A comparison of normalization methods for high density oligonucleotide array data based on variance and bias

MOTIVATION: When running experiments that involve multiple high density oligonucleotide arrays, it is important to remove sources of variation between arrays of non-biological origin. Normalization is a process for reducing this variation. It is common to see non-linear relations between arrays and the standard normalization provided by Affymetrix does not perform well in these situations. RESULTS: We present three methods of performing normalization at the probe intensity level. These methods are called complete data methods because they make use of data from all arrays in an experiment to form the normalizing relation. These algorithms are compared to two methods that make use of a baseline array: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure. Two publicly available datasets are used to carry out the comparisons. The simplest and quickest complete data method is found to perform favorably. AVAILABILITY: Software implementing all three of the complete data normalization methods is available as part of the R package Affy, which is a part of the Bioconductor project http://www.bioconductor.org. SUPPLEMENTARY INFORMATION: Additional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html     

Group normalization for genomic data

Data normalization is a crucial preliminary step in analyzing genomic datasets. The goal of normalization is to remove global variation to make readings across different experiments comparable. In addition, most genomic loci have non-uniform sensitivity to any given assay because of variation in local sequence properties. In microarray experiments, this non-uniform sensitivity is due to different DNA hybridization and cross-hybridization efficiencies, known as the probe effect. In this paper we introduce a new scheme, called Group Normalization (GN), to remove both global and local biases in one integrated step, whereby we determine the normalized probe signal by finding a set of reference probes with similar responses. Compared to conventional normalization methods such as Quantile normalization and physically motivated probe effect models, our proposed method is general in the sense that it does not require the assumption that the underlying signal distribution be identical for the treatment and control, and is flexible enough to correct for nonlinear and higher order probe effects. The Group Normalization algorithm is computationally efficient and easy to implement. We also describe a variant of the Group Normalization algorithm, called Cross Normalization, which efficiently amplifies biologically relevant differences between any two genomic datasets.     

aCGH.Spline--an R package for aCGH dye bias normalization

MOTIVATION: The careful normalization of array-based comparative genomic hybridization (aCGH) data is of critical importance for the accurate detection of copy number changes. The difference in labelling affinity between the two fluorophores used in aCGH-usually Cy5 and Cy3-can be observed as a bias within the intensity distributions. If left unchecked, this bias is likely to skew data interpretation during downstream analysis and lead to an increased number of false discoveries. RESULTS: In this study, we have developed aCGH.Spline, a natural cubic spline interpolation method followed by linear interpolation of outlier values, which is able to remove a large portion of the dye bias from large aCGH datasets in a quick and efficient manner. Conclusions: We have shown that removing this bias and reducing the experimental noise has a strong positive impact on the ability to detect accurately both copy number variation (CNV) and copy number alterations (CNA).     

A pseudolikelihood approach for simultaneous analysis of array comparative genomic hybridizations

DNA sequence copy number has been shown to be associated with cancer development and progression. Array-based comparative genomic hybridization (aCGH) is a recent development that seeks to identify the copy number ratio at large numbers of markers across the genome. Due to experimental and biological variations across chromosomes and hybridizations, current methods are limited to analyses of single chromosomes. We propose a more powerful approach that borrows strength across chromosomes and hybridizations. We assume a Gaussian mixture model, with a hidden Markov dependence structure and with random effects to allow for intertumoral variation, as well as intratumoral clonal variation. For ease of computation, we base estimation on a pseudolikelihood function. The method produces quantitative assessments of the likelihood of genetic alterations at each clone, along with a graphical display for simple visual interpretation. We assess the characteristics of the method through simulation studies and analysis of a brain tumor aCGH data set. We show that the pseudolikelihood approach is superior to existing methods both in detecting small regions of copy number alteration and in accurately classifying regions of change when intratumoral clonal variation is present. Software for this approach is available at http://www.biostat.harvard.edu/ approximately betensky/papers.html.     

寡聚核苷酸芯片表达谱系统偏移的校正算法

多芯片对比实验中,由于多方面的变异因素,使得芯片间存在明显的系统偏移.因此,芯片表达谱数据的校正处理是关键的数据预处理步骤.当前,已经提出了很多校正算法,比如:比例常数校正、非线性校正、分位数校正等.提出了一种新的校正算法.在选择的最小秩差异探针集上,进行非线性M-A校正.并采用迭代策略减弱基准芯片方法对基准芯片选择的敏感性.在标准测试集上,同几种已知的方法进行了对比分析.     

