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 maSigFunVisulazation 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:

  • Quantitative factor: name of the numerical variable of the experimental design, normally the time.
  • Qualitative factor: name of the categorical variable of the experimental design.
  • Control group: name of the reference series in the regression model (in the example is Ctr).
  • Annotations: the annotations can be uploaded in a two columns (gene tab annotation) txt file.
  • Polynomial degree: degree for the regression model. The maximum allowed degree is #time_points – 1.
  • Alpha: significant level for gene selection.
  • R-Squared cut-off: required level of the goodness of fit of the regression model. This parameter is between 0 and 1. Higher values indicate well fitted models. We recommend values between [0.4,0.8].

Parameters for maSigFun visualization
To show the trajectories in plots maSig visualization applies clustering methods to group functional categories with similar trends and summarize the graphical display.

  • Series to see: name of the series to visualize from the available series.
  • clustering method: available methods are:
    • 'hclust': hierarchical clustering
    • 'kmeans': k-means
  • Number of clusters: groups to split gene selection to show results.

masigfun.txt · Last modified: 2010/05/04 23:25 by aconesa
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