network analysis

Developing and applying tools for network analysis in high-dimensional -omics data.

Network analysis in genomic data is one of my primary research interests. Biological systems are highly complex and intrinsically interconnected, and there is much we can learn about diseases and other conditions by interrogating genomic networks. Many of my research projects have involved applying existing network analysis tools, but I have also worked on developing such tools like hdWGCNA.

Method development

One of my primary research interests is in method development for network analysis in genomics data. I am the lead developer of hdWGCNA, an R package for co-expression network analysis in single-cell and spatial transcriptomics data. This method constructs networks in specific contexts that reflect the underlying biology of individual cell types or disease conditions. hdWGCNA also includes downstream tools for data visualization and statistical testing. We developed this package to be user-friendly and we leveraged common data formats which enable a borad range of researchers to use this package. To date hdWGCNA has already gathered hundreds of users across many different fields of research. Check out our publication to learn more about this method (Morabito et al., 2023).

Applications

Nearly all of my research projects have involved applying network analysis approaches in some capacity. These approaches enable us to identify systems-level changes in disease or in specific cell states, beyond what differential expression testing is capable of revealing. For example, in our recent preprint (Miyoshi* et al., 2023) we performed multi-scale network analysis across different cortical layers in our spatial dataset to reveal an altered neuroinflammatory signature in disease. In some cases, I have also worked on other types of network analysis aside from co-expression networks, like transcription factor regulatory networks in (Morabito* et al., 2021).

References

2023

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

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

Cell Reports Methods, Aug 2023

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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, 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
bioRxiv

Spatial and single-nucleus transcriptomic analysis of genetic and sporadic forms of Alzheimer’s Disease

Emily Miyoshi* , Samuel Morabito* , Caden M. Henningfield , Negin Rahimzadeh , Sepideh Kiani Shabestari , and 19 more authors

bioRxiv, Aug 2023

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The majority of mammalian genes encode multiple transcript isoforms that result from differential promoter use, changes in exonic splicing, and alternative 3′ end choice. Detecting and quantifying transcript isoforms across tissues, cell types, and species has been extremely challenging because transcripts are much longer than the short reads normally used for RNA-seq. By contrast, long-read RNA-seq (LR-RNA-seq) gives the complete structure of most transcripts. We sequenced 264 LR-RNA-seq PacBio libraries totaling over 1 billion circular consensus reads (CCS) for 81 unique human and mouse samples. We detect at least one full-length transcript from 87.7% of annotated human protein coding genes and a total of \textbackslashtextasciitilde200,000 full-length transcripts, \textbackslashtextasciitilde40% of which have novel exon junction chains. To capture and compute on the three sources of transcript structure diversity, we introduce a gene and transcript annotation framework that uses triplets representing the transcript start site, exon junction chain, and transcript end site of each transcript. Using triplets in a simplex representation demonstrates how promoter selection, splice pattern, and 3′ processing are deployed across human tissues, with nearly half of multitranscript protein coding genes showing a clear bias toward one of the three diversity mechanisms. Evaluated across samples, the predominantly expressed transcript changes for 74% of protein coding genes. In evolution, the human and mouse transcriptomes are globally similar in types of transcript structure diversity, yet among individual orthologous gene pairs, more than half (57.8%) show substantial differences in mechanism of diversification in matching tissues. This initial large-scale survey of human and mouse long-read transcriptomes provides a foundation for further analyses of alternative transcript usage, and is complemented by short-read and microRNA data on the same samples and by epigenome data elsewhere in the ENCODE4 collection.

2021

Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease

Samuel Morabito* , Emily Miyoshi* , Neethu Michael , Saba Shahin , Alessandra Cadete Martini , and 5 more authors

Nature Genetics, Aug 2021

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The gene-regulatory landscape of the brain is highly dynamic in health and disease, coordinating a menagerie of biological processes across distinct cell types. Here, we present a multi-omic single-nucleus study of 191,890 nuclei in late-stage Alzheimer’s disease (AD), accessible through our web portal, profiling chromatin accessibility and gene expression in the same biological samples and uncovering vast cellular heterogeneity. We identified cell-type-specific, disease-associated candidate cis-regulatory elements and their candidate target genes, including an oligodendrocyte-associated regulatory module containing links to APOE and CLU. We describe cis-regulatory relationships in specific cell types at a subset of AD risk loci defined by genome-wide association studies, demonstrating the utility of this multi-omic single-nucleus approach. Trajectory analysis of glial populations identified disease-relevant transcription factors, such as SREBF1, and their regulatory targets. Finally, we introduce single-nucleus consensus weighted gene coexpression analysis, a coexpression network analysis strategy robust to sparse single-cell data, and perform a systems-level analysis of the AD transcriptome.