Gene Expression Data In Terms Research Articles

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm.

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.

Elastic Net Regularized Softmax Regression Methods for Multi-subtype Classification in Cancer

<P>Background: Various regularization methods have been proposed to improve the prediction accuracy in cancer diagnosis. Elastic net regularized logistic regression has been widely adopted for cancer classification and gene selection in genetics and molecular biology but is commonly applied to binary classification and regression. However, usually, the cancer subtypes can be more, and most likely cannot be decided precisely. </P><P> Objective: Besides the multi-class issue, the feature selection problem is also a critical problem for cancer subtype classification. </P><P> Methods: An Elastic Net Regularized Softmax Regression (ENRSR) for multi-classification is put forward to tackle the multiple classification issue. As an extension of elastic net regularized logistic regression, ENRSR enforces structure sparsity and ‘grouping effect’ for gene selection based on gene expression data, which may exhibit high correlation. The sparsity structure and ‘grouping effect’ help to select more propriate discriminable features for multi-classification. </P><P> Result: It is demonstrated that ENRSR gains more accurate and robust performance compared to the other 6 competing algorithms (K-means, Hierarchical Clustering, Expectation Maximization, Nonnegative Matrix Factorization, Support Vector Machine and Random Forest) in predicting cancer subtypes both on simulation data and real cancer gene expression data in terms of F measure. </P><P> Conclusion: Our proposed ENRSR method is a reliable regularized softmax regression for multisubtype classification.</P>

Gene set analysis methods: a systematic comparison

BackgroundGene set analysis is a valuable tool to summarize high-dimensional gene expression data in terms of biologically relevant sets. This is an active area of research and numerous gene set analysis methods have been developed. Despite this popularity, systematic comparative studies have been limited in scope.MethodsIn this study we present a semi-synthetic simulation study using real datasets in order to test and compare commonly used methods.ResultsA software pipeline, Flexible Algorithm for Novel Gene set Simulation (FANGS) develops simulated data based on a prostate cancer dataset where the KRAS and TGF-β pathways were differentially expressed. The FANGS software is compatible with other datasets and pathways. Comparisons of gene set analysis methods are presented for Gene Set Enrichment Analysis (GSEA), Significance Analysis of Function and Expression (SAFE), sigPathway, and Correlation Adjusted Mean RAnk (CAMERA) methods. All gene set analysis methods are tested using gene sets from the MSigDB knowledge base. The false positive rate and power are estimated and presented for comparison. Recommendations are made for the utility of the default settings of methods and each method’s sensitivity towards various effect sizes.ConclusionsThe results of this study provide empirical guidance to users of gene set analysis methods. The FANGS software is available for researchers for continued methods comparisons.

Model-Based Clustering With Data Correction For Removing Artifacts In Gene Expression Data.

The NIH Library of Integrated Network-based Cellular Signatures (LINCS) contains gene expression data from over a million experiments, using Luminex Bead technology. Only 500 colors are used to measure the expression levels of the 1,000 landmark genes measured, and the data for the resulting pairs of genes are deconvolved. The raw data are sometimes inadequate for reliable deconvolution, leading to artifacts in the final processed data. These include the expression levels of paired genes being flipped or given the same value, and clusters of values that are not at the true expression level. We propose a new method called model-based clustering with data correction (MCDC) that is able to identify and correct these three kinds of artifacts simultaneously. We show that MCDC improves the resulting gene expression data in terms of agreement with external baselines, as well as improving results from subsequent analysis.

Leukocyte Dynamics Influence Reference Gene Stability in Whole Blood: Data-Driven qRT-PCR Normalization Is a Robust Alternative for Measurement of Transcriptional Biomarkers.

The use of reference genes for normalization of whole blood qRT-PCR data may be problematic in conditions such as stroke which induce alterations in white blood cell differential. In this study, we assessed the influence of stroke on the stability of commonly employed reference genes, and we evaluated data-driven normalization as an alternative. Peripheral whole blood was sampled from 33 stroke patients and 29 controls, and qRT-PCR was used to measure the expression levels of 10 target genes whose transcripts are known stroke biomarkers. Target gene expression levels were normalized via those of 2 frequently cited reference genes (ACTB and B2M) as well as with the NORMA-Gene data-driven normalization algorithm. Whole blood expression levels of reference genes were significantly altered in stroke patients relative to controls. In comparison to normalization via reference genes, NORMA-Gene produced more robust target gene expression data in terms of differential expression dynamics, variance properties, and diagnostic performance. Our findings suggest that whole blood expression levels of commonly used reference genes may be sensitive to changes in white blood cell differential, and that data-driven qRT-PCR normalization approaches offer a powerful alternative.

