% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/biomarkerDiscovery.R
\name{biomarkerDiscovery}
\alias{biomarkerDiscovery}
\title{Multi-Omics Biomarker Discovery}
\usage{
biomarkerDiscovery(
  mae,
  dr_result,
  n_biomarkers = 50L,
  host_assay = "voom",
  mb_assay = "CLR"
)
}
\arguments{
\item{mae}{A \code{MultiAssayExperiment} from \code{\link{matchSamples}}.}

\item{dr_result}{A named \code{list} from \code{\link{jointDimReduction}}.}

\item{n_biomarkers}{An \code{integer(1)} number of top features to
select per omics layer. Default: \code{50}.}

\item{host_assay}{A \code{character(1)} assay in host SE for computing
cross-omics correlations. Default: \code{"voom"}.}

\item{mb_assay}{A \code{character(1)} assay in microbiome SE for
computing cross-omics correlations. Default: \code{"CLR"}.}
}
\value{
A \code{DataFrame} with one row per biomarker and columns:
  \describe{
    \item{\code{feature}}{Feature name (gene or taxon ID).}
    \item{\code{omics_layer}}{Either \code{"host"} or
      \code{"microbiome"}.}
    \item{\code{loading_score}}{L2 norm of DIABLO loadings across
      components.}
    \item{\code{rank}}{Within-layer ranking by loading score.}
    \item{\code{component}}{DIABLO component with highest absolute
      loading.}
    \item{\code{max_cross_cor}}{Maximum absolute Spearman correlation
      with a feature from the other omics layer.}
    \item{\code{top_partner}}{Name of the cross-omics feature with
      the highest absolute correlation.}
  }
}
\description{
Identifies and ranks host genes and microbial taxa that jointly predict
the outcome of interest using two complementary evidence streams: (1)
sparse feature loadings from the DIABLO joint dimensionality reduction
model, and (2) a cross-omics Spearman correlation network linking host
genes to microbial taxa. Features are ranked by their combined loading
score and annotated with their maximum cross-omics correlation.
}
\details{
The biomarker ranking combines:
\describe{
  \item{Sparse loading score}{The L2 norm of a feature's loadings across
    all DIABLO components. Genes/taxa with higher loading scores
    contribute more strongly to the latent integration axes.}
  \item{Cross-omics correlation}{For each selected host gene, the
    maximum absolute Spearman correlation with any selected microbial
    taxon (and vice versa). High cross-omics correlation indicates
    biologically relevant host-microbe co-variation.}
}
Hub features — those with both high loading scores and high cross-omics
correlations — represent the most credible multi-omics biomarker
candidates.
}
\examples{
set.seed(42)
host_counts <- matrix(rpois(500 * 20, 100), nrow = 500, ncol = 20,
    dimnames = list(paste0("Gene", 1:500), paste0("S", 1:20)))
host_counts[1:20, 11:20] <- host_counts[1:20, 11:20] * 5L
mb_counts <- matrix(rpois(60 * 20, 40), nrow = 60, ncol = 20,
    dimnames = list(paste0("Taxon", 1:60), paste0("S", 1:20)))

host_se <- loadHostData(host_counts)
mb_se   <- loadMicrobiomeData(mb_counts)
mae     <- matchSamples(host_se, mb_se)
outcome <- rep(c("ctrl", "treat"), each = 10)
dr_res  <- jointDimReduction(mae, outcome, n_components = 2,
                              n_features_host = 30, n_features_mb = 15)
bm <- biomarkerDiscovery(mae, dr_res, n_biomarkers = 20)
head(as.data.frame(bm))

}
\seealso{
\code{\link{jointDimReduction}}, \code{\link{plotBiomarkerNetwork}},
  \code{\link{MultiOmicsBridgeAnalysis}}
}
