{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:43:08Z","timestamp":1753875788642,"version":"3.41.2"},"reference-count":17,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T00:00:00Z","timestamp":1705536000000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P30 CA068485","U24 CA163056","P50 CA236733","P50 CA098131","U54 CA163072"],"award-info":[{"award-number":["P30 CA068485","U24 CA163056","P50 CA236733","P50 CA098131","U54 CA163072"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Single-cell RNA-seq normalization is an essential step to correct unwanted biases caused by sequencing depth, capture efficiency, dropout, and other technical factors. Existing normalization methods primarily reduce biases arising from sequencing depth by modeling count-depth relationship and\/or assuming a specific distribution for read counts. However, these methods may lead to over or under-correction due to presence of technical biases beyond sequencing depth and the restrictive assumption on models and distributions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We present scKWARN, a Kernel Weighted Average Robust Normalization designed to correct known or hidden technical confounders without assuming specific data distributions or count-depth relationships. scKWARN generates a pseudo expression profile for each cell by borrowing information from its fuzzy technical neighbors through a kernel smoother. It then compares this profile against the reference derived from cells with the same bimodality patterns to determine the normalization factor. As demonstrated in both simulated and real datasets, scKWARN outperforms existing methods in removing a variety of technical biases while preserving true biological heterogeneity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and Implementation<\/jats:title><jats:p>scKWARN is freely available at https:\/\/github.com\/cyhsuTN\/scKWARN.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae008","type":"journal-article","created":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T00:32:16Z","timestamp":1705624336000},"source":"Crossref","is-referenced-by-count":2,"title":["scKWARN: Kernel-weighted-average robust normalization for single-cell RNA-seq data"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8325-2112","authenticated-orcid":false,"given":"Chih-Yuan","family":"Hsu","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Vanderbilt University Medical Center , Nashville, TN 37203, United States"},{"name":"Center 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