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Criteria Heatmap with Optional Barplots

Source: R/gg_criteria.R
gg_criteria.Rd

Creates a tile-based heatmap visualization where samples are displayed on the y-axis and criteria are shown on the x-axis. Each tile shows a criterion value with color coding. Optional barplots can be added to the right side to display numeric metrics for each sample.

Usage

gg_criteria(
  data,
  id = "id",
  criteria = "_criteria",
  bar_column = NULL,
  bar_fill = NULL,
  panel_ratio = 0.3,
  tile_width = 0.7,
  tile_height = 0.7,
  tile_alpha = 1,
  tile_fill = NULL,
  show_text = TRUE,
  border_color = "black",
  border_width = 0.5,
  text_size = 8,
  show_legend = TRUE,
  quiet = FALSE
)

Arguments

data

A data frame containing the data to visualize.

id

Character string specifying the column name in data that contains sample identifiers. Default is "id".

criteria

Character string specifying either: (1) the column name in data that contains criterion names (long data, not yet supported), or (2) a regular expression pattern that identifies the criterion columns (wide data, e.g., "_criteria$" to match Pass_criteria, Fail_criteria, etc.). Default is "_criteria".

bar_column

Character vector specifying column name(s) in data to display as horizontal barplot(s) to the right of the heatmap. Columns must be numeric. Default is NULL (no barplots).

bar_fill

Character vector specifying the fill color(s) for bars. When bar_column contains multiple elements, colors are recycled if necessary to match the number of bars. If NULL (default), uses colors from the Brewer "Paired" palette.

panel_ratio

Numeric value specifying the total relative width of the barplot panel(s) compared to the heatmap panel (which has a reference width of 1). When multiple bar_column values are provided, this width is divided equally among them. For example, panel_ratio = 0.3 creates a 1:0.3 width ratio between heatmap and barplot panels. Only used when bar_column is not NULL. Default is 0.3.

tile_width

Numeric value (0-1) specifying the width of tiles as a proportion of available space. Default is 0.7.

tile_height

Numeric value (0-1) specifying the height of tiles as a proportion of available space. Default is 0.7.

tile_alpha

Numeric value (0-1) specifying transparency of tiles. Default is 1 (fully opaque).

tile_fill

Named character vector of colors for criterion values (e.g., c(Pass = "green", Fail = "red")). If NULL (default), uses the Brewer "Paired" palette.

show_text

Logical indicating whether to show criterion values as text labels on tiles. Default is TRUE.

border_color

Character string specifying the color of tile borders. Default is "black".

border_width

Numeric value specifying the width of tile borders. Default is 0.5.

text_size

Numeric value specifying the size of axis text. Default is 8.

show_legend

Logical indicating whether to show the fill legend. Default is TRUE.

quiet

Logical indicating whether to suppress messages. Default is FALSE.

Value

A ggplot2 object (or patchwork object if bar_column is used) showing the criteria heatmap. The plot displays:

  • Tiles at each sample-criterion intersection

  • Tile colors indicating criterion values

  • Criterion values as text labels within tiles

  • Optional horizontal barplots on the right side

  • A "recommended_dims" attribute with suggested width and height in inches

Details

The function currently supports wide format data where each criterion is a separate column. The column names should match the pattern specified in criteria (e.g., columns ending in "_crit"). The pattern is removed from column names to create criterion labels.

When bar_column is specified, horizontal barplots are added to the right side of the heatmap. Multiple barplots can be created by providing a vector of column names. Each barplot shows the numeric values with text labels positioned outside the bars.

The function calculates and suggests plot dimensions based on the number of samples and criteria. Access these via attr(plot, "recommended_dims").

WARNING: Alignment between heatmap and barplots depends on plot dimensions. The function provides recommended dimensions (accessible via attr(plot, "recommended_dims")) that ensure proper alignment. You can adjust these dimensions to improve appearance (e.g., reduce width to tighten spacing, or scale proportionally for size) while maintaining alignment.

