diff --git a/bin/bff.R b/bin/bff.R
index b0ea7af..bc31c06 100755
--- a/bin/bff.R
+++ b/bin/bff.R
@@ -18,7 +18,7 @@ parser$add_argument("--methodsForConsensus", help='By default, a consensus call
 parser$add_argument("--cellbarcodeWhitelist", help='A vector of expected cell barcodes. This allows reporting on the total set of expected barcodes, not just those in the filtered count matrix',default=NULL)
 parser$add_argument("--metricsFile", help='If provided, summary metrics will be written to this file.', default="metrics_cell_hash_r.csv")
 parser$add_argument("--doTSNE", help='If true, tSNE will be run on the resulting hashing calls after each caller.', default=TRUE)
-parser$add_argument("--preprocess", help='If true, the PreProcess function by CellHashR is executed', default=FALSE)
+parser$add_argument("--preprocess_bff", help='If given, the PreProcess function by CellHashR is executed', action="store_true")
 parser$add_argument("--barcodeWhitelist", help='A vector of barcode names to retain, used for preprocessing step', default=NULL)
 parser$add_argument("--doHeatmap", help='f true, Seurat::HTOHeatmap will be run on the results of each caller.', default=TRUE)
 parser$add_argument("--perCellSaturation", help='An optional dataframe with the columns cellbarcode and saturation',default=NULL)
@@ -31,6 +31,7 @@ parser$add_argument("--assignmentOutBff", help="Prefix name for the file contain
 parser$add_argument("--outputdir", help='Output directory')
 args <- parser$parse_args()
 
+print(args$preprocess_bff)
 #Parameters originally Null
 methodsForConsensus <- args$methodsForConsensus
 if(is.null(methodsForConsensus)){
@@ -62,7 +63,7 @@ Argument <- c("HTO-File", "methods", "methodsForConsensus", "cellbarcodeWhitelis
 Value <- c(args$fileHto, args$methods, methodsForConsensus, cellbarcodeWhitelist, args$metricsFile, perCellSaturation, majorityConsensusThreshold, callerDisagreementThreshold, args$doTSNE, args$doHeatmap,args$chemistry)
 params <- data.frame(Argument, Value)
 
-if(as.logical(args$preprocess)){
+if(args$preprocess_bff == TRUE){
   print("Preprocessing activated")
   print(args$preprocess)
   #get barcodes
@@ -90,7 +91,7 @@ if (!is.null(args$methodsForConsensus)) {
 perCell_args <- args$perCellSaturation
 perCell <- ifelse(perCell_args == "null" || perCell_args == "Null", NULL, perCell_args)
 
-if(args$methodsForConsensus=="bff_raw" || args$methodsForConsensus=="bff_cluster" || args$methodsForConsensus=="bff_raw,bff_cluster" || is.null(args$methodsForConsensus)  )
+if(args$methodsForConsensus=="bff_raw" || args$methodsForConsensus=="bff_cluster" || args$methodsForConsensus=="bff_raw,bff_cluster" || args$methodsForConsensus=="bff_cluster,bff_raw"|| is.null(args$methodsForConsensus)  )
   #Only Bff in its different variations is available
   if (args$methods == "bff_raw") {
     print("Executing BFF raw")
diff --git a/bin/summary_hash.py b/bin/summary_hash.py
index fac8ea8..f93bb27 100755
--- a/bin/summary_hash.py
+++ b/bin/summary_hash.py
@@ -18,6 +18,7 @@
 parser.add_argument("--generate_mudata", help="Generate mudata", action='store_true')
 parser.add_argument("--read_rna_mtx", help="10x-Genomics-formatted mtx directory for gene expression", default=None)
 parser.add_argument("--read_hto_mtx", help="10x-Genomics-formatted mtx directory for HTO expression", default=None)
+parser.add_argument("--sampleId", help="sampleID if multiple samples are demultiplexed", default=None)
 
 args = parser.parse_args()
 
