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A new non-linear normalization method for reducing variability in DNA microarray experiments.

ABSTRACT: BACKGROUND:Microarray data are subject to multiple sources of variation, of which biological sources are of interest whereas most others are only confounding. Recent work has identified systematic sources of variation that are intensity-dependent and non-linear in nature. Systematic sources of variation are not limited to the differing properties of the cyanine dyes Cy(5) and Cy(3) as observed in cDNA arrays, but are the general case for both oligonucleotide microarray (Affymetrix GeneChips) and cDNA microarray data. Current normalization techniques are most often linear and therefore not capable of fully correcting for these effects. RESULTS:We present here a simple and robust non-linear method for normalization using array signal distribution analysis and cubic splines. These methods compared favorably to normalization using robust local-linear regression (lowess). The application of these methods to oligonucleotide arrays reduced the relative error between replicates by 5-10% compared with a standard global normalization method. Application to cDNA arrays showed improvements over the standard method and over Cy(3)-Cy(5) normalization based on dye-swap replication. In addition, a set of known differentially regulated genes was ranked higher by the t-test. In either cDNA or Affymetrix technology, signal-dependent bias was more than ten times greater than the observed print-tip or spatial effects. CONCLUSIONS:Intensity-dependent normalization is important for both high-density oligonucleotide array and cDNA array data. Both the regression and spline-based methods described here performed better than existing linear methods when assessed on the variability of replicate arrays. Dye-swap normalization was less effective at Cy(3)-Cy(5) normalization than either regression or spline-based methods alone.

SUBMITTER: Workman C

PROVIDER: S-EPMC126873 | biostudies-literature | 2002 Aug

REPOSITORIES: biostudies-literature

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A new non-linear normalization method for reducing variability in DNA microarray experiments.

Workman Christopher C Jensen Lars Juhl LJ Jarmer Hanne H Berka Randy R Gautier Laurent L Nielser Henrik Bjørn HB Saxild Hans-Henrik HH Nielsen Claus C Brunak Søren S Knudsen Steen S

Genome biology 20020830 9

<h4>Background</h4>Microarray data are subject to multiple sources of variation, of which biological sources are of interest whereas most others are only confounding. Recent work has identified systematic sources of variation that are intensity-dependent and non-linear in nature. Systematic sources of variation are not limited to the differing properties of the cyanine dyes Cy(5) and Cy(3) as observed in cDNA arrays, but are the general case for both oligonucleotide microarray (Affymetrix GeneCh ...[more]

PMID: 12225587

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Project description:MotivationMultiplexed immunofluorescence bioimaging of single-cells and their spatial organization in tissue holds great promise to the development of future precision diagnostics and therapeutics. Current multiplexing pipelines typically involve multiple rounds of immunofluorescence staining across multiple tissue slides. This introduces experimental batch effects that can hide underlying biological signal. It is important to have robust algorithms that can correct for the batch effects while not introducing biases into the data. Performance of data normalization methods can vary among different assay pipelines. To evaluate differences, it is critical to have a ground truth dataset that is representative of the assay.ResultsA new immunoFLuorescence Image NOrmalization (FLINO) method is presented and evaluated against alternative methods and workflows. Multi-round immunofluorescence staining of the same tissue with the nuclear dye DAPI was used to represent virtual slides and a ground truth. DAPI was re-stained on a given tissue slide producing multiple images of the same underlying structure but undergoing multiple representative tissue handling steps. This ground truth dataset was used to evaluate and compare multiple normalization methods including median, quantile, smooth quantile, median ratio normalization (MRN) and trimmed mean of the M-values (TMM). These methods were applied in both an unbiased grid object and segmented cell object workflow to 24 multiplexed biomarkers. An upper quartile normalization of grid objects in log space was found to obtain almost equivalent performance to directly normalizing segmented cell objects by the middle quantile. The developed grid-based technique was then applied with on-slide controls for evaluation. Using five or fewer controls per slide can introduce biases into the data. Ten or more on-slide controls were able to robustly correct for batch effects.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

A new non-linear normalization method for reducing variability in DNA microarray experiments.

Publications

A new non-linear normalization method for reducing variability in DNA microarray experiments.

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