Introduction

Multi-omics integration is now central to mechanistic studies of complex diseases. Combining host transcriptomics (bulk RNA-seq) with gut microbiome profiling (16S rRNA or metagenomics) has revealed key host-microbe interactions in inflammatory bowel disease, tuberculosis, HIV, and metabolic syndrome. Yet the computational toolkit for this specific integration scenario remains fragmented: no single Bioconductor package takes a researcher from raw paired data through joint dimensionality reduction, biomarker discovery, and diagnostic classification.

MultiOmicsBridge closes this gap. The package provides a unified, reproducible workflow with five modules:

Module	Functions	Purpose
1. Harmonization	`loadHostData()`, `loadMicrobiomeData()`, `matchSamples()`	Import, normalize, pair data
2. Joint Dim. Reduction	`jointDimReduction()`	DIABLO sparse multi-block PLS-DA
3. Biomarker Discovery	`biomarkerDiscovery()`	Cross-omics ranked biomarkers
4. Classification	`diagnosticClassifier()`	Host-only vs microbiome-only vs joint
5. Visualization	`plotIntegration()`, `plotBiomarkerNetwork()`, `plotClassifierComparison()`, `plotSankey()`	Publication figures

The one-call wrapper MultiOmicsBridgeAnalysis() executes all five modules and returns a structured MOBResult S4 object.

Installation

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("MultiOmicsBridge")

Simulated Multi-Omics Dataset

We demonstrate the package on a simulated dataset that mimics a real paired study: 20 samples (10 healthy controls, 10 disease cases), 500 host genes, and 80 microbial taxa. To model a biologically realistic signal, we inject upregulation of the first 20 genes and enrichment of the first 5 taxa in the disease group.

library(MultiOmicsBridge)
library(SummarizedExperiment)
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#> 
#> Attaching package: 'MatrixGenerics'
#> The following objects are masked from 'package:matrixStats':
#> 
#>     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#>     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#>     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#>     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#>     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#>     colWeightedMeans, colWeightedMedians, colWeightedSds,
#>     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#>     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#>     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#>     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#>     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#>     rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> Loading required package: generics
#> 
#> Attaching package: 'generics'
#> The following objects are masked from 'package:base':
#> 
#>     as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
#>     setequal, union
#> 
#> Attaching package: 'BiocGenerics'
#> The following objects are masked from 'package:stats':
#> 
#>     IQR, mad, sd, var, xtabs
#> The following object is masked from 'package:utils':
#> 
#>     data
#> The following objects are masked from 'package:base':
#> 
#>     anyDuplicated, aperm, append, as.data.frame, basename, cbind,
#>     colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
#>     get, grep, grepl, is.unsorted, lapply, Map, mapply, match, mget,
#>     order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
#>     rbind, Reduce, rownames, sapply, saveRDS, scale, sequence, table,
#>     tapply, transform, unique, unsplit, which.max, which.min
#> Loading required package: S4Vectors
#> 
#> Attaching package: 'S4Vectors'
#> The following object is masked from 'package:utils':
#> 
#>     findMatches
#> The following objects are masked from 'package:base':
#> 
#>     expand.grid, I, unname
#> Loading required package: IRanges
#> Loading required package: Seqinfo
#> Loading required package: Biobase
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
#> 
#> Attaching package: 'Biobase'
#> The following object is masked from 'package:MatrixGenerics':
#> 
#>     rowMedians
#> The following objects are masked from 'package:matrixStats':
#> 
#>     anyMissing, rowMedians

set.seed(2026)
n_genes   <- 500
n_taxa    <- 80
n_samples <- 20

# Host RNA-seq count matrix (genes x samples)
host_counts <- matrix(
    rpois(n_genes * n_samples, lambda = 150L),
    nrow = n_genes, ncol = n_samples
)
rownames(host_counts) <- paste0("Gene",   seq_len(n_genes))
colnames(host_counts) <- paste0("Sample", seq_len(n_samples))

# Inject transcriptional signal: genes 1-20 upregulated in disease
host_counts[seq_len(20), seq(11, n_samples)] <-
    host_counts[seq_len(20), seq(11, n_samples)] * 5L