@@ -28,12 +29,15 @@ def demuxem_summary(demuxem_res, raw_adata, raw_mudata):
     for x in demuxem_res:
         obs_res_dir = os.path.join(x, [filename for filename in os.listdir(x) if filename.endswith("_obs.csv")][0])
         obs_res = pd.read_csv(obs_res_dir)
-        obs_res.loc[:, "barcodekey"] = obs_res["barcodekey"].apply(lambda x: x + "-1")
-        demuxem_assign = obs_res[["barcodekey", "assignment"]]
-        demuxem_assign.columns = ["Barcode",os.path.basename(x)]
-        demuxem_assign.set_index("Barcode", inplace=True)
+        obs_res.rename(columns={obs_res.columns[0]: "Barcode"}, inplace=True)
+        demuxem_assign = obs_res[["Barcode", "assignment"]]
+        demuxem_assign.columns = ["Barcode", os.path.basename(x)]
+        #demuxem_assign.loc[:, "Barcode"] = demuxem_assign["Barcode"].apply(lambda x: x + "-1")
+        #demuxem_assign["Barcode"] = demuxem_assign["Barcode"].astype(str) + "-1"
+        demuxem_assign.index = demuxem_assign.Barcode
+        demuxem_assign = demuxem_assign.drop(columns=['Barcode'])
         assign.append(demuxem_assign)
-        
+        print(assign)
         if raw_adata is not None:
             adata = raw_adata.copy()
             adata.obs = adata.obs.merge(demuxem_assign, left_index=True, right_index=True, how='left')
@@ -51,17 +55,21 @@ def demuxem_summary(demuxem_res, raw_adata, raw_mudata):
             mudata.update()
             mudata.write("hash_summary/mudata/mudata_with_mudata_"+ os.path.basename(x)+".h5mu") 
 
-        demuxem_classi = obs_res[["barcodekey", "demux_type"]]
+        demuxem_classi = obs_res[["Barcode", "demux_type"]]
         demuxem_classi.columns = ["Barcode", os.path.basename(x)]
         demuxem_classi = demuxem_classi.replace("unknown", "negative")
-        demuxem_classi.set_index("Barcode", inplace=True)
+        #demuxem_classi.loc[:, "Barcode"] = demuxem_classi["Barcode"].apply(lambda x: x + '-1')
+        demuxem_classi.index = demuxem_classi.Barcode
+        demuxem_classi = demuxem_classi.drop(columns=['Barcode'])
         classi.append(demuxem_classi)
 
         params_dir = os.path.join(x, [filename for filename in os.listdir(x) if filename.endswith("params.csv")][0])
         params_res = pd.read_csv(params_dir, keep_default_na=False, index_col=0)
+        #params_res.rename(columns={params_res.columns[1]: os.path.basename(x)}, inplace=True)
         params_res.columns = [os.path.basename(x)]
         params.append(params_res)
         
+
     assign = pd.concat(assign, axis=1)
     assign.to_csv("hash_summary/demuxem_assignment.csv", quoting=False)
 
@@ -104,9 +112,13 @@ def hashsolo_summary(hashsolo_res, raw_adata, raw_mudata):
             mudata.write("hash_summary/mudata/mudata_with_mudata_"+ os.path.basename(x)+".h5mu") 
 
         hashsolo_classi = obs_res[["most_likely_hypothesis"]]
-        hashsolo_classi['most_likely_hypothesis'] = hashsolo_classi['most_likely_hypothesis'].apply(lambda x: 'negative' if x == 0.0 else ('singlet' if x == 1.0 else 'doublet'))
-        hashsolo_classi.columns = [os.path.basename(x)]
-        classi.append(hashsolo_classi)
+        hashsolo_classi_copy = hashsolo_classi.copy()
+        hashsolo_classi_copy.loc[hashsolo_classi_copy["most_likely_hypothesis"] == 0.0 , "most_likely_hypothesis"] = "negative"
+        hashsolo_classi_copy.loc[hashsolo_classi_copy["most_likely_hypothesis"] == 1.0 , "most_likely_hypothesis"] = "singlet"
+        hashsolo_classi_copy.loc[hashsolo_classi_copy["most_likely_hypothesis"] == 2.0 , "most_likely_hypothesis"] = "doublet"
+        
+        hashsolo_classi_copy.columns = [os.path.basename(x)]
+        classi.append(hashsolo_classi_copy)
     
         params_dir = os.path.join(x, [filename for filename in os.listdir(x) if filename.endswith("params.csv")][0])
         params_res = pd.read_csv(params_dir, keep_default_na=False, index_col=0)
@@ -157,6 +169,7 @@ def hasheddrops_summary(hasheddrops_res, raw_adata, raw_mudata):
             
             mudata.write("hash_summary/mudata/mudata_with_mudata_"+ os.path.basename(x)+".h5mu") 
 
+
         hasheddrops_classi = obs_res[["Barcode", "Classification"]]
         hasheddrops_classi = hasheddrops_classi.rename(columns={"Classification": os.path.basename(x)})
         classi.append(hasheddrops_classi)
@@ -184,6 +197,7 @@ def multiseq_summary(multiseq_res, raw_adata, raw_mudata):
         multiseq_assign.columns = ["Barcode", os.path.basename(x)]
         multiseq_assign.set_index("Barcode", inplace=True)
         multiseq_assign.replace({"Doublet": "doublet", "Negative": "negative"}, inplace=True)
+
         assign.append(multiseq_assign)
         
         if raw_adata is not None:
@@ -203,6 +217,7 @@ def multiseq_summary(multiseq_res, raw_adata, raw_mudata):
             mudata.update()
             mudata.write("hash_summary/mudata/mudata_with_mudata_"+ os.path.basename(x)+".h5mu") 
 
