Statial

Statial

Workflows

Standard Workflow

Quantify context-aware spatial relationships between cell types using hierarchies and associate them with survival outcomes.

library(Statial)
library(spicyR)
library(SingleCellExperiment)
library(survival)
library(treekoR)

data("kerenSCE")

# Define hierarchy
kerenTree <- treekoR::getClusterTree(t(assay(kerenSCE, "intensities")), kerenSCE$cellType, hierarchy_method="hopach", hopach_K = 1)
parentDf <- parentCombinations(all = unique(kerenSCE$cellType), parentList = getParentPhylo(kerenTree))

# Run Kontextual
kerenKontextual <- Kontextual(cells = kerenSCE, parentDf = parentDf, r = 100, cores = 1)

# Prepare matrix and run survival
kerenSCE$event = 1 - kerenSCE$Censored
kerenSCE$survival = Surv(kerenSCE$Survival_days_capped, kerenSCE$event)
kontextMat <- prepMatrix(kerenKontextual)
kontextMat <- kontextMat[unique(kerenSCE$imageID), ]
kontextMat[is.na(kontextMat)] <- 0

survivalResults = spicy(cells = kerenSCE, alternateResult = kontextMat, condition = "survival", weights = TRUE)

Input: A SingleCellExperiment object containing cell coordinates, cell types, and survival data; Output: Survival analysis results associating context-aware spatial relationships with patient outcomes.

Continuous Cell State Changes

Identify continuous changes in cell state (marker expression) as a function of spatial proximity to other cell types, correcting for lateral marker spillover.

library(Statial)
library(SingleCellExperiment)

data("kerenSCE")

# Calculate spatial distances and abundances
kerenSCE <- getDistances(kerenSCE, maxDist = 200, nCores = 1)
kerenSCE <- getAbundances(kerenSCE, r = 200, nCores = 1)

# Calculate continuous state changes for a single image
stateChanges <- calcStateChanges(cells = kerenSCE, type = "distances", image = "6", from = "Keratin_Tumour", to = "Macrophages", marker = "p53", nCores = 1)

# Calculate contamination probabilities and run corrected state changes
kerenSCE <- calcContamination(kerenSCE)
stateChangesCorrected <- calcStateChanges(cells = kerenSCE, type = "distances", nCores = 1, minCells = 100, contamination = TRUE)

Input: A SingleCellExperiment object with cell coordinates and marker intensities; Output: A data frame of state changes corrected for lateral marker spillover.

When to Use

• Quantifying context-aware spatial relationships between cell types using Kontextual to avoid tissue structure biases.
• Identifying continuous changes in cell state (marker expression) relative to the distance to other cell types using calcStateChanges.
• Correcting for lateral marker spillover (contamination) in multiplexed imaging data using calcContamination.

When NOT to Use

• For non-spatial single-cell data, use standard differential expression packages like scran or scater because Statial requires spatial coordinates (x, y).
• For simple cell-type abundance comparisons without spatial localization or context, use spicyR directly.

Data Requirements

• A SingleCellExperiment object containing cell coordinates (x and y in colData), cell type annotations (cellType), and marker expression assays.

Key Parameters

• r (100): Radius on which the cell relationship is evaluated in Kontextual or getAbundances.
• maxDist (200): Maximum distance threshold for calculating spatial proximity in getDistances.
• contamination (TRUE): Logical indicating whether to include contamination probabilities as covariates in calcStateChanges.
• minCells (100): Minimum number of cells required to perform state change calculations in calcStateChanges.
• cores (1): Number of cores for parallel processing in Kontextual.

Best Practices

• Define cell type hierarchies using treekoR::getClusterTree or biological knowledge to specify appropriate contexts for Kontextual.
• Extract pairwise combinations of cell types and their parents using parentCombinations before running Kontextual.
• Run calcContamination to estimate lateral marker spillover probabilities before modeling continuous state changes.

Common Pitfalls

• Lateral marker spillover (contamination): Adjacent cells wrongly inherit marker expression from neighbors. Fix by setting contamination = TRUE in calcStateChanges after running calcContamination.
• Missing survival data alignment: Ensure the row names of the prepared matrix from prepMatrix match the unique image IDs in the SingleCellExperiment object before running spicy.

Alternatives

• spicyR: For spatial analysis of cell-cell localization without context-aware hierarchy modeling or continuous marker state changes.
• lisaClust: For clustering spatial regions rather than modeling continuous cell state changes.

Citations

• Keren et al. (2018), Cell.

References

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