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

2017 ◽  
Vol 263 (1-2) ◽  
pp. 405-428
Author(s):  
Ozan Cinar ◽  
Ozlem Ilk ◽  
Cem Iyigun
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Time Course ◽  
Expression Data ◽  
Short Time

scholarly journals Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data

2007 ◽  
Vol 8 (1) ◽  
Author(s):  
Miika Ahdesmäki ◽  
Harri Lähdesmäki ◽  
Andrew Gracey ◽  
llya Shmulevich ◽  
Olli Yli-Harja
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Time Course ◽  
Robust Regression ◽  
Expression Data ◽  
Periodicity Detection

scholarly journals Clustering short time series gene expression data

2005 ◽  
Vol 21 (Suppl 1) ◽  
pp. i159-i168 ◽  
Author(s):  
J. Ernst ◽  
G. J. Nau ◽  
Z. Bar-Joseph
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Gene Expression Data ◽  
Expression Data ◽  
Short Time Series ◽  
Time Series Gene Expression ◽  
Short Time

scholarly journals Differential and trajectory methods for time course gene expression data

2005 ◽  
Vol 21 (13) ◽  
pp. 3009-3016 ◽  
Author(s):  
Y. Liang ◽  
B. Tayo ◽  
X. Cai ◽  
A. Kelemen
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Time Course ◽  
Expression Data ◽  
Trajectory Methods

CURVE-BASED CLUSTERING OF TIME COURSE GENE EXPRESSION DATA USING SELF-ORGANIZING MAPS

2009 ◽  
Vol 07 (04) ◽  
pp. 645-661 ◽  
Author(s):  
XIN CHEN
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Regulatory Networks ◽  
Time Course ◽  
Clustering Algorithm ◽  
Expression Patterns ◽  
Self Organizing Map ◽  
Expression Data ◽  
Wide Range ◽  
Self Organizing

There is an increasing interest in clustering time course gene expression data to investigate a wide range of biological processes. However, developing a clustering algorithm ideal for time course gene express data is still challenging. As timing is an important factor in defining true clusters, a clustering algorithm shall explore expression correlations between time points in order to achieve a high clustering accuracy. Moreover, inter-cluster gene relationships are often desired in order to facilitate the computational inference of biological pathways and regulatory networks. In this paper, a new clustering algorithm called CurveSOM is developed to offer both features above. It first presents each gene by a cubic smoothing spline fitted to the time course expression profile, and then groups genes into clusters by applying a self-organizing map-based clustering on the resulting splines. CurveSOM has been tested on three well-studied yeast cell cycle datasets, and compared with four popular programs including Cluster 3.0, GENECLUSTER, MCLUST, and SSClust. The results show that CurveSOM is a very promising tool for the exploratory analysis of time course expression data, as it is not only able to group genes into clusters with high accuracy but also able to find true time-shifted correlations of expression patterns across clusters.

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