Multiple gene expression profile alignment for microarray time-series data clustering

Subhani, Numanul; Rueda, Luis; Ngom, Alioune; Burden, Conrad J.

doi:10.1093/bioinformatics/btq422

Cited by 17 publications

(10 citation statements)

References 19 publications

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“…In addition, clustering was used to divide the time series data into groups based on similarity, without advanced knowledge of the definitions of the groups [34], and has been applied in the fields of climate, environment, economy, finance, medicine, and biology [35][36][37][38][39]. Most of the applications in biology were related to genes [37,[40][41][42][43]. The present study provided the first application for bacterial growth analysis.…”

Section: Discussionmentioning

confidence: 99%

Clustering of Bacterial Growth Dynamics in Response to Growth Media by Dynamic Time Warping

2020

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Bacterial growth curves, representing population dynamics, are still poorly understood. The growth curves are commonly analyzed by model-based theoretical fitting, which is limited to typical S-shape fittings and does not elucidate the dynamics in their entirety. Thus, whether a certain growth condition results in any particular pattern of growth curve remains unclear. To address this question, up-to-date data mining techniques were applied to bacterial growth analysis for the first time. Dynamic time warping (DTW) and derivative DTW (DDTW) were used to compare the similarity among 1015 growth curves of 28 Escherichia coli strains growing in three different media. In the similarity evaluation, agglomerative hierarchical clustering, assessed with four statistic benchmarks, successfully categorized the growth curves into three clusters, roughly corresponding to the three media. Furthermore, a simple benchmark was newly proposed, providing a highly improved accuracy (~99%) in clustering the growth curves corresponding to the growth media. The biologically reasonable categorization of growth curves suggested that DTW and DDTW are applicable for bacterial growth analysis. The bottom-up clustering results indicate that the growth media determine some specific patterns of population dynamics, regardless of genomic variation, and thus have a higher priority of shaping the growth curves than the genomes do.

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Section: Discussionmentioning

confidence: 99%

Clustering of Bacterial Growth Dynamics in Response to Growth Media by Dynamic Time Warping

2020

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“…There are two issues that need to be resolved when assigning such smoothing splines: (1) The number of knots (control points) and (2) their spacing. Past approaches for using splines to model time series gene expression data have usually used the same number of control points for all genes regardless of their trajectories ( Subhani et al, 2010 ; Bar-Joseph et al, 2003b ), and mostly employed uniform knot placements. However, since our method needs to be able to adapt to any size of

as defined above, we also attempt to select the number of knots and their spacing.…”

Section: Methodsmentioning

confidence: 99%

Selecting the most appropriate time points to profile in high-throughput studies

Kleyman

Sefer

Nicola

et al. 2017

eLife

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Biological systems are increasingly being studied by high throughput profiling of molecular data over time. Determining the set of time points to sample in studies that profile several different types of molecular data is still challenging. Here we present the Time Point Selection (TPS) method that solves this combinatorial problem in a principled and practical way. TPS utilizes expression data from a small set of genes sampled at a high rate. As we show by applying TPS to study mouse lung development, the points selected by TPS can be used to reconstruct an accurate representation for the expression values of the non selected points. Further, even though the selection is only based on gene expression, these points are also appropriate for representing a much larger set of protein, miRNA and DNA methylation changes over time. TPS can thus serve as a key design strategy for high throughput time series experiments. Supporting Website: www.sb.cs.cmu.edu/TPSDOI: http://dx.doi.org/10.7554/eLife.18541.001

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“…Time-series clustering can be defined as follows [21]: given a dataset of n time-series images D = {F 1 , F 2 , • • • , F n }, the process of unsupervised partitioning of D into C = {C 1 , C 2 , • • • , C K } is conducted in such a way that the homogeneous time-series images are grouped together based on a certain similarity measure. Time-series clustering is widely used in the field of data mining, in applications such as seasonal retail pattern analysis [22], seismic wave, and mining explosion analysis [23], gene expression pattern extraction [24,25], climate analysis [26], and stock market trend analysis [27]. To date, the research into time-series clustering has mainly focused on the following four aspects [1]: (1) The representation method for the time-series data [28][29][30][31][32], because an effective representation method is crucial to the subsequent clustering process.…”

Section: Introductionmentioning

confidence: 99%

A Novel Relational-Based Transductive Transfer Learning Method for PolSAR Images via Time-Series Clustering

Qin

Sun

et al. 2019

Remote Sensing

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The combination of transfer learning and remote sensing image processing technology can effectively improve the automation level of image information extraction from a remote sensing time series. However, in the processing of polarimetric synthetic aperture radar (PolSAR) time-series images, the existing transfer learning methods often cannot make full use of the time-series information of the images, relying too much on the labeled samples in the target domain. Furthermore, the speckle noise inherent in synthetic aperture radar (SAR) imagery aggravates the difficulty of the manual selection of labeled samples, so these methods have difficulty in meeting the processing requirements of large data volumes and high efficiency. In lieu of these problems and the spatio-temporal relational knowledge of objects in time-series images, this paper introduces the theory of time-series clustering and proposes a new three-phase time-series clustering algorithm. Due to the full use of the inherent characteristics of the PolSAR images, this algorithm can accurately transfer the labels of the source domain samples to those samples that have not changed in the whole time series without relying on the target domain labeled samples, so as to realize transductive sample label transfer for PolSAR time-series images. Experiments were carried out using three different sets of PolSAR time-series images and the proposed method was compared with two of the existing methods. The experimental results showed that the transfer precision of the proposed method reaches a high level with different data and different objects and it performs significantly better than the existing methods. With strong reliability and practicability, the proposed method can provide a new solution for the rapid information extraction of remote sensing image time series.

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Multiple gene expression profile alignment for microarray time-series data clustering

Cited by 17 publications

References 19 publications

Clustering of Bacterial Growth Dynamics in Response to Growth Media by Dynamic Time Warping

Clustering of Bacterial Growth Dynamics in Response to Growth Media by Dynamic Time Warping

Selecting the most appropriate time points to profile in high-throughput studies

A Novel Relational-Based Transductive Transfer Learning Method for PolSAR Images via Time-Series Clustering

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