spatialsnake 0.0.1

A Python package for spatial transcriptomics analysis workflows.

Spatialsnake :

A Snakemake workflow for spatial transcriptomics powered by spatialdata framework

for more detail of the usage of the pipeline please read the documentation

• Snakemake workflow: spatialsnake
  • Usage
  • Deployment options
  • lzh
  • References
  • TODO

** How to install spatialsnake **

prepare the environment first.

## Create conda environment with the environment.yml file in github code page

conda env create -f environment.yml -n spatialsnake_env     ## [or setting your own conda env name]

conda activate spatialsnake_env

install the spatialsnake.

git clone https://github.com/l-zh007/spatialsnake.git

cd spatialsnake
pip install -e .                       # or [pip install .]  优选-e开发者模式

spatialsnake -h
spatialsnake install-packages          # (Install coordinate R packages)

mkdir project
cd project

start your analysis with file[sample.txt] and spatialdata in [data/*] and output dir [results]

please make sure your spatialdata folder name in [data/] in accordance with sample_name in  [sample.txt]

Examples:

Run only one step of ["integrate","preprocess","clustering","annotion_help","annotion","compare_analyze","advance_analysis"]
  spatialsnake <sample_channal> sample.txt <data_type> --option=<option_name> [other_params]

IF you want to run with integrate multiple sample:

Run compare_analysis
  spatialsnake compare_analysis sample.txt <data_type> --option=<option_name>
  
Run all basic steps on single sample (default behavior)
  spatialsnake <sample_channal> sample.txt <data_type> --option=all


IF you want to run with setting your own params:

  spatialsnake produce-file [--option=<analysis_option>]
  spatialsnake <sample_channal> sample.txt <data_type> --option=<option_name> --config-file <*.yaml>

We produce some useful_tool to help you analysis.

Split integrated data with barcode
  spatialsnake useful_tool integrated_data.zarr --data_barcode B_cell
  
Split integrated data with image coordinate
  spatialsnake useful_tool integrated_data.zarr --max_x --min_x --max_y --min_y2

Install coordinate R packages  
  spatialsnake install-packages

Usage:

spatialsnake <command> <INPUT> <TYPE> [--option=<analysis_option>] [options]
spatialsnake useful_tool [--option=<ways>] <INPUT> [options]
spatialsnake produce-file [--option=<analysis_option>]
spatialsnake install-packages
spatialsnake (-h | --help)
spatialsnake --version

commands:
  single_analysis      Process single spatial transcriptomics dataset (runs all basic steps except advance_analysis by default)
  compare_analysis     Compare multiple spatial transcriptomics datasets

analysis option:
    integrate
    preprocess
    clustering
    annotion_help
    annotion
    compare_analyze
    advance_analysis

Type Arguments:
    visium
    visium_segment
    visium_HD
    xenium
    Merfish
    slide_seq

INPUT Arguments:
    sample.txt
    annotion.txt
    filter_list

Basic Configuration:
    --configfile <FILE>    Configuration file in YAML format [default: config.yaml].

Integration Step Options (--option integrate):
    --cells_boundaries <BOOL>    xenium key in load in data [default: False].
    --nucleus_boundaries <BOOL>  xenium key in load in data [default: False].
    --nucleus_labels <BOOL>      xenium key in load in data [default: False].
    --morphology_mip <BOOL>      xenium key in load in data [default: False].

Preprocessing Step Options (--option preprocess):
    --min_cells <INT>         Minimum spots per gene [default: 3].
    --min_genes <INT>         Minimum genes per spot [default: 200].
    --seg_filter <BOOL>       to seg filter the differnet sample dataset when command compare_anaysis [default: False].
    --filter_list <FILE>      filename of filter [default: False]
    --batch_method <TEXT>     batch method for multiple sample analysis [default: harmony]
    --sketch <BOOL>           whether use sketch method to analysis [default: False]

Clustering Step Options (--option clustering):
    --resolution <FLOAT>        Cluster resolution [default: 0.5].
    --cluster_algorithm <TEXT>  Clustering algorithm [default: leiden].
    --tsene <BOOL>              umap [default:False].
    --n_clusters <INT>          kmeans params of cluster [default: 15].

