On the Recoverability of Causal Relations from Bulk Gene Expression Data
Bulk gene expression profiling, which aggregates pooled RNA across cells within a biological sample, remains important in the single-cell era because it is typically less noisy, more sensitive, and more cost-effective than single-cell assays. Accordingly, a growing body of computational methods seeks to recover causal relations among genes from bulk expression data. However, aggregation is a lossy, non-invertible coarsening of the underlying cellular system, and it remains unclear whether and under what conditions causal relations are recoverable from aggregated bulk gene expression data. To answer this, we formalize recoverability under aggregation through two notions of consistency: fun...
| Authors | Gongxu Luo, Boyang Sun, Kun Zhang |
|---|---|
| Primary category | cs.LG |
| Published | 30 May 2026 |
| Last updated at source | 8 June 2026 |
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