data_analysis

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data_analysis [2021/03/14 11:23]
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-### 1. GSRI +## 1. GSRI 
-####  1.1. Short description + 
-##### 1.1.1. An approach for “gene set analysis”, i.e. for assessing whether a group of functionally related features (genes, RNAs, proteins or …) are regulated. +### 1. Short description 
-#### 1.1.2. The GSRI estimates the fraction of significantly regulated features. +  1. An approach for “gene set analysis”, i.e. for assessing whether a group of functionally related features (genes, RNAs, proteins or …) are regulated. 
-#### 1.1.3. For estimation, the empirical cumulative density function (ecdf) of the p-values is analyzed. An iterative estimation procedure is used to unravel the difference to a uniform distribution of p-values (which corresponds to a diagonal line for the ecdf). It also enables calculation of standard errors for the fraction and significance statements. +  2. The GSRI estimates the fraction of significantly regulated features. 
-#### 1.1.4. In contrast to other similar approaches, no reference gene set which is NOT regulated (e.g. “all genes”) is required. +  3. For estimation, the empirical cumulative density function (ecdf) of the p-values is analyzed. An iterative estimation procedure is used to unravel the difference to a uniform distribution of p-values (which corresponds to a diagonal line for the ecdf). It also enables calculation of standard errors for the fraction and significance statements. 
-#### 1.1.5. The most prominent similar approach is GSEA (gene set enrichment analysis)+  4. In contrast to other similar approaches, no reference gene set which is NOT regulated (e.g. “all genes”) is required. 
 +  5. The most prominent similar approach is GSEA (gene set enrichment analysis)
  
 ### 1.2. Applicability/restrictions/pitfalls ### 1.2. Applicability/restrictions/pitfalls
-#### 1.2.1. The approach is applied several times in application project. It works. +  1. The approach is applied several times in application project. It works. 
-#### 1.2.2. Drawback: Collaborators weakly tend to more prominent approaches.+  2. Drawback: Collaborators weakly tend to more prominent approaches.
  
 ### 1.3. Code availability ### 1.3. Code availability
-#### 1.3.1. R-package “les” on Bioconductor+  1. R-package “les” on Bioconductor
  
 ### 1.4. Publications from the Timmer group ### 1.4. Publications from the Timmer group
-#### 1.4.1. https://doi.org/10.1089/cmb.2008.0226+  1. https://doi.org/10.1089/cmb.2008.0226
  
 ### 1.5. Side remark ### 1.5. Side remark
-#### 1.5.1. Weighting of the individual p-values leads to LES +  1. Weighting of the individual p-values leads to LES 
  
 ## 2. LES ## 2. LES
-### 2.1. Short description +### 1. Short description 
-### 2.2. Local estimate of the fraction of significant p-values +  1. Local estimate of the fraction of significant p-values 
-### 2.3. The approach has been developed for tiling arrays. +  2. The approach has been developed for tiling arrays. 
-### 2.4. The approach can be applied if p-values from statistical tests are available in a spatial order (e.g. along the genome) +  3. The approach can be applied if p-values from statistical tests are available in a spatial order (e.g. along the genome) 
-### 2.5. The GSRI estimates the fraction of significantly regulated features. +  4. The GSRI estimates the fraction of significantly regulated features. 
-### 2.6. A smoothing window is applied in combination (similar to GSRI). +  5. A smoothing window is applied in combination (similar to GSRI). 
-### 2.7. It enables significance statements whether at a certain position a significant fraction of p-values deviate from the uniform distribution. +  6. It enables significance statements whether at a certain position a significant fraction of p-values deviate from the uniform distribution. 
-### 2.8. The outcome can be used to rank genomic regions, i.e. for finding regions of interest. +  7. The outcome can be used to rank genomic regions, i.e. for finding regions of interest. 
-### 2.9. In contrast to other similar approaches, no reference gene set which is NOT regulated (e.g. “all genes”) is required. +  8. In contrast to other similar approaches, no reference gene set which is NOT regulated (e.g. “all genes”) is required. 
-### 2.10. The most prominent similar approach is GSEA (gene set enrichment analysis)+  9. The most prominent similar approach is GSEA (gene set enrichment analysis)
  
-## 3. Applicability/restrictions/pitfalls +### 3. Applicability/restrictions/pitfalls 
-### 3.1. The R-package was implemented by Julian Gehring. Is was one of the most experience R programmer in our group and later become group member in Wolfgang Huber’s lab (a major Bioconductor group). He utilized Bioconductor classes. +  1. The R-package was implemented by Julian Gehring. Is was one of the most experience R programmer in our group and later become group member in Wolfgang Huber’s lab (a major Bioconductor group). He utilized Bioconductor classes. 
-### 3.2. There were no other projects with similar data, i.e. where the approach could be applied.+  2. There were no other projects with similar data, i.e. where the approach could be applied.
  
-## 4. Code availability +### 4. Code availability 
-### 4.1. R-package “les” on Bioconductor+  1. R-package “les” on Bioconductor
  
-## 5. Publications from the Timmer group +### 5. Publications from the Timmer group 
-### 5.1. Julian Gehring’s Masters Thesis+  1. Julian Gehring’s Masters Thesis
  
-## 6. TSSi +## 3. TSSi 
-### 6.1. Short description +### 1. Short description 
-#### 6.1.1. Transcription Start Site Identification (TSSi) based on sequencing reads +  1. Transcription Start Site Identification (TSSi) based on sequencing reads 
-#### 6.1.2. The data did not uniquely indicate TSSs.  +  2. The data did not uniquely indicate TSSs.  
-#### 6.1.3. The approach has been applied for prediction TSS for the physcomitrella patens genome. The results were available in the standard genome browser for this organism.+  3. The approach has been applied for prediction TSS for the physcomitrella patens genome. The results were available in the standard genome browser for this organism.
  
