×

Clustering of short time-course gene expression data with dissimilar replicates. (English) Zbl 1392.92054

Summary: 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.

MSC:

92D10 Genetics and epigenetics
92C40 Biochemistry, molecular biology
62H30 Classification and discrimination; cluster analysis (statistical aspects)

Software:

EMMIX; ORIOGEN
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Alonso, A; Berrendero, J; Hernandez, A; Justel, A, Time series clustering based on forecast densities, Computational Statistics and Data Analysis, 51, 762-776, (2006) · Zbl 1157.62484
[2] Bar-Joseph, Z, Analyzing time series gene expression data, Bioinformatics, 20, 2493-2503, (2004)
[3] Bar-Joseph, Z; Gerber, GK; Gifford, DK; Jaakkola, TS; Simon, I, Continuous representations of time-series gene expression data, Journal of Computational Biology, 10, 341-356, (2003)
[4] Caiado, J; Crato, N; Pena, D, A periodogram-based metric for time series classification, Computational Statistics and Data Analysis, 50, 2668-2684, (2006) · Zbl 1445.62222
[5] Celeux, G; Martin, O; Lavergne, C, Mixture of linear mixed models for clustering gene expression profiles from repeated microarray experiments, Statistical Modelling, 5, 243-267, (2005) · Zbl 1111.62103
[6] Cho, RJ; Campbell, MJ; Winzeler, EA; Steinmetz, L; Wodicka, ACL; Wolfsberg, TG; Gabrielan, AE; Landsman, D; Lockhart, DJ; Davis, RW, A genome-wide transcriptioanal analysis of the mitotic cell cycle, Molecular Cell, 2, 65-73, (1998)
[7] Chu, S; DeRisi, J; Eisen, M; Mulholland, J; Botstein, D; Brown, PO, The transcriptional program of sporulation in budding yeast, Science, 282, 699-705, (1998)
[8] Corduas, M; Piccolo, D, Time series clustering and classification by the autoregressive metric, Computational Statistics and Data Analysis, 52, 1860-1872, (2008) · Zbl 1452.62624
[9] Díaz, SP; Vilar, JA, Comparing several parametric and nonparametric approaches to time series clustering: A simulation study, Journal of Classification, 27, 333-362, (2010) · Zbl 1337.62137
[10] Do, JH; Choi, D, Clustering approaches to identfying gene expression patterns from DNA microarray data, Molecules and Cells, 25, 279, (2008)
[11] Eisen, MB; Spellman, PT; Brown, PO; Boltstein, D, Cluster analysis and display of genome-wide expression patterns, Proceedings of the National Academy of Sciences, 95, 14,863-14,868, (1998)
[12] Ernst, J; Nau, GJ; Bar-Joseph, Z, Clustering short time series gene expression data, Bioinformatics, 21, i159-i168, (2005)
[13] Galbraith, J., & Jiaqing, L. (1999). Cluster and discriminant analysis on time series as a research tool UTIP Working Paper Number 6, The University of Texas at Austin, Austin: Lyndon B · Zbl 1445.62222
[14] Hackstadt, AJ; Hess, AM, Filtering for increased power for microarray data analysis, BMC Bioinformatics, 10, 1, (2009)
[15] Hakamada, K; Okamoto, M; Hanai, T, Novel technique for preprocessing high dimensional time-course data from DNA microarray: mathematical model-based clustering, Bioinformatics, 22, 843-848, (2006)
[16] Heard, NA; Holmes, CC; Stephens, DA; Hand, DJ; Dimopoulos, G, Bayesian coclustering of anopheles gene expression time series: study of immune defense response to multiple experimental challenges, Proceedings of the National Academy of Sciences of the United States of America, 102, 16,939-16,944, (2005)
[17] Heyer, LJ; Kruglyak, S; Yooseph, S, Exploring expression data: identification and analysis of coexpressed genes, Genome Research, 9, 1106-1115, (1999)
