
  impute_perc_of_min = 0.2,
  impute_NA = TRUE
)

Add mean, SD and CV

Description

This function calculates the mean, standard deviation (SD) and the coefficient of variation (CV) for each group and adds them to aggregated_data.

Usage

calculate_cv(
  metalyzer_se,
  groups = NULL,
  cv_thresholds = c(0.1, 0.2, 0.3),
  na.rm = TRUE
)

Arguments

metalyzer_se

A Metalyzer object

groups

A vector of column names of aggregated_data to calculate mean, SD and CV group wise. If the column does not exists in aggregated_data it is automatically added from meta data. The default value is set to NULL, which uses the existing grouping of aggregated_data.

cv_thresholds

A numeric vector of upper thresholds (CV <= t) between 0 and 1 for CV categorization.

na.rm

a logical evaluating to TRUE or FALSE indicating whether NA values should be stripped before the computation proceeds.

Value

An updated aggregated_data tibble data frame

Examples

metalyzer_se <- MetAlyzer_dataset(file_path = example_extraction_data())
metalyzer_se <- renameMetaData(
  metalyzer_se,
  Extraction_Method = "Sample Description"
)
metalyzer_se <- filterMetaData(
  metalyzer_se,
  Tissue == "Drosophila"
)
metalyzer_se <- calculate_cv(
  metalyzer_se,
  groups = c("Tissue", "Extraction_Method", "Metabolite"),
  cv_thresholds = c(0.1, 0.2, 0.3),
  na.rm = TRUE
)

Calculate log2 fold change

Description

This function calculates log2(FC), p-values, and adjusted p-values of the data using limma.

Usage

calculate_log2FC(
  metalyzer_se,
  categorical,
  impute_perc_of_min = 0.2,
  impute_NA = FALSE
)

Arguments

metalyzer_se

A Metalyzer object

categorical

A column specifying the two groups

impute_perc_of_min

A numeric value below 1

impute_NA

Logical value whether to impute NA values

Value

A data frame containing the log2 fold change for each metabolite

Examples

metalyzer_se <- MetAlyzer_dataset(file_path = example_mutation_data_xl())
metalyzer_se <- filterMetabolites(
  metalyzer_se,
  drop_metabolites = "Metabolism Indicators"
)
metalyzer_se <- renameMetaData(
  metalyzer_se,
  Mutant_Control = "Sample Description"
)

metalyzer_se <- calculate_log2FC(
  metalyzer_se,
  categorical = "Mutant_Control",
  impute_perc_of_min = 0.2,
  impute_NA = FALSE
)

Impute aggregated data

Description

This function imputes zero concentration values with a percentage of the minimal positive value. If all values are zero or NA, they are set to NA. The imputed values are added to plotting_data in an extra column imputed_Conc.

Usage

data_imputation(metalyzer_se, impute_perc_of_min, impute_NA)

Arguments

metalyzer_se

A MetAlyzer object

impute_perc_of_min

A numeric value between 0 and 1. Percentage of the minimal positive value, that is taken for imputation

impute_NA

Logical value whether to impute NA values

Transform aggregated data

Description

This function performs a transformation of imputed concentration values (imputed_Conc) with a given function. NA values are skipped. The transformed values are added to aggregated_data in an extra column transf_Conc.

Usage

data_transformation(metalyzer_se)

Arguments

metalyzer_se

a MetAlyzer object

Value

An updated aggregated_data tibble data frame

Get example extraction data

Description

This function returns the extraction_data_MxP_Quant_500.xlsx file path.

Usage

example_extraction_data()

Value

extraction_data_MxP_Quant_500.xlsx file path

Examples

fpath <- example_extraction_data()

Get example meta data

Description

This function returns the data frame loaded from example_meta_data.RDS.

Usage

example_meta_data()

Value

data frame loaded from example_meta_data.RDS

Examples

fpath <- example_meta_data()

Get example mutation data

Description

This function returns the mutation_data_MxP_Quant_500_XL.xlsx file path.

