Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data

Köksal, Ali Sinan; Beck, Kirsten; Cronin, Dylan; McKenna, Aaron; Camp, Nathan D.; Srivastava, Saurabh; MacGilvray, Matthew E.; Bodík, Rastislav; Wolf-Yadlin, Alejandro; Fraenkel, Ernest; Fisher, Jasmin; Gitter, Anthony

doi:10.1101/209676

Cited by 8 publications

(27 citation statements)

References 134 publications

(149 reference statements)

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“…For both PathLinker and NetBox, we used the interactome included as a part of their software packages. For PCSF and min-cost flow, we used an interactome from Köksal et al (2018) that merged protein interactions from the iRefIndex database v13 (Razick et al, 2008) and kinase-substrate interactions from PhosphoSitePlus (Hornbeck et al, 2014). The interactions from the iRefIndex database include confidence scores, while confidence scores for kinase-substrate interactions were inferred from the number of interactions for each kinase-substrate pair and the type of experiment that detected the interaction.…”

Section: Interactomesmentioning

confidence: 99%

“…Although biological pathway enrichment can be used to interpret omic data, pathways in curated databases are incomplete and also contain proteins or genes that are not involved in a particular biological context (Köksal et al, 2018). Thus, it is often preferable to infer a customized subnetwork specific to an experimental dataset starting from all known protein interactions, referred to as the interactome.…”

Section: Introductionmentioning

confidence: 99%

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Automating parameter selection to avoid implausible biological pathway models

Magnano

Gitter

2019

Preprint

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A common way to integrate and analyze large amounts of biological omic data is through pathway finding: creating a subnetwork of a generic background network that represents some process or cellular state using condition-specific omic data. A challenge in pathway finding is that adjusting pathway creation algorithms' parameters produces pathways with drastically different topological properties and biological interpretations. Due to the exploratory nature of pathway finding, there is no ground truth for direct evaluation, so parameter tuning methods typically used in statistics and machine learning are inapplicable. We developed the pathway parameter advising method to tune pathway finding algorithms to minimize biologically implausible predictions. We leverage background knowledge in pathway databases to select pathways whose high-level structure resembles that of manually curated biological pathways. At the core of this method is a graphlet decomposition metric, which measures topological similarity to curated biological pathways. In order to evaluate pathway parameter advising, we compare its performance in avoiding implausible networks and reconstructing pathways from the NetPath database with other parameter selection methods across four pathway finding algorithms. We also demonstrate how pathway parameter advising can guide construction of an influenza host factor network. Pathway parameter advising is method-agnostic; it is applicable to any pathway finding algorithm with tunable parameters. Our pathway parameter advising software is available on GitHub at https://github.com/gitter-lab/pathway-parameter-advising.

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Section: Interactomesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automating parameter selection to avoid implausible biological pathway models

Magnano

Gitter

2019

Preprint

Self Cite

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“…Zhao et al (2017) scored pathways and corrected for a deterministic impact of smaller sample sizes due to missing data on the pathway rank. Likewise, Köksal et al (2018) appealed to parsimony by assuming that missing values have an insignificant effect on the overall analysis.…”

Section: Introductionmentioning

confidence: 99%

Iterative integrated imputation for missing data and pathway models with applications to breast cancer subtypes

Linder

Zhang

2019

CSAM

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Tumor development is driven by complex combinations of biological elements. Recent advances suggest that molecularly distinct subtypes of breast cancers may respond differently to pathway-targeted therapies. Thus, it is important to dissect pathway disturbances by integrating multiple molecular profiles, such as genetic, genomic and epigenomic data. However, missing data are often present in the-omic profiles of interest. Motivated by genomic data integration and imputation, we present a new statistical framework for pathway significance analysis. Specifically, we develop a new strategy for imputation of missing data in large-scale genomic studies, which adapts low-rank, structured matrix completion. Our iterative strategy enables us to impute missing data in complex configurations across multiple data platforms. In turn, we perform large-scale pathway analysis integrating gene expression, copy number, and methylation data. The advantages of the proposed statistical framework are demonstrated through simulations and real applications to breast cancer subtypes. We demonstrate superior power to identify pathway disturbances, compared with other imputation strategies. We also identify differential pathway activity across different breast tumor subtypes.

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“…Causality is an essential component for a data explanation model, as it introduces predictability in the system and opens up opportunities for developing alteration strategies for a desired change. Causality is often part of the methods that infer new pathway relations from omic data [6][7][8] . On the other hand, despite being highly desired, causality is much less emphasized while explaining data with known pathways.…”

Section: Introductionmentioning

confidence: 99%

Causal interactions from proteomic profiles: molecular data meets pathway knowledge

Babur

Luna

Korkut

et al. 2018

Preprint

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Measurement of changes in protein levels and in post-translational modifications, such as phosphorylation, can be highly informative about the phenotypic consequences of genetic differences or about the dynamics of cellular processes. Typically, such proteomic profiles are interpreted intuitively or by simple correlation analysis. Here, we present a computational method to generate causal explanations for proteomic profiles using prior mechanistic knowledge in the literature, as recorded in cellular pathway maps. To demonstrate its potential, we use this method to analyze the cascading events after EGF stimulation of a cell line, to discover new pathways in platelet activation, to identify influential regulators of oncoproteins in breast cancer, to describe signaling characteristics in predefined subtypes of ovarian and breast cancers, and to highlight which pathway relations are most frequently activated across 32 cancer types. Causal pathway analysis, that combines molecular profiles with prior biological knowledge captured in computational form, may become a powerful discovery tool as the amount and quality of cellular profiling rapidly expands. The method is freely available at causalpath.org.

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Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data

Cited by 8 publications

References 134 publications

Automating parameter selection to avoid implausible biological pathway models

Automating parameter selection to avoid implausible biological pathway models

Iterative integrated imputation for missing data and pathway models with applications to breast cancer subtypes

Causal interactions from proteomic profiles: molecular data meets pathway knowledge

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