Local mean normalization of microarray element signal intensities across an array surface: quality control and correction of spatially systematic artifacts

Here we present a methodology for the normalization of element signal intensities to a mean intensity calculated locally across the surface of a DNA microarray. These methods allow the detection and/or correction of spatially systematic artifacts in microarray data. These include artifacts that can be introduced during the robotic printing, hybridization, washing, or imaging of microarrays. Using array element signal intensities alone, this local mean normalization process can correct for such artifacts because they vary across the surface of the array. The local mean normalization can be usedfor quality control and data correction purposes in the analysis of microarray data. These algorithms assume that array elements are not spatially ordered with regard to sequence or biological function and require that this spatial mapping is identical between the two sets of intensities to be compared. The tool described in this report was developed in the R statistical language and is freely available on the Internet as part of a larger gene expression analysis package. This Web implementation is interactive and user-friendly and allows the easy use of the local mean normalization tool described here, without programming expertise or downloading of additional software.     

Combining functional and linkage disequilibrium information in the selection of tag SNPs

We have developed an online program, WCLUSTAG, for tag SNP selection that allows the user to specify variable tagging thresholds for different SNPs. Tag SNPs are selected such that a SNP with user-specified tagging threshold C will have a minimum R2 of C with at least one tag SNP. This flexible feature is useful for researchers who wish to prioritize genomic regions or SNPs in an association study. AVAILABILITY: The online WCLUSTAG program is available at http://bioinfo.hku.hk/wclustag/     

OLIN: optimized normalization, visualization and quality testing of two-channel microarray data

SUMMARY: Microarray data are generated in complex experiments and frequently compromised by a variety of systematic errors. Subsequent data normalization aims to correct these errors. Although several normalization methods have recently been proposed, they frequently fail to account for the variability of systematic errors within and between microarray experiments. However, optimal adjustment of normalization procedures to the underlying data structure is crucial for the efficiency of normalization. To overcome this restriction of current methods, we have developed two normalization schemes based on iterative local regression combined with model selection. The schemes have been demonstrated to improve considerably the quality of normalization. They are implemented in a freely available R package. Additionally, functions for visualization and detection of systematic errors in microarray data have been incorporated in the software package. A graphical user interface is also available. AVAILABILITY: The R package can be downloaded from http://itb.biologie.hu-berlin.de/~futschik/software/R/OLIN. It underlies the GPL version 2. CONTACT: m.futschik@biologie.hu-berlin.de SUPPLEMENTARY INFORMATION: Further information about the methods used in the OLIN software package can be found at http://itb.biologie.hu-berlin.de/~futschik/software/R/OLIN.     

Genomic profiling by DNA amplification of laser capture microdissected tissues and array CGH

Comparative genomic hybridization by means of BAC microarrays (array CGH) allows high-resolution profiling of copy-number aberrations in tumor DNA. However, specific genetic lesions associated with small but clinically relevant tumor areas may pass undetected due to intra-tumor heterogeneity and/or the presence of contaminating normal cells. Here, we show that the combination of laser capture microdissection, 29 DNA polymerase-mediated isothermal genomic DNA amplification, and array CGH allows genomic profiling of very limited numbers of cells. Moreover, by means of simple statistical models, we were able to bypass the exclusion of amplification distortions and variability prone areas, and to detect tumor-specific chromosomal gains and losses. We applied this new combined experimental and analytical approach to the genomic profiling of colorectal adenomatous polyps and demonstrated our ability to accurately detect single copy gains and losses affecting either whole chromosomes or small genomic regions from as little as 2 ng of DNA or 1000 microdissected cells.     

NormalizeMets: assessing,selecting and implementing statistical methods for normalizing metabolomics data

Introduction

In metabolomics studies, unwanted variation inevitably arises from various sources. Normalization, that is the removal of unwanted variation, is an essential step in the statistical analysis of metabolomics data. However, metabolomics normalization is often considered an imprecise science due to the diverse sources of variation and the availability of a number of alternative strategies that may be implemented.

Objectives

We highlight the need for comparative evaluation of different normalization methods and present software strategies to help ease this task for both data-oriented and biological researchers.

