ArticlePloS one2015
GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 37 citations in OpenAlex.
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- Evolutionary and developmental specialization of foveal cell types in the marmoset.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- A universal system for boosting gene expression in eukaryotic cell-lines.Nature communications · 2024Article
- Representation and quantification of module activity from omics data with rROMA.NPJ systems biology and applications · 2024Article
- Double DAP-seq uncovered synergistic DNA binding of interacting bZIP transcription factors.Nature communications · 2023Article
- Adipocyte lysoplasmalogenase TMEM86A regulates plasmalogen homeostasis and protein kinase A-dependent energy metabolism.Nature communications · 2022Article
- Lipidome profile predictive of disease evolution and activity in rheumatoid arthritis.Experimental & molecular medicine · 2022Article
- Histone Deacetylase Inhibition Regulates Lipid Homeostasis in a Mouse Model of Amyotrophic Lateral Sclerosis.International journal of molecular sciences · 2021Article
- A deep dive into fat: Investigating blubber lipidomic fingerprint of killer whales and humpback whales in northern Norway.Ecology and evolution · 2021Article
- Benchmarking principal component analysis for large-scale single-cell RNA-sequencing.Genome biology · 2020Article
- Combinatorial prediction of marker panels from single-cell transcriptomic data.Molecular systems biology · 2019Article
- Early emergence of cortical interneuron diversity in the mouse embryo.Science (New York, N.Y.) · 2018Article
- Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads.Genome biology · 2018Article
- Pairwise gene GO-based measures for biclustering of high-dimensional expression data.BioData mining · 2018Article
- Massive transcriptome sequencing of human spinal cord tissues provides new insights into motor neuron degeneration in ALS.Scientific reports · 2017Article
- Revealing the vectors of cellular identity with single-cell genomics.Nature biotechnology · 2016Review
- A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.Cell · 2016Article
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1 author at 1 institution in 1 country.
Funding
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Abstract
methodGenome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a readily interpretable representation of biologically relevant similarities and differences. The robustness of the results obtained can be assessed by bootstrapping.
resultsI first applied GO-PCA to datasets containing diverse hematopoietic cell types from human and mouse, respectively. In both cases, GO-PCA generated a small number of signatures that represented the majority of lineages present, and whose labels reflected their respective biological characteristics. I then applied GO-PCA to human glioblastoma (GBM) data, and recovered signatures associated with four out of five previously defined GBM subtypes. My results demonstrate that GO-PCA is a powerful and versatile exploratory method that reduces an expression matrix containing thousands of genes to a much smaller set of interpretable signatures. In this way, GO-PCA aims to facilitate hypothesis generation, design of further analyses, and functional comparisons across datasets.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.