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Alzheimer’s disease (ad) is a devastating neurological disorder characterized by changes in cell-type proportions and consequently marked alterations of the transcriptome. Here we use a data-driven systems biology meta-analytical approach across three human ad cohorts, encompassing six cortical brain regions, and integrate with multi-scale datasets comprising of DNA methylation, histone acetylation, transcriptome- and genome-wide association studies, and quantitative trait loci to further characterize the genetic architecture of ad. We perform co-expression network analysis across more than twelve hundred human brain samples, identifying robust ad-associated dysregulation of the transcriptome, unaltered in normal human aging. We assess the cell-type specificity of ad gene co-expression changes and estimate cell-type proportion changes in human ad by integrating co-expression modules with single-cell transcriptome data generated from 27 321 nuclei from human postmortem prefrontal cortical tissue. We also show that genetic variants of ad are enriched in a microglial ad-associated module and identify key transcription factors regulating co-expressed modules. Additionally, we validate our results in multiple published human ad gene expression datasets, which can be easily accessed using our online resource (https://swaruplab.bio.uci.edu/consensusAD).

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Biological systems are immensely complex, organized into a multi-scale hierarchy of functional units based on tightly-regulated interactions between distinct molecules, cells, organs, and organisms. While experimental methods enable transcriptome-wide measurements across millions of cells, the most ubiquitous bioinformatic tools do not support systems-level analysis. Here we present hdWGCNA, a comprehensive framework for analyzing co-expression networks in high dimensional transcriptomics data such as single-cell and spatial RNA-seq. hdWGCNA provides built-in functions for network inference, gene module identification, functional gene enrichment analysis, statistical tests for network reproducibility, and data visualization. In addition to conventional single-cell RNA-seq, hdWGCNA is capable of performing isoform-level network analysis using long-read single-cell data. We showcase hdWGCNA using publicly available single-cell datasets from Autism spectrum disorder and Alzheimer's disease brain samples, identifying disease-relevant co-expression network modules in specific cell populations. hdWGCNA is directly compatible with Seurat, a widely-used R package for single-cell and spatial transcriptomics analysis, and we demonstrate the scalability of hdWGCNA by analyzing a dataset containing nearly one million cells.