From e39535bc2e5550310c806e70f70417b881fa06c1 Mon Sep 17 00:00:00 2001 From: Ali Khan Date: Tue, 13 Feb 2024 09:19:40 -0500 Subject: [PATCH] initial import --- .github/workflows/main.yml | 4 +- .github/workflows/release-please.yml | 2 +- .snakemake-workflow-catalog.yml | 6 +- README.md | 16 +- config/README.md | 3 +- config/config.yml | 74 + config/datasets.tsv | 2 + dag.pdf | Bin 0 -> 17735 bytes poetry.lock | 3406 +++++++++++++++++ pyproject.toml | 26 + workflow/Snakefile | 131 +- workflow/macros/AutostitchMacro.ijm | 41 + workflow/macros/FuseImageMacroZarr.ijm | 47 + workflow/notebooks/ome-zarr-dask.ipynb | 806 ++++ workflow/rules/atlasreg.smk | 198 + workflow/rules/bigstitcher.smk | 351 ++ workflow/rules/flatfield_corr.smk | 101 + workflow/rules/import.smk | 129 + workflow/rules/ome_zarr.smk | 121 + .../apply_basic_flatfield_corr_zarr.py | 53 + .../scripts/fit_basic_flatfield_corr_zarr.py | 62 + workflow/scripts/import_labelmapper_lut.py | 14 + workflow/scripts/ome_zarr_to_nii.py | 39 + workflow/scripts/raw_to_metadata.py | 88 + workflow/scripts/resample_labels_to_zarr.py | 97 + workflow/scripts/tif_to_zarr.py | 55 + workflow/scripts/zarr_to_n5_bdv.py | 116 + workflow/scripts/zarr_to_ome_zarr.py | 60 + workflow/scripts/zarr_to_ome_zarr_labels.py | 77 + 29 files changed, 6101 insertions(+), 24 deletions(-) create mode 100644 config/config.yml create mode 100644 config/datasets.tsv create mode 100644 dag.pdf create mode 100644 poetry.lock create mode 100644 pyproject.toml create mode 100644 workflow/macros/AutostitchMacro.ijm create mode 100644 workflow/macros/FuseImageMacroZarr.ijm create mode 100644 workflow/notebooks/ome-zarr-dask.ipynb create mode 100644 workflow/rules/atlasreg.smk create mode 100644 workflow/rules/bigstitcher.smk create mode 100644 workflow/rules/flatfield_corr.smk create mode 100644 workflow/rules/import.smk create mode 100644 workflow/rules/ome_zarr.smk create mode 100644 workflow/scripts/apply_basic_flatfield_corr_zarr.py create mode 100644 workflow/scripts/fit_basic_flatfield_corr_zarr.py create mode 100644 workflow/scripts/import_labelmapper_lut.py create mode 100644 workflow/scripts/ome_zarr_to_nii.py create mode 100644 workflow/scripts/raw_to_metadata.py create mode 100644 workflow/scripts/resample_labels_to_zarr.py create mode 100644 workflow/scripts/tif_to_zarr.py create mode 100644 workflow/scripts/zarr_to_n5_bdv.py create mode 100644 workflow/scripts/zarr_to_ome_zarr.py create mode 100644 workflow/scripts/zarr_to_ome_zarr_labels.py diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 5d28ca0..3d6facf 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -44,11 +44,11 @@ jobs: with: directory: .test snakefile: workflow/Snakefile - args: "--use-conda --show-failed-logs --cores 3 --conda-cleanup-pkgs cache --all-temp" + args: "--sdm apptainer --show-failed-logs --cores 1 --all-temp" - name: Test report uses: snakemake/snakemake-github-action@v1.24.0 with: directory: .test snakefile: workflow/Snakefile - args: "--report report.zip" \ No newline at end of file + args: "--report report.zip" diff --git a/.github/workflows/release-please.yml b/.github/workflows/release-please.yml index a3e26fb..030f2a8 100644 --- a/.github/workflows/release-please.yml +++ b/.github/workflows/release-please.yml @@ -14,4 +14,4 @@ jobs: id: release with: release-type: go # just keep a changelog, no version anywhere outside of git tags - package-name: \ No newline at end of file + package-name: SPIMprep diff --git a/.snakemake-workflow-catalog.yml b/.snakemake-workflow-catalog.yml index 3436e32..432b731 100644 --- a/.snakemake-workflow-catalog.yml +++ b/.snakemake-workflow-catalog.yml @@ -5,7 +5,7 @@ usage: desc: # describe your flags here in a few sentences (they will be inserted below the example commands) flags: # put your flags here software-stack-deployment: # definition of software deployment method (at least one of conda, singularity, or singularity+conda) - conda: true # whether pipeline works with --use-conda - singularity: false # whether pipeline works with --use-singularity + conda: false # whether pipeline works with --use-conda + singularity: true # whether pipeline works with --use-singularity singularity+conda: false # whether pipeline works with --use-singularity --use-conda - report: true # add this to confirm that the workflow allows to use 'snakemake --report report.zip' to generate a report containing all results and explanations \ No newline at end of file + report: true # add this to confirm that the workflow allows to use 'snakemake --report report.zip' to generate a report containing all results and explanations diff --git a/README.md b/README.md index aab998b..23aa98d 100644 --- a/README.md +++ b/README.md @@ -1,21 +1,13 @@ -# Snakemake workflow: `` +# Snakemake workflow: `SPIMprep` [![Snakemake](https://img.shields.io/badge/snakemake-≥6.3.0-brightgreen.svg)](https://snakemake.github.io) [![GitHub actions status](https://github.com///workflows/Tests/badge.svg?branch=main)](https://github.com///actions?query=branch%3Amain+workflow%3ATests) - -A Snakemake workflow for `` - +A Snakemake workflow forp re-processing single plane illumination microscopy (SPIM, aka lightsheet microscopy) ## Usage -The usage of this workflow is described in the [Snakemake Workflow Catalog](https://snakemake.github.io/snakemake-workflow-catalog/?usage=%2F). - -If you use this workflow in a paper, don't forget to give credits to the authors by citing the URL of this (original) sitory and its DOI (see above). +The usage of this workflow is described in the [Snakemake Workflow Catalog](https://snakemake.github.io/snakemake-workflow-catalog/?usage=khanlab%2Fspimprep). -# TODO -* Replace `` and `` everywhere in the template (also under .github/workflows) with the correct `` name and owning user or organization. -* Replace `` with the workflow name (can be the same as ``). -* Replace `` with a description of what the workflow does. -* The workflow will occur in the snakemake-workflow-catalog once it has been made public. Then the link under "Usage" will point to the usage instructions if `` and `` were correctly set. \ No newline at end of file +Note: Designed to work with data from the Miltenyi Biotec UltraMicroscope Blaze, but could be adapted to other types of data diff --git a/config/README.md b/config/README.md index 44c6f7e..3656873 100644 --- a/config/README.md +++ b/config/README.md @@ -1,2 +1 @@ -Describe how to configure the workflow (using config.yaml and maybe additional files). -All of them need to be present with example entries inside of the config folder. +Add datasets to datasets.tsv, and update config.yml to customize options. diff --git a/config/config.yml b/config/config.yml new file mode 100644 index 0000000..80ee425 --- /dev/null +++ b/config/config.yml @@ -0,0 +1,74 @@ +datasets: 'config/datasets.tsv' + + +root: 'results' +work: 'work' + +targets: + desc: + - stitchedflatcorr #e.g. can be stitchedraw to skip flatfield correction + +import: + raw_tif_pattern: "{prefix}_Blaze[{tilex} x {tiley}]_C{channel}_xyz-Table Z{zslice}.ome.tif" + raw_tif_glob: "{prefix}_Blaze[[]{tilex} x {tiley}[]]_C{channel}_xyz-Table Z{zslice}.ome.tif" + intensity_rescaling: 0.5 #raw images seem to be at the upper end of uint16 (over-saturated) -- causes wrapping issues when adjusting with flatfield correction etc. this rescales the raw data as it imports it.. + + + +basic_flatfield_corr: + max_n_images: 500 + fitting_opts: + get_darkfield: True + smoothness_flatfield: 1.0 + smoothness_darkfield: 1.0 + sparse_cost_darkfield: 0.01 + + +bigstitcher: + calc_pairwise_shifts: + downsample_in_x: 4 + downsample_in_y: 4 + downsample_in_z: 1 + filter_pairwise_shifts: + min_r: 0.7 + fuse_dataset: + downsampling: 1 + block_size_x: 1024 # for storage + block_size_y: 1024 + block_size_z: 1 + block_size_factor_x: 2 #e.g. 2 will use 2*block_size for computation + block_size_factor_y: 2 + block_size_factor_z: 1 + +ome_zarr: + max_downsampling_layers: 4 # e.g. 4 levels: { 0: orig, 1: ds2, 2: ds4, 3: ds8, 4: ds16} + rechunk_size: #z, y, x + - 1 + - 1024 + - 1024 + scaling_method: 'local_mean' #can be nearest, gaussian, local_mean, zoom (zoom uses spline interp) + +nifti: + levels: #cannot be higher than max_downsampling_layers in ome_zarr + - 3 + - 4 + +templates: + - ABAv3 + +atlases: + ABAv3: + anat: 'resources/ABAv3/P56_Atlas.nii.gz' + dseg: 'resources/ABAv3/P56_Annotation.nii.gz' + lut: 'resources/ABAv3/labelmapper_ABAv3_to_all.json' + +atlasreg: + stain: autof + level: 4 + desc: stitchedflatcorr + + + +containers: + spimprep: 'docker://khanlab/spimprep-deps:main' + miracl: 'docker://mgoubran/miracl:2.2.5' diff --git a/config/datasets.tsv b/config/datasets.tsv new file mode 100644 index 0000000..f9938be --- /dev/null +++ b/config/datasets.tsv @@ -0,0 +1,2 @@ +subject sample acq stain_0 stain_1 dataset_path +mouse1 brain 1x abeta autof input_data/test_lightsheet_ome.tar diff --git a/dag.pdf b/dag.pdf new file mode 100644 index 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a/workflow/Snakefile +++ b/workflow/Snakefile @@ -1,4 +1,127 @@ -# Main entrypoint of the workflow. -# Please follow the best practices: -# https://snakemake.readthedocs.io/en/stable/snakefiles/best_practices.html, -# in particular regarding the standardized folder structure mentioned there. +import json +from snakebids import bids +from pathlib import Path +import pandas as pd +import os + + +configfile: "config/config.yml" + + +container: config["containers"]["spimprep"] + + +# use expandvars so we can use e.g. '$SLURM_TMPDIR' +root = os.path.expandvars(config["root"]) +work = os.path.expandvars(config["work"]) + +# read datasets tsv +datasets = pd.read_csv( + config["datasets"], + sep="\t", + dtype={ + "subject": str, + "sample": str, + "acq": str, + "stain_0": str, + "stain_1": str, + "num_tiles": int, + }, +) + + +def get_all_targets(): + targets = [] + for i in range(len(datasets)): + targets.extend( + expand( + bids( + root=root, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + stain="{stain}", + suffix="spim.ome.zarr.zip", + ), + subject=datasets.loc[i, "subject"], + sample=datasets.loc[i, "sample"], + acq=datasets.loc[i, "acq"], + desc=config["targets"]["desc"], + stain=[datasets.loc[i, "stain_0"], datasets.loc[i, "stain_1"]], + ) + ) + targets.extend( + expand( + bids( + root=root, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + from_="{template}", + suffix="dseg.ome.zarr.zip", + ), + subject=datasets.loc[i, "subject"], + sample=datasets.loc[i, "sample"], + acq=datasets.loc[i, "acq"], + desc=config["targets"]["desc"], + template=config["templates"], + stain=[datasets.loc[i, "stain_0"], datasets.loc[i, "stain_1"]], + ) + ) + targets.extend( + expand( + bids( + root=root, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + stain="{stain}", + level="{level}", + suffix="spim.nii", + ), + subject=datasets.loc[i, "subject"], + sample=datasets.loc[i, "sample"], + acq=datasets.loc[i, "acq"], + desc=config["targets"]["desc"], + level=config["nifti"]["levels"], + stain=[datasets.loc[i, "stain_0"], datasets.loc[i, "stain_1"]], + ) + ) + + return targets + + +def get_dataset_path(wildcards): + df = datasets.query( + f"subject=='{wildcards.subject}' and sample=='{wildcards.sample}' and acq=='{wildcards.acq}'" + ) + return df.dataset_path.to_list()[0] + + +def get_stains(wildcards): + df = datasets.query( + f"subject=='{wildcards.subject}' and sample=='{wildcards.sample}' and acq=='{wildcards.acq}'" + ) + + return [ + df.stain_0.to_list()[0], + df.stain_1.to_list()[0], + ] + + +rule all: + input: + get_all_targets(), + + +include: "rules/import.smk" +include: "rules/flatfield_corr.smk" +include: "rules/bigstitcher.smk" +include: "rules/ome_zarr.smk" +include: "rules/atlasreg.smk" diff --git a/workflow/macros/AutostitchMacro.ijm b/workflow/macros/AutostitchMacro.ijm new file mode 100644 index 0000000..080f1f5 --- /dev/null +++ b/workflow/macros/AutostitchMacro.ijm @@ -0,0 +1,41 @@ +args = getArgument() +args = split(args, " "); + +dataset_xml = args[0] + +run("Calculate pairwise shifts ...", + "select=" + dataset_xml + + " process_angle=[All angles] " + + " process_channel=[All channels] " + + " process_illumination=[All illuminations] " + + " process_tile=[All tiles] " + + " process_timepoint=[All Timepoints] " + + " method=[Phase Correlation] " + + " channels=[Average Channels] " + + " downsample_in_x=" + ds_x + + " downsample_in_y=" + ds_y + + " downsample_in_z=" + ds_z ); + +run("Filter pairwise shifts ...", + "select=" + dataset_xml + + " min_r=" min_r + + " max_r=1 " + + " max_shift_in_x=0 " + + " max_shift_in_y=0 " + + " max_shift_in_z=0 " + + " max_displacement=0"); + +run("Optimize globally and apply shifts ...", + "select=" + dataset_xml + + " process_angle=[All angles] " + + " process_channel=[All channels] " + + " process_illumination=[All illuminations] " + + " process_tile=[All tiles] " + + " process_timepoint=[All Timepoints] " + + " relative=2.500 " + + " absolute=3.500 " + + " global_optimization_strategy=[Two-Round using Metadata to align unconnected Tiles and iterative dropping of bad links] " + + " fix_group_0-0,"); + +// quit after we are finished +eval("script", "System.exit(0);"); diff --git a/workflow/macros/FuseImageMacroZarr.ijm b/workflow/macros/FuseImageMacroZarr.ijm new file mode 100644 index 0000000..9d49b06 --- /dev/null +++ b/workflow/macros/FuseImageMacroZarr.ijm @@ -0,0 +1,47 @@ +args = getArgument() +args = split(args, " "); + +in_dataset_xml = args[0] +downsampling = args[1] +channel = args[2] +output_zarr = args[3] +block_size_x = args[4] +block_size_y = args[5] +block_size_z = args[6] +block_size_factor_x = args[7] +block_size_factor_y = args[8] +block_size_factor_z = args[9] + +run("Fuse dataset ...", + "select=" + in_dataset_xml + + " process_angle=[All angles] " + + " process_channel=[Single channel (Select from List)] " + + " processing_channel=[channel " + channel + "]" + + " process_illumination=[All illuminations] " + + " process_tile=[All tiles] " + + " process_timepoint=[All Timepoints] " + + " bounding_box=[Currently Selected Views] " + + " preserve_original " + + " downsampling=" + downsampling + + " interpolation=[Linear Interpolation] " + + " pixel_type=[16-bit unsigned