Normalization of Illumina Infinium whole-genome SNP data improves copy number estimates and allelic intensity ratios

Background  

Illumina Infinium whole genome genotyping (WGG) arrays are increasingly being applied in cancer genomics to study gene copy number alterations and allele-specific aberrations such as loss-of-heterozygosity (LOH). Methods developed for normalization of WGG arrays have mostly focused on diploid, normal samples. However, for cancer samples genomic aberrations may confound normalization and data interpretation. Therefore, we examined the effects of the conventionally used normalization method for Illumina Infinium arrays when applied to cancer samples.     

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.     

Ribosomal DNA deletions modulate genome-wide gene expression: "rDNA-sensitive" genes and natural variation

The ribosomal rDNA gene array is an epigenetically-regulated repeated gene locus. While rDNA copy number varies widely between and within species, the functional consequences of subtle copy number polymorphisms have been largely unknown. Deletions in the Drosophila Y-linked rDNA modifies heterochromatin-induced position effect variegation (PEV), but it has been unknown if the euchromatic component of the genome is affected by rDNA copy number. Polymorphisms of naturally occurring Y chromosomes affect both euchromatin and heterochromatin, although the elements responsible for these effects are unknown. Here we show that copy number of the Y-linked rDNA array is a source of genome-wide variation in gene expression. Induced deletions in the rDNA affect the expression of hundreds to thousands of euchromatic genes throughout the genome of males and females. Although the affected genes are not physically clustered, we observed functional enrichments for genes whose protein products are located in the mitochondria and are involved in electron transport. The affected genes significantly overlap with genes affected by natural polymorphisms on Y chromosomes, suggesting that polymorphic rDNA copy number is an important determinant of gene expression diversity in natural populations. Altogether, our results indicate that subtle changes to rDNA copy number between individuals may contribute to biologically relevant phenotypic variation.     

Spatial normalization of array-CGH data

Background  

Array-based comparative genomic hybridization (array-CGH) is a recently developed technique for analyzing changes in DNA copy number. As in all microarray analyses, normalization is required to correct for experimental artifacts while preserving the true biological signal. We investigated various sources of systematic variation in array-CGH data and identified two distinct types of spatial effect of no biological relevance as the predominant experimental artifacts: continuous spatial gradients and local spatial bias. Local spatial bias affects a large proportion of arrays, and has not previously been considered in array-CGH experiments.     

A new method for determining ribosomal DNA copy number shows differences between Saccharomyces cerevisiae populations

Ribosomal DNA genes (rDNA) encode the major ribosomal RNAs and in eukaryotes typically form tandem repeat arrays. Species have characteristic rDNA copy numbers, but there is substantial intra-species variation in copy number that results from frequent rDNA recombination. Copy number differences can have phenotypic consequences, however difficulties in quantifying copy number mean we lack a comprehensive understanding of how copy number evolves and the consequences. Here we present a genomic sequence read approach to estimate rDNA copy number based on modal coverage to help overcome limitations with existing mean coverage-based approaches. We validated our method using Saccharomyces cerevisiae strains with known rDNA copy numbers. Application of our pipeline to a global sample of S. cerevisiae isolates showed that different populations have different rDNA copy numbers. Our results demonstrate the utility of the modal coverage method, and highlight the high level of rDNA copy number variation within and between populations.     

定量PCR检测microRNA表达的数据归一化新技术

数据归一化技术对研究结果的分析具有重要的作用.在定量PCR实验中,通常利用稳定表达的看家基因作为实验数据归一化的内参,但最近的研究表明,这些看家基因的表达量在不同的生理病理过程中也表现出显著的变化,不适合作为数据归一化处理的基准.针对这一问题,提出一种新的数据处理技术,利用单细胞归一化方法(percellome),对定量PCR检测miRNA表达的数据进行处理,显著提高了数据处理的准确性.以8周龄/40周龄小鼠脑为实验材料,选择14种microRNA的表达情况进行了检测.在研究中,将3种不同拷贝数的人工合成的RNA片段(spike RNA)作为内参加入到样品中,用于microRNA表达检测的归一化基准.研究发现,未经处理的microRNA单细胞拷贝数的变化范围为2.0×105～4.3×105,而经单细胞归一处理,上述表达变化范围为2到26倍.该项研究还发现,看家基因U6 ncRNA和5S rRNA的表达水平存在显著的变化,在以基因组DNA为归一化基准时,其表达量变化为1.5和4.8倍,而在以RNA水平为归一化基准时,其表达量变化为5.8和3.8倍,表明这些基因不适合作为数据处理的基准.据此,为microRNA的定量研究提供了一种新的、可靠的归一化方案.     

