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. 2023 Jan 12;8(2):246–259. doi: 10.1038/s41564-022-01293-8

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© The Author(s) 2023, corrected publication 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Extended Data Fig. 2 — a, b, UMAP ordination of metabolomics data (N = 232), same as Fig. 1b, colored by Pos Early, Pos Late, and Polar platform batches (a; 2 batches) and by Neg platform batches (b; 3 batches). See Supplementary Table 4 for which metabolites were measured by each platform. Limited batch effect is noted, which is statistically significant only for the 3 batches (PERMANOVA P = 0.09 and P = 0.023 for 2 and 3 batches, respectively). c, The fraction of samples from each batch (y-axis; top, Pos Early, Pos Late, and Polar platform batches; bottom, Neg platform batches) whose metabolite profiles clustered to each metabolite cluster (MC; x-axis), shown for each MC separately. No significant batch effect was detected in MC assignments (Two-sided Fisher’s exact P > 0.05 for all without FDR correction). d, Heatmap showing odds ratio for sPTB (color bar) for each metabolite from Fig. 2a (x-axis) using a logistic regression model adjusting for batch (according to the appropriate platform for the metabolite, Supplementary Table 4), stratified by maternal race (y-axis). The exact odds ratio and confidence interval are written in the cell for all statistically significant associations (FDR < 0.1). e, sPTB classification accuracy (auROC, x-axis) for a prediction model similar to those used for the entire cohort (Fig. 4, Methods), that is: trained and evaluated in cross validation on batch 1 (N = 114; orange; auROC = 0.66; one-sided permutation P = 0.44 for lower accuracy than random draw); trained on batch 1 (N = 114) and evaluated on batch 2 (N = 118; violet; auROC = 0.66; P = 0.46); trained and evaluated in cross validation on batch 2 (N = 118; magenta; auROC = 0.66; P = 0.44); and trained on batch 2 (N = 118) and evaluated on batch 1 (N = 114; brown; auROC = 0.69; P = 0.66). Gray histogram (black line, KDE) shows accuracy of models evaluated in cross-validation on random samples (N = 116) from this cohort (mean auROC = 0.67). This analysis demonstrates that a prediction model trained on one of the two batches generalizes well to the other batch, and that both accuracies are to be expected given the limited sample size.