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PubMed2005

Visualization methods for statistical analysis of microarray clusters.

BACKGROUND: The most common method of identifying groups of functionally related genes in microarray data is to apply a clustering algorithm. However, it is impossible to determine which clustering algorithm is most appropriate to apply, and it is difficult to verify the results of any algorithm due to the lack of a gold-standard. Appropriate data visualization tools can aid this analysis process, but existing visualization methods do not specifically address this issue. RESULTS: We present several visualization techniques that incorporate meaningful statistics that are noise-robust for the purpose of analyzing the results of clustering algorithms on microarray data. This includes a rank-based visualization method that is more robust to noise, a difference display method to aid assessments of cluster quality and detection of outliers, and a projection of high dimensional data into a three dimensional space in order to examine relationships between clusters. Our methods are interactive and are dynamically linked together for comprehensive analysis. Further, our approach applies to both protein and gene expression microarrays, and our architecture is scalable for use on both desktop/laptop screens and large-scale display devices. This methodology is implemented in GeneVAnD (Genomic Visual ANalysis of Datasets) and is available at http://function.princeton.edu/GeneVAnD. CONCLUSION: Incorporating relevant statistical information into data visualizations is key for analysis of large biological datasets, particularly because of high levels of noise and the lack of a gold-standard for comparisons. We developed several new visualization techniques and demonstrated their effectiveness for evaluating cluster quality and relationships between clusters.

Algorithms↗
PubMed2005

Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models.

BACKGROUND: With the explosion of microarray studies, an enormous amount of data is being produced. Systematic integration of gene expression data from different sources increases statistical power of detecting differentially expressed genes and allows assessment of heterogeneity. The challenge, however, is in designing and implementing efficient analytic methodologies for combination of data generated by different research groups. RESULTS: We extended traditional effect size models to combine information from different microarray datasets by incorporating a quality measure for each gene in each study into the effect size estimation. We illustrated our method by integrating two datasets generated using different Affymetrix oligonucleotide types. Our results indicate that the proposed quality-adjusted weighting strategy for modelling inter-study variation of gene expression profiles not only increases consistency and decreases heterogeneous results between these two datasets, but also identifies many more differentially expressed genes than methods proposed previously. CONCLUSION: Data integration and synthesis is becoming increasingly important. We live in a high-throughput era where technologies constantly change leaving behind a trail of data with different forms, shapes and sizes. Statistical and computational methodologies are therefore critical for extracting the most out of these related but not identical sources of data.

Algorithms↗
PubMed2005

Feature selection and classification for microarray data analysis: evolutionary methods for identifying predictive genes.

BACKGROUND: In the clinical context, samples assayed by microarray are often classified by cell line or tumour type and it is of interest to discover a set of genes that can be used as class predictors. The leukemia dataset of Golub et al. 1 and the NCI60 dataset of Ross et al. 2 present multiclass classification problems where three tumour types and nine cell lines respectively must be identified. We apply an evolutionary algorithm to identify the near-optimal set of predictive genes that classify the data. We also examine the initial gene selection step whereby the most informative genes are selected from the genes assayed. RESULTS: In the absence of feature selection, classification accuracy on the training data is typically good, but not replicated on the testing data. Gene selection using the RankGene software 3 is shown to significantly improve performance on the testing data. Further, we show that the choice of feature selection criteria can have a significant effect on accuracy. The evolutionary algorithm is shown to perform stably across the space of possible parameter settings - indicating the robustness of the approach. We assess performance using a low variance estimation technique, and present an analysis of the genes most often selected as predictors. CONCLUSION: The computational methods we have developed perform robustly and accurately, and yield results in accord with clinical knowledge: A Z-score analysis of the genes most frequently selected identifies genes known to discriminate AML and Pre-T ALL leukemia. This study also confirms that significantly different sets of genes are found to be most discriminatory as the sample classes are refined.

Algorithms↗
PubMed2005

Comparison of normalization methods for CodeLink Bioarray data.

