The Stability of Gene Selection in Microarray Experiments

This work presents a comparative study of methods for building predictive models from data in high dimensionality spaces, i.e. where the number of features describing items to be classified is high as compared with the available number of items used to build the model and test its predictive performance. Application of such methods may be quite diverse, ranging from data analysis in life sciences (e.g., analysis of data from experiments generating thousands of feature-numbers per tested case, such a microarray or RT-PCR techniques), to analysis of monitoring data from a complex, highly reliable technical system, where the relationship is being sought between the monitoring data and the relatively infrequent occurrences of some event (such as a faulty or somewhat untypical state of the system). This latter case may be of special interest in early prediction of abnormal conditions in systems focused on dependability. The multidimensional data analysis challenges and generic methods are described in this paper using a very problem specific language of life sciences, namely classification of samples based on gene expression profiles obtained using DNA microarrays. We concentrate on feature selection methods (which in the context are gene selection methods). We also propose a method to evaluate performance of feature (gene) selection methods by looking at predictive power of classifiers based on selected features.

Development of public toxicogenomics software for microarray data management and analysis

The real-time system of the JT-60 has a cascade structure with the major and the minor control loops. The multi-variable feedback control is adopted in the major control loop in order to adjust non-linear effects which will appear in the high-/beta/region. By applying the concept of 'phase shift control' to the control program of the major control loop, a flexible tokamak discharge control is expected to be performed with taking account of machine protection.

Two important questions for the analysis of gene expression measurements from different sample classes are (1) how to classify samples and (2) how to identify meaningful gene signatures (ranked gene lists) exhibiting the differences between classes and sample subsets. Solutions to both questions have immediate biological and biomedical applications. To achieve optimal classification performance, a suitable combination of classifier and gene selection method needs to be specifically selected for a given dataset. The selected gene signatures can be unstable and the resulting classification accuracy unreliable, particularly when considering different subsets of samples. Both unstable gene signatures and overestimated classification accuracy can impair biological conclusions. We address these two issues by repeatedly evaluating the classification performance of all models, i.e. pairwise combinations of various gene selection and classification methods, for random subsets of arrays (sampling). A model score is used to select the most appropriate model for the given dataset. Consensus gene signatures are constructed by extracting those genes frequently selected over many samplings. Sampling additionally permits measurement of the stability of the classification performance for each model, which serves as a measure of model reliability. We analyzed a large gene expression dataset with 78 measurements of four different cartilage sample classes. Classifiers trained on subsets of measurements frequently produce models with highly variable performance. Our approach provides reliable classification performance estimates via sampling. In addition to reliable classification performance, we determined stable consensus signatures (i.e. gene lists) for sample classes. Manual literature screening showed that these genes are highly relevant to our gene expression experiment with osteoarthritic cartilage. We compared our approach to others based on a publicly available dataset on breast cancer. R package at http://www.bio.ifi.lmu.de/~davis/edaprakt

Gene selection is to select the most informative genes from the whole gene set. It's an important preprocessing procedure for the discriminant analysis of microarray data, because many of the genes are irrelevant or redundant to the discriminant problem. In this paper, the gene selection problem is considered as a gene ranking problem and a random subspace method based gene ranking (RSM-GR) algorithm is proposed. In RSM-GR, firstly subsets of the genes are randomly generated; then Support Vector Machines are respectively trained on each subset and thus produce the importance factor of each gene; finally, the importance of each gene obtained from these randomly selected subsets is combined to constitute its final importance. Experiments on two public datasets show that RSM-GR obtains gene sets leading to more accurate classification results than other gene selection methods, and it demands less computational time. RSM-GR can also better deal with datasets with a large number of genes and a big number of genes to be selected.

