diff --git a/workflow/rules/bigstitcher.smk b/workflow/rules/bigstitcher.smk
index 7e3c0c4..62d39f2 100644
--- a/workflow/rules/bigstitcher.smk
+++ b/workflow/rules/bigstitcher.smk
@@ -269,6 +269,20 @@ rule fuse_dataset_spark:
             suffix="bigstitcher.xml",
         ),
         ijm=Path(workflow.basedir) / "macros" / "FuseImageMacroZarr.ijm",
+    params:
+        channel=lambda wildcards: "--channelId={channel}".format(
+            channel=get_stains(wildcards).index(wildcards.stain)
+        ),
+        block_size="--blockSize={bsx},{bsy},{bsz}".format(
+            bsx=config["bigstitcher"]["fuse_dataset"]["block_size_x"],
+            bsy=config["bigstitcher"]["fuse_dataset"]["block_size_y"],
+            bsz=config["bigstitcher"]["fuse_dataset"]["block_size_z"],
+        ),
+        block_size_factor="--blockScale={bsfx},{bsfy},{bsfz}".format(
+            bsfx=config["bigstitcher"]["fuse_dataset"]["block_size_factor_x"],
+            bsfy=config["bigstitcher"]["fuse_dataset"]["block_size_factor_y"],
+            bsfz=config["bigstitcher"]["fuse_dataset"]["block_size_factor_z"],
+        ),
     output:
         zarr=temp(
             directory(
@@ -315,4 +329,10 @@ rule fuse_dataset_spark:
     group:
         "preproc"
     shell:
-        "affine-fusion ..."
+        "affine-fusion --preserveAnisotropy -x {input.dataset_xml} "
+        " -o {output.zarr} -d /fused/s0 -s ZARR "
+        " --UINT16 --minIntensity 0 --maxIntensity 65535 "
+
+
+#        " --UINT8 --minIntensity 0 --maxIntensity 255 "
+"{params}"  # all the params
diff --git a/workflow/scripts/apply_basic_flatfield_corr_zarr.py b/workflow/scripts/apply_basic_flatfield_corr_zarr.py
index 01bdddf..acbbb4e 100644
--- a/workflow/scripts/apply_basic_flatfield_corr_zarr.py
+++ b/workflow/scripts/apply_basic_flatfield_corr_zarr.py
@@ -41,7 +41,7 @@
     #now we want to apply correction to all images
     #define a function to map
     def apply_basic_parallel(x):
-        return np.reshape(basic.transform(x.squeeze()),(1,1,img_shape[0],img_shape[1])).astype('int16')
+        return np.reshape(basic.transform(x.squeeze()),(1,1,img_shape[0],img_shape[1])).astype('uint16')
     arr_corr = da.map_blocks(apply_basic_parallel,arr_chan)
 
     chan_arr_list.append(arr_corr)
diff --git a/workflow/scripts/tif_to_zarr.py b/workflow/scripts/tif_to_zarr.py
index 1b8aa75..bfd4a86 100644
--- a/workflow/scripts/tif_to_zarr.py
+++ b/workflow/scripts/tif_to_zarr.py
@@ -59,7 +59,7 @@ def single_imread(*args):
 
 #rescale intensities, and recast 
 darr = darr * snakemake.params.intensity_rescaling
-darr = darr.astype('int16')
+darr = darr.astype('uint16')
 
 #now we can do the computation itself, storing to zarr
 print('writing images to zarr with dask')
diff --git a/workflow/scripts/tif_to_zarr_gcs.py b/workflow/scripts/tif_to_zarr_gcs.py
index c49a770..01a9af1 100644
--- a/workflow/scripts/tif_to_zarr_gcs.py
+++ b/workflow/scripts/tif_to_zarr_gcs.py
@@ -77,7 +77,7 @@ def build_zstack(gcs_uris,fs):
 
 #rescale intensities, and recast 
 darr = darr * snakemake.params.intensity_rescaling
-darr = darr.astype('int16')
+darr = darr.astype('uint16')
 
 #now we can do the computation itself, storing to zarr
 print('writing images to zarr with dask')
diff --git a/workflow/scripts/zarr_to_n5_bdv.py b/workflow/scripts/zarr_to_n5_bdv.py
index 48fb89e..1ed8675 100644
--- a/workflow/scripts/zarr_to_n5_bdv.py
+++ b/workflow/scripts/zarr_to_n5_bdv.py
@@ -99,7 +99,7 @@ def update_xml_h5_to_n5(in_xml,out_xml,in_n5):
     ds_list=[] #for setup-level attrs
     for ds in range(max_downsampling_layers):
         step=2**ds  #1,2,4,8.. 
-        zstack = da.squeeze(darr[tile_i,chan_i,:,::step,::step]).astype(np.int16)
+        zstack = da.squeeze(darr[tile_i,chan_i,:,::step,::step])
         print(f'writing to setup{setup_i}/timepoint0/s{ds}')
         with ProgressBar():
             zstack.to_zarr(n5_store,component=f'setup{setup_i}/timepoint0/s{ds}',overwrite=True,compute=True) 
@@ -111,6 +111,6 @@ def update_xml_h5_to_n5(in_xml,out_xml,in_n5):
     #add attributes for downsampling as a list, and datatype to the setup# level
     g = zarr.open_group(store=n5_store,path=f'setup{setup_i}',mode='r+')
     g.attrs['downsamplingFactors']=ds_list
-    g.attrs['dataType']='int16'
+    g.attrs['dataType']='uint16'