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:

Rare populations: Stringent filtering may remove rare but viable cell types
Biological variation: Some healthy cells naturally have extreme values
Reversibility: Easier to filter more later than to recover lost cells
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

/single-cell-rna-qc

referencesscverse_qc_guidelines.md