+
         params_dir = os.path.join(x, [filename for filename in os.listdir(x) if filename.endswith("params.csv")][0])
         params_res = pd.read_csv(params_dir, usecols=[1, 2], keep_default_na=False, index_col=0)
         params_res.columns = [os.path.basename(x)]
@@ -212,7 +227,7 @@ def multiseq_summary(multiseq_res, raw_adata, raw_mudata):
     assign.to_csv("hash_summary/multiseq_assignment.csv", quoting=False)
 
     classi = assign.copy()
-    classi[~classi.isin(["doublet", "negative"])] = "singlet"
+    classi[(classi != "doublet") & (classi != "negative")] = "singlet"
     classi.to_csv("hash_summary/multiseq_classification.csv", quoting=False)
 
     params = pd.concat(params, axis=1)
@@ -226,8 +241,10 @@ def htodemux_summary(htodemux_res, raw_adata, raw_mudata):
         obs_res_dir = os.path.join(x, [filename for filename in os.listdir(x) if filename.endswith("_assignment_htodemux.csv")][0])
         htodemux_assign = pd.read_csv(obs_res_dir)
         htodemux_assign.columns = ["Barcode", os.path.basename(x)]
-        htodemux_assign.replace({"Doublet": "doublet", "Negative": "negative"}, inplace=True)
-        htodemux_assign.set_index("Barcode", inplace=True)
+        htodemux_assign.replace("Doublet", "doublet", inplace=True)
+        htodemux_assign.replace("Negative", "negative", inplace=True)
+        htodemux_assign.index = htodemux_assign.Barcode
+        htodemux_assign = htodemux_assign.drop(columns=['Barcode'])
         assign.append(htodemux_assign)
 
         if raw_adata is not None:
@@ -240,18 +257,22 @@ def htodemux_summary(htodemux_res, raw_adata, raw_mudata):
 
         if raw_mudata is not None:
             mudata = raw_mudata.copy()
-            mudata['rna'].obs = mudata['rna'].obs.merge(htodemux_assign, left_index=True, right_index=True, how='left')
+            mudata['rna'].obs = mudata['rna'].obs.merge(htodemux_assign, left_index=True, right_on='Barcode', how='left').set_index('Barcode')
             mudata['rna'].obs.rename(columns={mudata['rna'].obs.columns[0]: 'donor'}, inplace=True)
             mudata['rna'].obs.donor = mudata['rna'].obs.donor.fillna("negative")
             mudata['rna'].obs.donor = mudata['rna'].obs.donor.astype(str)
             mudata.update()
             mudata.write("hash_summary/mudata/mudata_with_mudata_"+ os.path.basename(x)+".h5mu") 
 
+
         obs_res_dir = os.path.join(x, [filename for filename in os.listdir(x) if filename.endswith("_classification_htodemux.csv")][0])
         htodemux_classi = pd.read_csv(obs_res_dir)
         htodemux_classi.columns = ["Barcode", os.path.basename(x)]
-        htodemux_classi.replace({"Doublet": "doublet", "Negative": "negative", "Singlet": "singlet"}, inplace=True)
-        htodemux_classi.set_index("Barcode", inplace=True)
+        htodemux_classi.replace("Singlet", "singlet", inplace=True)
+        htodemux_classi.replace("Doublet", "doublet", inplace=True)
+        htodemux_classi.replace("Negative", "negative", inplace=True)
+        htodemux_classi.index = htodemux_classi.Barcode
+        htodemux_classi = htodemux_classi.drop(columns=['Barcode'])
         classi.append(htodemux_classi)
 
         params_dir = os.path.join(x, [filename for filename in os.listdir(x) if filename == "params.csv"][0])
@@ -267,24 +288,21 @@ def htodemux_summary(htodemux_res, raw_adata, raw_mudata):
     
     params = pd.concat(params, axis=1)
     params.to_csv("hash_summary/htodemux_params.csv")
-
-
-
+        
 def gmm_summary(gmmDemux_res,raw_adata, raw_mudata):
     classi = []
     assign = []
     params = []
     for x in gmmDemux_res:
         obs_res_dir = os.path.join(x, [filename for filename in os.listdir(x) if filename.endswith("GMM_full.csv")][0])
-         
         #we get the number of hashes used for the experiment from the parameters
         #so that we now how many kinds of singlets we could find in the assignment
         params_dir = os.path.join(x, "params.csv")
         params_res = pd.read_csv(params_dir,index_col=False)
         params_res.columns = ["Argument", os.path.basename(x)]
         params.append(params_res)
-
         
