Batch effects removal for microbiome data via conditional quantile regression

Batch effects in microbiome data arise from differential processing of specimens and can lead to spurious findings and obscure true signals. Strategies designed for genomic data to mitigate batch effects usually fail to address the zero-inflated and over-dispersed microbiome data. Most strategies ta...

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Detalles Bibliográficos
Autores principales: Ling, Wodan, Lu, Jiuyao, Zhao, Ni, Lulla, Anju, Plantinga, Anna M., Fu, Weijia, Zhang, Angela, Liu, Hongjiao, Song, Hoseung, Li, Zhigang, Chen, Jun, Randolph, Timothy W., Koay, Wei Li A., White, James R., Launer, Lenore J., Fodor, Anthony A., Meyer, Katie A., Wu, Michael C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9477887/
https://www.ncbi.nlm.nih.gov/pubmed/36109499
http://dx.doi.org/10.1038/s41467-022-33071-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9477887/
https://www.ncbi.nlm.nih.gov/pubmed/36109499
http://dx.doi.org/10.1038/s41467-022-33071-9

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