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.

maSigPro (MicroArray Significant Profiles) [1] applies linear regression to model gene expression in (multiple)series time course microarray data and selects differentially expressed genes through a two-steps algorithm. First, responsive genes are identified by fitting a generic regression model with time as quantitative variable and series as dummy variables. Second, step-wise regression is applied on selected genes to adjust models and identify gene-specific variation patterns. maSigPro returns lists of genes with statistically significant changes along time and across the different series. Each list can be further investigated on the maSigPro visualization module where a cluster algorithm is applied on the gene selection to group genes of similar expression patterns and represent their profiles as trajectory charts.

Parameters for maSigPro gene 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:

Parameters for maSigPro 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.
  

[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.

ASCA-genes [2] is an adaptation of the ASCA method (ANOVA Simultaneous Component Analysis) [3] developed by Smilde and co-workers, to the analysis of multifactorial experiments in transcriptomics. Basically, ASCA uses ANOVA to decompose data variation associated to experimental factors, and PCA to discover principal patterns of variation associated to the experimental factors. ASCA-genes combines this multivariate descriptive analysis on time course expression data with a gene selection procedure. ASCA-genes has been implemented for designed experiments comprising either one, two or three experimental factors, one of them typically the time. The program returns trajectory charts representing major transcriptional changes and lists of selected genes that significantly follow these major changes. An additional list collects genes with expression profile changes different from the major trends.
Parameters:

• 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:

• Join: logical to indicate whether serial transcriptional changes should include time-zero non-null values (TRUE, default) or not (FALSE).
  
• Interaction: logical to indicate whether interactions between factors should be analyzed.
  
• Variability: average“ to explain more than the average variation of the principal components. A specific variation can be also indicated as 0.75 or 0.80…
  
• alpha: significant level for gene selection.
  
• R: number of bootstrap rounds for gene selection.
  

[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.