Extended Data Fig. 2: Metabolome profile of quiescent cells and benchmarking batch-effect correction using ComBat. | Nature Metabolism

Extended Data Fig. 2: Metabolome profile of quiescent cells and benchmarking batch-effect correction using ComBat.

From: A homoeostatic switch causing glycerol-3-phosphate and phosphoethanolamine accumulation triggers senescence by rewiring lipid metabolism

Extended Data Fig. 2

A: Heat map showing modules of temporally coexpressed metabolites in WI38 fibroblasts for quiescence at indicated time points using a hierarchical clustering method (WGCNA). Roman numerals refer to different metabolite clusters. Shown are the top forty metabolites based on the most significant adjusted p values. Data are expressed as row Z scores collected from three biologically independent experiments per condition.B: Experimental design for batch correction validation. DNA damage-induced senescence (DDIS) and quiescence (Q) samples correspond to the same, as shown in Fig. 1a. The validation dataset included technical replicates of a subset of those samples and was measured in the same mass spectrometry run. C-E: Visualization of the average computed values for (C) relative standard deviation (RSD), (D) repeatability, and (E) Bhattacharyya distance for each approach. F-H: Comparison between the obtained values for the ComBat approach following quantile normalization (QN) and the other approaches for each measured peak (or sample) based on three metrics: (F) RSD, (G) repeatability and (H) Bhattacharyya distance. Black lines show the identity function. I- J: PCA plots depicting the average of each sample used for batch correction validation (I) before and (J) after quantile normalization and ComBat.

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