# Microbiome count matrix (taxa x samples)
mb_counts <- matrix(
    rpois(n_taxa * n_samples, lambda = 40L),
    nrow = n_taxa, ncol = n_samples
)
rownames(mb_counts) <- paste0("Taxon",  seq_len(n_taxa))
colnames(mb_counts) <- paste0("Sample", seq_len(n_samples))

# Inject microbial enrichment: taxa 1-5 enriched in disease
mb_counts[seq_len(5), seq(11, n_samples)] <-
    mb_counts[seq_len(5), seq(11, n_samples)] * 4L

# Sample metadata
col_data <- data.frame(
    condition = rep(c("Control", "Disease"), each = 10),
    age       = sample(30:65, n_samples, replace = TRUE),
    sex       = sample(c("M", "F"), n_samples, replace = TRUE),
    row.names = paste0("Sample", seq_len(n_samples))
)

cat(sprintf("Host:       %d genes × %d samples\n", nrow(host_counts), ncol(host_counts)))
#> Host:       500 genes × 20 samples
cat(sprintf("Microbiome: %d taxa  × %d samples\n", nrow(mb_counts), ncol(mb_counts)))
#> Microbiome: 80 taxa  × 20 samples

Module 1: Data Harmonization

Step 1.1 — Load host RNA-seq data

loadHostData() applies TMM normalization (via edgeR) followed by limma-voom precision weighting. Both raw counts and log2-CPM values are stored in the output SummarizedExperiment.

host_se <- loadHostData(host_counts, col_data = col_data)
#> Normalizing 500 genes x 20 samples with TMM + voom...
#> loadHostData complete.
host_se
#> class: SummarizedExperiment 
#> dim: 500 20 
#> metadata(0):
#> assays(2): counts voom
#> rownames(500): Gene1 Gene2 ... Gene499 Gene500
#> rowData names(0):
#> colnames(20): Sample1 Sample2 ... Sample19 Sample20
#> colData names(3): condition age sex
assayNames(host_se)
#> [1] "counts" "voom"

Step 1.2 — Load microbiome data

loadMicrobiomeData() applies centered log-ratio (CLR) transformation. This is the compositionally appropriate normalization that removes the unit-sum constraint inherent to relative abundance data.

mb_se <- loadMicrobiomeData(mb_counts, normalization = "CLR",
                             min_prevalence = 0.1)
#> Normalizing 80 taxa x 20 samples with CLR...
#> loadMicrobiomeData complete.
mb_se
#> class: SummarizedExperiment 
#> dim: 80 20 
#> metadata(0):
#> assays(2): counts CLR
#> rownames(80): Taxon1 Taxon2 ... Taxon79 Taxon80
#> rowData names(0):
#> colnames(20): Sample1 Sample2 ... Sample19 Sample20
#> colData names(0):
assayNames(mb_se)
#> [1] "counts" "CLR"

# Verify CLR: each sample should have zero mean
cat("Column means of CLR matrix (should be ≈ 0):\n")
#> Column means of CLR matrix (should be ≈ 0):
print(round(colMeans(assay(mb_se, "CLR")), 10)[1:5])
#> Sample1 Sample2 Sample3 Sample4 Sample5 
#>       0       0       0       0       0

Step 1.3 — Match paired samples

matchSamples() finds the intersection of sample names across both SEs and packages them into a MultiAssayExperiment (MAE). Unpaired samples are transparently reported and excluded.

mae <- matchSamples(host_se, mb_se, min_paired = 10)
#> matchSamples: 20 host | 20 microbiome | 20 paired samples retained.
mae
#> A MultiAssayExperiment object of 2 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 2:
#>  [1] host: SummarizedExperiment with 500 rows and 20 columns
#>  [2] microbiome: SummarizedExperiment with 80 rows and 20 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files

All 20 samples are paired in this dataset. In real studies, matchSamples() will report and exclude any samples missing from either platform.

