Reliability analysis of microarray data using fuzzy c-means and normal mixture modeling based classification methods

Asyalı, Musa Hakan; Alcı, Musa

doi:10.1093/bioinformatics/bti036

Cited by 38 publications

(31 citation statements)

References 28 publications

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“…ncbi.nlm.nih.gov/geo/; accession number GPL-4229). We used a univariate, mixture-modeling algorithm (Asyali et al, 2004;Asyali and Alci, 2005) to identify miRNAs that were expressed in neurosphere cultures, and to eliminate nonexpressed miRNAs. ⌬CT ) indicates miRNA expression in cultures, normalized to U6 SNR .…”

Section: Resultsmentioning

confidence: 99%

“…Array features that were irrelevant to the analysis, including blank features, Cy3 control spots, and miRNA probe features for all species except mouse, were removed before additional analysis. We further eliminated genes classified as "undetected," based on a univariate mixture modeling and Bayesian statistical modeling algorithm (Asyali et al, 2004;Asyali and Alci, 2005). The data were analyzed further using GeneSifter (VizX Labs, Seattle, WA).…”

Section: Neurosphere Cultures and Alcohol Treatmentmentioning

confidence: 99%

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Competing Interactions between Micro-RNAs Determine Neural Progenitor Survival and Proliferation after Ethanol Exposure: Evidence from anEx VivoModel of the Fetal Cerebral Cortical Neuroepithelium

Sathyan¹,

Golden²,

Miranda³

2007

J. Neurosci.

262

280

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The fetal brain is sensitive to a variety of teratogens, including ethanol. We showed previously that ethanol induced mitosis and stem cell maturation, but not death, in fetal cerebral cortex-derived progenitors. We tested the hypothesis that micro-RNAs (miRNAs) could mediate the teratogenic effects of ethanol in a fetal mouse cerebral cortex-derived neurosphere culture model. Ethanol, at a level attained by alcoholics, significantly suppressed the expression of four miRNAs, miR-21, -335, -9, and -153, whereas a lower ethanol concentration, attainable during social drinking, induced miR-335 expression. A GABA A receptor-dependent mechanism mediated miR-21, but not miR-335 suppression, suggesting that divergent mechanisms regulate ethanol-sensitive miRNAs. Antisense-mediated suppression of miR-21 expression resulted in apoptosis, suggesting that miR-21 is an antiapoptotic factor. miR-335 knockdown promoted cell proliferation and prevented death induced by concurrently suppressing miR-21, indicating that miR-335 is a proapoptotic, antimitogenic factor whose actions are antagonistic to miR-21. Computational analyses identified two genes, Jagged-1, a Notch-receptor ligand, and embryonic-lethal abnormal vision, Drosophila-like 2 (ELAVL2), a brain-specific regulator of RNA stability, as presumptive targets of three of four ethanol-sensitive micro-RNAs. Combined knockdown of miR-335, -21, and -153 significantly increased Jagged-1 mRNA. Furthermore, ethanol induced both Jagged-1 and ELAVL2 mRNA. The collective suppression of micro-RNAs is consistent with ethanol induction of cell cycle and neuroepithelial maturation in the absence of apoptosis. These data identify a role for micro-RNAs as epigenetic intermediaries, which permit teratogens to shape complex, divergent developmental processes, and additionally demonstrate that coordinately regulated miRNAs exhibit both functional synergy and antagonism toward each other.

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Section: Resultsmentioning

confidence: 99%

Section: Neurosphere Cultures and Alcohol Treatmentmentioning

confidence: 99%

Competing Interactions between Micro-RNAs Determine Neural Progenitor Survival and Proliferation after Ethanol Exposure: Evidence from anEx VivoModel of the Fetal Cerebral Cortical Neuroepithelium

Sathyan¹,

Golden²,

Miranda³

2007

J. Neurosci.

262

280

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“…This way of partition is more realistic in labeling the regions of foreground spots from the background as well as from possible artifacts. The fuzzy c-means (FCM) based approaches have been introduced for several microarray data analysis [26][27][28][29][30]. In [26,30] FCM was used for grouping biologically relevant genes.…”

Section: Clustering Based Approachesmentioning

confidence: 99%

“…In [26,30] FCM was used for grouping biologically relevant genes. The study in [27] compared FCM and Gaussian normal mixture model approach in classifying microarray data into reliable and unreliable populations, showing FCM is computationally more efficient. The work in [28] introduced a new fuzzy approach and compared with FCM and SOM for gene expression profile analysis.…”

