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sc-data-profiler

Profile raw 10x Genomics single-cell matrices to output QC distributions as JSON.

Updated Mar 30, 2026
Search Tags:#data-analysis#quality-control#profile#10x#scrna-seq#mitochondrial-percent
One-click install
npx skills add https://github.com/ya-way/cytoclaw-skills --skill sc-data-profiler
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sc-data-profiler
Source: https://github.com/ya-way/cytoclaw-skills/tree/main/workspace/skills/sc-data-profiler
Command: npx skills add https://github.com/ya-way/cytoclaw-skills --skill sc-data-profiler

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scanpy, numpy, and includes scripts (resource) components.

What problem does it solve?

Profile raw 10x Genomics single-cell matrices without modification and provide robust QC statistics to guide adaptive preprocessing decisions, batch QC, and thresholding before filtering.

Core Features & Use Cases

  • Read raw 10x input (mtx/tsv or h5) without altering data and compute per-cell metrics (n_cells, genes per cell, mito percent).
  • Produce QC summaries including mito_pct_quantiles (q25, q50, q90) and recommended mito cutoff candidates for data-driven filtering.
  • Use cases: when a user asks for single-cell preprocessing, PBMC QC, basic data diagnostics, or threshold selection prior to filtering.

Quick Start

Provide the path to the raw 10x dataset to generate a QC summary JSON for downstream decisions.

Frequently Asked Questions about sc-data-profiler

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compute mitochondrial percent and gene counts for 10x Genomics single-cell data?▼

To compute mitochondrial percent and gene counts for 10x Genomics single-cell data, read raw matrices from mtx, tsv, or h5 files and calculate per-cell QC metrics like mito percent and total genes. This outputs a JSON summary for adaptive filtering.

What is the best way to select mitochondrial cutoff thresholds before filtering scRNA-seq data?▼

Selecting mitochondrial cutoff thresholds for scRNA-seq data requires profiling raw matrices to extract mito_pct_quantiles like q25, q50, and q90. These quantiles generate recommended mito cutoff candidates to guide data-driven filtering decisions.

Can I use scanpy to generate QC distributions for raw 10x single-cell matrices?▼

Yes, you can use scanpy alongside numpy to generate QC distributions for raw 10x single-cell matrices. The process reads input formats including mtx, tsv, and h5 to compute per-cell metrics without altering the original data.

How do I profile raw 10x single-cell matrices for PBMC QC and basic data diagnostics?▼

Profiling raw 10x single-cell matrices for PBMC QC involves reading the unfiltered data and calculating per-cell metrics such as n_cells and n_genes_median_per_cell. This generates a diagnostic JSON summary for threshold selection.

Does single-cell QC profiling modify my raw 10x Genomics input data?▼

Single-cell QC profiling does not modify raw 10x Genomics input data. It reads the original mtx, tsv, or h5 files without alteration to compute per-cell metrics and output QC statistics, ensuring your raw datasets remain intact for downstream workflows.

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