Overcoming biases in causal inference of molecular interactions

Abstract

Motivation Computer inference of biological mechanisms is increasingly approachable due to dynamically rich data sources such as single-cell genomics. Inferred molecular interactions can prioritize hypotheses for wet-lab experiments to expedite biological discovery. However, complex data often come with unwanted biological or technical variations, exposing biases over marginal distribution and sample size in current methods to favor spurious causal relationships. Results Considering function direction and strength as evidence for causality, we present an adapted functional chi-squared test (AdpFunChisq) that rewards functional patterns over non-functional or independent patterns. On synthetic and three biology datasets, we demonstrate the advantages of AdpFunChisq over ten methods on overcoming biases that give rise to wide fluctuations in the performance of alternative approaches. On single-cell multiomics data of multiple phenotype acute leukemia, we found that T-cell surface glycoprotein CD3 delta chain (CD3D) may causally mediate specific genes in the viral carcinogenesis pathway. Using the causality-by-functionality principle, AdpFunChisq offers a viable option for robust causal inference in dynamical systems. Availability The AdpFunChisq test is implemented in the R package ‘FunChisq’ (2.5.2 or above) at https://cran.r-project.org/package=FunChisq. All other source code along with pre-processed data is available at Code Ocean https://doi.org/10.24433/CO.2907738.v1 Supplementary information Supplementary materials are available at Bioinformatics online.

Integrated analysis of bulk and single-cell RNA-seq reveals the role of MYC signaling in lung adenocarcinoma

“…The expression matrix of scRNA-seq and the clinical information (such as histological type) of the samples were downloaded from the website ( https://doi.org/10.24433/CO.0121060.v1 ) ( Kumar and Song, 2022 ). The data contained a total of 114,489 cells from 10 LUAD samples and 10 normal lung tissue samples, and used 10x genomics for sequencing.…”

“…The data contained a total of 114,489 cells from 10 LUAD samples and 10 normal lung tissue samples, and used 10x genomics for sequencing. Genes below expression in 100 cells were filtered out using the “Seurat” R package ( Kumar and Song, 2022 ). Low-quality cells were filtered out by the criteria where the number of expressed genes was greater than 100 and less than 6,000 and the proportion of mitochondrial gene expression was less than 20.…”