Data augmented bootstrap constructs confidence intervals via approximately invariant transformations, recovering classical bootstrap, conformal prediction, wild bootstrap, and SymmPI as special cases with interpolated finite-sample to asymptotic coverage guarantees.
arXiv preprint arXiv:2409.05202 , year=
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SNR-ST-Mix is a geometry- and expression-aware mixup augmentation that constrains interpolation to k-nearest spatial neighbors and weights by transcriptomic similarity for spatial transcriptomics imputation.
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Data augmented bootstrap: Unifying confidence interval construction by approximate invariance
Data augmented bootstrap constructs confidence intervals via approximately invariant transformations, recovering classical bootstrap, conformal prediction, wild bootstrap, and SymmPI as special cases with interpolated finite-sample to asymptotic coverage guarantees.
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SNR-ST-Mix: Sample-specific Neighborhood Regression Mixup for Augmented Spatial Transcriptomics Imputation with Deep Neural Network
SNR-ST-Mix is a geometry- and expression-aware mixup augmentation that constrains interpolation to k-nearest spatial neighbors and weights by transcriptomic similarity for spatial transcriptomics imputation.