Module 2: Joint Dimensionality Reduction

jointDimReduction() runs DIABLO (Data Integration Analysis for Biomarker discovery using Latent cOmponents) from the mixOmics package. DIABLO simultaneously maximises the covariance between the host and microbiome latent variates while discriminating between outcome groups, using L1 sparsity to select only the most informative features.

outcome <- col_data$condition   # "Control" or "Disease"

dr_result <- jointDimReduction(
    mae,
    outcome         = outcome,
    n_components    = 2L,
    n_features_host = 40L,
    n_features_mb   = 15L,
    design_off_diag = 0.1
)
#> Running DIABLO: 20 samples | 500 host features | 80 mb features | 2 components
#> Design matrix has changed to include Y; each block will be
#>             linked to Y.
#> jointDimReduction complete.

cat("Score matrix dimensions:", dim(dr_result$scores), "\n")
#> Score matrix dimensions: 20 2
cat("Host loading matrix dimensions:", dim(dr_result$host_loadings), "\n")
#> Host loading matrix dimensions: 500 2
cat("Microbiome loading matrix dimensions:", dim(dr_result$mb_loadings), "\n")
#> Microbiome loading matrix dimensions: 80 2
cat("\nExplained variance per component:\n")
#> 
#> Explained variance per component:
print(round(dr_result$explained_variance, 4))
#>  Comp1  Comp2 
#> 0.0978 0.0507

Module 3: Biomarker Discovery

biomarkerDiscovery() ranks host genes and microbial taxa using their DIABLO sparse loading scores (L2 norm across components) and annotates each with its maximum Spearman correlation to a feature in the other omics layer. High-loading features with strong cross-omics correlations represent the most credible multi-omics biomarker candidates.

bm_table <- biomarkerDiscovery(mae, dr_result, n_biomarkers = 30)
#> Ranking biomarkers from DIABLO sparse loadings...
#> Computing cross-omics Spearman correlation network...
#> biomarkerDiscovery: 30 host genes, 30 microbial taxa selected.

# Show top 10 biomarkers
bm_df <- as.data.frame(bm_table)
bm_df_sorted <- bm_df[order(bm_df$loading_score, decreasing = TRUE), ]
head(bm_df_sorted[, c("feature","omics_layer","loading_score",
                        "component","max_cross_cor","top_partner")], 10)
#>         feature omics_layer loading_score component max_cross_cor top_partner
#> Taxon53 Taxon53  microbiome     0.4986963         2     0.5172932     Gene201
#> Taxon39 Taxon39  microbiome     0.4824716         2     0.6375940     Gene201
#> Gene201 Gene201        host     0.4510873         2     0.6375940     Taxon39
#> Gene303 Gene303        host     0.4438466         2     0.6481203     Taxon46
#> Taxon3   Taxon3  microbiome     0.4348072         1     0.8330827      Gene16
#> Taxon5   Taxon5  microbiome     0.4344162         1     0.8601504       Gene5
#> Taxon4   Taxon4  microbiome     0.4295783         1     0.9278195      Gene11
#> Taxon2   Taxon2  microbiome     0.4240883         1     0.8616541      Gene10
#> Taxon1   Taxon1  microbiome     0.4229655         1     0.8300752      Gene19
#> Gene113 Gene113        host     0.3532316         2     0.6887218     Taxon28

n_host <- sum(bm_df$omics_layer == "host")
n_mb   <- sum(bm_df$omics_layer == "microbiome")
cat(sprintf("Host biomarkers     : %d\n", n_host))
#> Host biomarkers     : 30
cat(sprintf("Microbiome biomarkers: %d\n", n_mb))
#> Microbiome biomarkers: 30

# Are the injected genes recovered?
injected <- paste0("Gene", seq_len(20))
detected <- intersect(bm_df$feature, injected)
cat(sprintf("Injected genes recovered: %d / 20\n", length(detected)))
#> Injected genes recovered: 20 / 20

Module 4: Diagnostic Classification

diagnosticClassifier() trains three Random Forest models using nested cross-validation and compares their AUC-ROC values. This comparison directly quantifies the added diagnostic value of multi-omics integration.

clf_results <- diagnosticClassifier(
    mae,
    outcome         = outcome,
    biomarker_table = bm_table,
    cv_folds        = 5L,
    seed            = 42L
)
#> diagnosticClassifier: 30 host | 30 microbiome | 5-fold CV
#>   Training host-only classifier...
#>   Training microbiome-only classifier...
#>   Training joint classifier...
#> AUC: host=1.000 | microbiome=1.000 | joint=1.000

cat("Classifier AUC-ROC (mean ± SD across 5-fold CV):\n")
#> Classifier AUC-ROC (mean ± SD across 5-fold CV):
for (nm in c("host_only", "microbiome_only", "joint")) {
    r <- clf_results[[nm]]
    cat(sprintf("  %-22s  %.3f ± %.3f\n",
                paste0(nm, ":"), r$mean_auc, r$sd_auc))
}
#>   host_only:              1.000 ± 0.000
#>   microbiome_only:        1.000 ± 0.000
#>   joint:                  1.000 ± 0.000

delta <- clf_results$joint$mean_auc - clf_results$host_only$mean_auc
cat(sprintf("\nMulti-omics gain vs host-only: +%.3f AUC\n", delta))
#> 
#> Multi-omics gain vs host-only: +0.000 AUC

