[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CZ4KVF3WEBPC239GCPBJZZ":3,"skill-same:01M6CZ4KVF3WEBPC239GCPBJZZ":64},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":24,"linkOnly":25,"markdownContent":26,"markdownBody":27,"frontMatter":28,"refName":29,"commitSha":30,"skillDirectory":31,"forksCount":32,"repositoryHtmlUrl":33,"repositoryHomepageUrl":6,"ownerHtmlUrl":34,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":35,"install":36,"resources":49},"01M6CZ4KVF3WEBPC239GCPBJZZ","scrnaseq-cellranger-count",null,"Generate feature-barcode count matrices from raw scRNA-seq FASTQ files using Cell Ranger, STARsolo, or alevin-fry.","Scientific-Tooling\u002Fstructured-intelligence","Scientific-Tooling","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F267453265?v=4","01M6CZ1SE6GGD4F67EAQ0PVX38","Scientific Tooling","01M6CZ1SJKCFVGH10Z3A152K02",4,"MIT","https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence\u002Fblob\u002Fmain\u002Fskills\u002Fscrnaseq-cellranger-count\u002FSKILL.md",[],[19,20,21,22,23],"scrnaseq cellranger count","generate","files","cell","matrices","2026-11-10 10:12:30",false,"---\nname: scrnaseq-cellranger-count\ndescription: Generate feature-barcode count matrices from raw scRNA-seq FASTQ files using Cell Ranger, STARsolo, or alevin-fry.\n---\n\n# Skill: scRNA-seq Cell Ranger Count\n\n## Use When\n\n- User has raw FASTQ files from a droplet-based single-cell protocol (10x Chromium, Parse Biosciences, etc.) and needs a feature-barcode count matrix.\n- User wants to run Cell Ranger count, STARsolo, or alevin-fry to produce per-cell gene expression matrices.\n- User needs to assess sequencing saturation or cell calling statistics before downstream analysis.\n- User is generating input for the scrnaseq-quality-control skill.\n\n## Inputs\n\n- Required:\n  - Raw FASTQ files (Cell Ranger naming: `{sample}_S{n}_L00{lane}_R{read}_001.fastq.gz`)\n  - Reference transcriptome (Cell Ranger reference package, STAR genome index, or salmon index)\n- Optional:\n  - Aligner choice: `cellranger`, `starsolo`, or `alevin-fry` (default: `cellranger`)\n  - 10x chemistry: `auto`, `threeprime`, `fiveprime`, `SC3Pv2`, `SC3Pv3`, `SC3Pv3.1`, `SC3Pv4` (default: `auto`)\n  - Number of threads (default: 8)\n  - Expected number of cells (leave unset to use automatic knee-point detection)\n  - Output directory (default: `.\u002Fcellranger_output`)\n  - Cell barcode whitelist (required for STARsolo; bundled in Cell Ranger reference)\n\n## Workflow\n\n1. 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.\n2. If Cell Ranger: run `cellranger count` with `--id`, `--transcriptome`, `--fastqs`, `--sample`, and `--chemistry` flags.\n3. If STARsolo: run STAR with `--soloType CB_UMI_Simple`, providing `--soloCBwhitelist`, `--soloCBstart\u002FEnd`, `--soloUMIstart\u002FEnd` for the appropriate chemistry.\n4. If alevin-fry: run `simpleaf quant` with the pre-built index and chemistry string; use `--resolution cr-like` for Cell Ranger-compatible output.\n5. Report cell calling statistics: estimated cells, mean reads per cell, median genes per cell, sequencing saturation, fraction reads in cells.\n6. Verify output: confirm existence of filtered and raw (unfiltered) feature-barcode matrices in MEX format.\n\n## Output Contract\n\n- Filtered feature-barcode matrix directory (`filtered_feature_bc_matrix\u002F`): `matrix.mtx.gz`, `barcodes.tsv.gz`, `features.tsv.gz`\n- Raw (unfiltered) feature-barcode matrix directory (`raw_feature_bc_matrix\u002F`)\n- Web summary HTML (Cell Ranger: `web_summary.html`) or equivalent STARsolo\u002Falevin-fry summary\n- Per-barcode metrics CSV (`metrics_summary.csv` for Cell Ranger)\n- BAM file and index (Cell Ranger only: `possorted_genome_bam.bam`, `.bai`)\n\n## Limits\n\n- Cell Ranger requires a commercial license (free for academic) and approximately 10–30 GB disk per sample for intermediate files.\n- STARsolo requires a STAR genome index and the appropriate cell barcode whitelist (bundled in Cell Ranger references or available from 10x Genomics).\n- alevin-fry is fastest and lowest-memory but requires salmon index built with `simpleaf index`.\n- 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.\n- Ambient RNA contamination is not removed at this step; handle in the scrnaseq-quality-control skill.