scholarly journals Clustering Longitudinal Life-Course Sequences using Mixtures of Exponential-Distance Models

2019 ◽  
Author(s):  
Keefe Murphy ◽  
Brendan Murphy ◽  
Raffaella Piccarreta ◽  
Isobel Claire Gormley
Keyword(s):  
Life Course ◽  
Hamming Distance ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
Model Fitting ◽  
Northern Irish ◽  
Sampling Weights ◽  
Data Set ◽  
Model Based Clustering ◽  
Exponential Distance

Sequence analysis is an increasingly popular approach for the analysis of life courses represented by an ordered collection of activities experienced by subjects over a given time period. Several criteria exist for measuring pairwise dissimilarities among sequences. Typically, dissimilarity matrices are employed as input to heuristic clustering algorithms, with the aim of identifying the most relevant patterns in the data.Here, we propose a model-based clustering approach for categorical sequence data. The technique is applied to a survey data set containing information on the career trajectories of a cohort of Northern Irish youths tracked between the ages of 16 and 22.Specifically, we develop a family of methods for clustering sequences directly, based on mixtures of exponential-distance models, which we call MEDseq. The use of the Hamming distance or weighted variants thereof as the distance metrics permits closed-form expressions for the normalising constant, thereby facilitating the development of an ECM algorithm for model fitting. Additionally, MEDseq models allow the probability of component membership to depend on fixed covariates. Sampling weights, which are often associated with life-course data arising from surveys, are also accommodated. Simultaneously including weights and covariates in the clustering process yields new insights on the Northern Irish data.

A Nonparametric Clustering Algorithm with a Quantile-Based Likelihood Estimator

2014 ◽  
Vol 26 (9) ◽  
pp. 2074-2101 ◽  
Author(s):  
Hideitsu Hino ◽  
Noboru Murata
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Conditional Entropy ◽  
Tuning Parameter ◽  
Clustering Methods ◽  
Sampling Weights ◽  
Data Set ◽  
Information Theoretic ◽  
Nonparametric Clustering ◽  
Conditional Information

Clustering is a representative of unsupervised learning and one of the important approaches in exploratory data analysis. By its very nature, clustering without strong assumption on data distribution is desirable. Information-theoretic clustering is a class of clustering methods that optimize information-theoretic quantities such as entropy and mutual information. These quantities can be estimated in a nonparametric manner, and information-theoretic clustering algorithms are capable of capturing various intrinsic data structures. It is also possible to estimate information-theoretic quantities using a data set with sampling weight for each datum. Assuming the data set is sampled from a certain cluster and assigning different sampling weights depending on the clusters, the cluster-conditional information-theoretic quantities are estimated. In this letter, a simple iterative clustering algorithm is proposed based on a nonparametric estimator of the log likelihood for weighted data sets. The clustering algorithm is also derived from the principle of conditional entropy minimization with maximum entropy regularization. The proposed algorithm does not contain a tuning parameter. The algorithm is experimentally shown to be comparable to or outperform conventional nonparametric clustering methods.

scholarly journals Selecting single cell clustering parameter values using subsampling-based robustness metrics

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ryan B. Patterson-Cross ◽  
Ariel J. Levine ◽  
Vilas Menon
Keyword(s):  
Single Cell ◽  
Optimal Parameter ◽  
Clustering Algorithms ◽  
Cell Types ◽  
Parameter Selection ◽  
Data Set ◽  
Biologically Relevant ◽  
Cell Clustering ◽  
Parameter Values ◽  
Robustness Metrics

