Mining gene expression data by interpreting principal components

Roden, J.; King, Brandon; Trout, Diane; Mortazavi, A; Wold, B; Hart, Christopher E.

doi:10.1186/1471-2105-7-194

Cited by 57 publications

(33 citation statements)

References 29 publications

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“…Principal component analysis (PCA) and independent component analysis (ICA) have been used to extract biological features and build functional gene sets (Alter et al, 2000; Chen et al, 2008; Engreitz et al, 2010; Frigyesi et al, 2006; Gong et al, 2007; Lutter et al, 2009; Ma and Kosorok, 2009; Raychaudhuri et al, 2000, 2000; Roden et al, 2006). We performed PCA and generated multiple ICA models from the same P. aeruginosa expression compendium and evaluated their KEGG/GO term coverage using the same procedures for eADAGE.…”

Section: Resultsmentioning

confidence: 99%

Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks

et al. 2017

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Summary Cross experiment comparisons in public data compendia are challenged by unmatched conditions and technical noise. The ADAGE method, which performs unsupervised integration with denoising autoencoder neural networks, can identify biological patterns, but because ADAGE models, like many neural networks, are over-parameterized, different ADAGE models perform equally well. To enhance model robustness and better build signatures consistent with biological pathways, we developed an ensemble ADAGE (eADAGE) that integrated stable signatures across models. We applied eADAGE to a compendium of Pseudomonas aeruginosa gene expression profiling experiments performed in 78 media. eADAGE revealed a phosphate starvation response controlled by PhoB in media with moderate phosphate and predicted that a second stimulus provided by the sensor kinase, KinB, is required for this PhoB activation. We validated this relationship using both targeted and unbiased genetic approaches. eADAGE, which captures stable biological patterns, enables cross-experiment comparisons that can highlight measured but undiscovered relationships.

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Section: Resultsmentioning

confidence: 99%

Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks

et al. 2017

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“…PC5 was able to separate all the developmental stages between the mutants and wild type (Figure 1) in abi 3-1. PCA is performed by considering either genes [3], [19] or the experimental conditions as the variable [20], [21].…”

Section: Discussionmentioning

confidence: 99%

Shortlisting genes important in seed maturation by principal component analysis of gene expression data

Pathak

Singla

Juneja

et al. 2020

Preprint

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Transcriptome data are widely used for functional analysis of genes. Denovo assembly of transcriptome gives a large number of unigenes. A large proportion of them remain unannotated. Efficient computational methods are required for identifying genes and modeling those for regulatory and functional roles. Principal component analysis (PCA) was used in a novel approach to shortlist genes, independently of annotation in genome expression data, taking seed development in Arabidopsis thaliana as a representative case. PCA was applied to published genome expression data from four lines of Arabidopsis, mutated in seed development.The PC separating all the developmental stages between a mutant and its respective wild type was selected for shortlisting genes as functionally more important. The shortlisted genes identified by PCA belong to a number of biological functions. The genes reported to give sensitivity to desiccation were identified in PCA analysis also in desiccation intolerant lines only. With respect to the network of 98 genes targeted by ABI3, a higher number of genes was identified as important in the mutants abi 3-5, fus 3-3 andlec 1-1 in comparison to abi 3-1. Ontological analysis and comparison with earlier studies suggest that PCA of genome expression data is useful for shortlisting functionally important genes.

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“…1A ). Cluster analysis of variables (oblique principal components) [ 7 ], principal component analysis (PCA) [ 19 ], and inferential analyses (Chi-square and ANOVA) were performed using the SAS System (Cary, NC). Statistical significance was set at P <0.05.…”

Section: Methodsmentioning

confidence: 99%

Response, use and habituation to a mouse house in C57BL/6J and BALB/c mice

et al. 2015

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Animal welfare depends on the possibility to express species-specific behaviours and can be strongly compromised in socially and environmentally deprived conditions. Nesting materials and refuges are very important resources to express these behaviours and should be considered as housing supplementation items. We evaluated the effects of one item of housing supplementation in standard settings in laboratory mice. C57BL/6JOlaHsd (B6) and BALB/cOlaHsd (BALB) young male and female mice, upon arrival, were housed in groups of four in standard laboratory cages and after 10 days of acclimatization, a red transparent plastic triangular-shaped Mouse House™ was introduced into half of the home cages. Animals with or without a mouse house were observed in various contexts for more than one month. Body weight gain and food intake, home cage behaviours, emotionality and response to standard cage changing procedures were evaluated. The presence of a mouse house in the home cage did not interfere with main developmental and behavioural parameters or emotionality of BALB and B6 male and female mice compared with controls. Both strains habituated to the mouse house in about a week, but made use of it differently, with BALB mice using the house more than the B6 strain. Our results suggest that mice habituated to the mouse house rather quickly without disrupting their home cage activities. Scientists can thus be encouraged to use mouse houses, also in view of the implementation of the EU Directive (2010/63/EU).

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Mining gene expression data by interpreting principal components

Cited by 57 publications

References 29 publications

Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks

Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks

Shortlisting genes important in seed maturation by principal component analysis of gene expression data

Response, use and habituation to a mouse house in C57BL/6J and BALB/c mice

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