-### 6.2. Applicability/restrictions/pitfalls +### 2. Applicability/restrictions/pitfalls 
-#### 6.2.1. I guess that similar data is not produced any more. Therefore, the approach might be obsolete.+  1. I guess that similar data is not produced any more. Therefore, the approach might be obsolete.
  
-### 6.3. Availability +### 3. Availability 
-#### 6.3.1. R-package TSSi+  1. R-package TSSi
  
-### 6.4. Publication from the Timmer group +### 4. Publication from the Timmer group 
-#### 6.4.1. https://doi.org/10.1093/bioinformatics/bts189+  1. https://doi.org/10.1093/bioinformatics/bts189
  
  
-## 7. Optimal Transformations +## 4. Optimal Transformations 
-### 7.1. Short description +### 1. Short description 
-#### 7.1.1. The Mean Optimal Transformation Approach (MOTA) was suggested for investigating non-identifiablities.  +  1. The Mean Optimal Transformation Approach (MOTA) was suggested for investigating non-identifiablities.  
-#### 7.1.2. Based on alternating conditional expectation (ACE) algorithm +  2. Based on alternating conditional expectation (ACE) algorithm 
-#### 7.1.3. Non-parametric method based on kernel estimation to unravel arbitrary dependencies in data +  3. Non-parametric method based on kernel estimation to unravel arbitrary dependencies in data 
-#### 7.1.4. Works also for relations, e.g. a circle +  4. Works also for relations, e.g. a circle 
  
-### 7.2. Applicability/restrictions/pitfalls +### 2. Applicability/restrictions/pitfalls 
-#### 7.2.1. Since based on kernel estimation restricted to low dimensional problems+  1. Since based on kernel estimation restricted to low dimensional problems
  
-### 7.3. Code availability +### 3. Code availability 
-#### 7.3.1. R-package MOTA (not maintained any more, see CRAN archive) +  1. R-package MOTA (not maintained any more, see CRAN archive) 
-#### 7.3.2. ACE is available in as R-package “acepack” +  2. ACE is available in as R-package “acepack” 
-#### 7.3.3. Matlab code for ACE is available internally (ask Clemens)+  3. Matlab code for ACE is available internally (ask Clemens)
  
-### 7.4. Other related methods +### 4. Other related methods 
-#### 7.4.1. ACE+  1. ACE
  
-### 7.5. Publications from the Timmer group +### 5. Publications from the Timmer group 
-#### 7.5.1. Hengl S et al. Data-based identifiability analysis of nonlinear dynamical models (2007)+  1. Hengl S et al. Data-based identifiability analysis of nonlinear dynamical models (2007)
  
-### 7.6. Publication from external groups +### 6. Publication from external groups 
-#### 7.6.1. Breiman & Friedman. Estimating optimal transformations for multiple regression and correlation. (1985)+  1. Breiman & Friedman. Estimating optimal transformations for multiple regression and correlation. (1985)
  
-## 8. Error Models+## 5. Error Models
  
-## 9. Retarded Transient Function +## 6. Retarded Transient Function 
-### 9.1. Under development +### 1. Under development 
-### 9.2. Short description +### 2. Short description 
-#### 9.2.1. An explicit function which has very similar shape as ODE solutions of signalling pathways +  1. An explicit function which has very similar shape as ODE solutions of signalling pathways 
-#### 9.2.2. If small amounts of data (observables) are available, the approach might serve as an alternative to traditional ODE modelling. +  2. If small amounts of data (observables) are available, the approach might serve as an alternative to traditional ODE modelling. 
-#### 9.2.3. The approach provides self-explained parameters (amplitudes, response times, time-scales) +  3. The approach provides self-explained parameters (amplitudes, response times, time-scales) 
-#### 9.2.4. It can be directly fit to data in order to have an explicit function describing the time dependency (like a smoothing spline) +  4. It can be directly fit to data in order to have an explicit function describing the time dependency (like a smoothing spline) 
-#### 9.2.5. It can be fit to ODEs in order to have an approximation of the dynamics as explicit function (e.g. for multiscale models)+  5. It can be fit to ODEs in order to have an approximation of the dynamics as explicit function (e.g. for multiscale models)
  
-### 9.3. Availability +### 3. Availability 
-#### 9.3.1. D2D is used for fitting +  1. D2D is used for fitting 
-#### 9.3.2. See: D2D Example folder (ToyModels/TransientFunction)+  2. See: D2D Example folder (ToyModels/TransientFunction)
  
-### 9.4. Applicability +### 4. Applicability 
-#### 9.4.1. Fitting is very robust +  1. Fitting is very robust 
-#### 9.4.2. For data, the outcome is great in 90% of cases +  2. For data, the outcome is great in 90% of cases 
-#### 9.4.3. For approximating ODEs, the performance depends on the model. The accuracy is better than uncertainties of data.+  3. For approximating ODEs, the performance depends on the model. The accuracy is better than uncertainties of data.
  
-### 9.5. Publication +### 5. Publication 
-#### 9.5.1. Submitted+  1. Submitted
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 +  - [[#gsri|GSRI]] \\ \\  
 +  - [[#les|LES]] \\ \\   
 +  - [[#tssi|TSSi]] \\ \\   
 +  - [[#optimal_transformations|Optimal Transformations]] \\ \\    
 +  - [[#error_models|Error Models]] \\ \\    
 +  - [[#retarded_transient_function|Retarded Transient Function]] \\ \\    
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