diff --git a/DESCRIPTION b/DESCRIPTION
index f5a0480..faa656c 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -7,7 +7,7 @@ Description: Cell-cell Interactions at Single-Cell Resolution
 License: GPL-3
 Encoding: UTF-8
 LazyData: yes
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 VignetteBuilder: knitr
 Depends:
     R (>= 4.0)
diff --git a/R/RunCellToCell.R b/R/RunCellToCell.R
index 29b1478..bfdf07e 100644
--- a/R/RunCellToCell.R
+++ b/R/RunCellToCell.R
@@ -5,8 +5,8 @@
 #' The default assay of this object is called "CellToCell" to distinguish it from normal Seurat objects.
 #' Meta.data slots by default contain "SendingType" "ReceivingType" and "VectorType" information.
 #' 
-#' @param sys.small A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings. 
-#' @param ground.truth Ground truth signaling mechanisms present in sys.small.
+#' @param filtered.obj A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings. 
+#' @param ground.truth Ground truth signaling mechanisms present in filtered.obj.
 #' @param assay The assay to run the SCC transformation on. Defaults to "RNA."
 #' @param meta.data.to.map A character vector of metadata names present in the original object which will be carried to the SCC objects
 #' @param output_format string. Choice of the output format. "seurat" will output a list of seurat objects, "raw" will output a list of lists with raw interaction matrix and compiled metadata
@@ -16,7 +16,7 @@
 #' @export
 
 
-RunCellToCell <- function(sys.small,
+RunCellToCell <- function(filtered.obj,
                           ground.truth,
                           assay,
                           meta.data.to.map,
@@ -25,13 +25,13 @@ RunCellToCell <- function(sys.small,
   
   ### CREATE MAPPING ###
 
-  # jc: Identify celltypes:names(table(Idents(sys.small))). Better to run check_celltypes, but harder to check
-  celltypes <- return_celltypes(sys.small)
+  # jc: Identify celltypes:names(table(Idents(filtered.obj))). Better to run check_celltypes, but harder to check
+  celltypes <- return_celltypes(filtered.obj)
   
   # Ligand dataset (listwise, for each celltype)
   lig.list <- list()
   for (i in 1:length(celltypes)){
-    temp <- subset(sys.small,idents = celltypes[i])
+    temp <- subset(filtered.obj,idents = celltypes[i])
     subunit.list <- list() # Builds sending (ligand) data for any number of ligand subunits
     for (s in 1:ncol(ground.truth$source.subunits)){ #For each subunit column...
       subunit.list[[s]] <- matrix(data = 1,nrow = nrow(ground.truth$source.subunits),ncol = ncol(temp)) #initialize a mechanism x barcode matrix of all NAs
@@ -47,7 +47,7 @@ RunCellToCell <- function(sys.small,
   # Receptor dataset (listwise, for each celltype)
   rec.list <- list()
   for (i in 1:length(celltypes)){
-    temp <- subset(sys.small,idents = celltypes[i])
+    temp <- subset(filtered.obj,idents = celltypes[i])
     subunit.list <- list() # Builds receiving (receptor) data for any number of receptor subunits
     for (t in 1:ncol(ground.truth$target.subunits)){
       subunit.list[[t]] <- matrix(data = 1,nrow = nrow(ground.truth$target.subunits),ncol = ncol(temp)) #initialize a mechanism x barcode matrix of all NAs
@@ -72,7 +72,7 @@ RunCellToCell <- function(sys.small,
   for (i in 1:length(celltypes)){
     
     # Define maximum number of comparisons for each pairing
-    num <- as.data.frame(table(Seurat::Idents(sys.small)))
+    num <- as.data.frame(table(Seurat::Idents(filtered.obj)))
     num$sender.freq <- ncol(lig.list[[i]])
     rownames(num) <- num$Var1
     num <- num[,-1]
@@ -98,8 +98,8 @@ RunCellToCell <- function(sys.small,
     rownames(scc.data[[i]]) <- paste(rownames(lig.data[[i]]),rownames(rec.data[[i]]),sep = '—')
     colnames(scc.data[[i]]) <- paste(colnames(lig.data[[i]]),colnames(rec.data[[i]]),sep = '—')
     
-    sending.cell.idents[[i]] <- as.character(Seurat::Idents(sys.small)[colnames(lig.data[[i]])])
-    receiving.cell.idents[[i]] <- as.character(Seurat::Idents(sys.small)[colnames(rec.data[[i]])])
+    sending.cell.idents[[i]] <- as.character(Seurat::Idents(filtered.obj)[colnames(lig.data[[i]])])
+    receiving.cell.idents[[i]] <- as.character(Seurat::Idents(filtered.obj)[colnames(rec.data[[i]])])
     
   }
   
@@ -109,8 +109,8 @@ RunCellToCell <- function(sys.small,
   #Use this matrix to create a Seurat object:
   demo <- Seurat::CreateSeuratObject(counts = as.matrix(scc),assay = 'CellToCell')
   # JC: Seurat V5 will not create data slot automatically, the following step is to manually add this slot
-  if(SeuratObject::Version(demo) >= 5){
-    demo <- NormalizeData(demo,assay = "CellToCell")  # Seura Object need to be >= 5.0.1
+  if(SeuratObject::Version(demo) >= "5.0.0"){
+    demo <- Seurat::NormalizeData(demo,assay = "CellToCell")  # Seura Object need to be >= 5.0.1
     demo@assays$CellToCell@layers$data <- demo@assays$CellToCell@layers$counts # Seura Object need to be>= 5.0.1
     
   }
@@ -133,9 +133,9 @@ RunCellToCell <- function(sys.small,
   # Identify sending and receiving barcodes
   sending.barcodes <- colnames(do.call(cbind,lig.data)) #This can be simplified if the above SCC construction is simplified
   receiving.barcodes <- colnames(do.call(cbind,rec.data)) #This can be simplified if the above SCC construction is simplified
-  # Pull and format sending and receiving metadata    jc: possible bug, change object to sys.small
-  sending.metadata <- as.matrix(sys.small@meta.data[,meta.data.to.map,drop=FALSE][sending.barcodes,])
-  receiving.metadata <- as.matrix(sys.small@meta.data[,meta.data.to.map,drop=FALSE][receiving.barcodes,])
+  # Pull and format sending and receiving metadata    jc: possible bug, change object to filtered.obj
+  sending.metadata <- as.matrix(filtered.obj@meta.data[,meta.data.to.map,drop=FALSE][sending.barcodes,])
+  receiving.metadata <- as.matrix(filtered.obj@meta.data[,meta.data.to.map,drop=FALSE][receiving.barcodes,])
   # Make joint metadata
   datArray <- abind::abind(sending.metadata,receiving.metadata,along=3)
   joint.metadata <- as.matrix(apply(datArray,1:2,function(x)paste(x[1],"-",x[2])))
@@ -154,7 +154,7 @@ RunCellToCell <- function(sys.small,
   Seurat::Idents(demo) <- demo$VectorType
   
