ArticleNucleic acids research2019
Learning common and specific patterns from data of multiple interrelated biological scenarios with matrix factorization.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- MOMHCA-SG: a multi-head cross-attention and similar graph convolutional network framework for Alzheimer's disease cell type classification.Frontiers in neuroscience · 2026Article
- sTPLS: identifying common and specific correlated patterns under multiple biological conditions.Briefings in bioinformatics · 2025Article
- Harmonizing heterogeneous single-cell gene expression data with individual-level covariate information.Bioinformatics advances · 2025Article
- Contemporary Precision Stratification and Prognostic Features of Primary Gliomas in a Southern Chinese Population.Research (Washington, D.C.) · 2025Article
- scStateDynamics: deciphering the drug-responsive tumor cell state dynamics by modeling single-cell level expression changes.Genome biology · 2024Article
- A new Bayesian factor analysis method improves detection of genes and biological processes affected by perturbations in single-cell CRISPR screening.Nature methods · 2023Article
- A universal framework for single-cell multi-omics data integration with graph convolutional networks.Briefings in bioinformatics · 2023Article
- Computational Methods for Single-cell DNA Methylome Analysis.Genomics, proteomics & bioinformatics · 2023Review
- Cross-species cell-type assignment from single-cell RNA-seq data by a heterogeneous graph neural network.Genome research · 2023Article
- The scINSIGHT Package for Integrating Single-Cell RNA-Seq Data from Different Biological Conditions.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Monitoring Wound Healing with Topically Applied Optical NanoFlare mRNA Nanosensors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Author Correction: scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data.Genome biology · 2022Article
- MOMA: a multi-task attention learning algorithm for multi-omics data interpretation and classification.Bioinformatics (Oxford, England) · 2022Article
- Shared and Cell-Type-Specific Gene Expression Patterns Associated With Autism Revealed by Integrative Regularized Non-Negative Matrix Factorization.Frontiers in genetics · 2022Article
- Approximate distance correlation for selecting highly interrelated genes across datasets.PLoS computational biology · 2021Article
- Single cell RNA sequencing for breast cancer: present and future.Cell death discovery · 2021Review
- Inference and analysis of cell-cell communication using CellChat.Nature communications · 2021Article
- scMC learns biological variation through the alignment of multiple single-cell genomics datasets.Genome biology · 2021Article
- Interpretable, Scalable, and Transferrable Functional Projection of Large-Scale Transcriptome Data Using Constrained Matrix Decomposition.Frontiers in genetics · 2021Article
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2 authors.
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Abstract
High-throughput biological technologies (e.g. ChIP-seq, RNA-seq and single-cell RNA-seq) rapidly accelerate the accumulation of genome-wide omics data in diverse interrelated biological scenarios (e.g. cells, tissues and conditions). Integration and differential analysis are two common paradigms for exploring and analyzing such data. However, current integrative methods usually ignore the differential part, and typical differential analysis methods either fail to identify combinatorial patterns of difference or require matched dimensions of the data. Here, we propose a flexible framework CSMF to combine them into one paradigm to simultaneously reveal Common and Specific patterns via Matrix Factorization from data generated under interrelated biological scenarios. We demonstrate the effectiveness of CSMF with four representative applications including pairwise ChIP-seq data describing the chromatin modification map between K562 and Huvec cell lines; pairwise RNA-seq data representing the expression profiles of two different cancers; RNA-seq data of three breast cancer subtypes; and single-cell RNA-seq data of human embryonic stem cell differentiation at six time points. Extensive analysis yields novel insights into hidden combinatorial patterns in these multi-modal data. Results demonstrate that CSMF is a powerful tool to uncover common and specific patterns with significant biological implications from data of interrelated biological scenarios.
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