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Deciphering programs of transcriptional regulation by combined deconvolution of multiple omics layers

https://doi.org/10.1101/199547

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Lineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene Promoters

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The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery

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Deciphering Signatures of Mutational Processes Operative in Human Cancer

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Simultaneous Multiplexed Measurement of RNA and Proteins in Single Cells

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Enhancers: The abundance and function of regulatory sequences beyond promoters

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Learning the parts of objects by non-negative matrix factorization

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Transcription regulation and animal diversity

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Signatures of mutational processes in human cancer

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An atlas of active enhancers across human cell types and tissues

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A promoter-level mammalian expression atlas

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A 17-gene stemness score for rapid determination of risk in acute leukaemia

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The NIH Roadmap Epigenomics Mapping Consortium

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Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma

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Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution

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Lineage-specific dynamic and pre-established enhancer–promoter contacts cooperate in terminal differentiation

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Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome

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Stem cell gene expression programs influence clinical outcome in human leukemia

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Metazoan promoters: emerging characteristics and insights into transcriptional regulation

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Metagenes and molecular pattern discovery using matrix factorization

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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

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ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells

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Runx3 and Runx1 are required for CD8 T cell development during thymopoiesis

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Improving molecular cancer class discovery through sparse non-negative matrix factorization

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Non-negative matrix factorization of gene expression profiles: a plug-in for BRB-ArrayTools

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Molecular signatures database (MSigDB) 3.0

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Robust classification of single-cell transcriptome data by nonnegative matrix factorization

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Combining multiple tools outperforms individual methods in gene set enrichment analyses

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MafB is an inducer of monocytic differentiation

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Isoform-level gene signature improves prognostic stratification and accurately classifies glioblastoma subtypes

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The Human Cell Atlas

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scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells

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The ENCODE (ENCyclopedia Of DNA Elements) Project

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Chromatin state dynamics during blood formation

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A flexible R package for nonnegative matrix factorization

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Biclustering of gene expression data by non-smooth non-negative matrix factorization

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NMF-mGPU: non-negative matrix factorization on multi-GPU systems

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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

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Gateways to the FANTOM5 promoter level mammalian expression atlas

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CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data

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Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction

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Identification of high-confidence RNA regulatory elements by combinatorial classification of RNA–protein binding sites

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Nonnegative Matrix Factorization: An Analytical and Interpretive Tool in Computational Biology

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ARHGAP22 Localizes at Endosomes and Regulates Actin Cytoskeleton

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Non-Negative Matrix Factorization for the Analysis of Complex Gene Expression Data: Identification of Clinically Relevant Tumor Subtypes

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Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization

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Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Evaluation of Whole Genome Sequencing Data

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A genetically defined signature of responsiveness to erlotinib in early-stage pancreatic cancer patients: Results from the CONKO-005 trial

inferred← citeshttps://doi.org/10.1016/j.semcancer.2018.01.010openalex-snapshot

Intratumor heterogeneity in epigenetic patterns

inferred← citeshttps://doi.org/10.1016/j.tig.2018.07.003openalex-snapshot

Enter the Matrix: Factorization Uncovers Knowledge from Omics

inferred← citeshttps://doi.org/10.1038/s41467-018-08205-7openalex-snapshot

Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity

inferred← citeshttps://doi.org/10.1038/s43588-021-00038-7openalex-snapshot

Machine learning for deciphering cell heterogeneity and gene regulation

inferred← citeshttps://doi.org/10.1101/196915openalex-snapshot

Enter the matrix: factorization uncovers knowledge from omics Names/Affiliations

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Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity

inferred← citeshttps://doi.org/10.3389/fgene.2020.00662openalex-snapshot

Unraveling Heterogeneity in Transcriptome and Its Regulation Through Single-Cell Multi-Omics Technologies

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Computational analysis and modelling of regulatory networks controlling embryonic development

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