   # How many vectors were captured by this sampling?
-  message(paste("\n",ncol(demo),'Cell-To-Cell edges computed, sampling',length(unique(demo$VectorType)),'distinct VectorTypes, out of',length(table(Seurat::Idents(sys.small)))^2,'total possible'))
+  message(paste("\n",ncol(demo),'Cell-To-Cell edges computed, sampling',length(unique(demo$VectorType)),'distinct VectorTypes, out of',length(table(Seurat::Idents(filtered.obj)))^2,'total possible'))
   
   if(output_format == "seurat") return(demo)
   else{
diff --git a/R/RunCellToCellSpatial.R b/R/RunCellToCellSpatial.R
index 9230ddd..f61601b 100644
--- a/R/RunCellToCellSpatial.R
+++ b/R/RunCellToCellSpatial.R
@@ -1,7 +1,7 @@
 #' RunCellToCellSpatial
 #'
-#' @param sys.small A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.
-#' @param ground.truth Ground truth signaling mechanisms present in sys.small.
+#' @param filtered.obj A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.
+#' @param ground.truth Ground truth signaling mechanisms present in filtered.obj.
 #' @param assay The assay to run the SCC transformation on. Defaults to "RNA."
 #' @param meta.data.to.map A character vector of metadata names present in the original object which will be carried to the NICHES objects
 #' @param edgelist data.frame. Each row is an directional edge between two spatially connected cells
@@ -11,7 +11,7 @@
 #' @importFrom dplyr %>%
 #' @export
 
-RunCellToCellSpatial <- function(sys.small,
+RunCellToCellSpatial <- function(filtered.obj,
                                  ground.truth,
                                  assay,
                                  meta.data.to.map,
@@ -22,30 +22,30 @@ RunCellToCellSpatial <- function(sys.small,
   
     # Make ligand matrix
 
-    #lig.data <- sys.small@assays[[assay]]@data[ligands,edgelist$from]
+    #lig.data <- filtered.obj@assays[[assay]]@data[ligands,edgelist$from]
 
     subunit.list <- list() # Builds sending (ligand) data for any number of ligand subunits
     for (s in 1:ncol(ground.truth$source.subunits)){ #For each subunit column...
-      subunit.list[[s]] <- matrix(data = 1,nrow = nrow(ground.truth$source.subunits),ncol = ncol(getSeuratAssay(sys.small,assay,"data")[,edgelist$from])) #initialize a mechanism x barcode matrix of all NAs
-      colnames(subunit.list[[s]]) <- colnames(getSeuratAssay(sys.small,assay,"data")[,edgelist$from])
+      subunit.list[[s]] <- matrix(data = 1,nrow = nrow(ground.truth$source.subunits),ncol = ncol(getSeuratAssay(filtered.obj,assay,"data")[,edgelist$from])) #initialize a mechanism x barcode matrix of all NAs
+      colnames(subunit.list[[s]]) <- colnames(getSeuratAssay(filtered.obj,assay,"data")[,edgelist$from])
       rownames(subunit.list[[s]]) <- rownames(ground.truth$source.subunits)
       non.na.indices <- !is.na(ground.truth$source.subunits[,s]) #Identify rows in the s-th column of the ground truth which are not NA
-      subunit.list[[s]][non.na.indices,] <- as.matrix(getSeuratAssay(sys.small,assay,"data")[ground.truth$source.subunits[non.na.indices,s],edgelist$from])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
+      subunit.list[[s]][non.na.indices,] <- as.matrix(getSeuratAssay(filtered.obj,assay,"data")[ground.truth$source.subunits[non.na.indices,s],edgelist$from])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
     }
     lig.data <- Reduce('*',subunit.list)
     rm(subunit.list)
     
   # Make receptor matrix
 
-    #rec.data <- sys.small@assays[[assay]]@data[receptors,edgelist$to]
+    #rec.data <- filtered.obj@assays[[assay]]@data[receptors,edgelist$to]
     
     subunit.list <- list() # Builds receiving (receptor) data for any number of receptor subunits
     for (t in 1:ncol(ground.truth$target.subunits)){
-      subunit.list[[t]] <- matrix(data = 1,nrow = nrow(ground.truth$target.subunits),ncol = ncol(getSeuratAssay(sys.small,assay,"data")[,edgelist$to])) #initialize a mechanism x barcode matrix of all NAs
-      colnames(subunit.list[[t]]) <- colnames(getSeuratAssay(sys.small,assay,"data")[,edgelist$to])
+      subunit.list[[t]] <- matrix(data = 1,nrow = nrow(ground.truth$target.subunits),ncol = ncol(getSeuratAssay(filtered.obj,assay,"data")[,edgelist$to])) #initialize a mechanism x barcode matrix of all NAs
+      colnames(subunit.list[[t]]) <- colnames(getSeuratAssay(filtered.obj,assay,"data")[,edgelist$to])
       rownames(subunit.list[[t]]) <- rownames(ground.truth$target.subunits)
       non.na.indices <- !is.na(ground.truth$target.subunits[,t]) #Identify rows in the t-th column of the ground truth which are not NA
-      subunit.list[[t]][non.na.indices,] <- as.matrix(getSeuratAssay(sys.small,assay,"data")[ground.truth$target.subunits[non.na.indices,t],edgelist$to])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
+      subunit.list[[t]][non.na.indices,] <- as.matrix(getSeuratAssay(filtered.obj,assay,"data")[ground.truth$target.subunits[non.na.indices,t],edgelist$to])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
     }
     rec.data <- Reduce('*',subunit.list)
     rm(subunit.list)
@@ -54,16 +54,16 @@ RunCellToCellSpatial <- function(sys.small,
   scc <- lig.data*rec.data
   rownames(scc) <- paste(rownames(lig.data),rownames(rec.data),sep = '—')
   colnames(scc) <- paste(colnames(lig.data),colnames(rec.data),sep = '—')
-  sending.cell.idents <- as.character(Seurat::Idents(sys.small)[colnames(lig.data)])
-  receiving.cell.idents <- as.character(Seurat::Idents(sys.small)[colnames(rec.data)])
+  sending.cell.idents <- as.character(Seurat::Idents(filtered.obj)[colnames(lig.data)])
+  receiving.cell.idents <- as.character(Seurat::Idents(filtered.obj)[colnames(rec.data)])
   dim(scc)
 
   # Use this matrix to create a Seurat object:
   demo <- Seurat::CreateSeuratObject(counts = as.matrix(scc),assay = 'CellToCellSpatial')
 
   # JC: Seurat V5 will not create data slot automatically, the following step is to manually add this slot
-  if(SeuratObject::Version(demo) >= 5){
-    demo <- NormalizeData(demo,assay = "CellToCellSpatial")  # Seura Object need to be >= 5.0.1
+  if(SeuratObject::Version(demo) >= "5.0.0"){
+    demo <- Seurat::NormalizeData(demo,assay = "CellToCellSpatial")  # Seura Object need to be >= 5.0.1
     demo@assays$CellToCellSpatial@layers$data <- demo@assays$CellToCellSpatial@layers$counts # Seura Object need to be >= 5.0.1
     
   }
@@ -87,9 +87,9 @@ RunCellToCellSpatial <- function(sys.small,
     sending.barcodes <- colnames(lig.data)
     receiving.barcodes <- colnames(rec.data)
     # Pull and format sending and receiving metadata
-    # jc: possible bug, change object to sys.small
-    sending.metadata <- as.matrix(sys.small@meta.data[,meta.data.to.map,drop=FALSE][sending.barcodes,])
-    receiving.metadata <- as.matrix(sys.small@meta.data[,meta.data.to.map,drop=FALSE][receiving.barcodes,])
+    # jc: possible bug, change object to filtered.obj
+    sending.metadata <- as.matrix(filtered.obj@meta.data[,meta.data.to.map,drop=FALSE][sending.barcodes,])
+    receiving.metadata <- as.matrix(filtered.obj@meta.data[,meta.data.to.map,drop=FALSE][receiving.barcodes,])
     # Make joint metadata
     datArray <- abind::abind(sending.metadata,receiving.metadata,along=3)
     joint.metadata <- as.matrix(apply(datArray,1:2,function(x)paste(x[1],"-",x[2])))
@@ -107,7 +107,7 @@ RunCellToCellSpatial <- function(sys.small,
   Seurat::Idents(demo) <- demo$VectorType
   
