We are analyzing https://link.springer.com/article/10.1186/1471-2105-6-191.

Title:
Evaluation of normalization methods for cDNA microarray data by k-NN classification | BMC Bioinformatics
Description:
Background Non-biological factors give rise to unwanted variations in cDNA microarray data. There are many normalization methods designed to remove such variations. However, to date there have been few published systematic evaluations of these techniques for removing variations arising from dye biases in the context of downstream, higher-order analytical tasks such as classification. Results Ten location normalization methods that adjust spatial- and/or intensity-dependent dye biases, and three scale methods that adjust scale differences were applied, individually and in combination, to five distinct, published, cancer biology-related cDNA microarray data sets. Leave-one-out cross-validation (LOOCV) classification error was employed as the quantitative end-point for assessing the effectiveness of a normalization method. In particular, a known classifier, k-nearest neighbor (k-NN), was estimated from data normalized using a given technique, and the LOOCV error rate of the ensuing model was computed. We found that k-NN classifiers are sensitive to dye biases in the data. Using N ONRM and GMEDIAN as baseline methods, our results show that single-bias-removal techniques which remove either spatial-dependent dye bias (referred later as spatial effect) or intensity-dependent dye bias (referred later as intensity effect) moderately reduce LOOCV classification errors; whereas double-bias-removal techniques which remove both spatial- and intensity effect reduce LOOCV classification errors even further. Of the 41 different strategies examined, three two-step processes, IG LOESS-SL FILTERW7, IST SPLINE-SL LOESS and IG LOESS-SL LOESS, all of which removed intensity effect globally and spatial effect locally, appear to reduce LOOCV classification errors most consistently and effectively across all data sets. We also found that the investigated scale normalization methods do not reduce LOOCV classification error. Conclusion Using LOOCV error of k-NNs as the evaluation criterion, three double-bias-removal normalization strategies, IG LOESS-SL FILTERW7, IST SPLINE-SL LOESS and IG LOESS-SL LOESS, outperform other strategies for removing spatial effect, intensity effect and scale differences from cDNA microarray data. The apparent sensitivity of k-NN LOOCV classification error to dye biases suggests that this criterion provides an informative measure for evaluating normalization methods. All the computational tools used in this study were implemented using the R language for statistical computing and graphics.
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Keywords {🔍}

data, normalization, methods, effect, loess, classification, microarray, spatial, scale, intensity, error, google, scholar, article, filterw, loocv, table, pubmed, values, sets, set, remove, knn, figure, location, microarrays, cdna, loesssl, results, method, onrm, expression, analysis, dye, strategies, size, lymphoma, cas, number, techniques, global, median, approaches, improvement, gene, local, probes, plots, cancer, normalized,

Topics {✒️}

org/staff/churchill/labsite/software/download resulting double-bias-removal technique single-bias-removal location approaches double-bias-removal location strategies org/src/contrib/descriptions/pamr double-bias-removal normalization strategies org/src/contrib/descriptions/vr double-bias-removal techniques single-bias-removal approach single-bias-removal techniques double-bias-removal methods high-dimensional classification tasks bioconductor package/function pamr/pamr single-bias-removal methods cross-validation error rates article download pdf k-nn classifiers depends k-nn algorithm predicts efficient exact k-nn k-fold cross-validation [34 intensity-dependent dye bias step bias-removal strategies k-nn classifiers estimated estimate k-nn classifiers key pre-processing steps spatial-dependent dye bias sl filterw3-related approaches 'housekeeping-gene'-related methods optimal k-nn classifiers post-normalization data processing full size image ig loess-sl filterw7 ist spline-sl filterw7 pre-normalization data processing ig loess-sl loess genome-wide expression patterns ist spline-sl loess class/package [45]class/knn ig loess-sl filterw3 ist spline-sl filterw3 qs pliner-sl filterw7 loocv classification errors qspline-related approaches require k-nn classification bias-removal methods qs pliner-sl loess transcriptional profiling platform single dye bias intensity-dependent normalization methods higher-order analytical tasks

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         headline:Evaluation of normalization methods for cDNA microarray data by k-NN classification
         description:Non-biological factors give rise to unwanted variations in cDNA microarray data. There are many normalization methods designed to remove such variations. However, to date there have been few published systematic evaluations of these techniques for removing variations arising from dye biases in the context of downstream, higher-order analytical tasks such as classification. Ten location normalization methods that adjust spatial- and/or intensity-dependent dye biases, and three scale methods that adjust scale differences were applied, individually and in combination, to five distinct, published, cancer biology-related cDNA microarray data sets. Leave-one-out cross-validation (LOOCV) classification error was employed as the quantitative end-point for assessing the effectiveness of a normalization method. In particular, a known classifier, k-nearest neighbor (k-NN), was estimated from data normalized using a given technique, and the LOOCV error rate of the ensuing model was computed. We found that k-NN classifiers are sensitive to dye biases in the data. Using N ONRM and GMEDIAN as baseline methods, our results show that single-bias-removal techniques which remove either spatial-dependent dye bias (referred later as spatial effect) or intensity-dependent dye bias (referred later as intensity effect) moderately reduce LOOCV classification errors; whereas double-bias-removal techniques which remove both spatial- and intensity effect reduce LOOCV classification errors even further. Of the 41 different strategies examined, three two-step processes, IG LOESS-SL FILTERW7, IST SPLINE-SL LOESS and IG LOESS-SL LOESS, all of which removed intensity effect globally and spatial effect locally, appear to reduce LOOCV classification errors most consistently and effectively across all data sets. We also found that the investigated scale normalization methods do not reduce LOOCV classification error. Using LOOCV error of k-NNs as the evaluation criterion, three double-bias-removal normalization strategies, IG LOESS-SL FILTERW7, IST SPLINE-SL LOESS and IG LOESS-SL LOESS, outperform other strategies for removing spatial effect, intensity effect and scale differences from cDNA microarray data. The apparent sensitivity of k-NN LOOCV classification error to dye biases suggests that this criterion provides an informative measure for evaluating normalization methods. All the computational tools used in this study were implemented using the R language for statistical computing and graphics.
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            Classification Error
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            Classification Error Rate
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            Computer Appl. in Life Sciences
            Algorithms
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      headline:Evaluation of normalization methods for cDNA microarray data by k-NN classification
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         Classification Error Rate
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         Microarrays
         Computational Biology/Bioinformatics
         Computer Appl. in Life Sciences
         Algorithms
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