GeneNetTools
Contents
GeneNetTools#
Note
This documentation is still a work in progress.
The GeneNetTools container implements the statistical techniques
developed in Bernal et al. [BSAB+22]. These implementations allow not
only to reproduce
some of the results in the paper but also
reuse
the functions with your own data without the need for
programming skills
. It is assumed that you have Docker installed.
Reproducible results#
For the time being, two figures can be reproduced with the
GeneNetTools container Figure 2 (b)
and Figure 3
.
Figure 2 (b). Partial correlations plot#
Save the following JSON object in an
shrunk.json
file{ "filename":"https://raw.githubusercontent.com/V-Bernal/GeneNetTools/venus/feature/container/GeneNetTools/tests/testthat/data/ecoli.csv", "verbose": true, "cutoff": 0.01 }
or download the file running the command:
wget https://raw.githubusercontent.com/V-Bernal/GeneNetTools/venus/feature/container/GeneNetTools/tests/testthat/params/shrunk.json
Run the command
docker run --rm -v "$PWD":/app/data venustiano/cds:genenettools-0.1.0 c_pcor_shrunk shrunk.json
Results
Opening parameters file: shrunk.json Reading all columns Number of samples = 9 Number of variables = 102 degrees of freedom k = 828.949258958985
and the plot in
Rplots.pdf
Figure 3. Differential network analysis#
Save the following JSON object in an
zscore.json
file{ "filename": "https://raw.githubusercontent.com/V-Bernal/GeneNetTools/venus/feature/container/GeneNetTools/tests/testthat/data/DBA_2J.csv", "filename2": "https://raw.githubusercontent.com/V-Bernal/GeneNetTools/venus/feature/container/GeneNetTools/tests/testthat/data/C57BL_6J.csv", "verbose": true, "cutoff": 0.01 }
or download the json file running the command:
wget https://raw.githubusercontent.com/V-Bernal/GeneNetTools/venus/feature/container/GeneNetTools/tests/testthat/params/zscore.json
Run the command
docker run --rm -v "$PWD":/app/data venustiano/cds:genenettools-0.1.0 c_zscore_shrunk zscore.json
Results
Opening parameters file: zscore.json Reading all columns Reading all columns Number of samples = 11 Number of variables = 221 degrees of freedom k = 465.630975024994 Number of samples = 10 Number of variables = 221 degrees of freedom k = 284.915155078846
Additional example#
Network for Escherichia coli microarray data Bernal et al. [BBG+19].
docker run --rm -v "$PWD":/app/data venustiano/cds:genenettools-0.1.0 c_pval_pcor_shrunk shrunk.json
Reusing the methods#
When using the methods with your own data, a couple of constraint are
that the variables/columns should be numeric and make sure that no
rownames
are in the data file. The above examples retrieve the data
is retrieved from internet but it can be stored in the same folder as
the JSON file.
Basic commands#
Running the container:
docker run --rm venustiano/cds:genenettools-0.1.0
will display the available functions in the container:
Index:
c_pcor_shrunk Partial correlation shrunk
c_pval_pcor_shrunk pval_pcor_shrunk
c_zscore_shrunk c_zscore_shrunk
compare.GGM compare.GGM
The c_ prefix in the function name stands for containerized and receives a JSON file name as a parameter. This file must contain information such as the data file, the parameters of the function and the output formats. Finally, the container will stop running and the –rm flag will remove it.
Function documentation#
The help
flag.
docker run --rm venustiano/cds:genenettools-0.1.0 c_pcor_shrunk help
c_pcor_shrunk package:GeneNetTools R Documentation
Partial correlation shrunk
Description:
This function computes confidence intervals for the partial
correlation with shrinkage.
Usage:
c_pcor_shrunk(lparams)
Arguments:
lparams: a list of parameters created using a JSON file. This file should
contain the following name/value pairs.
"filename": <string, required>
"variables": <array, strings representing column names>
"cutoff": <number, required threshold for the p-value of the
partial correlation>
"verbose": <boolean, required to display detailed description
on the terminal>
Value:
Forest plot of partial correlations in Rplot.pdf
Citation#
Todo
Generate Zenodo DOI
GitHub#
If you want to use the original GeneNetTools
source code or
install the R package, visit the main author’s GitHub repository.
References#
- BBG+19
Victor Bernal, Rainer Bischoff, Victor Guryev, Marco Grzegorczyk, and Peter Horvatovich. Exact hypothesis testing for shrinkage-based Gaussian graphical models. Bioinformatics, 35(23):5011–5017, 05 2019. URL: https://doi.org/10.1093/bioinformatics/btz357, arXiv:https://academic.oup.com/bioinformatics/article-pdf/35/23/5011/31278560/btz357.pdf, doi:10.1093/bioinformatics/btz357.
- BSAB+22
Victor Bernal, Venustiano Soancatl-Aguilar, Jonas Bulthuis, Victor Guryev, Peter Horvatovich, and Marco Grzegorczyk. GeneNetTools: tests for Gaussian graphical models with shrinkage. Bioinformatics, 09 2022. btac657. URL: https://doi.org/10.1093/bioinformatics/btac657, doi:10.1093/bioinformatics/btac657.