{"id":1187501,"date":"2026-09-28T13:58:19","date_gmt":"2026-09-28T20:58:19","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/artificial-variables-help-to-avoid-over-clustering-in-single-cell-rna-sequencing\/"},"modified":"2026-09-28T14:09:08","modified_gmt":"2026-09-28T21:09:08","slug":"artificial-variables-help-to-avoid-over-clustering-in-single-cell-rna-sequencing","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/artificial-variables-help-to-avoid-over-clustering-in-single-cell-rna-sequencing\/","title":{"rendered":"Artificial variables help to avoid over-clustering in single-cell RNA sequencing"},"content":{"rendered":"\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Standard single-cell RNA sequencing (scRNA-seq) pipelines nearly always include unsupervised clustering as a key step in identifying biologically distinct cell types. A follow-up step in these pipelines is to test for differential expression between the identified clusters. When algorithms over-cluster, downstream analyses can produce misleading results. In this work, we present &#8220;recall&#8221; (calibrated clustering with artificial variables), a method for protecting against over-clustering by controlling for the impact of reusing the same data twice when performing differential expression analysis, commonly known as &#8220;double dipping.&#8221; Importantly, our approach can be applied to a wide range of clustering algorithms. Using real and simulated data, we show that recall provides state-of-the-art clustering performance and can rapidly analyze large-scale scRNA-seq studies, even on a personal laptop.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Standard single-cell RNA sequencing (scRNA-seq) pipelines nearly always include unsupervised clustering as a key step in identifying biologically distinct cell types. A follow-up step in these pipelines is to test for differential expression between the identified clusters. When algorithms over-cluster, downstream analyses can produce misleading results. In this work, we present &#8220;recall&#8221; (calibrated clustering with [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"text","value":"Alan DenAdel","user_id":0},{"type":"text","value":"Michelle L. Ramseier","user_id":0},{"type":"text","value":"Andrew W. Navia","user_id":0},{"type":"text","value":"Alex K. Shalek","user_id":0},{"type":"text","value":"Srivatsan Raghavan","user_id":0},{"type":"text","value":"Peter S. 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