Variation among maize (Zea mays L.) accessions of Bendel State, Nigeria

Alika, J. E.; Aken'ova, M. E.; Fatokun, C. A.

doi:10.1007/bf02432144

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…PCA has been also applied in toxicological studies by the groups of Amin [34], Heijne [35] and Anderson [36] and to classification studies in the field of food quality control (coupled to cluster analysis and discriminant analysis): papers are present in literature about cheese classification [37] and identification of the protein content in wheat and bread [38][39][40].…”

Section: General Aspectsmentioning

confidence: 99%

Multivariate statistical tools applied to the characterization of the proteomic profiles of two human lymphoma cell lines by two‐dimensional gel electrophoresis

et al. 2006

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Mantle cell lymphoma (MCL) cell lines have been difficult to generate, since only few have been described so far and even fewer have been thoroughly characterized. Among them, there is only one cell line, called GRANTA-519, which is well established and universally adopted for most lymphoma studies. We succeeded in establishing a new MCL cell line, called MAVER-1, from a leukemic MCL, and performed a thorough phenotypical, cytogenetical and molecular characterization of the cell line. In the present report, the phenotypic expression of GRANTA-519 and MAVER-1 cell lines has been compared and evaluated by a proteomic approach, exploiting 2-D map analysis. By univariate statistical analysis (Student's t-test, as commonly used in most commercial software packages), most of the protein spots were found to be identical between the two cell lines. Thirty spots were found to be unique for the GRANTA-519, whereas another 11 polypeptides appeared to be expressed only by the MAVER-1 cell line. A number of these spots could be identified by MS. These data were confirmed and expanded by multivariate statistical tools (principal component analysis and soft-independent model of class analogy) that allowed identification of a larger number of differently expressed spots. Multivariate statistical tools have the advantage of reducing the risk of false positives and of identifying spots that are significantly altered in terms of correlated expression rather than absolute expression values. It is thus suggested that, in future work in differential proteomic profiling, both univariate and multivariate statistical tools should be adopted.

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Section: General Aspectsmentioning

confidence: 99%

Multivariate statistical tools applied to the characterization of the proteomic profiles of two human lymphoma cell lines by two‐dimensional gel electrophoresis

et al. 2006

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“…Different algorithms can be used as a measure of similarity, giving different cluster methodologies, i.e. k-means clustering [58], complete linkage clustering [34], ascendant hierarchical clustering [128][129][130], descendant hierarchical clustering [129], and average linkage cluster analysis [54]. The most similar objects/variables, and successive groups of objects/variables, are aggregated into new groups.…”

Section: Unsupervised Analysismentioning

confidence: 99%

The use of chemometrics to analyse protein patterns from gel electrophoresis

Færgestad¹,

Rye²,

Nhek³

et al. 2011

Acta Chromatographica

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Chemometrics involves strategies to analyse multivariate data using interdisciplinary approaches aiming to extract relevant information from complex data. Chemometric strategies comprise both the pre-processing of the data, where the choice of methodology is domain-specific, and analysis of the resulting data after preprocessing using multivariate methodology. Although use of multivariate data analysis for gel electrophoresis images has increased substantially in the last decade, its use is still much less frequent than use of univariate approaches. Considering the complexity of the electrophoresis gel images and the multivariate nature of the proteome, applying multivariate data analysis for gel electrophoresis images gives information which is otherwise lost. This paper is written as a review and guideline of chemometric strategies used for analysis of gel electrophoresis images. The multivariate data analyses described are, however, also relevant for other proteome data, for example mass spectrometry, and for functional genomics in general.

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“…Two of the first applications of multivariate techniques to proteomic datasets in botany and in the field of food quality and control were by Dewettinck et al (1997) and Alika et al (1995). In these applications, the samples were not described by the spot volumes (the authors applied SDS-PAGE) but by the volume profile along the SDS strip.…”

Section: Botany and Food Safetymentioning

confidence: 99%

“…Another study by Alika et al (1995), was devoted to the characterisation of 27 maize accessions (Bendel State, Nigeria) through the different band mobility of maize zein protein in SDS-PAGE, coupled to PCA and cluster analysis. Cluster analysis identified five clusters while PCA separated the accessions with yellow kernels from those with early maturity.…”

Section: Botany and Food Safetymentioning

confidence: 99%

Multivariate Statistical Tools for the Evaluation of Proteomic 2D-maps:Recent Achievements and Applications

Marengo¹,

Robotti²,

Bobba

2007

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Two dimensional polyacrylamide gel electrophoresis (2D-PAGE) maps represent an unavoidable tool in many fields connected with proteome research, such as development of new diagnostic assays or new drugs. Unfortunately the information contained in the maps is often so complex that its recognition and extraction usually requires complex statistical treatments. Statistics accompanies many phases of 2D-PAGE maps management -from the spot revelation to maps matching, as well as the extraction and rationalisation of useful information. This review describes and reports the most recent achievements in the field of statistical tools applied to proteome research by two-dimensional gel electrophoresis (2D-GE). The first section is devoted to briefly describe the theoretical aspects of the multivariate methods mostly adopted in this field such as Principal Component Analysis, Cluster Analysis, Classification methods, Artificial Neural Networks. The most recent applications are then described explaining the analysis of spot volume datasets from standard differential analysis as well as the direct analysis of 2D maps images. Applications are also reported about the use of multivariate tools in the analysis of DNA and RNA profiles.

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Variation among maize (Zea mays L.) accessions of Bendel State, Nigeria

Cited by 8 publications

References 10 publications

Multivariate statistical tools applied to the characterization of the proteomic profiles of two human lymphoma cell lines by two‐dimensional gel electrophoresis

Multivariate statistical tools applied to the characterization of the proteomic profiles of two human lymphoma cell lines by two‐dimensional gel electrophoresis

The use of chemometrics to analyse protein patterns from gel electrophoresis

Multivariate Statistical Tools for the Evaluation of Proteomic 2D-maps:Recent Achievements and Applications

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