Fig. 1: Cross-gene versus cross-individual gene expression prediction.
From: Personal transcriptome variation is poorly explained by current genomic deep learning models
a, Overview of our approach, illustrating the cross-gene (blue) and cross-individual (green) measures of performance. Colored nucleotides on the left represent genetic variants present in each example individual. b, Performance of all tested models on reference sequence prediction, cross-gene prediction and cross-individual prediction. Bar heights represent means and error bars represent s.d. over all individuals (n = 421) for cross-gene Spearman rank correlation or over all genes (n = 3,259) for cross-individual Spearman rank correlation. c, Distribution of Enformer cross-gene Spearman rank correlations for all individuals (left histogram) and Enformer cross-individual Spearman rank correlations for all genes (right histogram). Histograms for the other tested models are shown in Extended Data Figs. 2 and 3. d, Example genes with strong positive cross-individual correlation (SLFN5) and strong negative cross-individual correlation (SNHG5) of observed and predicted expression for Enformer.
