{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T16:49:19Z","timestamp":1782233359627,"version":"3.54.5"},"posted":{"date-parts":[[2026,6,19]]},"group-title":"Bioinformatics","reference-count":16,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T00:00:00Z","timestamp":1781827200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["MH111099"],"award-info":[{"award-number":["MH111099"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"crossref","award":["RGPIN-2016-05991"],"award-info":[{"award-number":["RGPIN-2016-05991"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]},{"name":"UBC Graduate Studies 4Y Fellowship"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,6,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Coexpression of transcripts presents the most common means of computational inference of transcription factor regulation, and is often combined with other data types to infer regulatory networks. With the growing popularity of single-cell approaches, there are questions about how best to extract coexpression information from the data. Recently we reported a simulation study that explored the differences among coexpression performed at different levels: across single cells (xCell, per cell type), across subjects from pseudobulked single-cell data (xSubject, per cell type), or across subjects using bulk tissue samples (xBulk). Here we test predictions made by those models using real data. We consider both preservation (consistency of coexpression findings across different levels of analysis of the same data) and replicability across independent studies, as well as biological interpretability. We find that preservation across levels is limited, indicating the choice of analysis level will affect outcomes. We show that xCell coexpression is more replicable across studies compared to xSubject. xBulk coexpression is dominated by patterns driven by variability in cellular composition and fails to capture much coexpression that is reliably detected at finer resolutions. While all modes of analysis exhibit some enrichment for known regulatory relationships, it was highest with the xCell mode. Finally, we present a case study of the effect of analysis modes on a schizophrenia-associated pattern, reinforcing the importance of analytic choices in the interpretation and replicability of coexpression analyses. Together with our modeling study, this work emphasizes the importance of understanding sources of expression covariation as they relate to the goals of the analysis, and recommend single-cell-based data with biological replicates should be the focus of attempts to infer dynamic regulatory interactions that are more likely to be replicable by others.<\/jats:p>","DOI":"10.64898\/2026.06.17.731965","type":"posted-content","created":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T10:25:14Z","timestamp":1782037514000},"source":"Crossref","is-referenced-by-count":0,"title":["Evaluation of analysis modes for RNA coexpression in single-cell and bulk tissue"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8339-9277","authenticated-orcid":false,"given":"Ching Pan","family":"Chu","sequence":"first","affiliation":[{"name":"Department of Psychiatry and Michael Smith Laboratories, University of British Columbia, Vancouver BC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nairuz","family":"Elazzabi","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Michael Smith Laboratories, University of British Columbia, Vancouver BC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jules","family":"Garreau","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Michael Smith Laboratories, University of British Columbia, Vancouver BC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brianna","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Michael Smith Laboratories, University of British Columbia, Vancouver BC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinrui Xiang","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Michael Smith Laboratories, University of British Columbia, Vancouver BC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Morin","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Michael Smith Laboratories, University of British Columbia, Vancouver BC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Pavlidis","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Michael Smith Laboratories, University of British Columbia, Vancouver BC"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026062308351858000_2026.06.17.731965v1.1","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1038\/nmeth.4463","article-title":"SCENIC: single-cell regulatory network inference and clustering","volume":"14","year":"2017","journal-title":"Nature Methods"},{"key":"2026062308351858000_2026.06.17.731965v1.2","doi-asserted-by":"crossref","unstructured":"Chu CP , Morin A , Pavlidis P (2025) Limits to the inference of gene regulation from bulk tissue expression data. :2024.10.24.619521 Available at: https:\/\/www.biorxiv.org\/content\/10.1101\/2024.10.24.619521v3 [Accessed October 2, 2025].","DOI":"10.1101\/2024.10.24.619521"},{"key":"2026062308351858000_2026.06.17.731965v1.3","doi-asserted-by":"crossref","first-page":"e1009484","DOI":"10.1371\/journal.pcbi.1009484","article-title":"Experiment level curation of transcriptional regulatory interactions in neurodevelopment","volume":"17","year":"2021","journal-title":"PLOS Computational Biology"},{"key":"2026062308351858000_2026.06.17.731965v1.4","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.95.25.14863"},{"key":"2026062308351858000_2026.06.17.731965v1.5","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty538"},{"key":"2026062308351858000_2026.06.17.731965v1.6","first-page":"gr.256735.119","article-title":"Untangling the effects of cellular composition on coexpression analysis","volume":"30","year":"2020","journal-title":"Genome Res"},{"key":"2026062308351858000_2026.06.17.731965v1.7","doi-asserted-by":"publisher","DOI":"10.1101\/gr.240663.118"},{"key":"2026062308351858000_2026.06.17.731965v1.8","doi-asserted-by":"crossref","unstructured":"Harris BD , Crow M , Fischer S , Gillis J (2021) Single-cell co-expression analysis reveals that transcriptional modules are shared across cell types in the brain. Cell Systems Available at: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2405471221001538 [Accessed May 25, 2021].","DOI":"10.1016\/j.cels.2021.04.010"},{"key":"2026062308351858000_2026.06.17.731965v1.9","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-9-559"},{"key":"2026062308351858000_2026.06.17.731965v1.10","unstructured":"Ling E et al. (2024) A concerted neuron\u2013astrocyte program declines in ageing and schizophrenia. Nature:1\u20138 Available at: https:\/\/www.nature.com\/articles\/s41586-024-07109-5 [Accessed March 7, 2024]."},{"key":"2026062308351858000_2026.06.17.731965v1.11","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0913357107"},{"key":"2026062308351858000_2026.06.17.731965v1.12","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1012962"},{"key":"2026062308351858000_2026.06.17.731965v1.13","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0690-6"},{"key":"2026062308351858000_2026.06.17.731965v1.14","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab503"},{"key":"2026062308351858000_2026.06.17.731965v1.15","doi-asserted-by":"crossref","first-page":"3273","DOI":"10.1091\/mbc.9.12.3273","article-title":"Comprehensive Identification of Cell Cycle\u2013regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization Fink GR, ed","volume":"9","year":"1998","journal-title":"MBoC"},{"key":"2026062308351858000_2026.06.17.731965v1.16","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1000770"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.64898\/2026.06.17.731965","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T15:56:58Z","timestamp":1782230218000},"score":1,"resource":{"primary":{"URL":"http:\/\/biorxiv.org\/lookup\/doi\/10.64898\/2026.06.17.731965"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,19]]},"references-count":16,"URL":"https:\/\/doi.org\/10.64898\/2026.06.17.731965","relation":{},"subject":[],"published":{"date-parts":[[2026,6,19]]},"subtype":"preprint"}}