Batch correction is a data processing step that addresses technical variations in scientific experiments. Biological experiments often involve factors unrelated to the biological question, such as different experimental runs, reagent lots, or instrument calibrations. These factors introduce non-biological variations, commonly called “batch effects,” which can obscure the true biological signals within the data. Correcting for these effects is important for accurate scientific analysis and interpretation, especially in modern high-throughput studies where large datasets are combined.
Understanding Batch Effects in Biological Data
Batch effects are systematic technical differences that arise when biological samples are processed and measured in different groups or “batches.” These variations are not related to the biological differences being studied but rather stem from the experimental procedures themselves. For instance, using different lab technicians, varying reagent lots, slight changes in instrument calibration, or even conducting experiments on different dates can introduce noticeable shifts in the data.
These non-biological variations can lead to artificial clustering or separation of data points that should be biologically similar, making it difficult to identify genuine biological differences or similarities. For example, if samples from a disease group are processed in one batch and control samples in another, any observed differences might be due to the batch effect rather than the disease itself. This confounding can lead to inaccurate conclusions and hinder the discovery of true biological insights.
How Harmony Integrates Diverse Datasets
Harmony is a computational method designed to remove batch effects from biological datasets, particularly in a low-dimensional space like Principal Component Analysis (PCA) embeddings. It works by iteratively adjusting for technical variations while preserving underlying biological differences.
The algorithm begins with an initial low-dimensional embedding of the cells, such as that derived from PCA. It then performs “soft clustering” of cells, meaning each cell can be assigned to multiple clusters with varying probabilities. These clusters represent cell types or states.
For each cluster, Harmony calculates a global centroid and dataset-specific centroids. It then computes a correction factor for each dataset within each cluster, based on these centroids. Each cell receives a personalized correction factor, a weighted average of these cluster-specific and dataset-specific factors, based on its soft cluster assignments. This iterative process continues until data points no longer significantly shift, indicating convergence.
Key Applications of Harmony Correction
Harmony is widely applied when researchers need to combine complex biological datasets generated under different experimental conditions. A prominent application is in single-cell RNA sequencing (scRNA-seq) experiments, which often involve data collected across multiple laboratories, using different platforms, or at various time points. When integrating scRNA-seq data, Harmony helps align cells from different batches so that similar cell types from different experiments cluster together, rather than by their original batch.
This capability allows researchers to pool data from diverse sources, significantly increasing the total number of cells available for analysis. By removing technical noise, Harmony enhances statistical power and enables the discovery of subtle biological insights that would otherwise be hidden by batch-related variations. For example, it can facilitate the identification of rare cell populations or the comparison of cell states across different disease conditions or treatments, even if samples were processed separately. Harmony is a widely used method for scRNA-seq data integration.
Important Considerations for Harmony Use
When implementing Harmony, it is important to understand the biological questions being asked and the structure of the data. While Harmony is a robust method, users should always validate the results to ensure that genuine biological signals are not unintentionally removed or distorted. This validation often involves visualizing the data before and after correction using dimensionality reduction techniques like UMAP or t-SNE, confirming that biological groups cluster appropriately while batch-specific separation is reduced.
Careful interpretation of the corrected data is also necessary, as batch correction methods make assumptions about the data. For instance, Harmony operates on a low-dimensional embedding, such as PCA, rather than directly modifying raw expression values. Users should consider adjusting parameters to achieve optimal integration while preserving biological distinctions, guided by the extent of the batch effect and desired correction.