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Identify all problem areas in spatial transcriptomics data

Source: R/edge_helper_functions_visium.R
problemAreas.Rd

Detects and characterizes all clusters of low-quality spots (problem areas) in tissue sections, including both edge and interior artifacts.

Usage

problemAreas(
  .xyz,
  offTissue,
  uniqueIdentifier = NA,
  shifted = FALSE,
  min_cluster_size = 40
)

Arguments

.xyz

Data frame with spot coordinates and QC metrics. Must contain:

  • Column 1: array_row coordinates

  • Column 2: array_col coordinates

  • Column 3: Binary outlier indicator (TRUE/FALSE or 1/0)

  • Row names: Spot identifiers

offTissue

Character vector of spot identifiers that are off-tissue

uniqueIdentifier

Character string used as prefix for cluster IDs. If NA, "X" will be used (default: NA)

shifted

Logical indicating whether to apply coordinate adjustment for hexagonal arrays (default: FALSE)

min_cluster_size

Numeric value for minimum cluster size in morphological operations (default: 40)

Value

Data frame with the following columns:

  • spotcode: Spot identifier

  • clumpID: Unique cluster identifier (format: "prefix_number")

  • clumpSize: Number of spots in the cluster

Returns empty data frame if no problem areas are found.

Details

This function identifies ALL connected components of outlier spots, not just those touching edges. Each cluster is assigned a unique ID and its size is calculated. This enables downstream filtering based on cluster characteristics.

Examples

# 1. Create a 5x5 grid of mock spot data
spot_data <- data.frame(
  array_row = rep(1:5, each = 5),
  array_col = rep(1:5, times = 5),
  outlier = FALSE
)
rownames(spot_data) <- paste0("spot", 1:25)

# 2. Create an "artifact" by flagging a 2x2 area as outliers
spot_data$outlier[spot_data$array_row %in% 2:3 & spot_data$array_col %in% 2:3] <- TRUE

# 3. Define off-tissue spots
offTissue <- character(0)

# 4. Identify all problem areas
problem_df <- problemAreas(
  spot_data,
  offTissue = offTissue,
  uniqueIdentifier = "Sample1",
  min_cluster_size = 3
)
print(problem_df)
#>   spotcode   clumpID clumpSize
#> 1    spot8 Sample1_1         4
#> 2    spot7 Sample1_1         4
#> 3   spot13 Sample1_1         4
#> 4   spot12 Sample1_1         4