BACKGROUND: The quality of microarray data can seriously affect the accuracy of downstream analyses. In order to reduce variability and enhance signal reproducibility in these data, many normalization methods have been proposed and evaluated, most of which are for data obtained from cDNA microarrays and Affymetrix GeneChips. CodeLink Bioarrays are a newly emerged, single-color oligonucleotide microarray platform. To date, there are no reported studies that evaluate normalization methods for CodeLink Bioarrays. RESULTS: We compared five existing normalization approaches, in terms of both noise reduction and signal retention: Median (suggested by the manufacturer), CyclicLoess, Quantile, Iset, and Qspline. These methods were applied to two real datasets (a time course dataset and a lung disease-related dataset) generated by CodeLink Bioarrays and were assessed using multiple statistical significance tests. Compared to Median, CyclicLoess and Qspline exhibit a significant and the most consistent improvement in reduction of variability and retention of signal. CyclicLoess appears to retain more signal than Qspline. Quantile reduces more variability than Median in both datasets, yet fails to consistently retain more signal in the time course dataset. Iset does not improve over Median in either noise reduction or signal enhancement in the time course dataset. CONCLUSION: Median is insufficient either to reduce variability or to retain signal effectively for CodeLink Bioarray data. CyclicLoess is a more suitable approach for normalizing these data. CyclicLoess also seems to be the most effective method among the five different normalization strategies examined.

Algorithms↗
PubMed2005

Can Zipf's law be adapted to normalize microarrays?

BACKGROUND: Normalization is the process of removing non-biological sources of variation between array experiments. Recent investigations of data in gene expression databases for varying organisms and tissues have shown that the majority of expressed genes exhibit a power-law distribution with an exponent close to -1 (i.e. obey Zipf's law). Based on the observation that our single channel and two channel microarray data sets also followed a power-law distribution, we were motivated to develop a normalization method based on this law, and examine how it compares with existing published techniques. A computationally simple and intuitively appealing technique based on this observation is presented. RESULTS: Using pairwise comparisons using MA plots (log ratio vs. log intensity), we compared this novel method to previously published normalization techniques, namely global normalization to the mean, the quantile method, and a variation on the loess normalization method designed specifically for boutique microarrays. Results indicated that, for single channel microarrays, the quantile method was superior with regard to eliminating intensity-dependent effects (banana curves), but Zipf's law normalization does minimize this effect by rotating the data distribution such that the maximal number of data points lie on the zero of the log ratio axis. For two channel boutique microarrays, the Zipf's law normalizations performed as well as, or better than existing techniques. CONCLUSION: Zipf's law normalization is a useful tool where the Quantile method cannot be applied, as is the case with microarrays containing functionally specific gene sets (boutique arrays).

Algorithms↗
PubMed2005

Feature selection and nearest centroid classification for protein mass spectrometry.

BACKGROUND: The use of mass spectrometry as a proteomics tool is poised to revolutionize early disease diagnosis and biomarker identification. Unfortunately, before standard supervised classification algorithms can be employed, the "curse of dimensionality" needs to be solved. Due to the sheer amount of information contained within the mass spectra, most standard machine learning techniques cannot be directly applied. Instead, feature selection techniques are used to first reduce the dimensionality of the input space and thus enable the subsequent use of classification algorithms. This paper examines feature selection techniques for proteomic mass spectrometry. RESULTS: This study examines the performance of the nearest centroid classifier coupled with the following feature selection algorithms. Student-t test, Kolmogorov-Smirnov test, and the P-test are univariate statistics used for filter-based feature ranking. From the wrapper approaches we tested sequential forward selection and a modified version of sequential backward selection. Embedded approaches included shrunken nearest centroid and a novel version of boosting based feature selection we developed. In addition, we tested several dimensionality reduction approaches, namely principal component analysis and principal component analysis coupled with linear discriminant analysis. To fairly assess each algorithm, evaluation was done using stratified cross validation with an internal leave-one-out cross-validation loop for automated feature selection. Comprehensive experiments, conducted on five popular cancer data sets, revealed that the less advocated sequential forward selection and boosted feature selection algorithms produce the most consistent results across all data sets. In contrast, the state-of-the-art performance reported on isolated data sets for several of the studied algorithms, does not hold across all data sets. CONCLUSION: This study tested a number of popular feature selection methods using the nearest centroid classifier and found that several reportedly state-of-the-art algorithms in fact perform rather poorly when tested via stratified cross-validation. The revealed inconsistencies provide clear evidence that algorithm evaluation should be performed on several data sets using a consistent (i.e., non-randomized, stratified) cross-validation procedure in order for the conclusions to be statistically sound.

Algorithms↗
PubMed2005

Correlation test to assess low-level processing of high-density oligonucleotide microarray data.

