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

https://doi.org/10.1101/196915

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What this work says

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

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Introns and gene expression: Cellular constraints, transcriptional regulation, and evolutionary consequences

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Principal component analysis

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Exact Matrix Completion via Convex Optimization

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Correspondence analysis applied to microarray data

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Singular value decomposition for genome-wide expression data processing and modeling

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MAD Bayes for Tumor Heterogeneity—Feature Allocation With Exponential Family Sampling

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Dimension reduction techniques for the integrative analysis of multi-omics data

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Dynamic modeling and network approaches for omics time course data: overview of computational approaches and applications

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Model-based clustering and data transformations for gene expression data

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Separation of samples into their constituents using gene expression data

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Linear modes of gene expression determined by independent component analysis

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Application of Bayesian Decomposition for analysing microarray data

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Probabilistic analysis of gene expression measurements from heterogeneous tissues

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CoGAPS: an R/C++ package to identify patterns and biological process activity in transcriptomic data

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PatternMarkers & GWCoGAPS for novel data-driven biomarkers via whole transcriptome NMF

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Improved data-driven likelihood factorizations for transcript abundance estimation

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Varying-Censoring Aware Matrix Factorization for Single Cell RNA-Sequencing

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Stochastic optimization for PCA and PLS

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Matrix factorization for transcriptional regulatory network inference

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Nonnegative Matrix Factorization: A Comprehensive Review

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Bayesian Inference for Intratumour Heterogeneity in Mutations and Copy Number Variation

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The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans

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Mutational signatures associated with tobacco smoking in human cancer

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Tensor Decompositions and Applications

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K -means clustering via principal component analysis

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Gene set enrichment analysis made simple

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Public Databases and Software for the Pathway Analysis of Cancer Genomes

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Determination of strongly overlapping signaling activity from microarray data

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Gene expression signatures modulated by epidermal growth factor receptor activation and their relationship to cetuximab resistance in head and neck squamous cell carcinoma

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Application of independent component analysis to microarrays

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Differential expression analysis for sequence count data

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PhyloWGS: Reconstructing subclonal composition and evolution from whole-genome sequencing of tumors

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ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis

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f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq

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Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance

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BayCount: A Bayesian decomposition method for inferring tumor heterogeneity using RNA-Seq counts

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Elucidating the Altered Transcriptional Programs in Breast Cancer using Independent Component Analysis

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Deconvolution of Blood Microarray Data Identifies Cellular Activation Patterns in Systemic Lupus Erythematosus

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Preferential Activation of the Hedgehog Pathway by Epigenetic Modulations in HPV Negative HNSCC Identified with Meta-Pathway Analysis

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SVD Identifies Transcript Length Distribution Functions from DNA Microarray Data and Reveals Evolutionary Forces Globally Affecting GBM Metabolism

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A Markov Chain Monte Carlo Technique for Identification of Combinations of Allelic Variants Underlying Complex Diseases in Humans

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Metric Learning: A Survey

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Multi-Task Feature Learning

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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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Exploring patterns enriched in a dataset with contrastive principal component analysis

inferred← citeshttps://doi.org/10.1039/d1mo00411eopenalex-snapshot

Multi-omics data integration approaches for precision oncology

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Comprehensive analysis of retinal development at single cell resolution identifies NFI factors as essential for mitotic exit and specification of late-born cells

inferred← citeshttps://doi.org/10.1186/s13059-021-02378-zopenalex-snapshot

GRiNCH: simultaneous smoothing and detection of topological units of genome organization from sparse chromatin contact count matrices with matrix factorization

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DeltaNMF: A Two-Stage Neural NMF for Differential Gene Program Discovery

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