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Differential Expression Analysis in scvi-tools

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Differential Expression Analysis in scvi-tools

This document provides detailed information about differential expression (DE) analysis using scvi-tools' probabilistic framework.

Overview

scvi-tools implements Bayesian differential expression testing that leverages the learned generative models to estimate expression differences between groups. This approach provides several advantages over traditional methods:

  • Batch correction: DE testing on batch-corrected representations
  • Uncertainty quantification: Probabilistic estimates of effect sizes
  • Zero-inflation handling: Proper modeling of dropout and zeros
  • Flexible comparisons: Between any groups or cell types
  • Multiple modalities: Works for RNA, proteins (totalVI), and accessibility (PeakVI)

Core Statistical Framework

Problem Definition

The goal is to estimate the log fold-change in expression between two conditions:

log fold-change = log(μ_B) - log(μ_A)

Where μ_A and μ_B are the mean expression levels in conditions A and B.

Three-Stage Process

Stage 1: Estimating Expression Levels

  • Sample from posterior distribution of cellular states
  • Generate expression values from the learned generative model
  • Aggregate across cells to get population-level estimates

Stage 2: Detecting Relevant Features (Hypothesis Testing)

  • Test for differential expression using Bayesian framework
  • Two testing modes available:
    • "vanilla" mode: Point null hypothesis (β = 0)
    • "change" mode: Composite hypothesis (|β| ≤ δ)

Stage 3: Controlling False Discovery

  • Posterior expected False Discovery Proportion (FDP) control
  • Selects maximum number of discoveries ensuring E[FDP] ≤ α

Basic Usage

Simple Two-Group Comparison

python
import scvi

# After training a model
model = scvi.model.SCVI(adata)
model.train()

# Compare two cell types
de_results = model.differential_expression(
    groupby="cell_type",
    group1="T cells",
    group2="B cells"
)

# View top DE genes
top_genes = de_results.sort_values("lfc_mean", ascending=False).head(20)
print(top_genes[["lfc_mean", "lfc_std", "bayes_factor", "is_de_fdr_0.05"]])

One vs. Rest Comparison

python
# Compare one group against all others
de_results = model.differential_expression(
    groupby="cell_type",
    group1="T cells"  # No group2 = compare to rest
)

All Pairwise Comparisons

python
# Compare all cell types pairwise
all_comparisons = {}

cell_types = adata.obs["cell_type"].unique()

for ct1 in cell_types:
    for ct2 in cell_types:
        if ct1 != ct2:
            key = f"{ct1}_vs_{ct2}"
            all_comparisons[key] = model.differential_expression(
                groupby="cell_type",
                group1=ct1,
                group2=ct2
            )

Key Parameters

groupby (required)

Column in adata.obs defining groups to compare.

python
# Must be a categorical variable
de_results = model.differential_expression(groupby="cell_type")

group1 and group2

Groups to compare. If group2 is None, compares group1 to all others.

python
# Specific comparison
de = model.differential_expression(groupby="condition", group1="treated", group2="control")

# One vs rest
de = model.differential_expression(groupby="cell_type", group1="T cells")

mode (Hypothesis Testing Mode)

"vanilla" mode (default): Point null hypothesis

  • Tests if β = 0 exactly
  • More sensitive, but may find trivially small effects

"change" mode: Composite null hypothesis

  • Tests if |β| ≤ δ
  • Requires biologically meaningful change
  • Reduces false discoveries of tiny effects
python
# Change mode with minimum effect size
de = model.differential_expression(
    groupby="cell_type",
    group1="T cells",
    group2="B cells",
    mode="change",
    delta=0.25  # Minimum log fold-change
)

delta

Minimum effect size threshold for "change" mode.

  • Typical values: 0.25, 0.5, 0.7 (log scale)
  • log2(1.5) ≈ 0.58 (1.5-fold change)
  • log2(2) = 1.0 (2-fold change)
python
# Require at least 1.5-fold change
de = model.differential_expression(
    groupby="condition",
    group1="disease",
    group2="healthy",
    mode="change",
    delta=0.58  # log2(1.5)
)

fdr_target

False discovery rate threshold (default: 0.05)

python
# More stringent FDR control
de = model.differential_expression(
    groupby="cell_type",
    group1="T cells",
    fdr_target=0.01
)

batch_correction

Whether to perform batch correction during DE testing (default: True)

python
# Test within a specific batch
de = model.differential_expression(
    groupby="cell_type",
    group1="T cells",
    group2="B cells",
    batch_correction=False
)

n_samples

Number of posterior samples for estimation (default: 5000)

  • More samples = more accurate but slower
  • Reduce for speed, increase for precision
python
# High precision analysis
de = model.differential_expression(
    groupby="cell_type",
    group1="T cells",
    n_samples=10000
)

Interpreting Results

Output Columns

The results DataFrame contains several important columns:

Effect Size Estimates:

  • lfc_mean: Mean log fold-change
  • lfc_median: Median log fold-change
  • lfc_std: Standard deviation of log fold-change
  • lfc_min: Lower bound of effect size
  • lfc_max: Upper bound of effect size

Statistical Significance:

  • bayes_factor: Bayes factor for differential expression
    • Higher values = stronger evidence

    • 3 often considered meaningful

  • is_de_fdr_0.05: Boolean indicating if gene is DE at FDR 0.05
  • is_de_fdr_0.1: Boolean indicating if gene is DE at FDR 0.1

Expression Levels:

  • mean1: Mean expression in group 1
  • mean2: Mean expression in group 2
  • non_zeros_proportion1: Proportion of non-zero cells in group 1
  • non_zeros_proportion2: Proportion of non-zero cells in group 2

Example Interpretation

python
de_results = model.differential_expression(
    groupby="cell_type",
    group1="T cells",
    group2="B cells"
)

# Find significantly upregulated genes in T cells
upreg_tcells = de_results[
    (de_results["is_de_fdr_0.05"]) &
    (de_results["lfc_mean"] > 0)
].sort_values("lfc_mean", ascending=False)

print(f"Upregulated genes in T cells: {len(upreg_tcells)}")
print(upreg_tcells.head(10))

# Find genes with large effect sizes
large_effect = de_results[
    (de_results["is_de_fdr_0.05"]) &
    (abs(de_results["lfc_mean"]) > 1)  # 2-fold change
]

Advanced Usage

DE Within Specific Cells

python
# Test DE only within a subset of cells
subset_indices = adata.obs["tissue"] == "lung"

de = model.differential_expression(
    idx1=adata.obs["cell_type"] == "T cells" & subset_indices,
    idx2=adata.obs["cell_type"] == "B cells" & subset_indices
)

Batch-Specific DE

python
# Test DE within each batch separately
batches = adata.obs["batch"].unique()

batch_de_results = {}
for batch in batches:
    batch_idx = adata.obs["batch"] == batch
    batch_de_results[batch] = model.differential_expression(
        idx1=(adata.obs["condition"] == "treated") & batch_idx,
        idx2=(adata.obs["condition"] == "control") & batch_idx
    )

Pseudo-bulk DE

python
# Aggregate cells before DE testing
# Useful for low cell counts per group

de = model.differential_expression(
    groupby="cell_type",
    group1="rare_cell_type",
    group2="common_cell_type",
    n_samples=10000,  # More samples for stability
    batch_correction=True
)

Visualization

Volcano Plot

python
import matplotlib.pyplot as plt
import numpy as np

de = model.differential_expression(
    groupby="condition",
    group1="treated",
    group2="control"
)

# Volcano plot
plt.figure(figsize=(10, 6))
plt.scatter(
    de["lfc_mean"],
    -np.log10(1 / (de["bayes_factor"] + 1)),
    c=de["is_de_fdr_0.05"],
    cmap="coolwarm",
    alpha=0.5
)
plt.xlabel("Log Fold Change")
plt.ylabel("-log10(1/Bayes Factor)")
plt.title("Volcano Plot: Treated vs Control")
plt.axvline(x=0, color='k', linestyle='--', linewidth=0.5)
plt.show()

Heatmap of Top DE Genes

python
import seaborn as sns

# Get top DE genes
top_genes = de.sort_values("lfc_mean", ascending=False).head(50).index

# Get normalized expression
norm_expr = model.get_normalized_expression(
    adata,
    indices=adata.obs["condition"].isin(["treated", "control"]),
    gene_list=top_genes
)

# Plot heatmap
plt.figure(figsize=(12, 10))
sns.heatmap(
    norm_expr.T,
    cmap="viridis",
    xticklabels=False,
    yticklabels=top_genes
)
plt.title("Top 50 DE Genes")
plt.show()

Ranked Gene Plot

python
# Plot genes ranked by effect size
de_sorted = de.sort_values("lfc_mean", ascending=False)

plt.figure(figsize=(12, 6))
plt.plot(range(len(de_sorted)), de_sorted["lfc_mean"].values)
plt.axhline(y=0, color='r', linestyle='--')
plt.xlabel("Gene Rank")
plt.ylabel("Log Fold Change")
plt.title("Genes Ranked by Effect Size")
plt.show()

Comparison with Traditional Methods

scvi-tools vs. Wilcoxon Test

python
import scanpy as sc

# Traditional Wilcoxon test
sc.tl.rank_genes_groups(
    adata,
    groupby="cell_type",
    method="wilcoxon",
    key_added="wilcoxon"
)

# scvi-tools DE
de_scvi = model.differential_expression(
    groupby="cell_type",
    group1="T cells"
)

# Compare results
wilcox_results = sc.get.rank_genes_groups_df(adata, group="T cells", key="wilcoxon")

Advantages of scvi-tools:

  • Accounts for batch effects automatically
  • Handles zero-inflation properly
  • Provides uncertainty quantification
  • No arbitrary pseudocount needed
  • Better statistical properties

When to use Wilcoxon:

  • Very quick exploratory analysis
  • Comparison with published results using Wilcoxon

Multi-Modal DE

Protein DE (totalVI)

python
# Train totalVI on CITE-seq data
totalvi_model = scvi.model.TOTALVI(adata)
totalvi_model.train()

# RNA differential expression
rna_de = totalvi_model.differential_expression(
    groupby="cell_type",
    group1="T cells",
    group2="B cells",
    protein_expression=False  # Default
)

# Protein differential expression
protein_de = totalvi_model.differential_expression(
    groupby="cell_type",
    group1="T cells",
    group2="B cells",
    protein_expression=True
)

print(f"DE genes: {rna_de['is_de_fdr_0.05'].sum()}")
print(f"DE proteins: {protein_de['is_de_fdr_0.05'].sum()}")

Differential Accessibility (PeakVI)

python
# Train PeakVI on ATAC-seq data
peakvi_model = scvi.model.PEAKVI(atac_adata)
peakvi_model.train()

# Differential accessibility
da = peakvi_model.differential_accessibility(
    groupby="cell_type",
    group1="T cells",
    group2="B cells"
)

# Same interpretation as DE

Handling Special Cases

Low Cell Count Groups

python
# Increase posterior samples for stability
de = model.differential_expression(
    groupby="cell_type",
    group1="rare_type",  # e.g., 50 cells
    group2="common_type",  # e.g., 5000 cells
    n_samples=10000
)

Imbalanced Comparisons

python
# When groups have very different sizes
# Use change mode to avoid tiny effects

de = model.differential_expression(
    groupby="condition",
    group1="rare_condition",
    group2="common_condition",
    mode="change",
    delta=0.5
)

Multiple Testing Correction

python
# Already included via FDP control
# But can apply additional corrections

from statsmodels.stats.multitest import multipletests

# Bonferroni correction (very conservative)
_, pvals_corrected, _, _ = multipletests(
    1 / (de["bayes_factor"] + 1),
    method="bonferroni"
)

Performance Considerations

Speed Optimization

python
# Faster DE testing for large datasets
de = model.differential_expression(
    groupby="cell_type",
    group1="T cells",
    n_samples=1000,  # Reduce samples
    batch_size=512    # Increase batch size
)

Memory Management

python
# For very large datasets
# Test one comparison at a time rather than all pairwise

cell_types = adata.obs["cell_type"].unique()
for ct in cell_types:
    de = model.differential_expression(
        groupby="cell_type",
        group1=ct
    )
    # Save results
    de.to_csv(f"de_results_{ct}.csv")

Best Practices

  1. Use "change" mode: For biologically interpretable results
  2. Set appropriate delta: Based on biological significance
  3. Check expression levels: Filter lowly expressed genes
  4. Validate findings: Check marker genes for sanity
  5. Visualize results: Always plot top DE genes
  6. Report parameters: Document mode, delta, FDR used
  7. Consider batch effects: Use batch_correction=True
  8. Multiple comparisons: Be aware of testing many groups
  9. Sample size: Ensure sufficient cells per group (>50 recommended)
  10. Biological validation: Follow up with functional experiments

Example: Complete DE Analysis Workflow

python
import scvi
import scanpy as sc
import matplotlib.pyplot as plt

# 1. Train model
scvi.model.SCVI.setup_anndata(adata, layer="counts", batch_key="batch")
model = scvi.model.SCVI(adata)
model.train()

# 2. Perform DE analysis
de_results = model.differential_expression(
    groupby="cell_type",
    group1="Disease_T_cells",
    group2="Healthy_T_cells",
    mode="change",
    delta=0.5,
    fdr_target=0.05
)

# 3. Filter and analyze
sig_genes = de_results[de_results["is_de_fdr_0.05"]]
upreg = sig_genes[sig_genes["lfc_mean"] > 0].sort_values("lfc_mean", ascending=False)
downreg = sig_genes[sig_genes["lfc_mean"] < 0].sort_values("lfc_mean")

print(f"Significant genes: {len(sig_genes)}")
print(f"Upregulated: {len(upreg)}")
print(f"Downregulated: {len(downreg)}")

# 4. Visualize top genes
top_genes = upreg.head(10).index.tolist() + downreg.head(10).index.tolist()

sc.pl.violin(
    adata[adata.obs["cell_type"].isin(["Disease_T_cells", "Healthy_T_cells"])],
    keys=top_genes,
    groupby="cell_type",
    rotation=90
)

# 5. Functional enrichment (using external tools)
# E.g., g:Profiler, DAVID, or gprofiler-official Python package
upreg_genes = upreg.head(100).index.tolist()
# Perform pathway analysis...

# 6. Save results
de_results.to_csv("de_results_disease_vs_healthy.csv")
upreg.to_csv("upregulated_genes.csv")
downreg.to_csv("downregulated_genes.csv")