New normalization methods for cDNA microarray data

MOTIVATION: The focus of this paper is on two new normalization methods for cDNA microarrays. After the image analysis has been performed on a microarray and before differentially expressed genes can be detected, some form of normalization must be applied to the microarrays. Normalization removes biases towards one or other of the fluorescent dyes used to label each mRNA sample allowing for proper evaluation of differential gene expression. RESULTS: The two normalization methods that we present here build on previously described non-linear normalization techniques. We extend these techniques by firstly introducing a normalization method that deals with smooth spatial trends in intensity across microarrays, an important issue that must be dealt with. Secondly we deal with normalization of a new type of cDNA microarray experiment that is coming into prevalence, the small scale specialty or 'boutique' array, where large proportions of the genes on the microarrays are expected to be highly differentially expressed. AVAILABILITY: The normalization methods described in this paper are available via http://www.pi.csiro.au/gena/ in a software suite called tRMA: tools for R Microarray Analysis upon request of the authors. Images and data used in this paper are also available via the same link.     

A robust two-way semi-linear model for normalization of cDNA microarray data

Background  

Normalization is a basic step in microarray data analysis. A proper normalization procedure ensures that the intensity ratios provide meaningful measures of relative expression values.     

Two-stage normalization using background intensities in cDNA microarray data

Background  

In the microarray experiment, many undesirable systematic variations are commonly observed. Normalization is the process of removing such variation that affects the measured gene expression levels. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization. One major source of variation is the background intensities. Recently, some methods have been employed for correcting the background intensities. However, all these methods focus on defining signal intensities appropriately from foreground and background intensities in the image analysis. Although a number of normalization methods have been proposed, no systematic methods have been proposed using the background intensities in the normalization process.     

Model selection and efficiency testing for normalization of cDNA microarray data

In this study we present two novel normalization schemes for cDNA microarrays. They are based on iterative local regression and optimization of model parameters by generalized cross-validation. Permutation tests assessing the efficiency of normalization demonstrated that the proposed schemes have an improved ability to remove systematic errors and to reduce variability in microarray data. The analysis also reveals that without parameter optimization local regression is frequently insufficient to remove systematic errors in microarray data.     

cDNA芯片阳性对照的制备及在芯片敏感性分析中的应用

cDNA芯片是一种高通量基因表达谱分析技术,在生理病理条件下细胞基因表达谱分析,新基因发现和功能研究等方面具有广阔应用前景。CDNA芯片阳性对照的选取以及CDNA芯片检测敏感性是芯片成功应用的关键问题之一。以在系统发育上与人类基因同源性小的荧火虫荧光素酶基因材料,制备了用于人类和其他动物基因表达谱CDNA芯片的通用型阳性对照探针和相应的mRNA参照物,经反转录对mRNA参照物进行Cy3荧光标记并与DNA芯片杂交后发现,mRNA参照物能特异性地与荧光酶基因cDNA片断杂交,而与人β-肌动蛋白基因,人G3PDH基因以及λDNA/HINDⅢ无杂交反应。把mRNA参照物以不同比例加入HepG2总RNA中,以反转录荧光标记后与CDNA芯片杂交,结果发现当总RNA中的MRNA含量为1/10^4稀释（即mRNA分子个数约为10^8个）时,CDNA芯片基本检测不出mRNA标记产物的杂交信号。而且,cDNA芯片检测的信号强度与芯片上固定的探针浓度密切相关,当探针浓度为2g/L时,杂交信号最强,随着探针浓度下降芯片的杂交信号趋于减弱。CDNA芯片通用型阳性参照物的制备以及应用于CDNA芯片检测敏感性研究为CDNA芯片应用于人和其他动物基因表达谱高通量分析和新基因功能研究提供了技术基础和理论依据。     

A software package for cDNA microarray data normalization and assessing confidence intervals

DNA microarray data are affected by variations from a number of sources. Before these data can be used to infer biological information, the extent of these variations must be assessed. Here we describe an open source software package, lcDNA, that provides tools for filtering, normalizing, and assessing the statistical significance of cDNA microarray data. The program employs a hierarchical Bayesian model and Markov Chain Monte Carlo simulation to estimate gene-specific confidence intervals for each gene in a cDNA microarray data set. This program is designed to perform these primary analytical operations on data from two-channel spotted, or in situ synthesized, DNA microarrays.     

