Pairwise

DESCRIPTION

@@ -1,14 +1,14 @@
 Type: Package
 Package: SpaceMarkers
 Title: Spatial Interaction Markers
-Version: 1.11.1
+Version: 1.1.2
 Authors@R: c(
     person(given = "Atul", family = "Deshpande", email = "[email protected]", role = c("aut", "cre"), comment = c(ORCID="0000-0001-5144-6924")),
     person(given = "Ludmila", family = "Danilova", email = "[email protected]", role = "ctb"),
-    person(given = "Dimitrijs", family = "Lvovs", email = "[email protected]", role = "ctb")
+    person(given = "Dmitrijs", family = "Lvovs", email = "[email protected]", role = "ctb", comment = c(ORCID = "0009-0003-2152-6853"))
   )
-BugReports: https://github.com/atuldeshpande/SpaceMarkers/issues
-URL: https://github.com/atuldeshpande/SpaceMarkers
+BugReports: https://github.com/DeshpandeLab/SpaceMarkers/issues
+URL: https://github.com/DeshpandeLab/SpaceMarkers
 Description: Spatial transcriptomic technologies have helped to resolve the connection
             between gene expression and the 2D orientation of tissues relative to each other.
             However, the limited single-cell resolution makes it difficult to highlight the most 
@@ -48,6 +48,8 @@ Suggests:
     testthat (>= 3.0.0),
     viridis,
     CoGAPS
+Enhances: 
+    BiocParallel
 VignetteBuilder: 
     knitr
 Config/testthat/edition: 3

NAMESPACE

@@ -1,6 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
 export(getInteractingGenes)
+export(getPairwiseInteractingGenes)
 export(getSpatialFeatures)
 export(getSpatialParameters)
 export(load10XCoords)

NEWS.md

@@ -1,4 +1,9 @@
-# SpaceMarkers development version
+# SpaceMarkers 1.1.2
+
+* Added getPairwiseInteractingGenes which enables pairwise analysis of 
+interacting patterns 
+
+# SpaceMarkers 1.1.1
 
 * `getSpatialFeatures`: add default method to infer the object passed to it. 
 

R/find_genes_of_interest.R

@@ -48,21 +48,15 @@ return(cbind(zVals,pvals))
 #===================
 #' find_genes_of_interest
 #' Identify genes associated with pattern interaction.
-
 #' This function identifies genes exhibiting significantly higher values of 
 #' testMat in the Interaction region of the two 
-#' patterns compared #' to regions with exclusive influence from either 
+#' patterns compared to regions with exclusive influence from either 
 #' pattern. It uses Kruskal-Wallis test followed by
 #' posthoc analysis using Dunn's Test to identify the genes.
 #'
 #' @usage
-#' find_genes_of_interest(
-#'     testMat,
-#'     goodGenes = NULL,
-#'     region,
-#'     fdr.level = 0.05,
-#'     analysis = c("enrichment", "overlap")
-#' )
+#' find_genes_of_interest(testMat, goodGenes, region, fdr.level=0.05,
+#'        analysis=c("enrichment","overlap"),...)
 #' @param    testMat A matrix of counts with cells as columns and genes as rows
 #' @param    goodGenes A vector of user specified genes expected to interact 
 #' a priori. The default for this is NULL as the function can find these genes 
@@ -72,18 +66,23 @@ return(cbind(zVals,pvals))
 #' @param    fdr.level False Discovery Rate. The default value is 0.05.
 #' @param    analysis a character string that specifies the type of analysis to 
 #' carry out, whether overlap or enrichment.
-#'
+#' @param    ... Additional arguments to be passed to lower level functions
 #' @return a list of genes exhibiting significantly higher values of testMat in 
 #' the Interaction region of the two #' patterns compared to regions with 
 #' exclusive influence from either pattern.
 
-
-
 find_genes_of_interest<-function(
         testMat,goodGenes=NULL,region, fdr.level=0.05,
-        analysis=c("enrichment","overlap")) {
+        analysis=c("enrichment","overlap"),...) {
+
+    # Default analysis = enrichment
+    if ("enrichment" %in% analysis) {analysis <- "enrichment"}
+    else if ("overlap" %in% analysis) {analysis <- "overlap"}
+    else stop("analysis must be either 'enrichment' or 'overlap'")
+
     region <- factor(region)
     patnames <- levels(region)[which(levels(region)!="Interacting")]
+
     pattern1 <- patnames[1]
     pattern2 <- patnames[2]
     if (!is.null(goodGenes)){
@@ -104,6 +103,12 @@ find_genes_of_interest<-function(
     qDunn$qvalues[ind,] <- qq$qvalue
     res_dunn_test <- cbind(res_dunn_test,qDunn$qvalues)
     colnames(res_dunn_test)[7:9] <- paste0(colnames(res_dunn_test)[7:9],".adj")
+    interactGenes <- buildInteractGenesdf(res_kruskal,
+        res_dunn_test,ind,fdr.level, pattern1,pattern2,analysis)
+    return(interactGenes)
+}
+buildInteractGenesdf <- function(res_kruskal,res_dunn_test,ind,
+                                fdr.level=0.05,pattern1,pattern2,analysis) {
     interact_patt1 <- res_dunn_test[ind,"pval_1_Int.adj"]<fdr.level
     interact_patt2 <- res_dunn_test[ind,"pval_2_Int.adj"]<fdr.level
     interacting_over_both_patterns <- interact_patt1 & interact_patt2