Towards a Data-Integrated Cell

N. Malod-Dognin, J. Petschnigg, S.F.L. Windels, J. Povh, H. Hemmingway, R. Ketteler and N. Pržulj

Corresponding author: Prof. N. Pržulj, natasa.przulj@mbzuai.ac.ae

Downloads

The two scripts needed to generate an iCell:

Main file

All the scripts and datasets used in the paper (all 4 parts are needed):

Put all 4 parts in one folder, then join and extract them:

macOS / Linux:

cat iCell_full_package.zip.00{1,2,3,4} > iCell_full_package.zip
unzip iCell_full_package.zip

Windows (Command Prompt):

copy /b iCell_full_package.zip.001+iCell_full_package.zip.002+iCell_full_package.zip.003+iCell_full_package.zip.004 iCell_full_package.zip

Then extract iCell_full_package.zip as usual.

Generating an iCell (Main file)

The Input_Nets folder contains three human generic networks to test the framework: PPI, co-expression (COEX) and genetic interaction (GI).

Step 1 (MATLAB):

run_SSNMTF()

Loads the three networks and runs SSNMTF.m for the data integration. Outputs the matrix factors G and S, and the correspondence between genes and rows of G.

Step 2 (terminal):

python Make_Integrated_Net.py

Uses the G factor and the gene correspondence file to build the integrated network, HSA_Generic_iCell.edgelist.

Contents of the full package

Each folder contains its own Readme.txt.

1–2: Generic and tissue-specific PPI, co-expression and genetic interaction networks
3–5: SSNMTF parameter setting (number of clusters, number of iterations, choice of input networks)
6–9: What is an iCell: integrated networks, graphlet degree vectors, model fitting, clustering and enrichment
10–12: Cancer-specific study: rewiring, driver gene enrichment, esiRNA experiments
13: Pan-cancer study

Citation

N. Malod-Dognin, J. Petschnigg, S.F.L. Windels, J. Povh, H. Hemmingway, R. Ketteler and N. Pržulj. Towards a data-integrated cell. Nature Communications 10, 805 (2019). https://doi.org/10.1038/s41467-019-08797-8