Methods

We present NormalizeMets—a joint graphical user interface within the familiar Microsoft Excel and freely-available R software for comparative evaluation of different normalization methods. The NormalizeMets R package along with the vignette describing the workflow can be downloaded from https://cran.r-project.org/web/packages/NormalizeMets/. The Excel Interface and the Excel user guide are available on https://metabolomicstats.github.io/ExNormalizeMets.

Results

NormalizeMets allows for comparative evaluation of normalization methods using criteria that depend on the given dataset and the ultimate research question. Hence it guides researchers to assess, select and implement a suitable normalization method using either the familiar Microsoft Excel and/or freely-available R software. In addition, the package can be used for visualisation of metabolomics data using interactive graphical displays and to obtain end statistical results for clustering, classification, biomarker identification adjusting for confounding variables, and correlation analysis.

Conclusion

NormalizeMets is designed for comparative evaluation of normalization methods, and can also be used to obtain end statistical results. The use of freely-available R software offers an attractive proposition for programming-oriented researchers, and the Excel interface offers a familiar alternative to most biological researchers. The package handles the data locally in the user’s own computer allowing for reproducible code to be stored locally.

     

Molekulare Karyotypisierung in der genetischen Diagnostik

Molecular karyotyping by array comparative genomic hybridization (CGH) and single nucleotide polymorphism (SNP) arrays allows for a high resolution scan of the entire genome. It detects gains or losses (copy number variants) that might be the underlying cause of a genetic disorder. This technique is mainly applied to cases with syndromal or non-syndromal impaired (intellectual) development and is used to characterize genetic aberrations of tumor samples. Furthermore, molecular karyotyping might be useful for resolving prenatal cases with abnormal ultrasound findings. The purpose of this article is to explain the basic techniques, their limitations and strengths and to provide an outlook on future prospects.     

MArray: analysing single,replicated or reversed microarray experiments

MArray is a Matlab toolbox with a graphical user interface that allows the user to analyse single or paired microarray datasets by direct input of the raw data output file from image analysis packages, such as QuantArray or GenePiX. The application provides simple procedures to manually evaluate the quality of each measurement, multiple approaches to both ratio normalization (simple normalization, intensity dependent normalization) and evaluation of the reproducibility of paired experiments (using the techniques 'simple statistical method' and 'quality control ellipse' and 'significance analysis of microarrays'). Specifically, interactive spot evaluation functions are available in MArray and an online gene information database (NCBI UniGene) is linked. The application may provide a valuable aid in selecting and optimizing experimental procedures, as well as serving as an analytical tool for two-state biological comparisons, such as a study of single-dose activation. It is entirely platform independent, and only requires Matlab installed. AVAILABILITY: http://matrise.uio.no/marray/marray.html     

IlluminaGUI: graphical user interface for analyzing gene expression data generated on the Illumina platform

IlluminaGUI is a graphical user interface implemented for analyzing microarray data from the Illumina BeadChip platform. All key components of a microarray experiment, including quality control, normalization, inference and classification methods are provided in a 'point and click' approach. IlluminaGUI is implemented as a R package based on the R-Tcl/Tk interface and is available for platforms on which R runs including Windows, Mac and Unix-type machines. AVAILABILITY: http://IlluminaGUI.dnsalias.org     

Estimation of parent specific DNA copy number in tumors using high-density genotyping arrays

Chromosomal gains and losses comprise an important type of genetic change in tumors, and can now be assayed using microarray hybridization-based experiments. Most current statistical models for DNA copy number estimate total copy number, which do not distinguish between the underlying quantities of the two inherited chromosomes. This latter information, sometimes called parent specific copy number, is important for identifying allele-specific amplifications and deletions, for quantifying normal cell contamination, and for giving a more complete molecular portrait of the tumor. We propose a stochastic segmentation model for parent-specific DNA copy number in tumor samples, and give an estimation procedure that is computationally efficient and can be applied to data from the current high density genotyping platforms. The proposed method does not require matched normal samples, and can estimate the unknown genotypes simultaneously with the parent specific copy number. The new method is used to analyze 223 glioblastoma samples from the Cancer Genome Atlas (TCGA) project, giving a more comprehensive summary of the copy number events in these samples. Detailed case studies on these samples reveal the additional insights that can be gained from an allele-specific copy number analysis, such as the quantification of fractional gains and losses, the identification of copy neutral loss of heterozygosity, and the characterization of regions of simultaneous changes of both inherited chromosomes.     