Assessment of algorithms for high throughput detection of genomic copy number variation in oligonucleotide microarray data

Background

Genomic deletions and duplications are important in the pathogenesis of diseases, such as cancer and mental retardation, and have recently been shown to occur frequently in unaffected individuals as polymorphisms. Affymetrix GeneChip whole genome sampling analysis (WGSA) combined with 100 K single nucleotide polymorphism (SNP) genotyping arrays is one of several microarray-based approaches that are now being used to detect such structural genomic changes. The popularity of this technology and its associated open source data format have resulted in the development of an increasing number of software packages for the analysis of copy number changes using these SNP arrays.

Results

We evaluated four publicly available software packages for high throughput copy number analysis using synthetic and empirical 100 K SNP array data sets, the latter obtained from 107 mental retardation (MR) patients and their unaffected parents and siblings. We evaluated the software with regards to overall suitability for high-throughput 100 K SNP array data analysis, as well as effectiveness of normalization, scaling with various reference sets and feature extraction, as well as true and false positive rates of genomic copy number variant (CNV) detection.

Conclusion

We observed considerable variation among the numbers and types of candidate CNVs detected by different analysis approaches, and found that multiple programs were needed to find all real aberrations in our test set. The frequency of false positive deletions was substantial, but could be greatly reduced by using the SNP genotype information to confirm loss of heterozygosity.     

Integrated study of copy number states and genotype calls using high-density SNP arrays

We propose a statistical framework, named genoCN, to simultaneously dissect copy number states and genotypes using high-density SNP (single nucleotide polymorphism) arrays. There are at least two types of genomic DNA copy number differences: copy number variations (CNVs) and copy number aberrations (CNAs). While CNVs are naturally occurring and inheritable, CNAs are acquired somatic alterations most often observed in tumor tissues only. CNVs tend to be short and more sparsely located in the genome compared with CNAs. GenoCN consists of two components, genoCNV and genoCNA, designed for CNV and CNA studies, respectively. In contrast to most existing methods, genoCN is more flexible in that the model parameters are estimated from the data instead of being decided a priori. GenoCNA also incorporates two important strategies for CNA studies. First, the effects of tissue contamination are explicitly modeled. Second, if SNP arrays are performed for both tumor and normal tissues of one individual, the genotype calls from normal tissue are used to study CNAs in tumor tissue. We evaluated genoCN by applications to 162 HapMap individuals and a brain tumor (glioblastoma) dataset and showed that our method can successfully identify both types of copy number differences and produce high-quality genotype calls.     

Normalization of cDNA microarray data

Normalization means to adjust microarray data for effects which arise from variation in the technology rather than from biological differences between the RNA samples or between the printed probes. This paper describes normalization methods based on the fact that dye balance typically varies with spot intensity and with spatial position on the array. Print-tip loess normalization provides a well-tested general purpose normalization method which has given good results on a wide range of arrays. The method may be refined by using quality weights for individual spots. The method is best combined with diagnostic plots of the data which display the spatial and intensity trends. When diagnostic plots show that biases still remain in the data after normalization, further normalization steps such as plate-order normalization or scale-normalization between the arrays may be undertaken. Composite normalization may be used when control spots are available which are known to be not differentially expressed. Variations on loess normalization include global loess normalization and two-dimensional normalization. Detailed commands are given to implement the normalization techniques using freely available software.     