Usage

example_mutation_data_xl()

Value

mutation_data_MxP_Quant_500_XL.xlsx file path

Examples

fpath <- example_mutation_data_xl()

Add column to aggregated_data

Description

This function adds a column to the aggregated_data tibble by using information from the meta data.

Usage

expand_aggregated_data(metalyzer_se, meta_data_column)

Arguments

metalyzer_se

A Metalyzer object

meta_data_column

A column from meta data

Value

An updated Metalyzer object

Export filtered raw data as csv

Description

This function exports the filtered raw data in the CSV format.

Usage

exportConcValues(metalyzer_se, ..., file_path = "metabolomics_data.csv")

Arguments

metalyzer_se

SummarizedExperiment

...

Additional columns from meta_data

file_path

file path

Examples

metalyzer_se <- MetAlyzer_dataset(file_path = example_extraction_data())

output_file <- file.path(tempdir(), "metabolomics_data.csv")
exportConcValues(
  metalyzer_se,
  `Sample Description`,
  Tissue,
  file_path = output_file
)
unlink(output_file)

Filter meta data

Description

This function updates the "Filter" column in meta_data to filter out samples.

Usage

filterMetaData(metalyzer_se, ..., inplace = FALSE)

Arguments

metalyzer_se

SummarizedExperiment

...

Use ´col_name´ and condition to filter selected variables.

inplace

If FALSE, return a copy. Otherwise, do operation inplace and return None.

Value

An updated SummarizedExperiment

Examples

metalyzer_se <- MetAlyzer_dataset(file_path = example_extraction_data())

metalyzer_se <- filterMetaData(metalyzer_se, !is.na(Tissue))
metalyzer_se <- filterMetaData(metalyzer_se, `Sample Description` %in% 1:6)
# or
filterMetaData(metalyzer_se, !is.na(Tissue), inplace = TRUE)
filterMetaData(metalyzer_se, `Sample Description` %in% 1:6, inplace = TRUE)

Filter metabolites

Description

This function filters out certain classes or metabolites of the metabolites vector. If aggregated_data is not empty, metabolites and class will also be filtered here.

Usage

filterMetabolites(
  metalyzer_se,
  drop_metabolites = c("Metabolism Indicators"),
  drop_NA_concentration = FALSE,
  drop_quant_status = NULL,
  min_percent_valid = NULL,
  valid_status = c("Valid", "LOQ"),
  per_group = NULL,
  inplace = FALSE
)

Arguments

metalyzer_se

SummarizedExperiment

drop_metabolites

A character vector defining metabolite classes or individual metabolites to be removed

drop_NA_concentration

A boolean whether to drop metabolites which have any NAs in their concentration value

drop_quant_status

A character, vector of characters or list of characters specifying which quantification status to remove. Metabolites with at least one quantification status of this vector will be removed.

min_percent_valid

A numeric lower threshold between 0 and 1 (t less than or equal to x) to remove invalid metabolites that do not meet a given percentage of valid measurements per group (default per Metabolite).

valid_status

A character vector that defines which quantification status is considered valid.

per_group

A character vector of column names from meta_data that will be used to split each metabolite into groups. The threshold 'min_percent_valid' will be applied for each group. The selected columns from meta_data will be added to aggregated_data.

inplace

If FALSE, return a copy. Otherwise, do operation inplace and return None.

Value

An updated SummarizedExperiment

Examples

metalyzer_se <- MetAlyzer_dataset(file_path = example_extraction_data())

drop_metabolites <- c("C0", "C2", "C3", "Metabolism Indicators",
  inplace = TRUE
)
metalyzer_se <- filterMetabolites(metalyzer_se, drop_metabolites)
# or
filterMetabolites(metalyzer_se, drop_metabolites, inplace = TRUE)

Get data range

Description

This function extracts rows and column indices to slice the .full_sheet into metabolites, conc_values and meta_data.

Usage

get_data_range(full_sheet)

Arguments

full_sheet

full_sheet

Get log2FC Data

Description

This function returns the tibble "log2FC".

