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pcamasigpro [2009/12/31 15:17]
aconesa
pcamasigpro [2014/05/12 12:42] (current)
jcarbonell
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 __Parameters for //​PCA-maSigFun gene selection//​__:​ __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.\\    ​*//​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.\\
 +\\
 +|Name|Array1|Array2|Array3|Array4|Array5|Array6|Array7|Array8|Array9|…|
 +|gene1|0.5|0.2|0.7|1.3|1.4|1.0|2.1|2.4|2.6|…|
 +|gene2|0.5|0.3|0.4|0.3|0.4|0.1|0.1|0.4|0.5|…|
 +|...|...|...|...|...|...|...|...|...|...|…|
 +\\
    ​*//​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:    ​*//​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:
 \\ \\
 |Time|3|3|3|9|9|9|27|27|27|…| |Time|3|3|3|9|9|9|27|27|27|…|
 |Treatment|Ctr|TrA|TrB|Ctr|TrA|TrB|Ctr|TrA|TrB|…| |Treatment|Ctr|TrA|TrB|Ctr|TrA|TrB|Ctr|TrA|TrB|…|
-   *//Annotations//: a two columns (gene tab annotation) txt file with functional data+\\  
-   *//Control.group//: name of the reference series in the model (in the example is Ctr) +   *//Quantitative factor//: name of the numerical variable of the experimental design, normally the time
-   ​*//​degree//: ​polynomial ​degree for the regression model (max. is # time-points ​– 1). \\ +   *//Qualitative factor//: name of the categorical variable of the experimental design. 
-   *//alpha//: significant level for gene selection.\\ +   ​*//​Control ​group//: name of the reference series in the regression ​model (in the example is Ctr). 
-   *//rsq//: ​cut-off ​value at the R-squared (goodness of fitregression parameter. \\ +   * //Annotations//:​ the annotations can be uploaded in a two columns (gene tab annotation) txt file. 
-   *//var.cutoff//: Variability level to select Principal Components. +   ​*//​Polynomial ​degree//: degree for the regression model. ​The maximum allowed degree ​is #time_points ​– 1. \\ 
-   *//fac.sel//:  criterion to select components can be: +   *//Alpha//: significant level for gene selection.\\ 
-      *'​%accum':​ percentage ​of accumulated ​variability +   *//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]. \\ 
-      *'​single%': ​ percentage ​of variability of that PC +   *//Cut-off//: Variability level to select Principal Components ​in each category
-      *'​abs.val'​absolute value of the variability ​of that PC +   *//Selection factor//:  criterion to select components can be: 
-      *'rel.abs': fold variability of tot.var/rank(X) +      * Proportion ​of acumulated ​variability. Posible cut-off values are in (0,1). 
-\\+      * Proportion ​of variability of each PC. Posible cut-off values are in (0,1). 
 +      * Averagecomponents are selected that explain more than "​cut-off"​ times the average component ​variability. ​The recommended "​cut-off"​ values are in [1,1.5]
 +   ​\\
 __Parameters for //​PCA-maSigFun visualization//​__\\ __Parameters for //​PCA-maSigFun visualization//​__\\
-   *//k//: number ​of clusters ​to split gene selection+To show the trajectories in plots //maSig visualization//​ applies clustering methods to group 
-   ​*//cluster.method//: ​clustering method. Possible values ​are: +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\\        * '​hclust':​ hierarchical clustering\\
-       * '​kmeans':​ k-means\\ +       * '​kmeans':​ k-means\\  
-   *//series.to.see//: number ​of the series ​to visualize from the available series.\\+   *//Number of clusters//: groups to split gene selection to show results. 
 +__PCA parameters:​__ 
 +Threshold for significant gene contribution for the PCA model. This threshold allows the identification 
 +of the genes that most contribute ​to the selected componentsIt can be computed 
 +by applying several procedures:​ 
 +   ​*//​Resampling//: where a null Leverage distribution is created by permuting columns ​of expression data and genes are selected at the "​alpha"​ percentile of the null distribution. 
 +   ​*//​minAS//:​ where a density function is calculated on the data and genes are selected on a local minimum basis [[references |[7]]]. 
 +   ​*//​Gamma//:​ where a gamma distribution is adjusted ​to the distributions of the gene loadings, and genes are selected at the "​alpha"​ percentile of the gamma distribution [[references |[7]]].  
 +   ​*//​Custom//:​ where the user can decide the threshold.
 \\ \\
  
  
  
 +       
  
pcamasigpro.1262269021.txt.gz · Last modified: 2009/12/31 15:17 by aconesa
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