Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks

Abstract

Background Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states. Results In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein–protein interaction networks that detects active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein–protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With scPPIN we expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the protein–protein interaction networks significantly enriched which represent biological pathways. In these pathways, scPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis. Conclusions The introduced scPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein interaction data. The detected modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues.

Toward modeling metabolic state from single-cell transcriptomics

“…For example, to pinpoint critical genes and metabolites scMetNet [ 39 ] constructs a metabolic network based on KEGG and then finds submodules with significant differential expression across cell populations. Similarly, scPPIN was developed for identification of differentially active modules in protein–protein interaction networks based on scRNA-seq data [ 67 ], but may be also applicable to metabolic networks. Another alternative is reporter metabolite analysis, such as perturb-Met, which aims to identify affected metabolites by analyzing changes in expression of metabolite-associated genes [ 40 ].…”

NetMix: A Network-Structured Mixture Model for Reduced-Bias Estimation of Altered Subnetworks

“…Moreover, the manually selected FDR allows users to selectively tune the value of this parameter to influence which genes are in the inferred altered subnetwork, analogous to ''p-hacking'' (Ioannidis, 2005;Head et al, 2015;Nuzzo, 2015). Indeed, recently published analyses using heinz use a wide range of FDR values, ranging anywhere from 10 -25 to 0:05 (Liang et al, 2012;Choi et al, 2016;He et al, 2017;Klimm et al, 2019). See the Supplementary Data for more details on the differences between heinz and NetMix.…”

“…Subsequently, Dittrich et al (2008) introduced heinz as ''the first approach that really tackles and solves the original problem raised by Ideker et al (2002) to optimality.'' jActiveModules and heinz have since become widely used tools with diverse applications; a few recent examples include mass-spectrometry proteomics (Kim and Hwang, 2016;Liu et al, 2018), damaging de novo mutations in schizophrenia and other neurological disorders (Gulsuner et al, 2013;Choi et al, 2016), and single-cell RNA-seq (Soul et al, 2015;Guo et al, 2016;Klimm et al, 2019).…”

Solving Steiner trees: Recent advances, challenges, and perspectives

“…Practically speaking, the authors modeled the problem as the PCSTP with a degree constraint at the root node. Their problem was solved using the code from Bailly‐Bechet et al [11] Most recently, Klimm et al [144] combined single‐cell RNA‐sequencing data with protein–protein interaction networks to detect active modules in cells of different transcriptional states. The open‐source code by Leitner et al [153] was employed to solve their underlying MWCS model.…”