Contrastive learning improves cell representations in single-cell foundation models
Foundation models for single-cell biology have relied on gene reconstruction as a pretraining objective, which optimizes for predicting masked expression values but leaves whole-cell representations underspecified for downstream tasks. This work proposes a contrastive learning framework that directly optimizes cell-level embeddings by constructing complementary transcriptomic views through co-expression-guided partitioning and expression-aware contrast sets. The approach addresses a fundamental mismatch in biological foundation models: gene-level objectives do not guarantee useful cell-level features. For practitioners building or fine-tuning models on transcriptomic data, this signals a shift toward task-aligned pretraining objectives that may improve performance on cell classification, clustering, and perturbation prediction without requiring task-specific retraining.
Modelwire context
ExplainerThe paper identifies a fundamental architectural mismatch in existing single-cell models: optimizing for gene reconstruction (predicting masked expression values) does not guarantee that the learned cell embeddings are useful for downstream tasks like classification or perturbation prediction. This is not a marginal efficiency gain but a recognition that the pretraining objective and the actual use case were misaligned.
This connects to a broader pattern visible in recent work on representation learning. The Model-Agnostic FDR Control paper from early August tackled the problem of identifying genuinely predictive features in neural models where traditional statistical assumptions break down. Here, the problem is inverted: instead of interpreting what a model learned, this work asks whether the model learned the right thing in the first place. Both papers reflect growing scrutiny of whether standard objectives (statistical hypothesis testing, gene reconstruction) actually serve the downstream goal (trustworthy inference, useful cell embeddings). The shift is toward task-aligned objectives rather than generic optimization targets.
If fine-tuning on this contrastive pretraining objective outperforms gene-reconstruction models on held-out cell classification benchmarks (PBMC, immune atlas datasets) by more than 3-5 percentage points without task-specific retraining, that validates the core claim. If performance gains vanish when evaluated on tasks the authors didn't explicitly optimize for, the approach may have simply moved the specificity problem rather than solved it.
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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “Beyond Gene Reconstruction: Learning Cell Representations through Complementary Transcriptomic Views”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.