ArticleGenome biology2020
Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations.
Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- STRIVE: interpretable multimodal subtype discovery in Women's cancers using an attention-weighted joint variational autoencoder.Frontiers in bioinformatics · 2026Article
- Detecting signatures underlying the composition of biological data.Nucleic acids research · 2025Article
- ZipAEr: A compressive convolutional autoencoder for high-dimensional spatial omics data at subcellular resolution.Research square · 2025Article
- Synthetic data generation method improves risk prediction model for early tumor recurrence after surgery in patients with pancreatic cancer.Scientific reports · 2025Article
- Exploring the latent space of transcriptomic data with topic modeling.NAR genomics and bioinformatics · 2025Article
- A Developmental Gene Expression Atlas Reveals Novel Biological Basis of Complex Phenotypes in Sheep.Genomics, proteomics & bioinformatics · 2025Article
- Deep profiling of gene expression across 18 human cancers.Nature biomedical engineering · 2025Article
- Diffusion-based generation of gene regulatory networks from scRNA-seq data with DigNet.Genome research · 2025Article
- BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk.PLoS computational biology · 2025Article
- A holistic approach to understanding immune-mediated inflammatory diseases: bioinformatic tools to integrate omics data.Computational and structural biotechnology journal · 2024Review
- Latent space arithmetic on data embeddings from healthy multi-tissue human RNA-seq decodes disease modules.Patterns (New York, N.Y.) · 2024Article
- A deep profile of gene expression across 18 human cancers.bioRxiv : the preprint server for biology · 2024Article
- iModulonMiner and PyModulon: Software for unsupervised mining of gene expression compendia.PLoS computational biology · 2024Article
- BuDDI:bioRxiv : the preprint server for biology · 2024Article
- Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework.Scientific reports · 2024Article
- Optimizer's dilemma: optimization strongly influences model selection in transcriptomic prediction.Bioinformatics advances · 2024Article
- Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS.Nature protocols · 2023Review
- Machine learning in rare disease.Nature methods · 2023Review
- Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2023Article
- PAUSE: principled feature attribution for unsupervised gene expression analysis.Genome biology · 2023Article
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5 authors.
Funding
Abstract
backgroundUnsupervised compression algorithms applied to gene expression data extract latent or hidden signals representing technical and biological sources of variation. However, these algorithms require a user to select a biologically appropriate latent space dimensionality. In practice, most researchers fit a single algorithm and latent dimensionality. We sought to determine the extent by which selecting only one fit limits the biological features captured in the latent representations and, consequently, limits what can be discovered with subsequent analyses.
resultsWe compress gene expression data from three large datasets consisting of adult normal tissue, adult cancer tissue, and pediatric cancer tissue. We train many different models across a large range of latent space dimensionalities and observe various performance differences. We identify more curated pathway gene sets significantly associated with individual dimensions in denoising autoencoder and variational autoencoder models trained using an intermediate number of latent dimensionalities. Combining compressed features across algorithms and dimensionalities captures the most pathway-associated representations. When trained with different latent dimensionalities, models learn strongly associated and generalizable biological representations including sex, neuroblastoma MYCN amplification, and cell types. Stronger signals, such as tumor type, are best captured in models trained at lower dimensionalities, while more subtle signals such as pathway activity are best identified in models trained with more latent dimensionalities.
conclusionsThere is no single best latent dimensionality or compression algorithm for analyzing gene expression data. Instead, using features derived from different compression models across multiple latent space dimensionalities enhances biological representations.
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