scholarly journals Change point detection for clustered expression data

2021 ◽  
Author(s):  
Miriam Sieg ◽  
Lina Katrin Sciesielski ◽  
Karin Michaela Kirschner ◽  
Jochen Kruppa
Keyword(s):  
Data Structure ◽  
Confidence Intervals ◽  
Change Point ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Research Question ◽  
Change Point Detection ◽  
Expression Data ◽  
Abrupt Changes ◽  
Point Detection

Abstract Background: To detect changes in biological processes samples are oftenmeasured at several time points. We observe expression data measured atdifferent developmental stages, or more broadly, historical data. Hence, the mainassumption of our proposed methodology is the independence between theobserved samples over time. In addition, the observations are clustered at eachpoint in time. The clustering is caused by measuring litter mates from relativelyfew mother mice at each development stage. The examination is lethal.Therefore, we have an independent data structure over the entire history, but adependent data structure at a particular point in time. Over the course of thehistorical data, we want to identify abrupt changes in the outcome - a changepoint. Results: In this paper, we demonstrate the application of generalized hypothesistesting using a linear mixed effects model as one possible method for detectingchange points. The coefficients from the linear mixed model are used in multiplecontrast tests. The effect estimates are then visualized with simultaneousconfidence intervals. The figure of the confidence intervals can be used for thedetermination of the change point. Multiple contrast tests depend on the choiceof the used contrast. A variety of possible usable contrasts exists. In smallsimulation studies, we model different courses with abrupt changes and illustratedifferent contrasts. We found two contrasts, both capable of answering differentresearch questions in change point detection. Sequen contrast to detectindividual points of change or McDermott contrast to illustrate overallprogression. In addition, we show the application on a clinical pilot study. Conclusion: Simultaneous confidence intervals estimated by multiple contrasttests using the model fit from a linear mixed model are usable to determinepossible change points in clustered expression data. The confidence intervalsdeliver direct interpretable effect estimates on the scale of the outcome for thestrength of the potential change point. Hence, scientists can define biologicallyrelevant limits of change depending on the research question. We found tworarely used contrast with the best properties to detect a possible change: theSequen and McDermott contrast. We provide R code for the direct applicationwith examples

Change Point Detection For Clustered Expression Data In Short Time Series.

2021 ◽  
Author(s):  
Miriam Sieg ◽  
Lina Katrin Sciesielski ◽  
Karin Kirschner ◽  
Jochen Kruppa
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Change Point ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Change Point Detection ◽  
Change Points ◽  
Point Detection ◽  
Over Time ◽  
Time Point

Abstract Background: In longitudinal studies, observations are made over time. Hence, the single observations at each time point are dependent, making them a repeated measurement. In this work, we explore a different, counterintuitive setting: At each developmental time point, a lethal observation is performed on the pregnant or nursing mother. Therefore, the single time points are independent. Furthermore, the observation in the offspring at each time point is correlated with each other because each litter consists of several (genetically linked) littermates. In addition, the observed time series is short from a statistical perspective as animal ethics prevent killing more mother mice than absolutely necessary, and murine development is short anyway. We solve these challenges by using multiple contrast tests and visualizing the change point by the use of confidence intervals.Results: We used linear mixed models to model the variability of the mother. The estimates from the linear mixed model are then used in multiple contrast tests.There are a variety of contrasts and intuitively, we would use the Changepoint method. However, it does not deliver satisfying results. Interestingly, we found two other contrasts, both capable of answering different research questions in change point detection: i) Should a single point with change direction be found, or ii) Should the overall progression be determined? The Sequen contrast answers the first, the McDermott the second. Confidence intervals deliver effect estimates for the strength of the potential change point. Therefore, the scientist can define a biologically relevant limit of change depending on the research question.Conclusion: We present a solution with effect estimates for short independent time series with observations nested at a given time point. Multiple contrast tests produce confidence intervals, which allow determining the position of change points or to visualize the expression course over time. We suggest to use McDermott’s method to determine if there is an overall significant change within the time frame, while Sequen is better in determining specific change points. In addition, we offer a short formula for the estimation of the maximal length of the time series.

