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Extractin information from two‐dimensional electrophoresis gels by partial least squares regression
Author(s) -
Jessen Flemming,
Lametsch René,
Bendixen Emøke,
Kjærsgård Inger V. H.,
Jørgensen Bo M.
Publication year - 2002
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/1615-9861(200201)2:1<32::aid-prot32>3.0.co;2-j
Subject(s) - partial least squares regression , multivariate statistics , matching (statistics) , feature selection , biological system , regression analysis , computer science , multivariate analysis , identification (biology) , two dimensional gel electrophoresis , regression , least squares function approximation , data mining , statistics , chromatography , chemistry , pattern recognition (psychology) , mathematics , artificial intelligence , proteomics , biology , biochemistry , botany , estimator , gene
Two‐dimensional gel electrophoresis (2‐DE) produces large amounts of data and extraction of relevant information from these data demands a cautious and time consuming process of spot pattern matching between gels. The classical approach of data analysis is to detect protein markers that appear or disappear depending on the experimental conditions. Such biomarkers are found by comparing the relative volumes of individual spots in the individual gels. Multivariate statistical analysis and modelling of 2‐DE data for comparison and classification is an alternative approach utilising the combination of all proteins/spots in the gels. In the present study it is demonstrated how information can be extracted by multivariate data analysis. The strategy is based on partial least squares regression followed by variable selection to find proteins that individually or in combination with other proteins vary informatively in relation to the experimental conditions. Finding of such coherent protein patterns leads to identification of potential relations between the involved proteins, and will be useful for focusing further investigation of proteins that relate to the chosen experimental conditions.