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pcamasigpro [2010/03/10 13:59]
mjnueda
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:
 \\ \\
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    ​*//​Alpha//:​ significant level for gene selection.\\    ​*//​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]. \\    ​*//​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]. \\
-   ​*//​Cut-off ​(0-1)//: Variability level to select Principal Components in each category.+   ​*//​Cut-off//:​ Variability level to select Principal Components in each category.
    ​*//​Selection factor//: ​ criterion to select components can be:    ​*//​Selection factor//: ​ criterion to select components can be:
-      * Proportion of acumulated variability. +      * Proportion of acumulated variability. Posible cut-off values are in (0,1)
-      * Proportion of variability of that PC.+      * Proportion of variability of each PC. Posible cut-off values are in (0,1). 
 +      * Average: components 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//​__\\
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 of the genes that most contribute to the selected components. It can be computed of the genes that most contribute to the selected components. It can be computed
 by applying several procedures: by applying several procedures:
-   ​*//​Resampling//: ​This option requires an alpha level and the number ​of permutations to compute+   ​*//​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//: ​This technique does not require any parameter+   ​*//​minAS//: ​where a density function is calculated on the data and genes are selected on a local minimum basis [[references |[7]]]
-   ​*//​Gamma//: ​An alpha level is required+   ​*//​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//: ​With this option ​the user can decide the threshold.+   ​*//​Custom//: ​where the user can decide the threshold.
 \\ \\
  
  
  
 +       
  
pcamasigpro.1268225971.txt.gz · Last modified: 2010/03/10 13:59 by mjnueda
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