Visualisation of the T cell differentiation programme by Canonical Correspondence Analysis of transcriptomes

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• 作者：Masahiro Ono (8)
  Reiko J Tanaka (9)
  Manabu Kano (10)
  
  8. Immunobiology Section ; UCL Institute of Child Health ; University College London ; 30 Guilford Street ; London ; WC1N 1EH ; UK
  9. Department of Bioengineering ; Imperial College London ; London ; SW7 2AZ ; UK
  10. Department of Systems Science ; Graduate School of Informatics ; Kyoto University ; Yoshida-Honmachi ; Sakyo-ku ; Kyoto ; 606-8501 ; Japan
  
• 关键词：Canonical Correspondence Analysis ; Multidimensional analysis ; Expression microarray ; RNA ; seq ; Immunological genomic data ; T cell differentiation ; Classification
• 刊名：BMC Genomics
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：15
• 期：1
• 全文大小：1,877 KB
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• 刊物主题：Life Sciences, general; Microarrays; Proteomics; Animal Genetics and Genomics; Microbial Genetics and Genomics; Plant Genetics & Genomics;
• 出版者：BioMed Central
• ISSN：1471-2164

文摘

Background Currently, in the era of post-genomics, immunology is facing a challenging problem to translate mutant phenotypes into gene functions based on high-throughput data, while taking into account the classifications and functions of immune cells, which requires new methods. Results Here we propose a novel application of a multidimensional analysis, Canonical Correspondence Analysis (CCA), to reveal the molecular characteristics of undefined cells in terms of cellular differentiation programmes by analysing two transcriptomic datasets. Using two independent datasets, whether RNA-seq or microarray data, CCA successfully visualised the cross-level relationships between genes, cells, and differentiation programmes, and thereby identified the immunological features of mutant cells (Gata3-KO T cells and Stat3-KO T cells) in a data-oriented manner. With a new concept, differentiation variable, CCA provides an automatic classification of cell samples, which had a high sensitivity and a comparable performance to other classification methods. In addition, we elaborate how CCA results can be interpreted, and reveal the features of CCA in comparison with other visualisation techniques. Conclusions CCA is a visualisation tool with a classification ability to reveal the cross-level relationships of genes, cells and differentiation programmes. This can be used for characterising the functional defect of cells of interest (e.g. mutant cells) in the context of cellular differentiation. The proposed approach fits with common hypothesis-oriented studies in immunology, and can be used for a wide range of molecular and genomic studies on cellular differentiation mechanisms.