[18] Irigoien, I; Vives, S; Arenas, C, Microarray time course experiments: finding profiles, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8, 464-475, (2011)
[19] Kakizawa, Y; Shumway, RH; Taniguchi, M, Discrimation and clustering for multivariate time series, Journal of the American Statistical Association, 93, 328-340, (1998) · Zbl 0906.62060
[20] Khan, J; Simon, R; Bittner, M; Chen, Y; Leighton, SB; Pohida, T; Smith, PD; Jiang, Y; Gooden, GC; Trent, JM; Meltzer, PS, Gene expression profiling of alveolar rhabdomyosarcoma with cdna microarrays, Cancer Research, 58, 5009-5013, (1998)
[21] Kim, BR; Zhang, L; Berg, A; Fan, J; Wu, R, A computational approach to the functional clustering of periodic gene-expression profiles, Genetics, 180, 821-834, (2008)
[22] Liao, TW, Clustering of time series data: A survey, Pattern Recognition, 38, 1857-1874, (2005) · Zbl 1077.68803
[23] Luan, Y; Li, H, Model-based methods for identifying periodically expressed genes based on time course microarray gene expression data, Bioinformatics, 20, 332-339, (2004)
[24] Maulik, U; Bandyopadhyay, S, Performance evaluation of some clustering algorithms and validity indices, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 1650-1654, (2002)
[25] McLachlan, GJ; Peel, D; Basford, KE; Adams, P, The emmix software for the Fitting of mixture of normal and t-components, Journal of Statistical Software, 4, 1-14, (1999)
[26] Möller-Levet, CS; Klawonn, F; Cho, KH; Yin, H; Wolkenhauer, O, Clustering of unevenly sampled gene expression time-series data, Fuzzy Sets and Systems, 152, 49-66, (2005) · Zbl 1066.92025
[27] Ng, SK; McLachlan, GJ; Wang, K; Jones, LBT; Ng, SW, A mixture of model with random effect components for clustering correlated gene-expression profiles, Bioinformatics, 22, 1745-1752, (2006)
[28] Peddada, S; Harris, S; Zajd, J; Harvey, E, Oriogen: order restricted inference for ordered gene expression data, Bioinformatics, 21, 3933-3934, (2005)
[29] Ramoni, MF; Sebastiani, P; Kohane, IS, Cluster analysis of gene expression dynamics, Proceedings of the National Academy of Sciences, 99, 9121-9126, (2002) · Zbl 1023.62110
[30] Schliep, A; Schönhuth, A; Steinhoff, C, Using hidden Markov models to analyze gene expression time course data, Bioinformatics, 19, i255-i263, (2003)
[31] Spellman, PT; Sherlock, G; Zhang, MQ; Iyer, VR; Anders, K; Eisen, MB; Brown, PO; Botstein, D; Futcher, B, Comprehensive identification of cell cycle-regulated of the yeast saccharomyces cerevisiae by microarray hybridization, Molecular Biology of the Cell, 9, 3273-3297, (1998)
[32] Storey, JD; Xiao, W; Leef, JT; Tompkins, RG; Davis, RW, Significance analysis of time course microarray experiments, Proceedings of the National Academy of Sciences of the America, 102, 12,837-12,842, (2005)
[33] Szekely, GJ; Rizzo, ML, Hierarchical clustering via joint between-within distances: extending ward’s minimum variance method, Journal of Classification, 22, 151-183, (2005) · Zbl 1336.62192
[34] Tamayo, P; Slonim, D; Mesirov, J; Zhu, Q; Kitareewan, S; Dmitrovsky, E; Lander, ES; Golub, TR, Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation, Proceedings of the National Academy of Sciences, 96, 2907-2912, (1999)
[35] Vilar, JA; Alonso, A; Vilar, JM, Non-linear time series clustering based on non-parametric forecast densities, Computational Statistics and Data Analysis, 54, 2850-2865, (2010) · Zbl 1284.62575
[36] Vilar, JM; Vilar, JA; Pertega, S, Classifying time series data: A nonparametric approach, Journal of Classification, 26, 3-28, (2009) · Zbl 1276.62042
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.