Feature Selection in the Presence of Monotone Batch Effects
Keywords: Batch Effect, Distribution Shift
Abstract: We study the feature selection problem in the presence of monotone batch effects when merging datasets from disparate technologies and different environments affects the underlying causal dependence of data features. We propose two novel algorithms for this task: 1) joint feature selection and batch effect correction through non-linear transformations matching the distribution of data batches; 2) transforming data using a batch-invariant characteristic (i.e., feature rank) to append datasets. To match the distribution of data batches during the feature selection procedure, we use the maximum mean discrepancy (MMD) distance. We assess the performance of the feature selection methods used in conjunction with our batch effect removal methods.
Our experiments on synthetic data show that the former method combined with Lasso improves the $F_1$ score significantly, even with few samples per dataset. This method outperforms popular batch effect removal algorithms, including Combat-Seq, Limma, and PCA. Comparatively, while the ranking method is computationally more efficient, its performance is worse due to the information loss resulting from ignoring the magnitude of data.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 9047
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