Single Cell Splicing

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/alternative-splicing/single-cell-splicingThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Analyzes alternative splicing at single-cell resolution. The first decision is library chemistry — 10X 3' is fundamentally limited (RT primes from poly-A, R2 falls in 3' UTR, <0.1 junction read per cell per AS event).…

---
name: bio-single-cell-splicing
description: Analyzes alternative splicing at single-cell resolution. The first decision is library chemistry — 10X 3' is fundamentally limited (RT primes from poly-A, R2 falls in 3' UTR, <0.1 junction read per cell per AS event). Plate-based full-length methods (Smart-seq3, FLASH-seq, VASA-seq, STORM-seq) and single-cell long-read (MAS-Iso-seq, scISOr-Seq2) are the chemistries that give per-cell isoform structure. Tools include MARVEL (R, Smart-seq integrated), BRIE2 (Bayesian PSI with regulatory features and ELBO_gain test), scQuint (junction-cluster, plate-based; not for 10X), SpliZ (annotation-free Z-score), Psix (graph-smoothness regulated AS), and Sierra (alternative polyadenylation, often confused with AS). Use when analyzing isoform usage in scRNA-seq, identifying cell-type-specific splicing, or determining whether scRNA-seq chemistry supports splicing analysis at all.
tool_type: python
primary_tool: MARVEL
---

## Version Compatibility

Reference examples tested with: MARVEL 2.0+, BRIE2 0.2.4+, scQuint 0.1+, SpliZ 0.0.1+, Sierra 1.0+, Psix 0.1+, anndata 0.10+, scanpy 1.10+, pandas 2.2+, scipy 1.13+

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.

# Single-Cell Splicing Analysis

The fundamental decision is **chemistry**, not tool. Most droplet 3' scRNA-seq cannot support transcriptome-wide splicing inference because reverse transcription primes from the poly(A) tail and most reads land in the 3' UTR — far from CDS-region splicing events. Plate-based full-length methods and single-cell long-read sequencing are the chemistries that give per-cell isoform structure across the gene body.

## The 10X 3' Problem (Quantified)

Three compounding mechanisms make 10X Chromium 3' (v3.1, GEM-X, v4) hostile to splicing:

1. **3' enrichment**: median fragment <1 kb from poly(A); >70% of unique reads fall within 3' UTR.
2. **Short R2 (~91 nt)**: each read straddles at most one junction; usually none, because R2 lands in 3' UTR.
3. **PCR concatemers and TSO artifacts**: pollute junction detection; UMI collapse is gene-level, not isoform-level.

**Quantitative estimate:** Only a small fraction of cassette exons sit close enough to the polyA site to be sampled by 3' chemistry (empirical estimates from APA/3'-end atlases — see Tian & Manley 2017 *Nat Rev Mol Cell Biol* for the 3' UTR isoform landscape). Effective junction read yield from 10X 3' is **<0.1 per cell per AS event** — vs the 5-10 needed for stable per-cell PSI. Most splicing analyses on 10X 3' data report artifacts.

**The 5' kit (10X 5' GEX) does not solve this** — it shifts capture from 3' UTR to 5' UTR / TSS-proximal regions. Marginal improvement; not a transcriptome-wide solution. Note that V(D)J recovery requires the **10X Chromium Single Cell Immune Profiling kit** (with TCR/BCR-specific enrichment), not 5' GEX alone — postdocs designing immune-repertoire experiments must use the dedicated V(D)J kit.

## Decision: Does the Chemistry Support Splicing Analysis?

| Chemistry | Splicing analysis viable? | Best alternative if no |
|-----------|----------------------------|--------------------------|
| 10X 3' (Chromium v3, GEM-X, v4, Flex) | No (transcriptome-wide); maybe near-3'-end events | Sierra for APA |
| 10X 5' GEX | Limited; near-5'-end events only | Sierra for alternative TSS; switch to MAS-Iso-seq |
| Smart-seq2 | Yes (full transcript) | MARVEL or BRIE2 |
| Smart-seq3 / Smart-seq3xpress | Yes + UMI molecule counting | MARVEL or BRIE2 |
| FLASH-seq | Yes (faster, cheaper Smart-seq3) | MARVEL or BRIE2 |
| VASA-seq | Yes + total RNA (incl. nascent, IR) | MARVEL with IR analysis |
| STORM-seq | Yes + total RNA + ribodepletion | MARVEL with IR analysis |
| MAS-Iso-seq + 10X 5' (PacBio Kinnex) | Yes — full isoforms per cell | FLAMES, scNanoGPS, IsoQuant, see long-read-splicing |
| scISOr-Seq2 (PacBio + 10X) | Yes — full isoforms with cell-typing | FLAMES, IsoQuant |
| ONT direct cDNA scRNA | Yes | FLAMES |
| ONT direct RNA scRNA | Yes + native modifications | FLAMES |

## Tool Selection Matrix

| Tool | Best for | Input | Strengths | Fails when |
|------|----------|-------|-----------|------------|
| MARVEL | Smart-seq plate-based and (v2+) 10X droplet unified workflow | Plate or droplet BAMs + Seurat | SE/A5SS/A3SS/MXE/RI/AFE/ALE; modality classification; native Seurat integration; v2 droplet support | R-only |
| BRIE2 | Plate-based with regulatory feature prior | Plate BAM + GFF3 events | Bayesian variational PSI + ELBO_gain test; principled uncertainty; CLI-driven (`brie-count`, `brie-quant`) | TensorFlow dependency; slow at scale |
| scQuint | Plate-based annotation-free junction-cluster quantification (validated on Smart-seq2) | STAR junctions across cells | Cluster-level junction usage; latent Dirichlet | Authors recommend AGAINST use on 10X 3'/5' data (3'-bias confounds); plate-based only |
| SpliZ | Annotation-free discovery of cell-state-associated splicing | STAR-aligned BAMs | Per-gene Z-score; no event database needed | Annotation-free = power tradeoff |
| Psix | Regulated AS along trajectories | PSI matrix + kNN graph | Tests graph smoothness; robust to dropout | Needs cell-state graph upstream |
| Sierra | APA in 10X 3' (NOT splicing) | 10X BAM + GTF | Peak-calling 3' ends; DEXSeq DTU on UTR isoforms | APA only; not for cassette exons |
| pseudobulk leafcutter / rMATS | Between-cell-type differential splicing | Aggregated BAMs | Bulk-level statistical power | Loses within-cluster heterogeneity |
| MAS-Iso-seq + FLAMES | Full-length single-cell isoforms | 10X 5' + PacBio Kinnex | F

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

pip install brie scanpy anndata scipy
pip install git+https://github.com/songlab-cal/scquint
pip install git+https://github.com/salzman-lab/SpliZ
pip install git+https://github.com/lareaulab/psix

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Single Cell Splicing

• github.com/GPTomics/bioSkills/tree/main/alternative-splicing/sin — the audited source at commit c8c1fc3a2b13 and every finding aboveObserved · 19 Oct 2026

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