hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data.

Morabito, Samuel; Reese, Fairlie; Rahimzadeh, Negin; Miyoshi, Emily; Swarup, Vivek

Morabito, Samuel; Reese, Fairlie; Rahimzadeh, Negin; Miyoshi, Emily; Swarup, Vivek.

Afiliación

Morabito S; Mathematical, Computational, and Systems Biology (MCSB) Program, University of California, Irvine, Irvine, CA, USA.
Reese F; Center for Complex Biological Systems (CCBS), University of California, Irvine, Irvine, CA, USA.
Rahimzadeh N; Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, Irvine, CA, USA.
Miyoshi E; Center for Complex Biological Systems (CCBS), University of California, Irvine, Irvine, CA, USA.
Swarup V; Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA.

Cell Rep Methods ; 3(6): 100498, 2023 06 26.

Article en En | MEDLINE | ID: mdl-37426759

RESUMEN

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, popular 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 sequencing (RNA-seq). hdWGCNA provides functions for network inference, gene module identification, gene enrichment analysis, statistical tests, and data visualization. Beyond conventional single-cell RNA-seq, hdWGCNA is capable of performing isoform-level network analysis using long-read single-cell data. We showcase hdWGCNA using data from autism spectrum disorder and Alzheimer's disease brain samples, identifying disease-relevant co-expression network modules. 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 1 million cells.

Asunto(s)

Enfermedad de Alzheimer; Trastorno del Espectro Autista; Humanos; Transcriptoma/genética; Trastorno del Espectro Autista/genética; Perfilación de la Expresión Génica; Redes Reguladoras de Genes/genética; Enfermedad de Alzheimer/genética

Palabras clave

Alzheimer's disease; Autism spectrum disorder; co-expression network; gene network; long-read RNA-seq; microglia; single-cell RNA-seq; single-cell genomics; spatial transcriptomics

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de Alzheimer / Trastorno del Espectro Autista Límite: Humans Idioma: En Revista: Cell Rep Methods Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

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