title: Comparative analysis of methods for batch correction in proteomics — a two‑batch case
metadata.name: dc.contributor.author
metadata.value: Danko, Katerina
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Danilov, Lavrentii
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Malashicheva, Anna
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Lobov, Arseniy
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2023-06-15T20:45:02Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2023-06-15T20:45:02Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2023-03
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Danko, K., Danilov, L., Malashicheva, A., and Lobov, A. 2023. Comparative analysis of methods for batch correction in proteomics — a two‑batch case. Bio. Comm. 68(1): 56–61. https://doi.org/10.21638/spbu03.2023.106
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/spbu03.2023.106
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/41587
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: A proper study design is vital for life science. Any effects unrelated to the studied
  ones (batch effects) should be avoided. Still, it is not always possible to exclude
  all batch effects in a complicated omics study. Here we discuss an appropriate
  way for analysis of proteomics data with an enormous technical batch
  effect. We re-analyzed the published dataset (PXD032212) with two batches of
  samples analyzed in two different years. Each batch includes control and differentiated
  cells. Control and differentiated cells form separate clusters with
  209 differentially expressed proteins (DEPs). Nevertheless, the differences
  between the batches were higher than between the cell types. Therefore, the
  analysis of only one of the batches gives 276 or 290 DEPs. Then we compared
  the efficiency of five methods for batch correction. ComBat was the most effective
  method for batch effect correction, and the analysis of the corrected
  dataset revealed 406 DEPs.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: The work was supported by
  the Russian Science Foundation for Basic
  Research, project No. 18-14-00152.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Biological Сommunications;Volume 68; Issue 1
metadata.lang: -
metadata.name: dc.subject
metadata.value: batch effect
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: proteomics
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: bioinformatics
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: batch effect correction
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Comparative analysis of methods for batch correction in proteomics — a two‑batch case
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 14385-Article Text-53888-1-10-20230502.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/41587/1/14385-Article%20Text-53888-1-10-20230502.pdf
resources.format: Adobe PDF
resources.size: 3,01 MB