Section: Clustering Based Approachesmentioning

confidence: 99%

A Comparison of Fuzzy Clustering Approaches for Quantification of Microarray Gene Expression

Wang

Gunampally

Chen³

et al. 2007

J Sign Process Syst Sign Image

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Despite the widespread application of microarray imaging for biomedical imaging research, barriers still exist regarding its reliability for clinical use. A critical major problem lies in accurate spot segmentation and the quantification of gene expression level (mRNA) from the microarray images. A variety of commercial and research freeware packages are available, but most cannot handle array spots with complex shapes such as donuts and scratches. Clustering approaches such as k-means and mixture models were introduced to overcome this difficulty, which use the hard labeling of each pixel. In this paper, we apply fuzzy clustering approaches for spot segmentation, which provides soft labeling of the pixel. We compare several fuzzy clustering approaches for microarray analysis and provide a comprehensive study of these approaches for spot segmentation. We show that possiblistic c-means clustering (PCM) provides the best performance in terms of stability criterion when testing on both a variety of simulated and real microarray images. In addition, we compared three statistical criteria in measuring gene expression levels and show that a new asymptotically unbiased statistic is able to quantify the gene expression level more accurately.

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“…Differentially expressed (DE) genes at each time point were determined by using empirical Bayes modeling and moderated t test methods implemented in the limma R package (28), with a significance threshold of a false discovery rate (FDR) of Ͻ0.05 and a fold change of Ͼ2. The time course profiles of the union of DE genes from all time points were standardized for visualization by using a fuzzy c-mean clustering algorithm (29). The time course R package was used to calculate the statistical significance of gene expression changes over time (30).…”

mentioning

confidence: 99%

ISL1 Regulates Peroxisome Proliferator-Activated Receptor γ Activation and Early Adipogenesis via Bone Morphogenetic Protein 4-Dependent and -Independent Mechanisms

Yang

Kaplan

et al. 2014

Molecular and Cellular Biology

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g While adipogenesis is controlled by a cascade of transcription factors, the global gene expression profiles in the early phase of adipogenesis are not well defined. Using microarray analysis of gene expression in 3T3-L1 cells, we have identified evidence for the activity of 2,568 genes during the early phase of adipocyte differentiation. One of these, the ISL1 gene, was of interest since its expression was markedly upregulated 1 h after initiation of differentiation, with a subsequent rapid decline. Overexpression of ISL1 at early times during adipocyte differentiation but not at later times was found to profoundly inhibit differentiation. This was accompanied by moderate downregulation of peroxisome proliferator-activated receptor ␥ (PPAR␥) levels, substantial downregulation of PPAR␥ downstream genes, and downregulation of bone morphogenetic protein 4 (BMP4) levels in preadipocytes. Readdition of BMP4 overcame the inhibitory effect of ISL1 on the expression of PPAR␥ but not aP2, a gene downstream of PPAR␥, and BMP4 also partially rescued ISL1 inhibition of adipogenesis, an effect which is additive with rosiglitazone. These results suggest that ISL1 is intimately involved in early regulation of adipogenesis, modulating PPAR␥ expression and activity via BMP4-dependent and -independent mechanisms. Our time course gene expression survey sets the stage for further studies to explore other early and immediate regulators.

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Reliability analysis of microarray data using fuzzy c-means and normal mixture modeling based classification methods

Abstract: asyali@kfshrc.edu.sa.

Cited by 38 publications

References 28 publications

Competing Interactions between Micro-RNAs Determine Neural Progenitor Survival and Proliferation after Ethanol Exposure: Evidence from anEx VivoModel of the Fetal Cerebral Cortical Neuroepithelium

Competing Interactions between Micro-RNAs Determine Neural Progenitor Survival and Proliferation after Ethanol Exposure: Evidence from anEx VivoModel of the Fetal Cerebral Cortical Neuroepithelium

A Comparison of Fuzzy Clustering Approaches for Quantification of Microarray Gene Expression

ISL1 Regulates Peroxisome Proliferator-Activated Receptor γ Activation and Early Adipogenesis via Bone Morphogenetic Protein 4-Dependent and -Independent Mechanisms

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