minor edits in predict_most_similar_cluster()

R/predict_most_similar_cluster.R

@@ -8,15 +8,29 @@
 #' For more options, please see \code{\link[stats]{p.adjust.methods}}.
 #'
 #' @return A data frame of the most similar reference cluster for each query cluster,
-#' which is the "match" with the highest adjusted p-value.
+#' which is the "match" with the highest adjusted p-value. If there are tied p-values, all will be returned.
 #'
 #' @export
 
 predict_most_similar_cluster <- function(rst.FRmatch, p.adj.method="BY"){
   pmat <- rst.FRmatch$pmat
   pmat.adj <- padj.FRmatch(pmat, p.adj.method=p.adj.method)
-  df.most.similar.cluster <- data.frame("query_cluster"=colnames(pmat),
-                                        "most_similar_ref_cluster"=rownames(pmat)[apply(pmat, 2, which.max)], #use the original pmat, independent of p.adj.method
-                                        "padj"=colMaxs(pmat.adj)) %>% arrange(desc(padj)) #return padj here
+
+  ## return ALL maximum p-values if there are multiple
+  cmax <- colMaxs(pmat.adj)
+  query_cluster <- most_similar_ref_cluster <- padj <- c()
+  for(j in 1:length(cmax)){
+    ind.j <- pmat.adj[,j] == cmax[j]
+    query_cluster <- c(query_cluster, rep(colnames(pmat.adj)[j], sum(ind.j)))
+    most_similar_ref_cluster <- c(most_similar_ref_cluster, rownames(pmat.adj)[ind.j])
+    padj <- c(padj, rep(cmax[j], sum(ind.j)))
+  }
+  df.most.similar.cluster <- data.frame(query_cluster, most_similar_ref_cluster, padj) %>% arrange(desc(padj))
+
+  # ## return the first maximum p-value
+  # df.most.similar.cluster <- data.frame("query_cluster"=colnames(pmat),
+  #                                       "most_similar_ref_cluster"=rownames(pmat)[apply(pmat, 2, which.max)], #use the original pmat, independent of p.adj.method
+  #                                       "padj"=colMaxs(pmat.adj)) %>% arrange(desc(padj)) #return padj here
+
   return(df.most.similar.cluster)
 }