The algorithm needs as an input one CSV matrix with gene expression/methylation/any other numerical data and one CSV file with a network.
Numerical data is accepted in the following format:
Our test data can be downloaded here and further used as a reference for the correct format.
We support both retrieving pre-built networks from NDex as well as uploading custom networks. Custom networks should be defined in a CSV file with two columns representing the interacting genes. The files must not have a header.
We also provide an example of a PPI here.
Add clinical data/survival data to enable further analysis. If you do not want to upload additional metadata, you can go to the next step.
For internal calculations, BiCoN normalizes the data by applying log2 transformation and then applying z-scores normalization. If your data was already log2 scaled, please uncheck "Log2 transform".
The main parameters of the algorithm are the sizes of the desired solution. Please, indicate the minimal and maximal number of genes you would like to have in each subnetwork.
The algorithm works such that in most cases default parameters deliver the optimal performance. If you want to set advanced parameters, please check "Use advanced parameters: Yes". You can specify the following parameters:
Olga Lazareva, Stefan Canzar, Kevin Yuan, Jan Baumbach, David B Blumenthal, Paolo Tieri, Tim Kacprowski*, Markus List*, BiCoN: Network-constrained biclustering of patients and omics data, Bioinformatics, 2020;, btaa1076, https://doi.org/10.1093/bioinformatics/btaa1076
** joint last author
If you want to contact us regarding BiCoN: