Integrative pathway enrichment analysis of multivariate omics data

Paczkowska, Marta; Barenboim, Jonathan; Sintupisut, Nardnisa; Fox, Natalie C.; Zhu, Helen He; Abd-Rabbo, Diala; Boutros, Paul C.; Reimand, Jüri

doi:10.1101/399113

Cited by 44 publications

(80 citation statements)

References 45 publications

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“…Single-cell datasets, on the other hand, are naturally sparser, with more peptides and proteins that can be transferred from other single-cell runs, and especially from the "library" runs (i.e., from boosting samples containing a higher number of cells). In such cases, using a more lenient (e.g., 2%) MBR FDR threshold may be considered, provided that downstream data analysis tools (e.g., for pathway-level analysis) are sufficiently robust toward quantification errors (48).…”

Section: Fdr-controlled Mbr In Single-cell Datamentioning

confidence: 99%

IonQuant enables accurate and sensitive label-free quantification with FDR-controlled match-between-runs

Haynes

Nesvizhskii

2020

Preprint

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Missing values weaken the power of label-free quantitative proteomic experiments to uncover true quantitative differences between biological samples or experimental conditions. Match-between-runs (MBR) has become a common approach to mitigate the missing value problem, where peptides identified by tandem mass spectra in one run are transferred to another by inference based on m/z, charge state, retention time, and ion mobility when applicable. Though tolerances are used to ensure such transferred identifications are reasonably located and meet certain quality thresholds, little work has been done to evaluate the statistical confidence of MBR. Here, we present a mixture model-based approach to estimate the false discovery rate (FDR) of peptide and protein identification transfer, which we implement in the label-free quantification tool IonQuant. Using several benchmarking datasets generated on both Orbitrap and timsTOF mass spectrometers, we demonstrate that IonQuant with FDR-controlled MBR results in superior performance compared to MaxQuant. We further illustrate the need for FDR-controlled MBR in sparse datasets such as those from single-cell proteomics experiments.

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Section: Fdr-controlled Mbr In Single-cell Datamentioning

confidence: 99%

IonQuant enables accurate and sensitive label-free quantification with FDR-controlled match-between-runs

Haynes

Nesvizhskii

2020

Preprint

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“…Multi-omics datasets represent distinct aspects of the central dogma of molecular biology, emphasizing the complementarity of underlying biology [12]. Previously, we proposed to prioritize functional gene regulators in CMSs by integrating genome, epigenome, transcriptome and interactome data, which play central roles in the corresponding subtypes [13].…”

Section: Methodsmentioning

confidence: 99%

“…However, many researchers seem to focus more on the regulation of miRNAs on FA metabolism in other cancer types than CRC. The relationship between miRNAs and FA metabolism in CRC is still unclear.Multi-omics datasets represent distinct aspects of the central dogma of molecular biology, emphasizing the complementarity of underlying biology [12]. Previously, we proposed to prioritize functional gene regulators in CMSs by integrating genome, epigenome, transcriptome and interactome data, which play central roles in the corresponding subtypes [13].…”

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confidence: 99%

Integrated Multi-Omics Analysis of Colorectal Cancer Reveals Mir-20a as a Regulator of Fatty Acid Metabolism in Consensus Molecular Subtype 3

Song¹,

Liu²,

Zhang³

et al. 2021

Preprint

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BackgroundmicroRNAs (miRNAs) serve important roles in metabolism. The consensus molecular subtype (CMS) 3 of colorectal cancer (CRC) is characterized by activated fatty acid (FA) metabolism. We aimed to identify essential miRNAs of CMS3-CRC and analyze the regulatory role in the FA metabolism. MethodsThe RobustRankAggreg method by integrating multi-omics data including genome, epigenome, transcriptome and interactome, was applied to filter out functional genes (Fgenes). The backward derivation approach based on Fgenes and miRNA-gene interactions was further applied to identify functional miRNAs (Fmirs). Nine human CRC cell lines with different CMSs were investigated. RT-qPCR, western blotting and immunofluorescence were performed to examine the effect of miR-20a on FA synthesis and Wnt/β-catenin signaling. The effect of miR-20a on cell proliferation and metastasis were studied by clone-formation, EdU assay, wound healing and transwell assay.ResultsWe identified 12 Fmirs by integrating multi-omics features in CMS3-CRC. These Fmirs exhibited significantly enriched CRC driver miRNAs and significant impacts on CMS3-CRC cell growth. Beyond the findings, miR-20a was significantly correlated with Wnt/β-catenin signaling and participated in FA metabolism subpathway. In vitro assays combined with bioinformatics analyses demonstrated that elevated miR-20a up-regulated FA synthesis enzymes FASN, ACAC and ACLY via Wnt/β-catenin signaling, and finally promoted proliferative and migration of CMS3-CRC cells. ConclusionsOverall, our study revealed that miR-20a promoted progression of CMS3-CRC by regulating FA metabolism and served as a potential target for preventing tumor metastasis.

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“…ActivePathways , implemented in R, is a tool for multivariate pathway enrichment analysis. Pathway enrichment analysis identifies gene sets, such as pathways or Gene Ontology terms, that are over‐represented in a list of genes of interest . ActivePathways implements an algorithm to perform pathway enrichment analysis on a dataset that contains multiple variables for each gene.…”

Section: Tools For Analytical Platformsmentioning

confidence: 99%

Tools and resources for metabolomics research community: A 2017–2018 update

Misra¹,

Mohapatra

2018

Electrophoresis

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The scale at which MS‐ and NMR‐based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences—ranging from biomedical to agricultural—is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017–2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.

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Integrative pathway enrichment analysis of multivariate omics data

Cited by 44 publications

References 45 publications

IonQuant enables accurate and sensitive label-free quantification with FDR-controlled match-between-runs

IonQuant enables accurate and sensitive label-free quantification with FDR-controlled match-between-runs

Integrated Multi-Omics Analysis of Colorectal Cancer Reveals Mir-20a as a Regulator of Fatty Acid Metabolism in Consensus Molecular Subtype 3

Tools and resources for metabolomics research community: A 2017–2018 update

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