Purpose
OmicsBraid analyzes the same biological contrast across scientifically ordered molecular layers while retaining layer-specific uncertainty and matched-subject dependence.
Minimal known-truth example
library(OmicsBraid)
sim <- simulate_braid_data(
n_per_group = 40,
omics = c("RNA", "Protein", "Phosphoprotein"),
rho = 0.4,
seed = 42
)
fit <- run_omics_braid(
sim$data,
group = "group",
reference = "Control",
comparison = "Disease",
omic_order = c("RNA", "Protein", "Phosphoprotein"),
bootstrap_B = 300,
equivalence_margin = 0.30,
trajectory_margin = 0.15,
pattern_draws = 1000,
seed = 42
)Inspect results
fit$effects
fit$integrated
fit$equivalence
fit$trend
fit$classification
braid_results_table(fit)fit$integrated contains the GLS consensus, omnibus
evidence, and cross-omic heterogeneity. The classification layer should
be interpreted together with pattern_status and the
underlying effect vector rather than as an isolated label.
Visualize one entity
plot_evidence_forest(fit, "inversion")
plot_effect_braid(fit, "inversion")For a genome-wide overview:
plot_concordance_map(fit)
plot_braid_heatmap(fit)Real data
Create an omics_braid_data object from analysis-ready
assay matrices and metadata. Column names must identify samples and
metadata must contain the same sample IDs.
obj <- omics_braid_data(
assays = list(RNA = rna, Protein = protein, Phosphoprotein = phospho),
metadata = metadata,
sample_id = "sample_id"
)
validate_omics_braid_data(obj)Do not feed raw sequencing counts or raw mass-spectrometry files directly into OmicsBraid.