Informative gene selection using Adaptive Analytic Hierarchy Process (A2HP)

BackgroundGene selection is one of the critical steps in the course of the classification of microarray data. Since particle swarm optimization has no complicated evolutionary operators and fewer parameters need to be adjusted, it has been used increasingly as an effective technique for gene selection. Since particle swarm optimization is apt to converge to local minima which lead to premature convergence, some particle swarm optimization based gene selection methods may select non-optimal genes with high probability. To select predictive genes with low redundancy as well as not filtering out key genes is still a challenge.ResultsTo obtain predictive genes with lower redundancy as well as overcome the deficiencies of traditional particle swarm optimization based gene selection methods, a hybrid gene selection method based on gene scoring strategy and improved particle swarm optimization is proposed in this paper. To select the genes highly related to out samples’ classes, a gene scoring strategy based on randomization and extreme learning machine is proposed to filter much irrelevant genes. With the third-level gene pool established by multiple filter strategy, an improved particle swarm optimization is proposed to perform gene selection. In the improved particle swarm optimization, to decrease the likelihood of the premature of the swarm the Metropolis criterion of simulated annealing algorithm is introduced to update the particles, and the half of the swarm are reinitialized when the swarm is trapped into local minima.ConclusionsCombining the gene scoring strategy with the improved particle swarm optimization, the new method could select functional gene subsets which are significantly sensitive to the samples’ classes. With the few discriminative genes selected by the proposed method, extreme learning machine and support vector machine classifiers achieve much high prediction accuracy on several public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.

Despite the considerable efforts that are done for Microarray gene expression profile, biomarker discovery or gene selection process remains a complex challenge for biologists and computer scientists. This article presented an Artificial Bee Colony (ABC) based approach for accurate classification of cancer microarray data using support vector machine (SVM) as a classifier. The approach is able to deal with the high dimensionality of the microarray data. According to literature for microarray data analysis, up to our knowledge, this is the first attempt to apply Artificial Bee Colony based algorithm as gene selection method for cancer classification problem using Microarray gene expression profile. Comparing with other algorithms that are proposed in the literature, we can conclude that our proposed algorithm (ABC-SVM) obtains promising results for gene selection and cancer classification problems.

In recent years, the application of single cell RNA-seq (scRNA-seq) has become more and more popular in fields such as biology and medical research. Analyzing scRNA-seq data can discover complex cell populations and infer single-cell trajectories in cell development. Clustering is one of the most important methods to analyze scRNA-seq data. In this paper, we focus on improving scRNA-seq clustering through gene selection, which also reduces the dimensionality of scRNA-seq data. Studies have shown that gene selection for scRNA-seq data can improve clustering accuracy. Therefore, it is important to select genes with cell type specificity. Gene selection not only helps to reduce the dimensionality of scRNA-seq data, but also can improve cell type identification in combination with clustering methods. Here, we proposed RFCell, a supervised gene selection method, which is based on permutation and random forest classification. We first use RFCell and three existing gene selection methods to select gene sets on 10 scRNA-seq data sets. Then, three classical clustering algorithms are used to cluster the cells obtained by these gene selection methods. We found that the gene selection performance of RFCell was better than other gene selection methods.

One problem with discriminant analysis of microarray data is representation of each sample by a large number of genes that are possibly irrelevant, insignificant, or redundant. Methods of variable selection are, therefore, of great significance in microarray data analysis. A new method for key gene selection has been proposed on the basis of interval segmentation purity that is defined as the purity of samples belonging to a certain class in intervals segmented by a mode search algorithm. This method identifies key variables most discriminative for each class, which offers possibility of unraveling the biological implication of selected genes. A salient advantage of the new strategy over existing methods is the capability of selecting genes that, though possibly exhibit a multimodal distribution, are the most discriminative for the classes of interest, considering that the expression levels of some genes may reflect systematic difference in within-class samples derived from different pathogenic mechanisms. On the basis of the key genes selected for individual classes, a support vector machine with block-wise kernel transform is developed for the classification of different classes. The combination of the proposed gene mining approach with support vector machine is demonstrated in cancer classification using two public data sets. The results reveal that significant genes have been identified for each class, and the classification model shows satisfactory performance in training and prediction for both data sets.

Microarray data analysis has been widely used for extracting relevant biological information from thousands of genes simultaneously expressed in a specific cell. Although many genes are expressed in a sample tissue, most of these are irrelevant or insignificant for clinical diagnosis or disease classification because of missing values and noises. Thus, finding a small, closely related gene set to accurately classify disease cells is an important research problem. At the same time, scalable gene selection methods are required for microarray data analysis due to rapidly increasing volume of microarray data. In this paper, we propose a scalable parallel gene selection method using the M a p R e u d c e programming model. The proposed method utilizes the kNN classifier algorithm for evaluating classification accuracy and uses four real and three synthetic datasets for experiments. Experimental results show that the proposed method can offer good scalability on large data with increasing number of nodes and it can also provide higher classification accuracy rather than using whole gene set for classification.

