L1 ⚛ L1-520 ⊙ Testnet

RNA-seq Cell-Type Classification (PWDR)

Computational Biology · Single-cell / bulk RNA-seq transcript quantification with marker-gene-panel cell-type categorical readout

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⬇ L1-520.md

# ⚛ L1 Principle — RNA-seq Cell-Type Classification (PWDR) **ID:** `L1-520` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Computational Biology — *Single-cell / bulk RNA-seq transcript quantification with marker-gene-panel cell-type categorical readout* > **🎯 Problem class:** linear inverse with categorical readout · **🧮 Solution space:** 1D celltype label > **📡 Carrier:** rna_molecules_with_sequencing · **🌫 Noise:** negative binomial > **⚖ Difficulty (δ):** 5 · **⛓ Block:** 41555318 --- ## 🧠 1. Introduction **RNA-seq Cell-Type Classification (PWDR)** is a **linear inverse with categorical readout** whose unknown lives in **1D celltype label** space, within the **Single-cell / bulk RNA-seq transcript quantification with marker-gene-panel cell-type categorical readout** sub-domain of **Computational Biology**. Measurements consist of rna molecules with sequencing via a **rnaseq with marker panel classifier** sensing mechanism. The forward operator applies, in order: L · poly a capture operator; L · reverse transcription operator; L · pcr amplification operator; L · sequencing operator; L · transcriptome alignment operator; L · transcript quantification operator; L · normalization operator; L · marker panel classifier operator; int · cell operator. Observations are corrupted by negative-binomial over-dispersed counts. Existence guaranteed within Omega bounds. Uniqueness conditional on adequate sequencing depth (typically >30k reads per cell for 3' chemistry) and adequate marker-panel coverage. Stability conditional with dropout_rate dominant for low-expressing markers; batch_effect dominant cross-sample; doublet_contamination dominant for high cell densities. Joint Hadamard well-posedness for the coupled RNA-seq + marker-panel-classifier forward established by Trapnell 2014 (foundational scRNA-seq), Macosko 2015 (Drop-seq), Stuart-Butler 2019 (Seurat v3 integration), Tabula Sapiens Consortium 2022, Zhang 2019 (CellMarker), Franzen 2019 (PanglaoDB). ## ⚙ 2. Forward Model Physical chain: **x** → L · poly a capture → L · reverse transcription → L · pcr amplification → L · sequencing → L · transcriptome alignment → L · transcript quantification → L · normalization → L · marker panel classifier → int · cell → **y** (detector). ``` y = `int.cell` `L.marker_panel_classifier` `L.normalization` `L.transcript_quantification` `L.transcriptome_alignment` `L.sequencing` `L.pcr_amplification` `L.reverse_transcription` `L.poly_a_capture` x ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `L.poly_a_capture` | L · poly a capture operator | | `L.reverse_transcription` | L · reverse transcription operator | | `L.pcr_amplification` | L · pcr amplification operator | | `L.sequencing` | L · sequencing operator | | `L.transcriptome_alignment` | L · transcriptome alignment operator | | `L.transcript_quantification` | L · transcript quantification operator | | `L.normalization` | L · normalization operator | | `L.marker_panel_classifier` | L · marker panel classifier operator | | `int.cell` | Int · cell operator | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Computational Biology | | Sub domain | Single-cell / bulk RNA-seq transcript quantification with marker-gene-panel cell-type categorical readout | | Carrier | rna_molecules_with_sequencing | | Problem class | linear_inverse_with_categorical_readout | | Solution space | 1D_celltype_label | | Noise model | negative_binomial | | Integration axis | molecular_cellular | | Difficulty delta | 5 | | L dag | 8.3 | ## 📡 4. Measurement Model Existence guaranteed within Omega bounds. Uniqueness conditional on adequate sequencing depth (typically >30k reads per cell for 3' chemistry) and adequate marker-panel coverage. Stability conditional with dropout_rate dominant for low-expressing markers; batch_effect dominant cross-sample; doublet_contamination dominant for high cell densities. Joint Hadamard well-posedness for the coupled RNA-seq + marker-panel-classifier forward established by Trapnell 2014 (foundational scRNA-seq), Macosko 2015 (Drop-seq), Stuart-Butler 2019 (Seurat v3 integration), Tabula Sapiens Consortium 2022, Zhang 2019 (CellMarker), Franzen 2019 (PanglaoDB). | Metric | Value | |---|---| | Metric | categorical_accuracy | | Secondary | macro_F1_per_celltype | ## 📏 5. Operating Range (Ω) **Center problem class:** `scrnaseq_celltype_pwdr` · **Forward operator:** `scrnaseq_celltype_pwdr_forward` **Center point:** | Parameter | Unit | Value | |---|---|---| | N cells | — | 5000 | | N genes | — | 20000 | | Batch effect | — | 0 | | Dropout rate | — | 0 | | Reads per cell | — | 50000 | | Sequencing depth | — | 50000 | | Library complexity | — | 0.85 | | Doublet contamination | — | 0 | | Taxonomy disagreement | — | 0 | | Ambient rna contamination | — | 0 | | Marker panel coverage uncertainty | — | 0 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | N cells | — | 100 – 1000000 | | N genes | — | 1000 – 60000 | | Dropout rate | — | 0.0 – 0.5 | | Reads per cell | — | 1000 – 1000000 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 0.85_accuracy | Metric | Range | |---|---| | Categorical accuracy | 0.4 – 0.99 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **categorical_accuracy**, with κ = `200` and δ = `5`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x673b55bb788633e2b747d4088754fce2dd908b8e80fb87a580eae551bba1a5d1` - **Chain tx hash:** `0xc076736d4f2f15c82c65bb5a3b13c16628e630d165c72e056fb413fb169ed1eb` - **Chain block:** `41555318` --- ## File Mapping This bundle consists of: `L1-520.md`, `L1-520.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-520.md` | Source of truth — edit this | Human or LLM | | `L1-520.json` | Structured metadata for the registry | LLM regenerates from the sections above | **Prompt for your LLM after editing this Markdown:** > Read the attached Markdown. Regenerate the sibling `.json` so every field matches. > Preserve the schema documented in the rows above. > Output each file in its own fenced code block tagged with the filename. > Output only the JSON object. _This Markdown was auto-synthesized from the catalog row for `L1-520`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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Read the attached Markdown (L1-520.md).
Regenerate L1-520.json so every field matches the Markdown.
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Output each file in its own fenced code block tagged with the filename.

