Biomedical subjects

Mats Rudemo

Publications and source records attributed to Mats Rudemo.

11 recordsLinked to original sources

PubMed2006

Discrimination and scoring using small sets of genes for two-sample microarray data.

Comparison of gene expression for two groups of individuals form an important subclass of microarray experiments. We study multivariate procedures, in particular use of Hotelling's T2 for discrimination between the groups with a special emphasis on methods based on few genes only. We apply the methods to data from an experiment with a group of atopic dermatitis patients compared with a control group. We also compare our methodology to other recently proposed methods on publicly available datasets. It is found that (i) use of several genes gives a much improved discrimination of the groups as compared to one gene only, (ii) the genes that play the most important role in the multivariate analysis are not necessarily those that rank first in univariate comparisons of the groups, (iii) Linear Discriminant Analysis carried out with sets of 2-5 genes selected according to their Hotelling T2 give results comparable to state-of-the-art methods using many more genes, a feature of our method which might be crucial in clinical applications. Finding groups of genes that together give optimal multivariate discrimination (given the size of the group) can identify crucial pathways and networks of genes responsible for a disease. The computer code that we developed to make computations is available as an R package.

Algorithms↗

PubMed2006

Separation of human adipocytes by size: hypertrophic fat cells display distinct gene expression.

Enlarged adipocytes are associated with insulin resistance and are an independent predictor of type 2 diabetes. To understand the molecular link between these diseases and adipocyte hypertrophy, we developed a technique to separate human adipocytes from an adipose tissue sample into populations of small cells (mean 57.6+/-3.54 microm) and large cells (mean 100.1+/-3.94 microm). Microarray analysis of the cell populations separated from adipose tissue from three subjects identified 14 genes, of which five immune-related, with more than fourfold higher expression in large cells than small cells. Two of these genes were serum amyloid A (SAA) and transmembrane 4 L six family member 1 (TM4SF1). Real-time RT-PCR analysis of SAA and TM4SF1 expression in adipocytes from seven subjects revealed 19-fold and 22-fold higher expression in the large cells, respectively, and a correlation between adipocyte size and both SAA and TM4SF1 expression. The results were verified using immunohistochemistry. In comparison with 17 other human tissues and cell types by microarray, large adipocytes displayed by far the highest SAA and TM4SF1 expression. Thus, we have identified genes with markedly higher expression in large, compared with small, human adipocytes. These genes may link hypertrophic obesity to insulin resistance/type 2 diabetes.

Adipocytes↗

PubMed2006

Quality optimised analysis of general paired microarray experiments.

In microarray experiments, several steps may cause sub-optimal quality and the need for quality control is strong. Often the experiments are complex, with several conditions studied simultaneously. A linear model for paired microarray experiments is proposed as a generalisation of the paired two-sample method by Kristiansson et al. (2005). Quality variation is modelled by different variance scales for different (pairs of) arrays, and shared sources of variation are modelled by covariances between arrays. The gene-wise variance estimates are moderated in an empirical Bayes approach. Due to correlations all data is typically used in the inference of any linear combination of parameters. Both real and simulated data are analysed. Unequal variances and strong correlations are found in real data, leading to further examination of the fit of the model and of the nature of the datasets in general. The empirical distributions of the test-statistics are found to have a considerably improved match to the null distribution compared to previous methods, which implies more correct p-values provided that most genes are non-differentially expressed. In fact, assuming independent observations with identical variances typically leads to optimistic p-values. The method is shown to perform better than the alternatives in the simulation study.

Animals↗

PubMed2005

Weighted analysis of paired microarray experiments.

In microarray experiments quality often varies, for example between samples and between arrays. The need for quality control is therefore strong. A statistical model and a corresponding analysis method is suggested for experiments with pairing, including designs with individuals observed before and after treatment and many experiments with two-colour spotted arrays. The model is of mixed type with some parameters estimated by an empirical Bayes method. Differences in quality are modelled by individual variances and correlations between repetitions. The method is applied to three real and several simulated datasets. Two of the real datasets are of Affymetrix type with patients profiled before and after treatment, and the third dataset is of two-colour spotted cDNA type. In all cases, the patients or arrays had different estimated variances, leading to distinctly unequal weights in the analysis. We suggest also plots which illustrate the variances and correlations that affect the weights computed by our analysis method. For simulated data the improvement relative to previously published methods without weighting is shown to be substantial.