CODEX: a normalization and copy number variation detection method for whole exome sequencing

High-throughput sequencing of DNA coding regions has become a common way of assaying genomic variation in the study of human diseases. Copy number variation (CNV) is an important type of genomic variation, but detecting and characterizing CNV from exome sequencing is challenging due to the high level of biases and artifacts. We propose CODEX, a normalization and CNV calling procedure for whole exome sequencing data. The Poisson latent factor model in CODEX includes terms that specifically remove biases due to GC content, exon capture and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based recursive segmentation procedure that explicitly models the count-based exome sequencing data. CODEX is compared to existing methods on a population analysis of HapMap samples from the 1000 Genomes Project, and shown to be more accurate on three microarray-based validation data sets. We further evaluate performance on 222 neuroblastoma samples with matched normals and focus on a well-studied rare somatic CNV within the ATRX gene. We show that the cross-sample normalization procedure of CODEX removes more noise than normalizing the tumor against the matched normal and that the segmentation procedure performs well in detecting CNVs with nested structures.     

Inferring tree models for oncogenesis from comparative genome hybridization data.

Comparative genome hybridization (CGH) is a laboratory method to measure gains and losses of chromosomal regions in tumor cells. It is believed that DNA gains and losses in tumor cells do not occur entirely at random, but partly through some flow of causality. Models that relate tumor progression to the occurrence of DNA gains and losses could be very useful in hunting cancer genes and in cancer diagnosis. We lay some mathematical foundations for inferring a model of tumor progression from a CGH data set. We consider a class of tree models that are more general than a path model that has been developed for colorectal cancer. We derive a tree model inference algorithm based on the idea of a maximum-weight branching in a graph, and we show that under plausible assumptions our algorithm infers the correct tree. We have implemented our methods in software, and we illustrate with a CGH data set for renal cancer.     

Inverted duplication pattern in anaphase bridges confirms the breakage-fusion-bridge (BFB) cycle model for 11q13 amplification

Conventional cytogenetic analyses and comparative genomic hybridization have revealed a complex and even chaotic nature of chromosomal aberrations in pleural malignant mesothelioma (MM). We set out to describe the complex gene copy number changes and screen for novel genetic aberrations using a high-density oligonucleotide microarray platform for comparative genomic hybridization (aCGH) of a series of 26 well-characterized MM tumor samples. The number of copy number changes varied from zero to 40 per sample. Gene copy number losses predominated over gains, and the most frequent region of loss was 9p21.3 (17/26 cases), the locus of CDKN2A and CDKN2B, both known to be commonly lost in MM. The most recurrent minimal regions of losses were 1p31.1--> p13.2, 3p22.1-->p14.2, 6q22.1, 9p21.3, 13cen-->q14.12, 14q22.1-->qter, and 22qcen-->q12.3. Previously unreported gains included 9p13.3, 7p22.3-->p22.2, 12q13.3, and 17q21.32-->qter. The results suggest that gene copy number losses are a major mechanism of MM carcinogenesis and reveal a recurrent pattern of copy number changes in MM.     

Genome wide measurement of DNA copy number changes in neuroblastoma: dissecting amplicons and mapping losses, gains and breakpoints

In the past few years high throughput methods for assessment of DNA copy number alterations have witnessed rapid progress. Both 'in house' developed BAC, cDNA, oligonucleotide and commercial arrays are now available and widely applied in the study of the human genome, particularly in the context of disease. Cancer cells are known to exhibit DNA losses, gains and amplifications affecting tumor suppressor genes and proto-oncogenes. Moreover, these patterns of genomic imbalances may be associated with particular tumor types or subtypes and may have prognostic value. Here we summarize recent array CGH findings in neuroblastoma, a pediatric tumor of the sympathetic nervous system. A total of 176 primary tumors and 53 cell lines have been analyzed on different platforms. Through these studies the genomic content and boundaries of deletions, gains and amplifications were characterized with unprecedented accuracy. Furthermore, in conjunction with cytogenetic findings, array CGH allows the mapping of breakpoints of unbalanced translocations at a very high resolution.     

