scrnaseq-cellranger-count
Skill4 starsChanged 12 days agoMITView on GitHub
Generate feature-barcode count matrices from raw scRNA-seq FASTQ files using Cell Ranger, STARsolo, or alevin-fry.
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The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 1 is distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
3
Very specific
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- Common
- Distinctive
- Very specific
- Very specific: line 31
If Cell Ranger: run cellranger count with --id, --transcriptome, --fastqs, --sample, and --chemistry flags.
- Very specific: line 32
If STARsolo: run STAR with --soloType CB_UMI_Simple, providing --soloCBwhitelist, --soloCBstart/End, --soloUMIstart/End for the appropriate …
- Very specific: line 33
If alevin-fry: run simpleaf quant with the pre-built index and chemistry string.
- Distinctive: line 35
Verify output: confirm existence of filtered and raw (unfiltered) feature-barcode matrices in MEX format.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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.
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