Return to start page
1. GSRI
1. Short description
- An approach for “gene set analysis”, i.e. for assessing whether a group of functionally related features (genes, RNAs, proteins or …) are regulated.
- The GSRI estimates the fraction of significantly regulated features.
- 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.
- In contrast to other similar approaches, no reference gene set which is NOT regulated (e.g. “all genes”) is required.
- The most prominent similar approach is GSEA (gene set enrichment analysis)
1.2. Applicability/restrictions/pitfalls
- The approach is applied several times in application project. It works.
- Drawback: Collaborators weakly tend to more prominent approaches.
1.3. Code availability
- R-package “les” on Bioconductor
1.4. Publications from the Timmer group
1.5. Side remark
- Weighting of the individual p-values leads to LES
2. LES
1. Short description
- Local estimate of the fraction of significant p-values
- The approach has been developed for tiling arrays.
- The approach can be applied if p-values from statistical tests are available in a spatial order (e.g. along the genome)
- The GSRI estimates the fraction of significantly regulated features.
- A smoothing window is applied in combination (similar to GSRI).
- It enables significance statements whether at a certain position a significant fraction of p-values deviate from the uniform distribution.
- The outcome can be used to rank genomic regions, i.e. for finding regions of interest.
- In contrast to other similar approaches, no reference gene set which is NOT regulated (e.g. “all genes”) is required.
- The most prominent similar approach is GSEA (gene set enrichment analysis)
3. Applicability/restrictions/pitfalls
- 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.
- There were no other projects with similar data, i.e. where the approach could be applied.
4. Code availability
- R-package “les” on Bioconductor
5. Publications from the Timmer group
- Julian Gehring’s Masters Thesis
3. TSSi
1. Short description
- Transcription Start Site Identification (TSSi) based on sequencing reads
- The data did not uniquely indicate TSSs.
- 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.
2. Applicability/restrictions/pitfalls
- I guess that similar data is not produced any more. Therefore, the approach might be obsolete.
3. Availability
- R-package TSSi
4. Publication from the Timmer group
4. Optimal Transformations
1. Short description
- The Mean Optimal Transformation Approach (MOTA) was suggested for investigating non-identifiablities.
- Based on alternating conditional expectation (ACE) algorithm
- Non-parametric method based on kernel estimation to unravel arbitrary dependencies in data
- Works also for relations, e.g. a circle
2. Applicability/restrictions/pitfalls
- Since based on kernel estimation restricted to low dimensional problems
3. Code availability
- R-package MOTA (not maintained any more, see CRAN archive)
- ACE is available in as R-package “acepack”
- Matlab code for ACE is available internally (ask Clemens)
4. Other related methods
- ACE
5. Publications from the Timmer group
- Hengl S et al. Data-based identifiability analysis of nonlinear dynamical models (2007)
6. Publication from external groups
- Breiman & Friedman. Estimating optimal transformations for multiple regression and correlation. (1985)
5. Error Models
6. Retarded Transient Function
1. Under development
2. Short description
- An explicit function which has very similar shape as ODE solutions of signalling pathways
- If small amounts of data (observables) are available, the approach might serve as an alternative to traditional ODE modelling.
- The approach provides self-explained parameters (amplitudes, response times, time-scales)
- It can be directly fit to data in order to have an explicit function describing the time dependency (like a smoothing spline)
- It can be fit to ODEs in order to have an approximation of the dynamics as explicit function (e.g. for multiscale models)
3. Availability
- D2D is used for fitting
- See: D2D Example folder (ToyModels/TransientFunction)
4. Applicability
- Fitting is very robust
- For data, the outcome is great in 90% of cases
- For approximating ODEs, the performance depends on the model. The accuracy is better than uncertainties of data.
5. Publication
- Submitted
</col>