Non-linear normalization and background correction in one-channel cDNA microarray studies

MOTIVATION: Data from one-channel cDNA microarray studies may exhibit poor reproducibility due to spatial heterogeneity, non-linear array-to-array variation and problems in correcting for background. Uncorrected, these phenomena can give rise to misleading conclusions. RESULTS: Spatial heterogeneity may be corrected using two-dimensional loess smoothing (Colantuoni et al., 2002). Non-linear between-array variation may be corrected using an iterative application of one-dimensional loess smoothing. A method for background correction using a smoothing function rather than simple subtraction is described. These techniques promote within-array spatial uniformity and between-array reproducibility. Their application is illustrated using data from a study of the effects of an insulin sensitizer, rosiglitazone, on gene expression in white adipose tissue in diabetic db/db mice. They may also be useful with data from two-channel cDNA microarrays and from oligonucleotide arrays. AVAILABILITY: R functions for the methods described are available on request from the author.     

Using Generalized Procrustes Analysis (GPA) for normalization of cDNA microarray data

Background  

Normalization is essential in dual-labelled microarray data analysis to remove non-biological variations and systematic biases. Many normalization methods have been used to remove such biases within slides (Global, Lowess) and across slides (Scale, Quantile and VSN). However, all these popular approaches have critical assumptions about data distribution, which is often not valid in practice.     

An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis

A systems-level understanding of a small but essential population of cells in development or adulthood (e.g. somatic stem cells) requires accurate quantitative monitoring of genome-wide gene expression, ideally from single cells. We report here a strategy to globally amplify mRNAs from single cells for highly quantitative high-density oligonucleotide microarray analysis that combines a small number of directional PCR cycles with subsequent linear amplification. Using this strategy, both the representation of gene expression profiles and reproducibility between individual experiments are unambiguously improved from the original method, along with high coverage and accuracy. The immediate application of this method to single cells in the undifferentiated inner cell masses of mouse blastocysts at embryonic day (E) 3.5 revealed the presence of two populations of cells, one with primitive endoderm (PE) expression and the other with pluripotent epiblast-like gene expression. The genes expressed differentially between these two populations were well preserved in morphologically differentiated PE and epiblast in the embryos one day later (E4.5), demonstrating that the method successfully detects subtle but essential differences in gene expression at the single-cell level among seemingly homogeneous cell populations. This study provides a strategy to analyze biophysical events in medicine as well as in neural, stem cell and developmental biology, where small numbers of distinctive or diseased cells play critical roles.     

A two-stage normalization method for partially degraded mRNA microarray data

MOTIVATION: The goal of the study is to obtain genetic information from exfoliated colonocytes in the fecal stream rather than directly from mucosa cells within the colon. The latter is obtained through invasive procedures. The difficulties encountered by this procedure are that certain probe information may be compromised due to partially degraded mRNA. Proper normalization is essential to obtaining useful information from these fecal array data. RESULTS: We propose a new two-stage semiparametric normalization method motivated by the features observed in fecal microarray data. A location-scale transformation and a robust inclusion step were used to roughly align arrays within the same treatment. A non-parametric estimated non-linear transformation was then used to remove the potential intensity-based biases. We compared the performance of the new method in analyzing a fecal microarray dataset with those achieved by two existing normalization approaches: global median transformation and quantile normalization. The new method favorably compared with the global median and quantile normalization methods. AVAILABILITY: The R codes implementing the two-stage method may be obtained from the corresponding author.     

