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However, scRNA-seq data often suffer from technical noise, dropout events and sparsity, hindering downstream analyses. Although existing works attempt to mitigate these issues by utilizing graph structures for data denoising, they involve the risk of propagating noise and fall short of fully leveraging the inherent data relationships, relying mainly on one of cell\u2013cell or gene\u2013gene associations and graphs constructed by initial noisy data. To this end, this study presents single-cell bilevel feature propagation (scBFP), two-step graph-based feature propagation method. It initially imputes zero values using non-zero values, ensuring that the imputation process does not affect the non-zero values due to dropout. Subsequently, it denoises the entire dataset by leveraging gene\u2013gene and cell\u2013cell relationships in the respective steps. Extensive experimental results on scRNA-seq data demonstrate the effectiveness of scBFP in various downstream tasks, uncovering valuable biological insights.<\/jats:p>","DOI":"10.1093\/bib\/bbae209","type":"journal-article","created":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T06:11:22Z","timestamp":1714975882000},"source":"Crossref","is-referenced-by-count":6,"title":["Single-cell RNA sequencing data imputation using bi-level feature propagation"],"prefix":"10.1093","volume":"25","author":[{"given":"Junseok","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Industrial and Systems Engineering, KAIST , 291 Daehak-ro, Yuseong-gu, Daejeon 34141 , Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sukwon","family":"Yun","sequence":"additional","affiliation":[{"name":"Department of Computer Science , 201 S. 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