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L2-520-001 RNA-seq Cell-Type Classification (PWDR) — Spec Skeleton

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📋 JSON metadata

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  "domain": "Computational Biology",
  "hardness_fn": {
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    "kappa": 200,
    "metric": "categorical_accuracy",
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  "layer": "L1",
  "observable_profile": {
    "metric": "categorical_accuracy",
    "regime": "Existence guaranteed within Omega bounds. Uniqueness conditional on adequate sequencing depth (typically \u003e30k reads per cell for 3\u0027 chemistry) and adequate marker-panel coverage. Stability conditional with dropout_rate dominant for low-expressing markers; batch_effect dominant cross-sample; doublet_contamination dominant for high cell densities. Joint Hadamard well-posedness for the coupled RNA-seq + marker-panel-classifier forward established by Trapnell 2014 (foundational scRNA-seq), Macosko 2015 (Drop-seq), Stuart-Butler 2019 (Seurat v3 integration), Tabula Sapiens Consortium 2022, Zhang 2019 (CellMarker), Franzen 2019 (PanglaoDB).",
    "secondary": "macro_F1_per_celltype"
  },
  "physics_fingerprint": {
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    "carrier": "rna_molecules_with_sequencing",
    "difficulty_delta": 5,
    "domain": "Computational Biology",
    "integration_axis": "molecular_cellular",
    "noise_model": "negative_binomial",
    "primitives": [
      "L.poly_a_capture",
      "L.reverse_transcription",
      "L.pcr_amplification",
      "L.sequencing",
      "L.transcriptome_alignment",
      "L.transcript_quantification",
      "L.normalization",
      "L.marker_panel_classifier",
      "int.cell"
    ],
    "problem_class": "linear_inverse_with_categorical_readout",
    "sensing_mechanism": "rnaseq_with_marker_panel_classifier",
    "solution_space": "1D_celltype_label",
    "sub_domain": "Single-cell / bulk RNA-seq transcript quantification with marker-gene-panel cell-type categorical readout",
    "title": "RNA-seq Cell-Type Classification (PWDR)"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "scrnaseq_celltype_pwdr_forward",
      "bulk_rnaseq_deconvolution_pwdr_forward",
      "scrnaseq_immune_pwdr_forward",
      "scrnaseq_atlas_pwdr_forward"
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    "allowed_omega_dimensions": [
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      "dropout_rate",
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      "doublet_contamination",
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      "marker_panel_coverage_uncertainty",
      "taxonomy_disagreement"
    ],
    "allowed_problem_classes": [
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      "scrnaseq_immune_celltype_pwdr",
      "scrnaseq_epithelial_pwdr",
      "scrnaseq_neural_pwdr",
      "bulk_rnaseq_deconvolution_pwdr"
    ],
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      "omega": {
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        "reads_per_cell": 50000,
        "sequencing_depth": 50000,
        "taxonomy_disagreement": 0.0
      },
      "problem_class": "scrnaseq_celltype_pwdr"
    },
    "epsilon_bounds": {
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        0.99
      ]
    },
    "omega_bounds": {
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        100,
        1000000
      ],
      "N_genes": [
        1000,
        60000
      ],
      "dropout_rate": [
        0.0,
        0.5
      ],
      "reads_per_cell": [
        1000,
        1000000
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Single-cell / bulk RNA-seq transcript quantification with marker-gene-panel cell-type categorical readout",
  "title": "RNA-seq Cell-Type Classification (PWDR)"
}

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