BACKGROUND: There are currently a number of competing techniques for low-level processing of oligonucleotide array data. The choice of technique has a profound effect on subsequent statistical analyses, but there is no method to assess whether a particular technique is appropriate for a specific data set, without reference to external data. RESULTS: We analyzed coregulation between genes in order to detect insufficient normalization between arrays, where coregulation is measured in terms of statistical correlation. In a large collection of genes, a random pair of genes should have on average zero correlation, hence allowing a correlation test. For all data sets that we evaluated, and the three most commonly used low-level processing procedures including MAS5, RMA and MBEI, the housekeeping-gene normalization failed the test. For a real clinical data set, RMA and MBEI showed significant correlation for absent genes. We also found that a second round of normalization on the probe set level improved normalization significantly throughout. CONCLUSION: Previous evaluation of low-level processing in the literature has been limited to artificial spike-in and mixture data sets. In the absence of a known gold-standard, the correlation criterion allows us to assess the appropriateness of low-level processing of a specific data set and the success of normalization for subsets of genes.

Algorithms↗
PubMed2005

The efficiency of different search strategies in estimating parsimony jackknife, bootstrap, and Bremer support.

BACKGROUND: For parsimony analyses, the most common way to estimate confidence is by resampling plans (nonparametric bootstrap, jackknife), and Bremer support (Decay indices). The recent literature reveals that parameter settings that are quite commonly employed are not those that are recommended by theoretical considerations and by previous empirical studies. The optimal search strategy to be applied during resampling was previously addressed solely via standard search strategies available in PAUP*. The question of a compromise between search extensiveness and improved support accuracy for Bremer support received even less attention. A set of experiments was conducted on different datasets to find an empirical cut-off point at which increased search extensiveness does not significantly change Bremer support and jackknife or bootstrap proportions any more. RESULTS: For the number of replicates needed for accurate estimates of support in resampling plans, a diagram is provided that helps to address the question whether apparently different support values really differ significantly. It is shown that the use of random addition cycles and parsimony ratchet iterations during bootstrapping does not translate into higher support, nor does any extension of the search extensiveness beyond the rather moderate effort of TBR (tree bisection and reconnection branch swapping) plus saving one tree per replicate. Instead, in case of very large matrices, saving more than one shortest tree per iteration and using a strict consensus tree of these yields decreased support compared to saving only one tree. This can be interpreted as a small risk of overestimating support but should be more than compensated by other factors that counteract an enhanced type I error. With regard to Bremer support, a rule of thumb can be derived stating that not much is gained relative to the surplus computational effort when searches are extended beyond 20 ratchet iterations per constrained node, at least not for datasets that fall within the size range found in the current literature. CONCLUSION: In view of these results, calculating bootstrap or jackknife proportions with narrow confidence intervals even for very large datasets can be achieved with less expense than often thought. In particular, iterated bootstrap methods that aim at reducing statistical bias inherent to these proportions are more feasible when the individual bootstrap searches require less time.

Bayes Theorem↗
PubMed2005

Recursive partitioning models for linkage in COGA data.

We have developed a recursive-partitioning (RP) algorithm for identifying phenotype and covariate groupings that interact with the evidence for linkage. This data-mining approach for detecting gene x environment interactions uses genotype and covariate data on affected relative pairs to find evidence for linkage heterogeneity across covariate-defined subgroups. We adapted a likelihood-ratio based test of linkage parameterized with relative risks to a recursive partitioning framework, including a cross-validation based deviance measurement for choosing optimal tree size and a bootstrap sampling procedure for choosing robust tree structure. ALDX2 category 5 individuals were considered affected, categories 1 and 3 unaffected, and all others unknown. We sampled non-overlapping affected relative pairs from each family; therefore, we used 144 affected pairs in the RP model. Twenty pair-level covariates were defined from smoking status, maximum drinks, ethnicity, sex, and age at onset. Using the all-pairs score in GENEHUNTER, the nonparametric linkage tests showed no regions with suggestive linkage evidence. However, using the RP model, several suggestive regions were found on chromosomes 2, 4, 6, 14, and 20, with detection of associated covariates such as sex and age at onset.

Alcoholism↗
PubMed2001

A tandem repeats database for bacterial genomes: application to the genotyping of Yersinia pestis and Bacillus anthracis.

