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PCA-maSigFun [4] identifies the major(s) gene expression changes within each functional class and evaluates whether these changes are significantly associated to the time. In summary, PCA is applied to the gene-expression submatrix associated to the genes belonging to each functional category. The scores of the relevant principal component(s) of these PCAs are taken as joined expression profile(s) for the functional class. The regression based time-course analysis methodology maSigPro [1] is then applied to the joined profiles (PC scores) to identify function-related subset of genes with expression changes significantly associated to the time. Note that each functional class can result in more than one joined profile when more than one subset of correlated genes exist within that functional category. that we consider that a functional block might contain several patterns of coordinative gene expression. This program returns lists of significant functional classes (an their representative joined profiles) for each of the series included in the experiment.

Parameters for PCA-maSigFun 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 PCA-maSigFun visualization

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

pcamasigpro.1262269021.txt.gz · Last modified: 2009/12/31 15:17 by aconesa
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