@@ -27,7 +27,6 @@ cellMatchR <- function(Atot,nSets, cnt, minNS=NULL, maxNS=NULL, ignore.NA=FALSE,

   }}

   if(ignore.NA==TRUE){Atot<-Atot[complete.cases(Atot),]}

-

 corcut<-function(Atot,minNS,cnt,cluster.method){

   corr.dist=cor(Atot)

   corr.dist=1-corr.dist

@@ -55,30 +54,35 @@ corcut<-function(Atot,minNS,cnt,cluster.method){

             RtoMeanPattern[[i]] <- sapply(1:nIN,function(j) {round(cor(x=Atot[,cut==i][,j],y=cMNs[,i]),3)})

         }

       }

-    PByClust[sapply(PByClust,is.null)]<-NULL

+    AByClust[sapply(AByClust,is.null)]<-NULL

     RtoMeanPattern[sapply(RtoMeanPattern,is.null)]<-NULL

     return(list("RtoMeanPattern"=RtoMeanPattern,"AByClust"=AByClust))

   }   

-

   cc<-corcut(Atot,minNS,cnt,cluster.method)

     ### split by maxNS

-    indx<-which(unlist(lapply(cc$PByClust,function(x) dim(x)[1]>maxNS)))

-    while(length(indx)>0){

-          icc<-corcut(cc$AByClust[[indx[1]]],minNS,2,cluster.method)

-          cc$AByClust[[indx[1]]]<-icc[[2]][[2]]

-          cc$RtoMeanPattern[[indx[1]]]<-icc[[1]][[2]]

-          if(length(icc[[2]])>1){

-                cc$AByClust<-append(cc$AByClust,icc[[2]][1])

-                cc$RtoMeanPattern<-append(cc$RtoMeanPattern,icc[[1]][1])

-          }

-      indx<-which(unlist(lapply(cc$PByClust,function(x) dim(x)[1]>maxNS)))

+    indx<-which(unlist(lapply(cc$AByClust,function(x) dim(x)[1]>maxNS)))

+    i<-1

+    while(length(indx)>0){ 

+            icc<-corcut(cc$AByClust[[indx[1]]],minNS,2,cluster.method)

+            if(length(icc[[2]])==0){

+              indx<-indx[-1]

+              next

+            } else{

+              cc$AByClust[[indx[1]]]<-icc[[2]][[1]]

+              cc$RtoMeanPattern[[indx[1]]]<-icc[[1]][[1]]

+              if(length(icc[[2]])>1){

+                cc$AByClust<-append(cc$AByClust,icc[[2]][2])

+                cc$RtoMeanPattern<-append(cc$RtoMeanPattern,icc[[1]][2])

+              } 

+              indx<-which(unlist(lapply(cc$AByClust,function(x) dim(x)[1]>maxNS)))

+            }

     }

+

 #weighted.mean(AByClustDrop[[1]],RtoMPDrop[[1]])

 AByCDSWavg<- t(sapply(1:length(cc$AByClust),function(z) apply(cc$AByClust[[z]],1,function(x) weighted.mean(x,(cc$RtoMeanPattern[[z]])^3))))

 rownames(AByCDSWavg) <- lapply(1:length(cc$AByClust),function(x) paste("Pattern",x))

-

 #scale As

 Amax <- apply(AByCDSWavg,1,max)

 AByCDSWavgScaled <- t(sapply(1:dim(AByCDSWavg)[1],function(x) AByCDSWavg[x,]/Amax[x]))

@@ -7,14 +7,14 @@

 #' @param nSets number of sets to partition the data into

 #' @param simulationName name used to identify files created by this simulation

 #' @param samplingRatio vector of relative quantities to use for sampling celltypes

-#' @param annotionObj vector of same length as number of columns of D 

+#' @param anotionObj vector of same length as number of columns of D 

 #' @param path character string indicating were to save resulting data objects. default is current working dir

 #' @return simulationName used to identify saved files

 #' @examples

 #' data(SimpSim)

 #' createscCoGAPSSets(SimpSim.D, SimpSim.S, nSets=2, "example")

 #' @export

-createscCoGAPSSets <- function(D, nSets, simulationName,samplingRatio=NULL,path="",annotionObj=NULL)

+createscCoGAPSSets <- function(D, nSets, simulationName,samplingRatio=NULL,path="",anotionObj=NULL)

 {

     # check gene names

     if (length(unique(colnames(D))) != length(colnames(D)))

@@ -34,8 +34,8 @@ createscCoGAPSSets <- function(D, nSets, simulationName,samplingRatio=NULL,path=

             cellset <- sample(cells, sampleSize, replace=FALSE)

             cells <- cells[!(cells %in% cellset)]

         } else {

-        if(length(unique(annotionObj))!=length(samplingRatio)){warning("Not all celltypes will be sampled from.")}

-        ct.indx<-lapply(unique(annotionObj),function(x) which(annotionObj == x))

+        if(length(unique(anotionObj))!=length(samplingRatio)){warning("Not all celltypes will be sampled from.")}

+        ct.indx<-lapply(unique(anotionObj),function(x) which(anotionObj == x))

         cellset<-sample(colnames(D)[ct.indx[[x]]], samplingRatio[x],replace=TRUE)

         }

@@ -8,17 +8,15 @@

 postFixed4SC <- function(AP.fixed, setAs)

 {

     ASummary <- AP.fixed[[1]]$Amean

-

-

     PSummary <- do.call(cbind,lapply(AP.fixed, function(x) x$Pmean))

     Psd <- do.call(cbind,lapply(AP.fixed, function(x) x$Psd))

-    Pmax <- apply(PSummary,2,max)

-    Pneu <- sweep(PSummary,2,Pmax,FUN="/")

-    Aneu <- sweep(ASummary,1,Pmax,FUN="*")

+    Amax <- apply(ASummary,2,max)

+    Aneu <- sweep(ASummary,2,Amax,FUN="/")

+    Pneu <- sweep(PSummary,1,Amax,FUN="*")

-    X <- apply(Pneu,2,range)

-    Y <- apply(setPs,2,range)

+    X <- apply(Aneu,2,range)

+    Y <- apply(setAs,2,range)

     colnames(X) <- colnames(Y)

     if (all.equal(X,Y,tolerance=0.01) != TRUE)

         warning("Patterns do not match fixed values.")