{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:21:23Z","timestamp":1785403283743,"version":"3.56.0"},"posted":{"date-parts":[[2026,7,22]]},"group-title":"Bioinformatics","reference-count":43,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"award":["1R21HD114953"],"award-info":[{"award-number":["1R21HD114953"]}],"id":[{"id":"https:\/\/ror.org\/01cwqze88","id-type":"ROR","asserted-by":"publisher"}]},{"award":["1R01AI180118"],"award-info":[{"award-number":["1R01AI180118"]}],"id":[{"id":"https:\/\/ror.org\/01cwqze88","id-type":"ROR","asserted-by":"publisher"}]},{"award":["P30 AR070155"],"award-info":[{"award-number":["P30 AR070155"]}],"id":[{"id":"https:\/\/ror.org\/01cwqze88","id-type":"ROR","asserted-by":"publisher"}]},{"award":["P01HD106414"],"award-info":[{"award-number":["P01HD106414"]}],"id":[{"id":"https:\/\/ror.org\/01cwqze88","id-type":"ROR","asserted-by":"publisher"}]},{"award":["1R01AG100879-0"],"award-info":[{"award-number":["1R01AG100879-0"]}],"id":[{"id":"https:\/\/ror.org\/01cwqze88","id-type":"ROR","asserted-by":"publisher"}]},{"name":"UCSF March of Dimes Prematurity Research Center"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,7,22]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:p>\n                  Transcriptome-based drug repurposing can accelerate therapeutic discovery, but is limited by fragmented resources, inconsistent quality control, and reliance on single perturbation databases. We developed CDRPipe (\n                  <jats:bold>C<\/jats:bold>\n                  omputational\n                  <jats:bold>D<\/jats:bold>\n                  rug\n                  <jats:bold>R<\/jats:bold>\n                  epurposing\n                  <jats:bold>Pipe<\/jats:bold>\n                  line), a unified framework that interrogates disease signatures against drug perturbation signatures generated by distinct experimental technologies. Specifically, CDRPipe harmonizes microarray perturbation profiles from the Connectivity Map (CMap; 1,968 quality-filtered experiments) with pseudo-bulk profiles derived from large-scale single-cell RNA sequencing experiments in the Tahoe-100M database (56,827 experiments). CDRPipe standardizes preprocessing, computes rank-based connectivity scores, and evaluates significance using empirical null models. We applied CDRPipe to 233 curated disease signatures from GEO and CREEDS and evaluated performance using known drug-disease associations from Open Targets. Single-cell-derived pseudo-bulk profiles recovered more annotated therapeutics than microarray profiles (median recall 50.0% vs. 6.2%; Wilcoxon p &lt; 10\u207b\u00b9\u00b9), thought these differences partly reflect differences in drug library composition and clinical annotation coverage. Importantly, the two resources were highly complementary, with only 3.5% overlap in recovered drugs, indicating that integrating predictions across independent perturbation resources expands therapeutic coverage and enables identification of high-confidence consensus candidates. Case studies in autoimmune disease and endometriosis further demonstrate that CDRPipe recovers clinically relevant therapies while revealing technology-dependent patterns of discovery. These results show that integrating heterogeneous transcriptomic perturbation resources improves the robustness and interpretability of transcriptional drug repurposing.\n                <\/jats:p>\n                <jats:sec>\n                  <jats:title>One Sentence Summary<\/jats:title>\n                  <jats:p>Integrating drug perturbation resources from distinct transcriptomic platforms improves the robustness and accuracy of drug repurposing predictions.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.07.17.739227","type":"posted-content","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T02:00:16Z","timestamp":1784772016000},"source":"Crossref","is-referenced-by-count":0,"title":["Integrating single-cell and bulk transcriptomic perturbation resources reveals complementary therapeutic spaces for drug repurposing"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7981-8133","authenticated-orcid":false,"given":"Enock","family":"Niyonkuru","sequence":"first","affiliation":[{"name":"Bakar Computational Health Sciences Institute, University of California, San Francisco; 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