scholarly journals Change point detection for clustered expression data

2021 ◽  
Author(s):  
Miriam Sieg ◽  
Lina Katrin Sciesielski ◽  
Karin Michaela Kirschner ◽  
Jochen Kruppa
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Change Point ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Change Point Detection ◽  
Change Points ◽  
Point Detection ◽  
Over Time ◽  
Time Point

Abstract Background: In longitudinal studies, observations are made over time. Hence, the single observations at each time point are dependent, making them a repeated measurement. In this work, we explore a different, counterintuitive setting: At each developmental time point, a lethal observation is performed on the pregnant or nursing mother. Therefore, the single time points are independent. Furthermore, the observation in the offspring at each time point is correlated with each other because each litter consists of several (genetically linked) littermates. In addition, the observed time series is short from a statistical perspective as animal ethics prevent killing more mother mice than absolutely necessary, and murine development is short anyway. We solve these challenges by using multiple contrast tests and visualizing the change point by the use of confidence intervals.Results: We used linear mixed models to model the variability of the mother. The estimates from the linear mixed model are then used in multiple contrast tests.There are a variety of contrasts and intuitively, we would use the Changepoint method. However, it does not deliver satisfying results. Interestingly, we found two other contrasts, both capable of answering different research questions in change point detection: i) Should a single point with change direction be found, or ii) Should the overall progression be determined? The Sequen contrast answers the first, the McDermott the second. Confidence intervals deliver effect estimates for the strength of the potential change point. Therefore, the scientist can define a biologically relevant limit of change depending on the research question.Conclusion: We present a solution with effect estimates for short independent time series with observations nested at a given time point. Multiple contrast tests produce confidence intervals, which allow determining the position of change points or to visualize the expression course over time. We suggest to use McDermott’s method to determine if there is an overall significant change within the time frame, while Sequen is better in determining specific change points. In addition, we offer a short formula for the estimation of the maximal length of the time series.

scholarly journals Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity

2018 ◽  
Vol 119 (4) ◽  
pp. 1394-1410 ◽  
Author(s):  
Sile Hu ◽  
Qiaosheng Zhang ◽  
Jing Wang ◽  
Zhe Chen
Keyword(s):  
Real Time ◽  
Spike Activity ◽  
Change Point ◽  
Particle Filtering ◽  
Jump Process ◽  
Change Point Detection ◽  
Stochastic Jump ◽  
Abrupt Changes ◽  
Non Gaussian ◽  
Point Detection

Sequential change-point detection from time series data is a common problem in many neuroscience applications, such as seizure detection, anomaly detection, and pain detection. In our previous work (Chen Z, Zhang Q, Tong AP, Manders TR, Wang J. J Neural Eng 14: 036023, 2017), we developed a latent state-space model, known as the Poisson linear dynamical system, for detecting abrupt changes in neuronal ensemble spike activity. In online brain-machine interface (BMI) applications, a recursive filtering algorithm is used to track the changes in the latent variable. However, previous methods have been restricted to Gaussian dynamical noise and have used Gaussian approximation for the Poisson likelihood. To improve the detection speed, we introduce non-Gaussian dynamical noise for modeling a stochastic jump process in the latent state space. To efficiently estimate the state posterior that accommodates non-Gaussian noise and non-Gaussian likelihood, we propose particle filtering and smoothing algorithms for the change-point detection problem. To speed up the computation, we implement the proposed particle filtering algorithms using advanced graphics processing unit computing technology. We validate our algorithms, using both computer simulations and experimental data for acute pain detection. Finally, we discuss several important practical issues in the context of real-time closed-loop BMI applications. NEW & NOTEWORTHY Sequential change-point detection is an important problem in closed-loop neuroscience experiments. This study proposes novel sequential Monte Carlo methods to quickly detect the onset and offset of a stochastic jump process that drives the population spike activity. This new approach is robust with respect to spike sorting noise and varying levels of signal-to-noise ratio. The GPU implementation of the computational algorithm allows for parallel processing in real time.

scholarly journals Change-point detection: application of Cusum method to real life data