   # How many vectors were captured by this sampling?
-  message(paste("\n",length(unique(demo$VectorType)),'distinct VectorTypes were computed, out of',length(table(Seurat::Idents(sys.small)))^2,'total possible'))
+  message(paste("\n",length(unique(demo$VectorType)),'distinct VectorTypes were computed, out of',length(table(Seurat::Idents(filtered.obj)))^2,'total possible'))
 
   
   if(output_format == "seurat") return(demo)
diff --git a/R/RunCellToNeighborhood.R b/R/RunCellToNeighborhood.R
index 9779572..fdb410f 100644
--- a/R/RunCellToNeighborhood.R
+++ b/R/RunCellToNeighborhood.R
@@ -1,7 +1,7 @@
 #' RunCellToNeighborhood
 #'
-#' @param sys.small A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.
-#' @param ground.truth Ground truth signaling mechanisms present in sys.small.
+#' @param filtered.obj A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.
+#' @param ground.truth Ground truth signaling mechanisms present in filtered.obj.
 #' @param assay The assay to run the SCC transformation on. Defaults to "RNA."
 #' @param meta.data.to.map A character vector of metadata names present in the original object which will be carried to the NICHES objects
 #' @param blend Choice of linear operator to combine edges. Defaults to "mean", also accepts "sum"
@@ -10,7 +10,7 @@
 #'
 #' @export
 
-RunCellToNeighborhood <- function(sys.small,
+RunCellToNeighborhood <- function(filtered.obj,
                                   ground.truth,
                                   assay,
                                   meta.data.to.map,
@@ -21,30 +21,30 @@ RunCellToNeighborhood <- function(sys.small,
 
   # Make ligand matrix
   
-  #lig.data <- sys.small@assays[[assay]]@data[ligands,edgelist$from]
+  #lig.data <- filtered.obj@assays[[assay]]@data[ligands,edgelist$from]
   
   subunit.list <- list() # Builds sending (ligand) data for any number of ligand subunits
   for (s in 1:ncol(ground.truth$source.subunits)){ #For each subunit column...
-    subunit.list[[s]] <- matrix(data = 1,nrow = nrow(ground.truth$source.subunits),ncol = ncol(getSeuratAssay(sys.small,assay,"data")[,edgelist$from])) #initialize a mechanism x barcode matrix of all NAs
-    colnames(subunit.list[[s]]) <- colnames(getSeuratAssay(sys.small,assay,"data")[,edgelist$from])
+    subunit.list[[s]] <- matrix(data = 1,nrow = nrow(ground.truth$source.subunits),ncol = ncol(getSeuratAssay(filtered.obj,assay,"data")[,edgelist$from])) #initialize a mechanism x barcode matrix of all NAs
+    colnames(subunit.list[[s]]) <- colnames(getSeuratAssay(filtered.obj,assay,"data")[,edgelist$from])
     rownames(subunit.list[[s]]) <- rownames(ground.truth$source.subunits)
     non.na.indices <- !is.na(ground.truth$source.subunits[,s]) #Identify rows in the s-th column of the ground truth which are not NA
-    subunit.list[[s]][non.na.indices,] <- as.matrix(getSeuratAssay(sys.small,assay,"data")[ground.truth$source.subunits[non.na.indices,s],edgelist$from])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
+    subunit.list[[s]][non.na.indices,] <- as.matrix(getSeuratAssay(filtered.obj,assay,"data")[ground.truth$source.subunits[non.na.indices,s],edgelist$from])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
   }
   lig.data <- Reduce('*',subunit.list)
   rm(subunit.list)
   
   # Make receptor matrix
   
-  #rec.data <- sys.small@assays[[assay]]@data[receptors,edgelist$to]
+  #rec.data <- filtered.obj@assays[[assay]]@data[receptors,edgelist$to]
   
   subunit.list <- list() # Builds receiving (receptor) data for any number of receptor subunits
   for (t in 1:ncol(ground.truth$target.subunits)){
-    subunit.list[[t]] <- matrix(data = 1,nrow = nrow(ground.truth$target.subunits),ncol = ncol(getSeuratAssay(sys.small,assay,"data")[,edgelist$to])) #initialize a mechanism x barcode matrix of all NAs
-    colnames(subunit.list[[t]]) <- colnames(getSeuratAssay(sys.small,assay,"data")[,edgelist$to])
+    subunit.list[[t]] <- matrix(data = 1,nrow = nrow(ground.truth$target.subunits),ncol = ncol(getSeuratAssay(filtered.obj,assay,"data")[,edgelist$to])) #initialize a mechanism x barcode matrix of all NAs
+    colnames(subunit.list[[t]]) <- colnames(getSeuratAssay(filtered.obj,assay,"data")[,edgelist$to])
     rownames(subunit.list[[t]]) <- rownames(ground.truth$target.subunits)
     non.na.indices <- !is.na(ground.truth$target.subunits[,t]) #Identify rows in the t-th column of the ground truth which are not NA
-    subunit.list[[t]][non.na.indices,] <- as.matrix(getSeuratAssay(sys.small,assay,"data")[ground.truth$target.subunits[non.na.indices,t],edgelist$to])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
+    subunit.list[[t]][non.na.indices,] <- as.matrix(getSeuratAssay(filtered.obj,assay,"data")[ground.truth$target.subunits[non.na.indices,t],edgelist$to])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
   }
   rec.data <- Reduce('*',subunit.list)
   rm(subunit.list)
@@ -63,24 +63,24 @@ RunCellToNeighborhood <- function(sys.small,
   
   # Label columns properly
   barcodes <- colnames(scc)
-  colnames(scc) <- paste(colnames(scc),'Neighborhood',sep = '—')
+  colnames(scc) <- paste(barcodes,'Neighborhood',sep = '—')
 
   # Use this matrix to create a Seurat object:
   demo <- Seurat::CreateSeuratObject(counts = as.matrix(scc),assay = 'CellToNeighborhood')
   # JC: Seurat V5 will not create data slot automatically, the following step is to manually add this slot
-  if(SeuratObject::Version(demo) >= 5){
-    demo <- NormalizeData(demo,assay = "CellToNeighborhood")  # Seura Object need to be >= 5.0.1
+  if(SeuratObject::Version(demo) >= "5.0.0"){
+    demo <- Seurat::NormalizeData(demo,assay = "CellToNeighborhood")  # Seura Object need to be >= 5.0.1
     demo@assays$CellToNeighborhood@layers$data <- demo@assays$CellToNeighborhood@layers$counts # Seura Object need to be >= 5.0.1
     
   }
   
   # Add metadata based on ident slot
-  demo <- Seurat::AddMetaData(demo,metadata = barcodes,col.name = 'SendingCell')
   # bug fix: add the Neighborhood - prefix
-  sending_type.meta <- data.frame(Seurat::Idents(sys.small)[barcodes])
-  rownames(sending_type.meta) <- paste(rownames(sending_type.meta),"Neighborhood",sep = '—')
+  sending_type.meta <- data.frame(SendingCell = barcodes,
+                                  SendingType = Seurat::Idents(filtered.obj)[barcodes],
+                                    row.names = paste(barcodes,"Neighborhood",sep = '—'))
   
-  demo <- Seurat::AddMetaData(demo,metadata = sending_type.meta,col.name = 'SendingType')
+  demo <- Seurat::AddMetaData(demo,metadata = sending_type.meta,col.name = c("SendingCell","SendingType"))
 
   # Gather and assemble additional metadata
   if (!is.null(meta.data.to.map)){
@@ -88,8 +88,8 @@ RunCellToNeighborhood <- function(sys.small,
     sending.barcodes <- barcodes # Only sending cell metadata applies for this function
     #receiving.barcodes <- colnames(rec.map)
     # Pull and format sending and receiving metadata
-    # jc: possible bug, change object to sys.small
-    sending.metadata <- as.matrix(sys.small@meta.data[,meta.data.to.map,drop=FALSE][sending.barcodes,])
+    # jc: possible bug, change object to filtered.obj
+    sending.metadata <- as.matrix(filtered.obj@meta.data[,meta.data.to.map,drop=FALSE][sending.barcodes,])
     #receiving.metadata <- as.matrix(object@meta.data[,meta.data.to.map][receiving.barcodes,])
     # Make joint metadata
     #datArray <- abind(sending.metadata,receiving.metadata,along=3)
diff --git a/R/RunCellToSystem.R b/R/RunCellToSystem.R
index 3ca87c8..93a195d 100644
--- a/R/RunCellToSystem.R
+++ b/R/RunCellToSystem.R
@@ -9,8 +9,8 @@
 #' Meta.data slots by default contain "SendingType" information, which is the celltypes for each point, 
 #' and "SendingCell" which is the exact cell barcode present in the original Seurat object.
 #' 
-#' @param sys.small A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.
-#' @param ground.truth Ground truth signaling mechanisms present in sys.small.
+#' @param filtered.obj A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.
+#' @param ground.truth Ground truth signaling mechanisms present in filtered.obj.
 #' @param assay The assay to run the CellToSystem transformation on. Defaults to "RNA."
 #' @param blend Choice of linear operator to combine edges. Defaults to "mean", also accepts "sum","mean.adj"
 #' @param meta.data.to.map A character vector of metadata names present in the original object which will be carried to the NICHES objects
@@ -19,7 +19,7 @@
 #' @export
 
 
-RunCellToSystem <- function(sys.small,
+RunCellToSystem <- function(filtered.obj,
                             ground.truth,
                             assay,
                             blend = 'mean',
@@ -33,11 +33,11 @@ RunCellToSystem <- function(sys.small,
   # Receptor data
   subunit.list <- list() # Builds receiving (receptor) data for any number of receptor subunits
   for (t in 1:ncol(ground.truth$target.subunits)){
-    subunit.list[[t]] <- matrix(data = 1,nrow = nrow(ground.truth$target.subunits),ncol = ncol(sys.small)) #initialize a mechanism x barcode matrix of all NAs
-    colnames(subunit.list[[t]]) <- colnames(sys.small)
+    subunit.list[[t]] <- matrix(data = 1,nrow = nrow(ground.truth$target.subunits),ncol = ncol(filtered.obj)) #initialize a mechanism x barcode matrix of all NAs
+    colnames(subunit.list[[t]]) <- colnames(filtered.obj)
     rownames(subunit.list[[t]]) <- rownames(ground.truth$target.subunits)
     non.na.indices <- !is.na(ground.truth$target.subunits[,t]) #Identify rows in the s-th column of the ground truth which are not NA
-    subunit.list[[t]][non.na.indices,] <- as.matrix(getSeuratAssay(sys.small,assay,"data")[ground.truth$target.subunits[non.na.indices,t],])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
+    subunit.list[[t]][non.na.indices,] <- as.matrix(getSeuratAssay(filtered.obj,assay,"data")[ground.truth$target.subunits[non.na.indices,t],])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
   }
   rec.map <- Reduce('*',subunit.list)
   rm(subunit.list)
@@ -61,11 +61,11 @@ RunCellToSystem <- function(sys.small,
   # Ligand data
   subunit.list <- list() # Builds sending (ligand) data for any number of ligand subunits
   for (s in 1:ncol(ground.truth$source.subunits)){ #For each subunit column...
-    subunit.list[[s]] <- matrix(data = 1,nrow = nrow(ground.truth$source.subunits),ncol = ncol(sys.small)) #initialize a mechanism x barcode matrix of all NAs
-    colnames(subunit.list[[s]]) <- colnames(sys.small)
+    subunit.list[[s]] <- matrix(data = 1,nrow = nrow(ground.truth$source.subunits),ncol = ncol(filtered.obj)) #initialize a mechanism x barcode matrix of all NAs
+    colnames(subunit.list[[s]]) <- colnames(filtered.obj)
     rownames(subunit.list[[s]]) <- rownames(ground.truth$source.subunits)
     non.na.indices <- !is.na(ground.truth$source.subunits[,s]) #Identify rows in the s-th column of the ground truth which are not NA
-    subunit.list[[s]][non.na.indices,] <- as.matrix(getSeuratAssay(sys.small,assay,"data")[ground.truth$source.subunits[non.na.indices,s],])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
+    subunit.list[[s]][non.na.indices,] <- as.matrix(getSeuratAssay(filtered.obj,assay,"data")[ground.truth$source.subunits[non.na.indices,s],])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
   }
   lig.map <- Reduce('*',subunit.list)
   rm(subunit.list)
@@ -83,8 +83,8 @@ RunCellToSystem <- function(sys.small,
   # Use this matrix to create a Seurat object:
   demo <- Seurat::CreateSeuratObject(counts = as.matrix(sc.connectome),assay = 'CellToSystem')
   # JC: Seurat V5 will not create data slot automatically, the following step is to manually add this slot
-  if(SeuratObject::Version(demo) >= 5){
-    demo <- NormalizeData(demo,assay = "CellToSystem")  # Seura Object need to be >= 5.0.1
+  if(SeuratObject::Version(demo) >= "5.0.0"){
+    demo <- Seurat::NormalizeData(demo,assay = "CellToSystem")  # Seura Object need to be >= 5.0.1
     demo@assays$CellToSystem@layers$data <- demo@assays$CellToSystem@layers$counts # Seura Object need to be >= 5.0.1
     
   }
@@ -95,7 +95,7 @@ RunCellToSystem <- function(sys.small,
   demo <- Seurat::AddMetaData(demo,metadata = meta.data.to.add,col.name = 'SendingCell')
   
   # bug fix: add the system - prefix
-  sending_type.meta <- data.frame(Seurat::Idents(sys.small))
+  sending_type.meta <- data.frame(Seurat::Idents(filtered.obj))
   rownames(sending_type.meta) <- paste(rownames(sending_type.meta),"System",sep = '—')
   
   demo <- Seurat::AddMetaData(demo,metadata = sending_type.meta,col.name = 'SendingType')
@@ -106,8 +106,8 @@ RunCellToSystem <- function(sys.small,
     sending.barcodes <- colnames(lig.map) # Only sending cell metadata applies for this function
     #receiving.barcodes <- colnames(rec.map) 
     # Pull and format sending and receiving metadata
-    # jc: possible bug, change object to sys.small
-    sending.metadata <- as.matrix(sys.small@meta.data[,meta.data.to.map,drop=FALSE][sending.barcodes,])
+    # jc: possible bug, change object to filtered.obj
+    sending.metadata <- as.matrix(filtered.obj@meta.data[,meta.data.to.map,drop=FALSE][sending.barcodes,])
     #receiving.metadata <- as.matrix(object@meta.data[,meta.data.to.map][receiving.barcodes,])
     # Make joint metadata
     #datArray <- abind(sending.metadata,receiving.metadata,along=3)
diff --git a/R/RunNICHES.R b/R/RunNICHES.R
index 3a324d2..3f3fcef 100644
--- a/R/RunNICHES.R
+++ b/R/RunNICHES.R
@@ -161,12 +161,12 @@ RunNICHES.default <- function(object,
   # jc: move the shared preprocessing steps here to avoid redundancy and reduce the number of parameters to be passed to other functions
 
