Batch integration graph

The task

This is a sub-task of the overall batch integration task. Batch (or data) integration methods integrate datasets across batches that arise from various biological and technical sources. Methods that integrate batches typically have three different types of output: a corrected feature matrix, a joint embedding across batches, and/or an integrated cell-cell similarity graph (e.g., a kNN graph). This sub-task focuses on all methods that can output integrated graphs, and includes methods that canonically output the other two data formats with subsequent postprocessing to generate a graph. Other sub-tasks for batch integration can be found for:

This sub-task was taken from a benchmarking study of data integration methods.

The metrics

Metrics for batch integration (graph) measure how well batches are mixed while biological signals are preserved. They are divided into batch correction and biological variance conservation metrics.

Batch correction

  • Graph connectivity: The graph connectivity metric assesses whether the kNN graph representation, G, of the integrated data connects all cells with the same cell identity label.

Biological variance removal

  • Adjusted rand index (ARI): The Rand index compares the overlap of two clusterings; it considers both correct clustering overlaps while also counting correct disagreements between two clusterings.
  • Iso label F1 score: Isolated cell labels are identified as the labels present in the least number of batches in the integration task. The score evaluates how well these isolated labels separate from other cell identities based on clustering.
  • Normalized mutual information (NMI): NMI compares the overlap of two clusterings. We used NMI to compare the cell-type labels with Louvain clusters computed on the integrated dataset.
DatasetBest MethodPaperCode
Immune (by batch)scANVI (hvg/unscaled)


Lung (Viera Braga et al.)scANVI (hvg/unscaled)


Pancreas (by batch)Scanorama (hvg/unscaled)