Genomic comparison using data mining techniques based on a possibilistic fuzzy sets model

Balzano, Walter; Sorbo, Maria Rosaria Del

doi:10.1016/j.biosystems.2006.07.014

Cited by 14 publications

(5 citation statements)

References 5 publications

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“…To this aim, we carried out unsupervised hierarchical clustering of the biopsies using the expression of 4–5 markers for aggregation purposes. This statistical method groups samples by similarity of expression in different groups or clusters [ 20 ]. To illustrate this point, the clustering of the 39 biopsies of control, deficit of Complex I activity and of the expression of myophosphorylase using the expression of NADH dehydrogenase subunit 9, myophosphorylase and the β-F1-ATPase/GAPDH ratio resulted in the distribution of the biopsies in three separate groups with a classification sensitivity of 95% and 100% for complex I and myophosphorylase deficiencies, respectively (Figure 3 A).…”

Section: Resultsmentioning

confidence: 99%

Quantitative analysis of proteins of metabolism by reverse phase protein microarrays identifies potential biomarkers of rare neuromuscular diseases

et al. 2015

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BackgroundMuscle diseases have been associated with changes in the expression of proteins involved in energy metabolism. To this aim we have developed a number of monoclonal antibodies against proteins of energy metabolism.MethodsHerein, we have used Reverse Phase Protein Microarrays (RPMA), a high throughput technique, to investigate quantitative changes in protein expression with the aim of identifying potential biomarkers in rare neuromuscular diseases. A cohort of 73 muscle biopsies that included samples from patients diagnosed of Duchenne (DMD), Becker (BMD), symptomatic forms of DMD and BMD in female carriers (Xp21 Carriers), Limb Girdle Muscular Dystrophy Type 2C (LGMD2C), neuronal ceroid lipofuscinosis (NCL), glycogenosis type V (Mc Ardle disease), isolated mitochondrial complex I deficiency, intensive care unit myopathy and control donors were investigated. The nineteen proteins of energy metabolism studied included members of the mitochondrial oxidation of pyruvate, the tricarboxylic acid cycle, β-oxidation of fatty acids, electron transport and oxidative phosphorylation, glycogen metabolism, glycolysis and oxidative stress using highly specific antibodies.ResultsThe results indicate that the phenotype of energy metabolism offers potential biomarkers that could be implemented to refine the understanding of the biological principles of rare diseases and, eventually, the management of these patients.ConclusionsWe suggest that some biomarkers of energy metabolism could be translated into the clinics to contribute to the improvement of the clinical handling of patients affected by rare diseases.Electronic supplementary materialThe online version of this article (doi:10.1186/s12967-015-0424-1) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Quantitative analysis of proteins of metabolism by reverse phase protein microarrays identifies potential biomarkers of rare neuromuscular diseases

et al. 2015

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“…A better normalization method will lead to higher and lower silhouette coefficients for biological and batch labels, respectively. The silhouette coefficients were computed using the function silhouette() from the R package cluster (version 2.1.2) 63 .…”

Section: Methodsmentioning

confidence: 99%

Removing unwanted variation from large-scale RNA sequencing data with PRPS

et al. 2022

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Accurate identification and effective removal of unwanted variation is essential to derive meaningful biological results from RNA sequencing (RNA-seq) data, especially when the data come from large and complex studies. Using RNA-seq data from The Cancer Genome Atlas (TCGA), we examined several sources of unwanted variation and demonstrate here how these can significantly compromise various downstream analyses, including cancer subtype identification, association between gene expression and survival outcomes and gene co-expression analysis. We propose a strategy, called pseudo-replicates of pseudo-samples (PRPS), for deploying our recently developed normalization method, called removing unwanted variation III (RUV-III), to remove the variation caused by library size, tumor purity and batch effects in TCGA RNA-seq data. We illustrate the value of our approach by comparing it to the standard TCGA normalizations on several TCGA RNA-seq datasets. RUV-III with PRPS can be used to integrate and normalize other large transcriptomic datasets coming from multiple laboratories or platforms.

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“…A better normalization method will lead to higher and lower silhouette coefficients for biological and batch labels respectively. The silhouette coefficients were computed using the function silhouette() from the R package cluster (version 2.1.2) [33].…”

Section: Silhouette Coefficient Analysismentioning

confidence: 99%

Removing unwanted variation from large-scale cancer RNA-sequencing data

Molania

Foroutan

Gagnon-Bartsch

et al. 2021

Preprint

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The accurate identification and effective removal of unwanted variation are essential to derive meaningful biological results from RNA-seq data, especially when the data come from large and complex studies. We have used The Cancer Genome Atlas (TCGA) RNA-seq data to show that library size, batch effects, and tumor purity are major sources of unwanted variation across all TCGA RNA-seq datasets and that existing gold standard approaches to normalizations fail to remove this unwanted variation. Additionally, we illustrate how different sources of unwanted variation can compromise downstream analyses, including gene co-expression, association between gene expression and survival outcomes, and cancer subtype identifications. Here, we propose the use of a novel strategy, pseudo-replicates of pseudo-samples (PRPS), to deploy the Removing Unwanted Variation III (RUV-III) method to remove different sources of unwanted variation from large and complex gene expression studies. Our approach requires at least one roughly known biologically homogenous subclass of samples shared across sources of unwanted variation. To create PRPS, we first need to identify the sources of unwanted variation, which we will call batches in the data. Then the gene expression measurements of biologically homogeneous sets of samples are averaged within batches, and the results called pseudo-samples. Pseudo-samples with the same biology and different batches are then defined to be pseudo-replicates and used in RUV-III as replicates. The variation between pseudo-samples of a set pseudo-replicates is mainly unwanted variation. We illustrate the value of our approach by comparing it to the TCGA normalizations on several TCGA RNA-seq datasets. RUV-III with PRPS can be used for any large genomics project involving multiple labs, technicians, or platforms.

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Genomic comparison using data mining techniques based on a possibilistic fuzzy sets model

Cited by 14 publications

References 5 publications

Quantitative analysis of proteins of metabolism by reverse phase protein microarrays identifies potential biomarkers of rare neuromuscular diseases

Quantitative analysis of proteins of metabolism by reverse phase protein microarrays identifies potential biomarkers of rare neuromuscular diseases

Removing unwanted variation from large-scale RNA sequencing data with PRPS

Removing unwanted variation from large-scale cancer RNA-sequencing data

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