BACKGROUND: Some pathogenic bacteria are genetically very homogeneous, making strain discrimination difficult. In the last few years, tandem repeats have been increasingly recognized as markers of choice for genotyping a number of pathogens. The rapid evolution of these structures appears to contribute to the phenotypic flexibility of pathogens. The availability of whole-genome sequences has opened the way to the systematic evaluation of tandem repeats diversity and application to epidemiological studies. RESULTS: This report presents a database (http://minisatellites.u-psud.fr) of tandem repeats from publicly available bacterial genomes which facilitates the identification and selection of tandem repeats. We illustrate the use of this database by the characterization of minisatellites from two important human pathogens, Yersinia pestis and Bacillus anthracis. In order to avoid simple sequence contingency loci which may be of limited value as epidemiological markers, and to provide genotyping tools amenable to ordinary agarose gel electrophoresis, only tandem repeats with repeat units at least 9 bp long were evaluated. Yersinia pestis contains 64 such minisatellites in which the unit is repeated at least 7 times. An additional collection of 12 loci with at least 6 units, and a high internal conservation were also evaluated. Forty-nine are polymorphic among five Yersinia strains (twenty-five among three Y. pestis strains). Bacillus anthracis contains 30 comparable structures in which the unit is repeated at least 10 times. Half of these tandem repeats show polymorphism among the strains tested. CONCLUSIONS: Analysis of the currently available bacterial genome sequences classifies Bacillus anthracis and Yersinia pestis as having an average (approximately 30 per Mb) density of tandem repeat arrays longer than 100 bp when compared to the other bacterial genomes analysed to date. In both cases, testing a fraction of these sequences for polymorphism was sufficient to quickly develop a set of more than fifteen informative markers, some of which show a very high degree of polymorphism. In one instance, the polymorphism information content index reaches 0.82 with allele length covering a wide size range (600-1950 bp), and nine alleles resolved in the small number of independent Bacillus anthracis strains typed here.

Bacillus anthracis↗
PubMed2003

Relationship between apolipoprotein(a) size polymorphism and coronary heart disease in overweight subjects.

BACKGROUND: Overweight is associated with an increased cardiovascular risk which is only partially explained by conventional risk factors. The objective of this study was to evaluate lipoprotein(a) [Lp(a)] plasma levels and apolipoprotein(a) [apo(a)] phenotypes in relation to coronary heart disease (CHD) in overweight subjects. METHODS: A total of 275 overweight (BMI > or = 27 kg/m2) subjects, of which 155 had experienced a CHD event, 337 normal weight subjects with prior CHD and 103 CHD-free normal weight subjects were enrolled in the study. Lp(a) levels were determined by an ELISA technique and apo(a) isoforms were detected by a high-resolution immunoblotting method. RESULTS: Lp(a) levels were similar in the three study groups. Overweight subjects with CHD had Lp(a) concentrations significantly higher than those without [median (interquartile range): 20 (5-50.3) versus 12.6 (2.6-38.6) mg/dl, P < 0.05]. Furthermore, overweight subjects with CHD showed a higher prevalence of low molecular weight apo(a) isoforms than those without (55.5% versus 40.8%, P < 0.05) and with respect to the control group (55.5% versus 39.8%, P < 0.05). Stepwise regression analysis showed that apo(a) phenotypes, but not Lp(a) levels, entered the model as significant independent predictors of CHD in overweight subjects. CONCLUSIONS: Our data indicate that small-sized apo(a) isoforms are associated with CHD in overweight subjects. The characterization of apo(a) phenotypes might serve as a reliable biomarker to better assess the overall CHD risk of each subject with elevated BMI, leading to more intensive treatment of modifiable cardiovascular risk factors.

Apolipoproteins A↗
PubMed2005

An AFLP-based genetic linkage map of Plasmodium chabaudi chabaudi.

BACKGROUND: Plasmodium chabaudi chabaudi can be considered as a rodent model of human malaria parasites in the genetic analysis of important characters such as drug resistance and immunity. Despite the availability of some genome sequence data, an extensive genetic linkage map is needed for mapping the genes involved in certain traits. METHODS: The inheritance of 672 Amplified Fragment Length Polymorphism (AFLP) markers from two parental clones (AS and AJ) of P. c. chabaudi was determined in 28 independent recombinant progeny clones. These, AFLP markers and 42 previously mapped Restriction Fragment Length Polymorphism (RFLP) markers (used as chromosomal anchors) were organized into linkage groups using Map Manager software. RESULTS: 614 AFLP markers formed linkage groups assigned to 10 of 14 chromosomes, and 12 other linkage groups not assigned to known chromosomes. The genetic length of the genome was estimated to be about 1676 centiMorgans (cM). The mean map unit size was estimated to be 13.7 kb/cM. This was slightly less then previous estimates for the human malaria parasite, Plasmodium falciparum CONCLUSION: The P. c. chabaudi genetic linkage map presented here is the most extensive and highly resolved so far available for this species. It can be used in conjunction with the genome databases of P. c chabaudi, P. falciparum and Plasmodium yoelii to identify genes underlying important phenotypes such as drug resistance and strain-specific immunity.

