Fundamentals of cDNA microarray data analysis

Leung, Yiu-Wing; Cavalieri, Duccio

doi:10.1016/j.tig.2003.09.015

Cited by 271 publications

(151 citation statements)

References 47 publications

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“…The complexity of these methods often creates a communicational gap between the data-producing biologists and the data-analyzing mathematicians and biostatisticians (7,8,36). To avoid this gap, and to keep the presentation of our results as directly connected to the samples they represent as possible, we have deliberately used very simple, straightforward methods to compare the overall GEP of different samples, rather than any of the more sophisticated software packages existing today.…”

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confidence: 99%

Getting the right cells to the array: Gene expression microarray analysis of cell mixtures and sorted cells

et al. 2004

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Background: Most biological samples are cell mixtures. Some basic questions are still unanswered about analyzing these heterogeneous samples using gene expression microarray technology (MAT). How meaningful is a cell mixture's overall gene expression profile (GEP)? Is it necessary to purify the cells of interest before microarray analysis, and how much purity is needed? How much does the purification itself distort the GEP, and how well can the GEP of a small cell subset be recovered? Methods: Model cell mixtures with different cell ratios were analyzed by both spotted and Affymetrix MAT. GEP distortion during cell purification and GEPs of purified cells were studied. CD34ϩ cord blood cells were purified and analyzed by MAT. Results: GEPs for mixed cell populations were found to mirror the cell ratios in the mixture. Over 75% pure

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confidence: 99%

Getting the right cells to the array: Gene expression microarray analysis of cell mixtures and sorted cells

et al. 2004

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“…28 As a result, TIMP-1 was completely separated from the other clusters of phenotypic markers or EBV infection status. This suggests that TIMP-1( þ ) diffuse large B-cell lymphoma is a distinct group separate from other cases of diffuse large B-cell lymphoma.…”

Section: Discussionmentioning

confidence: 98%

“…We used a correlation coefficient with centering; a similarity measure that is sensitive to the expression profile shape, regardless of the expression levels. 28 Antibodies clustered into groups that reflected the relatedness of expression of the cognate antigens. Interestingly, the greatest number of branches, of the antibody dendrogram, clearly separate anti-TIMP-1 from the other antibodies and EBER probe (BCL6, MUM-1, CD10, CD138, and EBER) (Figure 1).…”

Section: Hierarchical Clustering Analysis Of Tissue Microarray Datamentioning

confidence: 99%

Clinicopathologic implications of tissue inhibitor of metalloproteinase-1-positive diffuse large B-cell lymphoma

Choi

Lee

et al. 2006

Modern Pathology

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The tissue inhibitor of metalloproteinase-1 (TIMP-1) is a stromal factor that promotes plasmablastic differentiation, and the survival of germinal center B-cells. The expression of TIMP-1 is known to be correlated with a subset of non-Hodgkin lymphoma at the mRNA level, and Epstein-Barr virus infection in vitro. To characterize TIMP-1( þ ) diffuse large B-cell lymphoma, TIMP-1 expression was investigated in tissue microarrays from 182 cases of de novo diffuse large B-cell lymphoma and compared with prognostic factors, immunophenotypes, and Epstein-Barr virus infection status. TIMP-1 was expressed not only in tumor cells themselves, in 14 of 182 cases (8%), designated as TIMP-1( þ ) diffuse large B-cell lymphoma, but also in stromal cells like fibroblasts and endothelial cells. In univariate analysis and hierarchical clustering, our findings suggest that TIMP-1 expression may represent a distinct subgroup. In multivariate analysis, TIMP-1( þ ) diffuse large B-cell lymphoma (n ¼ 14) was associated with unfavorable outcomes compared to TIMP-1(À) diffuse large B-cell lymphoma (n ¼ 168) (odds ratio ¼ 2.5, P ¼ 0.049). Together with TIMP-1 expression, age (greater than 60 years), the presence of B-symptoms, abnormal lactate dehydrogenase level, or more advanced stage (III/IV) was correlated with a poor overall survival. However, TIMP-1 expression in diffuse large B-cell lymphoma was not correlated with other prognostic factors including: clinical stage, international prognostic index score, and nongerminal center B-cell phenotype, as well as Epstein-Barr virus infection. Our results suggest that TIMP-1 expression may be an independent negative prognostic factor in patients with diffuse large B-cell lymphoma.

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“…Recently, numerous studies have exploited the microarray approach (Leung and Cavalieri, 2003) to monitor changes in adipose tissue gene expression during calorie restriction.…”

Section: I3 Adipose Tissue and Calorie Restrictionmentioning

confidence: 99%

Genes involved in obesity: Adipocytes, brain and microflora

et al. 2006

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Obesity and obesity-related disorders such as cardiovascular diseases and metabolic abnormalities including hypertension, hyperinsulinemia, insulin-resistance and type 2 diabetes, are among the most pressing problems in the industrialized world (Friedman, 2003;Kannel et al., 1991 (Reitman et al., 2000), demonstrating that normal adipose tissue development and function is critical for metabolic homeostasis.Obesity results from complex interactions between genes and environmental factors such as diet, food components and/or way of life, and can be viewed as an energy storage disorder in which weight gain results from an energy imbalance (i.e. energy input exceeding output), with most of the excess calories stored as triglycerides in adipose tissue (Smith et al., 2006). Therefore a great deal of attention has focused on the mechanisms controlling food intake and adipose mass, and is of considerable importance for public health and clinical medicine.In this review, we prevalently highlight the complex relationships existing between the white adipose tissue, the central nervous system, the endogenous microbiota, and nutrition. Throughout, we emphasize the most recent findings in the field and consider how it shapes the past knowledge and forecasts the future of research on metabolic disorders, such as obesity.

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Fundamentals of cDNA microarray data analysis

Cited by 271 publications

References 47 publications

Getting the right cells to the array: Gene expression microarray analysis of cell mixtures and sorted cells

Getting the right cells to the array: Gene expression microarray analysis of cell mixtures and sorted cells

Clinicopathologic implications of tissue inhibitor of metalloproteinase-1-positive diffuse large B-cell lymphoma

Genes involved in obesity: Adipocytes, brain and microflora

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