compare_daa_results: Compare the Consistency of Statistically Significant Features

In ggpicrust2: Make 'PICRUSt2' Output Analysis and Visualization Easier

Compare the Consistency of Statistically Significant Features

Description

This function compares the consistency and inconsistency of statistically significant features obtained using different methods in 'pathway_daa' from the 'ggpicrust2' package. It creates a report showing the number of common and different features identified by each method, and the features themselves.

Usage

compare_daa_results(daa_results_list, method_names, p_values_threshold = 0.05)

Arguments

daa_results_list	A list of data frames containing statistically significant features obtained using different methods.
method_names	A character vector of names for each method used.
p_values_threshold	A numeric value representing the threshold for the p-values. Features with p-values less than this threshold are considered statistically significant. Default is 0.05. Must be in the range (0, 1].

Details

Each list element is one discovery set. Split multi-test output such as ALDEx2 by its method column before comparison; otherwise the tests' discoveries are pooled within that element. This function does not rerun testing or adjust p-values again.

For multi-group DAA output, each discovery is compared as a feature + group-pair unit. The group pair is treated as unordered for this set-level comparison, because the function compares whether methods identified a significant difference, not the effect-size direction. This prevents the same feature from being counted as method-consistent when different methods found it in different pairwise contrasts, while still treating A vs B and B vs A as the same biological comparison. Rows whose two group labels are identical are invalid self-comparisons and are rejected. If all significant discoveries share one group pair, the printed feature lists use feature IDs only for backward-readable output; otherwise feature lists include the canonical contrast as feature [group1 vs group2].

Value

A data frame with the comparison results. The data frame has the following columns:

• method: The name of the method.

• num_features: The total number of statistically significant features obtained by the method.

• num_common_features: The number of features that are common to other methods.

• num_diff_features: The number of features that are different from other methods.

• common_features: The names of the features that are common to all methods.

• diff_features: The names of the features that are different from other methods.

Examples

# Minimal DAA-like results from three methods (no external dependencies required)
deseq2_df <- data.frame(
  feature = c("ko00010", "ko00020", "ko00564"),
  group1 = c("A", "A", "A"),
  group2 = c("B", "B", "B"),
  p_adjust = c(0.01, 0.20, 0.03),
  stringsAsFactors = FALSE
)

edgeR_df <- data.frame(
  feature = c("ko00010", "ko00680", "ko00564"),
  group1 = c("A", "A", "A"),
  group2 = c("B", "B", "B"),
  p_adjust = c(0.02, 0.04, 0.01),
  stringsAsFactors = FALSE
)

maaslin2_df <- data.frame(
  feature = c("ko00010", "ko03030", "ko00564"),
  group1 = c("A", "A", "A"),
  group2 = c("B", "B", "B"),
  p_adjust = c(0.03, 0.02, 0.04),
  stringsAsFactors = FALSE
)

daa_results_list <- list(DESeq2 = deseq2_df, edgeR = edgeR_df, Maaslin2 = maaslin2_df)
comparison_results <- compare_daa_results(
  daa_results_list = daa_results_list,
  method_names = c("DESeq2", "edgeR", "Maaslin2"),
  p_values_threshold = 0.05
)
comparison_results

ggpicrust2 documentation built on Oct. 7, 2026, 1:07 a.m.