Analyzing Time-Course Microarray Data Using Functional Data Analysis - A Review

Coffey, Norma; Hinde, John

doi:10.2202/1544-6115.1671

Cited by 31 publications

(27 citation statements)

References 33 publications

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“…Among such problems are object silhouette recognition (Sebastian et al 2003), micro-array data analyses (Coffey et al 2011), handwriting recognition (Wirtz 1997), or the classic human growth curve alignment problem (Ramsay and Li 1998;Sangalli et al 2010). Here, we follow and extend the landmark registration approach introduced by Bookstein (1978) and coworkers (Kneip andGasser 1992, Kneip andRamsay 2008).…”

Section: Consensus Calculationmentioning

confidence: 99%

Microstructure alignment of wood density profiles: an approach to equalize radial differences in growth rate

Bender¹,

Mann

Backofen³

et al. 2012

Trees

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We studied intra-annual wood density profiles of Douglas-fir tree rings (Pseudotsuga menziesii [Mirb.] Franco) in southwestern Germany. Growth rate varies differently over time throughout the circumference of trees. This leads to differences in wood formation, which can be observed in the shape of the density profiles of the same tree ring measured in different radial directions. Due to this spatial variation in density profiles, we need a reliable method to determine an average profile, which preserves the common characteristics of the data. To this end, we developed a multiple interval-based curve alignment (MICA) procedure. It identifies characteristic points within the profiles such as minima, maxima and inflection points. These reference points are shifted gradually against each other within a proportionally defined base line interval. Using our progressive alignment approach, we are able to calculate an average profile that represents very well the characteristics of all measured curves of a specific tree ring. We applied the procedure to get year-specific average profiles using various trees. This results in representative mean density profiles that preserves the density variations common to all aligned profiles. Individual noise is reduced thereby enabling the analysis of the impact of weather variations on wood density.

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Section: Consensus Calculationmentioning

confidence: 99%

Microstructure alignment of wood density profiles: an approach to equalize radial differences in growth rate

Bender¹,

Mann

Backofen³

et al. 2012

Trees

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“…Inspired by a trend in the data analysis to fit the true underlying functions [20, 21], methods based on functional PCA (FPCA) were developed [22, 23]. The most recent method [23] can handle single replicated time course data, predict individual dynamics with PACE (Principal Component Analysis through Conditional Expectation) [24] and yields reasonable results for moderately slow expression dynamics.…”

Section: Introductionmentioning

confidence: 99%

TTCA: an R package for the identification of differentially expressed genes in time course microarray data

et al. 2017

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BackgroundThe analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements.ResultsThe method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF).ConclusionHere we describe a new, efficient method for the analysis of sparse and heterogeneous time course data with high detection sensitivity and transparency. It is implemented as R package TTCA (transcript time course analysis) and can be installed from the Comprehensive R Archive Network, CRAN. The source code is provided with the Additional file 1.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1440-8) contains supplementary material, which is available to authorized users.

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“…A popular method for summarising quantities measured over time is smoothing splines, which uses a piecewise polynomial function with a penalty term λ [24,25]. The challenging part of this method is to determine the correct smoothness parameter λ and the number of knots, which are very important for the function fit, but are often arbitrarily chosen.…”

Section: Challenges Of Emergent High-throughput Platformsmentioning

confidence: 99%

“…Widely used methods for clustering time course data are hierarchical clustering (HC) [22], kmeans (KM) [24], self-organizing maps (SOM) [40] and model based clustering (mclust) [25]. HC is popular because of the easy visualisation and the resulting information of the data structure.…”

Section: Clusteringmentioning

confidence: 99%

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Development of statistical tools for integrating time course ‘omics’ data

Straube¹

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Biological systems adapt to environmental and genetic changes via dynamic molecular expression and regulation. During the past decade, the use of 'omics' technologies to take a snapshot of this molecular behaviour has become ubiquitous. Expression quantities of a single molecular level like the transcriptome, proteome or metabolome have been a focus of study, however this is not sufficient to understand the complex intermolecular level regulations. Instead, taking a series of snapshots of molecular levels over time allows the study of molecular expression dynamics. In addition, the integration of multiple time course 'omics' experiments may result in better understanding of how molecular interactions give rise to the functions and behaviours of that system. The analysis of time course 'omics' data has various objectives. One is to identify molecules that change expression over time or between groups to infer their contribution to biological processes.Another is to cluster molecules with similar expression profiles. These co-expressed molecules are believed to be co-regulated or have similar molecular functions and are hypothesised to be the result of networks of molecule interaction. Therefore, researchers use time course 'omics' data to identify co-expressed molecules and differentially expressed molecules to understand the dynamics of biological systems at the molecular level. These data can also be used to model molecular interaction networks with the objective to understand the relationships of molecules that drive a biological system.Time course 'omics' experiments quantify thousands of molecules for each biological sample, but those experiments often only include very few biological and technical replications. The data generated are difficult to analyse because of the high number of missing values, as well as potentially high within and between individual variability. Furthermore, expression changes of co-expressed molecules can occur delayed in time. Therefore, powerful statistical methods are critical for answering key questions about system response and function. They need to efficiently analyse highdimensional data that are noisy and complex. However, so far researchers have been limited in capitalising on the wealth of 'omics' data because of the lack of powerful statistical methods. This thesis outlines the development of novel and user-friendly statistical tools to analyse time course 'omics' data. It consists of two core projects: firstly, the development of a framework to analyse time course 'omics' data obtained from a single molecular level (e.g. proteome); secondly, the development of statistical methods and tools to integrate and analyse time course 'omics' data from several functional levels (e.g. transcriptome and metabolome).For my first project, I developed a framework consisting of three stages: quality assessment and filtering, profile modelling, and analysis. The quality control approach was developed to remove molecules for which expression was highly noisy. I then further deve...

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Analyzing Time-Course Microarray Data Using Functional Data Analysis - A Review

Cited by 31 publications

References 33 publications

Microstructure alignment of wood density profiles: an approach to equalize radial differences in growth rate

Microstructure alignment of wood density profiles: an approach to equalize radial differences in growth rate

TTCA: an R package for the identification of differentially expressed genes in time course microarray data

Development of statistical tools for integrating time course ‘omics’ data

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