arXiv Machine Learning By Youssef Marrakchi, Davide D'Ascenzo, Sebastiano Cultrera di Montesano

Score Distributions, Not Cells: Evaluating Single-Cell Perturbations Under Class Overlap

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arXiv:2607. 04595v1 Announce Type: new Abstract: Most classification problems assume the classes are roughly separable, so that an individual sample can usually be assigned to one class.

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arXiv Machine Learning
Sep 22

Rethinking Class Imbalance for Single-Cell Foundation Models: A Systematic Benchmark Across Architectures and Long-Tail Loss Functions

The paper benchmarks six long‑tail loss functions—cross‑entropy, weighted CE, class‑balanced loss, focal loss, LDAM, and logit‑adjusted softmax—across three single‑cell foundation model architectures (scGPT, scBERT, Geneformer) and three datasets (Multiple Sclerosis, Zheng68K, human Pancreas). It shows that overall accuracy masks systematic failures on rare, disease‑relevant cell types, with a consistent gap between overall accuracy, Macro‑F1, and rare‑class recall under plain cross‑entropy. The study identifies two distinct regimes of rare‑class failure, predicts reweighting efficacy by absolute training‑set size, and finds class‑balanced loss and LDAM to be the most reliable across all settings.

By Zeyu Dong, Jiahui Zhong