Normalization of two-channel microarrays accounting for experimental design and intensity-dependent relationships.
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ABSTRACT:
SUBMITTER: Dabney AR
PROVIDER: S-EPMC1868928 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundMiR arrays distinguish themselves from gene expression arrays by their more limited number of probes, and the shorter and less flexible sequence in probe design. Robust data processing and analysis methods tailored to the unique characteristics of miR arrays are greatly needed. Assumptions underlying commonly used normalization methods for gene expression microarrays containing tens of thousands or more probes may not hold for miR microarrays. Findings from previous studies have sometimes been inconclusive or contradictory. Further studies to determine optimal normalization methods for miR microarrays are needed.MethodsWe evaluated many different normalization methods for data generated with a custom-made two channel miR microarray using two data sets that have technical replicates from several different cell lines. The impact of each normalization method was examined on both within miR error variance (between replicate arrays) and between miR variance to determine which normalization methods minimized differences between replicate samples while preserving differences between biologically distinct miRs.ResultsLowess normalization generally did not perform as well as the other methods, and quantile normalization based on an invariant set showed the best performance in many cases unless restricted to a very small invariant set. Global median and global mean methods performed reasonably well in both data sets and have the advantage of computational simplicity.ConclusionsResearchers need to consider carefully which assumptions underlying the different normalization methods appear most reasonable for their experimental setting and possibly consider more than one normalization approach to determine the sensitivity of their results to normalization method used.
| S-EPMC2917409 | biostudies-literature
Project description:MotivationA major challenge in utilizing microarray technologies to measure nucleic acid abundances is 'normalization', the goal of which is to separate biologically meaningful signal from other confounding sources of signal, often due to unavoidable technical factors. It is intuitively clear that true biological signal and confounding factors need to be simultaneously considered when performing normalization. However, the most popular normalization approaches do not utilize what is known about the study, both in terms of the biological variables of interest and the known technical factors in the study, such as batch or array processing date.ResultsWe show here that failing to include all study-specific biological and technical variables when performing normalization leads to biased downstream analyses. We propose a general normalization framework that fits a study-specific model employing every known variable that is relevant to the expression study. The proposed method is generally applicable to the full range of existing probe designs, as well as to both single-channel and dual-channel arrays. We show through real and simulated examples that the method has favorable operating characteristics in comparison to some of the most highly used normalization methods.AvailabilityAn R package called snm implementing the methodology will be made available from Bioconductor (http://bioconductor.org).Contactjstorey@princeton.eduSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC2865860 | biostudies-literature
Project description:Chromatin immunoprecipitation on tiling arrays (ChIP-chip) has been widely used to investigate the DNA binding sites for a variety of proteins on a genome-wide scale. However, several issues in the processing and analysis of ChIP-chip data have not been resolved fully, including the effect of background (mock control) subtraction and normalization within and across arrays.The binding profiles of Drosophila male-specific lethal (MSL) complex on a tiling array provide a unique opportunity for investigating these topics, as it is known to bind on the X chromosome but not on the autosomes. These large bound and control regions on the same array allow clear evaluation of analytical methods.We introduce a novel normalization scheme specifically designed for ChIP-chip data from dual-channel arrays and demonstrate that this step is critical for correcting systematic dye-bias that may exist in the data. Subtraction of the mock (non-specific antibody or no antibody) control data is generally needed to eliminate the bias, but appropriate normalization obviates the need for mock experiments and increases the correlation among replicates. The idea underlying the normalization can be used subsequently to estimate the background noise level in each array for normalization across arrays. We demonstrate the effectiveness of the methods with the MSL complex binding data and other publicly available data.Proper normalization is essential for ChIP-chip experiments. The proposed normalization technique can correct systematic errors and compensate for the lack of mock control data, thus reducing the experimental cost and producing more accurate results.
| S-EPMC1913544 | biostudies-literature
Project description:Multiple Arabidopsis thaliana clones from an experimental series of cDNA microarrays are evaluated in order to identify essential sources of noise in the spotting and hybridization process. Theoretical and experimental strategies for an improved quantitative evaluation of cDNA microarrays are proposed and tested on a series of differently diluted control clones. Several sources of noise are identified from the data. Systematic and stochastic fluctuations in the spotting process are reduced by control spots and statistical techniques. The reliability of slide to slide comparison is critically assessed within the statistical framework of pattern matching and classification.
| S-EPMC105386 | biostudies-literature
Project description:DNA microarray technology is a powerful tool for monitoring gene expression or for finding the location of DNA-bound proteins. DNA microarrays can suffer from gene-specific dye bias (GSDB), causing some probes to be affected more by the dye than by the sample. This results in large measurement errors, which vary considerably for different probes and also across different hybridizations. GSDB is not corrected by conventional normalization and has been difficult to address systematically because of its variance. We show that GSDB is influenced by label incorporation efficiency, explaining the variation of GSDB across different hybridizations. A correction method (Gene- And Slide-Specific Correction, GASSCO) is presented, whereby sequence-specific corrections are modulated by the overall bias of individual hybridizations. GASSCO outperforms earlier methods and works well on a variety of publically available datasets covering a range of platforms, organisms and applications, including ChIP on chip. A sequence-based model is also presented, which predicts which probes will suffer most from GSDB, useful for microarray probe design and correction of individual hybridizations. Software implementing the method is publicly available.