Journal Article↗

PubMed2005

Pixel-level signal modelling with spatial correlation for two-colour microarrays.

Statistical models for spot shapes and signal intensities are used in image analysis of laser scans of microarrays. Most models have essentially been based on the assumption of independent pixel intensity values, but models that allow for spatial correlation among neighbouring pixels can accommodate errors in the microarray slide and should improve the model fit. Five spatial correlation structures, exponential, Gaussian, linear, rational quadratic and spherical, are compared for a dataset with 50-mer two-colour oligonucleotide microarrays and 452 probes for selected Arabidopsis genes. Substantial improvement in model fit is obtained for all five correlation structures compared to the model with independent pixel values, and the Gaussian and the spherical models seem to be slightly better than the other three models. We also conclude that for the data set analysed the correlation seems negligible for non-neighbouring pixels.

Journal Article↗

PubMed2005

Evaluation of reference genes for studies of gene expression in human adipose tissue.

OBJECTIVE: The aim of this study was to evaluate reference genes for expression studies of human adipose tissue. RESEARCH METHODS AND PROCEDURES: Using 52 human adipose tissue expression profiles (HU95), 10 putative reference genes with the lowest variation in expression levels were selected for further studies. Expression stability of these 10 novel and 5 previously established reference genes was evaluated by real-time reverse transcriptase-polymerase chain reaction analysis. For this purpose, 44 adipose tissue biopsies from 27 subjects were chosen to include a wide range of parameters such as sex, age, BMI, depot origin, biopsy procedure, and effects of nutrition. RESULTS: LRP10 was identified as the gene with the least variation in expression levels. The frequently used reference genes RPLP0, 18S rRNA, PPIA, ACTB, and GAPD were ranked as 4, 6, 7, 8, and 10, respectively. DISCUSSION: Our results suggest that LRP10 is a better choice as reference for expression studies of human adipose tissue compared with the most frequently used reference genes.

Adipose Tissue↗

PubMed2004

Spot shape modelling and data transformations for microarrays.

MOTIVATION: To study lowly expressed genes in microarray experiments, it is useful to increase the photometric gain in the scanning. However, a large gain may cause some pixels for highly expressed genes to become saturated. Spatial statistical models that model spot shapes on the pixel level may be used to infer information about the saturated pixel intensities. Other possible applications for spot shape models include data quality control and accurate determination of spot centres and spot diameters. RESULTS: Spatial statistical models for spotted microarrays are studied including pixel level transformations and spot shape models. The models are applied to a dataset from 50mer oligonucleotide microarrays with 452 selected Arabidopsis genes. Logarithmic, Box-Cox and inverse hyperbolic sine transformations are compared in combination with four spot shape models: a cylindric plateau shape, an isotropic Gaussian distribution and a difference of two-scaled Gaussian distribution suggested in the literature, as well as a proposed new polynomial-hyperbolic spot shape model. A substantial improvement is obtained for the dataset studied by the polynomial-hyperbolic spot shape model in combination with the Box-Cox transformation. The spatial statistical models are used to correct spot measurements with saturation by extrapolating the censored data. AVAILABILITY: Source code for R is available at http://www.matfys.kvl.dk/~ekstrom/spotshapes/

Algorithms↗

PubMed2004

Statistical exploration of variation in quantitative two-dimensional gel electrophoresis data.

Two-dimensional gel electrophoresis is a major technique in global analysis at the protein level. This paper presents an examination of spot volume data from three gel sets with radioactively labeled yeast Saccharomyces cerevisiae proteins. A strong variance versus mean dependence in data was found to be stabilized by applying a shifted logarithmic transformation. However, transformed data showed a remaining substantial variance heterogeneity for different proteins. Furthermore, examination of studentized residuals revealed that transformed data were approximately normally distributed and that there were spatial correlations among the measurement errors in the gel.

Analysis of Variance↗

PubMed2004

Gene profiling reveals increased expression of uteroglobin and other anti-inflammatory genes in glucocorticoid-treated nasal polyps.

