Sentence examples for microarray normalization methods from inspiring English sources

In comparing Affymetrix microarray normalization methods, Bolstad et al [11] perform variance, pairwise and bias comparisons between arrays.

Most microarray normalization methods assume that the total mRNA quantity will remain constant across the different arrays and time points.

We address the uncertainty regarding the performance of different microarray normalization methods [15] by comparing the results of two algorithms -GCRMA and dChIP.

Given the lack of consensus within the bioinformatics community regarding the different microarray normalization methods [15], we analyzed our data using two different algorithms: GC-Robust Multi-Array (GCRMA) [16] and dChip [17].

Some of the most highly used microarray normalization methods are what we call 'unsupervised' methods.

Users can apply various microarray normalization methods to the imported datasets through the limma package functions 'normalizeBetweenArrays' and 'normalizeWithinArrays'normalizeWithinArrays

Loess normalization is a standard microarray normalization method that removes non-linear intensity-dependent artifacts from the data by iteratively fitting a series of local piecewise curves to the log-mean-difference plots of each pair of arrays, and effectively subtracting the curve from the data[25].

Subsequently, the generated dataset was normalized with two widely used microarray data normalization methods, as there exists no gold standard normalization method, with the scope to decrease the number of possible false positives during the statistical selection step.

Commonly, microarray data normalization methods assume that relatively few transcripts change from sample to sample [36].

However, the procedure for generating RNA-seq data is fundamentally different from that for microarray data, the normalization methods used in microarray data are therefore not directly applicable in RNA-seq data.

For these reasons, we have developed and validated in this study a "spot by spot" microarray data normalization method based on gDNA.