Skip to contents

Summarize and Visualize Analysis Pipeline Steps

Source: R/run_pipeline_summary.R
run_pipeline_summary.Rd

This function prints and visualizes the analysis steps stored in the metadata of a MultiAssayExperiment object. The steps are optionally printed to the console as a numbered list and can be rendered as a left-to-right Mermaid flowchart. The flowchart connects steps with arrows and includes step notes if requested.

Usage

run_pipeline_summary(
  expomicset,
  show_index = TRUE,
  console_print = TRUE,
  diagram_print = FALSE,
  include_notes = TRUE
)

Arguments

expomicset

A MultiAssayExperiment object that contains a "summary" entry in its metadata, which includes a list named steps.

show_index

Logical, default TRUE. If TRUE, prefixes each step with its index.

console_print

Logical, default TRUE. If TRUE, prints the step list to the console.

diagram_print

Logical, default FALSE. If TRUE, renders a Mermaid diagram of the steps.

include_notes

Logical, default TRUE. If TRUE, appends any "notes" associated with each step to the label.

Value

No return value. This function is called for its side effects: console output and/or diagram rendering.

Details

The Mermaid flowchart is rendered left-to-right and connects each step in sequence. Each node is labeled using the step name and, optionally, any attached notes.

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.

# Test for normality
mae <- mae |>
    run_normality_check() |>
    transform_exposure(exposure_cols = c("age", "bmi", "exposure_pm25"))
#> Checking Normality Using Shapiro-Wilk Test
#> 4 Exposure Variables are Normally Distributed
#> 0 Exposure Variables are NOT Normally Distributed
#> Applying the boxcox_best transformation.

# Run the pipeline summary
run_pipeline_summary(mae)
#> 1. run_normality_check - Assessed normality of 4 numeric exposure variables. 4 were normally distributed (p > 0.05), 0 were not.2. transform_exposure - Applied 'boxcox_best' transformation to 3 exposure variables. 3 passed normality (Shapiro-Wilk p > 0.05, 100%).