Arrow plot and CA maps on microarray preprocessing methods

Microarray allow to monitoring simultaneously thousands of genes, where the abundance of the transcripts under a same experimental condition at the same time can be quantified. Among various available array technologies, double channel cDNA microarray experiments have arisen in numerous technical protocols associated to genomic studies, which is the focus of this work. Microarray experiments involve many steps and each one can affect the quality of raw data. Background correction and normalization are preprocessing techniques to clean and correct the raw data when undesirable fluctuations arise from technical factors. Several recent studies showed that there is no preprocessing strategy that outperforms others in all circumstances and thus it seems difficult to provide general recommendations. In this work, it is proposed to use exploratory techniques to visualize the effects of preprocessing methods on statistical analysis of cancer two-channel microarray data sets, where the cancer types (classes) are known. For selecting differential expressed genes the arrow plot was used and the graph of profiles resultant from the correspondence analysis for visualizing the results. It was used 6 background methods and 6 normalization methods, performing 36 pre-processing methods and it was analyzed in a published cDNA microarray database (Liver) available at http://genome-www5.stanford.edu/ which microarrays were already classified by cancer type. All statistical analyses were performed using the R statistical software.

PHYLOViZ: phylogenetic inference and data visualization for sequence based typing methods

Background: With the decrease of DNA sequencing costs, sequence-based typing methods are rapidly becoming the gold standard for epidemiological surveillance. These methods provide reproducible and comparable results needed for a global scale bacterial population analysis, while retaining their usefulness for local epidemiological surveys. Online databases that collect the generated allelic profiles and associated epidemiological data are available but this wealth of data remains underused and are frequently poorly annotated since no user-friendly tool exists to analyze and explore it. Results: PHYLOViZ is platform independent Java software that allows the integrated analysis of sequence-based typing methods, including SNP data generated from whole genome sequence approaches, and associated epidemiological data. goeBURST and its Minimum Spanning Tree expansion are used for visualizing the possible evolutionary relationships between isolates. The results can be displayed as an annotated graph overlaying the query results of any other epidemiological data available. Conclusions: PHYLOViZ is a user-friendly software that allows the combined analysis of multiple data sources for microbial epidemiological and population studies. It is freely available at http://www.phyloviz.net.

Arrow Plot: a new graphical tool for selecting up and down regulated genes and genes differentially expressed on samples subgroups

Background: A common task in analyzing microarray data is to determine which genes are differentially expressed across two (or more) kind of tissue samples or samples submitted under experimental conditions. Several statistical methods have been proposed to accomplish this goal, generally based on measures of distance between classes. It is well known that biological samples are heterogeneous because of factors such as molecular subtypes or genetic background that are often unknown to the experimenter. For instance, in experiments which involve molecular classification of tumors it is important to identify significant subtypes of cancer. Bimodal or multimodal distributions often reflect the presence of subsamples mixtures. Consequently, there can be genes differentially expressed on sample subgroups which are missed if usual statistical approaches are used. In this paper we propose a new graphical tool which not only identifies genes with up and down regulations, but also genes with differential expression in different subclasses, that are usually missed if current statistical methods are used. This tool is based on two measures of distance between samples, namely the overlapping coefficient (OVL) between two densities and the area under the receiver operating characteristic (ROC) curve. The methodology proposed here was implemented in the open-source R software. Results: This method was applied to a publicly available dataset, as well as to a simulated dataset. We compared our results with the ones obtained using some of the standard methods for detecting differentially expressed genes, namely Welch t-statistic, fold change (FC), rank products (RP), average difference (AD), weighted average difference (WAD), moderated t-statistic (modT), intensity-based moderated t-statistic (ibmT), significance analysis of microarrays (samT) and area under the ROC curve (AUC). On both datasets all differentially expressed genes with bimodal or multimodal distributions were not selected by all standard selection procedures. We also compared our results with (i) area between ROC curve and rising area (ABCR) and (ii) the test for not proper ROC curves (TNRC). We found our methodology more comprehensive, because it detects both bimodal and multimodal distributions and different variances can be considered on both samples. Another advantage of our method is that we can analyze graphically the behavior of different kinds of differentially expressed genes. Conclusion: Our results indicate that the arrow plot represents a new flexible and useful tool for the analysis of gene expression profiles from microarrays.

Association of FTO and PPARG polymorphisms with obesity in Portuguese women

Purpose: We evaluated the association between risk of obesity in the Portuguese population and two obesity-related single-nucleotide gene polymorphisms: fat-mass and obesity-associated (FTO) rs9939609 and peroxisome proliferator-activated receptor gamma (PPARG) rs1801282. Patients and methods: A total of 194 Portuguese premenopausal female Caucasians aged between 18 and 50 years (95 with body mass index [BMI] ≥30 g/m2, 99 controls with BMI 18.5–24.9 kg/m2) participated in this study. The association of the single-nucleotide polymorphisms with obesity was determined by odds ratio calculation with 95% confidence intervals. Results: Significant differences in allelic expression of FTO rs9939609 (P<0.05) were found between control and case groups, indicating a 2.5-higher risk for obesity in the presence of both risk alleles when comparing the control group with the entire obese group. A fourfold-higher risk was found for subjects with class III obesity compared to those with classes I and II. No significant differences in BMI were found between the control and case groups for PPARG rs1801282 (P>0.05). Conclusion: For the first time, a study involving an adult Portuguese population shows that individuals harboring both risk alleles in the FTO gene locus are at higher risk for obesity, which is in agreement to what has been reported for other European populations.

