| Title: | A Metabolomics Analysis Tool for Intuitive Figures and Convenient Metadata Collection |
| Version: | 1.1.2 |
| 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. |
| Depends: | R (≥ 3.3.0) |
| Imports: | plyr, dplyr, stringr, httr, ggfortify, ggplot2, magrittr, tidyr, broom, FSA, rstatix, randomForest, caret |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.3 |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| URL: | https://github.com/connor-reid-tiffany/Omu, https://www.kegg.jp/kegg/rest/keggapi.html |
| BugReports: | https://github.com/connor-reid-tiffany/Omu/issues |
| NeedsCompilation: | no |
| Packaged: | 2024-03-06 16:12:49 UTC; connor |
| Author: | Connor Tiffany [aut, cre] |
| Maintainer: | Connor Tiffany <tiffanyc1@chop.edu> |
| Repository: | CRAN |
| Date/Publication: | 2024-03-06 23:40:02 UTC |
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 metabolomics count dataframe with a KEGG identifier columns |
Examples
## Not run:
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)
## End(Not run)
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")
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
## Not run:
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)
## End(Not run)
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 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)