PaSTa is a nextflow-based end-to-end image analysis pipeline for decoding image-based spatial transcriptomics data. It performs imaging cycle registration, cell segmentation and transcripts peak decoding. It is currently supports analysis of three types of ST technology: - in-situ sequencing-like encoding - MERFISH-like encoding - RNAScope-like labelling Prerequisites: 1. Nextflow. Installation guide: https://www.nextflow.io/docs/latest/getstarted.html 2. Docker or Singularity. Installation guide: https://docs.docker.com/get-docker/ or https://sylabs.io/guides/3.7/user-guide/quick_start.html Demo run with GitPod 1. Clone the repository ``` git clone https://github.com/cellgeni/Image-ST.git ``` 2. Prepare the run.config file * ``` process { withName: CELLPOSE { ext.args = "--channels [0,0]" storeDir = "./output/naive_cellpose_segmentation/" } withName: POSTCODE { memory = {20.Gb * task.attempt} storeDir = "./output/PoSTcode_decoding_output" } withName: TO_SPATIALDATA { memory = {20.Gb * task.attempt} ext.args = "--feature_col 'Name' --expansion_in_pixels 30 --save_label_img False" } withName: MERGE_OUTLINES { storeDir = "./output/merged_cellpose_segmentation/" } withName: BIOINFOTONGLI_MICROALIGNER { memory = {50.Gb * task.attempt} storeDir = "./output/registered_stacks" } withName: BIOINFOTONGLI_TILEDSPOTIFLOW { memory = {30.Gb * task.attempt} storeDir = "./output/spotiflow_peaks/" } withName: BIOINFOTONGLI_MERGEPEAKS { memory = {50.Gb * task.attempt} storeDir = "./output/spotiflow_peaks/" } withName: BIOINFOTONGLI_CONCATENATEWKTS { memory = {50.Gb * task.attempt} storeDir = "./output/spotiflow_peaks/" } withName: EXTRACT_PEAK_PROFILE { memory = {50.Gb * task.attempt} storeDir = "./output/peak_profiles/" } } ``` 3. Prepare the parameters file (e.g. iss.yaml) ``` images: - ['id': "test", [ "cycle1.ome.tiff", "cycle2.ome.tiff", "cycle3.ome.tiff", "cycle4.ome.tiff", "cycle5.ome.tiff", "cycle6.ome.tiff", ] ] cell_diameters: [30] chs_to_call_peaks: [1,2] // channels to call peaks, can be multiple codebook: - ['id': "test", "./codebook.csv", "./dummy.txt"] // has to match the meta in `images` variable segmentation_method: "CELLPOSE" // or DEEPCELL or STARDIST or INSTANSEG out_dir: "./output" ``` 4. Run the pipeline ``` nextflow run ./Image-ST/main.nf -profile lsf,singularity -c run.config -params-file iss.yaml -entry RUN_DECODING -resume ``` 5. Check the output in the specified storeDir. Spin up Napari with napari-spatialdata plugin installed (https://spatialdata.scverse.org/projects/napari/en/latest/notebooks/spatialdata.html) Then use the following command to visualize the output ``` from napari_spatialdata import Interactive import spatialdata as spd data = spd.read_zarr([path-to-.sdata-folder]) Interactive(data) ``` *: You may leave the process block empty if you want to use the default parameters. FAQ --- 1. My HOME dir is full when running Singularity image conversion on HPC. A quick solution is to manually specify singularity dir by setting: ``` singularity cache clean export SINGULARITY_CACHEDIR=./singularity_image_dir export NXF_SINGULARITY_CACHEDIR=./singularity_image_dir ``` 2. How do I modify parameters to specific process/step? By following nf-core standard, it is possible to add any parameters to the main script using ext.args=”--[key] [value]” in the run.config file. An example is withName: POSTCODE { ext.args = "--codebook_targer_col L-probe --codebook_code_col code " } 3. Cannot download pretrained model for the deep-learning tools (Spotiflow/CellPose) > Exception: URL fetch failure on https://drive.switch.ch/index.php/s/6AoTEgpIAeQMRvX/download: None -- [Errno -3] Temporary failure in name resolution Or CellPose urllib.error.URLError: Mostly likely you've reached max download (?), wait a bit and try later OR manually download those models and update the configuration file.