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bioRxiv · 10.64898/2026.04.03.716238

Representation Methods of Transcriptomics with Applications in Neuroimmune Biology

Abbasi, M.·Ochoa Zermeno, S.·Spendlove, M. D.·Tashi, Z.·Plaisier, C. L.·Bartelle, B. B.
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Abstract

Interpretable representations of gene expression are used to define cellular identities and the molecular programs active within cells, two related, but distinct phenomena. In the case of microglia, a cell type with high transcriptomic, functional, and morphological heterogeneity, the predominant representation of transcriptomic data presumes the adoption of distinct molecular identities, despite a lack of easily separable transcriptional states. Here, we explore alternative transcriptomic representations by comparing two single-cell analysis methods: differential expression analysis for identities and co-expression network analysis for molecular programs. For microglia, co-expression network analysis identifies highly significant functional ontologies not resolved by differential expression analysis. The identified co-expression modules are preserved across transcriptomic datasets and suggest reducible functional programs that activate and modulate depending on context. We conclude that co-expression analysis constitutes a best practice for single cell analysis of an individual cell type and describing microglia function as concurrent molecular programs offers a more parsimonious model of microglia function.

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Abbasi, M., Ochoa Zermeno, S., Spendlove, M. D., Tashi, Z., Plaisier, C. L., Bartelle, B. B.. 2026-04-07. Representation Methods of Transcriptomics with Applications in Neuroimmune Biology. https://doi.org/10.64898/2026.04.03.716238

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