322 lines
9.3 KiB
Markdown
322 lines
9.3 KiB
Markdown
# ATAC-seq and Chromatin Accessibility Models
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This document covers models for analyzing single-cell ATAC-seq and chromatin accessibility data in scvi-tools.
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## PeakVI
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**Purpose**: Analysis and integration of single-cell ATAC-seq data using peak counts.
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**Key Features**:
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- Variational autoencoder specifically designed for scATAC-seq peak data
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- Learns low-dimensional representations of chromatin accessibility
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- Performs batch correction across samples
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- Enables differential accessibility testing
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- Integrates multiple ATAC-seq datasets
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**When to Use**:
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- Analyzing scATAC-seq peak count matrices
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- Integrating multiple ATAC-seq experiments
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- Batch correction of chromatin accessibility data
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- Dimensionality reduction for ATAC-seq
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- Differential accessibility analysis between cell types or conditions
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**Data Requirements**:
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- Peak count matrix (cells × peaks)
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- Binary or count data for peak accessibility
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- Batch/sample annotations (optional, for batch correction)
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**Basic Usage**:
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```python
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import scvi
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# Prepare data (peaks should be in adata.X)
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# Optional: filter peaks
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sc.pp.filter_genes(adata, min_cells=3)
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# Setup data
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scvi.model.PEAKVI.setup_anndata(
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adata,
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batch_key="batch"
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)
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# Train model
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model = scvi.model.PEAKVI(adata)
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model.train()
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# Get latent representation (batch-corrected)
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latent = model.get_latent_representation()
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adata.obsm["X_PeakVI"] = latent
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# Differential accessibility
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da_results = model.differential_accessibility(
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groupby="cell_type",
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group1="TypeA",
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group2="TypeB"
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)
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```
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**Key Parameters**:
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- `n_latent`: Dimensionality of latent space (default: 10)
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- `n_hidden`: Number of nodes per hidden layer (default: 128)
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- `n_layers`: Number of hidden layers (default: 1)
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- `region_factors`: Whether to learn region-specific factors (default: True)
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- `latent_distribution`: Distribution for latent space ("normal" or "ln")
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**Outputs**:
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- `get_latent_representation()`: Low-dimensional embeddings for cells
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- `get_accessibility_estimates()`: Normalized accessibility values
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- `differential_accessibility()`: Statistical testing for differential peaks
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- `get_region_factors()`: Peak-specific scaling factors
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**Best Practices**:
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1. Filter out low-quality peaks (present in very few cells)
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2. Include batch information if integrating multiple samples
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3. Use latent representations for clustering and UMAP visualization
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4. Consider using `region_factors=True` for datasets with high technical variation
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5. Store latent embeddings in `adata.obsm` for downstream analysis with scanpy
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## PoissonVI
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**Purpose**: Quantitative analysis of scATAC-seq fragment counts (more detailed than peak counts).
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**Key Features**:
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- Models fragment counts directly (not just peak presence/absence)
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- Poisson distribution for count data
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- Captures quantitative differences in accessibility
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- Enables fine-grained analysis of chromatin state
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**When to Use**:
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- Analyzing fragment-level ATAC-seq data
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- Need quantitative accessibility measurements
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- Higher resolution analysis than binary peak calls
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- Investigating gradual changes in chromatin accessibility
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**Data Requirements**:
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- Fragment count matrix (cells × genomic regions)
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- Count data (not binary)
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**Basic Usage**:
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```python
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scvi.model.POISSONVI.setup_anndata(
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adata,
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batch_key="batch"
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)
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model = scvi.model.POISSONVI(adata)
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model.train()
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# Get results
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latent = model.get_latent_representation()
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accessibility = model.get_accessibility_estimates()
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```
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**Key Differences from PeakVI**:
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- **PeakVI**: Best for standard peak count matrices, faster
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- **PoissonVI**: Best for quantitative fragment counts, more detailed
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**When to Choose PoissonVI over PeakVI**:
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- Working with fragment counts rather than called peaks
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- Need to capture quantitative differences
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- Have high-quality, high-coverage data
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- Interested in subtle accessibility changes
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## scBasset
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**Purpose**: Deep learning approach to scATAC-seq analysis with interpretability and motif analysis.