Abstract Background Generating and analysing single-cell data has become a widespread approach to examine tissue heterogeneity, and numerous algorithms exist for clustering these datasets to identify putative cell types with shared transcriptomic signatures. However, many of these clustering workflows rely on user-tuned parameter values, tailored to each dataset, to identify a set of biologically relevant clusters. Whereas users often develop their own intuition as to the optimal range of parameters for clustering on each data set, the lack of systematic approaches to identify this range can be daunting to new users of any given workflow. In addition, an optimal parameter set does not guarantee that all clusters are equally well-resolved, given the heterogeneity in transcriptomic signatures in most biological systems. Results Here, we illustrate a subsampling-based approach (chooseR) that simultaneously guides parameter selection and characterizes cluster robustness. Through bootstrapped iterative clustering across a range of parameters, chooseR was used to select parameter values for two distinct clustering workflows (Seurat and scVI). In each case, chooseR identified parameters that produced biologically relevant clusters from both well-characterized (human PBMC) and complex (mouse spinal cord) datasets. Moreover, it provided a simple “robustness score” for each of these clusters, facilitating the assessment of cluster quality. Conclusion chooseR is a simple, conceptually understandable tool that can be used flexibly across clustering algorithms, workflows, and datasets to guide clustering parameter selection and characterize cluster robustness.

Population Subdivision and Molecular Sequence Variation: Theory and Analysis of Drosophila ananassae Data

Genetics ◽  
2003 ◽  
Vol 165 (3) ◽  
pp. 1385-1395
Author(s):  
Claus Vogl ◽  
Aparup Das ◽  
Mark Beaumont ◽  
Sujata Mohanty ◽  
Wolfgang Stephan
Keyword(s):  
Sequence Data ◽  
Isolation By Distance ◽  
Allele Frequencies ◽  
Drosophila Ananassae ◽  
Population Subdivision ◽  
Variation Theory ◽  
Peripheral Populations ◽  
Molecular Variation ◽  
Data Set ◽  
Evolutionary Forces

Abstract Population subdivision complicates analysis of molecular variation. Even if neutrality is assumed, three evolutionary forces need to be considered: migration, mutation, and drift. Simplification can be achieved by assuming that the process of migration among and drift within subpopulations is occurring fast compared to mutation and drift in the entire population. This allows a two-step approach in the analysis: (i) analysis of population subdivision and (ii) analysis of molecular variation in the migrant pool. We model population subdivision using an infinite island model, where we allow the migration/drift parameter 0398; to vary among populations. Thus, central and peripheral populations can be differentiated. For inference of 0398;, we use a coalescence approach, implemented via a Markov chain Monte Carlo (MCMC) integration method that allows estimation of allele frequencies in the migrant pool. The second step of this approach (analysis of molecular variation in the migrant pool) uses the estimated allele frequencies in the migrant pool for the study of molecular variation. We apply this method to a Drosophila ananassae sequence data set. We find little indication of isolation by distance, but large differences in the migration parameter among populations. The population as a whole seems to be expanding. A population from Bogor (Java, Indonesia) shows the highest variation and seems closest to the species center.

Frequent Closed Partial Orders Mining in Sequences

2013 ◽  
Vol 846-847 ◽  
pp. 1304-1307
Author(s):  
Ye Wang ◽  
Yan Jia ◽  
Lu Min Zhang
Keyword(s):  
Sequence Data ◽  
Real Data ◽  
Partial Orders ◽  
Hard Problem ◽  
Important Data ◽  
Data Set ◽  
Pruning Algorithm ◽  
Equal Chance ◽  
Np Hard Problem ◽  
General Sequences

Mining partial orders from sequence data is an important data mining task with broad applications. As partial orders mining is a NP-hard problem, many efficient pruning algorithm have been proposed. In this paper, we improve a classical algorithm of discovering frequent closed partial orders from string. For general sequences, we consider items appearing together having equal chance to calculate the detecting matrix used for pruning. Experimental evaluations from a real data set show that our algorithm can effectively mine FCPO from sequences.

The correlations of the function and positional distribution of the cis-elements CArG around the TSS in the genes of Mus musculus

Genome ◽  
2009 ◽  
Vol 52 (3) ◽  
pp. 217-221 ◽  
Author(s):  
Xia Shen ◽  
Bruce Walsh ◽  
Jing J. Li ◽  
Hong X. Pang ◽  
Wen J. Wang ◽  
...  
Keyword(s):  
Binding Sites ◽  
Copy Number ◽  
Serum Response Factor ◽  
Sequence Data ◽  
Positional Distribution ◽  
Cis Elements ◽  
Transcription Start ◽  
Data Set ◽  
Control Set ◽  
Serum Response

