DAssemble is an R package for ensemble differential-abundance / differential-expression (DA/DE) analysis across multiple omics domains. It wraps a collection of existing DA/DE methods as core methods, combines them via Cauchy Combination Tests (CCT), and augments them with lightweight enhancers.

You can install DAssemble directly from GitHub:

# install.packages("remotes")  # if not already installed
remotes::install_github("himelmallick/DAssemble")

After installation, load the package with:

library(DAssemble)

DAssemble depends on several packages from CRAN, Bioconductor, and GitHub for its core DA methods and enhancers. The script below will automatically install any missing packages and then load them.

## ===========================================
## Install + load all required DAssemble deps
## ===========================================

# All required packages
req_pkgs <- c(
  "MAST", "DESeq2", "edgeR", "limma", "metagenomeSeq",
  "dearseq", "SummarizedExperiment", "ALDEx2", "LinDA",
  "LOCOM", "Maaslin2", "maaslin3", "Tweedieverse",
  "Robseq", "ANCOMBC", "TreeSummarizedExperiment", "S4Vectors"
)

# GitHub-only packages
github_pkgs <- c(
  LinDA        = "zhouhj1994/LinDA",
  LOCOM        = "yijuanhu/LOCOM",
  Tweedieverse = "himelmallick/Tweedieverse",
  maaslin3     = "biobakery/maaslin3",
  Robseq       = "schatterjee30/Robseq"
)

# Install required managers
if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")

if (!requireNamespace("remotes", quietly = TRUE))
  install.packages("remotes")

# Install + load loop
for (pkg in req_pkgs) {

  # Install if missing
  if (!requireNamespace(pkg, quietly = TRUE)) {
    message("Installing missing package: ", pkg)

    if (pkg %in% names(github_pkgs)) {
      repo <- github_pkgs[[pkg]]
      message("  -> Installing from GitHub: ", repo)
      remotes::install_github(repo, dependencies = TRUE)
    } else {
      tryCatch({
        install.packages(pkg)
      }, error = function(e) {
        message("  -> CRAN failed, installing via Bioconductor: ", pkg)
        BiocManager::install(pkg, ask = FALSE)
      })
    }
  }

  # Load package
  message("Loading package: ", pkg)
  ok <- suppressPackageStartupMessages(
    require(pkg, character.only = TRUE)
  )
  if (!ok)
    stop("Failed to load package: ", pkg, call. = FALSE)
}

cat("All required packages installed and loaded successfully.\n")

Some methods already combine two models internally (e.g., hurdle or two‑part models) and/or directly output a combined p‑value. These should normally be used alone in an ensemble, i.e. as the only core method. Combining a hurdle model with other core methods can double‑count the same underlying signal and lead to inflated type I error. The following methods fall into this category:

• Maaslin3 – fits both a prevalence and abundance model under the hood;
• MAST – implements a hurdle model for single‑cell expression data;
• metagenomeSeq – fits a zero‑inflated log‑normal model.

You may still add enhancers to these models (e.g. to perform sensitivity analyses), but combining them with additional core methods is not recommended.

DAssemble can be run in a number of configurations depending on whether you include a core method, enhancers, or both. The current framework allows you to combine one core method with up to three enhancers, or to run enhancer‑only analyses when core_method = NULL. The table below lists the valid combinations of enhancers by the number of enhancers used. The currently available enhancers are WLX (Wilcoxon rank‑sum), LR logistic regression) and KS (Kolmogorov–Smirnov).

For example, you might call:

# core + two enhancers
DAssemble(X, metadata, core_method = "DESeq2", enhancers = c("WLX", "LR"), expVar = "group")

# enhancer‑only, all three
DAssemble(X, metadata, core_method = NULL, enhancers = c("WLX", "LR", "KS"), expVar = "group")

When return_subensembles = TRUE the returned $ensembles element will include every sub‑combination of the requested methods. For instance, with three enhancers and no core, the sub‑ensembles will report CCT results for each single test ("WLX", "LR", "KS"), each pair ("WLX+LR", etc.) and the full trio ("WLX+LR+KS").

DAssemble currently implements three enhancer methods:

• DA_fit_enhancer_WLX() – Wilcoxon rank-sum test (WLX)
• DA_fit_enhancer_LR() – presence–absence logistic regression (LR)
• DA_fit_enhancer_KS() – Kolmogorov–Smirnov test (KS)

All enhancers return:

feature     # character
pval_<TAG>  # numeric, where <TAG> is WLX / LR / KS

Within DAssemble(), you usually specify enhancers using their short tags:

enhancers = c("WLX", "LR", "KS")  # at most two at a time are currently supported

DAssemble then routes to the corresponding DA_fit_enhancer_*() functions.

