Noise sampling method: an ANOVA approach allowing robust selection of differentially regulated genes measured by DNA microarrays

Abstract

The novel method proposed here is based on ANOVA and uses replicate spots to estimate an empirical distribution of the noise. The measured intensity range is divided in a number of intervals. A noise distribution is constructed for each such interval. Bootstrapping is used to map the desired confidence levels from the noise distribution corresponding to a given interval to the measured log ratios in that interval. If the method is applied on individual arrays having replicate spots, the method can calculate an overall width of the noise distribution which can be used as an indicator of the array quality. We compared this method with the fold change and unusual ratio method. We also discuss the relationship with an ANOVA model proposed by Churchill et al. In silico experiments were performed while controlling the degree of regulation as well as the amount of noise. Such experiments show the performance of the classical methods can be very unsatisfactory. We also compared the results of the 2-fold method with the results of the noise sampling method using pre and post immortalization cell lines derived from the MDAH041 fibroblasts hybridized on Affymetrix GeneChip arrays. The 2-fold method reported 198 genes as upregulated and 493 genes as downregulated. The noise sampling method reported 98 gene upregulated and 240 genes downregulated at the 99.99% confidence level. The methods agreed on 221 genes downregulated and 66 genes upregulated. Fourteen genes from the subset of genes reported by both methods were all confirmed by Q-RT-PCR. Alternative assays on various subsets of genes on which the two methods disagreed suggested that the noise sampling method is likely to provide fewer false positives.

Soybean Homologs of MPK4 Negatively Regulate Defense Responses and Positively Regulate Growth and Development

“…org/AffyChip/), and the induced and suppressed gene sets were classified according to the Gene Ontology (GO) biological processes component. GO functional classes that were significantly overrepresented (P , 0.05) in the gene lists were obtained by using Fisher's exact test (Fisher, 1966;Draghici et al, 2003) with a Bonferroni correction to adjust for repetitive sampling. The majority of genes in the GO functional classes of biotic and abiotic defense responses were induced, whereas the majority of genes in GO functional classes of growth and development, cell cycle, and cell proliferation were repressed in GmMPK4-silenced plants relative to vector control plants (Table I; Fig.…”

Head and neck squamous cell carcinoma transcriptome analysis by comprehensive validated differential display

“…However, using cutoffs for the T/N mA-ratios, the correlation gradually increased to 100% (Figure 2d). mAs ratios may only be significant if they are at least 2-to 3-fold (Draghici et al, 2003). With cutoffs of 2 or 3, and a DD cutoff of 1, the correlations reached 83% (49/59) and 92% (33/36 genes), respectively.…”

Changes in extreme cold tolerance, membrane composition and cardiac transcriptome during the first day of thermal acclimation in the porcelain crabPetrolisthes cinctipes

“…Median fluorescence values for each feature were log transformed (Draghici et al, 2003), and a weighted linear least-squares regression LOWESS normalization was conducted on log 2 -transformed data using the joint LOWESS function (Wu et al, 2003). GenePix results files and log 2 -transformed LOWESS normalized data have been submitted to the NCBI Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/query/ acc.cgi?accGSE35307).…”