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Applied Sciences
By Daria A. Kashatnikova, Oleg A. Grebenchikov, Artem N. Kuzovlev and 1 others.
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Pooling public blood transcriptome studies never designed for integration introduces failures that standard diagnostics miss.
To define safeguards for confounding-aware integration and establish what an integrated stroke dataset can support.
Eleven GEO series (1885 samples) were integrated under six safeguards: expression fingerprinting for undeclared patient overlap; staged ComBat with leave-one-study-out PCA; scale-correct deconvolution; composition-adjusted differential expression; an estimability audit with leakage-safe cross-validation; and a provenance audit against the source studies’ deposited tables.
Fingerprinting found 33 shared patients in two series. After ComBat, study and platform explained ~0% of PC1, and cell composition explained 61.4%. Linear rather than log-scale deconvolution input changed the neutrophil fraction (0.07 to 0.25) but did not affect the level of cross-method agreement (r = 0.81 vs. 0.78). Neutrophil adjustment removed 65–84% of acute differentially expressed genes, and co-expression modules re-correlated with composition even among composition-robust genes. Stroke subtype was fully confounded with study (classifier AUC 0.882 from single-class folds). The provenance audit reclassified our novel candidate finding in cell-sorted data as replicative and confirmed the depositors’ differential expression (monocytes: r = 0.92, neutrophils: 0.90). The integrated dataset supported a direction-resolved acute signature, three temporal response programs in a 513-gene core, and seven candidate stroke-versus-trauma discriminators with divergent kinetics.
Passing integration diagnostics does not guarantee estimability. Estimability must be assessed before correction removes the evidence, and novelty must be checked against deposited data.
Published in Applied Sciences. The abstract is shown as published and remains the work of its authors; read the full article at the source.
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