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**Key Features**:
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- Convolutional neural network (CNN) architecture for sequence-based analysis
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- Models raw DNA sequences, not just peak counts
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- Enables motif discovery and transcription factor (TF) binding prediction
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- Provides interpretable feature importance
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- Performs batch correction
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**When to Use**:
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- Want to incorporate DNA sequence information
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- Interested in TF motif analysis
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- Need interpretable models (which sequences drive accessibility)
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- Analyzing regulatory elements and TF binding sites
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- Predicting accessibility from sequence alone
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**Data Requirements**:
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- Peak sequences (extracted from genome)
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- Peak accessibility matrix
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- Genome reference (for sequence extraction)
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**Basic Usage**:
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```python
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# scBasset requires sequence information
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# First, extract sequences for peaks
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from scbasset import utils
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sequences = utils.fetch_sequences(adata, genome="hg38")
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# Setup and train
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scvi.model.SCBASSET.setup_anndata(
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adata,
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batch_key="batch"
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)
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model = scvi.model.SCBASSET(adata, sequences=sequences)
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model.train()
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# Get latent representation
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latent = model.get_latent_representation()
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# Interpret model: which sequences/motifs are important
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importance_scores = model.get_feature_importance()
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```
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**Key Parameters**:
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- `n_latent`: Latent space dimensionality
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- `conv_layers`: Number of convolutional layers
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- `n_filters`: Number of filters per conv layer
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- `filter_size`: Size of convolutional filters
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**Advanced Features**:
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- **In silico mutagenesis**: Predict how sequence changes affect accessibility
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- **Motif enrichment**: Identify enriched TF motifs in accessible regions
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- **Batch correction**: Similar to other scvi-tools models
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- **Transfer learning**: Fine-tune on new datasets
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**Interpretability Tools**:
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```python
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# Get importance scores for sequences
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importance = model.get_sequence_importance(region_indices=[0, 1, 2])
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# Predict accessibility for new sequences
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predictions = model.predict_accessibility(new_sequences)
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```
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## Model Selection for ATAC-seq
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### PeakVI
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**Choose when**:
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- Standard scATAC-seq analysis workflow
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- Have peak count matrices (most common format)
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- Need fast, efficient batch correction
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- Want straightforward differential accessibility
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- Prioritize computational efficiency
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**Advantages**:
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- Fast training and inference
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- Proven track record for scATAC-seq
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- Easy integration with scanpy workflow
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- Robust batch correction
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### PoissonVI
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**Choose when**:
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- Have fragment-level count data
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- Need quantitative accessibility measures
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- Interested in subtle differences
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- Have high-coverage, high-quality data
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**Advantages**:
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- More detailed quantitative information
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- Better for gradient changes
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- Appropriate statistical model for counts
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### scBasset
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**Choose when**:
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- Want to incorporate DNA sequence
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- Need biological interpretation (motifs, TFs)
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- Interested in regulatory mechanisms
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- Have computational resources for CNN training
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- Want predictive power for new sequences
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**Advantages**:
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- Sequence-based, biologically interpretable
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- Motif and TF analysis built-in
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- Predictive modeling capabilities
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- In silico perturbation experiments
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## Workflow Example: Complete ATAC-seq Analysis
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```python
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import scvi
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import scanpy as sc
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# 1. Load and preprocess ATAC-seq data
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adata = sc.read_h5ad("atac_data.h5ad")
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# 2. Filter low-quality peaks
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sc.pp.filter_genes(adata, min_cells=10)
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# 3. Setup and train PeakVI
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scvi.model.PEAKVI.setup_anndata(
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adata,
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batch_key="sample"
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)
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model = scvi.model.PEAKVI(adata, n_latent=20)
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model.train(max_epochs=400)
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# 4. Extract latent representation
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latent = model.get_latent_representation()
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adata.obsm["X_PeakVI"] = latent
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# 5. Downstream analysis
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sc.pp.neighbors(adata, use_rep="X_PeakVI")
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sc.tl.umap(adata)
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sc.tl.leiden(adata, key_added="clusters")
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# 6. Differential accessibility
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da_results = model.differential_accessibility(
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groupby="clusters",
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group1="0",
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group2="1"
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)
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# 7. Save model
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model.save("peakvi_model")
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```
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## Integration with Gene Expression (RNA+ATAC)
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For paired multimodal data (RNA+ATAC from same cells), use **MultiVI** instead:
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```python
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# For 10x Multiome or similar paired data
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scvi.model.MULTIVI.setup_anndata(
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adata,
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batch_key="sample",
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modality_key="modality" # "RNA" or "ATAC"
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)
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model = scvi.model.MULTIVI(adata)
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model.train()
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# Get joint latent space
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latent = model.get_latent_representation()
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```
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See `models-multimodal.md` for more details on multimodal integration.
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## Best Practices for ATAC-seq Analysis
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1. **Quality Control**:
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- Filter cells with very low or very high peak counts
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- Remove peaks present in very few cells
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- Filter mitochondrial and sex chromosome peaks if needed
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2. **Batch Correction**:
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- Always include `batch_key` if integrating multiple samples
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- Consider technical covariates (sequencing depth, TSS enrichment)
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3. **Feature Selection**:
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- Unlike RNA-seq, all peaks are often used
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- Consider filtering very rare peaks for efficiency
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4. **Latent Dimensions**:
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- Start with `n_latent=10-30` depending on dataset complexity
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- Larger values for more heterogeneous datasets
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5. **Downstream Analysis**:
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- Use latent representations for clustering and visualization
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- Link peaks to genes for regulatory analysis
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- Perform motif enrichment on cluster-specific peaks
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6. **Computational Considerations**:
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- ATAC-seq matrices are often very large (many peaks)
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- Consider downsampling peaks for initial exploration
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- Use GPU acceleration for large datasets
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