tooluniverse-single-cell

What this skill does

# Single-Cell Genomics and Expression Matrix Analysis

## RULE ZERO — Check for pre-computed results FIRST

Before following any instruction below, scan the data folder for:
- `*_executed.ipynb` → read with `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` and cite its cell outputs as the authoritative answer
- Pre-computed result files (CSV/TSV with names like `*results*`, `*deseq*`, `*enrich*`, `*stats*`, `*_simplified.csv`) → read directly and report the requested value
- Canonical analysis scripts (`analysis.R`, `run_*.py`, `find_*.R`, `*.Rmd`) → execute as-is and read the output

Only follow this skill's re-analysis recipe below if **none** of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5-10× turn count).

---

Comprehensive single-cell RNA-seq analysis and expression matrix processing using scanpy, anndata, scipy, and ToolUniverse.

---

## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.

---

## When to Use This Skill

Apply when users:
- Have scRNA-seq data (h5ad, 10X, CSV count matrices) and want analysis
- Ask about cell type identification, clustering, or annotation
- Need differential expression analysis by cell type or condition
- Want gene-expression correlation analysis (e.g., gene length vs expression by cell type)
- Ask about PCA, UMAP, t-SNE for expression data
- Need Leiden/Louvain clustering on expression matrices
- Want statistical comparisons between cell types (t-test, ANOVA, fold change)
- Ask about marker genes, batch correction, trajectory, or cell-cell communication

**NOT for** (use other skills instead):
- Bulk RNA-seq DESeq2 only -> `tooluniverse-rnaseq-deseq2`
- Gene enrichment only -> `tooluniverse-gene-enrichment`
- VCF/variant analysis -> `tooluniverse-variant-analysis`

---

## Core Principles

1. **Data-first** - Load, inspect, validate before analysis
2. **AnnData-centric** - All data flows through anndata objects
3. **Cell type awareness** - Per-cell-type subsetting when needed
4. **Statistical rigor** - Normalization, multiple testing correction, effect sizes
5. **Question-driven** - Parse what the user is actually asking

---

## Required Packages

```python
import scanpy as sc, anndata as ad, pandas as pd, numpy as np
from scipy import stats
from scipy.cluster.hierarchy import linkage, fcluster
from sklearn.decomposition import PCA
from statsmodels.stats.multitest import multipletests
import gseapy as gp  # enrichment
import harmonypy     # batch correction (optional)
```

Install: `pip install scanpy anndata leidenalg umap-learn harmonypy gseapy pandas numpy scipy scikit-learn statsmodels`

---

## Workflow Decision Tree

```
START: User question about scRNA-seq data
|
+-- FULL PIPELINE (raw counts -> annotated clusters)
|   Workflow: QC -> Normalize -> HVG -> PCA -> Cluster -> Annotate -> DE
|   See: references/scanpy_workflow.md
|
+-- DIFFERENTIAL EXPRESSION (per-cell-type comparison)
|   Most common pattern: per-cell-type DE
|   See: analysis_patterns.md "Pattern 1"
|
+-- CORRELATION ANALYSIS (gene property vs expression)
|   Pattern: Gene length vs expression correlation
|   See: analysis_patterns.md "Pattern 2"
|
+-- CLUSTERING & PCA (expression matrix analysis)
|   See: references/clustering_guide.md
|
+-- CELL COMMUNICATION (ligand-receptor interactions)
|   See: references/cell_communication.md
|
+-- TRAJECTORY ANALYSIS (pseudotime)
    See: references/trajectory_analysis.md
```

**Data format handling**:
- h5ad -> `sc.read_h5ad()`
- 10X -> `sc.read_10x_mtx()` or `sc.read_10x_h5()`
- CSV/TSV -> `pd.read_csv()` -> Convert to AnnData (check orientation!)

