Assessing dissimilarity measures for sample-based hierarchical clustering of RNA sequencing data using plasmode datasets

Sample- and gene-based hierarchical cluster analyses have been widely adopted as tools for exploring gene expression data in high-throughput experiments. Gene expression values (read counts) generated by RNA sequencing technology (RNA-seq) are discrete variables with special statistical properties,...

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Autores principales: Reeb, Pablo D., Bramardi, Sergio Jorge, Steibel, Juan P.
Formato: Articulo
Lenguaje:Inglés
Publicado: 2015
Materias:
Ciencias Agrarias
RNA
Transcriptome
Novo transcriptome
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/86218
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-86218
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Agrarias
RNA
Transcriptome
Novo transcriptome
spellingShingle Ciencias Agrarias
RNA
Transcriptome
Novo transcriptome
Reeb, Pablo D.
Bramardi, Sergio Jorge
Steibel, Juan P.
Assessing dissimilarity measures for sample-based hierarchical clustering of RNA sequencing data using plasmode datasets
topic_facet Ciencias Agrarias
RNA
Transcriptome
Novo transcriptome
description Sample- and gene-based hierarchical cluster analyses have been widely adopted as tools for exploring gene expression data in high-throughput experiments. Gene expression values (read counts) generated by RNA sequencing technology (RNA-seq) are discrete variables with special statistical properties, such as over-dispersion and right-skewness. Additionally, read counts are subject to technology artifacts as differences in sequencing depth. This possesses a challenge to finding distance measures suitable for hierarchical clustering. Normalization and transformation procedures have been proposed to favor the use of Euclidean and correlation based distances. Additionally, novel model-based dissimilarities that account for RNA-seq data characteristics have also been proposed. Adequacy of dissimilarity measures has been assessed using parametric simulations or exemplar datasets that may limit the scope of the conclusions. Here, we propose the simulation of realistic conditions through creation of plasmode datasets, to assess the adequacy of dissimilarity measures for sample-based hierarchical clustering of RNA-seq data. Consistent results were obtained using plasmode datasets based on RNA-seq experiments conducted under widely different conditions. Dissimilarity measures based on Euclidean distance that only considered data normalization or data standardization were not reliable to represent the expected hierarchical structure. Conversely, using either a Poisson-based dissimilarity or a rank correlation based dissimilarity or an appropriate data transformation, resulted in dendrograms that resemble the expected hierarchical structure. Plasmode datasets can be generated for a wide range of scenarios upon which dissimilarity measures can be evaluated for sample-based hierarchical clustering analysis. We showed different ways of generating such plasmodes and applied them to the problem of selecting a suitable dissimilarity measure.We report several measures that are satisfactory and the choice of a particular measure may rely on the availability on the software pipeline of preference.
format Articulo
Articulo
author Reeb, Pablo D.
Bramardi, Sergio Jorge
Steibel, Juan P.
author_facet Reeb, Pablo D.
Bramardi, Sergio Jorge
Steibel, Juan P.
author_sort Reeb, Pablo D.
title Assessing dissimilarity measures for sample-based hierarchical clustering of RNA sequencing data using plasmode datasets
title_short Assessing dissimilarity measures for sample-based hierarchical clustering of RNA sequencing data using plasmode datasets
title_full Assessing dissimilarity measures for sample-based hierarchical clustering of RNA sequencing data using plasmode datasets
title_fullStr Assessing dissimilarity measures for sample-based hierarchical clustering of RNA sequencing data using plasmode datasets
title_full_unstemmed Assessing dissimilarity measures for sample-based hierarchical clustering of RNA sequencing data using plasmode datasets
title_sort assessing dissimilarity measures for sample-based hierarchical clustering of rna sequencing data using plasmode datasets
publishDate 2015
url http://sedici.unlp.edu.ar/handle/10915/86218
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