Normalization strategies for cDNA microarrays.
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ABSTRACT:
SUBMITTER: Schuchhardt J
PROVIDER: S-EPMC105386 | biostudies-literature | 2000 May
REPOSITORIES: biostudies-literature
Publications
Normalization strategies for cDNA microarrays.
Schuchhardt J J Beule D D Malik A A Wolski E E Eickhoff H H Lehrach H H Herzel H H
Nucleic acids research 20000501 10
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 co ...[more]
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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:BackgroundNormalization is a critical step in analysis of gene expression profiles. For dual-labeled arrays, global normalization assumes that the majority of the genes on the array are non-differentially expressed between the two channels and that the number of over-expressed genes approximately equals the number of under-expressed genes. These assumptions can be inappropriate for custom arrays or arrays in which the reference RNA is very different from the experimental samples.ResultsWe propose a mixture model based normalization method that adaptively identifies non-differentially expressed genes and thereby substantially improves normalization for dual-labeled arrays in settings where the assumptions of global normalization are problematic. The new method is evaluated using both simulated and real data.ConclusionsThe new normalization method is effective for general microarray platforms when samples with very different expression profile are co-hybridized and for custom arrays where the majority of genes are likely to be differentially expressed.
| S-EPMC552315 | biostudies-literature
Project description:BackgroundIn prokaryotic genomes, genes are organized in operons, and the genes within an operon tend to have similar levels of expression. Because of co-transcription of genes within an operon, borrowing information from other genes within the same operon can improve the estimation of relative transcript levels; the estimation of relative levels of transcript abundances is one of the most challenging tasks in experimental genomics due to the high noise level in microarray data. Therefore, techniques that can improve such estimations, and moreover are based on sound biological premises, are expected to benefit the field of microarray data analysisResultsIn this paper, we propose a hierarchical Bayesian model, which relies on borrowing information from other genes within the same operon, to improve the estimation of gene expression levels and, hence, the detection of differentially expressed genes. The simulation studies and the analysis of experiential data demonstrated that the proposed method outperformed other techniques that are routinely used to estimate transcript levels and detect differentially expressed genes, including the sample mean and SAM t statistics. The improvement became more significant as the noise level in microarray data increases.ConclusionBy borrowing information about transcriptional activity of genes within classified operons, we improved the estimation of gene expression levels and the detection of differentially expressed genes.
| S-EPMC1513396 | biostudies-literature
Project description:To identify gene expression changes along progression of bladder cancer, we compared the expression profiles of early-stage and advanced bladder tumors using cDNA microarrays containing 17,842 known genes and expressed sequence tags. The application of bootstrapping techniques to hierarchical clustering segregated early-stage and invasive transitional carcinomas into two main clusters. Multidimensional analysis confirmed these clusters and more importantly, it separated carcinoma in situ from papillary superficial lesions and subgroups within early-stage and invasive tumors displaying different overall survival. Additionally, it recognized early-stage tumors showing gene profiles similar to invasive disease. Different techniques including standard t-test, single-gene logistic regression, and support vector machine algorithms were applied to identify relevant genes involved in bladder cancer progression. Cytokeratin 20, neuropilin-2, p21, and p33ING1 were selected among the top ranked molecular targets differentially expressed and validated by immunohistochemistry using tissue microarrays (n = 173). Their expression patterns were significantly associated with pathological stage, tumor grade, and altered retinoblastoma (RB) expression. Moreover, p33ING1 expression levels were significantly associated with overall survival. Analysis of the annotation of the most significant genes revealed the relevance of critical genes and pathways during bladder cancer progression, including the overexpression of oncogenic genes such as DEK in superficial tumors or immune response genes such as Cd86 antigen in invasive disease. Gene profiling successfully classified bladder tumors based on their progression and clinical outcome. The present study has identified molecular biomarkers of potential clinical significance and critical molecular targets associated with bladder cancer progression.
| S-EPMC1868230 | biostudies-literature
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:Five microarrays from a larger dataset used to demonstrate a normalization technique base on Zipf's law. The original data set was generated by using Atlas Rat cDNA microarrays (Clontech, 588 genes) probed with rat brain tissue, from control (cerebellum n=10, olive n=10) and harmaline treated (cerebellum n=10, olive n=9) animals. Microarrays were probed according to established protocols and exposed to imaging plates overnight (BAS-MS 2325) and scanned at a 50 m resolution on a FLA-3000G phosphoimager (Raytest, Germany). Image gridding was carried out using VisualGrid® software (http://www.gpc-biotech.com). Keywords: other
2004-07-03 | GSE1509 | GEO
Project description:We developed a novel simple cDNA normalization method [termed duplex-specific nuclease (DSN) normalization] that may be effectively used for samples enriched with full-length cDNA sequences. DSN normalization involves the denaturation-reassociation of cDNA, degradation of the double-stranded (ds) fraction formed by abundant transcripts and PCR amplification of the equalized single-stranded (ss) DNA fraction. The key element of this method is the degradation of the ds fraction formed during reassociation of cDNA using the kamchatka crab DSN, as described recently. This thermostable enzyme displays a strong preference for cleaving ds DNA and DNA in DNA-RNA hybrid duplexes compared with ss DNA and RNA, irrespective of sequence length. We developed normalization protocols for both first-strand cDNA [when poly(A)+ RNA is available] and amplified cDNA (when only total RNA can be obtained). Both protocols were evaluated in model experiments using human skeletal muscle cDNA. We also employed DSN normalization to normalize cDNA from nervous tissues of the marine mollusc Aplysia californica (a popular model organism in neuroscience) to illustrate further the efficiency of the normalization technique.
| S-EPMC373426 | biostudies-literature
Project description:AIM:To study the pathogenetic processes and the role of gene expression by microarray analyses in expediting our understanding of the molecular pathophysiology of pancreatic adenocarcinoma, and to identify the novel cancer-associated genes. METHODS:Nine histologically defined pancreatic head adenocarcinoma specimens associated with clinical data were studied. Total RNA and mRNA were isolated and labeled by reverse transcription reaction with Cy5 and Cy3 for cDNA probe. The cDNA microarrays that represent a set of 4 096 human genes were hybridized with labeled cDNA probe and screened for molecular profiling analyses. RESULTS:Using this methodology, 184 genes were screened out for differences in gene expression level after nine couples of hybridizations. Of the 184 genes, 87 were upregulated and 97 downregulated, including 11 novel human genes. In pancreatic adenocarcinoma tissue, several invasion and metastasis related genes showed their high expression levels, suggesting that poor prognosis of pancreatic adenocarcinoma might have a solid molecular biological basis. CONCLUSION:The application of cDNA microarray technique for analysis of gene expression patterns is a powerful strategy to identify novel cancer-associated genes, and to rapidly explore their role in clinical pancreatic adenocarcinoma. Microarray profiles provide us new insights into the carcinogenesis and invasive process of pancreatic adenocarcinoma. Our results suggest that a highly organized and structured process of tumor invasion exists in the pancreas.
| S-EPMC4355802 | biostudies-literature
Project description:We compared the accuracy of microarray measurements obtained with oligonucleotide arrays (GeneChip, Affymetrix) with a laboratory-developed cDNA array by assaying test RNA samples from an experiment using a paradigm known to regulate many genes measured on both arrays. We selected 47 genes represented on both arrays, including both known regulated and unregulated transcripts, and established reference relative expression measurements for these genes in the test RNA samples using quantitative reverse transcriptase real-time PCR (QRTPCR) assays. The validity of the reproducible (average coefficient of variation = 11.8%) QRTPCR measurements were established through application of a new mathematical model. The performance of both array platforms in identifying regulated and non-regulated genes was identical. With either platform, 16 of 17 definitely regulated genes were correctly identified, and no definitely unregulated transcript was falsely identified as regulated. Accuracy of the fold-change measurements obtained with each platform was assessed by determining measurement bias. Both platforms consistently underestimate the relative changes in mRNA expression between experimental and control samples. The bias observed with cDNA arrays was predictable for fold-changes <250-fold by QRTPCR and could be corrected by the calibration function F(c) = F(a(cDNA))(q), where F(a(cDNA)) is the microarray-determined fold-change comparing experimental with control samples, q is the correction factor and F(c) is the calibrated value. The bias observed with the commercial oligonucleotide arrays was less predictable and calibration was unfeasible. Following calibration, fold-change measurements generated by custom cDNA arrays were more accurate than those obtained by commercial oligonucleotide arrays. Our study demonstrates systematic bias of microarray measurements and identifies a calibration function that improves the accuracy of cDNA array data.
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Project description:Microarray data are susceptible to a wide-range of artifacts, many of which occur on physical scales comparable to the spatial dimensions of the array. These artifacts introduce biases that are spatially correlated. The ability of current methodologies to detect and correct such biases is limited.We introduce a new approach for analyzing spatial artifacts, termed 'conditional residual analysis for microarrays' (CRAM). CRAM requires a microarray design that contains technical replicates of representative features and a limited number of negative controls, but is free of the assumptions that constrain existing analytical procedures. The key idea is to extract residuals from sets of matched replicates to generate residual images. The residual images reveal spatial artifacts with single-feature resolution. Surprisingly, spatial artifacts were found to coexist independently as additive and multiplicative errors. Efficient procedures for bias estimation were devised to correct the spatial artifacts on both intensity scales. In a survey of 484 published single-channel datasets, variance fell 4- to 12-fold in 5% of the datasets after bias correction. Thus, inclusion of technical replicates in a microarray design affords benefits far beyond what one might expect with a conventional 'n = 5' averaging, and should be considered when designing any microarray for which randomization is feasible.CRAM is implemented as version 2 of the hoptag software package for R, which is included in the Supplementary information.
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