   # NOTE: relies on Idents(object) to be cell types to subset
-  sys.small <- prepSeurat(object,assay,min.cells.per.ident,min.cells.per.gene) 
-  ground.truth <- lr_load(LR.database,custom_LR_database,species,rownames(sys.small@assays[[assay]]))
+  filtered.obj <- prepSeurat(object,assay,min.cells.per.ident,min.cells.per.gene) 
+  ground.truth <- lr_load(LR.database,custom_LR_database,species,rownames(filtered.obj@assays[[assay]]))
   if (org_names_indicator["CellToCellSpatial"] == T | org_names_indicator["CellToNeighborhood"] == T | org_names_indicator["NeighborhoodToCell"] == T){
     ## 1. Move the neighbor graph construction here
     ## 2. Enable a k-nearest-neighbor parameter as an alternative
-    edgelist <- compute_edgelist(sys.small,position.x,position.y,k,rad.set)
+    edgelist <- compute_edgelist(filtered.obj,position.x,position.y,k,rad.set)
   }
   
   # check the output format
@@ -179,20 +179,20 @@ RunNICHES.default <- function(object,
   # NOTE: RunCellToCell relies on Idents(object) to be cell types to subset
   #       Also each RunXXX function needs Idents(object) to build VectorType meta data 
 
-  if (CellToCell == T){output[[length(output)+1]] <- RunCellToCell(sys.small=sys.small,
+  if (CellToCell == T){output[[length(output)+1]] <- RunCellToCell(filtered.obj=filtered.obj,
                                                                    ground.truth=ground.truth,
                                                                    assay = assay,
                                                                    meta.data.to.map = meta.data.to.map,
                                                                    output_format = output_format
                                                                    )}
-  if (CellToSystem == T){output[[length(output)+1]] <- RunCellToSystem(sys.small=sys.small,
+  if (CellToSystem == T){output[[length(output)+1]] <- RunCellToSystem(filtered.obj=filtered.obj,
                                                                        ground.truth=ground.truth,
                                                                        assay = assay,
                                                                        meta.data.to.map = meta.data.to.map,
                                                                        blend = blend,
                                                                        output_format = output_format
                                                                        )}
-  if (SystemToCell == T){output[[length(output)+1]] <- RunSystemToCell(sys.small=sys.small,
+  if (SystemToCell == T){output[[length(output)+1]] <- RunSystemToCell(filtered.obj=filtered.obj,
                                                                        ground.truth=ground.truth,
                                                                        assay = assay,
                                                                        meta.data.to.map = meta.data.to.map,
@@ -201,14 +201,14 @@ RunNICHES.default <- function(object,
                                                                        )}
   
   
-  if (CellToCellSpatial == T){output[[length(output)+1]] <- RunCellToCellSpatial(sys.small=sys.small,
+  if (CellToCellSpatial == T){output[[length(output)+1]] <- RunCellToCellSpatial(filtered.obj=filtered.obj,
                                                                                  ground.truth=ground.truth,
                                                                                  assay = assay,
                                                                                  meta.data.to.map = meta.data.to.map,
                                                                                  edgelist = edgelist,
                                                                                  output_format = output_format
                                                                                  )} #Spatially-limited Cell-Cell vectors
-  if (CellToNeighborhood == T){output[[length(output)+1]] <- RunCellToNeighborhood(sys.small=sys.small,
+  if (CellToNeighborhood == T){output[[length(output)+1]] <- RunCellToNeighborhood(filtered.obj=filtered.obj,
                                                                                    ground.truth=ground.truth,
                                                                                    assay = assay,
                                                                                    meta.data.to.map = meta.data.to.map,
@@ -216,7 +216,7 @@ RunNICHES.default <- function(object,
                                                                                    edgelist = edgelist,
                                                                                    output_format = output_format
                                                                                    )} #Spatially-limited Cell-Neighborhood vectors
-  if (NeighborhoodToCell == T){output[[length(output)+1]] <- RunNeighborhoodToCell(sys.small=sys.small,
+  if (NeighborhoodToCell == T){output[[length(output)+1]] <- RunNeighborhoodToCell(filtered.obj=filtered.obj,
                                                                                    ground.truth=ground.truth,
                                                                                    assay = assay,
                                                                                    meta.data.to.map = meta.data.to.map,
diff --git a/R/RunNeighborhoodToCell.R b/R/RunNeighborhoodToCell.R
index 183198b..39895f8 100644
--- a/R/RunNeighborhoodToCell.R
+++ b/R/RunNeighborhoodToCell.R
@@ -1,7 +1,7 @@
 #' RunNeighborhoodToCell
 #' 
-#' @param sys.small A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.
-#' @param ground.truth Ground truth signaling mechanisms present in sys.small.
+#' @param filtered.obj A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.
+#' @param ground.truth Ground truth signaling mechanisms present in filtered.obj.
 #' @param assay The assay to run the SCC transformation on. Defaults to "RNA."
 #' @param meta.data.to.map A character vector of metadata names present in the original object which will be carried to the NICHES objects
 #' @param blend Choice of linear operator to combine edges. Defaults to "mean", also accepts "sum"
@@ -10,7 +10,7 @@
 #'
 #' @export
 
-RunNeighborhoodToCell <- function(sys.small,
+RunNeighborhoodToCell <- function(filtered.obj,
                                   ground.truth,
                                   assay,
                                   meta.data.to.map,
@@ -21,30 +21,30 @@ RunNeighborhoodToCell <- function(sys.small,
   
   # Make ligand matrix
   
-  #lig.data <- sys.small@assays[[assay]]@data[ligands,edgelist$from]
+  #lig.data <- filtered.obj@assays[[assay]]@data[ligands,edgelist$from]
   
   subunit.list <- list() # Builds sending (ligand) data for any number of ligand subunits
   for (s in 1:ncol(ground.truth$source.subunits)){ #For each subunit column...
-    subunit.list[[s]] <- matrix(data = 1,nrow = nrow(ground.truth$source.subunits),ncol = ncol(getSeuratAssay(sys.small,assay,"data")[,edgelist$from])) #initialize a mechanism x barcode matrix of all NAs
-    colnames(subunit.list[[s]]) <- colnames(getSeuratAssay(sys.small,assay,"data")[,edgelist$from])
+    subunit.list[[s]] <- matrix(data = 1,nrow = nrow(ground.truth$source.subunits),ncol = ncol(getSeuratAssay(filtered.obj,assay,"data")[,edgelist$from])) #initialize a mechanism x barcode matrix of all NAs
+    colnames(subunit.list[[s]]) <- colnames(getSeuratAssay(filtered.obj,assay,"data")[,edgelist$from])
     rownames(subunit.list[[s]]) <- rownames(ground.truth$source.subunits)
     non.na.indices <- !is.na(ground.truth$source.subunits[,s]) #Identify rows in the s-th column of the ground truth which are not NA
-    subunit.list[[s]][non.na.indices,] <- as.matrix(getSeuratAssay(sys.small,assay,"data")[ground.truth$source.subunits[non.na.indices,s],edgelist$from])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
+    subunit.list[[s]][non.na.indices,] <- as.matrix(getSeuratAssay(filtered.obj,assay,"data")[ground.truth$source.subunits[non.na.indices,s],edgelist$from])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
   }
   lig.data <- Reduce('*',subunit.list)
   rm(subunit.list)
   
