From 1bc5084781f13efd5c6b775504f2a16f904ed7b9 Mon Sep 17 00:00:00 2001 From: Ali Khan Date: Mon, 3 Feb 2025 12:58:53 -0500 Subject: [PATCH] Imaris import support (#57) * uses zarr copy and new metadata import function to import from imaris prestitched --- README.md | 11 +- config/config.yml | 2 +- config/samples.tsv | 1 + poetry.lock | 3877 ++++++++++---------- workflow/Snakefile | 1 + workflow/rules/common.smk | 13 +- workflow/rules/imaris.smk | 126 + workflow/rules/ome_zarr.smk | 37 +- workflow/scripts/imaris_channel_to_zarr.py | 13 + workflow/scripts/imaris_to_metadata.py | 31 + workflow/scripts/imaris_to_ome_zarr.py | 101 + 11 files changed, 2262 insertions(+), 1951 deletions(-) create mode 100644 workflow/rules/imaris.smk create mode 100644 workflow/scripts/imaris_channel_to_zarr.py create mode 100644 workflow/scripts/imaris_to_metadata.py create mode 100644 workflow/scripts/imaris_to_ome_zarr.py diff --git a/README.md b/README.md index 4f28778..d7ba76f 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,13 @@ A Snakemake workflow for pre-processing single plane illumination microscopy (SP Takes TIF images (tiled or prestitched) and outputs a validated BIDS Microscopy dataset, with a multi-channel multi-scale OME-Zarr file for each scan, along with downsampled nifti images (in a derivatives folder). +## Supported inputs: + +SPIMprep supports a range of inputs, with the type of acquisition specified by including +the short-hand name (in bold below) as a substring in the acquisition tag. + - **`blaze`**: Raw Ultramicroscope Blaze OME TIFF files, either as 2D or 3D TIFF files + - **`prestitched`**: Prestitched images, as a stack of 2D TIF files (e.g. from LifeCanvas) + - **`imaris`**: Prestitched into a single Imaris (.ims) file. ## Requirements @@ -17,10 +24,6 @@ Takes TIF images (tiled or prestitched) and outputs a validated BIDS Microscopy - (Note: container will be automatically pulled when you run the workflow) - Python >= 3.11 - Lightsheet data: - - Raw Ultramicroscope Blaze OME TIFF files (include `blaze` in the acquisition tag) - - can be 2D or 3D TIFF files - - Prestitched TIFF files (include `prestitched` in the acquisition tag) - ## Usage diff --git a/config/config.yml b/config/config.yml index 83967e9..3ac7293 100644 --- a/config/config.yml +++ b/config/config.yml @@ -162,5 +162,5 @@ report: containers: - spimprep: 'docker://khanlab/spimprep-deps:v0.1.0' + spimprep: 'docker://khanlab/spimprep-deps:v0.1.1' diff --git a/config/samples.tsv b/config/samples.tsv index e2b4614..5cb0c00 100644 --- a/config/samples.tsv +++ b/config/samples.tsv @@ -1,3 +1,4 @@ subject sample acq stain_0 stain_1 stain_2 sample_path mouse1 brain blaze Lectin PI Abeta .test/dryrun/data lifecanvas1 brain prestitched PI Abeta n/a .test/dryrun/data +brown brain imaris1x Iba1 GFAP YOPRO .test/dryrun/data diff --git a/poetry.lock b/poetry.lock index 567aa71..78af66f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,145 +1,162 @@ -# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. 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path to extracted sample or path to provided input folder""" sample_path = Path(get_sample_path(wildcards)) - if is_remote_gcs(sample_path): return rules.cp_from_gcs.output.ome_dir.format(**wildcards) @@ -175,6 +174,8 @@ def get_input_sample(wildcards): # sample was a tar file, so point to the extracted folder return rules.extract_sample.output.ome_dir.format(**wildcards) + elif sample_path.suffixes[-1] == ".ims": + return get_sample_path_remote(wildcards) else: print(f"unsupported input: {sample_path}") @@ -277,12 +278,12 @@ def get_output_ome_zarr(acq_type): if config["use_zipstore"]: return { "zarr": bids( - root=work, + root=root, subject="{subject}", datatype="micr", sample="{sample}", acq=f"{{acq,[a-zA-Z0-9]*{acq_type}[a-zA-Z0-9]*}}", - suffix="SPIM.ome.zarr", + suffix="SPIM.ome.zarr.zip", ) } else: @@ -302,12 +303,12 @@ def get_output_ome_zarr(acq_type): if config["use_zipstore"]: return { "zarr": bids( - root=work, + root=root, subject="{subject}", datatype="micr", sample="{sample}", acq=f"{{acq,[a-zA-Z0-9]*{acq_type}[a-zA-Z0-9]*}}", - suffix="SPIM.ome.zarr", + suffix="SPIM.ome.zarr.zip", ) } else: @@ -333,7 +334,7 @@ def get_input_ome_zarr_to_nii(wildcards): datatype="micr", sample="{sample}", acq="{acq}", - suffix="SPIM.ome.zarr", + suffix="SPIM.ome.zarr.zip", ).format(**wildcards) else: return bids( diff --git a/workflow/rules/imaris.smk b/workflow/rules/imaris.smk new file mode 100644 index 0000000..b6f2c07 --- /dev/null +++ b/workflow/rules/imaris.smk @@ -0,0 +1,126 @@ +rule imaris_to_metadata: + input: + ims=get_input_sample, + output: + metadata_json=bids( + root=root, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq,[a-zA-Z0-9]*imaris[a-zA-Z0-9]*}", + suffix="SPIM.json", + ), + benchmark: + bids( + root="benchmarks", + datatype="imaris_to_metdata", + subject="{subject}", + sample="{sample}", + acq="{acq}", + suffix="benchmark.tsv", + ) + log: + bids( + root="logs", + datatype="prestitched_to_metdata", + subject="{subject}", + sample="{sample}", + acq="{acq}", + suffix="log.txt", + ), + group: + "preproc" + container: + config["containers"]["spimprep"] + script: + "../scripts/imaris_to_metadata.py" + + +rule imaris_channel_to_zarr: + input: + ims=get_input_sample, + params: + channel=lambda wildcards: get_stains(wildcards).index(wildcards.stain), + output: + zarr=temp( + bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + stain="{stain}", + suffix="imaris.zarr.zip", + ) + ), + log: + bids( + root="logs", + subject="{subject}", + datatype="imaris_channel_to_zarr", + sample="{sample}", + acq="{acq}", + stain="{stain}", + suffix="log.txt", + ), + container: + config["containers"]["spimprep"] + group: + "preproc" + threads: 1 + resources: + runtime=360, + mem_mb=1000, + shadow: + "minimal" + script: + "../scripts/imaris_channel_to_zarr.py" + + +rule imaris_to_ome_zarr: + input: + zarr=lambda wildcards: expand( + bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + stain="{stain}", + suffix="imaris.zarr.zip", + ), + stain=get_stains(wildcards), + allow_missing=True, + ), + metadata_json=rules.prestitched_to_metadata.output.metadata_json, + params: + max_downsampling_layers=config["ome_zarr"]["max_downsampling_layers"], + rechunk_size=config["ome_zarr"]["rechunk_size"], + scaling_method=config["ome_zarr"]["scaling_method"], + downsampling=config["bigstitcher"]["fuse_dataset"]["downsampling"], + stains=get_stains, + uri=get_output_ome_zarr_uri(), + storage_provider_settings=workflow.storage_provider_settings, + output: + **get_output_ome_zarr("imaris"), + log: + bids( + root="logs", + subject="{subject}", + datatype="imaris_to_ome_zarr", + sample="{sample}", + acq="{acq}", + suffix="log.txt", + ), + container: + config["containers"]["spimprep"] + group: + "preproc" + threads: config["total_cores"] + resources: + runtime=360, + mem_mb=config["total_mem_mb"], + shadow: + "minimal" + script: + "../scripts/imaris_to_ome_zarr.py" diff --git a/workflow/rules/ome_zarr.smk b/workflow/rules/ome_zarr.smk index 26606ac..0516fac 100644 --- a/workflow/rules/ome_zarr.smk +++ b/workflow/rules/ome_zarr.smk @@ -72,7 +72,7 @@ rule tif_stacks_to_ome_zarr: bids( root="logs", subject="{subject}", - datatype="zarr_to_ome_zarr", + datatype="tif_stacks_to_ome_zarr", sample="{sample}", acq="{acq}", suffix="log.txt", @@ -89,41 +89,6 @@ rule tif_stacks_to_ome_zarr: "../scripts/tif_stacks_to_ome_zarr.py" -rule ome_zarr_to_zipstore: - """ use 7zip to create a zipstore """ - input: - zarr=bids( - root=work, - subject="{subject}", - datatype="micr", - sample="{sample}", - acq="{acq}", - suffix="SPIM.ome.zarr", - ), - output: - zarr_zip=bids( - root=root, - subject="{subject}", - datatype="micr", - sample="{sample}", - acq="{acq}", - suffix="SPIM.ome.zarr.zip", - ), - log: - bids( - root="logs", - subject="{subject}", - datatype="micr", - sample="{sample}", - acq="{acq}", - suffix="log.txt", - ), - group: - "preproc" - shell: - "7z a -mx0 -tzip {output.zarr_zip} {input.zarr}/. &> {log}" - - rule ome_zarr_to_nii: input: **get_storage_creds(), diff --git a/workflow/scripts/imaris_channel_to_zarr.py b/workflow/scripts/imaris_channel_to_zarr.py new file mode 100644 index 0000000..5b475cb --- /dev/null +++ b/workflow/scripts/imaris_channel_to_zarr.py @@ -0,0 +1,13 @@ +import h5py +import hdf5plugin +import zarr +from sys import stdout #change this to log file later.. + +source = h5py.File(snakemake.input.ims, mode='r') + +store = zarr.ZipStore(snakemake.output.zarr,dimension_separator='/',mode='x') +dest = zarr.group(store) + +zarr.copy(source['DataSet/ResolutionLevel 0/TimePoint 0/Channel {chan}/Data'.format(chan=snakemake.params.channel)], dest,log=stdout,compressor=None) +source.close() + diff --git a/workflow/scripts/imaris_to_metadata.py b/workflow/scripts/imaris_to_metadata.py new file mode 100644 index 0000000..99062bd --- /dev/null +++ b/workflow/scripts/imaris_to_metadata.py @@ -0,0 +1,31 @@ +import h5py +import xmltodict +import json + +with h5py.File(snakemake.input.ims, "r") as hdf5_file: + xml_data = hdf5_file['DataSetInfo/OME Image Tags/Image 0'][:] + + +# Convert byte array to string and then to a dictionary +xml_str = bytes(xml_data).decode('utf-8', errors='ignore') # Decode byte array to string + +try: + xml_dict = xmltodict.parse(f"{xml_str}", namespace_separator=':') +except Exception as e: + print(f"Error parsing XML: {e}") + + +metadata={} +metadata['physical_size_x'] = float(xml_dict['root']['ca:CustomAttributes']['DataAxis0']['@PhysicalUnit']) +metadata['physical_size_y'] = float(xml_dict['root']['ca:CustomAttributes']['DataAxis1']['@PhysicalUnit']) +metadata['physical_size_z'] = abs(float(xml_dict['root']['ca:CustomAttributes']['DataAxis3']['@PhysicalUnit'])) +metadata['PixelSize'] = [ metadata['physical_size_z']/1000.0, metadata['physical_size_y']/1000.0, metadata['physical_size_x']/1000.0] #zyx since OME-Zarr is ZYX +metadata['PixelSizeUnits'] = 'mm' + +#write metadata to json +with open(snakemake.output.metadata_json, 'w') as fp: + json.dump(metadata, fp,indent=4) + + + + diff --git a/workflow/scripts/imaris_to_ome_zarr.py