Sentence examples for been developed for microarray from inspiring English sources
To gain biologically relevant knowledge from the transcriptome studies without compromising statistical significance, we interrogated the data using multiple strategies that had been developed for microarray analysis and visualization, taking into consideration the strengths and limitations of each strategy.
Although not a major focus of this report, we compared several different statistical models including ANOVA, negative binomial regression, Poisson regression, and a Bayesian implementation of the modulated t-test that had originally been developed for microarray analysis (LIMMA).
Several different cDNA labeling methods have been developed for microarray based gene expression analysis.
Several other modified t-statistics, such as SAM [ 22], penalized t-statistics [ 23], or the local pooled error method used here [ 18], have been developed for microarray data analysis.
The main limitation is that most of the methods applied to metagenomic data sets to score differences in pathway abundance have been developed for microarrays.
Genetic algorithm-neural network (GANN) is the bespoke hybrid genetic algorithm (GA) and artificial neural network (ANN) program that was developed for microarray analysis [ 19- 21].
The R-based BioConductor package (www.bioconductor.org) provides access to a wide variety of data analysis methods and graphics facilities that were developed for microarray data.
Although triPOD was developed for microarray data from the Illumina platform, automated adjustments for sample-specific levels of quality and variation allow for application to other platforms from which SNP-specific genotypes, allelic ratios, and copy number data can be derived.
Alternative approaches must be developed for microarrays where the proportion of modulated genes and their distribution are unknown and they may be biased towards up- or down-modulated trends.
Specific databases for gene expression data have been set up (e.g. [ 10]), and software packages have been developed for analyzing microarray data in a largely automated way, e.g. [ 11, 12, 13, 14, 15], many of them integrate gene expression data with further information obtained from e.g. ontologies, pathway databases or text mining.
