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Study and Analysis of Gene Expression Clustering with Gaussian Mixed Effects Models and Smoothing


Tejal Upadhyay, Dr Samir Patel


Vol. 20  No. 5  pp. 76-82


A large number of longitudinal studies measuring gene expression aim to stratify the genes according to their differential temporal behaviors. Genes with similar expression patterns may reflect functional responses of biological relevance. However, these measurements come with intrinsic noise which makes their time series clustering a difficult task. Here, we have shown how to cluster such data with mixed effects models with nonparametric smoothing spline fitting and is able to robustly stratify genes by their complex time series patterns. The article has, besides the main clustering methods, a set of functionalities assisting the user to visualize and assess the clustering results, and to choose the optimal clustering solution. The first part is about the introduction to gene expression, how time series can be applied and how the clustering is important to gene expression. The Gaussian mixed effect model is also explain. The second part is about the related work already done with some references. The third part is about our own process and workflow with diagram. How the clustering is applied and diagrams of different cluster sets. The fourth part is about results and discussion, how the silhouette analysis is important and using 3 clusters and 4 clusters how the data sets look like. The fifth part is shown with applications of clustering effects, how the yeast data sets can be divided into clusters etc. The sixth part shows the methodology of mixed Gaussian effects and smoothing splines. The last part is about conclusion and references.


Clustering, Silhouette Analysis, Gaussian mixed effect model, Smoothing Splines