Examples

# Example: Gene prioritization criteria
gene_data <- data.frame(
  gene = c("BRCA1", "TP53", "EGFR", "KRAS", "MYC", 
           "PTEN", "APC", "CDKN2A", "RB1", "VHL"),
  `Missense Variant_crit` = c("Yes", "Yes", "Yes", NA, "Yes", 
                               "Yes", NA, "Yes", NA, "Yes"),
  `eQTL_crit` = c("Yes", "Yes", NA, "Yes", "Yes", 
                  "Yes", "Yes", "Yes", "Yes", NA),
  `pQTL_crit` = c("Yes", NA, "Yes", "Yes", NA, 
                  "Yes", "Yes", NA, "Yes", "Yes"),
  `GWAS Hit_crit` = c("Yes", "Yes", "Yes", "Yes", NA, 
                      "Yes", "Yes", "Yes", NA, NA),
  `Loss of Function_crit` = c(NA, "Yes", NA, NA, "Yes", 
                              "Yes", "Yes", NA, "Yes", NA),
  `High Conservation_crit` = c("Yes", "Yes", "Yes", "Yes", "Yes", 
                               "Yes", "Yes", "Yes", "Yes", "Yes"),
  `mRNA DE_crit` = c("Yes", NA, "Yes", NA, NA,
                     "Yes", "Yes", "Yes", NA, "Yes"),
  `Prot DE_crit` = c(NA, "Yes", NA, NA, NA,
                     "Yes", NA, NA, NA, "Yes"),
  check.names = FALSE
)

# Calculate total criteria met
crit_cols <- grep("_crit$", names(gene_data), value = TRUE)
gene_data$`Total` <- rowSums(gene_data[crit_cols] == "Yes", na.rm = TRUE)

# Base criteria plot
# **Warn**: space between heatmap and barplots depends on plot width
gg_criteria(
  data = gene_data,
  id = "gene",
  criteria = "_crit$",
  bar_column = "Total",
  show_text = FALSE,
  tile_fill = c(Yes = "#A6CEE3", No = "white"),
  bar_fill = "#A6CEE3",
  panel_ratio = 1
)
#> Recommended dimensions: 7.2 x 5.5 inches


# Example: VHH Variant Analysis
# Define amino acid chemistry colors
aa_colors <- c(
  "D" = "#E60A0A", "E" = "#E60A0A", # Acidic (red)
  "K" = "#145AFF", "R" = "#145AFF", # Basic (blue)
  "H" = "#8282D2",                  # Histidine (purple)
  "S" = "#FA9600", "T" = "#FA9600", # Polar uncharged (orange)
  "N" = "#00DCDC", "Q" = "#00DCDC", # Polar amides (cyan)
  "C" = "#E6E600",                  # Cysteine (yellow)
  "G" = "#EBEBEB",                  # Glycine (light gray)
  "P" = "#DC9682",                  # Proline (tan)
  "A" = "#C8C8C8",                  # Alanine (gray)
  "V" = "#0F820F", "I" = "#0F820F", # Hydrophobic (green)
  "L" = "#0F820F", "M" = "#0F820F",
  "F" = "#3232AA", "W" = "#B45AB4", # Aromatic (dark blue/purple)
  "Y" = "#3232AA"
)

vhh_variants <- data.frame(
  variant = c("WT", "Mut1", "Mut2", "Mut3", "Mut4", "Mut7", "Mut5",
              "Mut6", "Mut8", "Mut9", "Mut10", "Mut11"),
  Q5_mut = c(NA, "H", NA, NA, NA, NA, "H", "H", "H", "D", NA, "H"),
  S55_mut = c(NA, NA, "P", NA, NA, "P", "P", NA, "P", "P", NA, NA),
  N73_mut = c(NA, NA, NA, "E", NA, NA, NA, "E", "E", NA, "E", NA),
  K80_mut = c(NA, "L", NA, NA, "S", "V", NA, NA, NA, "L", "S", NA),
  F99_mut = c(NA, NA, "L", NA, NA, NA, NA, NA, NA, "W", NA, "W"),
  KD_nM = c(45, 18, 5.2, 38, 42, 20, 3.8, 15, 3.2, 4.5, 40, 22),
  yield_mg_L = c(12, 11.8, 10, 13, 11, 10, 10, 12, 10, 7.8, 12.5, 8.5),
  Tm_C = c(68.5, 67.8, 68, 72.3, 35, 66, 67.5, 70, 70.5, 72, 38, 74)
)

# Create the systematic variant heatmap
gg_criteria(
  data = vhh_variants,
  id = "variant",
  criteria = "_mut$",
  tile_fill = aa_colors,
  bar_column = c("KD_nM", "yield_mg_L", "Tm_C"),
  panel_ratio = 2,
  tile_width = 0.70,
  tile_height = 0.70,
  show_text = TRUE,
  border_color = "grey40",
  border_width = 0.4,
  text_size = 10,
  show_legend = FALSE
)
#> Recommended dimensions: 10.0 x 5.9 inches