diff --git a/R/CellHashing.R b/R/CellHashing.R
index e9f6752..d3867ec 100644
--- a/R/CellHashing.R
+++ b/R/CellHashing.R
@@ -703,10 +703,11 @@ GetExampleMarkdown <- function(dest) {
 #' @param molInfoFile An optional path to the 10x molecule_info.h5.
 #' @param majorityConsensusThreshold This applies to calculating a consensus call when multiple algorithms are used. If NULL, then all non-negative calls must agree or that cell is marked discordant. If non-NULL, then the number of algorithms returning the top call is divided by the total number of non-negative calls. If this ratio is above the majorityConsensusThreshold, that value is selected. For example, when majorityConsensusThreshold=0.6 and the calls are: HTO-1,HTO-1,Negative,HTO-2, then 2/3 calls are for HTO-1, giving 0.66. This is greater than the majorityConsensusThreshold of 0.6, so HTO-1 is returned. This can be useful for situations where most algorithms agree, but a single caller fails.
 #' @param callerDisagreementThreshold If provided, the agreement rate will be calculated between each caller and the simple majority call, ignoring discordant and no-call cells. If any caller has an disagreement rate above this threshold, it will be dropped and the consensus call re-calculated. The general idea is to drop a caller that is systematically discordant.
+#' @param datatypeName For output from CellRanger >= 3.0 with multiple data types, the result of Seurat::Read10X is a list. You need to supply the name of the Antibody Capture
 #' @param title A title for the HTML report
 #' @importFrom rmdformats html_clean
 #' @export
-CallAndGenerateReport <- function(rawCountData, reportFile, callFile, rawFeatureMatrixH5 = NULL, barcodeWhitelist = NULL, barcodeBlacklist = c('no_match', 'total_reads', 'unmapped'), cellbarcodeWhitelist = 'inputMatrix', methods = c('bff_cluster', 'gmm_demux', 'dropletutils'), methodsForConsensus = NULL, minCountPerCell = 5, title = NULL, metricsFile = NULL, rawCountsExport = NULL, skipNormalizationQc = FALSE, keepMarkdown = FALSE, molInfoFile = NULL, majorityConsensusThreshold = NULL, callerDisagreementThreshold = NULL, doTSNE = TRUE) {
+CallAndGenerateReport <- function(rawCountData, reportFile, callFile, rawFeatureMatrixH5 = NULL, barcodeWhitelist = NULL, barcodeBlacklist = c('no_match', 'total_reads', 'unmapped'), cellbarcodeWhitelist = 'inputMatrix', methods = c('bff_cluster', 'gmm_demux', 'dropletutils'), methodsForConsensus = NULL, minCountPerCell = 5, title = NULL, metricsFile = NULL, rawCountsExport = NULL, skipNormalizationQc = FALSE, keepMarkdown = FALSE, molInfoFile = NULL, majorityConsensusThreshold = NULL, callerDisagreementThreshold = NULL, doTSNE = TRUE, datatypeName = NULL) {
   rmd <- system.file("rmd/cellhashR.rmd", package = "cellhashR")
   if (!file.exists(rmd)) {
     stop(paste0('Unable to find file: ', rmd))
diff --git a/R/Preprocessing.R b/R/Preprocessing.R
index d0fde9c..ff25771 100644
--- a/R/Preprocessing.R
+++ b/R/Preprocessing.R
@@ -116,7 +116,7 @@ ProcessCountMatrix <- function(rawCountData=NA, minCountPerCell = 5, barcodeWhit
                 print(paste0('Using: ', datatypeName))
                 barcodeData <- barcodeData[[datatypeName]]
             } else {
-                print('The data were generated from multiple data types, which are listed above. Please use the datatypeName argument to specify which assay to use')
+                stop('The data were generated from multiple data types, which are listed above. Please use the datatypeName argument to specify which assay to use')
             }
         }
 
diff --git a/inst/rmd/cellhashR.rmd b/inst/rmd/cellhashR.rmd
index af47022..f44bcdc 100644
--- a/inst/rmd/cellhashR.rmd
+++ b/inst/rmd/cellhashR.rmd
@@ -39,13 +39,13 @@ if (!file.exists(rawCountData)) {
   stop(paste0('Could not find rawCountData: ', rawCountData))
 }
 
-optionalVars <- c('barcodeWhitelist', 'barcodeBlacklist', 'cellbarcodeWhitelist', 'minCountPerCell', 'metricsFile', 'rawCountsExport', 'molInfoFile', 'rawFeatureMatrixH5', 'methodsForConsensus', 'majorityConsensusThreshold', 'callerDisagreementThreshold', 'doTSNE')
+optionalVars <- c('barcodeWhitelist', 'barcodeBlacklist', 'cellbarcodeWhitelist', 'minCountPerCell', 'metricsFile', 'rawCountsExport', 'molInfoFile', 'rawFeatureMatrixH5', 'methodsForConsensus', 'majorityConsensusThreshold', 'callerDisagreementThreshold', 'doTSNE', 'datatypeName')
 for (v in optionalVars) {
 	if (!exists(v)) {
 		if (v == 'minCountPerCell') {
 			minCountPerCell <- 5
         } else if (v == 'doTSNE') {
-            doTSNE <- TRUE
+            t <- TRUE
 		} else {
 			assign(v, NULL)
 		}
@@ -72,7 +72,7 @@ if (!is.null(metricsFile)) {
 
 ```{r QC}
 
-barcodeData <- ProcessCountMatrix(rawCountData = rawCountData, minCountPerCell = minCountPerCell, barcodeWhitelist = barcodeWhitelist, barcodeBlacklist = barcodeBlacklist, cellbarcodeWhitelist = cellbarcodeWhitelist, saveOriginalCellBarcodeFile = saveOriginalCellBarcodeFile, metricsFile = metricsFile)
+barcodeData <- ProcessCountMatrix(rawCountData = rawCountData, minCountPerCell = minCountPerCell, barcodeWhitelist = barcodeWhitelist, barcodeBlacklist = barcodeBlacklist, cellbarcodeWhitelist = cellbarcodeWhitelist, saveOriginalCellBarcodeFile = saveOriginalCellBarcodeFile, metricsFile = metricsFile, datatypeName = datatypeName)
 if (nrow(barcodeData) == 0) {
   stop('No passing barcodes')
 }
diff --git a/man/CallAndGenerateReport.Rd b/man/CallAndGenerateReport.Rd
index 28d6b8a..443bad1 100644
--- a/man/CallAndGenerateReport.Rd
+++ b/man/CallAndGenerateReport.Rd
@@ -23,7 +23,8 @@ CallAndGenerateReport(
   molInfoFile = NULL,
   majorityConsensusThreshold = NULL,
   callerDisagreementThreshold = NULL,
-  doTSNE = TRUE
+  doTSNE = TRUE,
+  datatypeName = NULL
 )
 }
 \arguments{
@@ -64,6 +65,8 @@ CallAndGenerateReport(
 \item{callerDisagreementThreshold}{If provided, the agreement rate will be calculated between each caller and the simple majority call, ignoring discordant and no-call cells. If any caller has an disagreement rate above this threshold, it will be dropped and the consensus call re-calculated. The general idea is to drop a caller that is systematically discordant.}
 
 \item{doTSNE}{If true, tSNE will be run on results as part of QC. This can be memory intensive and is not strictly needed, so it can be skipped if desired.}
+
+\item{datatypeName}{For output from CellRanger >= 3.0 with multiple data types, the result of Seurat::Read10X is a list. You need to supply the name of the Antibody Capture}
 }
 \description{
 Runs the default processing pipeline