Important! Template update for nf-core/tools v2.10

.devcontainer/devcontainer.json

@@ -2,6 +2,7 @@
     "name": "nfcore",
     "image": "nfcore/gitpod:latest",
     "remoteUser": "gitpod",
+    "runArgs": ["--privileged"],
 
     // Configure tool-specific properties.
     "customizations": {

.github/CONTRIBUTING.md

@@ -9,7 +9,9 @@ Please use the pre-filled template to save time.
 However, don't be put off by this template - other more general issues and suggestions are welcome!
 Contributions to the code are even more welcome ;)
 
-> If you need help using or modifying nf-core/molkart then the best place to ask is on the nf-core Slack [#molkart](https://nfcore.slack.com/channels/molkart) channel ([join our Slack here](https://nf-co.re/join/slack)).
+:::info
+If you need help using or modifying nf-core/molkart then the best place to ask is on the nf-core Slack [#molkart](https://nfcore.slack.com/channels/molkart) channel ([join our Slack here](https://nf-co.re/join/slack)).
+:::
 
 ## Contribution workflow
 

.github/workflows/linting.yml

@@ -78,7 +78,7 @@ jobs:
 
       - uses: actions/setup-python@v4
         with:
-          python-version: "3.8"
+          python-version: "3.11"
           architecture: "x64"
 
       - name: Install dependencies

.github/workflows/release-announcments.yml

@@ -0,0 +1,68 @@
+name: release-announcements
+# Automatic release toot and tweet anouncements
+on:
+  release:
+    types: [published]
+  workflow_dispatch:
+
+jobs:
+  toot:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: rzr/fediverse-action@master
+        with:
+          access-token: ${{ secrets.MASTODON_ACCESS_TOKEN }}
+          host: "mstdn.science" # custom host if not "mastodon.social" (default)
+          # GitHub event payload
+          # https://docs.github.com/en/developers/webhooks-and-events/webhooks/webhook-events-and-payloads#release
+          message: |
+            Pipeline release! ${{ github.repository }} v${{ github.event.release.tag_name }} - ${{ github.event.release.name }}!
+
+            Please see the changelog: ${{ github.event.release.html_url }}
+
+  send-tweet:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+      - name: Install dependencies
+        run: pip install tweepy==4.14.0
+      - name: Send tweet
+        shell: python
+        run: |
+          import os
+          import tweepy
+
+          client = tweepy.Client(
+              access_token=os.getenv("TWITTER_ACCESS_TOKEN"),
+              access_token_secret=os.getenv("TWITTER_ACCESS_TOKEN_SECRET"),
+              consumer_key=os.getenv("TWITTER_CONSUMER_KEY"),
+              consumer_secret=os.getenv("TWITTER_CONSUMER_SECRET"),
+          )
+          tweet = os.getenv("TWEET")
+          client.create_tweet(text=tweet)
+        env:
+          TWEET: |
+            Pipeline release! ${{ github.repository }} v${{ github.event.release.tag_name }} - ${{ github.event.release.name }}!
+
+            Please see the changelog: ${{ github.event.release.html_url }}
+          TWITTER_CONSUMER_KEY: ${{ secrets.TWITTER_CONSUMER_KEY }}
+          TWITTER_CONSUMER_SECRET: ${{ secrets.TWITTER_CONSUMER_SECRET }}
+          TWITTER_ACCESS_TOKEN: ${{ secrets.TWITTER_ACCESS_TOKEN }}
+          TWITTER_ACCESS_TOKEN_SECRET: ${{ secrets.TWITTER_ACCESS_TOKEN_SECRET }}
+
+  bsky-post:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: zentered/[email protected]
+        with:
+          post: |
+            Pipeline release! ${{ github.repository }} v${{ github.event.release.tag_name }} - ${{ github.event.release.name }}!
+
+            Please see the changelog: ${{ github.event.release.html_url }}
+        env:
+          BSKY_IDENTIFIER: ${{ secrets.BSKY_IDENTIFIER }}
+          BSKY_PASSWORD: ${{ secrets.BSKY_PASSWORD }}
+          #

CITATIONS.md

@@ -12,7 +12,7 @@
 
 - [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
 
-  > Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online]. Available online https://www.bioinformatics.babraham.ac.uk/projects/fastqc/.
+  > Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online].
 
