scholarly journals Internal standard-based analysis of microarray data. Part 1: analysis of differential gene expressions

2009 ◽  
Vol 37 (19) ◽  
pp. 6323-6339 ◽  
Author(s):  
I. Dozmorov ◽  
I. Lefkovits
Keyword(s):  
Microarray Data ◽  
Internal Standard ◽  
Gene Expressions ◽  
Differential Gene

Screening for Differential Gene Expressions from Microarray Data

Screening ◽  
2006 ◽  
pp. 139-155
Author(s):  
Jason C. Hsu ◽  
Jane Y. Chang ◽  
Tao Wang
Keyword(s):  
Microarray Data ◽  
Gene Expressions ◽  
Differential Gene

scholarly journals 3D genome alterations associated with dysregulated HOXA13 expression in high-risk T-lineage acute lymphoblastic leukemia

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lu Yang ◽  
Fengling Chen ◽  
Haichuan Zhu ◽  
Yang Chen ◽  
Bingjie Dong ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Lymphoblastic Leukemia ◽  
T Cell Differentiation ◽  
Newly Diagnosed ◽  
Coding Region ◽  
Gene Expressions ◽  
3D Genome ◽  
Topologically Associated Domains ◽  
Differential Gene ◽  
Poor Outcomes

Abstract3D genome alternations can dysregulate gene expression by rewiring enhancer-promoter interactions and lead to diseases. We report integrated analyses of 3D genome alterations and differential gene expressions in 18 newly diagnosed T-lineage acute lymphoblastic leukemia (T-ALL) patients and 4 healthy controls. 3D genome organizations at the levels of compartment, topologically associated domains and loop could hierarchically classify different subtypes of T-ALL according to T cell differentiation trajectory, similar to gene expressions-based classification. Thirty-four previously unrecognized translocations and 44 translocation-mediated neo-loops are mapped by Hi-C analysis. We find that neo-loops formed in the non-coding region of the genome could potentially regulate ectopic expressions of TLX3, TAL2 and HOXA transcription factors via enhancer hijacking. Importantly, both translocation-mediated neo-loops and NUP98-related fusions are associated with HOXA13 ectopic expressions. Patients with HOXA11-A13 expressions, but not other genes in the HOXA cluster, have immature immunophenotype and poor outcomes. Here, we highlight the potentially important roles of 3D genome alterations in the etiology and prognosis of T-ALL.

scholarly journals Differential gene expressions in arbuscular mycorrhizal-colonized tomato grown under heavy metal stress

2005 ◽  
Vol 162 (6) ◽  
pp. 634-649 ◽  
Author(s):  
Fouad Ouziad ◽  
Ulrich Hildebrandt ◽  
Elmon Schmelzer ◽  
Hermann Bothe
Keyword(s):  
Heavy Metal ◽  
Arbuscular Mycorrhizal ◽  
Heavy Metal Stress ◽  
Metal Stress ◽  
Gene Expressions ◽  
Differential Gene

Soft Subspace Clustering for Cancer Microarray Data Analysis

2014 ◽  
pp. 131-145
Author(s):  
Natthakan Iam-On ◽  
Tossapon Boongoen
Keyword(s):  
Microarray Data ◽  
Dimensional Space ◽  
Subspace Clustering ◽  
Research Direction ◽  
Microarray Data Analysis ◽  
Consensus Clustering ◽  
Gene Expressions ◽  
Tumor Subtypes ◽  
Microarray Gene ◽  
New Research

A need has long been identified for a more effective methodology to understand, prevent, and cure cancer. Microarray technology provides a basis of achieving this goal, with cluster analysis of gene expression data leading to the discrimination of patients, identification of possible tumor subtypes, and individualized treatment. Recently, soft subspace clustering was introduced as an accurate alternative to conventional techniques. This practice has proven effective for high dimensional data, especially for microarray gene expressions. In this review, the basis of weighted dimensional space and different approaches to soft subspace clustering are described. Since most of the models are parameterized, the application of consensus clustering has been identified as a new research direction that is capable of turning the difficulty with parameter selection to an advantage of increasing diversity within an ensemble.

scholarly journals IMPROVED ESTIMATION OF COVARIANCE MATRIX IN HOTELLING’S T2 FOR MICROARRAY DATA

2016 ◽  
Vol 8 (2) ◽  
pp. 26 ◽  
Author(s):  
Suryaefiza Karjanto ◽  
Norazan Mohamed Ramli ◽  
Nor Azura Md Ghaninor Azura Md Ghani
Keyword(s):  
Covariance Matrix ◽  
Microarray Data ◽  
Simulated Data ◽  
Principal Component ◽  
Data Sets ◽  
Similar Data ◽  
Shrinkage Methods ◽  
Differential Gene ◽  
The Relationship ◽  
Simulated Data Sets

<p class="lead">The relationship between genes in gene set analysis in microarray data is analyzed using Hotelling’s <em>T</em><sup>2</sup> but the test cannot be applied when the number of samples is larger than the number of variables which is uncommon in the microarray. Thus, in this study, we proposed shrinkage approaches to estimating the covariance matrix in Hotelling’s <em>T<sup>2</sup></em> particularly to cater high dimensionality problem in microarray data. Three shrinkage covariance methods were proposed in this study and are referred as Shrink A, Shrink B and Shrink C. The analysis of the three proposed shrinkage methods was compared with the Regularized Covariance Matrix Approach and Kong’s Principal Component Analysis. The performances of the proposed methods were assessed using several cases of simulated data sets. In many cases, the Shrink A method performed the best, followed by the Shrink C and RCMAT methods. In contrast, both the Shrink B and KPCA methods showed relatively poor results. The study contributes to an establishment of modified multivariate approach to differential gene expression analysis and expected to be applied in other areas with similar data characteristics.</p>

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