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iDAS: Interpretable Differential Analysis of Genes Using Three Factors

Source: R/iDAS_functions.R
threefactors.Rd

This function implements the iDAS (Interpretable Differential Analysis Signature) framework to identify genes associated with three experimental factors (factor1, factor2, and factor3), as well as their interactions. The analysis involves an overall model test, interaction tests (two-way and three-way), and main effects tests. Results include adjusted p-values and test statistics for each gene.

Usage

threefactors(
  Z,
  factor1,
  factor2,
  factor3,
  random_effect = NULL,
  model_fit_function = "lm",
  test_function = "anova_test",
  pval_quantile_cutoff = 0.02,
  pval_cutoff_full = 0.05,
  pval_cutoff_interaction = 0.01,
  pval_cutoff_factor1 = 0.01,
  pval_cutoff_factor2 = 0.01,
  pval_cutoff_factor3 = 0.01,
  pval_cutoff_int12 = 0.01,
  pval_cutoff_int13 = 0.01,
  pval_cutoff_int23 = 0.01,
  pval_cutoff_int123 = 0.01,
  p_adjust_method = "BH",
  factor1_name = NULL,
  factor2_name = NULL,
  factor3_name = NULL,
  random_effect_name = NULL,
  ...
)

Arguments

Z

A numeric matrix or data frame where each column represents a gene's expression (or abundance) and each row corresponds to an observation (e.g., a sample).

factor1

A factor or vector representing the primary experimental factor.

factor2

A factor or vector representing the secondary experimental factor.

factor3

A factor or vector representing the tertiary experimental factor.

random_effect

An optional factor or vector for random effects (e.g., subject ID). Use NULL if no random effects are included.

model_fit_function

A character string specifying the model-fitting function (e.g., "lm" for linear models, "lmer" for mixed-effects models). Default is "lm".

test_function

A character string specifying the testing function to use (e.g., "anova_test"). Default is "anova_test".

pval_quantile_cutoff

A numeric threshold for the quantile-based filtering of overall p-values (i.e., only genes with overall p-values below the specified quantile are considered for further tests). Default is 0.02.

pval_cutoff_full

A numeric p-value cutoff for the overall model test. Default is 0.05.

pval_cutoff_interaction

A numeric p-value cutoff for the omnibus interaction test (i.e., is there any interaction?). Default is 0.01.

pval_cutoff_factor1

A numeric p-value cutoff for testing the main effect of factor1. Default is 0.01.

pval_cutoff_factor2

A numeric p-value cutoff for testing the main effect of factor2. Default is 0.01.

pval_cutoff_factor3

A numeric p-value cutoff for testing the main effect of factor3. Default is 0.01.

pval_cutoff_int12

A numeric p-value cutoff for the two-way interaction between factor1 and factor2. Default is 0.01.

pval_cutoff_int13

A numeric p-value cutoff for the two-way interaction between factor1 and factor3. Default is 0.01.

pval_cutoff_int23

A numeric p-value cutoff for the two-way interaction between factor2 and factor3. Default is 0.01.

pval_cutoff_int123

A numeric p-value cutoff for the three-way interaction among factor1, factor2, and factor3. Default is 0.01.

p_adjust_method

A character string specifying the method used to adjust p-values for multiple comparisons (e.g., "BH"). Default is "BH".

factor1_name

An optional character string to label factor1 in outputs or formulas. Default is NULL.

factor2_name

An optional character string to label factor2 in outputs or formulas. Default is NULL.

factor3_name

An optional character string to label factor3 in outputs or formulas. Default is NULL.

random_effect_name

An optional character string to label the random effect in outputs or formulas. Default is NULL.

...

Additional arguments passed to internal functions, model-fitting routines, or test functions.

Value

A list containing:

pval_matrix

A matrix of adjusted p-values for each gene, including main effects and interactions.

stat_matrix

A matrix of corresponding test statistics.

class_df

A data frame classifying each gene based on the significance of main effects and interactions.

Details

Internally, the function:

  1. Builds the appropriate model formulas for each gene, depending on model_fit_function (e.g., lm vs. lmer) and whether random_effect is provided.

  2. Performs an overall significance test for each gene (the pval_cutoff_full threshold).

  3. For those genes passing the overall test, conducts an omnibus interaction test and further specific tests (main effects or two-way/three-way interactions) controlled by the respective p-value cutoffs.

Multiple testing corrections are applied based on p_adjust_method.

Examples

if (FALSE) { # \dontrun{
# Generate sample data
set.seed(123)
Z <- matrix(rnorm(1000), ncol = 10)
colnames(Z)=paste0("gene",1:10)
factor1 <- as.factor(rep(1:2, each = 5))
factor2 <- as.factor(rep(1:2, times = 5))
factor3 <- as.factor(rep(1:2, length.out = 10))

# Run the differential analysis using iDAS
result <- threefactors(
  Z, factor1, factor2, factor3,
  model_fit_function = "lm",
  test_function = "anova_test",
  pval_quantile_cutoff = 0.02,
  pval_cutoff_full = 0.05,
  pval_cutoff_interaction = 0.01,
  pval_cutoff_factor1 = 0.01,
  pval_cutoff_factor2 = 0.01,
  pval_cutoff_factor3 = 0.01,
  pval_cutoff_int12 = 0.01,
  pval_cutoff_int13 = 0.01,
  pval_cutoff_int23 = 0.01,
  pval_cutoff_int123 = 0.01,
  p_adjust_method = "BH"
)

# Inspect results
head(result$pval_matrix)
head(result$stat_matrix)
head(result$class_df)
} # }