Brain Transcriptome Database (BrainTx, formerly CDT-DB) � Profiling of spatio-temporal gene expression during postnatal development of mouse brain

Brain Transcriptome Database (BrainTx, formerly CDT-DB) � Profiling of spatio-temporal gene expression during postnatal development of mouse brain

Comparative evaluation of set-level techniques in predictive classification of gene expression samples

BackgroundAnalysis of gene expression data in terms of a priori-defined gene sets has recently received significant attention as this approach typically yields more compact and interpretable results than those produced by traditional methods that rely on individual genes. The set-level strategy can also be adopted with similar benefits in predictive classification tasks accomplished with machine learning algorithms. Initial studies into the predictive performance of set-level classifiers have yielded rather controversial results. The goal of this study is to provide a more conclusive evaluation by testing various components of the set-level framework within a large collection of machine learning experiments.ResultsGenuine curated gene sets constitute better features for classification than sets assembled without biological relevance. For identifying the best gene sets for classification, the Global test outperforms the gene-set methods GSEA and SAM-GS as well as two generic feature selection methods. To aggregate expressions of genes into a feature value, the singular value decomposition (SVD) method as well as the SetSig technique improve on simple arithmetic averaging. Set-level classifiers learned with 10 features constituted by the Global test slightly outperform baseline gene-level classifiers learned with all original data features although they are slightly less accurate than gene-level classifiers learned with a prior feature-selection step.ConclusionSet-level classifiers do not boost predictive accuracy, however, they do achieve competitive accuracy if learned with the right combination of ingredients.AvailabilityOpen-source, publicly available software was used for classifier learning and testing. The gene expression datasets and the gene set database used are also publicly available. The full tabulation of experimental results is available at http://ida.felk.cvut.cz/CESLT.

Null space based feature selection method for gene expression data

Feature selection is quite an important process in gene expression data analysis. Feature selection methods discard unimportant genes from several thousands of genes for finding important genes or pathways for the target biological phenomenon like cancer. The obtained gene subset is used for statistical analysis for prediction such as survival as well as functional analysis for understanding biological characteristics. In this paper we propose a null space based feature selection method for gene expression data in terms of supervised classification. The proposed method discards the redundant genes by applying the information of null space of scatter matrices. We derive the method theoretically and demonstrate its effectiveness on several DNA gene expression datasets. The method is easy to implement and computationally efficient.

ECM remodelling components regulated during jaw periosteal cell osteogenesis

Human JPCs (jaw periosteal cells) are a promising source for the engineering of cell-based osteoinductive grafts in oral surgery. For this purpose, cell characteristics of this stem cell source should be elucidated in detail. Analysis of gene expression profiles may help us to evaluate key factors and cellular targets of JPC osteogenesis. Because little is known about the interplay of osteogenic-related components, we analysed the expression of different collagen types reflecting important players for extracellular matrix assembly and of TIMPs (tissue inhibitors of metalloproteinases) responsible for the inhibition of matrix degradation. Gene expression analyses using microarrays and quantitative RT-PCR (reverse transcription-PCR) during JPC osteogenesis revealed the induction of several collagen types' expression (VII, VIII, XI and XII), and some of them (types I, VIII and XI) seemed to be susceptible to BMP-2 (bone morphogenetic protein-2) that is known to be a potent osteogenic inducer of periosteal cells. Among the TIMPs, only TIMP-4 and RECK (reversion-inducing cysteine-rich protein with Kazal motifs) expressions were strongly up-regulated during JPC osteogenesis. Proteome profiler analysis of supernatants from untreated and differentiated JPCs confirmed the gene expression data in terms of TIMP expression. In summary, we identified new collagen types and TIMPs that seem to play important roles during the osteogenesis of jaw periosteal progenitor cells.

Analyzing gene expression data in terms of gene sets: methodological issues

Many statistical tests have been proposed in recent years for analyzing gene expression data in terms of gene sets, usually from Gene Ontology. These methods are based on widely different methodological assumptions. Some approaches test differential expression of each gene set against differential expression of the rest of the genes, whereas others test each gene set on its own. Also, some methods are based on a model in which the genes are the sampling units, whereas others treat the subjects as the sampling units. This article aims to clarify the assumptions behind different approaches and to indicate a preferential methodology of gene set testing. We identify some crucial assumptions which are needed by the majority of methods. P-values derived from methods that use a model which takes the genes as the sampling unit are easily misinterpreted, as they are based on a statistical model that does not resemble the biological experiment actually performed. Furthermore, because these models are based on a crucial and unrealistic independence assumption between genes, the P-values derived from such methods can be wildly anti-conservative, as a simulation experiment shows. We also argue that methods that competitively test each gene set against the rest of the genes create an unnecessary rift between single gene testing and gene set testing.