Full Pipeline with One Call

For standard analyses, MultiOmicsBridgeAnalysis() runs all modules in sequence and returns a MOBResult S4 object:

result <- MultiOmicsBridgeAnalysis(
    mae,
    outcome,
    n_components    = 2L,
    n_features_host = 40L,
    n_features_mb   = 15L,
    n_biomarkers    = 30L,
    cv_folds        = 5L,
    seed            = 42L
)
#> === MultiOmicsBridgeAnalysis ===
#> Samples: 20 | Outcome: Control vs Disease
#> 
#> -- Module 2: Joint Dimensionality Reduction (DIABLO) --
#> Running DIABLO: 20 samples | 500 host features | 80 mb features | 2 components
#> Design matrix has changed to include Y; each block will be
#>             linked to Y.
#> jointDimReduction complete.
#> 
#> -- Module 3: Biomarker Discovery --
#> Ranking biomarkers from DIABLO sparse loadings...
#> Computing cross-omics Spearman correlation network...
#> biomarkerDiscovery: 30 host genes, 30 microbial taxa selected.
#> 
#> -- Module 4: Diagnostic Classification --
#> diagnosticClassifier: 30 host | 30 microbiome | 5-fold CV
#>   Training host-only classifier...
#>   Training microbiome-only classifier...
#>   Training joint classifier...
#> AUC: host=1.000 | microbiome=1.000 | joint=1.000
#> 
#> === Analysis complete. Assembling MOBResult ===

result
#> MOBResult
#>   Integration    : DIABLO 
#>   Samples        : 20 
#>   Components     : 2 
#>   Biomarkers     : 60 (30 host, 30 microbiome) 
#>   -- Classifier AUC (mean +/- SD) -----------
#>   host_only:       1.000 +/- 0.000
#>   microbiome_only: 1.000 +/- 0.000
#>   joint:           1.000 +/- 0.000
#>   Outcome levels : Control vs Disease

Access individual results with typed accessor methods:

# Integration scores: samples x components
head(integrationScores(result))
#>             comp1      comp2
#> Sample1 -4.577809 -0.2783086
#> Sample2 -4.756835 -1.7897451
#> Sample3 -4.514106 -0.1633972
#> Sample4 -4.714052 -0.1824974
#> Sample5 -4.470973 -1.5173940
#> Sample6 -4.897541  2.4213213

# Top biomarkers
head(as.data.frame(biomarkers(result))[, c("feature","omics_layer",
                                             "loading_score")])
#>         feature omics_layer loading_score
#> Gene201 Gene201        host     0.4510873
#> Gene303 Gene303        host     0.4438466
#> Gene113 Gene113        host     0.3532316
#> Gene147 Gene147        host     0.2376692
#> Gene6     Gene6        host     0.2204654
#> Gene4     Gene4        host     0.2202405

# Classifier performance
perf <- performance(result)
cat(sprintf("Joint AUC: %.3f\n", perf$joint$mean_auc))
#> Joint AUC: 1.000

Module 5: Visualization

Joint integration biplot

plotIntegration(result, outcome = outcome, n_loading_arrows = 5)

DIABLO sample scores coloured by outcome group. Loading vectors show the top contributing features per omics layer.

Cross-omics biomarker network

plotBiomarkerNetwork(result, mae, n_host = 15, n_mb = 10)

Clustered heatmap of Spearman correlations between the top host genes and microbial taxa. Strong positive/negative correlations indicate candidate host-microbe interactions.

Classifier comparison (bar chart)

plotClassifierComparison(result, type = "bar")

Mean cross-validated AUC-ROC for host-only, microbiome-only, and joint classifiers. The multi-omics advantage is immediately visible.