Annotation Help Step Options (--option annotion_help):
    --markers_algorithm <TEXT>       Automatically detect marker genes [default: wilcoxon].
    --spacies <TEXT>            Automatically detect marker genes [default: human].

Compare_analyze option Options (--option compare_analysis)
    --cell_focus <TEXT>         celltype you focus to compare in different sample[default: None].
    --compare_algorithm <TEXT>  compare analysys [default: DEseq2].
Annotation option Options (--option annotion):
    --annotation-file <FILE>    Annotation file for cell typing (required for annotion step)
    --anno_algorithm <TEXT>     Annotation method [default: mannul].
    --shape_type <TEXT>         Automatically detect marker genes [default: cell_boundaries].
    --image_type <TEXT>         Automatically detect marker genes [default: hires].
    --device <TEXT>                 cpu or GPU accelerate [default: cuda].

Advanced Analysis option Options (--option advance_analysis):
    --runpipe <TEXT>        Run  which analysis analysis.[default: advance_analysis]
    --senic_input <DIR>   Input file for PySCENIC analysis.[default: sample.zarr]
    --motifs_input <FILE>      PySCENIC database directory.[default: motifs-v9-nr.hgnc-m0.001-o0.0.tbl]
    --feather_input <FILE> path for necessary file of pyscenic.[default: hg38_10kbp_up_10kbp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather]
    --tfs_input <FILE>     path for necessary file of pyscenic.[default: hs_hgnc_tfs.txt]
    --count-data <TEXT>       gene type for cellPhoneDB [default: hgnc_symbol].
    --threads <INT>           workers for cellphoneDB [default: 8].
    --output_name <TEXT>      output name for cellPhoneDB [default: Normal].
useful_tool params Option:
    --output_zarr_path <FILE> output dir for splitted file [default: results]
    --split_by <TEXT>          slice out with the barcode in table[anndata] .obs [default: clusters]
    --max_x   <FLOAT>         coordinate of image boundaries [default: 0]
    --min_x   <FLOAT>         coordinate of image boundaries [default: 2000]
    --max_y   <FLOAT>         coordinate of image boundaries [default: 2000]
    --min_y   <FLOAT>         coordinate of image boundaries [default: 0]

General Options:
    -j <INT>, --jobs <INT>   Number of CPU cores [default: 32].
    --results_folder <DIR>     Output directory [default: results].

Utility Options:
    --install-packages   Install required packages.
    -u, --unlock         Unlock stalled workflow.
    -r, --remove         Remove all output files.
    -d, --dry            Dry run (simulate execution).
    -h, --help           Show this help message.
    --version            Show version.

Authors

• Firstname Lastname
  • Affiliation
  • ORCID profile
  • home page

References

Köster, J., Mölder, F., Jablonski, K. P., Letcher, B., Hall, M. B., Tomkins-Tinch, C. H., Sochat, V., Forster, J., Lee, S., Twardziok, S. O., Kanitz, A., Wilm, A., Holtgrewe, M., Rahmann, S., & Nahnsen, S. Sustainable data analysis with Snakemake. F1000Research, 10:33, 10, 33, 2021. https://doi.org/10.12688/f1000research.29032.2.

TODO

• Replace <owner> and <repo> everywhere in the template with the correct user name/organization, and the repository name. The workflow will be automatically added to the snakemake workflow catalog once it is publicly available on Github.
• Replace <name> with the workflow name (can be the same as <repo>).
• Replace <description> with a description of what the workflow does.
• Update the deployment, authors and references sections.
• Update the README.md badges. Add or remove badges for conda/singularity/apptainer usage depending on the workflow's deployment options.
• Do not forget to also adjust the configuration-specific config/README.md file.