Usage

log2FC(metalyzer_se)

Arguments

metalyzer_se

SummarizedExperiment

Examples

metalyzer_se <- MetAlyzer_dataset(file_path = example_mutation_data_xl())
metalyzer_se <- filterMetabolites(
  metalyzer_se,
  drop_metabolites = "Metabolism Indicators"
)
metalyzer_se <- renameMetaData(
  metalyzer_se,
  Mutant_Control = "Sample Description"
)

metalyzer_se <- calculate_log2FC(
  metalyzer_se,
  categorical = "Mutant_Control",
  impute_perc_of_min = 0.2,
  impute_NA = TRUE
)

log2FC(metalyzer_se)

Description

This function prints "MetAlyzer" as an ASCII logo

Usage

metalyzer_ascii_logo()

Get MetAlyzer colors

Description

This function returns the vector loaded from metalyzer_colors.RDS.

Usage

metalyzer_colors()

Value

data frame loaded from metalyzer_colors.RDS

Examples

fpath <- metalyzer_colors()

Open Excel file

Description

This function opens the given MetIDQ output Excel file and reads the full given sheet.

Usage

open_file(starter_list)

Arguments

starter_list

contains the file path and the sheet index

Get pathway file path

Description

This function returns the pathway.xlsx file path.

Usage

pathway()

Value

pathway.xlsx file path

Examples

fpath <- pathway()

Plot log2 fold change

Description

This method plots the log2 fold change for each metabolite.

Usage

plot_log2FC(
  metalyzer_se,
  signif_colors = c(`#5F5F5F` = 1, `#FEBF6E` = 0.1, `#EE5C42` = 0.05, `#8B1A1A` = 0.01),
  hide_labels_for = c(),
  class_colors = "MetAlyzer",
  polarity_file = "MxPQuant500",
  vulcano = FALSE
)

Arguments

metalyzer_se

A Metalyzer object

signif_colors

signif_colors

hide_labels_for

vector of Metabolites or Classes for which no labels are printed

class_colors

class_colors

polarity_file

polarity_file

vulcano

boolean value to plot a vulcano plot

Value

ggplot object

Examples

metalyzer_se <- MetAlyzer_dataset(file_path = example_mutation_data_xl())
metalyzer_se <- filterMetabolites(
  metalyzer_se,
  drop_metabolites = "Metabolism Indicators"
)
metalyzer_se <- renameMetaData(
  metalyzer_se,
  Mutant_Control = "Sample Description"
)
metalyzer_se <- calculate_log2FC(
  metalyzer_se,
  categorical = "Mutant_Control",
  impute_perc_of_min = 0.2,
  impute_NA = TRUE
)

# p_vulcano <- plot_log2FC(metalyzer_se, vulcano=TRUE)
# p_fc <- plot_log2FC(metalyzer_se, vulcano=FALSE)

Plot Pathway Network

Description

This function plots the log2 fold change for each metabolite and visualizes it, in a pathway network.

Usage

plot_network(
  metalyzer_se,
  q_value = 0.05,
  metabolite_text_size = 3,
  connection_width = 0.75,
  pathway_text_size = 6,
  pathway_width = 4,
  scale_colors = c("green", "black", "magenta")
)

Arguments

metalyzer_se

A Metalyzer object

q_value

The q-value threshold for significance

metabolite_text_size

The text size of metabolite labels

connection_width

The line width of connections between metabolites

pathway_text_size

The text size of pathway annotations

pathway_width

The line width of pathway-specific connection coloring

scale_colors

A vector of length 3 with colors for low, mid and high of the gradient.

Value

ggplot object

Examples

metalyzer_se <- MetAlyzer_dataset(file_path = example_mutation_data_xl())
metalyzer_se <- filterMetabolites(
  metalyzer_se,
  drop_metabolites = "Metabolism Indicators"
)
metalyzer_se <- renameMetaData(
  metalyzer_se,
  Mutant_Control = "Sample Description"
)

metalyzer_se <- calculate_log2FC(
  metalyzer_se,
  categorical = "Mutant_Control",
  impute_perc_of_min = 0.2,
  impute_NA = FALSE
)

network <- plot_network(metalyzer_se, q_value = 0.05)

Plotly Log2FC Network Plot

Description

This function returns a list with interactive networkplot based on log2 fold change data.

