ArticleBioinformatics (Oxford, England)2008
Inferring differentiation pathways from gene expression.
Article in Bioinformatics (Oxford, England), 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- ChromGene: gene-based modeling of epigenomic data.Genome biology · 2023Article
- Article
- Leveraging global gene expression patterns to predict expression of unmeasured genes.BMC genomics · 2015Article
- A stochastic model dissects cell states in biological transition processes.Scientific reports · 2014Article
- On the selection of appropriate distances for gene expression data clustering.BMC bioinformatics · 2014Article
- Identifying multi-layer gene regulatory modules from multi-dimensional genomic data.Bioinformatics (Oxford, England) · 2012Article
- DREM 2.0: Improved reconstruction of dynamic regulatory networks from time-series expression data.BMC systems biology · 2012Article
- Lineage-based identification of cellular states and expression programs.Bioinformatics (Oxford, England) · 2012Article
- Predicting gene expression in T cell differentiation from histone modifications and transcription factor binding affinities by linear mixture models.BMC bioinformatics · 2011Article
- Probabilistic principal component analysis for metabolomic data.BMC bioinformatics · 2010Article
- PyMix--the python mixture package--a tool for clustering of heterogeneous biological data.BMC bioinformatics · 2010Article
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Authors and funding
4 authors.
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Abstract
motivationThe regulation of proliferation and differentiation of embryonic and adult stem cells into mature cells is central to developmental biology. Gene expression measured in distinguishable developmental stages helps to elucidate underlying molecular processes. In previous work we showed that functional gene modules, which act distinctly in the course of development, can be represented by a mixture of trees. In general, the similarities in the gene expression programs of cell populations reflect the similarities in the differentiation path.
resultsWe propose a novel model for gene expression profiles and an unsupervised learning method to estimate developmental similarity and infer differentiation pathways. We assess the performance of our model on simulated data and compare it with favorable results to related methods. We also infer differentiation pathways and predict functional modules in gene expression data of lymphoid development.
conclusionsWe demonstrate for the first time how, in principal, the incorporation of structural knowledge about the dependence structure helps to reveal differentiation pathways and potentially relevant functional gene modules from microarray datasets. Our method applies in any area of developmental biology where it is possible to obtain cells of distinguishable differentiation stages. AVAILABILITY: The implementation of our method (GPL license), data and additional results are available at http://algorithmics.molgen.mpg.de/Supplements/InfDif/. SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.
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