While many studies of cis-elements CArG bound by serum response factor (SRF) are in progress, little is known about the positional distribution of the functional CArG elements around the transcription start site (TSS) of genes that they influence. We use a validated CArG data set to calculate the distance distribution of functional CArG elements around the TSS. Distances between adjacent CArGs were also analyzed. We compare these distributions with those derived using a control set of randomly selected CArGs (that were not experimentally validated for function). Our results show that most functional CArG elements (108 of 152, 71%) exist upstream of the annotated TSS, with copy number increasing as one moves closer to the TSS. Moreover, the average number of the CArG elements in the CArG-containing genes is significantly more than that in the control genes. Our study extends earlier bioinformatic analyses of functional CArG elements and provides an application of comparative sequence data to the identification of transcription factor binding sites.

Molecular Systematics of Tribe Physarieae (Brassicaceae) Based on Nuclear ITS, LUMINIDEPENDENS, and Chloroplast ndhF

2021 ◽  
Author(s):  
Sara Fuentes-Soriano ◽  
Elizabeth A. Kellogg
Keyword(s):  
Sequence Data ◽  
Plastid Dna ◽  
Sister Relationship ◽  
Data Set ◽  
Woody Perennial ◽  
History Of ◽  
Nuclear Its ◽  
Phylogenetic Framework ◽  
Phylogenetic Hypotheses ◽  
Relationship Of

Physarieae is a small tribe of herbaceous annual and woody perennial mustards that are mostly endemic to North America, with its members including a large amount of variation in floral, fruit, and chromosomal variation. Building on a previous study of Physarieae based on morphology and ndhF plastid DNA, we reconstructed the evolutionary history of the tribe using new sequence data from two nuclear markers, and compared the new topologies against previously published cpDNA-based phylogenetic hypotheses. The novel analyses included ca. 420 new sequences of ITS and LUMINIDEPENDENS (LD) markers for 39 and 47 species, respectively, with sampling accounting for all seven genera of Physarieae, including nomenclatural type species, and 11 outgroup taxa. Maximum parsimony, maximum likelihood, and Bayesian analyses showed that these additional markers were largely consistent with the previous ndhF data that supported the monophyly of Physarieae and resolved two major clades within the tribe, i.e., DDNLS (Dithyrea, Dimorphocarpa, Nerisyrenia, Lyrocarpa, and Synthlipsis)and PP (Paysonia and Physaria). New analyses also increased internal resolution for some closely related species and lineages within both clades. The monophyly of Dithyrea and the sister relationship of Paysonia to Physaria was consistent in all trees, with the sister relationship of Nerisyrenia to Lyrocarpa supported by ndhF and ITS, and the positions of Dimorphocarpa and Synthlipsis shifted within the DDNLS Clade depending on the employed data set. Finally, using the strong, new phylogenetic framework of combined cpDNA + nDNA data, we discussed standing hypotheses of trichome evolution in the tribe suggested by ndhF.

Do Developmental and Life-Course Theory Risk Factors Equally Predict Age of Onset Among Juvenile Sexual and Nonsexual Offenders?

Sexual Abuse ◽  
2018 ◽  
Vol 32 (1) ◽  
pp. 55-78 ◽  
Author(s):  
Melanie Rosa ◽  
Bryanna Fox ◽  
Wesley G. Jennings
Keyword(s):  
Risk Factors ◽  
Sex Offenders ◽  
Life Course ◽  
Age Of Onset ◽  
Juvenile Sex Offenders ◽  
Criminological Theory ◽  
Sex Offending ◽  
Data Set ◽  
Florida Department ◽  
Nonsexual Offenders

Previous empirical inquiries into the etiology of juvenile sex offending have been largely atheoretical. Consequently, a call for studies conducted utilizing developmental and life-course (DLC) criminological theory has been made to better understand the onset, development, risk, and protective factors of juvenile sex offending. Therefore, this study contributes to the discussion by testing key predictions proposed by the DLC framework regarding the theoretical correlates of early onset offending, as applied to juvenile sex offenders (JSOs) and juvenile nonsex offenders (JNSOs). Drawing on a data set of more than 64,000 youth referred to the Florida Department of Juvenile Justice, results indicate that although the number and severity of risk factors for early age of onset differ between the JSOs and JNSOs, the specific type of risk factors that emerged align with DLC theory predictions. The implications of these findings and contributions for DLC theory are also discussed.