| S-EPMC2683724 | biostudies-literature
Project description:In two-colour microarrays, the ratio of signal intensities of two co-hybridized samples is used as a relative measure of gene expression. Ratio-based analysis becomes complicated and inefficient in multi-class comparisons. We therefore investigated the validity of an intensity-based analysis procedure. To this end, two different cRNA targets were hybridized together, separately, with a common reference and in a self-self fashion on spotted 65mer oligonucleotide microarrays. We found that the signal intensity of the cRNA targets was not influenced by the presence of a target labelled in the opposite colour. This indicates that targets do not compete for binding sites on the array, which is essential for intensity-based analysis. It is demonstrated that, for good-quality arrays, the correlation of signal intensity measurements between the different hybridization designs is high (R > 0.9). Furthermore, ratio calculations from ratio- and intensity-based analyses correlated well (R > 0.8). Based on these results, we advocate the use of separate intensities rather than ratios in the analysis of two-colour long-oligonucleotide microarrays. Intensity-based analysis makes microarray experiments more efficient and more flexible: It allows for direct comparisons between all hybridized samples, while circumventing the need for a reference sample that occupies half of the hybridization capacity.
| S-EPMC390313 | biostudies-literature
Project description:BACKGROUND: Multifactorial experimental designs using DNA microarrays are becoming increasingly common, but the extent of the transitivity of cDNA microarray expression measurements across multiple samples has yet to be explored. RESULTS: A strong correlation between direct and transitive inference for significantly differentially expressed genes is demonstrated, using subsets of a dye-swap loop design. CONCLUSIONS: In experimental design, opportunities for transitive inference should be exploited, while always ensuring that comparisons of greatest interest comprise direct hybridizations.
| S-EPMC239860 | biostudies-literature
Project description:BackgroundIn many microarray experiments, analysis is severely hindered by a major difficulty: the small number of samples for which expression data has been measured. When one searches for differentially expressed genes, the small number of samples gives rise to an inaccurate estimation of the experimental noise. This, in turn, leads to loss of statistical power.ResultsWe show that the measurement noise of genes with similar expression levels (intensity) is identically and independently distributed, and that this (intensity dependent) distribution is approximately normal. Our method can be easily adapted and used to test whether these statement hold for data from any particular microarray experiment. We propose a method that provides an accurate estimation of the intensity-dependent variance of the noise distribution, and demonstrate that using this estimation we can detect differential expression with much better statistical power than that of standard t-test, and can compare the noise levels of different experiments and platforms.ConclusionsWhen the number of samples is small, the simple method we propose improves significantly the statistical power in identifying differentially expressed genes.
| S-EPMC2920277 | biostudies-literature
Project description:Microbial community samples can be efficiently surveyed in high throughput by sequencing markers such as the 16S ribosomal RNA gene. Often, a collection of samples is then selected for subsequent metagenomic, metabolomic or other follow-up. Two-stage study design has long been used in ecology but has not yet been studied in-depth for high-throughput microbial community investigations. To avoid ad hoc sample selection, we developed and validated several purposive sample selection methods for two-stage studies (that is, biological criteria) targeting differing types of microbial communities. These methods select follow-up samples from large community surveys, with criteria including samples typical of the initially surveyed population, targeting specific microbial clades or rare species, maximizing diversity, representing extreme or deviant communities, or identifying communities distinct or discriminating among environment or host phenotypes. The accuracies of each sampling technique and their influences on the characteristics of the resulting selected microbial community were evaluated using both simulated and experimental data. Specifically, all criteria were able to identify samples whose properties were accurately retained in 318 paired 16S amplicon and whole-community metagenomic (follow-up) samples from the Human Microbiome Project. Some selection criteria resulted in follow-up samples that were strongly non-representative of the original survey population; diversity maximization particularly undersampled community configurations. Only selection of intentionally representative samples minimized differences in the selected sample set from the original microbial survey. An implementation is provided as the microPITA (Microbiomes: Picking Interesting Taxa for Analysis) software for two-stage study design of microbial communities.
| S-EPMC3834858 | biostudies-other
Project description:In the past 5 years, RNA-Seq has become a powerful tool in transcriptome analysis even though computational methods dedicated to the analysis of high-throughput sequencing data are yet to be standardized. It is, however, now commonly accepted that the choice of a normalization procedure is an important step in such a process, for example in differential gene expression analysis. The present article highlights the similarities between three normalization methods: TMM from edgeR R package, RLE from DESeq2 R package, and MRN. Both TMM and DESeq2 are widely used for differential gene expression analysis. This paper introduces properties that show when these three methods will give exactly the same results. These properties are proven mathematically and illustrated by performing in silico calculations on a given RNA-Seq data set.
| S-EPMC5025571 | biostudies-literature