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.