The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks

Guebila, Marouen Ben; Lopes‐Ramos, Camila M.; Fanfani, Viola; Weighill, Des; Burkholz, Rebekka; Schlauch, Daniel; Paulson, Joseph N.; Altenbuchinger, Michael; Shutta, Katherine H.; Sonawane, Abhijeet R.; Lim, James; Calderer, Genis; IJzendoorn, David G.P. van; Morgan, Daniel; Marin, Alessandro; Chen, Cho-Yi; Song, Qi; Saha, Enakshi; DeMeo, Dawn L.; Padi, Megha; Platig, John; Kuijjer, Marieke L.; Glass, Kimberly; Quackenbush, John

doi:10.1186/s13059-023-02877-1

Cited by 18 publications

(9 citation statements)

References 103 publications

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“…Using COBRA-corrected co-expression data from the same THCA study, we demonstrated that COBRA estimated batch-corrected, covariate-specific co-expression matrices that can be effectively used to estimate and compare GRNs between conditions. As both COBRA and PANDA are available through netZoo [Ben Guebila et al, 2023], we modified PANDA to allow COBRA correction to be incorporated into the analysis when the user supplies the parameter cobra_design_matrix . We found that the CEBP (CCAAT/enhancer-binding protein) and POU (Pit-Oct-Unc) families of transcription factors play important roles in THCA.…”

Section: Discussionmentioning

confidence: 99%

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Higher-order correction of persistent batch effects in correlation networks

Micheletti,

Schlauch,

Quackenbush

et al. 2023

Preprint

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Systems biology methods often rely on correlations in gene expression profiles to infer co-expression networks, commonly used as input for gene regulatory network inference or to identify functional modules of co-expressed or co-regulated genes. While systematic biases, including batch effects, are known to induce spurious associations and confound differential gene expression analyses (DE), the impact of batch effects on gene co-expression has not been fully explored. Methods have been developed to adjust expression values, ensuring conditional independence of mean and variance from batch or other covariates for each gene. These adjustments have been shown to improve the fidelity of DE analysis. However, these methods do not address the potential for spurious differential co-expression (DC) between groups. Consequently, uncorrected, artifactual DC can skew the correlation structure, leading network inference methods that use gene co-expression to identify false, nonbiological associations, even when the input data is corrected using standard batch correction.In this work, we demonstrate the persistence of confounders in covariance after standard batch correction using synthetic and real-world gene expression data examples. Subsequently, we introduce Co-expression Batch Reduction Adjustment (COBRA), a method for computing a batch-corrected gene co-expression matrix based on estimating a conditional covariance matrix. COBRA estimates a reduced set of parameters expressing the co-expression matrix as a function of the sample covariates, allowing control for continuous and categorical covariates. COBRA is computationally efficient, leveraging the inherently modular structure of genomic data to estimate accurate gene regulatory associations and facilitate functional analysis for high-dimensional genomic data.

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Section: Discussionmentioning

confidence: 99%

“…COBRA is available in R and Python through the netZoo packages [Ben Guebila et al, 2023] netZooR v1.5 and netZooPy v0.9.16 (https://netzoo.github.io). We also provide tutorials in R and Python through Netbooks (http://netbooks.networkmedicine.org/) [Ben Guebila et al, 2022b].…”

Section: Code and Data Availabilitymentioning

confidence: 99%

Higher-order correction of persistent batch effects in correlation networks

Micheletti,

Schlauch,

Quackenbush

et al. 2023

Preprint

Self Cite

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“…By using the default parameters, we did not transform or filter the Z-Scores from the PANDA networks, as previously reported [ 21 , 23 ]. Then we compared weighted in-degree between the sexes by conducting a Wilcoxon rank sum test with continuity correction applied across each tissue and then made a Bonferroni-corrected p -value adjustment [ 43 ]. We also calculated the median of the weighted in-degree difference between the male and female drug metabolism genes for each tissue.…”

Section: Methodsmentioning

confidence: 99%

Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes

Fisher,

Clark,

Jones

et al. 2024

BMC Pharmacol Toxicol

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Background Previous pharmacovigilance studies and a retroactive review of cancer clinical trial studies identified that women were more likely to experience drug adverse events (i.e., any unintended effects of medication), and men were more likely to experience adverse events that resulted in hospitalization or death. These sex-biased adverse events (SBAEs) are due to many factors not entirely understood, including differences in body mass, hormones, pharmacokinetics, and liver drug metabolism enzymes and transporters. Methods We first identified drugs associated with SBAEs from the FDA Adverse Event Reporting System (FAERS) database. Next, we evaluated sex-specific gene expression of the known drug targets and metabolism enzymes for those SBAE-associated drugs. We also constructed sex-specific tissue gene-regulatory networks to determine if these known drug targets and metabolism enzymes from the SBAE-associated drugs had sex-specific gene-regulatory network properties and predicted regulatory relationships. Results We identified liver-specific gene-regulatory differences for drug metabolism genes between males and females, which could explain observed sex differences in pharmacokinetics and pharmacodynamics. In addition, we found that ~ 85% of SBAE-associated drug targets had sex-biased gene expression or were core genes of sex- and tissue-specific network communities, significantly higher than randomly selected drug targets. Lastly, we provide the sex-biased drug-adverse event pairs, drug targets, and drug metabolism enzymes as a resource for the research community. Conclusions Overall, we provide evidence that many SBAEs are associated with drug targets and drug metabolism genes that are differentially expressed and regulated between males and females. These SBAE-associated drug metabolism enzymes and drug targets may be useful for future studies seeking to explain or predict SBAEs.

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“…Later, Bayesian inference approaches were used to construct highly probable molecular network structures [48,49]. More recently, due to the developments in AI/ML approaches, several automatic network inference tools have been proposed [50][51][52][53][54][55]. Although directly inferring networks from the data seem the most reliable way for further analyses and hypotheses generation, it is still very challenging to computationally extract true causality due to the lack of data and multimodal approaches.…”

Section: Constructing Molecular Networkmentioning

confidence: 99%

From Data to Knowledge: A Mini-Review on Molecular Network Modeling and Analysis for Therapeutic Target Discovery

Ozen

Abdi

2023

Arch Pharmacol Ther

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Successful drug development is a risky and lengthy process that can take over ten years and consume billions of dollars. Target discovery is a critical stage of drug development for the identification of key molecules and pathways that can be targeted by novel therapeutics to find more effective treatments. Due to the rapid development in artificial intelligence and machine learning techniques over the past decade, computational approaches have now emerged as powerful tools to unravel complex interactions within biological systems to identify novel therapeutic targets. In particular, modeling and analysis of intracellular molecular networks play a pivotal role in target discovery by enabling researchers to efficiently and simultaneously navigate massive amounts of biological data to identify potential therapeutic targets. Such technologies can significantly accelerate the prolonged process of development of innovative therapies for complex diseases. Besides highlighting the findings of the recently introduced extreme signaling failures in intracellular molecular networks, here we briefly review various methods for modeling and analysis of intracellular molecular networks and discuss how they can be utilized to predict potential drug targets within such complex signaling systems. Overall, this review emphasizes the significance of modeling and analysis of molecular networks for fast-tracking and rapid discovery of novel therapeutic targets; to pave the way for the development of more effective treatments.

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The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks

Cited by 18 publications

References 103 publications

Higher-order correction of persistent batch effects in correlation networks

Higher-order correction of persistent batch effects in correlation networks

Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes

From Data to Knowledge: A Mini-Review on Molecular Network Modeling and Analysis for Therapeutic Target Discovery

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