integer] " + + " interest_points_for_non_rigid=[-= Disable Non-Rigid =-] " + + " blend produce=[Each timepoint & channel] " + + " fused_image=[ZARR/N5/HDF5 export using N5-API] " + + " define_input=[Auto-load from input data (values shown below)] " + + " export=ZARR create_0 " + + " zarr_dataset_path=" + output_zarr + + " zarr_base_dataset=/ " + + " zarr_dataset_extension=/s0 " + + " show_advanced_block_size_options " + + " block_size_x=" + block_size_x + + " block_size_y=" + block_size_y + + " block_size_z=" + block_size_z + + " block_size_factor_x=" + block_size_factor_x + + " block_size_factor_y=" + block_size_factor_y + + " block_size_factor_z=" + block_size_factor_z + + " subsampling_factors=[{ {1,1,1}}]"); + + +// quit after we are finished +eval("script", "System.exit(0);"); diff --git a/workflow/notebooks/ome-zarr-dask.ipynb b/workflow/notebooks/ome-zarr-dask.ipynb new file mode 100644 index 0000000..74390a7 --- /dev/null +++ b/workflow/notebooks/ome-zarr-dask.ipynb @@ -0,0 +1,806 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 26, + "id": "c47a9c83-fa81-4ac4-88be-a35566472107", + "metadata": {}, + "outputs": [], + "source": [ + "import dask\n", + "import json\n", + "import zarr\n", + "import numpy as np\n", + "from ome_zarr.writer import write_image\n", + "from ome_zarr.scale import Scaler" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "42a733cf-8f3c-4575-9f7f-1173382ee582", + "metadata": {}, + "outputs": [], + "source": [ + "downsampling='4'\n", + "in_zarr=f'results/sub-mouse1/micr/sub-mouse1_acq-1x_desc-raw_sample-brain_downsampling-{downsampling}x_stain-autof_fusedspim.zarr'\n", + "metadata_json='results/sub-mouse1/micr/sub-mouse1_acq-1x_sample-brain_spim.metadata.json'\n", + "max_layer=4 #number of downsamplings by 2 to include in zarr\n", + "rechunk_size=(128,256,256)\n", + "out_zarr='results/sub-mouse1/micr/sub-mouse1_acq-1x_desc-raw_sample-brain_downsampling-4x_stain-autof_fusedspim.ome.zarr'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "9b1002a3-56d7-45e5-90fe-149ab2957bbd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + "dask.arrayu2, chunksize=(128, 256, 256), chunktype=numpy.ndarray>" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#open zarr to get group name\n", + "zi = zarr.open(in_zarr)\n", + "group_name = [g for g in zi.group_keys()][0]\n", + "\n", + "da = dask.array.from_zarr(in_zarr,component=f'{group_name}/s0',chunks=rechunk_size)\n", + "da" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "736d0f36-2df4-45d1-b110-7cbcbdb03942", + "metadata": {}, + "outputs": [], + "source": [ + "# prepare metadata for ome-zarr\n", + "\n", + "with open(metadata_json) as fp:\n", + " metadata = json.load(fp)\n", + "\n", + "voxdim = float(metadata['physical_size_x']) * float(downsampling) / 1000.0\n", + "\n", + "coordinate_transformations = []\n", + "#for each resolution (dataset), we have a list of dicts, transformations to apply.. \n", + "#in this case just a single one (scaling by voxel size)\n", + "\n", + "for l in range(max_layer+1):\n", + " \n", + " coordinate_transformations.append( [{'scale': [(2**l)*voxdim for i in range(3)],\n", + " 'type': 'scale'}]) #isotropic \n", + "\n", + "\n", + "axes = [ {'name': ax, 'type': 'space', 'unit': 'micrometer'} for ax in ['z','y','x'] ] \n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "9625fa20-e6db-408b-ba40-b698b97d2cb9", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "store = zarr.DirectoryStore(out_zarr)\n", + "root = zarr.group(store,path='/fused',overwrite=True)\n", + "scaler = Scaler(max_layer=max_layer)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "bb87efbb-2b63-4a14-9e29-ea742dc272e9", + "metadata": {}, + "outputs": [], + "source": [ + "dasks = write_image(image=da,\n", + " group=root,\n", + " scaler=scaler,\n", + " coordinate_transformations=coordinate_transformations,\n", + " axes=axes,\n", + " compute=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "d2a922ae-3371-4372-9786-10321d980ba0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/local/scratch/pypoetry_venv/bigstitcher-smk-EmIKbLsN-py3.11/lib/python3.11/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n", + "Perhaps you already have a cluster running?\n", + "Hosting the HTTP server on port 36279 instead\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from dask.distributed import Client, progress\n", + "client = Client(threads_per_worker=2, n_workers=6)\n", + "client" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "ee606614-d12f-443b-b0ba-b82e006d0b29", + "metadata": {}, + "outputs": [], + "source": [ + "compute_status = dask.compute(*dasks)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "02a6c7eb-e5a4-4737-9554-da9728f634f5", + "metadata": {}, + "outputs": [], + "source": [ + "zo = zarr.open(out_zarr)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "35b82370-c17f-4f48-9442-65f837e30bc3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Name/
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"metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "zo.groups()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59de6725-9feb-4b30-9a5b-f2e56524cd63", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/workflow/rules/atlasreg.smk b/workflow/rules/atlasreg.smk new file mode 100644 index 0000000..1c7da67 --- /dev/null +++ b/workflow/rules/atlasreg.smk @@ -0,0 +1,198 @@ +def bids_tpl(root, template, **entities): + """bids() wrapper for files in tpl-template folder""" + return str(Path(bids(root=root, tpl=template)) / bids(tpl=template, **entities)) + + +rule import_anat: + input: + anat=lambda wildcards: config["atlases"][wildcards.template]["anat"], + output: + anat=bids_tpl(root=root, template="{template}", suffix="anat.nii.gz"), + shell: + "cp {input} {output}" + + +rule import_dseg: + input: + dseg=lambda wildcards: config["atlases"][wildcards.template]["dseg"], + output: + dseg=bids_tpl(root=root, template="{template}", suffix="dseg.nii.gz"), + shell: + "cp {input} {output}" + + +rule import_lut: + input: + json=lambda wildcards: config["atlases"][wildcards.template]["lut"], + output: + tsv=bids_tpl(root=root, template="{template}", suffix="dseg.tsv"), + script: + "../scripts/import_labelmapper_lut.py" + + +rule affine_reg: + input: + template=bids_tpl(root=root, template="{template}", suffix="anat.nii.gz"), + subject=bids( + root=root, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc=config["atlasreg"]["desc"], + stain=config["atlasreg"]["stain"], + level=config["atlasreg"]["level"], + suffix="spim.nii", + ), + output: + xfm_ras=bids( + root=root, + subject="{subject}", + datatype="warps", + sample="{sample}", + acq="{acq}", + from_="subject", + to="{template}", + type_="ras", + desc="affine", + suffix="xfm.txt", + ), + warped=bids( + root=root, + subject="{subject}", + datatype="warps", + sample="{sample}", + acq="{acq}", + space="{template}", + desc="affinewarped", + suffix="spim.nii", + ), + shell: + "greedy -d 3 -i {input.template} {input.subject} " + " -a -dof 12 -ia-image-centers -m NMI -o {output.xfm_ras} && " + " greedy -d 3 -rf {input.template} " + " -rm {input.subject} {output.warped} " + " -r {output.xfm_ras}" + + +rule deform_reg: + input: + template=bids_tpl(root=root, template="{template}", suffix="anat.nii.gz"), + subject=bids( + root=root, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc=config["atlasreg"]["desc"], + stain=config["atlasreg"]["stain"], + level=config["atlasreg"]["level"], + suffix="spim.nii", + ), + xfm_ras=rules.affine_reg.output.xfm_ras, + output: + warp=bids( + root=root, + subject="{subject}", + datatype="warps", + sample="{sample}", + acq="{acq}", + from_="subject", + to="{template}", + suffix="warp.nii", + ), + warped=bids( + root=root, + subject="{subject}", + datatype="warps", + sample="{sample}", + acq="{acq}", + space="{template}", + desc="deformwarped", + suffix="spim.nii", + ), + shell: + "greedy -d 3 -i {input.template} {input.subject} " + " -it {input.xfm_ras} -m NMI " + " -o {output.warp} -n 100x50x0x0 && " + " greedy -d 3 -rf {input.template} " + " -rm {input.subject} {output.warped} " + " -r {output.warp} {input.xfm_ras}" + + +rule resample_labels_to_zarr: + """TODO: add required OME metadata""" + input: + dseg=rules.import_dseg.output.dseg, + xfm_ras=rules.affine_reg.output.xfm_ras, + zarr_zip=bids( + root=root, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + stain=config["atlasreg"]["stain"], + suffix="spim.ome.zarr.zip", + ), + output: + zarr=temp( + directory( + bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + from_="{template}", + suffix="dseg.zarr", + ) + ) + ), + script: + "../scripts/resample_labels_to_zarr.py" + + +rule zarr_to_ome_zarr_labels: + input: + zarr=bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + from_="{template}", + suffix="dseg.zarr", + ), + metadata_json=rules.raw_to_metadata.output.metadata_json, + label_tsv=bids_tpl(root=root, template="{template}", suffix="dseg.tsv"), + params: + max_downsampling_layers=config["ome_zarr"]["max_downsampling_layers"], + rechunk_size=config["ome_zarr"]["rechunk_size"], + scaling_method="nearest", + downsampling=config["bigstitcher"]["fuse_dataset"]["downsampling"], + label_name="dseg_{template}", + output: + zarr=temp( + directory( + bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + from_="{template}", + suffix="dseg.ome.zarr", + ) + ) + ), + threads: 32 + container: + config["containers"]["spimprep"] + group: + "preproc" + script: + "../scripts/zarr_to_ome_zarr_labels.py" diff --git a/workflow/rules/bigstitcher.smk b/workflow/rules/bigstitcher.smk new file mode 100644 index 0000000..18847a2 --- /dev/null +++ b/workflow/rules/bigstitcher.smk @@ -0,0 +1,351 @@ +rule zarr_to_bdv: + input: + zarr=bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + suffix="spim.zarr", + ), + metadata_json=rules.raw_to_metadata.output.metadata_json, + params: + max_downsampling_layers=5, #1,2,4,8,16 + temp_h5=str( + Path( + bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + suffix="bdv", + ) + ) + / "dataset.h5" + ), + #only temporary, is promptly deleted + temp_xml=str( + Path( + bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + suffix="bdv", + ) + ) + / "dataset.xml" + ), + #only temporary, is promptly deleted + output: + bdv_n5=temp( + directory( + bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + suffix="bdv.n5", + ) + ) + ), + bdv_xml=temp( + bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + suffix="bdv.xml", + ) + ), + benchmark: + bids( + root="benchmarks", + datatype="zarr_to_n5_bdv", + subject="{subject}", + sample="{sample}", + acq="{acq}", + desc="{desc}", + suffix="benchmark.tsv", + ) + threads: 32 + group: + "preproc" + container: + config["containers"]["spimprep"] + script: + "../scripts/zarr_to_n5_bdv.py" + + +def get_fiji_launcher_cmd(wildcards, output, threads, resources): + launcher_opts_find = "-Xincgc" + launcher_opts_replace = f"-XX:+UseG1GC -verbose:gc -XX:+PrintGCDateStamps -XX:ActiveProcessorCount={threads}" + pipe_cmds = [] + pipe_cmds.append("ImageJ-linux64 --dry-run --headless --console") + pipe_cmds.append(f"sed 's/{launcher_opts_find}/{launcher_opts_replace}'/") + pipe_cmds.append( + f"sed 's/-Xmx[0-9a-z]\+/-Xmx{resources.mem_mb}m -Xms{resources.mem_mb}m/'" + ) + pipe_cmds.append("tr --delete '\\n'") + return "|".join(pipe_cmds) + f" > {output.launcher} && chmod a+x {output.launcher} " + + +def get_macro_args_bigstitcher(wildcards, input, output): + return "{dataset_xml} {ds_x} {ds_y} {ds_z} {min_r}".format( + dataset_xml=input.dataset_xml, + ds_x=config["bigstitcher"]["calc_pairwise_shifts"]["downsample_in_x"], + ds_y=config["bigstitcher"]["calc_pairwise_shifts"]["downsample_in_y"], + ds_z=config["bigstitcher"]["calc_pairwise_shifts"]["downsample_in_z"], + min_r=config["bigstitcher"]["filter_pairwise_shifts"]["min_r"], + ) + + +rule bigstitcher: + input: + dataset_n5=rules.zarr_to_bdv.output.bdv_n5, + dataset_xml=rules.zarr_to_bdv.output.bdv_xml, + ijm=Path(workflow.basedir) / "macros" / "AutostitchMacro.ijm", + params: + fiji_launcher_cmd=get_fiji_launcher_cmd, + macro_args=get_macro_args_bigstitcher, + rm_old_xml=lambda wildcards, output: f"rm -f {output.dataset_xml}~?", + output: + launcher=temp( + bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + suffix="bigstitcherproc.sh", + ) + ), + dataset_xml=temp( + bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + suffix="bigstitcher.xml", + ) + ), + benchmark: + bids( + root="benchmarks", + datatype="bigstitcherproc", + subject="{subject}", + sample="{sample}", + acq="{acq}", + desc="{desc}", + suffix="benchmark.tsv", + ) + log: + bids( + root="logs", + datatype="bigstitcherproc", + subject="{subject}", + sample="{sample}", + acq="{acq}", + desc="{desc}", + suffix="log.txt", + ), + container: + config["containers"]["spimprep"] + resources: + runtime=30, #this should be proportional to the number of images and image size + mem_mb=10000, + threads: 32 + group: + "preproc" + shell: + "cp {input.dataset_xml} {output.dataset_xml} && " + " {params.fiji_launcher_cmd} && " + " echo ' -macro {input.ijm} \"{params.macro_args}\"' >> {output.launcher} " + " && {output.launcher} &> {log} && {params.rm_old_xml}" + + +def get_macro_args_zarr_fusion(wildcards, input, output): + return "{dataset_xml} {downsampling} {channel:02d} {output_zarr} {bsx} {bsy} {bsz} {bsfx} {bsfy} {bsfz}".format( + dataset_xml=input.dataset_xml, + downsampling=config["bigstitcher"]["fuse_dataset"]["downsampling"], + channel=get_stains(wildcards).index(wildcards.stain), + output_zarr=output.zarr, + bsx=config["bigstitcher"]["fuse_dataset"]["block_size_x"], + bsy=config["bigstitcher"]["fuse_dataset"]["block_size_y"], + bsz=config["bigstitcher"]["fuse_dataset"]["block_size_z"], + 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"], + ) + + +rule fuse_dataset: + input: + dataset_n5=bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + suffix="bdv.n5", + ), + dataset_xml=bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + suffix="bigstitcher.xml", + ), + ijm=Path(workflow.basedir) / "macros" / "FuseImageMacroZarr.ijm", + params: + fiji_launcher_cmd=get_fiji_launcher_cmd, + macro_args=get_macro_args_zarr_fusion, + output: + launcher=temp( + bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="stitched{desc}", + stain="{stain}", + suffix="fuseimagen5.sh", + ) + ), + zarr=temp( + directory( + bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="stitched{desc}", + stain="{stain}", + suffix="spim.zarr", + ) + ) + ), + benchmark: + bids( + root="benchmarks", + datatype="fuse_dataset", + subject="{subject}", + sample="{sample}", + acq="{acq}", + desc="stitched{desc}", + stain="{stain}", + suffix="benchmark.tsv", + ) + log: + bids( + root="logs", + datatype="fuse_dataset", + subject="{subject}", + sample="{sample}", + acq="{acq}", + desc="stitched{desc}", + stain="{stain}", + suffix="log.txt", + ), + container: + config["containers"]["spimprep"] + resources: + runtime=30, + mem_mb=20000, + threads: 32 + group: + "preproc" + shell: + " {params.fiji_launcher_cmd} && " + " echo ' -macro {input.ijm} \"{params.macro_args}\"' >> {output.launcher} " + " && {output.launcher} &> {log}" + + +rule fuse_dataset_spark: + input: + dataset_n5=bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + suffix="bdv.n5", + ), + dataset_xml=bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + suffix="bigstitcher.xml", + ), + ijm=Path(workflow.basedir) / "macros" / "FuseImageMacroZarr.ijm", + params: + output: + zarr=temp( + directory( + bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="sparkstitched{desc}", + stain="{stain}", + suffix="spim.zarr", + ) + ) + ), + benchmark: + bids( + root="benchmarks", + datatype="fuse_dataset_spark", + subject="{subject}", + sample="{sample}", + acq="{acq}", + desc="stitched{desc}", + stain="{stain}", + suffix="benchmark.tsv", + ) + log: + bids( + root="logs", + datatype="fuse_dataset_spark", + subject="{subject}", + sample="{sample}", + acq="{acq}", + desc="stitched{desc}", + stain="{stain}", + suffix="log.txt", + ), + container: + config["containers"]["spimprep"] + resources: + runtime=30, + mem_mb=20000, + threads: 32 + group: + "preproc" + shell: + "affine-fusion ..." + + diff --git a/workflow/rules/flatfield_corr.smk b/workflow/rules/flatfield_corr.smk new file mode 100644 index 0000000..034b8e4 --- /dev/null +++ b/workflow/rules/flatfield_corr.smk @@ -0,0 +1,101 @@ + +rule fit_basic_flatfield_corr: + """ BaSiC flatfield correction""" + input: + zarr=bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="raw", + suffix="spim.zarr", + ), + params: + channel=lambda wildcards: get_stains(wildcards).index(wildcards.stain), + max_n_images=config["basic_flatfield_corr"]["max_n_images"], #sets maximum number of images to use for fitting (selected randomly) + basic_opts=config["basic_flatfield_corr"]["fitting_opts"], + output: + model_dir=temp( + directory( + bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + stain="{stain}", + suffix="basicmodel", + ) + ) + ), + resources: + runtime=90, #this should be proportional to the number of images and image size + mem_mb=64000, + threads: 8 + benchmark: + bids( + root="benchmarks", + datatype="fit_basic_flatfield", + subject="{subject}", + sample="{sample}", + acq="{acq}", + stain="{stain}", + suffix="benchmark.tsv", + ) + group: + "preproc" + container: + config["containers"]["spimprep"] + script: + "../scripts/fit_basic_flatfield_corr_zarr.py" + + +rule apply_basic_flatfield_corr: + """ apply BaSiC flatfield correction """ + input: + zarr=bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="raw", + suffix="spim.zarr", + ), + model_dirs=lambda wildcards: expand( + rules.fit_basic_flatfield_corr.output.model_dir, + stain=get_stains(wildcards), + allow_missing=True, + ), + output: + zarr=temp( + directory( + bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="flatcorr", + suffix="spim.zarr", + ) + ) + ), + benchmark: + bids( + root="benchmarks", + datatype="apply_basic_flatfield", + subject="{subject}", + sample="{sample}", + acq="{acq}", + suffix="benchmark.tsv", + ) + resources: + runtime=60, #this should be proportional to the number of images and image size + mem_mb=32000, + threads: 32 + group: + "preproc" + script: + "../scripts/apply_basic_flatfield_corr_zarr.py" diff --git a/workflow/rules/import.smk b/workflow/rules/import.smk new file mode 100644 index 0000000..71ca961 --- /dev/null +++ b/workflow/rules/import.smk @@ -0,0 +1,129 @@ +def cmd_get_dataset(wildcards, input, output): + cmds = [] + import tarfile + + # supports tar, tar.gz, tgz, zip, or folder name + dataset_path = Path(input.dataset_path) + suffix = dataset_path.suffix + if dataset_path.is_dir(): + # we have a directory: + # return command to copy folder + cmds.append(f"ln -sr {input} {output}") + + elif tarfile.is_tarfile(dataset_path): + # we have a tar file + # check if gzipped: + cmds.append(f"mkdir -p {output}") + if suffix == "gz" or suffix == "tgz": + cmds.append(f"tar -xzf {input} -C {output}") + else: + cmds.append(f"tar -xf {input} -C {output}") + + else: + print(f"unsupported input: {dataset_path}") + + return " && ".join(cmds) + + +rule get_dataset: + input: + dataset_path=get_dataset_path, + params: + cmd=cmd_get_dataset, + output: + ome_dir=temp( + directory( + bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="raw", + suffix="spim", + ) + ) + ), + group: + "preproc" + shell: + "{params.cmd}" + + +rule raw_to_metadata: + input: + ome_dir=rules.get_dataset.output.ome_dir, + params: + in_tif_glob=lambda wildcards, input: os.path.join( + input.ome_dir, + config["import"]["raw_tif_pattern"], + ), + output: + metadata_json=bids( + root=root, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + suffix="spim.metadata.json", + ), + benchmark: + bids( + root="benchmarks", + datatype="raw_to_metdata", + subject="{subject}", + sample="{sample}", + acq="{acq}", + suffix="benchmark.tsv", + ) + group: + "preproc" + container: + config["containers"]["spimprep"] + script: + "../scripts/raw_to_metadata.py" + + +rule tif_to_zarr: + """ use dask to load tifs in parallel and write to zarr + output shape is (tiles,channels,z,y,x), with the 2d + images as the chunks""" + input: + ome_dir=rules.get_dataset.output.ome_dir, + metadata_json=rules.raw_to_metadata.output.metadata_json, + params: + in_tif_glob=lambda wildcards, input: os.path.join( + input.ome_dir, + config["import"]["raw_tif_glob"], + ), + intensity_rescaling=config["import"]["intensity_rescaling"], + output: + zarr=temp( + directory( + bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="raw", + suffix="spim.zarr", + ) + ) + ), + benchmark: + bids( + root="benchmarks", + datatype="tif_to_zarr", + subject="{subject}", + sample="{sample}", + acq="{acq}", + suffix="benchmark.tsv", + ) + group: + "preproc" + threads: 32 + container: + config["containers"]["spimprep"] + script: + "../scripts/tif_to_zarr.py" diff --git a/workflow/rules/ome_zarr.smk b/workflow/rules/ome_zarr.smk new file mode 100644 index 0000000..7cf1b75 --- /dev/null +++ b/workflow/rules/ome_zarr.smk @@ -0,0 +1,121 @@ +rule zarr_to_ome_zarr: + input: + zarr=bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + stain="{stain}", + suffix="spim.zarr", + ), + metadata_json=rules.raw_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"], + output: + zarr=temp( + directory( + bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + stain="{stain}", + suffix="spim.ome.zarr", + ) + ) + ), + threads: 32 + container: + config["containers"]["spimprep"] + group: + "preproc" + script: + "../scripts/zarr_to_ome_zarr.py" + + +rule ome_zarr_to_zipstore: + """ generic rule to process any ome.zarr from work """ + input: + zarr=f"{work}/{{prefix}}.ome.zarr", + output: + zarr_zip=f"{root}/{{prefix}}.ome.zarr.zip", + log: + "logs/ome_zarr_to_zipstore/{prefix}.log", + group: + "preproc" + shell: + "7z a -mx0 -tzip {output.zarr_zip} {input.zarr}/. &> {log}" + + +rule ome_zarr_to_nii: + input: + zarr=bids( + root=work, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + stain="{stain}", + suffix="spim.ome.zarr.zip", + ), + output: + nii=bids( + root=root, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + stain="{stain}", + level="{level}", + suffix="spim.nii", + ), + benchmark: + bids( + root="benchmarks", + datatype="ome_zarr_to_nifti", + subject="{subject}", + sample="{sample}", + acq="{acq}", + desc="{desc}", + stain="{stain}", + level="{level}", + suffix="benchmark.tsv", + ) + group: + "preproc" + threads: 32 + container: + config["containers"]["spimprep"] + script: + "../scripts/ome_zarr_to_nii.py" + + +rule zarr_masking_wip: + input: + zarr=rules.zarr_to_ome_zarr.output.zarr, + output: + zarr=directory( + bids( + root=root, + subject="{subject}", + datatype="micr", + sample="{sample}", + acq="{acq}", + desc="{desc}", + stain="{stain}", + suffix="mask.ome.zarr", + ) + ), + container: + config["containers"]["spimprep"] + notebook: + "../notebooks/zarr_masking.py.ipynb" diff --git a/workflow/scripts/apply_basic_flatfield_corr_zarr.py b/workflow/scripts/apply_basic_flatfield_corr_zarr.py new file mode 100644 index 0000000..73b2a7d --- /dev/null +++ b/workflow/scripts/apply_basic_flatfield_corr_zarr.py @@ -0,0 +1,53 @@ +import json +from basicpy import BaSiC +from pathlib import Path +import numpy as np +from skimage.transform import resize +import dask.array as da +from dask.diagnostics import ProgressBar + + + +in_zarr = snakemake.input.zarr + +#load data +arr = da.from_zarr(in_zarr) + +img_shape = (arr.shape[3],arr.shape[4]) + +chan_arr_list=[] + +#loops over channels (one model for each channel): +for i_channel,model in enumerate(snakemake.input.model_dirs): + + + #load model (doing it here instead of using load_model function as it was buggy-- fix that later).. + model_path = Path(model) + with open(model_path / 'settings.json' ) as fp: + model = json.load(fp) + + profiles = np.load(model_path / 'profiles.npy') + model["flatfield"] = profiles[0] + model["darkfield"] = profiles[1] + + basic = BaSiC() + basic.flatfield = resize(model['flatfield'],img_shape,preserve_range=True) + basic.darkfield = resize(model['darkfield'],img_shape,preserve_range=True) + basic.baseline = 0 + + #select the channel first + arr_chan = arr[:,i_channel,:,:,:] + + #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('uint16') + arr_corr = da.map_blocks(apply_basic_parallel,arr_chan) + + chan_arr_list.append(arr_corr) + +#stack along chans +arr_stacked = da.stack(chan_arr_list,axis=1) + +with ProgressBar(): + da.to_zarr(arr_stacked,snakemake.output.zarr,overwrite=True) diff --git a/workflow/scripts/fit_basic_flatfield_corr_zarr.py b/workflow/scripts/fit_basic_flatfield_corr_zarr.py new file mode 100644 index 0000000..88c7021 --- /dev/null +++ b/workflow/scripts/fit_basic_flatfield_corr_zarr.py @@ -0,0 +1,62 @@ +from random import shuffle +from basicpy import BaSiC +from skimage.transform import resize +import dask.array as da +import zarr +from dask.diagnostics import ProgressBar + +in_zarr = snakemake.input.zarr +channel=int(snakemake.params.channel) +max_n_images=int(snakemake.params.max_n_images) + +arr = da.from_zarr(in_zarr) +shape = arr.shape +n_tiles=shape[0] +n_chans=shape[1] +n_slices=shape[2] +n_x=shape[3] +n_y=shape[4] + +#select the channel first +arr = arr[:,channel,:,:,:] + +arr = da.reshape(arr,(n_tiles*n_slices,n_x,n_y)) + + +#if number of images less than the max, then use entire array +if arr.shape[0] > max_n_images: + #want to pick a random sample of max_n_images indices + indices = [i for i in range(arr.shape[0])] + shuffle(indices) + arr = arr[indices[:max_n_images],:,:] + +#now we want to apply resizing to all the images in the array +#but do it in parallel + +def resize_parallel(x): + return resize(x.squeeze(),(128,128),preserve_range=True).reshape((1,128,128)) + +#since each slice is a chunk/block, can use map_blocks to apply the function to all blocks +arr_resized = da.map_blocks(resize_parallel,arr) + +print('concatenating and resizing with dask') +with ProgressBar(): + concat_images = arr_resized.compute() + +basic = BaSiC(max_workers=snakemake.threads, + **snakemake.params.basic_opts + ) + + +print('fitting BaSiC...') +basic.fit(concat_images) +print('fitting BaSiC...done') + + +#add plot and save model here +print('saving model') +basic.save_model(snakemake.output.model_dir) + + + + diff --git a/workflow/scripts/import_labelmapper_lut.py b/workflow/scripts/import_labelmapper_lut.py new file mode 100644 index 0000000..c790a92 --- /dev/null +++ b/workflow/scripts/import_labelmapper_lut.py @@ -0,0 +1,14 @@ +import json +import pandas as pd + +col_order=['name','abbreviation','color'] + +with open(snakemake.input.json) as fp: + lut=json.load(fp) + +(pd.DataFrame(lut, columns = ['index', 'color','abbreviation','name']) + .set_index('index') + [col_order] + .to_csv(snakemake.output.tsv,sep='\t') +) + diff --git a/workflow/scripts/ome_zarr_to_nii.py b/workflow/scripts/ome_zarr_to_nii.py new file mode 100644 index 0000000..679e106 --- /dev/null +++ b/workflow/scripts/ome_zarr_to_nii.py @@ -0,0 +1,39 @@ +import zarr +import json +import numpy as np +import nibabel as nib +import dask.array as da +from ome_zarr.io import parse_url +from ome_zarr.reader import Reader + +in_zarr = snakemake.input.zarr + +zi = zarr.open(in_zarr) + +attrs=zi['fused'].attrs.asdict() + +level=int(snakemake.wildcards.level) + +#read coordinate transform from ome-zarr +transforms = attrs['multiscales'][0]['datasets'][level]['coordinateTransformations'] + + +#zarr uses z,y,x ordering, we reverse this for nifti +# also flip to set orientation properly +affine = np.eye(4) +affine[0,0]=-transforms[0]['scale'][2] #z +affine[1,1]=-transforms[0]['scale'][1] #y +affine[2,2]=-transforms[0]['scale'][0] #x + +darr = da.from_zarr(in_zarr,component=f'fused/{level}') + +#input array axes are ZYX +#writing to nifti we want XYZ +out_arr = np.moveaxis(darr,(0,1,2),(2,1,0)) + +nii = nib.Nifti1Image(out_arr, + affine=affine + ) + + +nii.to_filename(snakemake.output.nii) diff --git a/workflow/scripts/raw_to_metadata.py b/workflow/scripts/raw_to_metadata.py new file mode 100644 index 0000000..14d73db --- /dev/null +++ b/workflow/scripts/raw_to_metadata.py @@ -0,0 +1,88 @@ +import tifffile +import xmltodict +import json +import re +from itertools import product +from snakemake.io import glob_wildcards + +in_tif_glob = snakemake.params.in_tif_glob + +#parse the filenames to get number of channels, tiles etc.. +prefix, tilex, tiley, channel, zslice = glob_wildcards(in_tif_glob) + +tiles_x = sorted(list(set(tilex))) +tiles_y = sorted(list(set(tiley))) +channels = sorted(list(set(channel))) +zslices = sorted(list(set(zslice))) +prefixes = sorted(list(set(prefix))) + +#read in series metadata from first file +in_tif = in_tif_glob.format(tilex=tiles_x[0],tiley=tiles_y[0],prefix=prefixes[0],channel=channels[0],zslice=zslices[0]) + +raw_tif = tifffile.TiffFile(in_tif) + +axes = raw_tif.series[0].get_axes() +shape = raw_tif.series[0].get_shape() + +#axes normally should be CZYX - if not, then we fail out +if not axes == 'CZYX': + print('ERROR: cannot deal with axes other than CZYX') + #sys.exit(1) + +ome_dict = xmltodict.parse(raw_tif.ome_metadata) +physical_size_x = ome_dict['OME']['Image']['Pixels']['@PhysicalSizeX'] +physical_size_y = ome_dict['OME']['Image']['Pixels']['@PhysicalSizeY'] +physical_size_z = ome_dict['OME']['Image']['Pixels']['@PhysicalSizeZ'] +custom_metadata = ome_dict['OME']['Image']['ca:CustomAttributes'] + + + +#read tile configuration from the microscope metadata + +tile_config_pattern=r"Blaze\[(?P[0-9]+) x (?P[0-9]+)\]_C(?P[0-9]+)_xyz-Table Z(?P[0-9]+).ome.tif;;\((?P\S+), (?P\S+),(?P\S+), (?P\S+)\)" +tile_pattern = re.compile(tile_config_pattern) + +#put it in 3 maps, one for each coord, indexed by tilex, tiley, channel, and aslice +map_x=dict() +map_y=dict() +map_z=dict() + +map_tiles_to_chunk=dict() +chunks = [] +for chunk,(tilex,tiley) in enumerate(product(tiles_x,tiles_y)): + map_tiles_to_chunk[tilex+tiley] = chunk + chunks.append(chunk) + +for line in custom_metadata['TileConfiguration']['@TileConfiguration'].split(' ')[1:]: + + d = re.search(tile_pattern,line).groupdict() + chunk = map_tiles_to_chunk[d['tilex']+d['tiley']] # want the key to have chunk instad of tilex,tiley, so map to that first + + #key is: tile-{chunk}_chan-{channel}_z-{zslice} + key = f"tile-{chunk}_chan-{d['channel']}_z-{d['zslice']}" + + map_x[key] = float(d['x']) + map_y[key] = float(d['y']) + map_z[key] = float(d['z']) + + +metadata={} +metadata['tiles_x'] = tiles_x +metadata['tiles_y'] = tiles_y +metadata['channels'] = channels +metadata['zslices'] = zslices +metadata['prefixes'] = prefixes +metadata['chunks'] = chunks +metadata['axes'] = axes +metadata['shape'] = shape +metadata['physical_size_x'] = physical_size_x +metadata['physical_size_y'] = physical_size_y +metadata['physical_size_z'] = physical_size_z +metadata['lookup_tile_offset_x'] = map_x +metadata['lookup_tile_offset_y'] = map_y +metadata['lookup_tile_offset_z'] = map_z +metadata['ome_full_metadata'] = ome_dict + +#write metadata to json +with open(snakemake.output.metadata_json, 'w') as fp: + json.dump(metadata, fp,indent=4) diff --git a/workflow/scripts/resample_labels_to_zarr.py b/workflow/scripts/resample_labels_to_zarr.py new file mode 100644 index 0000000..bffb457 --- /dev/null +++ b/workflow/scripts/resample_labels_to_zarr.py @@ -0,0 +1,97 @@ +import zarr +import dask.array as da +import nibabel as nib +import numpy as np +from scipy.interpolate import interpn +from dask.diagnostics import ProgressBar + + + +in_zarr=snakemake.input.zarr_zip +in_xfm=snakemake.input.xfm_ras +in_template_dseg=snakemake.input.dseg +out_zarr=snakemake.output.zarr + +#load dask array from zarr reference image +darr = da.from_zarr(in_zarr,component='fused/0') + +#load template dseg +dseg_nib = nib.load(in_template_dseg) + +# get dseg volume for interpolation, +dseg_vol = dseg_nib.get_fdata() + +# along with grid points for interpolation +grid_points = (np.arange(dseg_vol.shape[0]), + np.arange(dseg_vol.shape[1]), + np.arange(dseg_vol.shape[2])) + + + +#read coordinate transform from ome-zarr +zi = zarr.open(in_zarr) +attrs=zi['fused'].attrs.asdict() +level=0 +transforms = attrs['multiscales'][0]['datasets'][level]['coordinateTransformations'] + +#need to put together the sequence of transforms to apply + +# 1. scaling_xfm (vox2ras in spim space) +#note: zarr uses z,y,x ordering, we reverse this for nifti; also the negation to flip +# this matches what the ome_zarr_to_nii affine has +scaling_xfm = np.eye(4) +scaling_xfm[0,0]=-transforms[0]['scale'][2] #x +scaling_xfm[1,1]=-transforms[0]['scale'][1] #y +scaling_xfm[2,2]=-transforms[0]['scale'][0] #z + +# 2. affine_inv_xfm (from registration, takes points from spim ras to template ras) +affine_inv_xfm = np.linalg.inv(np.loadtxt(in_xfm)) + + +# 3. ras2vox in template space +ras2vox = np.linalg.inv(dseg_nib.affine) + +# concatenate all three +concat_xfm = ras2vox @ affine_inv_xfm @ scaling_xfm + + + +def interp_label(x,block_info=None): + # print(block_info) + + arr_location = block_info[0]['array-location'] + + xv,yv,zv=np.meshgrid(np.arange(arr_location[2][0],arr_location[2][1]), + np.arange(arr_location[1][0],arr_location[1][1]), + np.arange(arr_location[0][0],arr_location[0][1])) + + #print(f'x shape: {x.shape}') + #print(f'xv shape: {xv.shape}') + + #reshape them into a vectors (x,y,z,1) for each point, so we can matrix multiply + xvf=xv.reshape((1,np.product(xv.shape))) + yvf=yv.reshape((1,np.product(yv.shape))) + zvf=zv.reshape((1,np.product(zv.shape))) + homog=np.ones(xvf.shape) + + vecs=np.vstack((xvf,yvf,zvf,homog)) + + xfm_vecs = concat_xfm @ vecs + + #then finally interpolate those points on the template dseg volume + interpolated = interpn(grid_points,dseg_vol, + xfm_vecs[:3,:].T, # + method='nearest', + bounds_error=False, + fill_value=-100) + + return interpolated.reshape(x.shape) + + +#perform interpolation on each block of spim zarr, in parallel +darr_map=darr.map_blocks(interp_label, dtype=np.uint16) + +with ProgressBar(): + da.to_zarr(darr_map,out_zarr,overwrite=True) + + diff --git a/workflow/scripts/tif_to_zarr.py b/workflow/scripts/tif_to_zarr.py new file mode 100644 index 0000000..7efc4c5 --- /dev/null +++ b/workflow/scripts/tif_to_zarr.py @@ -0,0 +1,55 @@ +import tifffile +import json +import dask.array as da +import dask.array.image +from itertools import product +from dask.diagnostics import ProgressBar + +in_tif_glob = snakemake.params.in_tif_glob + +#create function handle to tifffile.imread that sets key=0 +def single_imread(*args): + return tifffile.imread(*args,key=0) + +#read metadata json +with open(snakemake.input.metadata_json) as fp: + metadata = json.load(fp) + +#TODO: put these in top-level metadata for easier access.. +size_x=metadata['ome_full_metadata']['OME']['Image']['Pixels']['@SizeX'] +size_y=metadata['ome_full_metadata']['OME']['Image']['Pixels']['@SizeY'] +size_z=metadata['ome_full_metadata']['OME']['Image']['Pixels']['@SizeZ'] +size_c=metadata['ome_full_metadata']['OME']['Image']['Pixels']['@SizeC'] +size_tiles=len(metadata['tiles_x'])*len(metadata['tiles_y']) + + +#now get the first channel and first zslice tif +tiles=[] +for i_tile,(tilex,tiley) in enumerate(product(metadata['tiles_x'],metadata['tiles_y'])): + + zstacks=[] + for i_chan,channel in enumerate(metadata['channels']): + + + zstacks.append(dask.array.image.imread(in_tif_glob.format(tilex=tilex,tiley=tiley,prefix=metadata['prefixes'][0],channel=channel,zslice='*'), imread=single_imread)) + + + #have list of zstack dask arrays for the tile, one for each channel + #stack them up