Fig. 3: Ratio-based scaling enables horizontal integration.

a,b, Scatterplots of the feature abundance of inter-batch D5 samples in methylation, miRNA-seq, RNA-seq, proteomics and metabolomics datasets at the absolute level (raw data; a) and ratio level (ratio scaling to the D6 sample; b). The x and y axes show the average expression of the three D5 technical replicates from the two best quality batches from different labs (ranked by SNR). At the absolute level, features with a CV less than 0.2 for the technical replicates of D5 in both batches were retained; at the ratio level, features with a CV less than 0.2 for the technical replicates of D5 and D6 in both batches were retained. r denotes the Pearson correlation coefficient, and m denotes the number of features. Linear fits were performed on the basis of the feature abundance. c, Lollipop plots of CV in feature abundance for six D5 samples across two batches. The x axis represents the exhaustive two-by-two combination of all batches for each omics type. d,e, PCA plots of horizontal integration of all batches of methylation, miRNA-seq, RNA-seq, proteomics and metabolomics datasets at the absolute level (d) and ratio level (e). n denotes the number of samples, and m denotes the number of features. f, Scatterplots between SNR and degree of sample class-batch balance. Blue, absolute level; red, ratio level.