+        ##### Get number of hashes used for the experiment
         result_row = params_res[params_res['Argument'].str.contains('hto_name_gmm', case=False, na=False)]
         hashes_used = ""
         if not result_row.empty:
@@ -293,46 +311,75 @@ def gmm_summary(gmmDemux_res,raw_adata, raw_mudata):
             print("No row contains the number of hashes")
         hashes = hashes_used.split(',')
         number_of_hashes = len(hashes)
-        #GMM assignement file
+        #-----------------
+
+        #GMM assignement file - results GMM
         gmm_classi = pd.read_csv(obs_res_dir)
-        #GMM full is the name given by GMM_demux per default to all results
+        
+
+        #GMM full is the name given by GMM_demux per default to all results 
+        ##classif_file - contains the mapping of results
         classification_config = os.path.join(x, "GMM_full.config")
         classif_file = pd.read_csv(classification_config,header=None)
         
         #Classification and Assigment come from the same file
         gmm_dt = pd.DataFrame(gmm_classi)
         classification_dt = pd.DataFrame(classif_file)
-
+        
         #change column names
         classification_dt = classification_dt.rename(columns={0: "Cluster_id", 1: "assignment"})
-
+        
         gmm_dt = gmm_dt.rename(columns={"Unnamed: 0": "Barcode"})
+        
         #Create classification following the assignment found for the barcodes
         #we keep the original assigment and add a classification column
         def _classify_hash(row,number_hashes):
+            print(f"current row: {row}")
             if row == 0:
                 return 'negative'
-            elif 0 > row <= number_hashes:
+            elif row > 0 and row <= number_hashes:
+                print("singlet found")
                 return 'singlet'
             else:
                 return 'doublet'
 
         classification_dt['Classification'] = classification_dt['Cluster_id'].apply(lambda x: _classify_hash(x, number_of_hashes))
-
+        
         #Compare classification guide file with classification found
-        merged = pd.merge(classification_dt, gmm_dt, on='Cluster_id', how='left')
+        #merged = pd.merge(classification_dt, gmm_dt, on='Cluster_id', how='left')
+        new_rows = []
+        for index, row in gmm_dt.iterrows():
+            cluster_id = row['Cluster_id']
+            matching_row_map = classification_dt[classification_dt['Cluster_id'] == cluster_id]
+            if not matching_row_map.empty:
+                # Extract assignment and classification values from the matching row in df2
+                assignment_gmm = matching_row_map.iloc[0]['assignment']
+                classification_gmm = matching_row_map.iloc[0]['Classification']
+
+                new_row = {
+                'Barcode': row['Barcode'],
+                'Cluster_id': cluster_id,
+                'assignment': assignment_gmm,
+                'Classification': classification_gmm
+                }
+                new_rows.append(new_row)
+        merged = pd.DataFrame(new_rows)
+        
+        merged['assignment'] = merged.apply(lambda row: 'doublet' if 'doublet' in row['Classification'] else row['assignment'], axis=1)
+        print(merged)
+
         gmm_dt['Classification'] = merged['Classification']
         gmm_dt['Assignment'] = merged['assignment']
         #instead of multiple hashes, add doublet
-
         gmm_dt["Assignment"] = gmm_dt["Assignment"].apply(lambda x: "doublet" if "-" in x else x)
         classification_dt['Classification'] = classification_dt['Classification'].str.replace(' ', '')
+        
+        
         #Assigment for GMM-Demux
         #'Cluster_id',
         gmm_dt_assign = gmm_dt.drop(['Cluster_id','Confidence','Classification' ], axis=1)
         gmm_dt_assign['Assignment'] = gmm_dt_assign['Assignment'].str.replace(' ', '')
         gmm_dt_assign.columns = ["Barcode", os.path.basename(x)]
-   
         assign.append(gmm_dt_assign)
 
         if raw_adata is not None:
@@ -357,7 +404,11 @@ def _classify_hash(row,number_hashes):
         gmm_dt_classi.columns =["Barcode", os.path.basename(x)]
         classi.append(gmm_dt_classi)
 
-        
+        params_dir = os.path.join(x, "params.csv")
+        params_res = pd.read_csv(params_dir,index_col=False)
+        params_res.columns = ["Argument", os.path.basename(x)]
+        params.append(params_res)
+
     classi_df = pd.concat(classi, axis=1, join="outer")
     classi_df.to_csv("hash_summary"  +"/GMM_classification.csv", index=False)
     
