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KMID : 0381120220440070793
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Genes and Genomics
2022 Volume.44 No. 7 p.793 ~ p.799
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TissueSpace: a web tool for rank-based transcriptome representation and its applications in molecular medicine
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He Yiruo
Liu Wei
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Abstract
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Background: Cross-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.
Objective: Use of reduced vector to represent transcriptome data independent of platforms.
Methods: Thus, 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.
Results: Results 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.
Conclusions: TissueSpace could be used to generate testable hypotheses for translational medicine. The TissueSpace tool is available at http://bioinformatics.fafu.edu.cn/tissuespace/.
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KEYWORD
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NAFLD, Microarray, RNA-Seq, Cancer, Sepsis, Survival, Web tool
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