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gene_set_analysis [2012/04/23 18:12]
mbleda [Logistic model]
gene_set_analysis [2012/04/23 18:13]
mbleda [FatiScan]
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 **FatiScan** implements a segmentation test which checks for **asymmetrical distributions of regulatory elements** (microRNA and Transcription factors) associated to genes ranked in a list.  **FatiScan** implements a segmentation test which checks for **asymmetrical distributions of regulatory elements** (microRNA and Transcription factors) associated to genes ranked in a list. 
  
-{{:​images:​methods:​fatiscan.png?​170 |FatiScan}}+{{:​images:​methods:​fatiscan.png?​200 |FatiScan}}
  
 Unique in this type of approaches, this test **only** needs the list of **ordered genes** and not the original data which generated the sorting. This means that can be applied to the study of the relationship of regulatory elements to any type of experiment whose outcome is a sorted list of genes. Genes sorted by differential expression between two experimental conditions can be studied, but also genes correlated to a clinical variable (such as the level of a metabolite) or even to survival. Moreover, other lists of genes ranked by any other experimental or theoretical criteria can be studied (e.g. genes arranged by physico-chemical properties, mutability, structural parameters, etc.) in order to understand whether there is any regulatory element which is related to the experimental parameter studied. Unique in this type of approaches, this test **only** needs the list of **ordered genes** and not the original data which generated the sorting. This means that can be applied to the study of the relationship of regulatory elements to any type of experiment whose outcome is a sorted list of genes. Genes sorted by differential expression between two experimental conditions can be studied, but also genes correlated to a clinical variable (such as the level of a metabolite) or even to survival. Moreover, other lists of genes ranked by any other experimental or theoretical criteria can be studied (e.g. genes arranged by physico-chemical properties, mutability, structural parameters, etc.) in order to understand whether there is any regulatory element which is related to the experimental parameter studied.
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   - **Ranking**:​ Firstly a **list of genes is ordered using experimental information** on their differential expression, according to the phenotype studied in the experiment, or to other type of value (e.g. large-scale genotyping, evolutionary analysis, etc.). For example, genes can be ordered on the basis of their differential expression among two experimental conditions (e.g. healthy versus diseased samples, etc.).   - **Ranking**:​ Firstly a **list of genes is ordered using experimental information** on their differential expression, according to the phenotype studied in the experiment, or to other type of value (e.g. large-scale genotyping, evolutionary analysis, etc.). For example, genes can be ordered on the basis of their differential expression among two experimental conditions (e.g. healthy versus diseased samples, etc.).
   - **Distribution of regulatory elements**: The second step involves the **study of the distribution of functional terms in different partitions of this list**. Using a fisher exact test to compare such partitions, FatiScan extracts significantly under- and over-represented functional terms in a set of genes. In the figure, rows transcription factor 1 (TF1), TF2 and TF3 represent the position of the genes that are targets of this TF across the ranking. In this case, TF1 is completely uncorrelated with the arrangement while TF2 and 3 are clearly associated to high expression in the experimental conditions B and A, respectively.   - **Distribution of regulatory elements**: The second step involves the **study of the distribution of functional terms in different partitions of this list**. Using a fisher exact test to compare such partitions, FatiScan extracts significantly under- and over-represented functional terms in a set of genes. In the figure, rows transcription factor 1 (TF1), TF2 and TF3 represent the position of the genes that are targets of this TF across the ranking. In this case, TF1 is completely uncorrelated with the arrangement while TF2 and 3 are clearly associated to high expression in the experimental conditions B and A, respectively.
-  - Finally, a **table with the significant terms** obtained upon the application of the test can be used to detect significant asymmetrical distributions of genes, responsible for diverse biological processes, across the list.+  - **table with the significant terms** obtained upon the application of the test can be used to detect significant asymmetrical distributions of genes, responsible for diverse biological processes, across the list
 +  - **Multiple testing correction**:​ The P-values from the test of each regulatory element, are adjusted for multiple testing by controlling the false discovery rate (FDR) (Benjamini et al., 1995; Storey andTibshirani,​ 2003).
  
 ====== Logistic model ====== ====== Logistic model ======
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   - **Multiple testing correction**:​ The P-values from the test of each regulatory element, are adjusted for multiple testing by controlling the false discovery rate (FDR) (Benjamini et al., 1995; Storey andTibshirani,​ 2003).   - **Multiple testing correction**:​ The P-values from the test of each regulatory element, are adjusted for multiple testing by controlling the false discovery rate (FDR) (Benjamini et al., 1995; Storey andTibshirani,​ 2003).
    
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gene_set_analysis.txt · Last modified: 2012/04/23 18:13 by mbleda
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