Confounder-Aware Feature Correction for Single-Cell Batch Integration
Abstract
Batch integration is a central preprocessing step in single-cell genomics, where datasets collected across experiments, donors, and protocols must be combined despite pervasive technical batch effects. The leading integration methods produce a shared low-dimensional embedding, which discards the corrected gene-expression values that downstream differential-expression, biomarker, and other analyses depend on. The feature-space (expression-correcting) methods that do preserve genes typically align the marginal expression distributions of batches and thereby risk erasing genuine biological variation whenever cell-type composition differs across batches, i.e. whenever batch effect and biological signal are confounded. We recast single-cell batch integration as confounded domain adaptation and apply ConDo, a method that matches conditional expression distributions given the cell-type annotation rather than marginal distributions. To extend ConDo's pairwise source-to-target adapter to the many-batch setting, we introduce an agglomerative compatibility-graph integrator: batches are nodes connected when they share a cell type, and we greedily merge each best-scoring neighbor into a growing reference by fitting one ConDo adapter. On the Open Problems Batch Integration Benchmark, ConDo is the strongest feature-space integrator, ranking first among feature methods on five of six datasets. Furthermore, it is competitive with or better than deep embedding methods on the overall score, ranking first across all methods on four of six datasets while returning corrected expression rather than an opaque embedding.
Related
- A multidimensional benchmark of confounder-aware differential abundance strategies for single- and multi-cohort microbiome studies
- DAF-EfficientNetV2: distribution-aware feature enhancement for plant leaf disease recognition in complex environments
- Large cranial windows distort auditory cortical maps and degrade feature integration in secondary auditory cortex
- MPFIL: Multi-Level Protein Feature Integration for Predicting Mutation-Induced Protein-Protein Binding Affinity Changes
- miRAssist: a context-aware, evidence integration framework for interpretable miRNA-target prioritization
BibTeX
@misc{mccarter2026confounderaware,
title = {{Confounder-Aware Feature Correction for Single-Cell Batch Integration}},
author = {Calvin McCarter},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.28849},
eprint = {2608.28849},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.28849},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Confounder-Aware Feature Correction for Single-Cell Batch Integration
AU - Calvin McCarter
PY - 2026
DA - 2026/08/28
PB - arXiv
DO - 10.48550/arxiv.2608.28849
UR - https://arxiv.org/abs/2608.28849
AB - Batch integration is a central preprocessing step in single-cell genomics, where datasets collected across experiments, donors, and protocols must be combined despite pervasive technical batch effects. The leading integration methods produce a shared low-dimensional embedding, which discards the corrected gene-expression values that downstream differential-expression, biomarker, and other analyses depend on. The feature-space (expression-correcting) methods that do preserve genes typically align the marginal expression distributions of batches and thereby risk erasing genuine biological variation whenever cell-type composition differs across batches, i.e. whenever batch effect and biological signal are confounded. We recast single-cell batch integration as confounded domain adaptation and apply ConDo, a method that matches conditional expression distributions given the cell-type annotation rather than marginal distributions. To extend ConDo's pairwise source-to-target adapter to the many-batch setting, we introduce an agglomerative compatibility-graph integrator: batches are nodes connected when they share a cell type, and we greedily merge each best-scoring neighbor into a growing reference by fitting one ConDo adapter. On the Open Problems Batch Integration Benchmark, ConDo is the strongest feature-space integrator, ranking first among feature methods on five of six datasets. Furthermore, it is competitive with or better than deep embedding methods on the overall score, ranking first across all methods on four of six datasets while returning corrected expression rather than an opaque embedding.
ER -