   # Make receptor matrix
   
-  #rec.data <- sys.small@assays[[assay]]@data[receptors,edgelist$to]
+  #rec.data <- filtered.obj@assays[[assay]]@data[receptors,edgelist$to]
   
   subunit.list <- list() # Builds receiving (receptor) data for any number of receptor subunits
   for (t in 1:ncol(ground.truth$target.subunits)){
-    subunit.list[[t]] <- matrix(data = 1,nrow = nrow(ground.truth$target.subunits),ncol = ncol(getSeuratAssay(sys.small,assay,"data")[,edgelist$to])) #initialize a mechanism x barcode matrix of all NAs
-    colnames(subunit.list[[t]]) <- colnames(getSeuratAssay(sys.small,assay,"data")[,edgelist$to])
+    subunit.list[[t]] <- matrix(data = 1,nrow = nrow(ground.truth$target.subunits),ncol = ncol(getSeuratAssay(filtered.obj,assay,"data")[,edgelist$to])) #initialize a mechanism x barcode matrix of all NAs
+    colnames(subunit.list[[t]]) <- colnames(getSeuratAssay(filtered.obj,assay,"data")[,edgelist$to])
     rownames(subunit.list[[t]]) <- rownames(ground.truth$target.subunits)
     non.na.indices <- !is.na(ground.truth$target.subunits[,t]) #Identify rows in the t-th column of the ground truth which are not NA
-    subunit.list[[t]][non.na.indices,] <- as.matrix(getSeuratAssay(sys.small,assay,"data")[ground.truth$target.subunits[non.na.indices,t],edgelist$to])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
+    subunit.list[[t]][non.na.indices,] <- as.matrix(getSeuratAssay(filtered.obj,assay,"data")[ground.truth$target.subunits[non.na.indices,t],edgelist$to])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
   }
   rec.data <- Reduce('*',subunit.list)
   rm(subunit.list)
@@ -63,24 +63,24 @@ RunNeighborhoodToCell <- function(sys.small,
     
   # Label columns properly
   barcodes <- colnames(scc)
-  colnames(scc) <- paste('Neighborhood',colnames(scc),sep = '—')
+  colnames(scc) <- paste('Neighborhood',barcodes,sep = '—')
   
   # Use this matrix to create a Seurat object:
   demo <- Seurat::CreateSeuratObject(counts = as.matrix(scc),assay = 'NeighborhoodToCell')
   # JC: Seurat V5 will not create data slot automatically, the following step is to manually add this slot
-  if(SeuratObject::Version(demo) >= 5){
-    demo <- NormalizeData(demo,assay = "NeighborhoodToCell")  # Seura Object need to be >= 5.0.1
+  if(SeuratObject::Version(demo) >= "5.0.0"){
+    demo <- Seurat::NormalizeData(demo,assay = "NeighborhoodToCell")  # Seura Object need to be >= 5.0.1
     demo@assays$NeighborhoodToCell@layers$data <- demo@assays$NeighborhoodToCell@layers$counts # Seura Object need to be >= 5.0.1
     
   }
   
   # Add metadata based on ident slot
-  demo <- Seurat::AddMetaData(demo,metadata = barcodes,col.name = 'ReceivingCell')
   # bug fix: add the Neighborhood - prefix
-  receiving_type.meta <- data.frame(Seurat::Idents(sys.small)[barcodes])
-  rownames(receiving_type.meta) <- paste("Neighborhood",rownames(receiving_type.meta),sep = '—')
+  receiving_type.meta <- data.frame(ReceivingCell = barcodes,
+                                    ReceivingType = Seurat::Idents(filtered.obj)[barcodes],
+                                    row.names = paste("Neighborhood",barcodes,sep = '—'))
   
-  demo <- Seurat::AddMetaData(demo,metadata = receiving_type.meta,col.name = 'ReceivingType')
+  demo <- Seurat::AddMetaData(demo,metadata = receiving_type.meta,col.name = c("ReceivingCell","ReceivingType"))
   
   # Gather and assemble additional metadata
   if (!is.null(meta.data.to.map)){
@@ -89,8 +89,8 @@ RunNeighborhoodToCell <- function(sys.small,
     receiving.barcodes <- barcodes # Only receiving cell metadata applies for this function
     # Pull and format sending and receiving metadata
     #sending.metadata <- as.matrix(object@meta.data[,meta.data.to.map][sending.barcodes,])
-    # jc: possible bug, change object to sys.small
-    receiving.metadata <- as.matrix(sys.small@meta.data[,meta.data.to.map,drop=FALSE][receiving.barcodes,])
+    # jc: possible bug, change object to filtered.obj
+    receiving.metadata <- as.matrix(filtered.obj@meta.data[,meta.data.to.map,drop=FALSE][receiving.barcodes,])
     # Make joint metadata
     #datArray <- abind(sending.metadata,receiving.metadata,along=3)
     #joint.metadata <- as.matrix(apply(datArray,1:2,function(x)paste(x[1],"-",x[2])))
diff --git a/R/RunSystemToCell.R b/R/RunSystemToCell.R
index e69abab..475234d 100644
--- a/R/RunSystemToCell.R
+++ b/R/RunSystemToCell.R
@@ -9,8 +9,8 @@
 #' Meta.data slots by default contain "ReceivingType" information, which is the celltypes for each point, 
 #' and "ReceivingCell" which is the exact cell barcode present in the original Seurat object.
 #' 
-#' @param sys.small A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings. 
-#' @param ground.truth Ground truth signaling mechanisms present in sys.small.
+#' @param filtered.obj A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings. 
+#' @param ground.truth Ground truth signaling mechanisms present in filtered.obj.
 #' @param assay The assay to run the SystemToCell transformation on. Defaults to "RNA."
 #' @param blend Choice of linear operator to combine edges. Defaults to "mean", also accepts "sum", "mean.adj"
 #' @param meta.data.to.map A character vector of metadata names present in the original object which will be carried to the NICHES objects
@@ -19,7 +19,7 @@
 #' @export
 
