Package 'omu'

Title: A Metabolomics Analysis Tool for Intuitive Figures and Convenient Metadata Collection
Description: Facilitates the creation of intuitive figures to describe metabolomics data by utilizing Kyoto Encyclopedia of Genes and Genomes (KEGG) hierarchy data, and gathers functional orthology and gene data from the KEGG-REST API.
Authors: Connor Tiffany [aut, cre]
Maintainer: Connor Tiffany <[email protected]>
License: GPL-2
Version: 1.1.0
Built: 2025-03-13 06:05:17 UTC
Source: https://github.com/connor-reid-tiffany/omu

Help Index

Assign hierarchy metadata

Description

Assigns hierarchy metadata to a metabolomics count matrix using identifier values. It can assign KEGG compound hierarchy, orthology hierarchy, or organism hierarchy data.

Usage

assign_hierarchy(count_data, keep_unknowns, identifier)

Arguments

count_data

a metabolomics count data frame with either a KEGG compound, orthology, or a gene identifier column
keep_unknowns

a boolean of either TRUE or FALSE. TRUE keeps unannotated compounds, FALSE removes them
identifier

a string that is either "KEGG" for metabolite, "KO" for orthology, "Prokaryote" for organism, or "Eukaryote" for organism

Examples

assign_hierarchy(count_data = c57_nos2KO_mouse_countDF, keep_unknowns = TRUE, identifier = "KEGG")

c57b6J nos2KO metabolomics count matrix

Description

A dataset containing metabolomics counts for an experiment done using c57b6J wild type and c57b6J nos2 knockout mice

Usage

c57_nos2KO_mouse_countDF

Format

A data frame with 668 rows and 36 variables:

c57b6J nos2KO meta data

Description

A a meta data file for the c57b6J metabolomics count matrix

Usage

c57_nos2KO_mouse_metadata

Format

A data frame with 29 rows and 4 variables:

Check data for zeros across samples within factor levels. Will determine if there are more zeros than a user specified threshold within any given factor level(s). Returns a vector of Metabolites that are 0 above the threshold in any given factor level.

Description

Check data for zeros across samples within factor levels. Will determine if there are more zeros than a user specified threshold within any given factor level(s). Returns a vector of Metabolites that are 0 above the threshold in any given factor level.

Usage

check_zeros(
  count_data,
  metadata,
  numerator = NULL,
  denominator = NULL,
  threshold = 25,
  response_variable = "Metabolite",
  Factor
)

Arguments

count_data

A metabolomics count data frame
metadata

Metadata dataframe for the metabolomics count data frame
numerator

String of the first independent variable you wish to test. Defualt is NULL
denominator

String of the second independent variable you wish to test. Default is NULL.
threshold

Integer. A percentage threshold for the number of zeros in a Metabolite. Default is 25.
response_variable

String of the column header for the response variables, usually "Metabolite"
Factor

A factor with levels to test for zeros.

Examples

check_zeros(count_data = c57_nos2KO_mouse_countDF, metadata = c57_nos2KO_mouse_metadata,
Factor = "Treatment")

check_zeros(count_data = c57_nos2KO_mouse_countDF, metadata = c57_nos2KO_mouse_metadata,
Factor = "Treatment",numerator = "Strep", denominator = "Mock", threshold = 10)

Get counts for significant fold changes by metabolite class.

Description

Takes an input data frame from the output of omu_summary and creates a data frame of counts for significantly changed metabolites by class hierarchy data.

Usage

count_fold_changes(count_data, column, sig_threshold, keep_unknowns)

Arguments

count_data

Output dataframe from the omu_summary function or omu_anova.
column

Metabolite metadata you want to group by, i.e. "Class", "Subclass_1".
sig_threshold

Significance threshold for compounds that go towars the count, sig_threshold = 0.05
keep_unknowns

TRUE or FALSE for whether to drop compounds that weren't assigned hierarchy metadata

Examples

c57_nos2KO_mouse_countDF <- assign_hierarchy(c57_nos2KO_mouse_countDF, TRUE, "KEGG")

t_test_df <- omu_summary(count_data = c57_nos2KO_mouse_countDF,
metadata = c57_nos2KO_mouse_metadata,
numerator = "Strep", denominator = "Mock", response_variable = "Metabolite",
Factor = "Treatment", log_transform = TRUE, p_adjust = "BH", test_type = "welch")

fold_change_counts <- count_fold_changes(count_data = t_test_df,
column = "Class", sig_threshold = 0.05, keep_unknowns = "FALSE")

Get nucleotide and amino acid sequences for genes

Description

Function that gets nt and aa seqs for gene data from KEGG_gather

Usage

get_seqs(gene_data)

Arguments

gene_data

A dataframe with genes from KEGG_gather, with class seqs

Examples

gene_data <- c57_nos2KO_mouse_countDF[(1:2),]

gene_data <- KEGG_gather(gene_data)

gene_data <- KEGG_gather(gene_data)
gene_data <- gene_data[1:2,]

gene_data <- get_seqs(gene_data)

Gather metadata from KEGG for metabolites

Description

Method for gathering metadata from the KEGG API.

