Clustering of short time-course gene expression data with dissimilar replicates

Microarrays are used in genetics and medicine to examine large numbers of genes simultaneously through their expression levels under any condition such as a disease of interest. The information from these experiments can be enriched by following the expression levels through time and biological replicates. The purpose of this study is to propose an algorithm which clusters the genes with respect to the similarities between their behaviors through time. The algorithm is also aimed at highlighting the genes which show different behaviors between the replicates and separating the constant genes that keep their baseline expression levels throughout the study. Finally, we aim to feature cluster validation techniques to suggest a sensible number of clusters when it is not known a priori. The illustrations show that the proposed algorithm in this study offers a fast approach to clustering the genes with respect to their behavior similarities, and also separates the constant genes and the genes with dissimilar replicates without any need for pre-processing. Moreover, it is also successful at suggesting the correct number of clusters when that is not known.


Comparing Clustering Techniques for Real Microarray Data
Purutçuoğlu Gazi, Vilda (2012-08-29)
The clustering of genes detected as significant or differentially expressed provides useful information to biologists about functions and functional relationship of genes. There are variant types of clustering methods that can be applied in genomic data. These are mainly divided into the two groups, namely, hierarchical and partitional methods. In this paper, as the novelty, we perform a detailed clustering analysis for the recently collected boron microarray dataset to investigate biologically more interes...
Mixed effects models for time series gene expression data
Erkan, İbrahim; İlk Dağ, Özlem; Batmaz, İnci; Department of Statistics (2011)
The experimental factors such as the cell type and the treatment may have different impact on expression levels of individual genes which are quantitative measurements from microarrays. The measurements can be collected at a few unevenly spaced time points with replicates. The aim of this study is to consider cell type, treatment and short time series attributes and to infer about their effects on individual genes. A mixed effects model (LME) was proposed to model the gene expression data and the performanc...
Training of ANFIS Network by Genetic Algorithm for Diagnosis of Leukemia Cancer Subtypes Using Gene Expression Profile
Arslan, Mustafa Turan; Haznedar, Bülent; Kalınlı, Adem (2017-05-12)
In this study, subtypes of Leukemia cancer has classified by using microarray gene expression profiles. An approach is proposed to train Adaptive Neuro Fuzzy Inference System (ANFIS) network by using a population-based Genetic Algorithm (GA) to classify this cancer data. The classification success of the proposed model has compared with the successes of Backpropagation (BP)-ANFIS and Hybrid-ANFIS, which are derivative based ANFIS models. According to obtained results, GA-ANFIS model has performed ve...
Meta analysis of alzheimer’s disease at the gene expression level
İzgi, Hamit; Somel, Mehmet; Department of Biology (2017)
In this study, publicly available microarray gene expression datasets are used to investigate common gene expression changes in different postmortem brain regions in Alzheimer’s Disease (AD) patients compared to control subjects, and to find possible functional associations related to these changes. The hypothesis is that pathogenesis of the disease converges into common patterns of dysregulation/alteration or dysfunction in molecular pathways across different brain regions in AD. In total, I studied 13 dat...
Işıldak, Ulaş; Somel, Mehmet; Department of Biology (2022-9-1)
Aging is a complex process associated with the accumulation of stochastic genetic and epigenetic alterations, leading to functional decline and increased risk for disease and death. Although some previous studies demonstrated a tendency towards increased inter-individual heterogeneity during aging, whether it is a function of time that starts at the beginning of life is unknown. Its functional consequences and regulations have also not been systematically studied. In this study, I addressed these questions ...
Citation Formats
O. Cinar, Ö. İlk Dağ, and C. İyigün, “Clustering of short time-course gene expression data with dissimilar replicates,” ANNALS OF OPERATIONS RESEARCH, pp. 405–428, 2018, Accessed: 00, 2020. [Online]. Available: