Scvi Tools

# 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