Usage

plotly_network(
  metalyzer_se,
  q_value = 0.05,
  metabolite_node_size = 11,
  connection_width = 1.25,
  pathway_text_size = 20,
  pathway_width = 10,
  plot_height = 800
)

Arguments

metalyzer_se

A MetAlyzer Object

q_value

A numeric value specifying the cutoff for q-value

metabolite_node_size

The text size of the metabolite Nodes

connection_width

The line width of connections between metabolites

pathway_text_size

The text size of pathway annotations

pathway_width

The line width of pathway-specific connection coloring

plot_height

The height of the Plot in pixel [px]

Value

plotly object

Examples

metalyzer_se <- MetAlyzer_dataset(file_path = example_mutation_data_xl())
metalyzer_se <- filterMetabolites(
  metalyzer_se,
  drop_metabolites = "Metabolism Indicators"
)
metalyzer_se <- renameMetaData(
  metalyzer_se,
  Mutant_Control = "Sample Description"
)

metalyzer_se <- calculate_log2FC(
  metalyzer_se,
  categorical = "Mutant_Control",
  impute_perc_of_min = 0.2,
  impute_NA = FALSE
)

p_network <- plotly_network(metalyzer_se, q_value = 0.05)

Plotly Log2FC Scatter Plot

Description

This function returns a list with an interactive scatterplot based on log2 fold change data and a comprehensive Legend.

Usage

plotly_scatter(
  metalyzer_se,
  signif_colors = c(`#5F5F5F` = 1, `#FEBF6E` = 0.1, `#EE5C42` = 0.05, `#8B1A1A` = 0.01),
  class_colors = metalyzer_colors()
)

Arguments

metalyzer_se

A Metalyzer object

signif_colors

signif_colors

class_colors

A csv file containing class colors hexcodes

Value

plotly object

Examples


metalyzer_se <- MetAlyzer_dataset(file_path = example_mutation_data_xl())
metalyzer_se <- filterMetabolites(
  metalyzer_se,
  drop_metabolites = "Metabolism Indicators"
)
metalyzer_se <- renameMetaData(
  metalyzer_se,
  Mutant_Control = "Sample Description"
)
metalyzer_se <- calculate_log2FC(
  metalyzer_se,
  categorical = "Mutant_Control",
  impute_perc_of_min = 0.2,
  impute_NA = TRUE
)

p_scatter <- plotly_scatter(metalyzer_se)

Plotly Log2FC Vulcano Plot

Description

This function returns a list with interactive vulcanoplot based on log2 fold change data.

Usage

plotly_vulcano(
  metalyzer_se,
  cutoff_y = 0.05,
  cutoff_x = 1.5,
  class_colors = metalyzer_colors()
)

Arguments

metalyzer_se

A Metalyzer object

cutoff_y

A numeric value specifying the cutoff for q-value

cutoff_x

A numeric value specifying the cutoff for log2 fold change

class_colors

A csv file containing class colors hexcodes

Value

plotly object

Examples


metalyzer_se <- MetAlyzer_dataset(file_path = example_mutation_data_xl())
metalyzer_se <- filterMetabolites(
  metalyzer_se,
  drop_metabolites = "Metabolism Indicators"
)
metalyzer_se <- renameMetaData(
  metalyzer_se,
  Mutant_Control = "Sample Description"
)
metalyzer_se <- calculate_log2FC(
  metalyzer_se,
  categorical = "Mutant_Control",
  impute_perc_of_min = 0.2,
  impute_NA = TRUE
)

p_vulcano <- plotly_vulcano(metalyzer_se, 
                       cutoff_y = 0.05,
                       cutoff_x = 1.5)

Get polarity file path

Description

This function returns the polarity.csv file path.

Usage

polarity()

Value

polarity.csv file path

Examples

fpath <- polarity()

Read Named Regions

Description

This function reads in the named regions of an excel file.

Usage

read_named_region(file_path, named_region)

Arguments

file_path

The file path of the file

named_region

The region name u want to read in

Read quantification status

Description

This function gets the background color of each cell in full_sheet and assigns it to the corresponding quantification status.