Usage

KEGG_gather(count_data)

## S3 method for class 'cpd'
KEGG_gather(count_data)

## S3 method for class 'rxn'
KEGG_gather(count_data)

## S3 method for class 'KO'
KEGG_gather(count_data)

Arguments

count_data

A metabolmics count dataframe with a KEGG identifier columns

Examples

count_data <- assign_hierarchy(count_data = c57_nos2KO_mouse_countDF,
keep_unknowns = TRUE, identifier = "KEGG")

count_data <- subset(count_data, Subclass_2=="Aldoses")

count_data <- KEGG_gather(count_data = count_data)

Get metadata from KEGG API

Description

Internal function for KEGG_Gather

Usage

make_omelette(count_data, column, first_char)

Arguments

count_data

The metabolomics count data
column

The name of the KEGG identifier being sent to the KEGG API
first_char

firct character in number being fed to KEGG database

Perform anova

Description

Performs an anova across all response variables, followed by a Tukeys test on every possible contrast in your model and calculates group means and fold changes for each contrast. Returns a list of data frames for each contrast, and includes a dataframe of model residuals

Usage

omu_anova(
  count_data,
  metadata,
  response_variable = "Metabolite",
  model,
  log_transform = FALSE,
  method = "anova"
)

Arguments

count_data

A metabolomics count data frame
metadata

Metadata dataframe for the metabolomics count data frame
response_variable

String of the column header for the response variables, usually "Metabolite"
model

A formual class object, see ?formula for more info on formulas in R. an interaction between independent variables. Optional parameter
log_transform

Boolean of TRUE or FALSE for whether or not you wish to log transform your metabolite counts
method

A string of 'anova', 'kruskal', or 'welch'. anova performs an anova with a post hoc tukeys test, kruskal performs a kruskal wallis with a post hoc dunn test, welch performs a welch's anova with a post hoc games howell test

Examples

anova_df <- omu_anova(count_data = c57_nos2KO_mouse_countDF, metadata = c57_nos2KO_mouse_metadata,
response_variable = "Metabolite", model = ~ Treatment, log_transform = TRUE)

anova_df <- omu_anova(count_data = c57_nos2KO_mouse_countDF, metadata = c57_nos2KO_mouse_metadata,
response_variable = "Metabolite", model = ~ Treatment + Background, log_transform = TRUE)

anova_df <- omu_anova(count_data = c57_nos2KO_mouse_countDF, metadata = c57_nos2KO_mouse_metadata,
response_variable = "Metabolite", model = ~ Treatment + Background + Treatment*Background,
log_transform = TRUE)

omu_summary Performs comparison of means between two independent variables, standard deviation, standard error, FDR correction, fold change, log2FoldChange. The order effects the fold change values

Description

omu_summary Performs comparison of means between two independent variables, standard deviation, standard error, FDR correction, fold change, log2FoldChange. The order effects the fold change values

Usage

omu_summary(
  count_data,
  metadata,
  numerator,
  denominator,
  response_variable = "Metabolite",
  Factor,
  log_transform = FALSE,
  p_adjust = "BH",
  test_type = "welch",
  paired = FALSE
)

Arguments

count_data

should be a metabolomics count data frame
metadata

is meta data
numerator

is the variable you wish to compare against the denominator, in quotes
denominator

see above, in quotes
response_variable

the name of the column with your response variables
Factor

the column name for your independent variables
log_transform

TRUE or FALSE value for whether or not log transformation of data is performed before the t test
p_adjust

Method for adjusting the p value, i.e. "BH"
test_type

One of "mwu", "students", or "welch" to determine which model to use
paired

A boolean of TRUE or FALSE. If TRUE, performs a paired sample test. To perform a paired sample test, metadata must have a column named 'ID' containing the subject IDs.