 - [MultiQC](https://pubmed.ncbi.nlm.nih.gov/27312411/)
 

README.md

@@ -1,6 +1,7 @@
 # ![nf-core/molkart](docs/images/nf-core-molkart_logo_light.png#gh-light-mode-only) ![nf-core/molkart](docs/images/nf-core-molkart_logo_dark.png#gh-dark-mode-only)
 
-[![AWS CI](https://img.shields.io/badge/CI%20tests-full%20size-FF9900?labelColor=000000&logo=Amazon%20AWS)](https://nf-co.re/molkart/results)[![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.XXXXXXX-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.XXXXXXX)
+[![GitHub Actions CI Status](https://github.com/nf-core/molkart/workflows/nf-core%20CI/badge.svg)](https://github.com/nf-core/molkart/actions?query=workflow%3A%22nf-core+CI%22)
+[![GitHub Actions Linting Status](https://github.com/nf-core/molkart/workflows/nf-core%20linting/badge.svg)](https://github.com/nf-core/molkart/actions?query=workflow%3A%22nf-core+linting%22)[![AWS CI](https://img.shields.io/badge/CI%20tests-full%20size-FF9900?labelColor=000000&logo=Amazon%20AWS)](https://nf-co.re/molkart/results)[![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.XXXXXXX-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.XXXXXXX)
 
 [![Nextflow](https://img.shields.io/badge/nextflow%20DSL2-%E2%89%A523.04.0-23aa62.svg)](https://www.nextflow.io/)
 [![run with conda](http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000&logo=anaconda)](https://docs.conda.io/en/latest/)
@@ -11,12 +12,12 @@
 [![Get help on Slack](http://img.shields.io/badge/slack-nf--core%20%23molkart-4A154B?labelColor=000000&logo=slack)](https://nfcore.slack.com/channels/molkart)[![Follow on Twitter](http://img.shields.io/badge/twitter-%40nf__core-1DA1F2?labelColor=000000&logo=twitter)](https://twitter.com/nf_core)[![Follow on Mastodon](https://img.shields.io/badge/mastodon-nf__core-6364ff?labelColor=FFFFFF&logo=mastodon)](https://mstdn.science/@nf_core)[![Watch on YouTube](http://img.shields.io/badge/youtube-nf--core-FF0000?labelColor=000000&logo=youtube)](https://www.youtube.com/c/nf-core)
 
 ## Important note
+
 This pipeline is under active development and does not work at this point. If you want to process your Resolve Bioscience data in the meantime, please go to https://github.com/FloWuenne/nf-MolKart, where you can find a working nextflow pipeline, outside the nf-core ecosystem. In the coming weeks, nf-MolKart will be completely transitioned into nf-core/molkart for future use.
 
 ## Introduction
 
-**nf-core/molkart** is a pipeline for processing  Molecular Cartography data from Resolve Bioscience (combinatorial FISH). It takes as input a table of FISH spot positions (x,y,z,gene), a corresponding DAPI image (tiff format) and optionally a membrane staining image in tiff format. nf-core/molkart performs end-to-end processing of the data including image processing,  QC filtering of spots, cell segmentation, spot to cell assignment and reports quality metrics such as the spot assignment rate, average spots per cell and segmentation mask size ranges.
-
+**nf-core/molkart** is a pipeline for processing Molecular Cartography data from Resolve Bioscience (combinatorial FISH). It takes as input a table of FISH spot positions (x,y,z,gene), a corresponding DAPI image (tiff format) and optionally a membrane staining image in tiff format. nf-core/molkart performs end-to-end processing of the data including image processing, QC filtering of spots, cell segmentation, spot to cell assignment and reports quality metrics such as the spot assignment rate, average spots per cell and segmentation mask size ranges.
 
 <!-- TODO nf-core: Include a figure that guides the user through the major workflow steps. Many nf-core
      workflows use the "tube map" design for that. See https://nf-co.re/docs/contributing/design_guidelines#examples for examples.   -->
@@ -27,10 +28,11 @@ This pipeline is under active development and does not work at this point. If yo
 
 ## Usage
 
-> **Note**
-> If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/usage/installation) on how
-> to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline)
-> with `-profile test` before running the workflow on actual data.
+:::note
+If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/usage/installation) on how
+to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline)
+with `-profile test` before running the workflow on actual data.
+:::
 