Fat intake interacts with polymorphisms of Caspase9, FasLigand and PPARgamma apoptotic genes in modulating Crohn's disease activity

Background & aims: Crohn’s disease (CD) is a multifactorial disease where resistance to apoptosis is one major defect. Also, dietary fat intake has been shown to modulate disease activity. We aimed to explore the interaction between four single nucleotide polymorphisms (SNPs) in apoptotic genes and dietary fat intake in modulating disease activity in CD patients. Methods: Polymerase Chain Reaction (PCR) and Restriction Fragment Length Polymorphism (RFLP) techniques were used to analyze Caspase9þ93C/T, FasLigand-843C/T, Peroxisome Proliferator-Activated Receptor gammaþ161C/T and Peroxisome Proliferator-Activated Receptor gamma Pro12Ala SNPs in 99 patients with CD and 116 healthy controls. Interactions between SNPs and fat intake in modulating disease activity were analyzed using regression analysis. Results: None of the polymorphisms analyzed influenced disease susceptibility and/or activity, but a high intake of total, saturated and monounsaturated fats and a higher ratio of n-6/n-3 polyunsaturated fatty acids (PUFA), was associated with a more active phenotype (p < 0.05). We observed that the detrimental effect of a high intake of total and trans fat was more marked in wild type carriers of the Caspase9þ93C/T polymorphism [O.R (95%CI) 4.64 (1.27e16.89) and O.R (95%CI) 4.84 (1.34e17.50)]. In the Peroxisome Proliferator-Activated Receptor gamma Pro12Ala SNP, we also observed that a high intake of saturated and monounsaturated fat was associated to a more active disease in wild type carriers [OR (95%CI) 4.21 (1.33e13.26) and 4.37 (1.52e12.51)]. Finally, a high intake of n-6 PUFA was associated with a more active disease in wild type carriers for the FasLigand-843C/T polymorphism [O.R (95%CI) 5.15 (1.07e24.74)]. Conclusions: To our knowledge, this is the first study to disclose a synergism between fat intake and SNPs in apoptotic genes in modulating disease activity in CD patients.

Relationship of PON1 192 and 55 gene polymorphisms to calcific valvular aortic stenosis

Introduction and Objectives - Paraoxonases may exert anti-atherogenic action by reducing lipid peroxidation. Previous studies examined associations between polymorphisms in the paraoxonase 1 (PON1) gene and development of coronary artery disease (CAD), with inconsistent results. Given the similarities in clinical and pathophysiological risk factors of CAD and calcific aortic valve stenosis (CAVS), we postulated a link between PON1 alleles and CAVS progression. Methods - We investigated the association between PON1 55 and 192 single nucleotide polymorphisms (SNPs), their enzyme activity, and CAVS progression assessed by aortic valve area and transvalvular peak velocity in 67 consecutive patients with moderate CAVS and 251 healthy controls. Results - PON1 paraoxonase activity was higher in CAVS patients (P<0.001). The PON1 genotype Q192R SNP (P=0.03) and variant allele (R192) (P=0.01) frequencies differed between CAVS patients and controls. Significant association existed between PON1 enzyme activity, phenotypic effects of PON1 192 genotype polymorphisms, and CAVS progression, but not between PON1 55 and high-density lipoprotein (P=0.44) or low-density lipoprotein cholesterol (P=0.12), between 192 genotype and high-density lipoprotein (P=0.24) or low-density lipoprotein cholesterol (P=0.52). Conclusion - The PON1 genotype Q192R SNP has an important effect on CAVS disease progression. This study helps outline a genotype-phenotype relationship for PON1 in this unique population.

Clinical and genetic factors predicting response to therapy in patients with Crohn’s disease

Aim - To identify clinical and/or genetic predictors of response to several therapies in Crohn’s disease (CD) patients. Methods - We included 242 patients with CD (133 females) aged (mean ± standard deviation) 39 ± 12 years and a disease duration of 12 ± 8 years. The single-nucleotide polymorphisms (SNPs) studied were ABCB1 C3435T and G2677T/A, IL23R G1142A, C2370A, and G9T, CASP9 C93T, Fas G670A and LgC844T, and ATG16L1 A898G. Genotyping was performed with real-time PCR with Taqman probes. Results - Older patients responded better to 5-aminosalicylic acid (5-ASA) and to azathioprine (OR 1.07, p = 0.003 and OR 1.03, p = 0.01, respectively) while younger ones responded better to biologicals (OR 0.95, p = 0.06). Previous surgery negatively influenced response to 5-ASA compounds (OR 0.25, p = 0.05), but favoured response to azathioprine (OR 2.1, p = 0.04). In respect to genetic predictors, we observed that heterozygotes for ATGL16L1 SNP had a significantly higher chance of responding to corticosteroids (OR 2.51, p = 0.04), while homozygotes for Casp9 C93T SNP had a lower chance of responding both to corticosteroids and to azathioprine (OR 0.23, p = 0.03 and OR 0.08, p = 0.02,). TT carriers of ABCB1 C3435T SNP had a higher chance of responding to azathioprine (OR 2.38, p = 0.01), while carriers of ABCB1 G2677T/A SNP, as well as responding better to azathioprine (OR 1.89, p = 0.07), had a lower chance of responding to biologicals (OR 0.31, p = 0.07), which became significant after adjusting for gender (OR 0.75, p = 0.005). Conclusions - In the present study, we were able to identify a number of clinical and genetic predictors of response to several therapies which may become of potential utility in clinical practice. These are preliminary results that need to be replicated in future pharmacogenomic studies.

An unsupervised approach to feature discretization and selection

Many learning problems require handling high dimensional datasets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevant (or even detrimental) for the learning tasks. It is thus clear that there is a need for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for medium and high-dimensional datasets. The experimental results on several standard datasets, with both sparse and dense features, show the efficiency of the proposed techniques as well as improvements over previous related techniques.