AEGIS Quick Start

Step 1. Load data and markers

data("aegis_example", package = "AEGIS")
seu <- aegis_example
markers <- aegis_default_markers()

Step 2. Simulate deconvolution outputs

deconv <- simulate_deconv_results(
  seu,
  methods = c("RCTD", "SPOTlight", "cell2location"),
  seed = 2026
)
#> Loading required namespace: SeuratObject

Step 3. Run the core pipeline

obj <- run_aegis(seu, deconv = deconv, markers = markers)

Step 4. Score and rank methods (RRA + mean-rank)

obj <- score_methods(obj)
obj_rra <- rank_methods(obj, method = "rra")
obj_meta <- rank_methods(obj, method = "mean_rank")

rra_cols <- intersect(
  c("method", "overall_rank", "overall_score", "rra_pvalue", "aggregation_used", "recommendation"),
  colnames(obj_rra$consensus$method_ranking)
)
meta_cols <- intersect(
  c("method", "overall_rank", "overall_score", "aggregation_used", "recommendation"),
  colnames(obj_meta$consensus$method_ranking)
)

knitr::kable(obj_rra$consensus$method_ranking[, rra_cols, drop = FALSE], digits = 4)

	method	overall_rank	overall_score	rra_pvalue	aggregation_used	recommendation
2	SPOTlight	1.0	0.1023	0.7901	rra	preferred
1	RCTD	2.5	0.0000	1.0000	rra	acceptable
3	cell2location	2.5	0.0000	1.0000	rra	acceptable

knitr::kable(obj_meta$consensus$method_ranking[, meta_cols, drop = FALSE], digits = 4)

	method	overall_rank	overall_score	aggregation_used	recommendation
2	SPOTlight	1.5	-1.5	mean_rank	preferred
1	RCTD	2.0	-2.0	mean_rank	acceptable
3	cell2location	2.5	-2.5	mean_rank	acceptable

best_method <- obj_meta$consensus$method_ranking$method[[1]]
best_method
#> [1] "SPOTlight"

Step 5. Build weighted consensus from top-ranked methods

obj_meta <- compute_consensus(obj_meta, strategy = "weighted", top_n = 2)
obj_meta$consensus$result$methods_used
#> [1] "SPOTlight" "RCTD"

Step 6. Visualize audits and consensus (simplified plotting API)

plot_audit(obj_meta, type = "dominance", method = best_method)

plot_compare(obj_meta, type = "heatmap")

plot_compare(obj_meta, type = "spot_agreement")

plot_compare(obj_meta, type = "consensus_map")

plot_compare(obj_meta, type = "ranking")

plot_compare(type = "ranking") is the primary ranking view used throughout the tutorials.

plot_compare(obj_meta, type = "disagreement_map")

plot_compare(obj_meta, type = "confidence_map")

Step 7. Generate report

render_report(obj_meta, output_file = "aegis_quick_start_report.html")

This quick start shows the minimum workflow from input data to ranking and consensus outputs.

For a full “deconvolution from scratch -> downstream analysis” walkthrough, see: vignettes/AEGIS-complete-tutorial.Rmd.