scrnaseq-cellranger-count
Generate feature-barcode count matrices from raw scRNA-seq FASTQ files using Cell Ranger, STARsolo, or alevin-fry.
- License 许可
- MIT
- 最近更新
- 2026-11-03
- Stars
- ★ 4
- Forks
- 1
01 前言 Front Matter
- name
- scrnaseq-cellranger-count
- description
- Generate feature-barcode count matrices from raw scRNA-seq FASTQ files using Cell Ranger, STARsolo, or alevin-fry.
02 资源文件 3
- 参考文档agents/claude.yaml320 B
- 参考文档agents/openai.yaml321 B
- 参考文档config.yaml301 B
03 SKILL.md 正文
Skill: scRNA-seq Cell Ranger Count
Use When
- User has raw FASTQ files from a droplet-based single-cell protocol (10x Chromium, Parse Biosciences, etc.) and needs a feature-barcode count matrix.
- User wants to run Cell Ranger count, STARsolo, or alevin-fry to produce per-cell gene expression matrices.
- User needs to assess sequencing saturation or cell calling statistics before downstream analysis.
- User is generating input for the scrnaseq-quality-control skill.
Inputs
- Required:
- Raw FASTQ files (Cell Ranger naming:
{sample}_S{n}_L00{lane}_R{read}_001.fastq.gz) - Reference transcriptome (Cell Ranger reference package, STAR genome index, or salmon index)
- Raw FASTQ files (Cell Ranger naming:
- Optional:
- Aligner choice:
cellranger,starsolo, oralevin-fry(default:cellranger) - 10x chemistry:
auto,threeprime,fiveprime,SC3Pv2,SC3Pv3,SC3Pv3.1,SC3Pv4(default:auto) - Number of threads (default: 8)
- Expected number of cells (leave unset to use automatic knee-point detection)
- Output directory (default:
./cellranger_output) - Cell barcode whitelist (required for STARsolo; bundled in Cell Ranger reference)
- Aligner choice:
Workflow
- Validate FASTQ naming convention. Cell Ranger requires
{sample}_S{n}_L00{lane}_R{read}_001.fastq.gz; rename files if needed. STARsolo accepts generic paired FASTQ names. - If Cell Ranger: run
cellranger countwith--id,--transcriptome,--fastqs,--sample, and--chemistryflags. - If STARsolo: run STAR with
--soloType CB_UMI_Simple, providing--soloCBwhitelist,--soloCBstart/End,--soloUMIstart/Endfor the appropriate chemistry. - If alevin-fry: run
simpleaf quantwith the pre-built index and chemistry string; use--resolution cr-likefor Cell Ranger-compatible output. - Report cell calling statistics: estimated cells, mean reads per cell, median genes per cell, sequencing saturation, fraction reads in cells.
- Verify output: confirm existence of filtered and raw (unfiltered) feature-barcode matrices in MEX format.
Output Contract
- Filtered feature-barcode matrix directory (
filtered_feature_bc_matrix/):matrix.mtx.gz,barcodes.tsv.gz,features.tsv.gz - Raw (unfiltered) feature-barcode matrix directory (
raw_feature_bc_matrix/) - Web summary HTML (Cell Ranger:
web_summary.html) or equivalent STARsolo/alevin-fry summary - Per-barcode metrics CSV (
metrics_summary.csvfor Cell Ranger) - BAM file and index (Cell Ranger only:
possorted_genome_bam.bam,.bai)
Limits
- Cell Ranger requires a commercial license (free for academic) and approximately 10–30 GB disk per sample for intermediate files.
- STARsolo requires a STAR genome index and the appropriate cell barcode whitelist (bundled in Cell Ranger references or available from 10x Genomics).
- alevin-fry is fastest and lowest-memory but requires salmon index built with
simpleaf index. - Cell calling at the knee-point may under-call rare cell types; consider running EmptyDrops (via DropletUtils in R) on the raw matrix for improved sensitivity.
- Ambient RNA contamination is not removed at this step; handle in the scrnaseq-quality-control skill.
- Reference genome and transcriptome annotation must match (same genome build and GTF version).
- Common failure cases:
- FASTQ naming mismatch causing Cell Ranger to find no reads for the sample.
- Chemistry mismatch (
autodetection fails on unusual protocols); specify explicitly. - Insufficient disk space for BAM file (~10x raw FASTQ size).
- Reference built with a different Cell Ranger version causing incompatibility.
04 同仓库更多 Skills
structured-intelligence-skill-creator
Create or update repository skills that conform to local templates, provider metadata requirements, registry rules, and validation workflows.
stat-survival-analysis
Estimate and compare survival functions using Kaplan-Meier curves and log-rank test, with optional Cox proportional hazards regression.
stat-pca
Perform PCA on a numeric data matrix to reduce dimensionality, visualize sample structure, and identify top contributing features.
stat-pairwise-correlation
Compute pairwise correlations and significance tests across all numeric variables, and produce a correlation heatmap.
stat-nonlinear-embedding
Embed high-dimensional data into 2D using t-SNE or UMAP for exploratory visualization of sample or cell relationships.
stat-logistic-regression
Fit a logistic regression model for binary outcomes, report odds ratios and model performance (AUC-ROC, confusion matrix).