Make a heatmap summarizing sample marker expression patterns in CyTOF data

This function makes a heatmap of sample-to-sample marker expression patterns in single-cell data. Markers are plotted along the horizontal (x-) axis of the heatmap and sample IDs are plotted along the vertical (y-) axis of the heatmap.

Usage

tof_plot_sample_features(
  feature_tibble,
  sample_col,
  feature_cols = where(tof_is_numeric),
  scale_featurewise = FALSE,
  scale_samplewise = FALSE,
  line_width = 0.25,
  theme = ggplot2::theme_minimal()
)

Arguments

feature_tibble

A tbl_df or data.frame of aggregated sample-level features, such as that generated by tof_extract_features.

sample_col

An unquoted column name indicating which column in `tof_tibble` stores the IDs for each sample. If no sample IDs are present, a numeric ID will be assigned to each row of `feature_tibble` based on its row index.

feature_cols

Unquoted column names indicating which column in `feature_tibble` should be interpreted as features to be plotted along the x-axis of the heatmap. Supports tidyselect helpers.

scale_featurewise

A boolean value indicating if the heatmap should rescale the columns of the heatmap such that the maximum value for each marker is 1 and the minimum value is 0. Defaults to FALSE.

scale_samplewise

A boolean value indicating if the heatmap should rescale the rows of the heatmap such that the maximum value for each sample is 1 and the minimum value is 0. Defaults to FALSE.

line_width

A numeric value indicating how thick the lines separating the tiles of the heatmap should be. Defaults to 0.25.

theme

A ggplot2 theme to apply to the heatmap. Defaults to theme_minimal

Value

A ggplot object.

Examples

# simulate single-cell data
sim_data <-
    dplyr::tibble(
        cd45 = rnorm(n = 1000),
        cd38 = rnorm(n = 1000),
        cd34 = rnorm(n = 1000),
        cd19 = rnorm(n = 1000),
        cluster_id = sample(letters, size = 1000, replace = TRUE),
        sample_id = sample(paste0("sample", 1:5), size = 1000, replace = TRUE)
    )

# extract cluster proportions in each simulated patient
feature_data <-
    tof_extract_proportion(
        tof_tibble = sim_data,
        cluster_col = cluster_id,
        group_cols = sample_id
    )

# plot the heatmap
heatmap <- tof_plot_sample_features(feature_tibble = feature_data)