Multiplex Immunofluorescence Analysis

Usage

multiplexanalysis(
  data,
  marker_vars,
  x_coord = NULL,
  y_coord = NULL,
  cell_type_var = NULL,
  sample_id_var = NULL,
  group_var = NULL,
  analysis_focus = "comprehensive",
  correlation_method = "pearson",
  positivity_cutpoint = 0.1,
  perform_clustering = TRUE,
  n_clusters = 0,
  perform_pca = TRUE,
  show_loadings = TRUE,
  immune_contexture = FALSE,
  diversity_metrics = TRUE,
  spatial_analysis = FALSE,
  proximity_threshold = 20,
  show_plots = TRUE,
  show_clustering_plots = TRUE,
  biomarker_panel = "custom",
  normalization_method = "zscore",
  multiple_testing = "fdr",
  confidence_level = 0.95
)

Arguments

data: the data as a data frame
marker_vars: expression levels or intensities for multiple biomarkers
x_coord: X spatial coordinates for proximity analysis
y_coord: Y spatial coordinates for proximity analysis
cell_type_var: pre-defined cell type classifications
sample_id_var: unique identifier for samples/regions
group_var: grouping variable for comparative analysis
analysis_focus: primary focus of multiplex analysis
correlation_method: correlation method for co-expression analysis
positivity_cutpoint: threshold for defining positive marker expression
perform_clustering: identify cell populations through clustering
n_clusters: number of clusters (0 = auto-determine)
perform_pca: dimensionality reduction and visualization
show_loadings: display loading vectors on PCA plot
immune_contexture: calculate immunoscore and T-cell infiltration metrics
diversity_metrics: Shannon and Simpson diversity for cellular composition
spatial_analysis: cell-cell interaction analysis (requires coordinates)
proximity_threshold: distance threshold for defining cell proximity
show_plots: display correlation, PCA, and expression plots
show_clustering_plots: display cluster analysis visualizations
biomarker_panel: type of biomarker panel for optimized analysis
normalization_method: normalization method for marker expressions
multiple_testing: correction for multiple comparisons
confidence_level: confidence level for statistical intervals

Value

A results object containing:

`results$interpretation`					a html
`results$expressiontable`					Descriptive statistics for each biomarker
`results$correlationtable`					Pairwise correlations between biomarkers
`results$clusteringtable`					Identified cell clusters and phenotype suggestions
`results$pcatable`					PCA results and variance explained
`results$correlationplot`					Heatmap showing pairwise marker correlations
`results$heatmapplot`					Comprehensive marker expression patterns
`results$pcaplot`					PCA visualization with optional loading vectors
`results$clusterplot`					Visualization of identified cell populations
`results$spatialanalysis$proximityresults`					Distance-based interaction analysis
`results$spatialanalysis$spatialplot`					an image
`results$immunecontexture$immunescores`					T-cell infiltration and immune context metrics
`results$immunecontexture$immuneplot`					an image
`results$diversityanalysis$diversitytable`					Shannon, Simpson, and evenness indices
`results$qualitycontrol$qcmetrics`					Missing data, outliers, and technical quality
`results$qualitycontrol$batcheffects`					Analysis of systematic technical variation
`results$advancedanalysis$networkanalysis`					Network-based marker interaction analysis
`results$advancedanalysis$pathwayenrichment`					Biological pathway associations
`results$clinicalreport`					Copy-ready clinical interpretation and summary
`results$exportdata`					Formatted data for external analysis

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

results$expressiontable$asDF

as.data.frame(results$expressiontable)