https://github.com/csoneson/iCOBRA
Interactive benchmarking of ranking and assignment methods
https://github.com/csoneson/iCOBRA
Last synced: 4 months ago
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Interactive benchmarking of ranking and assignment methods
- Host: GitHub
- URL: https://github.com/csoneson/iCOBRA
- Owner: csoneson
- Created: 2015-10-12T13:53:25.000Z (over 9 years ago)
- Default Branch: devel
- Last Pushed: 2024-11-02T16:05:07.000Z (5 months ago)
- Last Synced: 2024-11-24T09:51:43.675Z (5 months ago)
- Language: R
- Homepage:
- Size: 18.3 MB
- Stars: 14
- Watchers: 7
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS
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README
# iCOBRA - Interactive benchmarking of ranking and assignment methods
`iCOBRA` is a package to calculate and visualize performance metrics for
ranking and binary assignment methods. A typical use case could be,
for example, comparing methods calling differential expression in
gene expression experiments, which could be seen as either a ranking
problem (estimating the correct effect size and ordering the genes by
significance) or a binary assignment problem (classifying the genes
into differentially expressed and non-differentially expressed).
`iCOBRA` can be used either directly from the console, or via the
interactive shiny application (see the function `COBRAapp()`). It can also
be accessed from the web server [http://imlspenticton.uzh.ch:3838/iCOBRA/](http://imlspenticton.uzh.ch:3838/iCOBRA/)
We have also collected a set of benchmarking data sets, addressing different aspects of genomic data analysis. The collection is reachable via the following link: [http://imlspenticton.uzh.ch/robinson_lab/benchmark_collection/](http://imlspenticton.uzh.ch/robinson_lab/benchmark_collection/)
## Installation
`iCOBRA` can be installed from `Bioconductor` using `BiocManager`:
```
install.packages("BiocManager")
BiocManager::install("iCOBRA")
```
or, optionally,
```
BiocManager::install("markrobinsonuzh/iCOBRA")
```
## Quick start guide
The `iCOBRA` workflow starts from an object of class `COBRAData`,
containing (adjusted) p-values and/or scores for a set of features as
well as the true status of the features. An example data set is provided in
the package
```
library(iCOBRA)
data(cobradata_example)
```
The function `calculate_performance()` calculates the different performance
metrics for a `COBRAData` object. By default, all performance metrics are
calculated, but a subset can be selected using the `aspects` argument.
```
cobraperf <- calculate_performance(cobradata_example, binary_truth = "status",
cont_truth = "logFC",
aspects = c("fdrtpr", "fdrtprcurve",
"corr"))
```
Next, the performance metrics are prepared for plotting using the
`prepare_for_plot()` function. This function defines colors for the
various methods and can also be used for selecting only a subset of the
methods for visualization, without having to recalculate the performance metrics.
```
cobraplot <- prepare_data_for_plot(cobraperf, colorscheme = "Set2",
keepmethods = c("voom", "edgeR"))
```
The resulting object can then be used to generate plots of the selected aspects.
```
plot_fdrtprcurve(cobraplot)
plot_corr(cobraplot, corrtype = "spearman")
```
## Vignette
The vignette can be found in the `vignettes/` directory. Further
information is also available in the 'Instructions' tab of the shiny app.
After installation, the vignette can be accessed from the R console by typing
```
browseVignettes("iCOBRA")
```
## Benchmarking data set collection
To facilitate future benchmarking studies, we have collected a set of benchmarking
data sets on [http://imlspenticton.uzh.ch/robinson_lab/benchmark_collection/](http://imlspenticton.uzh.ch/robinson_lab/benchmark_collection/). The page provides
links to raw data as well as evaluation results suitable for import into `iCOBRA`.