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Bio Expression Matrix Sparse Handling

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gptomics·vlatest·backend·MIT

Optimizes large biological expression matrices by efficiently managing sparse data for faster analysis and reduced storage needs.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add gptomics/bioskills --skill bio-expression-matrix-sparse-handling --full-depth -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

gptomics

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Install target

Live repository

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No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

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  • No credential access: passed

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  • Pinned to a commit: passed

    Scanned gptomics/bioskills at d91ed3d563.

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Commit
d91ed3d563
Manifest
expression-matrix/sparse-handling/SKILL.md
Scanned
3 files, 674 lines
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About this skill

What it does

This skill efficiently handles sparse bio-expression matrices, a common data structure in bioinformatics. It allows for optimized storage and computation on these matrices, significantly reducing memory usage and processing time compared to dense matrix representations. Specifically, it facilitates operations like normalization, filtering, and statistical analysis of gene expression data.

When to use it

  • Analyzing single-cell RNA sequencing (scRNA-seq) data where most genes have zero expression in many cells.
  • Performing differential expression analysis on large datasets with sparse matrices.
  • Building machine learning models that require efficient processing of high-dimensional, sparse gene expression data.
  • Calculating correlations or performing other statistical operations on bio-expression data.

Key capabilities

  • Sparse matrix representation and storage
  • Normalization of sparse matrices
  • Filtering of rows/columns in sparse matrices
  • Efficient mathematical operations on sparse matrices

Example prompts

  • "Normalize this gene expression matrix using the 'CPM' method."
  • "Filter out genes with an average expression below 0.5 from this matrix."
  • “Calculate the Pearson correlation coefficient between these two sparse bio-expression matrices.”

Tips & gotchas

This skill is most effective when dealing with genuinely sparse matrices (matrices containing a high proportion of zero values). Ensure your input data is in a suitable format for efficient processing.