@@ -384,6 +435,7 @@ def bff_summary(bff_res,raw_adata, raw_mudata):
             df = pd.DataFrame(columns=column_names)
             classi.append(df)
             assign.append(df)
+
         else:
             #df contain data and we save it in the same way
             dt_assign = data_bff.copy()
@@ -392,12 +444,16 @@ def bff_summary(bff_res,raw_adata, raw_mudata):
             for column in column_names:
                 if column in dt_assign.columns:
                     dt_assign = dt_assign.drop([column], axis=1)
+            #dt_assign.loc[dt_assign["consensuscall"] == "Singlet", "consensuscall"] = "singlet"
             dt_assign.loc[dt_assign["consensuscall"] == "Doublet", "consensuscall"] = "doublet"
             dt_assign.loc[dt_assign["consensuscall"] == "Negative", "consensuscall"] = "negative"
             dt_assign['consensuscall'] = dt_assign['consensuscall'].astype('category')
             dt_assign = dt_assign.rename(columns={"cellbarcode": "Barcode", "consensuscall": os.path.basename(x)})
-            dt_assign['Barcode'] = dt_assign['Barcode'].apply(lambda x: str(x) + "-1")
+            dt_assign["Barcode"] = dt_assign["Barcode"].apply(lambda x: x + "-1" if isinstance(x, str) else x)
+            
             assign.append(dt_assign)
+            print("assign bff barcodes")
+            print(assign)
             if raw_adata is not None:
                 adata = raw_adata.copy()
                 adata.obs = adata.obs.merge(dt_assign, left_index=True, right_index=True, how='left')
@@ -416,24 +472,24 @@ def bff_summary(bff_res,raw_adata, raw_mudata):
                 mudata.update()
                 mudata.write("hash_summary/mudata/mudata_with_mudata_"+ os.path.basename(x)+".h5mu") 
 
-
+                
             dt_classi = data_bff.copy()
             column_names_class = ["bff_raw","bff_cluster","consensuscall"]
             for column in column_names_class:
-                if column in dt_classi.columns:
+                if column in dt_assign.columns:
                     dt_classi = dt_classi.drop([column], axis=1)
             dt_classi.loc[dt_classi["consensuscall.global"] == "Singlet", "consensuscall.global"] = "singlet"
             dt_classi.loc[dt_classi["consensuscall.global"] == "Doublet", "consensuscall.global"] = "doublet"
             dt_classi.loc[dt_classi["consensuscall.global"] == "Negative", "consensuscall.global"] = "negative"
             dt_classi = dt_classi.rename(columns={"cellbarcode": "Barcode", "consensuscall.global": os.path.basename(x)})
-            dt_classi['Barcode'] = dt_classi['Barcode'].apply(lambda x: str(x) + "-1")
+            dt_classi["Barcode"] = dt_classi["Barcode"].apply(lambda x: x + "-1" if isinstance(x, str) else x)
+            print("classification bff barcodes")
+            print(assign)
             classi.append(dt_classi)
-            
+
         params_dir = os.path.join(x, [filename for filename in os.listdir(x) if filename == "params.csv"][0])
-        params_res = pd.read_csv(params_dir,sep=';',index_col=None)
-        params_res = params_res.drop(['Unnamed: 0' ], axis=1)  
-        params_res = params_res.dropna(subset=['Argument', 'Value'], how='any') 
-        params_res.columns = ["Argument",os.path.basename(x)]
+        params_res = pd.read_csv(params_dir, usecols=[1, 2], keep_default_na=False, index_col=0)     
+        params_res.columns = [os.path.basename(x)]
         params.append(params_res)
 
     classi_df = pd.concat(classi, axis=1, join="outer")
@@ -453,14 +509,13 @@ def bff_summary(bff_res,raw_adata, raw_mudata):
         os.mkdir("hash_summary")
 
     if args.generate_anndata is True:
-        if not os.path.exists("hash_summary/adata"):
-            os.mkdir("hash_summary/adata")
+        os.mkdir("hash_summary/adata")
         adata = sc.read_10x_mtx(args.read_rna_mtx)
 
     if args.generate_mudata is True:
-        if not os.path.exists("hash_summary/mudata"):
-            os.mkdir("hash_summary/mudata")
+        os.mkdir("hash_summary/mudata")
         rna_data = sc.read_10x_mtx(args.read_rna_mtx)
+        path_hto = args.read_hto_mtx
         hto_data = sc.read_10x_mtx(args.read_hto_mtx, gex_only=False)
         mudata = MuData({"rna": rna_data, "hto": hto_data })
         
@@ -483,6 +538,7 @@ def bff_summary(bff_res,raw_adata, raw_mudata):
     if args.htodemux is not None:
         htodemux_res = args.htodemux.split(':')
         htodemux_summary(htodemux_res, adata, mudata)
+    
 
     if args.gmm_demux is not None:
         gmmDemux_res = args.gmm_demux.split(':')
@@ -492,25 +548,29 @@ def bff_summary(bff_res,raw_adata, raw_mudata):
         bff_res = args.bff.split(':')
         bff_summary(bff_res, adata, mudata)
 
