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Randomization in laboratory procedure is key to obtaining reproducible microarray results.

ABSTRACT: The quality of gene expression microarray data has improved dramatically since the first arrays were introduced in the late 1990s. However, the reproducibility of data generated at multiple laboratory sites remains a matter of concern, especially for scientists who are attempting to combine and analyze data from public repositories. We have carried out a study in which a common set of RNA samples was assayed five times in four different laboratories using Affymetrix GeneChip arrays. We observed dramatic differences in the results across laboratories and identified batch effects in array processing as one of the primary causes for these differences. When batch processing of samples is confounded with experimental factors of interest it is not possible to separate their effects, and lists of differentially expressed genes may include many artifacts. This study demonstrates the substantial impact of sample processing on microarray analysis results and underscores the need for randomization in the laboratory as a means to avoid confounding of biological factors with procedural effects.

SUBMITTER: Yang H

PROVIDER: S-EPMC2579585 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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Randomization in laboratory procedure is key to obtaining reproducible microarray results.

Yang Hyuna H Harrington Christina A CA Vartanian Kristina K Coldren Christopher D CD Hall Rob R Churchill Gary A GA

PloS one 20081114 11

The quality of gene expression microarray data has improved dramatically since the first arrays were introduced in the late 1990s. However, the reproducibility of data generated at multiple laboratory sites remains a matter of concern, especially for scientists who are attempting to combine and analyze data from public repositories. We have carried out a study in which a common set of RNA samples was assayed five times in four different laboratories using Affymetrix GeneChip arrays. We observed ...[more]

PMID: 19009020

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Project description:MotivationIn cluster analysis, the validity of specific solutions, algorithms, and procedures present significant challenges because there is no null hypothesis to test and no 'right answer'. It has been noted that a replicable classification is not necessarily a useful one, but a useful one that characterizes some aspect of the population must be replicable. By replicable we mean reproducible across multiple samplings from the same population. Methodologists have suggested that the validity of clustering methods should be based on classifications that yield reproducible findings beyond chance levels. We used this approach to determine the performance of commonly used clustering algorithms and the degree of replicability achieved using several microarray datasets.MethodsWe considered four commonly used iterative partitioning algorithms (Self Organizing Maps (SOM), K-means, Clutsering LARge Applications (CLARA), and Fuzzy C-means) and evaluated their performances on 37 microarray datasets, with sample sizes ranging from 12 to 172. We assessed reproducibility of the clustering algorithm by measuring the strength of relationship between clustering outputs of subsamples of 37 datasets. Cluster stability was quantified using Cramer's v2 from a kXk table. Cramer's v2 is equivalent to the squared canonical correlation coefficient between two sets of nominal variables. Potential scores range from 0 to 1, with 1 denoting perfect reproducibility.ResultsAll four clustering routines show increased stability with larger sample sizes. K-means and SOM showed a gradual increase in stability with increasing sample size. CLARA and Fuzzy C-means, however, yielded low stability scores until sample sizes approached 30 and then gradually increased thereafter. Average stability never exceeded 0.55 for the four clustering routines, even at a sample size of 50. These findings suggest several plausible scenarios: (1) microarray datasets lack natural clustering structure thereby producing low stability scores on all four methods; (2) the algorithms studied do not produce reliable results and/or (3) sample sizes typically used in microarray research may be too small to support derivation of reliable clustering results. Further research should be directed towards evaluating stability performances of more clustering algorithms on more datasets specially having larger sample sizes with larger numbers of clusters considered.

Project description:BackgroundAlmost all genome sequencing projects neglect the fact that diploid organisms contain two genome copies and consequently what is published is a composite of the two. This means that the relationship between alternate alleles at two or more linked loci is lost. We have developed a simplified method of directly obtaining the haploid sequences of each genome copy from an individual organism.ResultsThe diploid sequences of three groups of cattle samples were obtained using a simple sample preparation procedure requiring only a microscope and a haemocytometer. Samples were: 1) lymphocytes from a single Angus steer; 2) sperm cells from an Angus bull; 3) lymphocytes from East African Zebu (EAZ) cattle collected and processed in a field laboratory in Eastern Kenya. Haploid sequence from a fosmid library prepared from lymphocytes of an EAZ cow was used for comparison. Cells were serially diluted to a concentration of one cell per microlitre by counting with a haemocytometer at each dilution. One microlitre samples, each potentially containing a single cell, were lysed and divided into six aliquots (except for the sperm samples which were not divided into aliquots). Each aliquot was amplified with phi29 polymerase and sequenced. Contigs were obtained by mapping to the bovine UMD3.1 reference genome assembly and scaffolds were assembled by joining adjacent contigs that were within a threshold distance of each other. Scaffolds that appeared to contain artefacts of CNV or repeats were filtered out leaving scaffolds with an N50 length of 27-133 kb and a 88-98 % genome coverage. SNP haplotypes were assembled with the Single Individual Haplotyper program to generate an N50 size of 97-201 kb but only ~27-68 % genome coverage. This method can be used in any laboratory with no special equipment at only slightly higher costs than conventional diploid genome sequencing. A substantial body of software for analysis and workflow management was written and is available as supplementary data.ConclusionsWe have developed a set of laboratory protocols and software tools that will enable any laboratory to obtain haplotype sequences at only modestly greater cost than traditional mixed diploid sequences.

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Randomization in laboratory procedure is key to obtaining reproducible microarray results.

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Randomization in laboratory procedure is key to obtaining reproducible microarray results.

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