The main entry point is:

DAssemble(
  X,
  expVar,
  coVars        = NULL,
  core_method   = "DESeq2",
  enhancers     = c("WLX", "KS"),
  domain        = c("bulkrnaseq", "singlecell", "microbiome", "none"),
  p_adj         = "BH",
  orientation   = c("features_by_samples", "samples_by_features", "auto"),
  return_components   = FALSE,
  return_subensembles = FALSE,
  ...
)

Key arguments:

• X – feature matrix of counts / abundances
• expVar – name (or column) of the primary exposure variable (binary, 2 levels)
• coVars – optional covariates (used by some core methods)
• core_method – one of the supported core method names (see above), or NULL / "none" to run an enhancer-only analysis
• enhancers – NULL or a subset of c("WLX", "LR", "KS")
• domain – "bulkrnaseq", "singlecell", "microbiome", or "none"; used only by the enhancers to choose modality-specific normalization
• p_adj – multiple testing correction method (passed to p.adjust)
• orientation – "features_by_samples", "samples_by_features", or "auto"
• return_components – if TRUE, return per-method results in $components
• return_subensembles – if TRUE, compute CCT sub-ensembles and return them in $ensembles

The main output includes:

• $results – data frame of features with ensemble p-values (raw and adjusted)
• $components – (optional) list of per-method result tables
• $ensembles – (optional) table of sub-ensemble CCT results

If return_subensembles = TRUE and the helper DA_build_subensembles() is available, DAssemble computes sub-ensembles in addition to the main joint CCT p-values.

• When core_method is not NULL, sub-ensembles typically correspond to:

  • the core alone,
  • each core + single-enhancer combination,
  • and (optionally) the full core + all-enhancers ensemble.

• When core_method = NULL (enhancer-only mode), the sub-ensembles are defined purely in terms of the enhancers.
  For example, with enhancers = c("WLX", "LR", "KS"), the sub-ensembles can include:

  • WLX alone, LR alone, KS alone
  • WLX + LR, WLX + KS, LR + KS
  • WLX + LR + KS

In the resulting $ensembles object, the sub_ensemble (or similarly named) column labels each combination (e.g., "WLX", "WLX+LR", "LR+KS", "WLX+LR+KS").

This is useful for sensitivity analysis: you can see how conclusions change as you move from individual tests to different CCT combinations, including the enhancer-only setting when no core method is used.

To illustrate how to use DAssemble on real datasets, this section provides two short examples: one for a bulk RNA‑Seq experiment and one for a microbiome study. These examples rely on publicly available data from Bioconductor packages; citations are provided for a brief description of each dataset.

The airway dataset from the Bioconductor package airway is a small RNA‑Seq experiment in which four airway smooth muscle cell lines are treated with the asthma medication dexamethasone. It is often used as an introductory example for differential expression analysis; the dataset comprises counts for ~63 k genes measured in eight samples (4 × 2 design). A Bioconductor vignette notes that the airway dataset provides a typical small‑scale RNA‑Seq experiment where four ASM cell lines are treated with dexamethasone.

To run DAssemble on this dataset using the DESeq2 core and two enhancers (Wilcoxon and logistic regression):

library(DESeq2)
library(airway)
library(DAssemble)

# load counts and metadata
data("airway")
counts   <- assay(airway, "counts")
metadata <- as.data.frame(colData(airway))

# the exposure variable is dex (treated vs untreated)
res <- DAssemble(
  features    = t(counts),
  metadata    = metadata,
  core_method = "DESeq2",
  enhancers   = c("WLX", "LR"),
  expVar      = "dex",
  p_adj       = "BH",
  enhancer_norm = "tmm",
  return_components = TRUE,
  return_subensembles = TRUE
)

# inspect the top differential genes
head(res$res)
# inspect per‑method p‑values
names(res$components)

This call normalizes the counts using the TMM scheme (enhancer_norm = "tmm"), combines DESeq2 with the Wilcoxon and logistic regression enhancers, and returns the full set of sub‑ensembles. You can access individual method outputs through the $components element of the result.

The Global Patterns dataset, available via the phyloseq package, contains 16S rRNA profiles for 25 environmental samples and three synthetic “mock communities,” representing nine sample types in total at an average sequencing depth of 3.1 million reads per sample【156744987103578†L128-L133】. It has been used to explore diversity patterns across a variety of ecosystems.

To run an enhancer‑only DAssemble analysis on a microbiome count table extracted from GlobalPatterns:

library(phyloseq)
library(DAssemble)

# load Global Patterns as a phyloseq object
data("GlobalPatterns")
gp <- GlobalPatterns

# extract count matrix and sample metadata
otu  <- as(otu_table(gp), "matrix")
meta <- as.data.frame(sample_data(gp))

# here we compare human stool samples against soil samples as an example
keep <- meta$SampleType %in% c("Stool", "Soil")
X    <- t(otu[, keep])
meta <- meta[keep, , drop = FALSE]
meta$group <- droplevels(factor(meta$SampleType))

res <- DAssemble(
  features    = X,
  metadata    = meta,
  core_method = NULL,
  enhancers   = c("WLX", "LR", "KS"),
  expVar      = "group",
  enhancer_norm = "clr",
  return_components = TRUE,
  return_subensembles = TRUE
)

head(res$res)

In this example the core is set to NULL, so the analysis combines three nonparametric enhancers only. Because microbiome data are compositional, we use centered log‑ratio (CLR) normalization (enhancer_norm = "clr"). The $components element contains the individual Wilcoxon, logistic regression and KS results, while $ensembles summarises every sub‑combination of the three enhancers.