Identifying cancer pathway dysregulations using differential causal effects

Abstract

Motivation Signaling pathways control cellular behavior. Dysregulated pathways, for example, due to mutations that cause genes and proteins to be expressed abnormally, can lead to diseases, such as cancer. Results We introduce a novel computational approach, called Differential Causal Effects (dce), which compares normal to cancerous cells using the statistical framework of causality. The method allows to detect individual edges in a signaling pathway that are dysregulated in cancer cells, while accounting for confounding. Hence, technical artifacts have less influence on the results and dce is more likely to detect the true biological signals. We extend the approach to handle unobserved dense confounding, where each latent variable, such as, for example, batch effects or cell cycle states, affects many covariates. We show that dce outperforms competing methods on synthetic data sets and on CRISPR knockout screens. We validate its latent confounding adjustment properties on a GTEx dataset. Finally, in an exploratory analysis on breast cancer data from TCGA, we recover known and discover new genes involved in breast cancer progression. Availability The method dce is freely available as an R package on Bioconductor (https://bioconductor.org/packages/release/bioc/html/dce.html) as well as on https://github.com/cbg-ethz/dce. The GitHub repository also contains the Snakemake workflows needed to reproduce all results presented here. Supplementary information Supplementary data are available at Bioinformatics online.

A Bayesian approach to differential edges with probabilistic interactions: applications in association and classification

“…Systematic studies to investigate their differences would be worth pursuing. Furthermore, the idea of PRIDE may be applied in the study of differential causal effects (CE) recently proposed ( Tian et al 2016 , Wang et al 2018 , Jablonski et al 2022 ) to extend their approaches from testing CE to estimation of CE. In summary, the Bayesian approach to differential network analysis has opened up a promising research direction where the D-Net can be utilized in both association and classification studies.…”

Exploring tumor-normal cross-talk with TranNet: role of the environment in tumor progression

“…We then selected the genes that are differentially expressed between control and tumor samples, and expression data for the remaining genes (non-differentially expressed) are compressed in the principal components that preserve 99% of covariate information of the data [15]. From these principal components (PCs), only differentiated (differentially expressed) components are assumed as additional meta nodes in the network, representing the presence of potential covariates in the transition network, coming from the non-differentially expressed genes [14]. The conceptual idea for defining the differentially expressed principal components and for including them as additional variables in the presenting analysis is described in Section 2.1 and Figure 2B.…”

“…The expressions of the genes that were not selected as tumor genes (DE genes) might have such influence. To adjust for such effects on the transition mapping, we include meta nodes (adjustment variables in [14]) that represent the principal components of the expression data for these non-tumor genes (see Section 6). By the definition, principal components are vector representations of the general trends in a multi-dimensional data [15].…”

Exploring tumor-normal cross-talk with TranNet: Role of the environment in tumor progression

“…The expressions of the genes that were not selected as tumor genes (DE genes) might have such influence. To adjust for such effects on the transition mapping, we include meta nodes (adjustment variables in [14]) that represent the principal components of the expression data for these non-tumor genes (see Section 'Materials and methods'). By the definition, principal components are vector representations of the general trends in a multidimensional data [15].…”