Usage

read_quant_status(
  starter_list,
  sheet_dim,
  data_ranges,
  metabolites,
  status_list,
  silent
)

Arguments

starter_list

starter_list

sheet_dim

sheet_dim

data_ranges

data_ranges

metabolites

metabolites

status_list

status_list

silent

silent

Rename meta data

Description

This function renames a column of meta_data.

Usage

renameMetaData(metalyzer_se, ..., inplace = FALSE)

Arguments

metalyzer_se

SummarizedExperiment

...

Use new_name = old_name to rename selected variables

inplace

If FALSE, return a copy. Otherwise, do operation inplace and return None.

Value

An updated SummarizedExperiment

Examples

metalyzer_se <- MetAlyzer_dataset(file_path = example_extraction_data())

metalyzer_se <- renameMetaData(
  metalyzer_se,
  Method = `Sample Description`
)
# or
renameMetaData(metalyzer_se, Model_Organism = Tissue, inplace = TRUE)

Threshold CV

Description

This function assigns a CV value according to a vector of thresholds.

Usage

set_threshold(x, cv_threshs)

Arguments

x

A CV value

cv_threshs

A numeric vector of thresholds

Slice concentration values

Description

This function slices measurements from .full_sheet into conc_values.

Usage

slice_conc_values(full_sheet, data_ranges, metabolites)

Arguments

full_sheet

full_sheet

data_ranges

data_ranges

metabolites

metabolites

Slice meta data

Description

This function slices meta data from .full_sheet into meta_data.

Usage

slice_meta_data(full_sheet, data_ranges)

Arguments

full_sheet

full_sheet

data_ranges

data_ranges

Slice metabolites

Description

This function extracts metabolites with their corresponding metabolite class from .full_sheet into metabolites.

Usage

slice_metabolites(full_sheet, data_ranges)

Arguments

full_sheet

full_sheet

data_ranges

data_ranges

Summarize concentration values

Description

This function prints quantiles and NAs of raw data.

Usage

summarizeConcValues(metalyzer_se)

Arguments

metalyzer_se

SummarizedExperiment

Examples

metalyzer_se <- MetAlyzer_dataset(file_path = example_extraction_data())

summarizeConcValues(metalyzer_se)

Summarize quantification status

Description

This function lists the number of each quantification status and its percentage.

Usage

summarizeQuantData(metalyzer_se)

Arguments

metalyzer_se

SummarizedExperiment

Examples

metalyzer_se <- MetAlyzer_dataset(file_path = example_extraction_data())

summarizeQuantData(metalyzer_se)

Transformation

Description

This function performs transformation of imputed concentration values (imputed_Conc).

Usage

transform(vec, func)

Arguments

vec

MetAlyzer metalyzer

func

A function for transformation

Unify hex codes

Description

This function gets hex codes of different length and returns them in a unified format.

Usage

unify_hex(hex)

Arguments

hex

A 3, 4, 6 or 8 digit hex code

Update meta data

Description

This function adds another column to filtered meta_data.

Usage

updateMetaData(metalyzer_se, ..., inplace = FALSE)

Arguments

metalyzer_se

SummarizedExperiment

...

Use ´new_col_name = new_column´ to rename selected variables

inplace

If FALSE, return a copy. Otherwise, do operation inplace and return None.

Value

An updated SummarizedExperiment

Examples

metalyzer_se <- MetAlyzer_dataset(file_path = example_extraction_data())

metalyzer_se <- updateMetaData(
  metalyzer_se,
  Date = Sys.Date(), Analyzed = TRUE
)
# or
updateMetaData(
  metalyzer_se,
  Date = Sys.Date(), Analyzed = TRUE, inplace = TRUE
)

Imputation of zero values

Description

This function performs zero imputation with the minimal positive value times impute_perc_of_min.

Usage

zero_imputation(vec, impute_perc_of_min, impute_NA)

Arguments

vec

A numeric vector containing the concentration values

impute_perc_of_min

A numeric value below 1

impute_NA

Logical value whether to impute NA values

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