Spatial Statistics from Coordinates

Usage

spatialanalysis(
  data,
  coords_x,
  coords_y,
  cell_types,
  groups,
  roi_id,
  perform_ripley = TRUE,
  perform_nnd = TRUE,
  perform_hotspot = TRUE,
  perform_interaction = TRUE,
  show_plots = TRUE,
  analysis_scope = "comprehensive",
  distance_method = "euclidean",
  correction_method = "both",
  significance_level = 0.05,
  min_points = 10
)

Arguments

data: the data as a data frame
coords_x: X coordinate variable (numeric). Must contain spatial position data for each observation.
coords_y: Y coordinate variable (numeric). Must contain spatial position data for each observation.
cell_types: Optional cell type variable for multi-type spatial analysis. Should be a factor or character variable identifying different cell populations.
groups: Optional grouping variable for comparing spatial patterns between conditions (e.g., treatment vs control, tumor vs normal).
roi_id: Optional ROI identifier for analyzing multiple tissue regions separately.
perform_ripley: Perform Ripley's K-function analysis to detect spatial clustering or dispersion patterns.
perform_nnd: Calculate nearest neighbor distances and perform Clark-Evans test for spatial randomness.
perform_hotspot: Detect spatial hotspots using kernel density estimation and statistical thresholds.
perform_interaction: Perform multi-type spatial interaction analysis when cell types are specified.
show_plots: Create spatial distribution plots showing coordinate data with optional cell type coloring.
analysis_scope: Analysis scope: basic (core statistics), comprehensive (all methods), or clinical (pathology-focused interpretation).
distance_method: Distance calculation method for spatial analysis.
correction_method: Edge correction method for handling boundary effects in spatial analysis.
significance_level: Alpha level for statistical significance testing.
min_points: Minimum number of complete coordinate pairs required to perform spatial analysis.

Value

A results object containing:

`results$text`					a html
`results$copysummary`					Plain-language summary of spatial analysis results ready for copy-paste
`results$summary`					Basic spatial metrics including point counts, density, and study area
`results$ripley`					Spatial clustering analysis at multiple distance scales
`results$nearestneighbor`					Nearest neighbor distances and Clark-Evans randomness test
`results$hotspots`					Spatial hotspot analysis using kernel density estimation
`results$interaction`					Cross-type spatial relationships and interaction patterns
`results$spatialplot`					Visualization of spatial coordinate data with optional cell type coloring
`results$interpretation`					Clinical significance and pathology applications of spatial patterns

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$summary$asDF

as.data.frame(results$summary)