 
-RunSystemToCell <- function(sys.small,
+RunSystemToCell <- function(filtered.obj,
                             ground.truth,
                             assay,
                             blend = 'mean',
@@ -33,11 +33,11 @@ RunSystemToCell <- function(sys.small,
   # Ligand data
   subunit.list <- list() # Builds sending (ligand) data for any number of ligand subunits
   for (s in 1:ncol(ground.truth$source.subunits)){ #For each subunit column...
-    subunit.list[[s]] <- matrix(data = 1,nrow = nrow(ground.truth$source.subunits),ncol = ncol(sys.small)) #initialize a mechanism x barcode matrix of all NAs
-    colnames(subunit.list[[s]]) <- colnames(sys.small)
+    subunit.list[[s]] <- matrix(data = 1,nrow = nrow(ground.truth$source.subunits),ncol = ncol(filtered.obj)) #initialize a mechanism x barcode matrix of all NAs
+    colnames(subunit.list[[s]]) <- colnames(filtered.obj)
     rownames(subunit.list[[s]]) <- rownames(ground.truth$source.subunits)
     non.na.indices <- !is.na(ground.truth$source.subunits[,s]) #Identify rows in the s-th column of the ground truth which are not NA
-    subunit.list[[s]][non.na.indices,] <- as.matrix(getSeuratAssay(sys.small,assay,"data")[ground.truth$source.subunits[non.na.indices,s],])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
+    subunit.list[[s]][non.na.indices,] <- as.matrix(getSeuratAssay(filtered.obj,assay,"data")[ground.truth$source.subunits[non.na.indices,s],])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
   }
   lig.map <- Reduce('*',subunit.list)
   rm(subunit.list)
@@ -60,11 +60,11 @@ RunSystemToCell <- function(sys.small,
   # Receptor data
   subunit.list <- list() # Builds receiving (receptor) data for any number of receptor subunits
   for (t in 1:ncol(ground.truth$target.subunits)){
-    subunit.list[[t]] <- matrix(data = 1,nrow = nrow(ground.truth$target.subunits),ncol = ncol(sys.small)) #initialize a mechanism x barcode matrix of all NAs
-    colnames(subunit.list[[t]]) <- colnames(sys.small)
+    subunit.list[[t]] <- matrix(data = 1,nrow = nrow(ground.truth$target.subunits),ncol = ncol(filtered.obj)) #initialize a mechanism x barcode matrix of all NAs
+    colnames(subunit.list[[t]]) <- colnames(filtered.obj)
     rownames(subunit.list[[t]]) <- rownames(ground.truth$target.subunits)
     non.na.indices <- !is.na(ground.truth$target.subunits[,t]) #Identify rows in the s-th column of the ground truth which are not NA
-    subunit.list[[t]][non.na.indices,] <- as.matrix(getSeuratAssay(sys.small,assay,"data")[ground.truth$target.subunits[non.na.indices,t],])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
+    subunit.list[[t]][non.na.indices,] <- as.matrix(getSeuratAssay(filtered.obj,assay,"data")[ground.truth$target.subunits[non.na.indices,t],])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
   }
   rec.map <- Reduce('*',subunit.list)
   rm(subunit.list)
@@ -81,8 +81,8 @@ RunSystemToCell <- function(sys.small,
   #Use this matrix to create a Seurat object:
   demo <- Seurat::CreateSeuratObject(counts = as.matrix(sc.connectome),assay = 'SystemToCell')
   # JC: Seurat V5 will not create data slot automatically, the following step is to manually add this slot
-  if(SeuratObject::Version(demo) >= 5){
-    demo <- NormalizeData(demo,assay = "SystemToCell")  # Seurat Object need to be >= 5.0.1
+  if(SeuratObject::Version(demo) >= "5.0.0"){
+    demo <- Seurat::NormalizeData(demo,assay = "SystemToCell")  # Seurat Object need to be >= 5.0.1
     demo@assays$SystemToCell@layers$data <- demo@assays$SystemToCell@layers$counts # Seurat Object need to be >= 5.0.1
     
   }
@@ -93,7 +93,7 @@ RunSystemToCell <- function(sys.small,
   demo <- Seurat::AddMetaData(demo,metadata = meta.data.to.add,col.name = 'ReceivingCell')
   
   # bug fix: add the system - prefix
-  receiving_type.meta <- data.frame(Seurat::Idents(sys.small))
+  receiving_type.meta <- data.frame(Seurat::Idents(filtered.obj))
   rownames(receiving_type.meta) <- paste("System",rownames(receiving_type.meta),sep = '—')
   
   demo <- Seurat::AddMetaData(demo,metadata = receiving_type.meta,col.name = 'ReceivingType')
@@ -105,8 +105,8 @@ RunSystemToCell <- function(sys.small,
     receiving.barcodes <- colnames(rec.map) 
     # Pull and format sending and receiving metadata
     #sending.metadata <- as.matrix(object@meta.data[,meta.data.to.map][sending.barcodes,])
-    # jc: possible bug, change object to sys.small
-    receiving.metadata <- as.matrix(sys.small@meta.data[,meta.data.to.map,drop=FALSE][receiving.barcodes,])
+    # jc: possible bug, change object to filtered.obj
+    receiving.metadata <- as.matrix(filtered.obj@meta.data[,meta.data.to.map,drop=FALSE][receiving.barcodes,])
     # Make joint metadata
     #datArray <- abind(sending.metadata,receiving.metadata,along=3)
     #joint.metadata <- as.matrix(apply(datArray,1:2,function(x)paste(x[1],"-",x[2])))
diff --git a/R/utils.R b/R/utils.R
index 11b3b02..a7af241 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -7,7 +7,7 @@
 #' @return the specified assay data
 #' @export
 getSeuratAssay <- function(object,assay,slot){
-  if(SeuratObject::Version(object) >= 5) return(Seurat::GetAssayData(object,assay=assay,layer=slot))
+  if(SeuratObject::Version(object) >= "5.0.0") return(Seurat::GetAssayData(object,assay=assay,layer=slot))
   else return(Seurat::GetAssayData(object,assay=assay,slot=slot))
 }
 
@@ -26,14 +26,14 @@ prepSeurat <- function(object,assay,min.cells.per.ident,min.cells.per.gene){
   Seurat::DefaultAssay(object) <- assay
   
   # Stash object
-  sys.small <- object
+  filtered.obj <- object
   
   
   # Limit object to cell populations larger than requested minimum
   if (!is.null(min.cells.per.ident)){
     message(paste("\n",'Subsetting to populations with greater than',min.cells.per.ident,'cells'))
-    idents.include <- names(table(Seurat::Idents(sys.small)))[table(Seurat::Idents(sys.small)) > min.cells.per.ident]
-    sys.small <- subset(sys.small,idents = idents.include)
+    idents.include <- names(table(Seurat::Idents(filtered.obj)))[table(Seurat::Idents(filtered.obj)) > min.cells.per.ident]
+    filtered.obj <- subset(filtered.obj,idents = idents.include)
   }
   
   # Limit analysis to interaction involving genes expressed above minimum threshold number of cells in the system
@@ -41,18 +41,18 @@ prepSeurat <- function(object,assay,min.cells.per.ident,min.cells.per.gene){
     message(paste("\n",'Subsetting to genes expressed in greater than',min.cells.per.gene,'cells'))
     
     # jc: return an error if the count matrix in the given assay is empty
-    if(!length(getSeuratAssay(sys.small,assay,"counts"))) stop("Unable to subset: the count matrix in the given assay is empty.")
+    if(!length(getSeuratAssay(filtered.obj,assay,"counts"))) stop("Unable to subset: the count matrix in the given assay is empty.")
     
-    cells.per.gene <- data.frame(non.zero.cells = Matrix::rowSums(getSeuratAssay(sys.small,assay,"counts")>0))
+    cells.per.gene <- data.frame(non.zero.cells = Matrix::rowSums(getSeuratAssay(filtered.obj,assay,"counts")>0))
     GOI <- subset(cells.per.gene,non.zero.cells > min.cells.per.gene)
-    sys.small <- sys.small[rownames(GOI),]
+    filtered.obj <- filtered.obj[rownames(GOI),]
   }
   
-  num.cells <- ncol(sys.small)
+  num.cells <- ncol(filtered.obj)
   message(paste("\n",num.cells,'distinct cells from',
-                length(names(table(Seurat::Idents(sys.small)))),'celltypes to be analyzed'))
+                length(names(table(Seurat::Idents(filtered.obj)))),'celltypes to be analyzed'))
   
