Abstract
The understanding of pathological processes is based on the comparison between physiological and pathological conditions, and transcriptomic analysis has been extensively applied to various diseases for this purpose. However, the way in which the transcriptomic data of pathological cells relate to the transcriptomes of normal cellular counterparts has not been fully explored, and may provide new and unbiased insights into the mechanisms of these diseases. To achieve this, it is necessary to develop a method to simultaneously analyse components across different levels, namely genes, normal cells, and diseases. Here we propose a multidimensional method that visualises the cross-level relationships between these components at three different levels based on transcriptomic data of physiological and pathological processes, by adapting Canonical Correspondence Analysis, which was developed in ecology and sociology, to microarray data (CCA on Microarray data, CCAM). Using CCAM, we have analysed transcriptomes of haematological disorders and those of normal haematopoietic cell differentiation. First, by analysing leukaemia data, CCAM successfully visualised known relationships between leukaemia subtypes and cellular differentiation, and their characteristic genes, which confirmed the relevance of CCAM. Next, by analysing transcriptomes of myelodysplastic syndromes (MDS), we have shown that CCAM was effective in both generating and testing hypotheses. CCAM showed that among MDS patients, high-risk patients had transcriptomes that were more similar to those of both haematopoietic stem cells (HSC) and megakaryocyte-erythroid progenitors (MEP) than low-risk patients, and provided a prognostic model. Collectively, CCAM reveals hidden relationships between pathological and physiological processes and gene expression, providing meaningful clinical insights into haematological diseases, and these could not be revealed by other univariate and multivariate methods. Furthermore, CCAM was effective in identifying candidate genes that are correlated with cellular phenotypes of interest. We expect that CCAM will benefit a wide range of medical fields.
Highlights
In order to fully understand pathological processes in clinical settings at the genomic level, it is necessary to compare the transcriptomes of pathological processes in individual patients with those of the physiological processes of normal counterparts
We have developed a new multidimensional and canonical analysis of two microarray datasets by adapting Canonical Correspondence Analysis, which was developed in ecology and sociology, to microarray analysis
CCA on Microarray data (CCAM) provides a practical solution for comparing multiple groups of samples with multiple cellular phenotypes
Summary
In order to fully understand pathological processes in clinical settings at the genomic level, it is necessary to compare the transcriptomes of pathological processes in individual patients with those of the physiological processes of normal counterparts. It is necessary to develop a method to simultaneously analyse components across different levels, namely genes and physiological and pathological processes (e.g. normal and abnormal cellular phenotypes) It is anticipated, if successful, this approach will reveal hidden relationships between pathogenesis, developmental mechanisms, and gene regulation. Sandberg et al developed a method using Singular Value Decomposition (SVD) to measure the similarities between cancer subtypes and cell lines [6], providing univariate scores for individual cell lines These approaches are easy to deal with and can be understood intuitively, there is a pitfall when they are applied to compare disease and normal cellular phenotypes: it cannot be assumed that the two cell signatures to be analysed are independent from each other.
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