Alleles↗
PubMed2000

TNF gene polymorphism and its relation to intracerebral production of TNFalpha and TNFbeta in AD.

OBJECTIVE: To analyze the extent of tumor necrosis factor-alpha (TNFalpha) and TNFbeta gene polymorphism in patients with AD and to relate it to intrathecal levels of these cytokines. METHODS: Analyses of TNFalpha and TNFbeta gene polymorphism were performed using PCR in 52 patients with AD and in 25 control subjects, and the levels of corresponding cytokines were analyzed using ELISA. RESULTS: Patients with AD displayed significantly higher intrathecal levels of TNFalpha, but not TNFbeta, compared with the control subjects. The levels of these cytokines did not differ significantly in patients displaying different alleles of the TNF gene. CONCLUSIONS: Results indicate that increased intrathecal production of TNFalpha in AD is preferentially controlled by environmental stimuli rather than genetic makeup.

Aged↗
PubMed2002

On the efficiency of composite interval mapping in an outbred population.

Composite interval mapping (CIM) has been successfully applied to the detection of QTL in experimental animals and plants. However, practical analyses based on CIM have been reported mainly for populations derived from cross between inbred lines. There are few studies on QTL analyses with CIM in outbred populations. To evaluate the applicability of CIM to outbred populations is prerequisite for the fine mapping of QTL in industrial animals such as pig and chicken. Some markers are usually not fully informative in outbred populations. In application of CIM to outbred populations, the influence of inclusion of such uninformative markers used as covariates on the efficiency of CIM should be investigated. In this paper a least-squares method for CIM was formalized in an F(2) population derived by crossing two outbred lines. The efficiencies of CIM were evaluated for outbred populations in comparison with simple interval mapping (SIM) for several cases of marker informativeness using simulations. By incorporating markers linked to a tested position as well as those unlinked, CIM showed a higher efficiency to separate two linked QTL over SIM. The efficiency of dissection was enhanced as the marker informativeness was increased. The power of CIM to detect an isolated QTL was improved by excluding markers linked to a tested position from covariates and higher than SIM regardless of marker informativeness. In conclusion, CIM is a useful procedure for the analysis of QTL in outbred populations even under low marker informativeness.

Animals↗
PubMed2004

Population history and natural selection shape patterns of genetic variation in 132 genes.

Identifying regions of the human genome that have been targets of natural selection will provide important insights into human evolutionary history and may facilitate the identification of complex disease genes. Although the signature that natural selection imparts on DNA sequence variation is difficult to disentangle from the effects of neutral processes such as population demographic history, selective and demographic forces can be distinguished by analyzing multiple loci dispersed throughout the genome. We studied the molecular evolution of 132 genes by comprehensively resequencing them in 24 African-Americans and 23 European-Americans. We developed a rigorous computational approach for taking into account multiple hypothesis tests and demographic history and found that while many apparent selective events can instead be explained by demography, there is also strong evidence for positive or balancing selection at eight genes in the European-American population, but none in the African-American population. Our results suggest that the migration of modern humans out of Africa into new environments was accompanied by genetic adaptations to emergent selective forces. In addition, a region containing four contiguous genes on Chromosome 7 showed striking evidence of a recent selective sweep in European-Americans. More generally, our results have important implications for mapping genes underlying complex human diseases.

Black People↗
PubMed1999

The index of individuality is often a misinterpreted quantity characteristic.

The concept of the "index of individuality" was introduced by Eugene Harris in 1974. The index of individuality, calculated as (CV(A)2 + CV(I)2)(1/2)/CV(G), where CV(A), CV(I), and CV(G) are analytical, within-subject, and between-subject coefficients of variation respectively, has been used by many to investigate the utility of conventional population-based reference values. For a high index of individuality, > 1.4, it has been said that reference intervals will be more useful than for a low index, < 0.6. The validity of these concepts is investigated here and a number of our findings are at odds with the generally held opinion. The index of individuality has no impact on the fraction of individuals classified using population-based reference values, as long as the change in concentration from the usual state is of the same absolute magnitude and one sample is assayed to detect disease. However, when a measurement falling outside a reference limit is repeated in order to verify the finding, the index of individuality has considerable influence. For quantities with very low indices, the repeat test result, will be close to the first and give no new information, whereas for quantities with high indices, a repeat test will decrease the number of true positives and false positives.

Biometry↗