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Serial Expression Analysis

Serial Expression Analysis (SEA) is a web site for the analysis of serial gene expression data. Serial data is understood as multifactorial experimental designs where one of the factors is a quantitative variable such as time or treatment dose. The site offers five different methodologies for the identification of genes and functional classes which significant changes across series.

maSigFun

maSigFun [4] methodology derives from maSigPro [1], a regression-based approach for the analysis of multiple series time-course microarray data . In maSigFun the regression model is not gene-wise fitted as in maSigPro, but to the data matrix composed by the expression values of all genes belonging to the functional class, thereby being one regression model computed for each functional category. The program returns lists of functional categories with a statistically significant expression changes, as a whole, along time and across series. As in maSigPro, the trajectory charts for significant functional categories can be plotted through the maSigFun visulazation module.
Parameters for maSigFun class selection: *Data: txt file with expression data, genes in rows, arrays in columns. The file must contain an additional row with arrays names and a column with gene names.

  • Covariates: txt file with experimental design information, containing as many columns as arrays and as many rows as experimental factor. Each cell contains the value of the array in the experimental factor. E.g:


Time333999272727
TreatmentCtrTrATrBCtrTrATrBCtrTrATrB

Parameters for maSigFun visualization

  • k: number of clusters to split gene selection.
  • cluster.method: clustering method. Possible values are:
    • “hclust”: hierarchical clustering
    • “kmeans”: k-means
  • series.to.see: number of the series to visualize from the available series.



References

[1] Conesa, A.; Nueda, M.J.; Ferrer, A. and Talón, M. (2006) maSigPro: a Method to Identify Significantly Differential Expression Profiles in Time-Course Microarray Experiments. Bioinformatics, 22 (9), 1096-1102.
[2] Nueda, M.J.; Conesa, A.; Westerhuis, J.A.; Hoefsloot, H.C.J.; Smilde, A.K.; Talón, M. and Ferrer, A. (2007) Discovering gene expression patterns in Time Course Microarray Experiments by ANOVA-SCA. Bioinformatics, 23 (14), 1792-1800.
[3] Smilde, A.K.; Jansen, J.J.; Hoefsloot, H.C.J.; Lamers, R.J.A.N.; Van der Greef, J. and Timmerman, M.E. (2005) ANOVA-Simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data. Bioinformatics, 21 (13), 3043-3048. [4] Nueda, M.J.; Sebastián, P.; Tarazona, S.; García-García, F.; Dopazo, J.; Ferrer, A. and Conesa, A. (2009) Functional Assessment of Time Course Microarray data. BMC Bioinformatics. 10 (suppl 6): S9.

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