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Scverse Qc Guidelines · Single Cell Rna Qc · anthropics/knowledge-work-plugins · Skills Docs
This document provides detailed information about quality control best practices for single-cell RNA-seq data, following the scverse ecosystem recommendations.
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Quality Control Metrics
What it measures : Total number of UMI/reads per cellWhy it matters : Low count cells may be empty droplets, debris, or poorly captured cellsTypical range : 500-50,000 counts per cell (varies by protocol)Red flags : Bimodal distributions may indicate mixing of high and low-quality cells
What it measures : Number of genes with at least 1 countWhy it matters : Strongly correlates with count depth; low values indicate poor captureTypical range : 200-5,000 genes per cellRed flags : Very low values (<200) suggest technical failures
What it measures : Percentage of counts from mitochondrial genesWhy it matters : High MT% indicates cell stress, apoptosis, or lysed cellsTypical range : <5% for most tissues, up to 10-15% for metabolically active cellsSpecies-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
What it measures : Percentage of counts from ribosomal protein genesWhy it matters : Can indicate cell state or contaminationPatterns : 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
What it measures : Percentage of counts from hemoglobin genesWhy it matters : Indicates blood contamination in non-blood tissuesPatterns : Genes matching ‘^Hb[^(p)]’ or ‘^HB[^(P)]’ (excludes Hbp1/HBP1)When to use : Particularly important for tissue samples (brain, liver, etc.) 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:
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
The default thresholds intentionally err on the side of keeping cells because:
Rare populations : Stringent filtering may remove rare but viable cell typesBiological variation : Some healthy cells naturally have extreme valuesReversibility : Easier to filter more later than to recover lost cellsDownstream robustness : Modern normalization methods handle moderate quality variation
Bimodal distributions : May indicate mixing of cell types or quality issuesLong tails : Common for count depth; MAD filtering handles thisSharp cutoffs : May indicate prior filtering or technical artifacts
Shows distribution shape and density
Median (line) and mean (diamond) should be similar for symmetric distributions
Wide distributions suggest high heterogeneity
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
Negative correlation expected (dying cells have fewer counts)
Vertical stratification may indicate batch effects
Cells with high counts + high MT% are suspicious
Similar to counts vs MT%, but often weaker correlation
Useful for identifying cells with gene detection issues
After filtering cells, remove genes detected in fewer than 20 cells:
Why 20? : Balances noise reduction with information retentionBenefits : Reduces dataset size, speeds up computation, removes noisy genesTrade-offs : May lose very rare markers; adjust to 10 if studying rare populations
Mitochondrial genes: mt-* (lowercase)
Ribosomal genes: Rpl*, Rps* (capitalized first letter)
Hemoglobin genes: Hb* (but not Hbp1)
Mitochondrial genes: MT-* (uppercase)
Ribosomal genes: RPL*, RPS* (all uppercase)
Hemoglobin genes: HB* (but not HBP1)
Adjust gene name patterns in the script to match your organism’s gene nomenclature. Consult Ensembl or your reference annotation for correct prefixes.
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
Tool: SoupX, CellBender, DecontX
When: High background RNA in droplet-based data
Effect: Removes contamination from lysed cells
Tool: scDblFinder, scrublet, DoubletFinder
When: Always recommended for droplet-based data
Effect: Identifies and removes multiplets (2+ cells in one droplet)
Tool: scanpy’s score_genes_cell_cycle
When: Cell cycle effects confound biological signal
Effect: Allows regressing out or accounting for cell cycle phase
Tool: Harmony, scVI, ComBat
When: Integrating data from multiple batches/experiments
Effect: Removes technical batch effects while preserving biology
references/scverse_qc_guidelines.md