A robust neural networks approach for spatial and intensity-dependent normalization of cDNA microarray data

MOTIVATION: Microarray experiments are affected by numerous sources of non-biological variation that contribute systematic bias to the resulting data. In a dual-label (two-color) cDNA or long-oligonucleotide microarray, these systematic biases are often manifested as an imbalance of measured fluorescent intensities corresponding to Sample A versus those corresponding to Sample B. Systematic biases also affect between-slide comparisons. Making effective corrections for these systematic biases is a requisite for detecting the underlying biological variation between samples. Effective data normalization is therefore an essential step in the confident identification of biologically relevant differences in gene expression profiles. Several normalization methods for the correction of systemic bias have been described. While many of these methods have addressed intensity-dependent bias, few have addressed both intensity-dependent and spatiality-dependent bias. RESULTS: We present a neural network-based normalization method for correcting the intensity- and spatiality-dependent bias in cDNA microarray datasets. In this normalization method, the dependence of the log-intensity ratio (M) on the average log-intensity (A) as well as on the spatial coordinates (X,Y) of spots is approximated with a feed-forward neural network function. Resistance to outliers is provided by assigning weights to each spot based on how distant their M values is from the median over the spots whose A values are similar, as well as by using pseudospatial coordinates instead of spot row and column indices. A comparison of the robust neural network method with other published methods demonstrates its potential in reducing both intensity-dependent bias and spatial-dependent bias, which translates to more reliable identification of truly regulated genes.     

SED, a normalization free method for DNA microarray data analysis

Background  

Analysis of DNA microarray data usually begins with a normalization step where intensities of different arrays are adjusted to the same scale so that the intensity levels from different arrays can be compared with one other. Both simple total array intensity-based as well as more complex "local intensity level" dependent normalization methods have been developed, some of which are widely used. Much less developed methods for microarray data analysis include those that bypass the normalization step and therefore yield results that are not confounded by potential normalization errors.     

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

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 Cy5 and Cy3 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.     

一种标记cDNA芯片探针的新方法

探讨mRNA长片段反转录PCR技术(RT-LDPCR)在cDNA芯片微量探针标记和信号放大中的应用.首先提取BEP2D细胞的总RNA,然后用两种不同的方法进行标记,一种为RT-LDPCR,用荧光素Cy3-dCTP进行标记;另一种为传统的RNA反转录,用荧光素Cy5-dCTP进行标记.将两种方法标记好的探针等量混合后与含有440个点(44个基因)的cDNA芯片同时杂交,发现二者具有很高的一致性(0.5＜Cy3/Cy5＞2.0).由于RNA反转录法为cDNA芯片探针标记的传统方法,从而验证了RT-LDPCR用于cDNA芯片探针标记的可行性.RT-LDPCR具有对样品总RNA的需要量少和可对样品中信号进行放大的优点,特别适合于对材料来源受到限制的RNA进行标记.     

Evaluation of normalization methods for microarray data

Background

Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. This novel technique helps us to understand gene regulation as well as gene by gene interactions more systematically. In the microarray experiment, however, many undesirable systematic variations are observed. Even in replicated experiment, some variations are commonly observed. Normalization is the process of removing some sources of variation which affect the measured gene expression levels. Although a number of normalization methods have been proposed, it has been difficult to decide which methods perform best. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization.

Results

In this paper, we use the variability among the replicated slides to compare performance of normalization methods. We also compare normalization methods with regard to bias and mean square error using simulated data.

Conclusions

Our results show that intensity-dependent normalization often performs better than global normalization methods, and that linear and nonlinear normalization methods perform similarly. These conclusions are based on analysis of 36 cDNA microarrays of 3,840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells. Simulation studies confirm our findings.

     

MASQOT: a method for cDNA microarray spot quality control

Background  

cDNA microarray technology has emerged as a major player in the parallel detection of biomolecules, but still suffers from fundamental technical problems. Identifying and removing unreliable data is crucial to prevent the risk of receiving illusive analysis results. Visual assessment of spot quality is still a common procedure, despite the time-consuming work of manually inspecting spots in the range of hundreds of thousands or more.     

CTAB-urea method purifies RNA from melanin for cDNA microarray analysis

Melanin represents a major problem for the study of melanoma by microarrays since it is retained during RNA extraction and inhibits the enzymatic reactions used for probe preparation. Here we report a new method for cleaning RNA from melanin, based on the use of the cationic detergent cetyl-trimethylammonium bromide (CTAB)-urea for RNA precipitation. This method is easy to perform and has a low cost. Purified RNA is recovered with high quality and good yield. CTAB-urea treated RNA from highly pigmented melanoma cells can be successfully reverse transcribed and labeled to obtain probes which can be subsequently used in cDNA microarray experiments, giving consistent and reproducible results.