Between-group analysis of microarray data

MOTIVATION: Most supervised classification methods are limited by the requirement for more cases than variables. In microarray data the number of variables (genes) far exceeds the number of cases (arrays), and thus filtering and pre-selection of genes is required. We describe the application of Between Group Analysis (BGA) to the analysis of microarray data. A feature of BGA is that it can be used when the number of variables (genes) exceeds the number of cases (arrays). BGA is based on carrying out an ordination of groups of samples, using a standard method such as Correspondence Analysis (COA), rather than an ordination of the individual microarray samples. As such, it can be viewed as a method of carrying out COA with grouped data. RESULTS: We illustrate the power of the method using two cancer data sets. In both cases, we can quickly and accurately classify test samples from any number of specified a priori groups and identify the genes which characterize these groups. We obtained very high rates of correct classification, as determined by jack-knife or validation experiments with training and test sets. The results are comparable to those from other methods in terms of accuracy but the power and flexibility of BGA make it an especially attractive method for the analysis of microarray cancer data.     

Normalization for probabilistic inference with neurons

Recently, there have been a number of proposals regarding how biologically plausible neural networks might perform probabilistic inference (Rao, Neural Computation, 16(1):1-38, 2004; Eliasmith and Anderson, Neural engineering: computation, representation and dynamics in neurobiological systems, 2003; Ma et?al., Nature Neuroscience, 9(11):1432-1438, 2006; Sahani and Dayan, Neural Computation, 15(10):2255-2279, 2003). To be able to repeatedly perform such inference, it is essential that the represented distributions be appropriately normalized. Past approaches have considered normalization mechanisms independently of inference, often leaving them unexplored, or appealing to a notion of divisive normalization that requires pooling across many neurons. Here, we demonstrate how normalization and inference can be combined into an appropriate connection matrix, eliminating the need for pooling or a division-like operation. We algebraically demonstrate that such a solution is available regardless of the inference being performed. We show that such a solution is relevant to neural computation by implementing it in a recurrent spiking neural network.     

On the frequency of copy number variants

MOTIVATION: Estimating the frequency distribution of copy number variants (CNVs) is an important aspect of the effort to characterize this new type of genetic variation. Currently, most studies report a strong skew toward low-frequency CNVs. In this article, our goal is to investigate the frequencies of CNVs. We employ a two-step procedure for the CNV frequency estimation process. We use family information a posteriori to select only the most reliable CNV regions, i.e. those showing high rates of Mendelian transmission. RESULTS: Our results suggest that the current skew toward low-frequency CNVs may not be representative of the true frequency distribution, but may be due, among other reasons, to the non-negligible false negative rates that characterize CNV detection methods. Moreover, false positives are also likely, as low-frequency CNVs are hard to detect with small sample sizes and technologies that are not ideally suited for their detection. Without appropriate validation methods, such as incorporation of biologically relevant information (for example, in our case, the transmission of heritable CNVs from parents to offspring), it is difficult to assess the validity of specific CNVs, and even harder to obtain reliable frequency estimates.     

A genome-wide study of cytogenetic changes in colorectal cancer using SNP microarrays: opportunities for future personalized treatment

In colorectal cancer (CRC), chromosomal instability (CIN) is typically studied using comparative-genomic hybridization (CGH) arrays. We studied paired (tumor and surrounding healthy) fresh frozen tissue from 86 CRC patients using Illumina's Infinium-based SNP array. This method allowed us to study CIN in CRC, with simultaneous analysis of copy number (CN) and B-allele frequency (BAF)--a representation of allelic composition. These data helped us to detect mono-allelic and bi-allelic amplifications/deletion, copy neutral loss of heterozygosity, and levels of mosaicism for mixed cell populations, some of which can not be assessed with other methods that do not measure BAF. We identified associations between CN abnormalities and different CRC phenotypes (histological diagnosis, location, tumor grade, stage, MSI and presence of lymph node metastasis). We showed commonalities between regions of CN change observed in CRC and the regions reported in previous studies of other solid cancers (e.g. amplifications of 20q, 13q, 8q, 5p and deletions of 18q, 17p and 8p). From Therapeutic Target Database, we identified relevant drugs, targeted to the genes located in these regions with CN changes, approved or in trials for other cancers and common diseases. These drugs may be considered for future therapeutic trials in CRC, based on personalized cytogenetic diagnosis. We also found many regions, harboring genes, which are not currently targeted by any relevant drugs that may be considered for future drug discovery studies. Our study shows the application of high density SNP arrays for cytogenetic study in CRC and its potential utility for personalized treatment.