scholarly journals SHI7 Is a Self-Learning Pipeline for Multipurpose Short-Read DNA Quality Control

mSystems ◽  
2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Gabriel A. Al-Ghalith ◽  
Benjamin Hillmann ◽  
Kaiwei Ang ◽  
Robin Shields-Cutler ◽  
Dan Knights
Keyword(s):  
Quality Control ◽  
Dna Sequences ◽  
Sequence Data ◽  
Background Knowledge ◽  
Sequencing Technology ◽  
Data Set ◽  
Short Read ◽  
Dna Quality ◽  
Public Data ◽  
User Friendly

ABSTRACT Next-generation sequencing technology is of great importance for many biological disciplines; however, due to technical and biological limitations, the short DNA sequences produced by modern sequencers require numerous quality control (QC) measures to reduce errors, remove technical contaminants, or merge paired-end reads together into longer or higher-quality contigs. Many tools for each step exist, but choosing the appropriate methods and usage parameters can be challenging because the parameterization of each step depends on the particularities of the sequencing technology used, the type of samples being analyzed, and the stochasticity of the instrumentation and sample preparation. Furthermore, end users may not know all of the relevant information about how their data were generated, such as the expected overlap for paired-end sequences or type of adaptors used to make informed choices. This increasing complexity and nuance demand a pipeline that combines existing steps together in a user-friendly way and, when possible, learns reasonable quality parameters from the data automatically. We propose a user-friendly quality control pipeline called SHI7 (canonically pronounced “shizen”), which aims to simplify quality control of short-read data for the end user by predicting presence and/or type of common sequencing adaptors, what quality scores to trim, whether the data set is shotgun or amplicon sequencing, whether reads are paired end or single end, and whether pairs are stitchable, including the expected amount of pair overlap. We hope that SHI7 will make it easier for all researchers, expert and novice alike, to follow reasonable practices for short-read data quality control. IMPORTANCE Quality control of high-throughput DNA sequencing data is an important but sometimes laborious task requiring background knowledge of the sequencing protocol used (such as adaptor type, sequencing technology, insert size/stitchability, paired-endedness, etc.). Quality control protocols typically require applying this background knowledge to selecting and executing numerous quality control steps with the appropriate parameters, which is especially difficult when working with public data or data from collaborators who use different protocols. We have created a streamlined quality control pipeline intended to substantially simplify the process of DNA quality control from raw machine output files to actionable sequence data. In contrast to other methods, our proposed pipeline is easy to install and use and attempts to learn the necessary parameters from the data automatically with a single command.

scholarly journals Functional Data Analysis in Sport Science: Example of Swimmers’ Progression Curves Clustering

2018 ◽  
Vol 8 (10) ◽  
pp. 1766 ◽  
Author(s):  
Arthur Leroy ◽  
Andy MARC ◽  
Olivier DUPAS ◽  
Jean Lionel REY ◽  
Servane Gey
Keyword(s):  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Clustering Algorithms ◽  
Principal Component ◽  
Continuous Functions ◽  
Functional Principal Component Analysis ◽  
Data Set ◽  
Functional Clustering ◽  
Sport Science

Many data collected in sport science come from time dependent phenomenon. This article focuses on Functional Data Analysis (FDA), which study longitudinal data by modelling them as continuous functions. After a brief review of several FDA methods, some useful practical tools such as Functional Principal Component Analysis (FPCA) or functional clustering algorithms are presented and compared on simulated data. Finally, the problem of the detection of promising young swimmers is addressed through a curve clustering procedure on a real data set of performance progression curves. This study reveals that the fastest improvement of young swimmers generally appears before 16 years old. Moreover, several patterns of improvement are identified and the functional clustering procedure provides a useful detection tool.

Sign in / Sign up

Export Citation Format

Share Document