Extended Data Fig. 1: Optimised processing of the AHBA identified three generalisable components. | Nature Neuroscience

Extended Data Fig. 1: Optimised processing of the AHBA identified three generalisable components.

From: Cortical gene expression architecture links healthy neurodevelopment to the imaging, transcriptomics and genetics of autism and schizophrenia

Extended Data Fig. 1

a, In the HCP-MMP parcellation, 43/180 regions are matched to samples representing less than 3 of the 6 AHBA donors. b, Distribution of differential stability of genes measured in the AHBA dataset processed in the HCP-MMP parcellation. c, Generalisability of first five components of the AHBA dataset computed with either principal components analysis (PCA) or diffusion map embedding (DME). Color represents generalisability g, defined as the median absolute correlation between matched components computed across all 10 disjoint triplet pairs (Methods); x-axis represents variation in the proportion of genes filtered out by differential stability prior to PCA/DME; y-axis represents variation in which regions are filtered out prior to PCA/DME. Tick mark indicates parameter combinations that exceed generalisability g > 0.6. Green highlights for C3 indicate the best parameter option with PCA and DME respectively, showing that switching to DME achieves similar generalisability while retaining more genes. d, Scatter plots of regional scores for AHBA components computed using the best PCA/DME options, demonstrating that PCA and DME derive spatially equivalent components.

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