DoIntegPPI: Integration of gene expression matrix and PPI network

In aet21/SCENT: Single Cell Entropy

Integration of gene expression matrix and PPI network

Description

This function finds the common genes between the scRNA-Seq data matrix and the genes present in the PPI network, and constructs the maximally connected subnetwork and reduced expression matrix for the computation of signaling entropy.

Usage

DoIntegPPI(exp.m, ppiA.m);

Arguments

exp.m	The scRNA-Seq data matrix normalized for library size and log2-transformed with a pseudocount of 1.1
ppiA.m	The adjacency matrix of a user-given PPI network with rownames and colnames labeling genes (same gene identifier as in exp.m)

Value

A list of two objects:

expMC	Reduced expression matrix with genes in the maximally connected subnetwork.
adjMC	Adjacency matrix of the maximally connected subnetwork.

References

Teschendorff AE, Tariq Enver. Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome. Nature communications 8 (2017): 15599. doi: 10.1038/ncomms15599.

Teschendorff AE, Banerji CR, Severini S, Kuehn R, Sollich P. Increased signaling entropy in cancer requires the scale-free property of protein interaction networks. Scientific reports 5 (2015): 9646. doi: 10.1038/srep09646.

Banerji, Christopher RS, et al. Intra-tumour signalling entropy determines clinical outcome in breast and lung cancer. PLoS computational biology 11.3 (2015): e1004115. doi: 10.1371/journal.pcbi.1004115.

Teschendorff, Andrew E., Peter Sollich, and Reimer Kuehn. Signalling entropy: A novel network-theoretical framework for systems analysis and interpretation of functional omic data. Methods 67.3 (2014): 282-293. doi: 10.1016/j.ymeth.2014.03.013.

Banerji, Christopher RS, et al. Cellular network entropy as the energy potential in Waddington's differentiation landscape. Scientific reports 3 (2013): 3039. doi: 10.1038/srep03039.

aet21/SCENT documentation built on Aug. 1, 2022, 12:05 p.m.