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TissueSpace: a web tool for rank-based transcriptome representation and its applications in molecular medicine

TissueSpace: a web tool for rank-based transcriptome representation and its applications in... BackgroundCross-platform or cross-experiment transcriptome data is hard to compare as the original gene expression values from different platforms cannot be compared directly. The inherent gene expression ranking information is rarely utilized.ObjectiveUse of reduced vector to represent transcriptome data independent of platforms.MethodsThus, we turned the expression profile into a rank vector, where a higher expression has a higher rank value, then applied Latent semantic analysis (LSA) to get compact and continuous 100-dimensional vector representations for samples.ResultsResults showed that the reconstructed vector has a precision of 96.7% in recovering tissue labels from an independent dataset. A user-friendly tool TissueSpace was developed, which provides users the following functionalities: (1) convert different gene ID types to Ensembl gene IDs; (2) project any human transcriptome profile to get vector representation for downstream analysis; (3) functional enrichment for each of the 100-dimensional vector features. Case studies for its applications in human common diseases indicate its usefulness.ConclusionsTissueSpace could be used to generate testable hypotheses for translational medicine. The TissueSpace tool is available at http://bioinformatics.fafu.edu.cn/tissuespace/. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Genes & Genomics Springer Journals

TissueSpace: a web tool for rank-based transcriptome representation and its applications in molecular medicine

Genes & Genomics , Volume 44 (7) – Jul 1, 2022

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References (36)

Publisher
Springer Journals
Copyright
Copyright © The Author(s) under exclusive licence to The Genetics Society of Korea 2022
ISSN
1976-9571
eISSN
2092-9293
DOI
10.1007/s13258-022-01245-w
Publisher site
See Article on Publisher Site

Abstract

BackgroundCross-platform or cross-experiment transcriptome data is hard to compare as the original gene expression values from different platforms cannot be compared directly. The inherent gene expression ranking information is rarely utilized.ObjectiveUse of reduced vector to represent transcriptome data independent of platforms.MethodsThus, we turned the expression profile into a rank vector, where a higher expression has a higher rank value, then applied Latent semantic analysis (LSA) to get compact and continuous 100-dimensional vector representations for samples.ResultsResults showed that the reconstructed vector has a precision of 96.7% in recovering tissue labels from an independent dataset. A user-friendly tool TissueSpace was developed, which provides users the following functionalities: (1) convert different gene ID types to Ensembl gene IDs; (2) project any human transcriptome profile to get vector representation for downstream analysis; (3) functional enrichment for each of the 100-dimensional vector features. Case studies for its applications in human common diseases indicate its usefulness.ConclusionsTissueSpace could be used to generate testable hypotheses for translational medicine. The TissueSpace tool is available at http://bioinformatics.fafu.edu.cn/tissuespace/.

Journal

Genes & GenomicsSpringer Journals

Published: Jul 1, 2022

Keywords: NAFLD; Microarray; RNA-Seq; Cancer; Sepsis; Survival; Web tool

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