-  return(sys.small)
+  return(filtered.obj)
 }
 
 
@@ -100,7 +100,7 @@ return_celltypes <- function(seurat_object){
 
 #' Compute an edgelist based on the spatial coordinates
 #'
-#' @param sys.small A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.
+#' @param filtered.obj A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.
 #' @param position.x string. Optional. Default: NULL. The name of the meta.data column specifying location on the spatial x-axis. Only required for spatial omics data.
 #' @param position.y string. Optional. Default: NULL. The name of the meta.data column specifying location on the spatial y-axis. Only required for spatial omics data.
 #' @param k integer. Optional. Default: 4. Number of neighbors in a knn graph. Used to compute a mutual nearest neighbor graph based on the spatial coordinates of the spatial transcriptomic datasets.  
@@ -108,7 +108,7 @@ return_celltypes <- function(seurat_object){
 #'
 #' @export
 #'
-compute_edgelist <- function(sys.small,
+compute_edgelist <- function(filtered.obj,
                              position.x,
                              position.y,
                              k=4,
@@ -120,8 +120,8 @@ compute_edgelist <- function(sys.small,
   # Create adjacency matrix
   # Adapted from :: https://stackoverflow.com/questions/16075232/how-to-create-adjacency-matrix-from-grid-coordinates-in-r
   # Setup numbering and labeling
-  # jc: possible bug, change object to sys.small
-  df <- data.frame(x = sys.small[[position.x]], y = sys.small[[position.y]])
+  # jc: possible bug, change object to filtered.obj
+  df <- data.frame(x = filtered.obj[[position.x]], y = filtered.obj[[position.y]])
   df$barcode <- rownames(df)
   df$x <- as.character(df$x)
   df$y <- as.character(df$y)
diff --git a/man/RunCellToCell.Rd b/man/RunCellToCell.Rd
index 0f77f19..84e45b1 100644
--- a/man/RunCellToCell.Rd
+++ b/man/RunCellToCell.Rd
@@ -4,12 +4,18 @@
 \alias{RunCellToCell}
 \title{RunCellToCell}
 \usage{
-RunCellToCell(sys.small, ground.truth, assay, meta.data.to.map, output_format)
+RunCellToCell(
+  filtered.obj,
+  ground.truth,
+  assay,
+  meta.data.to.map,
+  output_format
+)
 }
 \arguments{
-\item{sys.small}{A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.}
+\item{filtered.obj}{A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.}
 
-\item{ground.truth}{Ground truth signaling mechanisms present in sys.small.}
+\item{ground.truth}{Ground truth signaling mechanisms present in filtered.obj.}
 
 \item{assay}{The assay to run the SCC transformation on. Defaults to "RNA."}
 
diff --git a/man/RunCellToCellSpatial.Rd b/man/RunCellToCellSpatial.Rd
index 75268d0..24b5a29 100644
--- a/man/RunCellToCellSpatial.Rd
+++ b/man/RunCellToCellSpatial.Rd
@@ -5,7 +5,7 @@
 \title{RunCellToCellSpatial}
 \usage{
 RunCellToCellSpatial(
-  sys.small,
+  filtered.obj,
   ground.truth,
   assay,
   meta.data.to.map,
@@ -14,9 +14,9 @@ RunCellToCellSpatial(
 )
 }
 \arguments{
-\item{sys.small}{A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.}
+\item{filtered.obj}{A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.}
 
-\item{ground.truth}{Ground truth signaling mechanisms present in sys.small.}
+\item{ground.truth}{Ground truth signaling mechanisms present in filtered.obj.}
 
 \item{assay}{The assay to run the SCC transformation on. Defaults to "RNA."}
 
diff --git a/man/RunCellToNeighborhood.Rd b/man/RunCellToNeighborhood.Rd
index a95665a..480ff03 100644
--- a/man/RunCellToNeighborhood.Rd
+++ b/man/RunCellToNeighborhood.Rd
@@ -5,7 +5,7 @@
 \title{RunCellToNeighborhood}
 \usage{
 RunCellToNeighborhood(
-  sys.small,
+  filtered.obj,
   ground.truth,
   assay,
   meta.data.to.map,
@@ -15,9 +15,9 @@ RunCellToNeighborhood(
 )
 }
 \arguments{
-\item{sys.small}{A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.}
+\item{filtered.obj}{A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.}
 
-\item{ground.truth}{Ground truth signaling mechanisms present in sys.small.}
+\item{ground.truth}{Ground truth signaling mechanisms present in filtered.obj.}
 
 \item{assay}{The assay to run the SCC transformation on. Defaults to "RNA."}
 
diff --git a/man/RunCellToSystem.Rd b/man/RunCellToSystem.Rd
index 4a5e6fa..5913dc1 100644
--- a/man/RunCellToSystem.Rd
+++ b/man/RunCellToSystem.Rd
@@ -5,7 +5,7 @@
 \title{RunCellToSystem}
 \usage{
 RunCellToSystem(
-  sys.small,
+  filtered.obj,
   ground.truth,
   assay,
   blend = "mean",
@@ -14,9 +14,9 @@ RunCellToSystem(
 )
 }
 \arguments{
-\item{sys.small}{A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.}
+\item{filtered.obj}{A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.}
 
-\item{ground.truth}{Ground truth signaling mechanisms present in sys.small.}
+\item{ground.truth}{Ground truth signaling mechanisms present in filtered.obj.}
 
 \item{assay}{The assay to run the CellToSystem transformation on. Defaults to "RNA."}
 
diff --git a/man/RunNeighborhoodToCell.Rd b/man/RunNeighborhoodToCell.Rd
index aef0e6a..3876c49 100644
--- a/man/RunNeighborhoodToCell.Rd
+++ b/man/RunNeighborhoodToCell.Rd
@@ -5,7 +5,7 @@
 \title{RunNeighborhoodToCell}
 \usage{
 RunNeighborhoodToCell(
-  sys.small,
+  filtered.obj,
   ground.truth,
   assay,
   meta.data.to.map,
@@ -15,9 +15,9 @@ RunNeighborhoodToCell(
 )
 }
 \arguments{
-\item{sys.small}{A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.}
+\item{filtered.obj}{A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.}
 
-\item{ground.truth}{Ground truth signaling mechanisms present in sys.small.}
+\item{ground.truth}{Ground truth signaling mechanisms present in filtered.obj.}
 
 \item{assay}{The assay to run the SCC transformation on. Defaults to "RNA."}
 
diff --git a/man/RunSystemToCell.Rd b/man/RunSystemToCell.Rd
index 97c73e8..4e602a9 100644
--- a/man/RunSystemToCell.Rd
+++ b/man/RunSystemToCell.Rd
@@ -5,7 +5,7 @@
 \title{RunSystemToCell}
 \usage{
 RunSystemToCell(
-  sys.small,
+  filtered.obj,
   ground.truth,
   assay,
   blend = "mean",
@@ -14,9 +14,9 @@ RunSystemToCell(
 )
 }
 \arguments{
-\item{sys.small}{A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.}
+\item{filtered.obj}{A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.}
 
-\item{ground.truth}{Ground truth signaling mechanisms present in sys.small.}
+\item{ground.truth}{Ground truth signaling mechanisms present in filtered.obj.}
 
 \item{assay}{The assay to run the SystemToCell transformation on. Defaults to "RNA."}
 
diff --git a/man/compute_edgelist.Rd b/man/compute_edgelist.Rd
index f79e8d6..240a4bc 100644
--- a/man/compute_edgelist.Rd
+++ b/man/compute_edgelist.Rd
@@ -4,10 +4,10 @@
 \alias{compute_edgelist}
 \title{Compute an edgelist based on the spatial coordinates}
 \usage{
-compute_edgelist(sys.small, position.x, position.y, k = 4, rad.set = NULL)
+compute_edgelist(filtered.obj, position.x, position.y, k = 4, rad.set = NULL)
 }
 \arguments{
-\item{sys.small}{A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.}
+\item{filtered.obj}{A filtered Seurat object. The active identity will be used to define populations for connectomic sampling and crossings.}
 
 \item{position.x}{string. Optional. Default: NULL. The name of the meta.data column specifying location on the spatial x-axis. Only required for spatial omics data.}