A locally adaptive statistical procedure (LAP) to identify differentially expressed chromosomal regions

Callegaro, Andrea; Basso, Dario; Bicciato, Silvio

doi:10.1093/bioinformatics/btl455

Cited by 32 publications

(45 citation statements)

References 25 publications

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“…The chromosomal regions with modulated gene expression signals were identified using a non-parametric model-free statistical method called locally adaptive statistical procedure (LAP). 29 For the purposes of this study, the intensity levels were generated from CEL files using RMA and, after annotation, the probe sets without any chromosomal location information were filtered out, as were those on chromosomes X and Y. The differential expressions between the ∆…”

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confidence: 99%

Integrative genomic analysis reveals distinct transcriptional and genetic features associated with chromosome 13 deletion in multiple myeloma

Agnelli¹,

Bicciato²,

Fabris³

et al. 2007

Haematologica

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Background and Objectives The chromosome 13 deletion (Delta(13)) is one of the most frequent chromosomal alterations in multiple myeloma (MM). Delta(13) is associated with an unfavorable prognosis, although there is increasing agreement that its prognostic relevance must be related to the ploidy status and the presence of different chromosomal translocations. The aim of this study was to provide a comprehensive analysis of the transcriptional features of Delta(13) in MM.Design and Methods Highly purified plasma cells from 80 newly diagnosed MM patients were characterized by means of fluorescence in situ hybridization (FISH) and high-density oligonucleotide microarray for gene expression profiling and chromosomal alterations.Results We identified 67 differentially expressed genes in the patients with and without the chromosome 13 deletion, all of which were downregulated in the cases with Delta(13): 44 mapped along the whole chromosome 13, seven on chromosome 11 and three on chromosome 19. Functional analyses of the selected genes indicated their involvement in protein biosynthesis, ubiquitination and transcriptional regulation. An integrative genomic approach based on regional analyses of the gene expression data identified distinct chromosomal regions whose global expression modulation could differentiate Delta(13)-positive cases, in particular the upregulation of 1q21-1q42 and the downregulation of 19p and almost the entire chromosome 11. FISH analyses confirmed the close relationship between Delta(13)-positivity and the presence of extra copies of 1q21-1q42 (p=6x10(-4)) or the absence of chromosome 11 and 19 trisomy (p=5x10(-4)).Interpretation and Conclusions Our results indicate that distinct types of chromosomal aberrations are closely related to the transcriptional profiles of Delta(13)-positive cases, suggesting that the contribution of Delta(13) to the malignancy should be considered together with associated abnormalities

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confidence: 99%

Integrative genomic analysis reveals distinct transcriptional and genetic features associated with chromosome 13 deletion in multiple myeloma

Agnelli¹,

Bicciato²,

Fabris³

et al. 2007

Haematologica

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“…To further validate our results, we chose the two supervised approaches MACAT and LAP (Callegaro et al, 2006;Toedling et al, 2005). For MACAT, we used k-nearest-neighbors, prior parameter optimization and 1000 permutations of the sample labels.…”

Section: Meta-analysismentioning

confidence: 99%

“…The sample groups defined by tumor grade 3 versus grade 1 and 2 in three data sets (1,5,9) served as input for the methods LAP and MACAT (Callegaro et al, 2006;Toedling et al, 2005). LAP (MACAT) identified 299 (373), 687 (101) and 100 (136) genes in the data sets 1, 5 and 9, respectively.…”

Section: Gene Expression Associates With Tumor Gradementioning

confidence: 99%

“…Additionally, chromosomal localization has been integrated in several methods which aim to detect differential gene expression of adjacent genes. One method predicts regions of DNA amplification for each individual tumor sample based on its standardized expression profile, whereas other methods compare defined specimen collectives, like healthy versus cancerous tissues, to identify regional gene expression alterations (Callegaro et al, 2006;Caron et al, 2001;Crawley and Furge, 2002;Levin et al, 2005;Myers et al, 2004;Staub et al, 2006;Toedling et al, 2005;Zhou et al, 2003). If exceptionally high or low expression is present in only a subset of one group, e.g.…”

Section: Introductionmentioning

confidence: 99%

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Identification of aberrant chromosomal regions from gene expression microarray studies applied to human breast cancer

et al. 2007

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Motivation: In cancer, chromosomal imbalances like amplifications and deletions, or changes in epigenetic mechanisms like DNA methylation influence the transcriptional activity. These alterations are often not limited to a single gene but affect several genes of the genomic region and may be relevant for the disease status. For example, the ERBB2 amplicon (17q21) in breast cancer is associated with poor patient prognosis. We present a general, unsupervised method for genome-wide gene expression data to systematically detect tumor patients with chromosomal regions of distinct transcriptional activity. The method aims to find expression patterns of adjacent genes with a consistently decreased or increased level of gene expression in tumor samples. Such patterns have been found to be associated with chromosomal aberrations and clinical parameters like tumor grading and thus can be useful for risk stratification or therapy. Results: Our approach was applied to 12 independent human breast cancer microarray studies comprising 1422 tumor samples. We prioritized chromosomal regions and genes predominantly found across all studies. The result highlighted not only regions which are well known to be amplified like 17q21 and 11q13, but also others like 8q24 (distal to MYC) and 17q24-q25 which may harbor novel putative oncogenes. Since our approach can be applied to any microarray study it may become a valuable tool for the exploration of transcriptional changes in diverse disease types. Availability: The R source codes which implement the method and an exemplary analysis are available at

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“…Methods like CGMA (comparative genomic microarray analysis) [7], MACAT (MicroArray Chromosome Analysis Tool) [8] or LAP (Locally Adaptive statistical Procedure) [9] require replicated measurements of tumor and normal reference samples for the identification of differentially expressed genes. Such methods cannot be applied to the analysis of individual tumor expression profiles in large screenings for which repeated profiling of the same sample is typically not done to reduce costs and to increase the number of screened tumors.…”

Section: Introductionmentioning

confidence: 99%

Autoregressive Higher-Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles

et al. 2014

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Changes in gene expression programs play a central role in cancer. Chromosomal aberrations such as deletions, duplications and translocations of DNA segments can lead to highly significant positive correlations of gene expression levels of neighboring genes. This should be utilized to improve the analysis of tumor expression profiles. Here, we develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in tumor. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the simultaneous usage of higher-order state-transitions and autoregressive emissions as novel model features. We apply autoregressive higher-order HMMs to the analysis of breast cancer and glioma gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other related methods. The performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions. This benefit could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumors independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles. Moreover, we reveal interesting novel details of systematic alterations of gene expression levels in known cancer signaling pathways distinguishing oligodendrogliomas, astrocytomas and glioblastomas. An implementation is available under www.jstacs.de/index.php/ARHMM.

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A locally adaptive statistical procedure (LAP) to identify differentially expressed chromosomal regions

Abstract: Functions in R for implementing the LAP method are available at http://www.dpci.unipd.it/Bioeng/Publications/LAP.htm

Cited by 32 publications

References 25 publications

Integrative genomic analysis reveals distinct transcriptional and genetic features associated with chromosome 13 deletion in multiple myeloma

Integrative genomic analysis reveals distinct transcriptional and genetic features associated with chromosome 13 deletion in multiple myeloma

Identification of aberrant chromosomal regions from gene expression microarray studies applied to human breast cancer

Autoregressive Higher-Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles

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