Lisaclust

lisaClust

Workflows

Standard Workflow

Simultaneously calculate LISA curves, perform k-means clustering, and visualize the identified tissue regions and cell-type enrichments.

library(lisaClust)
library(SingleCellExperiment)
library(SpatialDatasets)

# Load breast cancer dataset
kerenSPE <- SpatialDatasets::spe_Keren_2018()
kerenSPE <- kerenSPE[, kerenSPE$imageID %in% c("5", "6")]

# Run lisaClust to calculate LISA curves and cluster cells
kerenSPE <- lisaClust(kerenSPE, k = 5)

# Examine cell-type enrichment across regions
regionMap(kerenSPE, type = "bubble")

# Visualize spatial regions using hatchingPlot
hatchingPlot(kerenSPE, nbp = 300)

Input: A SingleCellExperiment or SpatialExperiment object with spatial coordinates and cell-type annotations. Output: Region cluster assignments stored in colData, an enrichment bubble plot, and a spatial hatching plot.

Custom Lisa Clustering

Calculate LISA curves separately to allow custom clustering algorithms (e.g., manual k-means) before storing and plotting.

library(lisaClust)
library(SingleCellExperiment)

# Generate toy data
set.seed(51773)
x <- round(runif(100), 4) * 100
y <- round(runif(100), 4) * 100
cellType <- factor(rep(c("c1", "c2"), 50))
imageID <- rep("s1", 100)
cells <- data.frame(x, y, cellType, imageID)

# Create SingleCellExperiment object
SCE <- SingleCellExperiment(colData = cells)

# Calculate LISA curves
lisaCurves <- lisa(SCE, Rs = c(20, 50, 100))

# Perform custom k-means clustering
kM <- kmeans(lisaCurves, 2)

# Store custom cluster assignments back into colData
colData(SCE)$custom_region <- paste("region", kM$cluster, sep = "_")

Input: A SingleCellExperiment object. Output: A matrix of calculated LISA curves and custom region cluster assignments stored in colData.

When to Use

• Identifying Tissue Regions: Use to identify and visualize regions of cell-type colocalization in multiplexed imaging data segmented at single-cell resolution.
• LISA Curve Generation: Use lisa() to calculate Local Indicators of Spatial Association curves as a localized summary of spatial organization.
• Cell-Type Enrichment Analysis: Use regionMap() to examine which cell types appear more or less frequently in each identified region than expected by chance.
• Multi-Region Visualization: Use hatchingPlot() to plot both spatial regions (using hatching patterns) and cell types simultaneously on a single visualization.

When NOT to Use

• Pairwise Spatial Association Testing: For testing pairwise spatial associations between cell types without clustering them into regions, use spicyR directly.
• Non-Spatial Clustering: For clustering cells based purely on single-cell expression data without spatial coordinates, use standard clustering workflows in scran or Seurat.

Data Requirements

• Input Format: A SingleCellExperiment or SpatialExperiment object.
• Required Metadata (colData):
  • Spatial coordinates (e.g., x and y columns).
  • Cell-type annotations (e.g., cellType column).
  • Image or sample identifiers (e.g., imageID column).

Key Parameters

• k (2): The number of clusters to identify when running lisaClust().
• Rs (c(20, 50, 100) in lisa): The radii over which the LISA curves will be calculated.
• type ("bubble" in regionMap): The plot type used to visualize cell-type enrichment across regions.
• nbp (300 in hatchingPlot): Parameter controlling the grid resolution for the hatching plot.
• useImages (NULL): A character vector specifying which images to plot in hatchingPlot().

Best Practices

• Store cell coordinates, cell types, and image IDs in the colData of a SingleCellExperiment object before running spatial analyses.
• Use lisaClust() as a convenient wrapper to simultaneously calculate LISA curves and perform k-means clustering.
• For custom clustering methods (e.g., SOM or manual k-means), calculate the curves first using lisa(), cluster them, and store the assignments back in colData(SCE).
• Use regionMap(type = "bubble") to identify which cell types are significantly enriched or depleted in each spatial region.

Common Pitfalls

• Missing Spatial Coordinates: Ensure that spatial coordinates, cell-type annotations, and image IDs are correctly specified and match the column names in colData exactly.
• Inappropriate Radii (Rs): Choosing radii that are too small or too large relative to cell density can lead to uninformative LISA curves.

Alternatives

• spicyR: For pairwise spatial association analysis.
• scran: For non-spatial single-cell clustering.
• Seurat: For general single-cell analysis and clustering.

Citations

• Keren et al. (2018). A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging. Cell.

References

• Homepage: bioconductor.org/packages/lisaclust
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/lisaclust/inst/doc/lisaClust.html