Classifier comparison (ROC curves)

plotClassifierComparison(result, type = "roc")

Overlaid ROC curves from the last cross-validation fold for each classifier configuration.

Feature flow (Sankey) diagram

plotSankey(result, n_features = 8)

Feature flow from omics layer through selected biomarkers to outcome classes. Edge width is proportional to loading score.

Structured text report

generateReport(result, n_top = 8)
#> ============================================================
#>   MultiOmicsBridge Analysis Report
#>   Generated: 2026-06-09 13:49
#> ============================================================
#> 
#> MODULE 1: Data Summary
#> ------------------------------------------------------------
#>   Paired samples      : 20
#>   Latent components   : 2
#>   Integration method  : DIABLO
#>   Outcome             : Control vs Disease
#>   Host features used  : 40 per component
#>   MB features used    : 15 per component
#> 
#> MODULE 3: Multi-Omics Biomarkers
#> ------------------------------------------------------------
#>   Total biomarkers    : 60 (30 host, 30 microbiome)
#>   Top 8 biomarkers (by loading score):
#>   Rank Feature                   Layer        Loading
#>      1 Taxon53                   microbiome   0.4987
#>      2 Taxon39                   microbiome   0.4825
#>      1 Gene201                   host         0.4511
#>      2 Gene303                   host         0.4438
#>      3 Taxon3                    microbiome   0.4348
#>      4 Taxon5                    microbiome   0.4344
#>      5 Taxon4                    microbiome   0.4296
#>      6 Taxon2                    microbiome   0.4241
#> 
#>   Top cross-omics hubs (highest partner correlation):
#>   Taxon4                    -> Gene11                    (r=0.928)
#>   Taxon2                    -> Gene10                    (r=0.862)
#>   Taxon5                    -> Gene5                     (r=0.860)
#>   Taxon3                    -> Gene16                    (r=0.833)
#>   Gene303                   -> Taxon46                   (r=0.648)
#> 
#> MODULE 4: Diagnostic Classifier Performance
#> ------------------------------------------------------------
#>   Cross-validation    : 5-fold
#>   Host RNA-seq only       AUC = 1.000 +/- 0.000
#>   Microbiome only         AUC = 1.000 +/- 0.000
#>   Joint (multi-omics)     AUC = 1.000 +/- 0.000
#> 
#>   Multi-omics gain vs host-only: +0.000 AUC
#> 
#> ============================================================
#>   End of report
#> ============================================================

Working with Real Data

HMP2 / IBDMDB dataset

The IBDMDB dataset (Franzosa et al. 2019) is the recommended primary validation dataset. To use it with MultiOmicsBridge:

# The HMP2 data is available from the IBDMDB portal.
# After downloading, load as:
library(MultiOmicsBridge)

# Host RNA-seq
host_se <- loadHostData(
    counts   = host_count_matrix,   # genes x samples
    col_data = sample_metadata
)

# Microbiome 16S
mb_se <- loadMicrobiomeData(
    taxa_table    = taxa_count_matrix,   # taxa x samples
    normalization = "CLR"
)

# Match and run
mae    <- matchSamples(host_se, mb_se)
result <- MultiOmicsBridgeAnalysis(mae, outcome = sample_metadata$diagnosis)
result

Complex disease contexts

MultiOmicsBridge is designed to generalize to complex disease contexts. For tuberculosis studies (e.g. GEO: GSE79362), the same workflow applies:

# TB study: blood RNA-seq + sputum microbiome
host_se <- loadHostData(blood_counts, col_data = patient_metadata)
mb_se   <- loadMicrobiomeData(sputum_taxa, normalization = "CLR")
mae     <- matchSamples(host_se, mb_se)

result  <- MultiOmicsBridgeAnalysis(
    mae,
    outcome         = patient_metadata$tb_status,
    n_features_host = 100,
    n_features_mb   = 30,
    cv_folds        = 5
)
plotClassifierComparison(result, type = "bar")

Design Principles

Why CLR for microbiome data?

