Data-Adaptive Multi-Bin Rarefying for Alpha Diversity Association Analysis under Heterogeneous Library Sizes

Abstract

Alpha diversity association analysis is sensitive to heterogeneous library sizes, and overall rarefying to a common depth can either discard many reads from high-depth samples or exclude low-depth samples. We propose MBRarefy, a data-adaptive framework for selecting multi-bin rarefying designs. The method chooses library size cutpoints by minimizing an objective that combines residual within-bin library size dependence with a fragmentation penalty, subject to a minimum bin size constraint. The framework supports both fixed-K and varying-K cutpoint selection. In T-cell receptor (TCR) motivated simulations, the primary fixed-K procedure controlled depth-induced false positives and improved power relative to overall rarefying when group and depth confounding was absent. Applications to TCR immunosequencing and wild baboon gut microbiome data illustrate its ability to reduce residual library size dependence while improving read and sample retention.

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BibTeX RIS

BibTeX

@misc{li2026dataadaptive,
  title = {{Data-Adaptive Multi-Bin Rarefying for Alpha Diversity Association Analysis under Heterogeneous Library Sizes}},
  author = {Mo Li},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2606.21000},
  eprint = {2606.21000},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.21000},
}

RIS

TY  - UNPB
TI  - Data-Adaptive Multi-Bin Rarefying for Alpha Diversity Association Analysis under Heterogeneous Library Sizes
AU  - Mo Li
PY  - 2026
DA  - 2026/10/21
PB  - arXiv
DO  - 10.48550/arxiv.2606.21000
UR  - https://arxiv.org/abs/2606.21000
AB  - Alpha diversity association analysis is sensitive to heterogeneous library sizes, and overall rarefying to a common depth can either discard many reads from high-depth samples or exclude low-depth samples. We propose MBRarefy, a data-adaptive framework for selecting multi-bin rarefying designs. The method chooses library size cutpoints by minimizing an objective that combines residual within-bin library size dependence with a fragmentation penalty, subject to a minimum bin size constraint. The framework supports both fixed-K and varying-K cutpoint selection. In T-cell receptor (TCR) motivated simulations, the primary fixed-K procedure controlled depth-induced false positives and improved power relative to overall rarefying when group and depth confounding was absent. Applications to TCR immunosequencing and wild baboon gut microbiome data illustrate its ability to reduce residual library size dependence while improving read and sample retention.
ER  -

Abstract

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