GAAS: Gene Array Analyzer Software for management, analysis and visualization of gene expression data

Gene expression data often contain missing expression values. For the purpose of conducting an effective clustering analysis and since many algorithms for gene expression data analysis require a complete matrix of gene array values, choosing the most effective missing value estimation method is necessary. In this paper, the most commonly used imputation methods from literature are critically reviewed and analyzed to explain the proper use, weakness and point the observations on each published method. From the conducted analysis, we conclude that the Local Least Square (LLS) and Support Vector Regression (SVR) algorithms have achieved the best performances. SVR can be considered as a complement algorithm for LLS especially when applied to noisy data. However, both algorithms suffer from some deficiencies presented in choosing the value of Number of Selected Genes (K) and the appropriate kernel function. To overcome these drawbacks, the need for new method that automatically chooses the parameters of the function and it also has an appropriate computational complexity is imperative.

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Correlation of Dynamic PET and Gene Array Data in Patients with Gastrointestinal Stromal Tumors

The Scientific World JOURNAL ◽

10.1100/2012/721313 ◽

2012 ◽

Vol 2012 ◽

pp. 1-5 ◽

Cited By ~ 3

Author(s):

Ludwig G. Strauss ◽

Antonia Dimitrakopoulou-Strauss ◽

Dirk Koczan ◽

Leyun Pan ◽

Peter Hohenberger

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Gastrointestinal Stromal Tumors ◽

Gene Array ◽

Tracer Uptake ◽

Cell Cycle Gene ◽

Dynamic Pet ◽

Expression Data ◽

Array Data ◽

Stromal Tumors

Introduction. The results obtained with dynamic PET (dPET) were compared to gene expression data obtained in patients with gastrointestinal stromal tumors (GIST). The primary aim was to assess the association of the dPET results and gene expression data.Material and Methods. dPET was performed following the injection of F-18-fluorodeoxyglucose (FDG) in 22 patients with GIST. All patients were examined prior to surgery for staging purpose. Compartment and noncompartment models were used for the quantitative evaluation of the dPET examinations. Gene array data were based on tumor specimen obtained by surgery after the PET examinations.Results. The data analysis revealed significant correlations for the dPET parameters and the expression of zinc finger genes (znf43, znf85, znf91, znf189). Furthermore, the transport of FDG (k1) was associated with VEGF-A. The cell cycle gene cyclin-dependent kinase inhibitor 1C was correlated with the maximum tracer uptake (SUVmax) in the tumors.Conclusions. The data demonstrate a dependency of the tracer kinetics on genes associated with prognosis in GIST. Furthermore, angiogenesis and cell proliferation have an impact on the tracer uptake.

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Systematic Identification of Housekeeping Genes Possibly Used as References in Caenorhabditis elegans by Large-Scale Data Integration

Cells ◽

10.3390/cells9030786 ◽

2020 ◽

Vol 9 (3) ◽

pp. 786 ◽

Cited By ~ 1

Author(s):

Jingxin Tao ◽

Youjin Hao ◽

Xudong Li ◽

Huachun Yin ◽

Xiner Nie ◽

...

Keyword(s):

Gene Expression ◽

Caenorhabditis Elegans ◽

Gene Expression Data ◽

Reference Genes ◽

Large Scale ◽

Gene Array ◽

Housekeeping Genes ◽

Functional Enrichment ◽

Expression Data ◽

Data Types

For accurate gene expression quantification, normalization of gene expression data against reliable reference genes is required. It is known that the expression levels of commonly used reference genes vary considerably under different experimental conditions, and therefore, their use for data normalization is limited. In this study, an unbiased identification of reference genes in Caenorhabditis elegans was performed based on 145 microarray datasets (2296 gene array samples) covering different developmental stages, different tissues, drug treatments, lifestyle, and various stresses. As a result, thirteen housekeeping genes (rps-23, rps-26, rps-27, rps-16, rps-2, rps-4, rps-17, rpl-24.1, rpl-27, rpl-33, rpl-36, rpl-35, and rpl-15) with enhanced stability were comprehensively identified by using six popular normalization algorithms and RankAggreg method. Functional enrichment analysis revealed that these genes were significantly overrepresented in GO terms or KEGG pathways related to ribosomes. Validation analysis using recently published datasets revealed that the expressions of newly identified candidate reference genes were more stable than the commonly used reference genes. Based on the results, we recommended using rpl-33 and rps-26 as the optimal reference genes for microarray and rps-2 and rps-4 for RNA-sequencing data validation. More importantly, the most stable rps-23 should be a promising reference gene for both data types. This study, for the first time, successfully displays a large-scale microarray data driven genome-wide identification of stable reference genes for normalizing gene expression data and provides a potential guideline on the selection of universal internal reference genes in C. elegans, for quantitative gene expression analysis.

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