Clustering of RNA-Seq samples: Comparison study on cancer data

Pablo Andretta Jaskowiak, Ivan G. Costa, Ricardo J.G.B. Campello*

*Kontaktforfatter

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Abstract

RNA-Seq is becoming the standard technology for large-scale gene expression level measurements, as it offers a number of advantages over microarrays. Standards for RNA-Seq data analysis are, however, in its infancy when compared to those of microarrays. Clustering, which is essential for understanding gene expression data, has been widely investigated w.r.t. microarrays. In what concerns the clustering of RNA-Seq data, however, a number of questions remain open, resulting in a lack of guidelines to practitioners. Here we evaluate computational steps relevant for clustering cancer samples via an empirical analysis of 15 mRNA-seq datasets. Our evaluation considers strategies regarding expression estimates, number of genes after non-specific filtering and data transformations. We evaluate the performance of four clustering algorithms and twelve distance measures, which are commonly used for gene expression analysis. Results support that clustering cancer samples based on a gene quantification should be preferred. The use of non-specific filtering leading to a small number of features (1,000) presents, in general, superior results. Data should be log-transformed previously to cluster analysis. Regarding the choice of clustering algorithms, Average-Linkage and k-medoids provide, in general, superior recoveries. Although specific cases can benefit from a careful selection of a distance measure, Symmetric Rank-Magnitude correlation provides consistent and sound results in different scenarios.

OriginalsprogEngelsk
TidsskriftMethods
Vol/bind132
Sider (fra-til)42-49
ISSN1046-2023
DOI
StatusUdgivet - 1. jan. 2018
Udgivet eksterntJa

Bibliografisk note

Funding Information:
This project was partially funded by Brazilian research agencies FAPESP (Process 2011/04247-5 ), CNPq (Processes 304137/2013-8 , 400772/2014-0 , and 164595/2015-5 ), and by the Interdisciplinary Center for Clinical Research (IZKF) within the faculty of Medicine at the RWTH Aachen University.

Funding Information:
This project was partially funded by Brazilian research agencies FAPESP (Process 2011/04247-5), CNPq (Processes 304137/2013-8, 400772/2014-0, and 164595/2015-5), and by the Interdisciplinary Center for Clinical Research (IZKF) within the faculty of Medicine at the RWTH Aachen University.

Publisher Copyright:
© 2017 Elsevier Inc.

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