We are analyzing https://link.springer.com/article/10.1186/1471-2105-7-261.

Title:
Empirical array quality weights in the analysis of microarray data | BMC Bioinformatics
Description:
Background Assessment of array quality is an essential step in the analysis of data from microarray experiments. Once detected, less reliable arrays are typically excluded or "filtered" from further analysis to avoid misleading results. Results In this article, a graduated approach to array quality is considered based on empirical reproducibility of the gene expression measures from replicate arrays. Weights are assigned to each microarray by fitting a heteroscedastic linear model with shared array variance terms. A novel gene-by-gene update algorithm is used to efficiently estimate the array variances. The inverse variances are used as weights in the linear model analysis to identify differentially expressed genes. The method successfully assigns lower weights to less reproducible arrays from different experiments. Down-weighting the observations from suspect arrays increases the power to detect differential expression. In smaller experiments, this approach outperforms the usual method of filtering the data. The method is available in the limma software package which is implemented in the R software environment. Conclusion This method complements existing normalisation and spot quality procedures, and allows poorer quality arrays, which would otherwise be discarded, to be included in an analysis. It is applicable to microarray data from experiments with some level of replication.
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Keywords {🔍}

array, data, weights, arrays, quality, analysis, expression, microarray, genes, article, gene, model, reml, google, scholar, variance, values, algorithm, figure, pubmed, full, experiments, method, methods, results, linear, genebygene, tstatistics, set, experiment, parameters, matrix, false, spot, lines, update, simulated, equal, simulation, approach, spots, sets, control, lms, information, number, differential, probes, estimates, table,

Topics {✒️}

ryan van laar profile reml log-likelihood article google scholar open access article purely gene-wise manner au/iap/spot/spotmanual high-density oligonucleotide microarrays author information authors log-linear variance model summarised log-intensity values profile reml likelihood large moderated t-statistics gene-wise variance factors high-density oligonucleotide arrays model-based 'normexp' method 𝑗 𝑘 𝑗 𝑘 2 article download pdf probe-array variance model related subjects 3mm-0mm treatment comparisons authors’ original file individual array log-ratios gene-wise parameters β 3mm-0mm fold-change large dimensional problem conditional reml estimator gene-wise coefficients β privacy choices/manage cookies false discovery rates quality indicators increase unknown variance factors full size image heteroscedastic variance model highly expressed probes false discovery rate empirical bayes methods quality assessment procedures quality assessment steps quality assessment tools array weight method heteroscedastic linear model empirical array quality assigned high weight catastrophically poor quality neuroblastoma cell response moderated t-statistics detect differential expression high background levels 𝑗 𝑗 2

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         description:Assessment of array quality is an essential step in the analysis of data from microarray experiments. Once detected, less reliable arrays are typically excluded or "filtered" from further analysis to avoid misleading results. In this article, a graduated approach to array quality is considered based on empirical reproducibility of the gene expression measures from replicate arrays. Weights are assigned to each microarray by fitting a heteroscedastic linear model with shared array variance terms. A novel gene-by-gene update algorithm is used to efficiently estimate the array variances. The inverse variances are used as weights in the linear model analysis to identify differentially expressed genes. The method successfully assigns lower weights to less reproducible arrays from different experiments. Down-weighting the observations from suspect arrays increases the power to detect differential expression. In smaller experiments, this approach outperforms the usual method of filtering the data. The method is available in the limma software package which is implemented in the R software environment. This method complements existing normalisation and spot quality procedures, and allows poorer quality arrays, which would otherwise be discarded, to be included in an analysis. It is applicable to microarray data from experiments with some level of replication.
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            Array Weight
            REML Estimator
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            Computer Appl. in Life Sciences
            Algorithms
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      headline:Empirical array quality weights in the analysis of microarray data
      description:Assessment of array quality is an essential step in the analysis of data from microarray experiments. Once detected, less reliable arrays are typically excluded or "filtered" from further analysis to avoid misleading results. In this article, a graduated approach to array quality is considered based on empirical reproducibility of the gene expression measures from replicate arrays. Weights are assigned to each microarray by fitting a heteroscedastic linear model with shared array variance terms. A novel gene-by-gene update algorithm is used to efficiently estimate the array variances. The inverse variances are used as weights in the linear model analysis to identify differentially expressed genes. The method successfully assigns lower weights to less reproducible arrays from different experiments. Down-weighting the observations from suspect arrays increases the power to detect differential expression. In smaller experiments, this approach outperforms the usual method of filtering the data. The method is available in the limma software package which is implemented in the R software environment. This method complements existing normalisation and spot quality procedures, and allows poorer quality arrays, which would otherwise be discarded, to be included in an analysis. It is applicable to microarray data from experiments with some level of replication.
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         REML Estimator
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         Microarrays
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         Computer Appl. in Life Sciences
         Algorithms
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