Addressing Batch Correction with SAKURA

Batch effects are technical variations that occur between different experimental batches, which can confound biological signals in single-cell RNA sequencing data. SAKURA utilizes flexible approaches to handle batch effects, allowing users to choose the strategy that best fits their data and analysis goals.

Approach 1: Pre-corrected Expression Input

Perform batch correction methods on the raw expression matrix prior to utilizing SAKURA. The resulting expression matrix will serve as the input dataset for implementing SAKURA.

Advantages:

  • Corrects at the expression level, preserving biological signals

  • Flexible choice of established methods with extensive validation

  • SAKURA operates normally without modifications

Considerations:

  • May over-correct and remove subtle biological variations

  • Requires careful parameter tuning for optimal results due to transformed statistical properties and potential information loss in the batch corrected data

Suitable Methods:

  • Seurat CCA/RPCA: Batch effect correction aligning canonical basis vectors or reciprocal PCA

  • scVI: Probabilistic modeling of batch effects

  • Liger: Batch effect correction relies on integrative non-negative matrix factorization

Approach 2: Post-hoc Embedding Correction

Use SAKURA result embeddings as input to external batch correction methods, and then specifically remove technical batch variation within this low-dimensional space already enriched for biological signal.

Note

Use SAKURA embeddings as a functional substitute for PCA coordinates in batch correction, but ensure the method does not mathematically depends on PCA-specific constructs (e.g., gene loadings for matrix reconstruction).

Typical Workflow:

  1. Extract batch information from metadata during data preprocessing (batch, donor, sequencing_run, etc.)

  2. Generate SAKURA embeddings using standard training pipeline

  3. Apply batch correction to SAKURA embeddings using external tools

  4. Use corrected embeddings for clustering, visualization, and analysis

Advantages:

  • Preserves SAKURA’s biological signal learning

  • Reduces correction computational cost and time with low-dimensional SAKURA embedding

  • Enables modular evaluation on both SAKURA feature learning quality and batch correction efficacy

Considerations:

  • Requires compatible correction methods applied after feature learning

  • May involve iterative optimization between external correction methods and SAKURA targeting two objectives, i.e. biological signal learning and batch effect correction

Suitable Methods:

  • Harmony: Integration using diversity clustering correction

  • fastMNN: Batch effect correction aligning mutual nearest neighbors by cosine distance

Approach 3: Knowledge-Guided Training with Pre-Corrected Embeddings

Use pre-computed, batch-corrected low dimensional embeddings as the knowledge input for SAKURA’s feature training.

Note

This option is designed for scenarios where a reliable, batch-corrected low-dimensional representation of the data already exists or is preferred to be generated upstream.The core idea is to format prior knowledge about the desired, batch-effect-free cell-state together with necessary cell and feature metadata as the knowledge input to SAKURA for feature learning.

Configuration Example:

{
    "dataset": {
        ...
        "pheno_csv_path": "./<dataset>/pheno_df_with_batch_effect_correction_features.csv",
        "pheno_meta_path": "./<dataset>_<signature/phenotype>_bec/pheno_meta.json",
        "selected_pheno": [
            "BE_cor",
            ...
        ],
        ...
    },
    ...
    "story": [
      {
        ...
        "<pheno_with_>batch_effect_correction": {
            "use_split": "overall_train",
            "batch_size": 100,
            "train_main_latent": "False",
            "train_pheno": "True",
            "train_signature": "False",
            "selected_pheno": {
                ...
                "BE_cor": {
                    "loss": "*",
                    "regularization": "*"
                }
            }
        },
      }
    ]
}
Advantages:

  • Separates the complex problem of batch correction from knowledge-guided deep learning

  • Flexible integration with existing workflows where batch correction is a standardized upstream step

Considerations:

  • Requires appropriate choice of upstream batch correction methods and decent validation

  • Requires careful parameter tuning balancing the intensity of batch correction and other biological signal learning

  • Evaluation is holistic; difficult to disentangle the combined effect due to the fused learning.

Suitable Methods:

  • Harmony: Integration using diversity clustering correction

  • BBKNN: Batch balanced k-nearest neighbors

  • Scanorama: Panoramic stitching of datasets via mutual nearest neighbors

Conclusion

SAKURA’s flexible architecture supports multiple strategies for batch effect handling. The choice between pre-correction, post-hoc correction, or corrected embedding knowledge input depends on the severity of batch effects, data complexity, and specific research questions.

For most applications, we recommend starting with Approach 1 (pre-corrected expression) for well-established datasets, or Approach 2 (post-hoc embedding correction) when SAKURA’s biological feature extraction is prioritized.