2021 ◽  
Vol 8 (1) ◽  
pp. 1041-1047
Author(s):  
Edoh Katchekpele ◽  
Tchilabalo Abozou Kpanzou ◽  
Jean-Etienne Ouindllassida Ouédraogo
Keyword(s):  
Change Point ◽  
Real Life ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Test Statistic ◽  
Life Data ◽  
Real Life Data ◽  
Abrupt Changes ◽  
Cusum Method ◽  
Point Detection

Several procedures have been developed for the detection of abrupt changes in time series. Among these procedures, it can be mentioned the Cumulative Sum (Cusum) type method. It is in such a perspective that Katchekpele et al. (2017) proposed a method using a Cusum type test to detect a change-point in the unconditional variance of the generalised autoregressive conditional heteroskedasticity(GARCH) models. The aim of this paper is to present an application of their technique. After briefly recalling how the test statistic was constructed, the change-point detection algorithm is given and it is shown how it is applied to some real life data.

Understanding the CoVID-19 pandemic Curve through statistical approach (Preprint)

2020 ◽  
Author(s):  
Ibrar Ul Hassan Akhtar
Keyword(s):  
Probability Density ◽  
Change Point ◽  
Statistical Approach ◽  
Sudden Change ◽  
Global Scale ◽  
Change Point Detection ◽  
Segment Length ◽  
Infected Population ◽  
The Mean ◽  
Point Detection

UNSTRUCTURED Current research is an attempt to understand the CoVID-19 pandemic curve through statistical approach of probability density function with associated skewness and kurtosis measures, change point detection and polynomial fitting to estimate infected population along with 30 days projection. The pandemic curve has been explored for above average affected countries, six regions and global scale during 64 days of 22nd January to 24th March, 2020. The global cases infection as well as recovery rate curves remained in the ranged of 0 ‒ 9.89 and 0 ‒ 8.89%, respectively. The confirmed cases probability density curve is high positive skewed and leptokurtic with mean global infected daily population of 6620. The recovered cases showed bimodal positive skewed curve of leptokurtic type with daily recovery of 1708. The change point detection helped to understand the CoVID-19 curve in term of sudden change in term of mean or mean with variance. This pointed out disease curve is consist of three phases and last segment that varies in term of day lengths. The mean with variance based change detection is better in differentiating phases and associated segment length as compared to mean. Global infected population might rise in the range of 0.750 to 4.680 million by 24th April 2020, depending upon the pandemic curve progress beyond 24th March, 2020. Expected most affected countries will be USA, Italy, China, Spain, Germany, France, Switzerland, Iran and UK with at least infected population of over 0.100 million. Infected population polynomial projection errors remained in the range of -78.8 to 49.0%.

scholarly journals Change point detection with multiple alternatives reveals parallel evaluation of the same stream of evidence along distinct timescales

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexa Booras ◽  
Tanner Stevenson ◽  
Connor N. McCormack ◽  
Marie E. Rhoads ◽  
Timothy D. Hanks
Keyword(s):  
Change Point ◽  
Neural Mechanism ◽  
Detection Task ◽  
Change Point Detection ◽  
Model Free ◽  
Evidence Evaluation ◽  
Parallel Evaluation ◽  
Point Detection ◽  
Improved Accuracy

AbstractIn order to behave appropriately in a rapidly changing world, individuals must be able to detect when changes occur in that environment. However, at any given moment, there are a multitude of potential changes of behavioral significance that could occur. Here we investigate how knowledge about the space of possible changes affects human change point detection. We used a stochastic auditory change point detection task that allowed model-free and model-based characterization of the decision process people employ. We found that subjects can simultaneously apply distinct timescales of evidence evaluation to the same stream of evidence when there are multiple types of changes possible. Informative cues that specified the nature of the change led to improved accuracy for change point detection through mechanisms involving both the timescales of evidence evaluation and adjustments of decision bounds. These results establish three important capacities of information processing for decision making that any proposed neural mechanism of evidence evaluation must be able to support: the ability to simultaneously employ multiple timescales of evidence evaluation, the ability to rapidly adjust those timescales, and the ability to modify the amount of information required to make a decision in the context of flexible timescales.

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