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Using a cross‐model loadings plot to identify protein spots causing 2‐DE gels to become outliers in PCA
Author(s) -
Kristiansen Luise Cederkvist,
Jacobsen Susanne,
Jessen Flemming,
Jørgensen Bo M.
Publication year - 2010
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200900318
Subject(s) - outlier , multivariate statistics , cluster analysis , identification (biology) , principal component analysis , pattern recognition (psychology) , plot (graphics) , spots , computer science , artificial intelligence , exploratory analysis , data mining , biological system , chemistry , mathematics , statistics , biology , machine learning , botany , data science
The multivariate method PCA is an exploratory tool often used to get an overview of multivariate data, such as the quantified spot volumes of digitized 2‐DE gels. PCA can reveal hidden structures present in the data, and thus enables identification of potential outliers and clustering. Based on PCA, we here present an approach for identification of protein spots causing 2‐DE gels to become outliers. The approach can potentially obviate analytical exclusion of entire 2‐DE gels.