RT info:eu-repo/semantics/doctoralThesis
T1 Statistical methodology and software to analyse oscillatory signals with applications to biology
A1 Larriba González, Yolanda
A2 Universidad de Valladolid. Facultad de Ciencias
K1 Metodología estadística
K1 12 Matemáticas
AB Many physiological and biological phenomena, such as menstrual cycles(Draper et al. (2018)), reproductive activity (Simonneaux and Bahougne (2015),Caba et al. (2018)), cell cycle (Liu et al. (2017)) or circadian biology (Hugheset al. (2009), Zhang et al. (2014), Andreani et al. (2015), Seney et al. (2019)),are governed by oscillatory systems consisting of numerous signals that exhibitrhythmic patterns over time. For example, the circadian clock is a molecularpacemaker that orchestrates daily functional activity including metabolic state,endocrine activity or neural excitability. Genes involved in those processes thatexhibit rhythmic expression patterns along ~24-hour periods are called circadiangenes. The study of such signals with temporal rhythmic patterns, andhow these patterns change under different conditions, is called chronobiology.Chronobiology has been an active area of research during the past twodecades, with major impact on treating cardiovascular disorders like hypertension(Halberg et al. (2013)), detecting genes associated with neurodegenerativedisorders (Li et al. (2013)) or depression (Chauhan et al. (2017)), and improvingthe effectiveness of cancer treatments (Chan et al. (2017)). For instance, Haus(2009) demonstrated that the timing of radiation according to host and/or tumourrhythms improves the toxic/therapeutic ratio of the treatment. These andother findings in biomedical sciences have increased interest in chronobiologicalexperiments.From a statistical point of view, the analysis of rhythmic signals ( ) inchronobiology has several challenges because of: (a)   displays a wide varietyof rhythmic patterns over time, which are not exactly sinusoidal or even symmetric(Koren£i£ et al. (2012), Zhang et al. (2014), Rueda et al. (2019)); (b)the density of the time points and the number of periods of data is usually verysmall (Panda et al. (2002), Hughes et al. (2007, 2009), Yang and Su (2010));(c) the intrinsic circular nature of data from oscillatory systems; (d) the vari-1ability in time course expression data due to noisy nature of the data; (e) insome applications, the temporal order among samples may be unknown. Forthese reasons, standard time series or Fourier models are not convenient for theanalysis of chronobiological rhythms (Elkum and Myles (2006), Wijnen et al.(2006), Leise (2013)). Models based on parametric functions of time, such asCosinor, have been proposed in chronobiology to model these patterns (Tong(1976), Cornelissen (2014)). The main drawback of these approaches is thatsuch parametric functions are too rigid, as signals in oscillatory systems veryoften exhibit asymmetric patterns.There are several commonly encountered problems in chronobiology. Themain problem to solve in this context is rhythmicity detection as not all patternsobserved in an oscillatory system display rhythmic patterns. For a given signal ; rhythmicity detection can be formulated as the following hypothesis test:H0 :   is a flat signal (1.1)H1 :   is rhythmic signal.This problem has been studied extensively in literature, existing a widevariety of procedures to address it including, among others, those based on sinusoidalcurve fitting (Liu et al. (2004), Straume (2004), Cornelissen (2014)),autocorrelation (Levine et al. (2002)) or Fourier analyses (Wichert et al. (2004)).Some non-parametric approaches, such as JTK_Cycle (JTK) (Hughes et al.(2010)) and RAIN (Thaben and Westermark (2014)), based on Jonckheere-Terpstra test and Kendall's tau correlation, are widely employed by biologists.However, these two latter approaches do not detect asymmetric rhythmic patternsproperly.A fundamental assumption made in the above discussion is that the timecorresponding to each biological sample is known. However, in many instances,such as when dealing with samples obtained from human cadavers (Li et al.(2013), Seney et al. (2019)) or human organ biopsies, (Lamb et al. (2011), Bosséet al. (2012)), the exact time corresponding to each biological sample may beunknown. In such cases, one needs to first estimate or determine the time associatedwith each sample before investigating rhythmicity. This problem, knownas temporal order estimation, is other crucial issue in chronobiology. Some recentprocedures to cope with this problem are Oscope (Leng et al. (2015)) andCYCLOPS (Anafi et al. (2017)). Oscope was specifically designed to recovercell cycle dynamic, and it is only applicable in single cell RNA-Seq experiments.CYCLOPS is far from a mathematical close-fitting formulation. It is based on aneural network framework (which is like a black box) and uses additional rhythmicityinformation which is not always available.In addition to the major rhythmicity issues mentioned above, other interestingquestions related to the analysis of oscillatory signals, such as peak time2estimation or rhythm-pattern comparisons, deserve consideration. For instance,when dealing with circadian genes, time peak estimation reveals crucial informationfor biologists about the timings at which genes' biological function iscarried out.The main motivation of this thesis was to solve appealing rhythmicity questionsspecifically related to the analysis of circadian gene expression. In particular,the starting problem of this thesis was to identify among the severalthousand of genes registered in a genetic study, those that display rhythmicexpression patterns.
YR 2020
FD 2020
LK http://uvadoc.uva.es/handle/10324/43370
UL http://uvadoc.uva.es/handle/10324/43370
LA eng
NO Departamento de Estadística e Investigación Operativa
DS UVaDOC
RD 17-jul-2024