Examples

omu_summary(count_data = c57_nos2KO_mouse_countDF, metadata = c57_nos2KO_mouse_metadata,
numerator = "Strep", denominator = "Mock", response_variable = "Metabolite", Factor = "Treatment",
log_transform = TRUE, p_adjust = "BH", test_type = "welch")

Create a PCA plot

Description

Performs an ordination and outputs a PCA plot using a metabolomics count data frame and metabolomics metadata

Usage

PCA_plot(
  count_data,
  metadata,
  variable,
  color,
  response_variable = "Metabolite",
  label = FALSE,
  size = 2,
  ellipse = FALSE
)

Arguments

count_data

Metabolomics count data
metadata

Metabolomics metadata
variable

The independent variable you wish to compare and contrast
color

String of what you want to color by. Usually should be the same as variable.
response_variable

String of the response_variable, usually should be "Metabolite"
label

TRUE or FALSE, whether to add point labels or not
size

An integer for point size.
ellipse

TRUE or FALSE, whether to add confidence interval ellipses or not.

Examples

PCA_plot(count_data = c57_nos2KO_mouse_countDF, metadata = c57_nos2KO_mouse_metadata,
variable = "Treatment", color = "Treatment", response_variable = "Metabolite")

Create a pie chart

Description

Creates a pie chart as ggplot2 object using the output from ra_table.

Usage

pie_chart(ratio_data, variable, column, color)

Arguments

ratio_data

a dataframe object of percents. output from ra_table function
variable

The metadata variable you are measuring, i.e. "Class"
column

either "Increase", "Decrease", or "Significant_Changes"
color

string denoting color for outline. use NA for no outline

Examples

c57_nos2KO_mouse_countDF <- assign_hierarchy(c57_nos2KO_mouse_countDF, TRUE, "KEGG")

t_test_df <- omu_summary(count_data = c57_nos2KO_mouse_countDF,
metadata = c57_nos2KO_mouse_metadata,
numerator = "Strep", denominator = "Mock", response_variable = "Metabolite",
Factor = "Treatment",
log_transform = TRUE, p_adjust = "BH", test_type = "welch")

fold_change_counts <- count_fold_changes(count_data = t_test_df,
column = "Class", sig_threshold = 0.05, keep_unknowns = FALSE)

ra_table <- ra_table(fc_data = fold_change_counts, variable = "Class")

pie_chart(ratio_data = ra_table, variable = "Class", column = "Decrease", color = "black")

plate_omelette Internal method for KEGG_Gather which parses flat text files

Description

plate_omelette Internal method for KEGG_Gather which parses flat text files

Usage

plate_omelette(output)

## S3 method for class 'rxn'
plate_omelette(output)

## S3 method for class 'genes'
plate_omelette(output)

## S3 method for class 'KO'
plate_omelette(output)

Arguments

output

The metabolomics count dataframe

Clean up orthology metadata

Description

Internal function for KEGG_Gather.rxn method KEGG_Gather.rxn requires dispatch on multiple elements, so There was no way to incorporate as a method

Usage

plate_omelette_rxnko(output)

Arguments

output

output from plate_omelette

Create a bar plot

Description

Creates a ggplot2 object using the output file from the count_fold_changes function

Usage

plot_bar(fc_data, fill, size = c(1, 1), outline_color = c("black", "black"))

Arguments

fc_data

The output file from Count_Fold_Changes
fill

A character vector of length 2 containing colors for filling the bars, the first color is for the "Decrease" bar while the second is for "Increase"
size

A numeric vector of 2 numbers for the size of the bar outlines.
outline_color

A character vector of length 2 containing colors for the bar outlines

Examples

c57_nos2KO_mouse_countDF <- assign_hierarchy(c57_nos2KO_mouse_countDF, TRUE, "KEGG")

t_test_df <- omu_summary(count_data = c57_nos2KO_mouse_countDF,
metadata = c57_nos2KO_mouse_metadata, numerator = "Strep", denominator = "Mock",
response_variable = "Metabolite", Factor = "Treatment",
log_transform = TRUE, p_adjust = "BH", test_type = "welch")

fold_change_counts <- count_fold_changes(count_data = t_test_df,
column = "Class", sig_threshold = 0.05, keep_unknowns = FALSE)

plot_bar(fc_data = fold_change_counts, fill = c("firebrick2", "dodgerblue2"),
outline_color = c("black", "black"), size = c(1,1))

Create a box plot

Description

Takes a metabolomics count data frame and creates boxplots. It is recommended to either subset, truncate, or agglomerate by hierarchical metadata.