---

## Data Loading

AnnData expects: **cells/samples as rows (obs), genes as columns (var)**

```python
adata = sc.read_h5ad("data.h5ad")  # h5ad already oriented

# CSV/TSV: check orientation
df = pd.read_csv("counts.csv", index_col=0)
if df.shape[0] > df.shape[1] * 5:  # genes > samples by 5x => transpose
    df = df.T
adata = ad.AnnData(df)

# Load metadata
meta = pd.read_csv("metadata.csv", index_col=0)
common = adata.obs_names.intersection(meta.index)
adata = adata[common].copy()
for col in meta.columns:
    adata.obs[col] = meta.loc[common, col]
```

---

## Quality Control

```python
adata.var['mt'] = adata.var_names.str.startswith(('MT-', 'mt-'))
sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], inplace=True)
sc.pp.filter_cells(adata, min_genes=200)
adata = adata[adata.obs['pct_counts_mt'] < 20].copy()
sc.pp.filter_genes(adata, min_cells=3)
```

See: references/scanpy_workflow.md for details

---

## Complete Pipeline (Quick Reference)

```python
import scanpy as sc

adata = sc.read_10x_h5("filtered_feature_bc_matrix.h5")

# QC
adata.var['mt'] = adata.var_names.str.startswith('MT-')
sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], inplace=True)
adata = adata[adata.obs['pct_counts_mt'] < 20].copy()
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)

# Normalize + HVG + PCA
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
adata.raw = adata.copy()
sc.pp.highly_variable_genes(adata, n_top_genes=2000)
sc.tl.pca(adata, n_comps=50)

# Cluster + UMAP
sc.pp.neighbors(adata, n_pcs=30)
sc.tl.leiden(adata, resolution=0.5)
sc.tl.umap(adata)

# Find markers + Annotate + Per-cell-type DE
sc.tl.rank_genes_groups(adata, groupby='leiden', method='wilcoxon')
```

---

## Differential Expression Decision Tree

```
Single-Cell DE (many cells per condition):
  Use: sc.tl.rank_genes_groups(), methods: wilcoxon, t-test, logreg
  Best for: Per-cell-type DE, marker gene finding

Pseudo-Bulk DE (aggregate counts by sample):
  Use: R DESeq2 via `tu run run_deseq2_analysis` or Rscript (NOT pydeseq2 — gives different DEG counts)
  Best for: Sample-level comparisons with replicates

Statistical Tests Only:
  Use: scipy.stats (ttest_ind, f_oneway, pearsonr)
  Best for: Correlation, ANOVA, t-tests on summaries
```

---

## Statistical Tests (Quick Reference)

```python
from scipy import stats
from statsmodels.stats.multitest import multipletests

# Pearson/Spearman correlation
r, p = stats.pearsonr(gene_lengths, mean_expression)

# Welch's t-test
t_stat, p_val = stats.ttest_ind(group1, group2, equal_var=False)

# ANOVA
f_stat, p_val = stats.f_oneway(group1, group2, group3)

# Multiple testing correction (BH)
reject, pvals_adj, _, _ = multipletests(pvals, method='fdr_bh')
```

---

## Batch Correction (Harmony)

```python
import harmonypy
sc.tl.pca(adata, n_comps=50)
ho = harmonypy.run_harmony(adata.obsm['X_pca'][:, :30], adata.obs, 'batch', random_state=0)
adata.obsm['X_pca_harmony'] = ho.Z_corr.T
sc.pp.neighbors(adata, use_rep='X_pca_harmony')
sc.tl.leiden(adata, resolution=0.5)
sc.tl.umap(adata)
```

---

## ToolUniverse Integration

### Data Discovery (before analysis)
- **CxGDisc_search_datasets**: Search CELLxGENE Discover for scRNA-seq datasets by disease, tissue, organism. Use broad disease terms (e.g., "breast cancer" not "triple-negative").
- **GEO_search_rnaseq_datasets** / **geo_search_datasets**: Search GEO for scRNA-seq studies
- **NCBI_SRA_search_runs**: Search SRA for sequencing runs (query="single cell RNA-seq [disease]")
- **OmicsDI_search_datasets**: Cross-repository dataset search

### Cell Type Markers
- **CellMarker_search_by_cell_type**: Tissue-specific cell markers (use `CellMarker_list_cell_types` first — exact names required, e.g., "Regulatory T(Treg) cell" not "Regulatory T cell")
- **CellMarker_search_cancer_markers**: Cancer-context markers with experimental evidence
- **CellMarker_search_by_gene**: Reverse lookup — which cell types express a gene?
- **HPA_search_genes_by_query**: Cell-type marker gene search

### Gene Annotation
- **MyGene_query_genes** / **MyGene_batch_query**: Gene ID conversion
- **ensembl_lookup_ge