 <!-- TODO nf-core: Describe the minimum required steps to execute the pipeline, e.g. how to prepare samplesheets.
      Explain what rows and columns represent. For instance (please edit as appropriate):
@@ -59,10 +61,11 @@ nextflow run nf-core/molkart \
    --outdir <OUTDIR>
 ```
 
-> **Warning:**
-> Please provide pipeline parameters via the CLI or Nextflow `-params-file` option. Custom config files including those
-> provided by the `-c` Nextflow option can be used to provide any configuration _**except for parameters**_;
-> see [docs](https://nf-co.re/usage/configuration#custom-configuration-files).
+:::warning
+Please provide pipeline parameters via the CLI or Nextflow `-params-file` option. Custom config files including those
+provided by the `-c` Nextflow option can be used to provide any configuration _**except for parameters**_;
+see [docs](https://nf-co.re/usage/configuration#custom-configuration-files).
+:::
 
 For more details and further functionality, please refer to the [usage documentation](https://nf-co.re/molkart/usage) and the [parameter documentation](https://nf-co.re/molkart/parameters).
 

assets/multiqc_config.yml

@@ -1,7 +1,7 @@
 report_comment: >
-  This report has been generated by the <a href="https://github.com/nf-core/molkart/1.0dev" target="_blank">nf-core/molkart</a>
+  This report has been generated by the <a href="https://github.com/nf-core/molkart/releases/tag/dev" target="_blank">nf-core/molkart</a>
   analysis pipeline. For information about how to interpret these results, please see the
-  <a href="https://nf-co.re/molkart/1.0dev/output" target="_blank">documentation</a>.
+  <a href="https://nf-co.re/molkart/dev/docs/output" target="_blank">documentation</a>.
 report_section_order:
   "nf-core-molkart-methods-description":
     order: -1000

bin/apply_clahe.dask.py

@@ -22,7 +22,8 @@
 from os.path import abspath
 from argparse import ArgumentParser as AP
 import time
-#from memory_profiler import profile
+
+# from memory_profiler import profile
 # This API is apparently changing in skimage 1.0 but it's not clear to
 # me what the replacement will be, if any. We'll explicitly import
 # this so it will break loudly if someone tries this with skimage 1.0.
@@ -34,7 +35,7 @@
 
 def get_args():
     # Script description
-    description="""Easy-to-use, large scale CLAHE"""
+    description = """Easy-to-use, large scale CLAHE"""
 
     # Add parser
     parser = AP(description=description, formatter_class=argparse.RawDescriptionHelpFormatter)
@@ -43,10 +44,16 @@ def get_args():
     inputs = parser.add_argument_group(title="Required Input", description="Path to required input file")
     inputs.add_argument("-r", "--raw", dest="raw", action="store", required=True, help="File path to input image.")
     inputs.add_argument("-o", "--output", dest="output", action="store", required=True, help="Path to output image.")
-    inputs.add_argument("-c", "--channel", dest="channel", action="store", required=True, help="Channel on which CLAHE will be applied")
+    inputs.add_argument(
+        "-c", "--channel", dest="channel", action="store", required=True, help="Channel on which CLAHE will be applied"
+    )
     inputs.add_argument("-l", "--cliplimit", dest="clip", action="store", required=True, help="Clip Limit for CLAHE")
-    inputs.add_argument("--kernel", dest="kernel", action="store", required=False, default=None, help="Kernel size for CLAHE")
-    inputs.add_argument("-g", "--nbins", dest="nbins", action="store", required=False, default=256, help="Number of bins for CLAHE")
+    inputs.add_argument(
+        "--kernel", dest="kernel", action="store", required=False, default=None, help="Kernel size for CLAHE"
+    )
+    inputs.add_argument(
+        "-g", "--nbins", dest="nbins", action="store", required=False, default=256, help="Number of bins for CLAHE"
+    )
     inputs.add_argument("-p", "--pixel-size", dest="pixel_size", action="store", required=True, help="Image pixel size")
 
     arg = parser.parse_args()
@@ -61,54 +68,58 @@ def get_args():
 
     return arg
 
+
 def preduce(coords, img_in, img_out):
     print(img_in.dtype)
     (iy1, ix1), (iy2, ix2) = coords
     (oy1, ox1), (oy2, ox2) = np.array(coords) // 2
     tile = skimage.img_as_float32(img_in[iy1:iy2, ix1:ix2])
     tile = skimage.transform.downscale_local_mean(tile, (2, 2))
-    tile = dtype_convert(tile, 'uint16')
-    #tile = dtype_convert(tile, img_in.dtype)
+    tile = dtype_convert(tile, "uint16")
+    # tile = dtype_convert(tile, img_in.dtype)
     img_out[oy1:oy2, ox1:ox2] = tile
 
+
 def format_shape(shape):
     return "%dx%d" % (shape[1], shape[0])
 
+
 def subres_tiles(level, level_full_shapes, tile_shapes, outpath, scale):
     print(f"\n processing level {level}")
     assert level >= 1
     num_channels, h, w = level_full_shapes[level]
     tshape = tile_shapes[level] or (h, w)
