Merge branch 'hotfix/1'

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: clusterCells
 Title: Compare clusterings for single-cell sequencing
-Version: 0.0.0.9000
+Version: 0.0.0.9002
 Description: This package provides functions for running and comparing many different clusterings of single-cell sequencing data.
 Authors@R: c(person("Elizabeth", "Purdom", email = "epurdom@stat.berkeley.edu",
             role = c("aut", "cre", "cph")),

R/compareChoices.R

@@ -57,7 +57,7 @@
 #'	system.time(clusterTrack<-compareChoices(simData, ks=2:15, 
 #'	alphas=c(0.1,0.2,0.3), findBestK=c(TRUE,FALSE),sequential=c(FALSE),
 #'	subsample=c(FALSE),removeSil=c(TRUE), clusterMethod="pam", 
-#'	clusterArgs = list(minSize = 5,kRange=2:15),ncores=1,random.seed=48120))
+#'	clusterDArgs = list(minSize = 5,kRange=2:15),ncores=1,random.seed=48120))
 #' }
 #' 
 
@@ -97,11 +97,18 @@ removeSil=FALSE, subsample=FALSE,silCutoff=0,
 		#also deals with just in case the user gave duplicated values of something by mistake.
 		typeK<-which(param[,"clusterMethod"] %in% c("pam"))
 		if(length(typeK)>0){
-			param[typeK,"alpha"]<-0.01 #just a nothing value
+			param[typeK,"alpha"]<-NA #just a nothing value
 			whFindBestK<-which(param[,"findBestK"])
 			if(length(whFindBestK)>0){ #remove 'k' and see if same
 				if(!"kRange" %in% names(clusterDArgs)) clusterDArgs[["kRange"]]<-ks
+        #if findBestK=TRUE, and sequential=FALSE, then user needs to set k via subsampleArgs
 				param[whFindBestK,"k"]<-NA
+        whNoSeq<-which(!param[,"sequential"])
+        if(length(intersect(whFindBestK,whNoSeq))>0){
+          if(is.null(subsampleArgs[["k"]])) stop("must provide k in subsampleArgs for those with findBestK=TRUE and sequential=FALSE")
+  #        else param[intersect(whFindBestK,whNoSeq),"k"]<-subsampleArgs[["k"]]
+        } 
+
 			}
 		}
 		type01<-which(param[,"clusterMethod"] %in% c("hierarchical","tight"))

R/findSharedClusters.R

@@ -43,7 +43,8 @@ findSharedClusters<-function(clusterMat,proportion=1,clusterFunction="hierarchic
 		##Make clusterMat character, just in case
 		clusterMat<-apply(clusterMat,2,as.character)
 		clusterMat[clusterMat== "-1"]<-NA
-		sharedPerct<-.hammingdist(t(clusterMat)) #works on columns. gives a nsample x nsample matrix back
+		sharedPerct<-.hammingdist(t(clusterMat)) #works on columns. gives a nsample x nsample matrix back.
+		sharedPerct[is.na(sharedPerct)|is.nan(sharedPerct)]<-0 #have no clusterings for which they are both not '-1'
 		cl<-clusterD(D=sharedPerct,clusterFunction=clusterFunction,alpha = 1-proportion, minSize=minSize, format="vector",clusterArgs=list(evalClusterMethod=c("average")))
 		if(plot && require(NMF)) NMF::aheatmap(sharedPerct,annCol=data.frame("Cluster"=factor(cl)),Colv="Rowv",annColors=list(bigPalette))