and append to list of tiles + tiles.append(da.stack(zstacks)) + + +#now we have list of tiles, each a dask array +#stack them up to get our final array +darr = da.stack(tiles) + +#rescale intensities, and recast +darr = darr * snakemake.params.intensity_rescaling +darr = darr.astype('uint16') + +#now we can do the computation itself, storing to zarr +print('writing images to zarr with dask') +with ProgressBar(): + da.to_zarr(darr,snakemake.output.zarr,overwrite=True) + + diff --git a/workflow/scripts/zarr_to_n5_bdv.py b/workflow/scripts/zarr_to_n5_bdv.py new file mode 100644 index 0000000..14a40e2 --- /dev/null +++ b/workflow/scripts/zarr_to_n5_bdv.py @@ -0,0 +1,116 @@ +import json +import numpy as np +import npy2bdv +import tifffile +import zarr +import dask +import dask.array as da +from pathlib import Path +from shutil import rmtree +from itertools import product +from dask.diagnostics import ProgressBar +import xml.etree.ElementTree as ET + +def update_xml_h5_to_n5(in_xml,out_xml,in_n5): + """updates the dataset.xml file + in-place to replace hdf5 with n5""" + tree = ET.parse(in_xml) + + root = tree.getroot() + for elem in root.iter(): + if 'format' in elem.keys(): + elem.set('format','bdv.n5') + elem.set('version','1.0') + for child_elem in elem.iter('hdf5'): + child_elem.tag = 'n5' + child_elem.text = str(Path(in_n5).name) + + tree.write(out_xml,encoding='utf-8') + return + + +in_zarr = snakemake.input.zarr + +max_downsampling_layers=snakemake.params.max_downsampling_layers + +#load data (tiles,chans,zslices,x,y) +darr = da.from_zarr(in_zarr) + +(n_tiles,n_chans,n_z,n_x,n_y) = darr.shape + +#read metadata json +with open(snakemake.input.metadata_json) as fp: + metadata = json.load(fp) + + +#create temp_folder for h5/xml +temp_bdv_dir=Path(snakemake.params.temp_h5).parent +temp_bdv_dir.mkdir(parents=True, exist_ok=True) + +#create new bdv volume +bdv_writer = npy2bdv.BdvWriter(snakemake.params.temp_h5, + overwrite=True, + nchannels=len(metadata['channels']), + ntiles=len(metadata['tiles_x'])*len(metadata['tiles_y']), + blockdim=((1,256,256),)) + +bdv_writer.set_attribute_labels('channel', metadata['channels']) + + +for i_chan,channel in enumerate(metadata['channels']): + for i_tile in metadata['chunks']: + + key = f'tile-{i_tile}_chan-{channel}_z-0000' + affine = np.eye(3,4) + affine[0,3] = metadata['lookup_tile_offset_x'][key] / float(metadata['physical_size_x']) + affine[1,3] = -metadata['lookup_tile_offset_y'][key] / float(metadata['physical_size_y']) + affine[2,3] = -metadata['lookup_tile_offset_z'][key] / float(metadata['physical_size_z']) + bdv_writer.append_view(stack=None, + #virtual_stack_dim=(shape[3],shape[2],shape[1]), + virtual_stack_dim=(metadata['shape'][1],metadata['shape'][2],metadata['shape'][3]), + tile=i_tile,channel=i_chan, + voxel_units='μm', + voxel_size_xyz=(metadata['physical_size_x'], + metadata['physical_size_y'], + metadata['physical_size_z']), + m_affine=affine, + name_affine='Translation to Regular Grid', + calibration=(1,1,float(metadata['physical_size_z'])/float(metadata['physical_size_x'])), + ) + + + + +print('writing dataset xml and (empty h5) using npy2bdv') +bdv_writer.write_xml() +bdv_writer.close() + +print('updating xml to point to n5 instead of h5') +update_xml_h5_to_n5(snakemake.params.temp_xml,snakemake.output.bdv_xml,snakemake.output.bdv_n5) + +print('removing empty bdv h5/xml') +rmtree(temp_bdv_dir) + +print('writing data to n5') +n5_store = zarr.n5.N5Store(snakemake.output.bdv_n5) + +for setup_i,((chan_i,chan),(tile_i,tile)) in enumerate(product(enumerate(metadata["channels"]),enumerate(metadata["chunks"]))): + + 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]) + 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) + #add attributes for downsampling + a = zarr.open_array(store=n5_store,path=f'setup{setup_i}/timepoint0/s{ds}',mode='r+') + a.attrs['downsamplingFactors']=[step,step,1] + ds_list.append([step,step,1]) + + #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']='uint16' + + diff --git a/workflow/scripts/zarr_to_ome_zarr.py b/workflow/scripts/zarr_to_ome_zarr.py new file mode 100644 index 0000000..eb449b9 --- /dev/null +++ b/workflow/scripts/zarr_to_ome_zarr.py @@ -0,0 +1,60 @@ +import json +import zarr +import dask.array as da +from ome_zarr.writer import write_image +from ome_zarr.scale import Scaler +from dask.diagnostics import ProgressBar + +in_zarr=snakemake.input.zarr +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 + +#open zarr to get group name +zi = zarr.open(in_zarr) +group_name = [g for g in zi.group_keys()][0] + +darr = da.from_zarr(in_zarr,component=f'{group_name}/s0',chunks=rechunk_size) +darr + + +# 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': [voxdim[0],(2**l)*voxdim[1],(2**l)*voxdim[2]], #image-pyramids in XY only + 'type': 'scale'}]) + + +axes = [ {'name': ax, 'type': 'space', 'unit': 'micrometer'} for ax in ['z','y','x'] ] + + + +store = zarr.DirectoryStore(out_zarr) +root = zarr.group(store,path='/fused',overwrite=True) +scaler = Scaler(max_layer=max_layer,method=scaling_method) + + + +with ProgressBar(): + write_image(image=darr, + group=root, + scaler=scaler, + coordinate_transformations=coordinate_transformations, + axes=axes) + + diff --git a/workflow/scripts/zarr_to_ome_zarr_labels.py b/workflow/scripts/zarr_to_ome_zarr_labels.py new file mode 100644 index 0000000..7660f7d --- /dev/null +++ b/workflow/scripts/zarr_to_ome_zarr_labels.py @@ -0,0 +1,77 @@ +import json +import zarr +import dask.array as da +import pandas as pd +from ome_zarr.writer import write_labels, write_label_metadata +from ome_zarr.scale import Scaler +from dask.diagnostics import ProgressBar + + + +in_zarr=snakemake.input.zarr +metadata_json=snakemake.input.metadata_json +label_tsv = snakemake.input.label_tsv +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 +scaling_method=snakemake.params.scaling_method +label_name=snakemake.params.label_name +out_zarr=snakemake.output.zarr + + +darr = da.from_zarr(in_zarr) + +# 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': [voxdim[0],(2**l)*voxdim[1],(2**l)*voxdim[2]], #image-pyramids in XY only + 'type': 'scale'}]) + + +axes = [ {'name': ax, 'type': 'space', 'unit': 'micrometer'} for ax in ['z','y','x'] ] + + + +store = zarr.DirectoryStore(out_zarr) +root = zarr.group(store,path='/',overwrite=True) +scaler = Scaler(max_layer=max_layer,method='nearest') + + +#label metadata - convert from bids tsv to list of dicts +df = pd.read_csv(label_tsv,sep='\t',index_col='index') + +colors=[] +properties=[] +for i, row in df.iterrows(): + if i>0: #leave out bg label + hex=row['color'].lstrip("#") + rgba = tuple(int(hex[i:i+2], 16) for i in (0, 2, 4)) + colors.append({'label-value': i, 'rgba': rgba}) + properties.append({'label-value': i, 'name': row['name'], 'abbreviation': row['abbreviation']}) + + +with ProgressBar(): + write_labels(labels=darr, + group=root, + scaler=scaler, + name=label_name, + coordinate_transformations=coordinate_transformations, + axes=axes) + + write_label_metadata(group=root, + name=f'/labels/{label_name}', + colors=colors, + properties=properties) +