Two-Factor Differential Analysis

Conducts a differential analysis for each feature (column) in the data matrix Z using two experimental factors. The function employs either a linear model (lm) or a mixed-effects model (lmer) depending on the presence of a random effect, and can perform follow-up tests (e.g., ANOVA tests) for interactions and main effects.

Usage

twofactors(
  Z,
  factor1,
  factor2,
  random_effect = NULL,
  model_fit_function = "lm",
  p_adjust_method_for_factors_and_interation = FALSE,
  pval_quantile_cutoff = 0.02,
  pval_cutoff_full = 0.05,
  test_function = "anova_test",
  pval_cutoff_interaction = 0.01,
  pval_cutoff_factor1 = 0.01,
  pval_cutoff_factor2 = 0.01,
  p_adjust_method = "BH",
  factor1_name = NULL,
  factor2_name = NULL,
  random_effect_name = NULL,
  ...
)

Arguments

Z

A matrix or data frame where each column represents a feature and each row represents an observation.

factor1

A factor or vector representing the first categorical variable.

factor2

A factor or vector representing the second categorical variable.

random_effect

An optional factor or vector representing a random effect (e.g., subject ID). Use NULL if not applicable.

model_fit_function

A character string specifying the model fitting function to use. Acceptable values are "lm" for linear models or "lmer" for mixed-effects models.

p_adjust_method_for_factors_and_interation

Logical indicating whether to apply p-value adjustment for follow-up tests on factors and interaction. Defaults to FALSE.

pval_quantile_cutoff

Numeric; a quantile cutoff used for an overall significance filter in the analysis. Defaults to 0.02.

pval_cutoff_full

Numeric; the p-value threshold for the overall model test. Defaults to 0.05.

test_function

A character string specifying which test function to use for statistical comparison. Defaults to "anova_test". Other valid functions (if implemented) might include "perm_anova_test", etc.

pval_cutoff_interaction

Numeric; the p-value threshold for testing the interaction effect. Defaults to 0.01.

pval_cutoff_factor1

Numeric; the p-value threshold for testing the main effect of factor1. Defaults to 0.01.

pval_cutoff_factor2

Numeric; the p-value threshold for testing the main effect of factor2. Defaults to 0.01.

p_adjust_method

A character string specifying the method for p-value adjustment (e.g., "BH"). Defaults to "BH".

factor1_name

An optional character string to label factor1 in model formulas; if NULL, a default is used.

factor2_name

An optional character string to label factor2 in model formulas; if NULL, a default is used.

random_effect_name

An optional character string to label the random effect in model formulas; if NULL, a default is used.

...

Additional arguments passed to internal functions (e.g., model-fitting functions or test functions).

Value

A list containing:

pval_matrix

A matrix of adjusted p-values for the overall test, interaction test, and main effects.

stat_matrix

A matrix of test statistics corresponding to the computed p-values.

class_df

A data frame summarizing the significance classification for each feature based on the analysis.

Examples

if (FALSE) { # \dontrun{
results <- twofactors(
  Z = my_data_matrix,
  factor1 = my_factor1,
  factor2 = my_factor2,
  model_fit_function = "lm",
  p_adjust_method_for_factors_and_interation = FALSE,
  pval_quantile_cutoff = 0.02,
  pval_cutoff_full = 0.05,
  test_function = "anova_test",
  pval_cutoff_interaction = 0.01,
  pval_cutoff_factor1 = 0.01,
  pval_cutoff_factor2 = 0.01,
  p_adjust_method = "BH",
  factor1_name = "Group",
  factor2_name = "Treatment"
)
# Inspect the output
print(results$pval_matrix)
print(results$stat_matrix)
print(results$class_df)
} # }