Usage

plot_boxplot(
  count_data,
  metadata,
  aggregate_by,
  log_transform = FALSE,
  Factor,
  response_variable = "Metabolite",
  fill_list
)

Arguments

count_data

A metabolomics count data frame, either from read_metabo or omu_summary
metadata

The descriptive meta data for the samples
aggregate_by

Hierarchical metadata value to sum metabolite values by, i.e. "Class"
log_transform

TRUE or FALSE. Recommended for visualization purposes. If true data is transformed by the natural log
Factor

The column name for the experimental variable
response_variable

The response variable for the data, i.e. "Metabolite"
fill_list

Colors for the plot which is colored by Factor, in the form of c("")

Examples

c57_nos2KO_mouse_countDF <- c57_nos2KO_mouse_countDF[1:5,]
c57_nos2KO_mouse_countDF <- assign_hierarchy(c57_nos2KO_mouse_countDF, TRUE, "KEGG")

plot_boxplot(count_data = c57_nos2KO_mouse_countDF, metadata = c57_nos2KO_mouse_metadata,
log_transform = TRUE, Factor = "Treatment", response_variable = "Metabolite",
aggregate_by = "Subclass_2", fill_list = c("darkgoldenrod1", "dodgerblue2"))

Create a heatmap

Description

Takes a metabolomics count data frame and creates a heatmap. It is recommended to either subset, truncate, or agglomerate by metabolite metadata to improve legibility.

Usage

plot_heatmap(
  count_data,
  metadata,
  Factor,
  response_variable,
  log_transform = FALSE,
  high_color,
  low_color,
  aggregate_by
)

Arguments

count_data

A metabolomics count data frame.
metadata

The descriptive meta data for the samples.
Factor

The column name for the independent variable in your metadata.
response_variable

The response variable for the data, i.e. "Metabolite"
log_transform

TRUE or FALSE. Recommended for visualization purposes. If true data is transformed by the natural log.
high_color

Color for high abundance values
low_color

Color for low abundance values
aggregate_by

Hierarchical metadata value to sum metabolite values by, i.e. "Class"

Examples

c57_nos2KO_mouse_countDF <- assign_hierarchy(c57_nos2KO_mouse_countDF, TRUE, "KEGG")

plot_heatmap(count_data = c57_nos2KO_mouse_countDF, metadata = c57_nos2KO_mouse_metadata,
log_transform = TRUE, Factor = "Treatment", response_variable = "Metabolite",
aggregate_by = "Subclass_2", high_color = "darkgoldenrod1", low_color = "dodgerblue2")

plot_rf_PCA

Description

PCA plot of the proximity matrix from a random forest classification model

Usage

plot_rf_PCA(rf_list, color, size, ellipse = FALSE, label = FALSE)

Arguments

rf_list

The output from the random_forest function. This only works on classification models.
color

A grouping factor. Use the one that was the LHS of your model parameter in the random_forest funciton
size

The number for point size in the plot
ellipse

TRUE or FALSE. Whether to plot with confidence interval ellipses or not.
label

TRUE or FALSE. Whether to include point labels or not.

Examples

rf_list <- random_forest(c57_nos2KO_mouse_countDF,c57_nos2KO_mouse_metadata,
Treatment ~.,c(60,40),500)
plot_rf_PCA(rf_list = rf_list, color = "Treatment", size = 1.5)

plot_variable_importance

Description

Plot the variable importance from a random forest model. Mean Decrease Gini for Classification and

Usage

plot_variable_importance(rf_list, color = "Class", n_metabolites = 10)

Arguments

rf_list

The output from the random_forest function
color

Metabolite metadata to color by
n_metabolites

The number of metabolites to include. Metabolites are sorted by decreasing importance.

Examples

rf_list <- random_forest(c57_nos2KO_mouse_countDF,c57_nos2KO_mouse_metadata,
Treatment ~.,c(60,40),500)
plot_variable_importance(rf_list = rf_list, color = "Class", n_metabolites = 10)

Create a volcano plot

Description

Creates a volcano plot as ggplot2 object using the output of omu_summary

Usage

plot_volcano(
  count_data,
  column,
  size,
  strpattern,
  fill,
  sig_threshold,
  alpha,
  shape,
  color
)

Arguments

count_data

The output file from the omu_summary function.
column

The column with metadata you want to highlight points in the plot with, i.e. "Class"
size

Size of the points in the plot
strpattern

A character vector of levels of the column you want the plot to focus on, i.e. strpattern = c("Carbohydrates", "Organicacids")
fill

