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- Title
Independent Component Analysis (ICA) based-clustering of temporal RNA-seq data.
- Authors
Nascimento, Moysés; Nascimento, Ana Carolina Campana; Barroso, Laís Mayara Azevedo; Ferreira Azevedo, Camila; Silva, Fabyano Fonseca e; Ferreira, Talles Eduardo Maciel; Guimarães, Simone Eliza Faccione; Sáfadi, Thelma; Serão, Nick Vergara Lopes
- Abstract
Gene expression time series (GETS) analysis aims to characterize sets of genes according to their longitudinal patterns of expression. Due to the large number of genes evaluated in GETS analysis, an useful strategy to summarize biological functional processes and regulatory mechanisms is through clustering of genes that present similar expression pattern over time. Traditional cluster methods usually ignore the challenges in GETS, such as the lack of data normality and small number of temporal observations. Independent Component Analysis (ICA) is a statistical procedure that uses a transformation to convert raw time series data into sets of values of independent variables, which can be used for cluster analysis to identify sets of genes with similar temporal expression patterns. ICA allows clustering small series of distribution-free data while accounting for the dependence between subsequent time-points. Using temporal simulated and real (four libraries of two pig breeds at 21, 40, 70 and 90 days of gestation) RNA-seq data set we present a methodology (ICAclust) that jointly considers independent components analysis (ICA) and a hierarchical method for clustering GETS. We compare ICAclust results with those obtained for K-means clustering. ICAclust presented, on average, an absolute gain of 5.15% over the best K-means scenario. Considering the worst scenario for K-means, the gain was of 84.85%, when compared with the best ICAclust result. For the real data set, genes were grouped into six distinct clusters with 89, 51, 153, 67, 40, and 58 genes each, respectively. In general, it can be observed that the 6 clusters presented very distinct expression patterns. Overall, the proposed two-step clustering method (ICAclust) performed well compared to K-means, a traditional method used for cluster analysis of temporal gene expression data. In ICAclust, genes with similar expression pattern over time were clustered together.
- Subjects
RNA sequencing; GENE expression; INDEPENDENT component analysis; TIME series analysis; K-means clustering; MATHEMATICAL models
- Publication
PLoS ONE, 2017, Vol 12, Issue 7, p1
- ISSN
1932-6203
- Publication type
Article
- DOI
10.1371/journal.pone.0181195