Consolidate BANKSY documentation and workflows for spatial transcriptomics analysis.
npx skills add https://github.com/Ketomihine/my_skills --skill banksy-merged
Please help me install this Agent Skill. Skill: banksy-merged Source: https://github.com/Ketomihine/my_skills/tree/main/banksy-merged Command: npx skills add https://github.com/Ketomihine/my_skills --skill banksy-merged
This skill consolidates BANKSY documentation and example workflows into a single, deduplicated resource, enabling researchers to quickly access APIs, notebooks, and reference material without navigating multiple sources.
Read the BANKSY documentation in references/, view the notebooks in references/notebooks.md, and run example workflows from references/notebooks_html. Use the included references to understand API signatures and pipeline steps, then adapt to your dataset.
High-intent search queries and answers about installing and using this skill.
To generate a BANKSY matrix for spatial transcriptomics clustering, you construct a spatial graph and apply Azimuthal Gabor Filters to capture neighborhood interactions. This consolidated resource provides guided notebooks detailing the API signatures and pipeline steps needed to build the matrix and run clustering.
Comparing BANKSY clustering with nonspatial methods across multiple samples is best handled using the provided multi-sample workflows. These workflows include plotting and reporting utilities that enable direct comparisons of spatial versus nonspatial clustering results within a single, deduplicated pipeline.
Yes, you can run multi-sample spatial transcriptomics workflows using the BANKSY algorithm. The consolidated documentation includes example workflows specifically designed to process and compare multiple samples, allowing you to adapt the provided references and notebooks directly to your dataset.
No external dependencies are required to access the BANKSY documentation, as this skill operates independently to provide deduplicated references. You only need the core BANKSY library installed in your environment to execute the spatial graph construction and visualization workflows described in the notebooks.
You can find BANKSY API signatures and parameters for spatial transcriptomics analysis in the consolidated references directory. This single resource aggregates core library documentation, PCA references, and notebook examples, eliminating the need to navigate multiple separate documentation sources.
Azimuthal Gabor Filters are used in BANKSY workflows to encode spatial neighborhood interactions into the matrix, enhancing clustering accuracy. The included guided notebooks explain how to configure these filter parameters to capture spatial domain boundaries effectively.
KetomihineOrchestrates BANKSY-based spatial transcriptomics analysis from graphs to clustering and visualization in Python and optionally R.
KetomihineAutomate BANKSY spatial transcriptomics analysis for multisample clustering and visualization.
TianGzlabLoad, quality control, normalize, and preprocess spatial transcriptomics data into a standardized AnnData object.
TianGzlabIdentify tissue regions from spatial transcriptomics data using clustering algorithms.