A character vector of colors you want your points to be. Must be of length 1 + length(strpattern) to account for points not in strpattern. Levels of a factor are organzed alphabetically. All levels not in the strpattern argument will be set to NA.
sig_threshold

An integer. Creates a horizontal dashed line for a significance threshold. i.e. sig_threshold = 0.05. Defaut value is 0.05
alpha

A character vector for setting transparency of factor levels.Must be of length 1 + length(strpattern) to account for points not in strpattern.
shape

A character vector for setting the shapes for your column levels. Must be of length 1 + length(strpattern) to account for points not in strpattern. See ggplot2 for an index of shape values.
color

A character vector of colors for the column levels. Must be of length 1 + length(strpattern) to account for points not in strpattern. If you choose to use shapes with outlines, this list will set the outline colors.

Examples

c57_nos2KO_mouse_countDF <- assign_hierarchy(c57_nos2KO_mouse_countDF, TRUE, "KEGG")

t_test_df <-  omu_summary(count_data = c57_nos2KO_mouse_countDF,
metadata = c57_nos2KO_mouse_metadata, numerator = "Strep", denominator = "Mock",
response_variable = "Metabolite", Factor = "Treatment",
log_transform = TRUE, p_adjust = "BH", test_type = "welch")

plot_volcano(count_data = t_test_df, column = "Class", strpattern = c("Carbohydrates"),
fill = c("firebrick2", "white"), sig_threshold = 0.05, alpha = c(1,1),
shape = c(1,24), color = c("black", "black"), size = 2)

plot_volcano(count_data = t_test_df, sig_threshold = 0.05, size = 2)

Creates a ratio table from the count_fold_changes function output.

Description

Create a ratio table

Usage

ra_table(fc_data, variable)

Arguments

fc_data

data frame output from the count_fold_changes function
variable

metadata from count_fold_changes, i.e. "Class"

Examples

c57_nos2KO_mouse_countDF <- assign_hierarchy(c57_nos2KO_mouse_countDF, TRUE, "KEGG")

t_test_df <- omu_summary(count_data = c57_nos2KO_mouse_countDF,
metadata = c57_nos2KO_mouse_metadata, numerator = "Strep", denominator = "Mock",
response_variable = "Metabolite", Factor = "Treatment",
log_transform = TRUE, p_adjust = "BH", test_type = "welch")

fold_change_counts <- count_fold_changes(count_data = t_test_df,
column = "Class", sig_threshold = 0.05, keep_unknowns = FALSE)

ra_table(fc_data = fold_change_counts, variable = "Class")

random_forest Perform a classification or regression random forest model

Description

a wrapper built around the randomForest function from package randomForest. Returns a list with a randomForest object list, training data set, testing data set, metabolite metadata, and confusion matrices for training and testing data (if type was classification).

Usage

random_forest(
  count_data,
  metadata,
  model,
  training_proportion = c(80, 20),
  n_tree = 500
)

Arguments

count_data

Metabolomics data
metadata

sample data
model

a model of format variable ~.
training_proportion

a numeric vector of length 2, first element is the percent of samples to use for training the model, second element is the percent of samples used to test the models accuracy
n_tree

number of decision trees to create

Examples

rf_list <- random_forest(count_data = c57_nos2KO_mouse_countDF,metadata = c57_nos2KO_mouse_metadata,
model = Treatment ~.,training_proportion = c(60,40),n_tree = 500)

Import a metabolomics count data frame

Description

Wrapper for read.csv that appends the "cpd" class and sets blank cells to NA. Used to import metabolomics count data into R.

Usage

read_metabo(filepath)

Arguments

filepath

a file path to your metabolomics count data

Examples

filepath_to_yourdata = paste0(system.file(package = "omu"), "/extdata/read_metabo_test.csv")
count_data <- read_metabo(filepath_to_yourdata)

transform_metabolites

Description

A functional to transform metabolomics data across metabolites.

Usage

transform_metabolites(count_data, func)

Arguments

count_data

Metabolomics data
func

a function to transform metabolites by. can be an anonymous function

Examples

data_pareto_scaled <- transform_samples(count_data = c57_nos2KO_mouse_countDF,
function(x) x/sqrt(sd(x)))

transform_samples

Description

A functional to transform metabolomics data across samples.

Usage

transform_samples(count_data, func)

Arguments

count_data

Metabolomics data
func

a function to transform samples by. can be an anonymous function

Examples

data_ln <- transform_samples(count_data = c57_nos2KO_mouse_countDF, log)