BACKGROUND: Treatment with local glucocorticoids (GCs) decreases symptoms and the size of nasal polyps. This might depend on the downregulation of proinflammatory genes, as well as the upregulation of anti-inflammatory genes. OBJECTIVE: We sought to identify GC-regulated anti-inflammatory genes in nasal polyps. METHODS: Affymetrix DNA microarrays were used to analyze the expression of 22,283 genes in 4 nasal polyps before and after local treatment with fluticasone (400 microg/d). Expression of uteroglobin and mammaglobin B was analyzed with real-time PCR in 6 nasal polyps and in nasal biopsy specimens from 6 healthy control subjects. RESULTS: Two hundred three genes had changed in expression in treated polyps, and 139 had known functions: 54 genes were downregulated, and 85 were upregulated. Genes associated with inflammation constituted the largest single functional group. These genes affected key steps in inflammation (eg, immunoglobulin production; antigen processing and presentation; and the chemoattraction and activation of granulocytes, T cells, and B cells). Several proinflammatory genes were downregulated. In contrast, some anti-inflammatory genes were upregulated. The gene that increased most in terms of expression was uteroglobin. This was confirmed with real-time PCR. By contrast, expression of uteroglobin was lower in untreated polyps than in healthy nasal mucosa. Immunohistochemical investigation showed staining of uteroglobin in the epithelium and in seromucous glands in control subjects and in nasal polyps. CONCLUSION: Upregulation of anti-inflammatory genes, such as uteroglobin, might contribute to the effects of local treatment with GCs in nasal polyps.

Adult↗

PubMed2004

DNA microarray analysis of chromosomal susceptibility regions to identify candidate genes for allergic disease: a pilot study.

OBJECTIVE: To examine whether DNA microarray analysis of chromosomal susceptibility regions for allergy can help to identify candidate genes. MATERIAL AND METHODS: Nasal biopsies were obtained from 23 patients with allergic rhinitis and 12 healthy controls. RNA was extracted from the biopsies and pooled into three patient and three control pools. These were then analysed in duplicate with DNA microarrays containing 12626 genes. Candidate genes were further examined in nasal biopsies (real-time polymerase chain reaction) and blood samples (single nucleotide polymorphisms) from other patients with allergic rhinitis and from controls. RESULTS: A total of 37 differentially expressed genes were identified according to criteria involving both the size and consistency of the gene expression levels. The chromosomal location of these genes was compared with the chromosomal susceptibility regions for allergic disease. Using a statistical method, five genes were identified in these regions, including serine protease inhibitor, Kazal type, 5 (SPINK5) and HLA-DRB2. The relevance of these genes was examined in other patients with allergic rhinitis and in controls; none of the genes were differentially expressed in nasal biopsies. Moreover, no association between allergic rhinitis and SPINK5 polymorphisms was found, at either the genotype or haplotype level. CONCLUSIONS: DNA microarray analysis of chromosomal susceptibility regions did not lead to identification of candidate genes that could be validated in a new material. However, because gene polymorphisms may cause differential gene expression, further studies, including validation data, are needed to examine this approach.

Adolescent↗

PubMed2002

Warping two-dimensional electrophoresis gel images to correct for geometric distortions of the spot pattern.

A crucial step in two-dimensional gel based protein expression analysis is to match spots in different gel images that correspond to the same protein. It still requires extensive and time-consuming manual interference, although several semiautomatic techniques exist. Geometric distortion of the protein patterns inherent to the electrophoresis procedure is one of the main causes of these difficulties. An image warping method to reduce this problem is presented. A warping is a function that deforms images by mapping between image domains. The method proceeds in two steps. Firstly, a simple physicochemical model is formulated and applied for warping of each gel image to correct for what might be one of the main causes of the distortions: current leakage across the sides during the second-dimensional electrophoresis. Secondly, the images are automatically aligned by maximizing a penalized likelihood criterion. The method is applied to a set of ten gel images showing the radioactively labeled proteome of yeast Saccharomyces cerevisiae during normal and steady-state saline growth. The improvement in matching when given the warped images instead of the original ones is exemplified by a comparison within a commercially available software.

Algorithms↗