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Plot Sensitivity Analysis Summary

Source: R/plot_sensitivity_summary.R
plot_sensitivity_summary.Rd

Generates a ridge plot and bar chart summarizing feature stability scores across assays.

Usage

plot_sensitivity_summary(
  expomicset,
  stability_score_thresh = NULL,
  stability_metric = "stability_score",
  title = "Distribution of Stability Scores"
)

Arguments

expomicset

A MultiAssayExperiment object containing sensitivity analysis results in metadata(expomicset)$sensitivity_analysis.

stability_score_thresh

A numeric threshold for stability scores. Default is NULL, which uses the threshold stored in metadata(expomicset)$sensitivity_analysis$score_thresh.

stability_metric

A character string specifying which stability metric to plot (e.g., "stability_score", "logp_weighted_score"). Default is "stability_score".

title

A character string specifying the title of the ridge plot. Default is "Distribution of Stability Scores".

Value

A patchwork object combining a ridge plot and a bar chart.

Details

This function:

  • Extracts feature stability scores from metadata(expomicset)$sensitivity_analysis$feature_stability.

  • Displays a ridge plot of stability score distributions per assay.

  • Displays a bar chart of the number of features per assay.

  • Prints the number of features with stability scores above the threshold.

Examples

# create example data
mae <- make_example_data(
    n_samples = 20,
    return_mae = TRUE
)
#> Ensuring all omics datasets are matrices with column names.
#> Creating SummarizedExperiment objects.
#> Creating MultiAssayExperiment object.
#> MultiAssayExperiment created successfully.


# Run differential abundance
mae <- run_differential_abundance(
    expomicset = mae,
    formula = ~ smoker + sex,
    abundance_col = "counts",
    method = "limma_voom",
    action = "add"
)
#> Running differential abundance testing.
#> Processing assay: mRNA
#> No group or design set. Assuming all samples belong to one group.
#> tidybulk says: The design column names are "(Intercept), smokeryes, sexM"
#> tidybulk says: to access the raw results (fitted GLM) do `attr(..., "internals")$limma_voom`
#> Processing assay: proteomics
#> No group or design set. Assuming all samples belong to one group.
#> tidybulk says: The design column names are "(Intercept), smokeryes, sexM"
#> tidybulk says: to access the raw results (fitted GLM) do `attr(..., "internals")$limma_voom`
#> Differential abundance testing completed.

# Run the sensitivity analysis
mae <- run_sensitivity_analysis(
    expomicset = mae,
    base_formula = ~ smoker + sex,
    methods = c("limma_voom"),
    scaling_methods = c("none"),
    covariates_to_remove = "sex",
    pval_col = "P.Value",
    logfc_col = "logFC",
    pval_threshold = 0.05,
    logFC_threshold = 0,
    bootstrap_n = 3,
    action = "add"
)
#> Running bootstrap iteration 1 of 3
#> No group or design set. Assuming all samples belong to one group.
#> tidybulk says: The design column names are "(Intercept), smokeryes, sexM"
#> tidybulk says: to access the raw results (fitted GLM) do `attr(..., "internals")$limma_voom`
#> No group or design set. Assuming all samples belong to one group.
#> tidybulk says: The design column names are "(Intercept), smokeryes, sexM"
#> tidybulk says: to access the raw results (fitted GLM) do `attr(..., "internals")$limma_voom`
#> Running bootstrap iteration 2 of 3
#> No group or design set. Assuming all samples belong to one group.
#> tidybulk says: The design column names are "(Intercept), smokeryes, sexM"
#> tidybulk says: to access the raw results (fitted GLM) do `attr(..., "internals")$limma_voom`
#> No group or design set. Assuming all samples belong to one group.
#> tidybulk says: The design column names are "(Intercept), smokeryes, sexM"
#> tidybulk says: to access the raw results (fitted GLM) do `attr(..., "internals")$limma_voom`
#> Running bootstrap iteration 3 of 3
#> No group or design set. Assuming all samples belong to one group.
#> tidybulk says: The design column names are "(Intercept), smokeryes, sexM"
#> tidybulk says: to access the raw results (fitted GLM) do `attr(..., "internals")$limma_voom`
#> No group or design set. Assuming all samples belong to one group.
#> tidybulk says: The design column names are "(Intercept), smokeryes, sexM"
#> tidybulk says: to access the raw results (fitted GLM) do `attr(..., "internals")$limma_voom`
#> Computing feature stability across sensitivity conditions.
#> Feature stability analysis completed.
#> Number Of Features Above Threshold Of 0.46:
#> ----------------------------------------
#> mRNA: 2/128
#> proteomics: 1/80

# create the sensitivity summary plot
sens_sum_p <- mae |>
    plot_sensitivity_summary()
#> Number of Features with stability_score > 0.463486276597003:
#> mRNA: 0 / 128
#> proteomics: 0 / 80