+
     # Read and combine assignment files
     assignment = [file for file in os.listdir("hash_summary") if file.endswith("_assignment.csv")]
-    assignment_all = pd.DataFrame()
+    assignment_all = pd.read_csv(os.path.join("hash_summary", assignment[0]))
+    
     
-    for file in assignment:
-        file_path = os.path.join("hash_summary", file)
-        df = pd.read_csv(file_path, usecols=['Barcode', pd.read_csv(file_path).columns[1]])
-        assignment_all = pd.concat([assignment_all, df.set_index('Barcode')], axis=1, join='outer')
-
-    # Reset the index to have 'Barcode' as a regular column
-    assignment_all.reset_index(inplace=True)
 
+    if len(assignment) > 1:
+        for df in assignment[1:]:
+            
+            df = pd.read_csv(os.path.join("hash_summary", df))
+            assignment_all = pd.merge(assignment_all, df, on='Barcode', how='outer')
+            #print("---------assignment all--------")
+            #print(assignment_all)
+            #print("-----------------")
+    
     assignment_all.to_csv("hash_summary/hashing_assignment_all.csv", index=False)
 
     # Read and combine classification files
     classification = [file for file in os.listdir("hash_summary") if file.endswith("_classification.csv")]
     classification_all = pd.read_csv(os.path.join("hash_summary", classification[0]))
-    
-    if len(classification) > 1: 
+
+    if len(classification) > 1:
         for df in classification[1:]:
             df = pd.read_csv(os.path.join("hash_summary", df))
             classification_all = pd.merge(classification_all, df, on='Barcode', how='outer')
diff --git a/modules/hash_demulti/bff.nf b/modules/hash_demulti/bff.nf
index c656133..37d6a36 100644
--- a/modules/hash_demulti/bff.nf
+++ b/modules/hash_demulti/bff.nf
@@ -29,13 +29,14 @@ process bff {
 
     script:
 
+        def run_preprocess = preprocess_bff != 'False' ? " --preprocess_bff" : ''
         """
         mkdir bff_${sampleId}
         bff.R --fileHto hto_data --methods $methods --methodsForConsensus $methodsForConsensus \
-                --cellbarcodeWhitelist $cellbarcodeWhitelist --metricsFile bff_${sampleId}_$metricsFile \
-                --doTSNE $doTSNE --doHeatmap $doHeatmap --perCellSaturation $perCellSaturation --majorityConsensusThreshold $majorityConsensusThreshold \
-                --chemistry $chemistry --callerDisagreementThreshold $callerDisagreementThreshold --outputdir bff_${sampleId} \
-                --assignmentOutBff $assignmentOutBff --preprocess $preprocess_bff --barcodeWhitelist $barcodeWhitelist
+        --cellbarcodeWhitelist $cellbarcodeWhitelist --metricsFile bff_${sampleId}_$metricsFile \
+        --doTSNE $doTSNE --doHeatmap $doHeatmap --perCellSaturation $perCellSaturation --majorityConsensusThreshold $majorityConsensusThreshold \
+        --chemistry $chemistry --callerDisagreementThreshold $callerDisagreementThreshold --outputdir bff_${sampleId} \
+        --assignmentOutBff $assignmentOutBff ${run_preprocess} --barcodeWhitelist $barcodeWhitelist
         """
 }
 
diff --git a/modules/hash_demulti/gmm_demux.nf b/modules/hash_demulti/gmm_demux.nf
index bb98533..5eb2d6e 100644
--- a/modules/hash_demulti/gmm_demux.nf
+++ b/modules/hash_demulti/gmm_demux.nf
@@ -61,7 +61,7 @@ process gmm_demux{
 
 workflow gmm_demux_hashing{
 take: 
-        hto_matrix
+        input_list
   main:
         summary = params.summary
         report_gmm = params.report_gmm
@@ -70,7 +70,7 @@ take:
         threshold_gmm = params.threshold_gmm
         ambiguous = params.ambiguous
 
-        gmm_demux(hto_matrix,summary,report_gmm,mode,extract,threshold_gmm,ambiguous)
+        gmm_demux(input_list,summary,report_gmm,mode,extract,threshold_gmm,ambiguous)
   