Spotted cDNA microarrays are emerging as a powerful and cost-effective tool for large-scale analysis of gene expression. Microarrays can be used to measure the relative quantities of specific mRNAs in two or more tissue samples for thousands of genes simultaneously. While the power of this technology has been recognized, many open questions remain about appropriate analysis of microarray data. One question is how to make valid estimates of the relative expression for genes that are not biased by ancillary sources of variation. Recognizing that there is inherent "noise" in microarray data, how does one estimate the error variation associated with an estimated change in expression, i.e., how does one construct the error bars? We demonstrate that ANOVA methods can be used to normalize microarray data and provide estimates of changes in gene expression that are corrected for potential confounding effects. This approach establishes a framework for the general analysis and interpretation of microarray data.

The analysis of high-volume microarray data faces challenges such as limited sample size, computational complexity, and the risk of inappropriate gene selection. The scarcity of samples hampers computational analysis and classification complexity, while reducing the classification's ability to generalize and predict new samples. Moreover, datasets with a high gene-to-sample ratio raise concerns about the selection of relevant genes for accurate predictive models. Interpreting disease-causing genes becomes intricate as only a subset of genes offers a precise biological insight into the disease. To address these issues, a focus on a smaller set of gene expression data is crucial for a more effective understanding of informative genes. Hence, the primary objective in microarray data analysis is to significantly reduce the number of genes through discriminative gene selection, enhancing the precision of information contained in the data. This article conducts gene expression classification on various cancer types, including colon cancer, breast cancer, leukemia, prostate tumors, and DLBCL. Each cancer type is independently evaluated in the feature selection cycle and classified using varying numbers of features. This approach aims to overcome challenges in microarray data analysis and improve the accuracy and interpretability of gene expression classification.

Introduction: Drug response prediction, especially in terms of cell viability prediction, is a well-studied research problem with significant implications for personalized medicine. It enables the identification of the most effective drugs based on individual genetic profiles, aids in selecting potential drug candidates, and helps identify biomarkers that predict drug efficacy and toxicity.A deeper investigation on drug response prediction reveals that drugs exert their effects by targeting specific proteins, which in turn perturb related genes in cascading ways. This perturbation affects cellular pathways and regulatory networks, ultimately influencing the cellular response to the drug. Identifying which genes are perturbed and how they interact can provide critical insights into the mechanisms of drug action. Hence, the problem of predicting drug response can be framed as a dual problem involving both the prediction of drug efficacy and the selection of drug-specific genes. Identifying these drug-specific genes (biomarkers) is crucial because they serve as indicators of how the drug will affect the biological system, thereby facilitating both drug response prediction and biomarker discovery.Methods: In this study, we propose DGDRP (Drug-specific Gene selection for Drug Response Prediction), a graph neural network (GNN)-based model that uses a novel rank-and-re-rank process for drug-specific gene selection. DGDRP first ranks genes using a pathway knowledge-enhanced network propagation algorithm based on drug target information, ensuring biological relevance. It then re-ranks genes based on the similarity between gene and drug target embeddings learned from the GNN, incorporating semantic relationships. Thus, our model adaptively learns to select drug mechanism-associated genes that contribute to drug response prediction. This integrated approach not only improves drug response predictions compared to other gene selection methods but also allows for effective biomarker discovery.Discussion: As a result, our approach demonstrates improved drug response predictions compared to other gene selection methods and demonstrates comparability with state-of-the-art deep learning models. Case studies further support our method by showing alignment of selected gene sets with the mechanisms of action of input drugs.Conclusion: Overall, DGDRP represents a deep learning based re-ranking strategy, offering a robust gene selection framework for more accurate drug response prediction. The source code for DGDRP can be found at: https://github.com/minwoopak/heteronet.

Application of the S-score algorithm for analysis of oligonucleotide microarrays