Package 'MetAlyzer' reference manual

Title:	Read and Analyze 'MetIDQ™' Software Output Files
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/>).
Authors:	Nils Mechtel [aut, cre] , Luis Herfurth [aut] , Carolin Andresen [aut] , Daniel Huebschmann [aut]
Maintainer:	Nils Mechtel <[email protected]>
License:	GPL-3
Version:	1.1.0
Built:	2024-12-06 18:41:30 UTC
Source:	CRAN

Get Aggregated Data

Description

This function returns the tibble "aggregated_data".

Usage

aggregatedData(metalyzer_se)
aggregatedData(metalyzer_se)

Arguments

metalyzer_se

SummarizedExperiment

Examples

metalyzer_se <- MetAlyzer_dataset(file_path = example_extraction_data())

aggregatedData(metalyzer_se)
metalyzer_se <- MetAlyzer_dataset(file_path = example_extraction_data())

aggregatedData(metalyzer_se)

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

Get example extraction data

Description

This function returns the extraction_data_MxP_Quant_500.xlsx file path.

Usage

example_extraction_data()
example_extraction_data()

Value

extraction_data_MxP_Quant_500.xlsx file path

Examples

fpath <- example_extraction_data()
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()
example_meta_data()

Value

data frame loaded from example_meta_data.RDS

Examples

fpath <- example_meta_data()
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()
example_mutation_data_xl()

Value

mutation_data_MxP_Quant_500_XL.xlsx file path

Examples

fpath <- example_mutation_data_xl()
fpath <- example_mutation_data_xl()

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")
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)
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 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
)
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)
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)

Filter meta data

Description

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

Usage

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

Get log2FC Data

Description

This function returns the tibble "log2FC".

Usage

log2FC(metalyzer_se)
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)
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)

Get MetAlyzer colors

Description

This function returns the vector loaded from metalyzer_colors.RDS.

Usage

metalyzer_colors()
metalyzer_colors()

Value

data frame loaded from metalyzer_colors.RDS

Examples

fpath <- metalyzer_colors()
fpath <- metalyzer_colors()

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
)
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())
metalyzer_se <- MetAlyzer_dataset(file_path = example_extraction_data())

Get pathway file path

Description

This function returns the pathway.xlsx file path.

Usage

pathway()
pathway()

Value

pathway.xlsx file path

Examples

fpath <- pathway()
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
)
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)
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")
)
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)
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
)
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)
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()
)
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)
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()
)
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)

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()
polarity()

Value

polarity.csv file path

Examples

fpath <- polarity()
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)
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

Rename meta data

Description

This function renames a column of meta_data.

Usage

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

Summarize concentration values

Description

This function prints quantiles and NAs of raw data.

Usage

summarizeConcValues(metalyzer_se)
summarizeConcValues(metalyzer_se)

Arguments

metalyzer_se

SummarizedExperiment

Examples

metalyzer_se <- MetAlyzer_dataset(file_path = example_extraction_data())

summarizeConcValues(metalyzer_se)
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)
summarizeQuantData(metalyzer_se)

Arguments

metalyzer_se

SummarizedExperiment

Examples

metalyzer_se <- MetAlyzer_dataset(file_path = example_extraction_data())

summarizeQuantData(metalyzer_se)
metalyzer_se <- MetAlyzer_dataset(file_path = example_extraction_data())

summarizeQuantData(metalyzer_se)

Update meta data

Description

This function adds another column to filtered meta_data.

Usage

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

Package 'MetAlyzer'

Help Index

Get Aggregated Data

Description

Usage

Arguments

Examples

One-way ANOVA

Description

Usage

Arguments

Value

Examples

Add mean, SD and CV

Description

Usage

Arguments

Value

Examples

Calculate log2 fold change

Description

Usage

Arguments

Value

Examples

Get example extraction data

Description

Usage

Value

Examples

Get example meta data

Description

Usage

Value

Examples

Get example mutation data

Description

Usage

Value

Examples

Export filtered raw data as csv

Description

Usage

Arguments

Examples

Filter metabolites

Description

Usage

Arguments

Value

Examples

Filter meta data

Description

Usage

Arguments

Value

Examples

Get log2FC Data

Description

Usage

Arguments

Examples

Get MetAlyzer colors

Description

Usage

Value

Examples

Open file and read data

Description

Usage

Arguments

Value

Examples

Get pathway file path

Description

Usage

Value

Examples

Plot log2 fold change

Description