   emit:
         gmm_demux.out.collect()
diff --git a/modules/hash_demultiplexing.nf b/modules/hash_demultiplexing.nf
index ad32ef9..542bac5 100644
--- a/modules/hash_demultiplexing.nf
+++ b/modules/hash_demultiplexing.nf
@@ -179,8 +179,12 @@ workflow hash_demultiplexing {
     if (params.gmmDemux == 'True') {
         Channel.fromPath(params.multi_input) \
                 | splitCsv(header:true) \
-                | map { row-> tuple(row.sampleId, params.hto_matrix_gmm_demux == 'raw' ? row.hto_matrix_raw : row.hto_matrix_filtered, row.hto_name_gmm) }
-                | gmm_demux_hashing
+
+                | map { row-> tuple(row.sampleId, 
+                        params.hto_matrix_gmm_demux == "raw" ? row.hto_matrix_raw : row.hto_matrix_filtered, 
+                        row.hto_name_gmm )}
+                | set {input_list_gmm_demux}
+                | gmm_demux_hashing(input_list_gmm_demux)
         gmmDemux_out = gmm_demux_hashing.out
     }
     else {
diff --git a/modules/single/hash_demulti/bff.nf b/modules/single/hash_demulti/bff.nf
index 0e4082f..3070299 100644
--- a/modules/single/hash_demulti/bff.nf
+++ b/modules/single/hash_demulti/bff.nf
@@ -28,6 +28,7 @@ process bff {
         path "bff_${task.index}"
 
     script:
+        def run_preprocess = preprocess_bff != 'False' ? " --preprocess_bff" : ''
 
         """
         mkdir bff_${task.index}
@@ -35,7 +36,7 @@ process bff {
         --cellbarcodeWhitelist $cellbarcodeWhitelist --metricsFile bff_${task.index}_$metricsFile \
         --doTSNE $doTSNE --doHeatmap $doHeatmap --perCellSaturation $perCellSaturation --majorityConsensusThreshold $majorityConsensusThreshold \
         --chemistry $chemistry --callerDisagreementThreshold $callerDisagreementThreshold  --outputdir bff_${task.index} --assignmentOutBff $assignmentOutBff \
-        --preprocess $preprocess_bff --barcodeWhitelist $barcodeWhitelist
+        ${run_preprocess} --barcodeWhitelist $barcodeWhitelist
         """
 }
 
diff --git a/modules/single/hash_demulti/gmm_demux.nf b/modules/single/hash_demulti/gmm_demux.nf
index a2f2f4e..9febe1b 100644
--- a/modules/single/hash_demulti/gmm_demux.nf
+++ b/modules/single/hash_demulti/gmm_demux.nf
@@ -1,15 +1,17 @@
 #!/usr/bin/env nextflow
 nextflow.enable.dsl = 2
 
-process gmm_demux {
-    publishDir "$projectDir/$params.outdir/$sampleId/$params.mode/hash_demulti/gmm_demux", mode:'copy'
+
+process gmm_demux{
+    publishDir "$projectDir/$params.outdir/$params.mode/hash_demulti/gmm_demux", mode:'copy'
+
     label 'small_mem'
     conda "$projectDir/conda/gmm_demux.yml"
 
     input:
-        tuple val(sampleId), path(filtered_hto_matrix_dir), val(hto_name_gmm)
+        path filtered_hto_matrix_dir
         //HTO names as string separated by commas
-        //val hto_name_gmm
+        val hto_name_gmm
         //mode 2
         //need estimate number of cells in the single cell assay
         //obligatory
@@ -26,7 +28,9 @@ process gmm_demux {
         val ambiguous
 
     output:
-        path "gmm_demux_${sampleId}"
+
+        path "gmm_demux_${task.index}"
+        
 
     script:
         def extract_droplets = extract != 'None' ? " -x ${extract}" : ''
@@ -34,21 +38,21 @@ process gmm_demux {
 