Incidence of numerical chromosome aberrations in meningioma tumors as revealed by fluorescence in situ hybridization using 10 chromosome-specific probes

OBJECTIVE: Although information on the cytogenetic characteristics of meningioma tumors has accumulated progressively over the past few decades, information on the genetic heterogeneity of meningiomas is still scanty. The aim of the present study was to analyze by interphase fluorescence in situ hybridization (FISH) the incidence of numerical abnormalities for chromosomes 1, 9, 10, 11, 14, 15, 17, 22, X, and Y in a group of 70 consecutive meningioma tumors. Another goal was to establish the potential associations among the altered chromosomes, as a way to assess both intertumoral and intratumoral heterogeneity. METHODS: For the purpose of the study, 70 patients diagnosed with meningioma were analyzed. Interphase FISH for the detection of numerical abnormalities for chromosomes 1, 9, 10, 11, 14, 15, 17, 22, X, and Y was applied to fresh tumor samples from each of the patients studied. RESULTS: The overall incidence of numerical abnormalities was 76%. Chromosome Y in males and chromosome 22 in the whole series were the most common abnormalities (46% and 61%, respectively). Despite the finding that monosomy of chromosome 22/22q(-) deletions are the most frequent individual abnormality (53%), we have observed that chromosome gains are significantly more common than chromosome losses (60% versus 40%). Chromosome gains corresponded to abnormalities of chromosomes 1 (27%), 9 (25%), 10 (23%), 11 (22%), 14 (33%), 15 (22%), 17 (23%), and X in females (35%) and males (23%) whereas chromosome losses apart from chromosome 22 frequently involved chromosomes 14 (19%), X in males (23%), and Y in males (32%). Although an association was found among most gained chromosomes on one side and chromosome losses on the other side, different association patterns were observed. Furthermore, in the latter group, monosomy 22/22q(-) was associated with monosomy X in females and monosomy 14/14q(-) was associated with nulisomy Y in males. In addition, chromosome losses usually involved a large proportion of the tumor cells whereas chromosome gains were restricted to small tumor cell clones, including tetraploid cells. CONCLUSIONS: Our results show that meningiomas are genetically heterogeneous tumors that display different patterns of numerical chromosome changes, as assessed by interphase FISH.     

SpotWhatR: a user-friendly microarray data analysis system

SpotWhatR is a user-friendly microarray data analysis tool that runs under a widely and freely available R statistical language (http://www.r-project.org) for Windows and Linux operational systems. The aim of SpotWhatR is to help the researcher to analyze microarray data by providing basic tools for data visualization, normalization, determination of differentially expressed genes, summarization by Gene Ontology terms, and clustering analysis. SpotWhatR allows researchers who are not familiar with computational programming to choose the most suitable analysis for their microarray dataset. Along with well-known procedures used in microarray data analysis, we have introduced a stand-alone implementation of the HTself method, especially designed to find differentially expressed genes in low-replication contexts. This approach is more compatible with our local reality than the usual statistical methods. We provide several examples derived from the Blastocladiella emersonii and Xylella fastidiosa Microarray Projects. SpotWhatR is freely available at http://blasto.iq.usp.br/~tkoide/SpotWhatR, in English and Portuguese versions. In addition, the user can choose between "single experiment" and "batch processing" versions.     

Malin: maximum likelihood analysis of intron evolution in eukaryotes

Malin is a software package for the analysis of eukaryotic gene structure evolution. It provides a graphical user interface for various tasks commonly used to infer the evolution of exon-intron structure in protein-coding orthologs. Implemented tasks include the identification of conserved homologous intron sites in protein alignments, as well as the estimation of ancestral intron content, lineage-specific intron losses and gains. Estimates are computed either with parsimony, or with a probabilistic model that incorporates rate variation across lineages and intron sites. Availability: Malin is available as a stand-alone Java application, as well as an application bundle for MacOS X, at the website http://www.iro.umontreal.ca/~csuros/introns/malin/. The software is distributed under a BSD-style license.