Plot Correlation Tilemap

Visualizes a correlation matrix as a heatmap tile plot using correlation results stored in the metadata of a MultiAssayExperiment object. When feature_type = "pcs", the function forces PCs to appear on the x-axis and exposures on the y-axis, and it adds a barplot showing how many PCs are significantly associated with each exposure. It also suppresses nonsignificant tiles based on a specified p-value threshold.

Usage

plot_correlation_tile(
  expomicset,
  feature_type = c("pcs", "degs", "omics", "factors", "factor_features", "exposures"),
  pval_cutoff = 0.05,
  na_color = "grey100",
  fill_limits = c(-1, 1),
  midpoint = 0
)

Arguments

expomicset

A MultiAssayExperiment object containing correlation results in metadata.

feature_type

Type of correlation results to plot. One of "pcs", "degs", "omics", "factors", "factor_features", or "exposures". Must match the key used in metadata(expomicset)$correlation[[feature_type]].

pval_cutoff

Numeric p-value cutoff below which correlations are displayed. Nonsignificant tiles are rendered in the na_color. Default is 0.05.

na_color

Color used to represent nonsignificant or missing correlations. Default is "grey100".

fill_limits

Numeric vector of length 2 defining the scale limits for correlation values. Default is c(-1, 1).

midpoint

Numeric value for centering the fill gradient. Default is 0.

Value

A ggplot2 tile plot (or a combined tile + barplot if feature_type = "pcs").

Examples

# create example data
mae <- make_example_data(
    n_samples = 10,
    return_mae = TRUE
)
#> Ensuring all omics datasets are matrices with column names.
#> Creating SummarizedExperiment objects.
#> Creating MultiAssayExperiment object.
#> MultiAssayExperiment created successfully.

# run pca
mae <- mae |>
    run_pca()
#> Identifying common samples.
#> Subsetting exposure data.
#> Subsetting omics data.
#> Performing PCA on Feature Space.
#> Performing PCA on Sample Space.
#> No outliers detected.

# correlate with exposures
mae <- mae |>
    run_correlation(
        feature_type = "pcs",
        exposure_cols = c("exposure_pm25", "exposure_no2", "age", "bmi")
    )

# make the correlation tile plot
cor_tile_p <- mae |>
    plot_correlation_tile(
        feature_type = "pcs"
    )