         if (mode_GMM == 'csv') {
             """
-            mkdir gmm_demux_${sampleId}
-            touch gmm_demux_${sampleId}_$report_gmm
-
-            GMM-demux -c $filtered_hto_matrix_dir $hto_name_gmm -u $summary --report gmm_demux_${sampleId}_$report_gmm --full gmm_demux_${sampleId} $extract_droplets -t $threshold_gmm
-            gmm_demux_params.py --path_hto $filtered_hto_matrix_dir --hto_name_gmm $hto_name_gmm --summary $summary --report gmm_demux_${sampleId}_$report_gmm   --mode $mode_GMM  $extract_droplets --threshold_gmm $threshold_gmm $ambiguous_droplets  --outputdir gmm_demux_${sampleId}
-
+            mkdir gmm_demux_${task.index}
+            touch gmm_demux_${task.index}_$report_gmm
+            
+            GMM-demux -c $filtered_hto_matrix_dir $hto_name_gmm -u $summary --report gmm_demux_${task.index}_$report_gmm --full gmm_demux_${task.index} $extract_droplets -t $threshold_gmm
+            gmm_demux_params.py --path_hto $filtered_hto_matrix_dir --hto_name_gmm $hto_name_gmm --summary $summary --report gmm_demux_${task.index}_$report_gmm   --mode $mode_GMM  $extract_droplets --threshold_gmm $threshold_gmm $ambiguous_droplets  --outputdir gmm_demux_${task.index}
+            
             """
         }else {
             """
-            mkdir gmm_demux_${sampleId}
-            touch gmm_demux_${sampleId}_$report_gmm
-
-            GMM-demux $filtered_hto_matrix_dir $hto_name_gmm -u $summary -r gmm_demux_${sampleId}_$report_gmm --full gmm_demux_${sampleId} -o gmm_demux_${sampleId} $extract_droplets -t $threshold_gmm
-            gmm_demux_params.py --path_hto $filtered_hto_matrix_dir --hto_name_gmm $hto_name_gmm --summary $summary --report gmm_demux_${sampleId}_$report_gmm --mode $mode_GMM $extract_droplets  --threshold_gmm $threshold_gmm $ambiguous_droplets --outputdir gmm_demux_${sampleId}
-
+            mkdir gmm_demux_${task.index}
+            touch gmm_demux_${task.index}_$report_gmm
+            
+            GMM-demux $filtered_hto_matrix_dir $hto_name_gmm -u $summary -r gmm_demux_${task.index}_$report_gmm --full gmm_demux_${task.index} -o gmm_demux_${task.index} $extract_droplets -t $threshold_gmm
+            gmm_demux_params.py --path_hto $filtered_hto_matrix_dir --hto_name_gmm $hto_name_gmm --summary $summary --report gmm_demux_${task.index}_$report_gmm --mode $mode_GMM $extract_droplets  --threshold_gmm $threshold_gmm $ambiguous_droplets --outputdir gmm_demux_${task.index}
+            
             """
         }
 }
@@ -57,6 +61,7 @@ workflow gmm_demux_hashing {
 take:
         hto_matrix
   main:
+        hto_name_gmm = params.hto_name_gmm
         summary = params.summary
         report_gmm = params.report_gmm
         mode = params.mode_GMM
@@ -64,8 +69,9 @@ take:
         threshold_gmm = params.threshold_gmm
         ambiguous = params.ambiguous
 
-        gmm_demux(hto_matrix, summary, report_gmm, mode, extract, threshold_gmm, ambiguous)
 
+        gmm_demux(hto_matrix,hto_name_gmm,summary,report_gmm,mode,extract,threshold_gmm,ambiguous)
+  
   emit:
         gmm_demux.out.collect()
 }
diff --git a/test.config b/test.config
index 5c74183..f1c0044 100644
--- a/test.config
+++ b/test.config
@@ -1,8 +1,8 @@
 params {
   outdir = "result_test"
   // input for hashing-based deconvolution
-  hto_matrix_raw = "$projectDir/test_data/hto"
-  hto_matrix_filtered = "$projectDir/test_data/hto"
+  hto_matrix_raw = "$projectDir/test_data/hto_gz"
+  hto_matrix_filtered = "$projectDir/test_data/hto_gz"
   rna_matrix_raw = "$projectDir/test_data/rna"
   rna_matrix_filtered = "$projectDir/test_data/rna"
 
@@ -23,4 +23,7 @@ params {
   // donor genotype file provided by popscle doesnt work on souporcell
   use_known_genotype = "False"
   ignore = "True"
+  //gmm_demux input
+  hto_name_gmm = "hto_1,hto_2"
+
 }
diff --git a/test_data/hto_gz/barcodes.tsv.gz b/test_data/hto_gz/barcodes.tsv.gz
new file mode 100644
index 0000000..dd04944
Binary files /dev/null and b/test_data/hto_gz/barcodes.tsv.gz differ
diff --git a/test_data/hto_gz/features.tsv.gz b/test_data/hto_gz/features.tsv.gz
new file mode 100644
index 0000000..38d469b
Binary files /dev/null and b/test_data/hto_gz/features.tsv.gz differ
diff --git a/test_data/hto_gz/matrix.mtx.gz b/test_data/hto_gz/matrix.mtx.gz
new file mode 100644
index 0000000..e9e65fc
Binary files /dev/null and b/test_data/hto_gz/matrix.mtx.gz differ
diff --git a/test_data/rna_gz/barcodes.tsv.gz b/test_data/rna_gz/barcodes.tsv.gz
new file mode 100644
index 0000000..dd04944
Binary files /dev/null and b/test_data/rna_gz/barcodes.tsv.gz differ
diff --git a/test_data/rna_gz/genes.tsv.gz b/test_data/rna_gz/genes.tsv.gz
new file mode 100644
index 0000000..c240cef
Binary files /dev/null and b/test_data/rna_gz/genes.tsv.gz differ
diff --git a/test_data/rna_gz/matrix.mtx.gz b/test_data/rna_gz/matrix.mtx.gz
new file mode 100644
index 0000000..d0e9510
Binary files /dev/null and b/test_data/rna_gz/matrix.mtx.gz differ