Microbiome count data is compositional: only relative abundances are observed. Analyzing composites with Euclidean distances or Pearson correlations leads to spurious results (the Aitchison problem). The CLR transformation maps compositional data to real space by:

\[clr(x_j) = \log\left(\frac{x_j + \delta}{g_\delta(x)}\right)\]

where \(g_\delta(x) = \exp\left(\frac{1}{D}\sum_{k}\log(x_k + \delta)\right)\) is the geometric mean with pseudocount \(\delta\). This removes the unit-sum constraint and enables standard Euclidean geometry, making correlations between host genes and microbial taxa statistically valid.

Why DIABLO for integration?

Unlike concatenation (simply merging feature matrices) or independent analysis of each omics layer, DIABLO enforces cross-block correlation: the latent variates from the host and microbiome blocks are constrained to covary. This means DIABLO identifies features that change together across conditions, which is biologically more meaningful than features that are simply marginally associated with the outcome in each dataset independently.

Quantifying the multi-omics advantage

A key scientific contribution of every multi-omics study is demonstrating that integrating two data types improves diagnostic performance over either alone. diagnosticClassifier() makes this comparison automatic and transparent, following best practices from clinical prediction modelling: nested cross-validation with stratified folds to avoid overfitting bias.

Session information

sessionInfo()
#> R version 4.6.0 (2026-04-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] SummarizedExperiment_1.43.0 Biobase_2.73.1             
#>  [3] GenomicRanges_1.65.0        Seqinfo_1.3.0              
#>  [5] IRanges_2.47.2              S4Vectors_0.51.3           
#>  [7] BiocGenerics_0.59.7         generics_0.1.4             
#>  [9] MatrixGenerics_1.25.0       matrixStats_1.5.0          
#> [11] MultiOmicsBridge_0.99.0     BiocStyle_2.41.0           
#> 
#> loaded via a namespace (and not attached):
#>  [1] ellipse_0.5.0               gtable_0.3.6               
#>  [3] xfun_0.58                   bslib_0.11.0               
#>  [5] ggplot2_4.0.3               ggrepel_0.9.8              
#>  [7] lattice_0.22-9              vctrs_0.7.3                
#>  [9] tools_4.6.0                 parallel_4.6.0             
#> [11] tibble_3.3.1                rARPACK_0.11-0             
#> [13] pkgconfig_2.0.3             Matrix_1.7-5               
#> [15] RColorBrewer_1.1-3          S7_0.2.2                   
#> [17] mixOmics_6.37.0             lifecycle_1.0.5            
#> [19] stringr_1.6.0               compiler_4.6.0             
#> [21] farver_2.1.2                statmod_1.5.2              
#> [23] codetools_0.2-20            htmltools_0.5.9            
#> [25] sys_3.4.3                   buildtools_1.0.0           
#> [27] sass_0.4.10                 yaml_2.3.12                
#> [29] tidyr_1.3.2                 pillar_1.11.1              
#> [31] jquerylib_0.1.4             MASS_7.3-65                
#> [33] BiocParallel_1.47.0         DelayedArray_0.39.3        
#> [35] cachem_1.1.0                limma_3.69.2               
#> [37] abind_1.4-8                 RSpectra_0.16-2            
#> [39] tidyselect_1.2.1            locfit_1.5-9.12            
#> [41] digest_0.6.39               stringi_1.8.7              
#> [43] purrr_1.2.2                 reshape2_1.4.5             
#> [45] dplyr_1.2.1                 labeling_0.4.3             
#> [47] maketools_1.3.2             fastmap_1.2.0              
#> [49] grid_4.6.0                  cli_3.6.6                  
#> [51] SparseArray_1.13.2          magrittr_2.0.5             
#> [53] S4Arrays_1.13.0             withr_3.0.2                
#> [55] edgeR_4.11.1                corpcor_1.6.10             
#> [57] scales_1.4.0                rmarkdown_2.31             
#> [59] XVector_0.53.0              igraph_2.3.2               
#> [61] otel_0.2.0                  gridExtra_2.3              
#> [63] ranger_0.18.0               evaluate_1.0.5             
#> [65] knitr_1.51                  MultiAssayExperiment_1.39.0
#> [67] rlang_1.2.0                 Rcpp_1.1.1-1.1             
#> [69] glue_1.8.1                  BiocManager_1.30.27        
#> [71] pROC_1.19.0.1               jsonlite_2.0.0             
#> [73] plyr_1.8.9                  R6_2.6.1

Integrative Multi-Omics Analysis with MultiOmicsBridge