omu (version 1.1.2)

omu_anova: 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

c57_nos2KO_mouse_countDF <- c57_nos2KO_mouse_countDF[1:12,];
c57_nos2KO_mouse_metadata <- c57_nos2KO_mouse_metadata;
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)