\n- Reference genome and transcriptome annotation must match (same genome build and GTF version).\n- Common failure cases:\n  - FASTQ naming mismatch causing Cell Ranger to find no reads for the sample.\n  - Chemistry mismatch (`auto` detection fails on unusual protocols); specify explicitly.\n  - Insufficient disk space for BAM file (~10x raw FASTQ size).\n  - Reference built with a different Cell Ranger version causing incompatibility.\n","# Skill: scRNA-seq Cell Ranger Count\n\n## Use When\n\n- User has raw FASTQ files from a droplet-based single-cell protocol (10x Chromium, Parse Biosciences, etc.) and needs a feature-barcode count matrix.\n- User wants to run Cell Ranger count, STARsolo, or alevin-fry to produce per-cell gene expression matrices.\n- User needs to assess sequencing saturation or cell calling statistics before downstream analysis.\n- User is generating input for the scrnaseq-quality-control skill.\n\n## Inputs\n\n- Required:\n  - Raw FASTQ files (Cell Ranger naming: `{sample}_S{n}_L00{lane}_R{read}_001.fastq.gz`)\n  - Reference transcriptome (Cell Ranger reference package, STAR genome index, or salmon index)\n- Optional:\n  - Aligner choice: `cellranger`, `starsolo`, or `alevin-fry` (default: `cellranger`)\n  - 10x chemistry: `auto`, `threeprime`, `fiveprime`, `SC3Pv2`, `SC3Pv3`, `SC3Pv3.1`, `SC3Pv4` (default: `auto`)\n  - Number of threads (default: 8)\n  - Expected number of cells (leave unset to use automatic knee-point detection)\n  - Output directory (default: `.\u002Fcellranger_output`)\n  - Cell barcode whitelist (required for STARsolo; bundled in Cell Ranger reference)\n\n## Workflow\n\n1. 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.\n2. If Cell Ranger: run `cellranger count` with `--id`, `--transcriptome`, `--fastqs`, `--sample`, and `--chemistry` flags.\n3. If STARsolo: run STAR with `--soloType CB_UMI_Simple`, providing `--soloCBwhitelist`, `--soloCBstart\u002FEnd`, `--soloUMIstart\u002FEnd` for the appropriate chemistry.\n4. If alevin-fry: run `simpleaf quant` with the pre-built index and chemistry string; use `--resolution cr-like` for Cell Ranger-compatible output.\n5. Report cell calling statistics: estimated cells, mean reads per cell, median genes per cell, sequencing saturation, fraction reads in cells.\n6. Verify output: confirm existence of filtered and raw (unfiltered) feature-barcode matrices in MEX format.\n\n## Output Contract\n\n- Filtered feature-barcode matrix directory (`filtered_feature_bc_matrix\u002F`): `matrix.mtx.gz`, `barcodes.tsv.gz`, `features.tsv.gz`\n- Raw (unfiltered) feature-barcode matrix directory (`raw_feature_bc_matrix\u002F`)\n- Web summary HTML (Cell Ranger: `web_summary.html`) or equivalent STARsolo\u002Falevin-fry summary\n- Per-barcode metrics CSV (`metrics_summary.csv` for Cell Ranger)\n- BAM file and index (Cell Ranger only: `possorted_genome_bam.bam`, `.bai`)\n\n## Limits\n\n- Cell Ranger requires a commercial license (free for academic) and approximately 10–30 GB disk per sample for intermediate files.\n- STARsolo requires a STAR genome index and the appropriate cell barcode whitelist (bundled in Cell Ranger references or available from 10x Genomics).\n- alevin-fry is fastest and lowest-memory but requires salmon index built with `simpleaf index`.\n- 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.\n- Ambient RNA contamination is not removed at this step; handle in the scrnaseq-quality-control skill.\n- Reference genome and transcriptome annotation must match (same genome build and GTF version).\n- Common failure cases:\n  - FASTQ naming mismatch causing Cell Ranger to find no reads for the sample.\n  - Chemistry mismatch (`auto` detection fails on unusual protocols); specify explicitly.\n  - Insufficient disk space for BAM file (~10x raw FASTQ size).\n  - Reference built with a different Cell Ranger version causing incompatibility.\n",{"name":5,"description":7},"main","5118e7fda890e9aa819cb71e003cd243d3a31970","skills\u002Fscrnaseq-cellranger-count",1,"https:\u002F\u002Fgithub.com\u002FScientific-Tooling\u002Fstructured-intelligence","https:\u002F\u002Fgithub.com\u002FScientific-Tooling","2026-11-03 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