CellMontage: similar expression profile search server

Fujibuchi, Wataru; Kiseleva, Larisa; Taniguchi, Tadatsugu; Harada, Hajime; Horton, Paul

doi:10.1093/bioinformatics/btm462

Cited by 40 publications

(29 citation statements)

References 7 publications

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“…First, the differential expression profiles of all genes were compared by using Spearman rank correlation (CellMontage) [4]. The other 4 methods used gene signatures, which comprise a preselected number of top/down DEGs, extracted only from the query profile.…”

Section: Methodsmentioning

confidence: 99%

Drug Similarity Search Based on Combined Signatures in Gene Expression Profiles

Cha

Kim

et al. 2014

Healthc Inform Res

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ObjectivesRecently, comparison of drug responses on gene expression has been a major approach to identifying the functional similarity of drugs. Previous studies have mostly focused on a single feature, the expression differences of individual genes. We provide a more robust and accurate method to compare the functional similarity of drugs by diversifying the features of comparison in gene expression and considering the sample dependent variations.MethodsFor differentially expressed gene measurement, we modified the conventional t-test to normalize variations in diverse experimental conditions of individual samples. To extract significant differentially co-expressed gene modules, we searched maximal cliques among the co-expressed gene network. Finally, we calculated a combined similarity score by averaging the two scaled scores from the above two measurements.ResultsThis method shows significant performance improvement in comparison to other approaches in the test with Connectivity Map data. In the test to find the drugs based on their own expression profiles with leave-one-out cross validation, the proposed method showed an area under the curve (AUC) score of 0.99, which is much higher than scores obtained with previous methods, ranging from 0.71 to 0.93. In the drug networks, we could find well clustered drugs having the same target proteins and novel relations among drugs implying the possibility of drug repurposing.ConclusionsInclusion of the features of a co-expressed module provides more implications to infer drug action. We propose that this method be used to find collaborative cellular mechanisms associated with drug action and to simply identify drugs having similar responses.

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Section: Methodsmentioning

confidence: 99%

Drug Similarity Search Based on Combined Signatures in Gene Expression Profiles

Cha

Kim

et al. 2014

Healthc Inform Res

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“…Our choice of similarity measure is based on the fact that the Spearman correlation coefficient is a well-performing robust nonparametric estimator of statistical dependence [16]. Additionally, it has been previously shown to perform well in the transcriptomics meta-analysis setting [9],[17].…”

Section: Resultsmentioning

confidence: 99%

Global Meta-Analysis of Transcriptomics Studies

Caldas

Vinga

2014

PLoS ONE

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Transcriptomics meta-analysis aims at re-using existing data to derive novel biological hypotheses, and is motivated by the public availability of a large number of independent studies. Current methods are based on breaking down studies into multiple comparisons between phenotypes (e.g. disease vs. healthy), based on the studies' experimental designs, followed by computing the overlap between the resulting differential expression signatures. While useful, in this methodology each study yields multiple independent phenotype comparisons, and connections are established not between studies, but rather between subsets of the studies corresponding to phenotype comparisons. We propose a rank-based statistical meta-analysis framework that establishes global connections between transcriptomics studies without breaking down studies into sets of phenotype comparisons. By using a rank product method, our framework extracts global features from each study, corresponding to genes that are consistently among the most expressed or differentially expressed genes in that study. Those features are then statistically modelled via a term-frequency inverse-document frequency (TF-IDF) model, which is then used for connecting studies. Our framework is fast and parameter-free; when applied to large collections of Homo sapiens and Streptococcus pneumoniae transcriptomics studies, it performs better than similarity-based approaches in retrieving related studies, using a Medical Subject Headings gold standard. Finally, we highlight via case studies how the framework can be used to derive novel biological hypotheses regarding related studies and the genes that drive those connections. Our proposed statistical framework shows that it is possible to perform a meta-analysis of transcriptomics studies with arbitrary experimental designs by deriving global expression features rather than decomposing studies into multiple phenotype comparisons.

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“…We have developed and maintained a matching system for gene expression data called ‘CellMontage’ in our laboratory (33), and have added the CELLPEDIA dataset to the database selection menu on the CellMontage homepage (http://cellmontage.cbrc.jp/) so that the user can check whether the expression profiles of artificially produced cells are similar to the profiles of naturally existing cells. As we consider CellMontage to be a part of CELLPEDIA project, the ‘Profile Matching’ and ‘Profile Retrieval’ pages of CellMontage are now available in the ‘Cell Analysis Tools’ in CELLPEDIA.…”

Section: Applications Of Cellpediamentioning

confidence: 99%

CELLPEDIA: a repository for human cell information for cell studies and differentiation analyses

et al. 2011

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CELLPEDIA is a repository database for current knowledge about human cells. It contains various types of information, such as cell morphologies, gene expression and literature references. The major role of CELLPEDIA is to provide a digital dictionary of human cells for the biomedical field, including support for the characterization of artificially generated cells in regenerative medicine. CELLPEDIA features (i) its own cell classification scheme, in which whole human cells are classified by their physical locations in addition to conventional taxonomy; and (ii) cell differentiation pathways compiled from biomedical textbooks and journal papers. Currently, human differentiated cells and stem cells are classified into 2260 and 66 cell taxonomy keys, respectively, from which 934 parent–child relationships reported in cell differentiation or transdifferentiation pathways are retrievable. As far as we know, this is the first attempt to develop a digital cell bank to function as a public resource for the accumulation of current knowledge about human cells. The CELLPEDIA homepage is freely accessible except for the data submission pages that require authentication (please send a password request to cell-info@cbrc.jp).Database URL: http://cellpedia.cbrc.jp/

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CellMontage: similar expression profile search server

Cited by 40 publications

References 7 publications

Drug Similarity Search Based on Combined Signatures in Gene Expression Profiles

Drug Similarity Search Based on Combined Signatures in Gene Expression Profiles

Global Meta-Analysis of Transcriptomics Studies

CELLPEDIA: a repository for human cell information for cell studies and differentiation analyses

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