Skill-to-MCP

# scverse Quality Control Guidelines This document provides detailed information about quality control best practices for single-cell RNA-seq data, following the scverse ecosystem recommendations. ## Quality Control Metrics ### Count Depth (Total Counts) - **What it measures**: Total number of UMI/reads per cell - **Why it matters**: Low count cells may be empty droplets, debris, or poorly captured cells - **Typical range**: 500-50,000 counts per cell (varies by protocol) - **Red flags**: Bimodal distributions may indicate mixing of high and low-quality cells ### Gene Detection (Genes per Cell) - **What it measures**: Number of genes with at least 1 count - **Why it matters**: Strongly correlates with count depth; low values indicate poor capture - **Typical range**: 200-5,000 genes per cell - **Red flags**: Very low values (<200) suggest technical failures ### Mitochondrial Content - **What it measures**: Percentage of counts from mitochondrial genes - **Why it matters**: High MT% indicates cell stress, apoptosis, or lysed cells - **Typical range**: <5% for most tissues, up to 10-15% for metabolically active cells - **Species-specific patterns**: - Mouse: Genes start with 'mt-' (e.g., mt-Nd1, mt-Co1) - Human: Genes start with 'MT-' (e.g., MT-ND1, MT-CO1) - **Context matters**: Some cell types (cardiomyocytes, neurons) naturally have higher MT content ### Ribosomal Content - **What it measures**: Percentage of counts from ribosomal protein genes - **Why it matters**: Can indicate cell state or contamination - **Patterns**: Genes start with 'Rpl'/'RPL' (large subunit) or 'Rps'/'RPS' (small subunit) - **Note**: High ribosomal content isn't always bad - metabolically active cells have more ribosomes ### Hemoglobin Content - **What it measures**: Percentage of counts from hemoglobin genes - **Why it matters**: Indicates blood contamination in non-blood tissues - **Patterns**: Genes matching '^Hb[^(p)]' or '^HB[^(P)]' (excludes Hbp1/HBP1) - **When to use**: Particularly important for tissue samples (brain, liver, etc.) ## MAD-Based Filtering Rationale ### Why MAD Instead of Fixed Thresholds? Fixed thresholds (e.g., "remove cells with <500 genes") fail because: - Different protocols yield different ranges - Different tissues have different characteristics - Different species have different gene counts - Fixed thresholds are arbitrary and not data-driven MAD (Median Absolute Deviation) is robust to outliers and adapts to your dataset: ``` MAD = median(|X - median(X)|) Outlier bounds = median ± n_MADs × MAD ``` ### Recommended MAD Thresholds Following scverse best practices (deliberately permissive): **5 MADs for count depth (log-transformed)** - Very permissive to retain rare cell populations - Catches extreme outliers (empty droplets, debris) - Log transformation handles the typical right-skewed distribution **5 MADs for gene counts (log-transformed)** - Parallels count depth filtering - Most informative when combined with count filtering - Log transformation normalizes the distribution **3 MADs for mitochondrial percentage** - More stringent because high MT% strongly indicates dying cells - Uses raw percentages (not log-transformed) - Combined with hard threshold for extra stringency **Hard threshold: 8% mitochondrial content** - Additional filter beyond MAD-based detection - Conservative cutoff that works across most tissues - Adjust higher (10-15%) for metabolically active cell types ### Why Be Permissive? The default thresholds intentionally err on the side of keeping cells because: 1. **Rare populations**: Stringent filtering may remove rare but viable cell types 2. **Biological variation**: Some healthy cells naturally have extreme values 3. **Reversibility**: Easier to filter more later than to recover lost cells 4. **Downstream robustness**: Modern normalization methods handle moderate quality variation ## Interpreting QC Visualizations ### Histograms - **Bimodal distributions**: May indicate mixing of cell types or quality issues - **Long tails**: Common for count depth; MAD filtering handles this - **Sharp cutoffs**: May indicate prior filtering or technical artifacts ### Violin Plots - Shows distribution shape and density - Median (line) and mean (diamond) should be similar for symmetric distributions - Wide distributions suggest high heterogeneity ### Scatter Plots **Counts vs Genes (colored by MT%)** - Should show strong positive correlation (R² > 0.8 typical) - Points deviating from trend may be outliers - High MT% cells often cluster at low counts/genes **Counts vs MT%** - Negative correlation expected (dying cells have fewer counts) - Vertical stratification may indicate batch effects - Cells with high counts + high MT% are suspicious **Genes vs MT%** - Similar to counts vs MT%, but often weaker correlation - Useful for identifying cells with gene detection issues ## Gene Filtering After filtering cells, remove genes detected in fewer than 20 cells: - **Why 20?**: Balances noise reduction with information retention - **Benefits**: Reduces dataset size, speeds up computation, removes noisy genes - **Trade-offs**: May lose very rare markers; adjust to 10 if studying rare populations ## Species-Specific Considerations ### Mouse (Mus musculus) - Mitochondrial genes: mt-* (lowercase) - Ribosomal genes: Rpl*, Rps* (capitalized first letter) - Hemoglobin genes: Hb* (but not Hbp1) ### Human (Homo sapiens) - Mitochondrial genes: MT-* (uppercase) - Ribosomal genes: RPL*, RPS* (all uppercase) - Hemoglobin genes: HB* (but not HBP1) ### Other Species Adjust gene name patterns in the script to match your organism's gene nomenclature. Consult Ensembl or your reference annotation for correct prefixes. ## When to Adjust Parameters Consider adjusting filtering thresholds when: **More stringent (lower MADs)** - High ambient RNA contamination suspected - Many low-quality cells observed in visualizations - Downstream analysis shows quality-driven clustering **More permissive (higher MADs)** - Studying rare cell populations - Dataset has high technical quality - Cell types naturally have extreme values (e.g., neurons with high MT%) **Tissue-specific adjustments** - Brain/neurons: May need higher MT% threshold (10-15%) - Blood: Can be more stringent with MT% (5-8%) - Tumor samples: Often need more permissive thresholds due to biological variation ## Advanced QC Considerations ### Not Included in This Workflow **Ambient RNA correction** - Tool: SoupX, CellBender, DecontX - When: High background RNA in droplet-based data - Effect: Removes contamination from lysed cells **Doublet detection** - Tool: scDblFinder, scrublet, DoubletFinder - When: Always recommended for droplet-based data - Effect: Identifies and removes multiplets (2+ cells in one droplet) **Cell cycle scoring** - Tool: scanpy's score_genes_cell_cycle - When: Cell cycle effects confound biological signal - Effect: Allows regressing out or accounting for cell cycle phase **Batch correction** - Tool: Harmony, scVI, ComBat - When: Integrating data from multiple batches/experiments - Effect: Removes technical batch effects while preserving biology ## References - scverse Best Practices: https://www.sc-best-practices.org/preprocessing_visualization/quality_control.html - Luecken & Theis (2019): Current best practices in single-cell RNA-seq analysis - Osorio & Cai (2021): Systematic determination of the mitochondrial proportion in human and mouse genomes - Germain et al. (2020): Doublet identification in single-cell sequencing data using scDblFinder