Denoiseit: denoising gene expression data using rank based isolation trees
DenoiseIt was applied to both bulk and single-cell RNA-seq data collected from TCGA and a COVID-19 cohort to show that it proficiently identified and removed genes exhibiting expression anomalies confined to specific samples rather than a known group. DenoiseIt also showed to reduce the level of technical noise while preserving a higher proportion of biologically relevant genes compared to existing methods. The DenoiseIt Software is publicly available on GitHub at https://github.com/cobi-git/DenoiseIt.
Authors
- Inuk Jung (ORCID: https://orcid.org/0000-0003-0675-4244)
- Jaemin Jeon
- Hye-Yeong Jo (ORCID: https://orcid.org/0000-0002-2343-7729)
- Sang Cheol Kim (ORCID: https://orcid.org/0000-0003-4389-9178)
- Youjeong Suk
- Kwangsoo Kim (ORCID: https://orcid.org/0000-0003-0112-1464)
Institutions
- Seoul National University (KR)
- Seoul National University Hospital (KR)
- Kyungpook National University (KR)
- Korea National Institute of Health (KR)
- National University College (PR)
- Korea Disease Control and Prevention Agency (KR)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-08-21
- DOI
- https://doi.org/10.1186/s12859-024-05899-z
- Citations
- 1
- Primary Topic
- Gene expression and cancer classification
- Type
- article
- Field-Weighted Citation Impact
- 0.23
Funders
- Korea National Institute of Health