Perturbational similarity maps are among the most ambitious artifacts in modern biology. The idea is elegantly simple: knock out or repress thousands of genes one at a time, record the molecular or cellular consequences of each perturbation, and then compare those consequence profiles to one another. Genes whose disruptions produce similar effects are inferred to act in the same pathways or protein complexes, turning a mountain of raw screening data into a functional network. If such maps were transferable, a perturbation atlas painstakingly assembled in one system, for example the mouse brain, could be used to interpret screens performed in human cell lines, dramatically multiplying the value of expensive experiments and accelerating the translation of animal findings into human biology.

Testing that assumption required a careful matching infrastructure. Wen began by restricting the comparison to genes with strict one-to-one orthology between mouse and human, a conservative choice that retained 71 shared perturbation targets for comparison with the K562 Perturb-seq dataset, 90 for the RPE1 Perturb-seq dataset, 695 for HeLa morphology profiles from the PERISCOPE resource, and between 273 and 311 for nine L1000 cell-line matrices. Even before any statistics were computed, the attrition was striking: the vast majority of perturbations profiled in any single system simply have no directly comparable counterpart in the others, a practical constraint that any cross-system transfer strategy must confront.

The core of the analysis rested on three complementary endpoints, each designed to probe a different notion of transferability. The first was global pair-rank concordance: if two perturbed genes are ranked as similar neighbors within the mouse brain map, do they receive similar ranks in the human datasets? This was quantified with Spearman rank correlation across all pairs of shared targets. The second was source-defined top-k neighbor preservation, a stricter test asking whether a perturbation’s ten nearest neighbors in the source map remain among its top neighbors in the target dataset. The third was within-validation retrieval against CORUM, a curated database of protein complexes, which checks whether external perturbation matrices show internal structure consistent with known molecular assemblies.

The headline numbers were sobering. Under the primary zero-anchored root-mean-square representation of the response features, the Spearman correlation between mouse and human pair ranks was 0.026 in K562 and 0.035 in RPE1, effectively indistinguishable from zero. The two HeLa morphology conditions yielded correlations of 0.0022 and 0.0055, and across the nine L1000 cell lines the median was 0.0055. In other words, knowing which perturbations resemble each other in the mouse whole-brain atlas provided essentially no information about which perturbations resemble each other in any of these human systems. The neighbor-preservation test fared no better: across all thirteen tested contexts, not a single source-defined top-10 preservation result survived statistical correction for multiple comparisons, with the smallest adjusted q-value reaching only 0.240.

One of the study’s most technically important findings concerns how sensitive these conclusions are to the mathematical representation of the data. Perturbation profiles are high-dimensional vectors, and the choice of how to normalize and scale them can reshape the geometry of the entire map. When Wen switched from the primary zero-anchored RMS representation to across-target standardization in the RPE1 dataset, the correlation rose to 0.102, with a 95 percent confidence interval of 0.027 to 0.201, a weak but statistically detectable signal. Omitting the feature RMS scaling step instead produced a descriptive correlation of negative 0.034. The lesson is double-edged: representation choices can manufacture or destroy apparent transferability, which means that any published claim of cross-system concordance must report and justify its preprocessing pipeline in detail.

The study also probed the reliability of the maps themselves, asking how much of the failure to transfer could be blamed on noise within each dataset. Across fifty random cell-level splits of the K562 and RPE1 Perturb-seq data, the median global correlation between independently derived maps was 0.600 and 0.710 respectively, with top-10 neighbor overlap of roughly 0.50. Across fifty mouse-level splits of the K562- and RPE1-matched source profiles, the median global correlations were 0.731 and 0.703. These figures indicate that the individual maps are internally reproducible to a moderate degree, yet they also reveal substantial split-to-split variability. Crucially, Wen notes that a true cross-species noise ceiling, the maximum correlation one could expect even for perfectly transferable biology given measurement noise, remains unestimated, so the near-zero observed correlations cannot yet be cleanly separated from an unfavorable signal-to-noise regime.

The within-validation CORUM analysis added an important nuance. Several of the external human matrices did show evidence of protein-complex structure when analyzed on their own terms, meaning that perturbations targeting members of the same complex tended to cluster together within those datasets. But Wen is careful to point out that this result does not test preservation of the source map’s neighbor structure; a dataset can be internally biologically coherent while remaining globally discordant with the mouse brain atlas. This distinction matters for the field because within-dataset validation is often presented as evidence of quality, when the more demanding question, whether functional structure survives the journey between systems, is a separate and harder test.

The implications reach well beyond cell culture. In vivo Perturb-seq has recently been used to map transcriptional networks in the developing cortex, to dissect astrocyte function, and to link psychiatric disease risk genes to specific molecular mechanisms, and such whole-brain atlases are increasingly proposed as reference resources for interpreting human genetic data. The new results do not invalidate that enterprise, but they do impose a discipline: conclusions drawn from a perturbation map in one species or modality should not be assumed to hold in another without direct empirical verification, and the choice of feature representation must be treated as a first-class scientific decision rather than a technical afterthought.

Wen is equally explicit about the limits of the negative findings. The results are conditional on the specific representations and feature selections tested, they concern non-neural human systems that differ profoundly from brain tissue, and they do not establish that transferability is absent, particularly for neural systems where matched perturbation data do not yet exist at comparable scale. The analysis code, fixed random seeds, processed inputs, and reviewer-requested analyses have been deposited in a versioned Zenodo archive, and the underlying public datasets from the whole-brain atlas, the Replogle Perturb-seq screens, PERISCOPE, and the Connectivity Map remain freely available. That transparency should make it straightforward for other groups to extend the audit, perhaps to neural organoids, assembloid screens, or emerging spatial Perturb-seq platforms, as those resources mature. For now, the study stands as a rigorous caution: in perturbational genomics, the map is not yet the territory, and every boundary between species, cell systems, and assay modalities must be tested rather than assumed.

Subject of Research: Cross-species and cross-platform transferability of perturbational similarity maps in functional genomics

Article Title: Testing the Transferability of Perturbational Similarity Maps Across Species, Cell Systems, and Assay Modalities

Article References: Wen, J. (2026). Testing the Transferability of Perturbational Similarity Maps Across Species, Cell Systems, and Assay Modalities. Neuroinformatics, 24(4), Article 66. https://doi.org/10.1007/s12021-026-09821-1

Image Credits: AI Generated

DOI: 10.1007/s12021-026-09821-1

Keywords: Perturb-seq, Neuroinformatics, perturbation atlas, cross-species comparison, L1000, Connectivity Map, protein complexes, CORUM, reproducibility, representation transfer, CRISPR screens, whole-brain mapping