     tiff = tifffile.TiffFile(outpath)
-    zimg = zarr.open(tiff.aszarr(series=0, level=level-1, squeeze=False))
+    zimg = zarr.open(tiff.aszarr(series=0, level=level - 1, squeeze=False))
     for c in range(num_channels):
-        sys.stdout.write(
-            f"\r  processing channel {c + 1}/{num_channels}"
-        )
+        sys.stdout.write(f"\r  processing channel {c + 1}/{num_channels}")
         sys.stdout.flush()
         th = tshape[0] * scale
         tw = tshape[1] * scale
         for y in range(0, zimg.shape[1], th):
             for x in range(0, zimg.shape[2], tw):
-                a = zimg[c, y:y+th, x:x+tw, 0]
-                a = skimage.transform.downscale_local_mean(
-                    a, (scale, scale)
-                )
+                a = zimg[c, y : y + th, x : x + tw, 0]
+                a = skimage.transform.downscale_local_mean(a, (scale, scale))
                 if np.issubdtype(zimg.dtype, np.integer):
                     a = np.around(a)
-                a = a.astype('uint16')
+                a = a.astype("uint16")
                 yield a
 
+
 def main(args):
     print(f"Head directory = {args.raw}")
-    print(f"Channel = {args.channel}, ClipLimit = {args.clip}, nbins = {args.nbins}, kernel_size = {args.kernel}, pixel_size = {args.pixel_size}")
+    print(
+        f"Channel = {args.channel}, ClipLimit = {args.clip}, nbins = {args.nbins}, kernel_size = {args.kernel}, pixel_size = {args.pixel_size}"
+    )
 
-    #clahe = cv2.createCLAHE(clipLimit = int(args.clip), tileGridSize=tuple(map(int, args.grid)))
+    # clahe = cv2.createCLAHE(clipLimit = int(args.clip), tileGridSize=tuple(map(int, args.grid)))
 
     img_raw = AI.AICSImage(args.raw)
-    img_dask = img_raw.get_image_dask_data("CYX").astype('uint16')
-    adapted = img_dask[args.channel].compute()/65535
-    adapted = (equalize_adapthist(adapted, kernel_size=args.kernel, clip_limit=args.clip, nbins=args.nbins)*65535).astype('uint16')
+    img_dask = img_raw.get_image_dask_data("CYX").astype("uint16")
+    adapted = img_dask[args.channel].compute() / 65535
+    adapted = (
+        equalize_adapthist(adapted, kernel_size=args.kernel, clip_limit=args.clip, nbins=args.nbins) * 65535
+    ).astype("uint16")
     img_dask[args.channel] = adapted
 
     # construct levels
@@ -121,7 +132,7 @@ def main(args):
     num_channels = img_dask.shape[0]
     num_levels = (np.ceil(np.log2(max(base_shape) / tile_size)) + 1).astype(int)
     factors = 2 ** np.arange(num_levels)
-    shapes = (np.ceil(np.array(base_shape) / factors[:,None])).astype(int)
+    shapes = (np.ceil(np.array(base_shape) / factors[:, None])).astype(int)
 
     print("Pyramid level sizes: ")
     for i, shape in enumerate(shapes):
@@ -137,40 +148,35 @@ def main(args):
         level_full_shapes.append((num_channels, shape[0], shape[1]))
     level_shapes = shapes
     tip_level = np.argmax(np.all(level_shapes < tile_size, axis=1))
-    tile_shapes = [
-        (tile_size, tile_size) if i <= tip_level else None
-        for i in range(len(level_shapes))
-    ]
+    tile_shapes = [(tile_size, tile_size) if i <= tip_level else None for i in range(len(level_shapes))]
 
     # write pyramid
     with tifffile.TiffWriter(args.output, ome=True, bigtiff=True) as tiff:
         tiff.write(
-            data = img_dask,
-            shape = level_full_shapes[0],
-            subifds=int(num_levels-1),
+            data=img_dask,
+            shape=level_full_shapes[0],
+            subifds=int(num_levels - 1),
             dtype=dtype,
             resolution=(10000 / pixel_size, 10000 / pixel_size, "centimeter"),
-            tile=tile_shapes[0]
+            tile=tile_shapes[0],
         )
-        for level, (shape, tile_shape) in enumerate(
-                zip(level_full_shapes[1:], tile_shapes[1:]), 1
-        ):
+        for level, (shape, tile_shape) in enumerate(zip(level_full_shapes[1:], tile_shapes[1:]), 1):
             tiff.write(
-                data = subres_tiles(level, level_full_shapes, tile_shapes, args.output, scale),
+                data=subres_tiles(level, level_full_shapes, tile_shapes, args.output, scale),
                 shape=shape,
                 subfiletype=1,
                 dtype=dtype,
-                tile=tile_shape
+                tile=tile_shape,
             )
 
     # note about metadata: the channels, planes etc were adjusted not to include the removed channels, however
     # the channel ids have stayed the same as before removal. E.g if channels 1 and 2 are removed,
     # the channel ids in the metadata will skip indices 1 and 2 (channel_id:0, channel_id:3, channel_id:4 ...)
-    #tifffile.tiffcomment(args.output, to_xml(metadata))
+    # tifffile.tiffcomment(args.output, to_xml(metadata))
     print()
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     # Read in arguments
     args = get_args()