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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
Published on: January 10, 2019
What a Near-Perfect AUROC Does Not Show: Random-Panel and Label-Permutation Controls for a Sepsis Blood-Transcriptome
Mikhail Kolev1,2, Irina Naskinova2, Meglena Lazarova3
1Department of Applied Computer Science and Mathematical Modelling, Faculty of Mathematics and Computer Science, University of Warmia and Mazury in Olsztyn, Słoneczna 54, 10-710 Olsztyn, Poland.
Abstract:
Sepsis blood-transcriptome signatures are usually judged against healthy controls. Two questions are rarely separated: whether the classifier exceeds chance, and whether its genes outperform an arbitrary list of equal size. We tested both on public microarray data (discovery GSE65682, n = 359; validation GSE57065, n = 53), using the 9983 of 11,222 genes measurable on both platforms. A 10-gene signature was selected by nested cross-validation and applied to validation without refitting. It separated sepsis from health perfectly (area under the receiver operating characteristic curve, AUROC, 1.000). The pipeline exceeded chance: no label permutation in 1000 matched it (p = 0.001). The genes did not outperform random lists: 12.5% of 2000 random 10-gene panels matched or exceeded the signature, as did 67.6% of panels drawn from the same 300 preselected candidates. Against non-infected intensive care patients, cross-fitted AUROC fell to 0.730 (95% confidence interval 0.642-0.818), unchanged after age and sex matching. At a threshold of 0.5, the cross-fitted model called 70 of these 75 patients septic. Selection was unstable: 200 bootstrap resamples gave a median of 6 genes, from 109 distinct genes. A near-perfect AUROC against healthy controls reflects an easy benchmark, not a specific gene list.