b/workflow/scripts/imaris_to_ome_zarr.py new file mode 100644 index 0000000..9ff692c --- /dev/null +++ b/workflow/scripts/imaris_to_ome_zarr.py @@ -0,0 +1,101 @@ +import h5py +import hdf5plugin +import zarr +import json +import zarr +import dask.array as da +from ome_zarr.writer import write_image +from ome_zarr.format import format_from_version +from ome_zarr.scale import Scaler +from dask.diagnostics import ProgressBar +from upath import UPath as Path +from lib.cloud_io import get_fsspec, is_remote + + + +stains=snakemake.params.stains +metadata_json=snakemake.input.metadata_json +downsampling=snakemake.params.downsampling +max_layer=snakemake.params.max_downsampling_layers #number of downsamplings by 2 to include in zarr +rechunk_size=snakemake.params.rechunk_size +out_zarr=snakemake.output.zarr +scaling_method=snakemake.params.scaling_method +uri = snakemake.params.uri + + + +# prepare metadata for ome-zarr +with open(metadata_json) as fp: + metadata = json.load(fp) + +voxdim = [float(metadata['physical_size_z']) * float(downsampling) / 1000.0, + float(metadata['physical_size_y']) * float(downsampling) / 1000.0, + float(metadata['physical_size_x']) * float(downsampling) / 1000.0] + + +coordinate_transformations = [] +#for each resolution (dataset), we have a list of dicts, transformations to apply.. +#in this case just a single one (scaling by voxel size) + +for l in range(max_layer+1): + + coordinate_transformations.append( [{'scale': [1,voxdim[0],(2**l)*voxdim[1],(2**l)*voxdim[2]], #image-pyramids in XY only + 'type': 'scale'}]) + + +axes = [{'name': 'c', 'type': 'channel'}] + [{'name': ax, 'type': 'space', 'unit': 'millimeter'} for ax in ['z','y','x'] ] + + +#init omero metadata +omero={key:val for key,val in snakemake.config['ome_zarr']['omero_metadata']['defaults'].items()} +omero['channels']=[] + + +if is_remote(uri): + fs_args={'storage_provider_settings':snakemake.params.storage_provider_settings,'creds':snakemake.input.creds} + fs = get_fsspec(uri,**fs_args) + #TODO: use zipstore here too if enabled + store = zarr.storage.FSStore(Path(uri).path,fs=fs,dimension_separator='/',mode='w') +else: + if Path(out_zarr).suffixes[-1] == '.zip': + store = zarr.ZipStore(out_zarr,dimension_separator='/',mode='x') + else: + store = zarr.DirectoryStore(out_zarr,dimension_separator='/') + + +darr_list=[] +for zarr_i,in_zarr in enumerate(snakemake.input.zarr): + #open zarr to get group name + zi = zarr.open(in_zarr) + darr_list.append(da.from_zarr(in_zarr,component='Data').rechunk(rechunk_size)) + + + + #append to omero metadata + channel_metadata={key:val for key,val in snakemake.config['ome_zarr']['omero_metadata']['channels']['defaults'].items()} + channel_name=stains[zarr_i] + channel_metadata['label'] = channel_name + default_color=snakemake.config['ome_zarr']['omero_metadata']['channels']['default_color'] + color=snakemake.config['ome_zarr']['omero_metadata']['channels']['color_mapping'].get(channel_name,default_color) + channel_metadata['color'] = color + omero['channels'].append(channel_metadata) + + +darr_channels = da.stack(darr_list) + + + +group = zarr.group(store,overwrite=True) +scaler = Scaler(max_layer=max_layer,method=scaling_method) + + +with ProgressBar(): + write_image(image=darr_channels, + group=group, + scaler=scaler, + coordinate_transformations=coordinate_transformations, + axes=axes, + metadata={'omero':omero} + ) + +