Rank-in: enabling integrative analysis across microarray and RNA-seq for cancer

Though transcriptomics technologies evolve rapidly in the past decades, integrative analysis of mixed data between microarray and RNA-seq remains challenging due to the inherent variability difference between them. Here, Rank-In was proposed to correct the nonbiological effects across the two techno...

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Detalles Bibliográficos
Autores principales: Tang, Kailin, Ji, Xuejie, Zhou, Mengdi, Deng, Zeliang, Huang, Yuwei, Zheng, Genhui, Cao, Zhiwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Methods Online
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8464058/
https://www.ncbi.nlm.nih.gov/pubmed/34214174
http://dx.doi.org/10.1093/nar/gkab554

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8464058/
https://www.ncbi.nlm.nih.gov/pubmed/34214174
http://dx.doi.org/10.1093/nar/gkab554

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