Study Questions Whether Causal Gene Relationships Can Be Reliably Recovered from Bulk Gene Expression Data
A new computational study examines whether causal relationships between genes can be accurately determined from bulk gene expression data, which pools RNA across many cells. The researchers found that causal relations are theoretically recoverable only under strict conditions involving linear aggregation and affine structural equations. Their analysis of real datasets suggests these conditions are rarely met in practice, raising concerns about the validity of methods that attempt to infer gene causality from bulk expression data.
Researchers have formalized the mathematical conditions under which causal relationships among genes can be recovered from bulk gene expression data—a common and cost-effective approach that aggregates RNA measurements across many cells in a sample. The study identifies two types of consistency required for recoverability: functional-form consistency and conditional-independence consistency. The authors derive necessary and sufficient conditions showing that causal relations can only be reliably recovered when data aggregation is linear (such as summing or averaging) and the underlying gene regulatory relationships follow affine structural equations. However, empirical analysis of eight real gene expression datasets—four bulk and four single-cell—reveals that actual pairwise regulatory functions among genes deviate significantly from the required linearity assumptions in both data types. This gap between theoretical requirements and empirical reality suggests that existing computational methods attempting to infer causal gene networks from bulk expression data may produce unreliable results without additional strong assumptions.
What's missing
The study does not discuss potential workarounds or alternative approaches that researchers might employ when linearity assumptions are violated, nor does it quantify how severely real-world deviations from linearity affect the accuracy of causal inference in practical applications.
What different sources said
- bioRxivCenter
When batch correction corrupts gene expression: uncovering distortions in correlation structures
- arXiv cs.LGCenter
On the Recoverability of Causal Relations from Bulk Gene Expression Data
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