Identifying Gene-Trait Correlations in Alzheimer's Disease

First, for each major cell-type, a gene-wise correlation coefficient (Spearman’s rank correlation coefficient) was computed using gene expression and AD-related neuropathological trait values across all the annotated cells as variables. The AD-related neuropathological traits included in this analysis were cogn_global_lv (global cognitive function - last valid score), age_death (age at death), educ (years of education), msex (self-reported sex), parksc_lv (global parkinsonian summary score - last valid score), gpath (global AD pathology burden), gpath_3neocort (global measure of neocortical pathology), pmi (post-mortem interval), amyloid (Overall amyloid level), plaq_d (diffuse plaque burden), plaq_n (neuritic plaque burden), nft (neurofibrillary tangle burden), and tangles (tangle density). Only significant correlations after Bonferroni correction at P<0.01 were considered. The resulting correlation matrices for each major cell-type were concatenated and analyzed using a computational algorithm (self-organizing map, SOM)³⁷ . All SOMs were created using the Kohonen R package⁵⁶ . To identify the territories of the SOM most strongly correlated with AD-related neuropathological traits, we used an image segmentation method and further manual curation to identify territories (gene-trait correlation modules) based on all the individual cell-type-specific SOM plots for each neuropathological trait. Enrichment analysis for Gene Ontology (GO) terms among the genes of a gene-trait correlation module was performed using Metascape⁵³ . The robustness of gene-trait associations to single-cell heterogeneity and noise was confirmed examining individual-level correlations for the genes in gene-trait module. Individual-level correlations were computed by first averaging for each individual normalized gene expression profiles across cells of the same cell. This resulted in cell-type-specific averaged gene expression profiles across the 48 individuals. Average profiles were subsequently mean-centered and scaled to finally compute gene-wise correlation coefficients versus corresponding pathological values. Individual-level gen-trait correlations were computed independently for all 48 individuals, for only 24 male individuals, and for only 24 female individuals. The robustness of gene-trait associations to potential confounding variables was corroborated by confirming the cell-type specific recovery of identified gene-trait modules using partial correlation. Briefly, the partial Pearson’s correlation coefficient between average gene expression and each pathological trait, after correcting for the effect of PMI, age, gender, and education level of each individual; was computed by first orthogonalizing the normalized expression with respect to the normalized covariates and then computing the correlation in the residual subspace.