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• W4293537710 abstract "Cell-cell interactions (CCI) play significant roles in manipulating biological functions of cells. Analyzing the differences in CCI between healthy and diseased conditions of a biological system yields greater insight than analyzing either conditions alone. There has been a recent and rapid growth of methods to infer CCI from single-cell RNA-sequencing (scRNA-seq), revealing complex CCI networks at a previously inaccessible scale. However, the majority of current CCI analyses from scRNA-seq data focus on direct comparisons between individual CCI networks of individual samples from patients, rather than group-level comparisons between sample groups of patients comprising different conditions. To illustrate new biological features among different disease statuses, we investigated the diversity of key network features on groups of CCI networks, as defined by different disease statuses. We considered three levels of network features: node level, as defined by cell type; node-to-node level; and network level. By applying these analysis to a large-scale single-cell RNA-sequencing dataset of coronavirus disease 2019 (COVID-19), we observe biologically meaningful patterns aligned with the progression and subsequent convalescence of COVID-19." @default.
• W4293537710 created "2022-08-30" @default.
• W4293537710 creator A5034376017 @default.
• W4293537710 creator A5040523461 @default.
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• W4293537710 date "2022-08-29" @default.
• W4293537710 modified "2023-09-26" @default.
• W4293537710 title "Analyzing network diversity of cell–cell interactions in COVID-19 using single-cell transcriptomics" @default.
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• W4293537710 doi "https://doi.org/10.3389/fgene.2022.948508" @default.
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• W4293537710 hasPublicationYear "2022" @default.
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