Association Rules and Compositional Data Analysis: Implications to Big Data

2017 ◽  
Author(s):  
Ron S. Kenett ◽  
Josep Antoni Marttn-Fernnndez ◽  
Santiago Thii-Henestrosa ◽  
Marina Vives-Mestres
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Association Rules ◽  
Compositional Data ◽  
Compositional Data Analysis

scholarly journals Effects of Two Randomized and Controlled Multi-Component Interventions Focusing On 24-Hour Movement Behavior among Office Workers: A Compositional Data Analysis

2021 ◽  
Vol 18 (8) ◽  
pp. 4191
Author(s):  
Lisa-Marie Larisch ◽  
Emil Bojsen-Møller ◽  
Carla F. J. Nooijen ◽  
Victoria Blom ◽  
Maria Ekblom ◽  
...  
Keyword(s):  
Physical Activity ◽  
Data Analysis ◽  
Cardiorespiratory Fitness ◽  
Leisure Time ◽  
Compositional Data ◽  
Office Workers ◽  
Movement Behavior ◽  
Compositional Data Analysis ◽  
Intervention Effects ◽  
Time In Bed

Intervention studies aiming at changing movement behavior have usually not accounted for the compositional nature of time-use data. Compositional data analysis (CoDA) has been suggested as a useful strategy for analyzing such data. The aim of this study was to examine the effects of two multi-component interventions on 24-h movement behavior (using CoDA) and on cardiorespiratory fitness among office workers; one focusing on reducing sedentariness and the other on increasing physical activity. Office workers (n = 263) were cluster randomized into one of two 6-month intervention groups, or a control group. Time spent in sedentary behavior, light-intensity, moderate and vigorous physical activity, and time in bed were assessed using accelerometers and diaries, both for 24 h in total, and for work and leisure time separately. Cardiorespiratory fitness was estimated using a sub-maximal cycle ergometer test. Intervention effects were analyzed using linear mixed models. No intervention effects were found, either for 24-h behaviors in total, or for work and leisure time behaviors separately. Cardiorespiratory fitness did not change significantly. Despite a thorough analysis of 24-h behaviors using CoDA, no intervention effects were found, neither for behaviors in total, nor for work and leisure time behaviors separately. Cardiorespiratory fitness did not change significantly. Although the design of the multi-component interventions was based on theoretical frameworks, and included cognitive behavioral therapy counselling, which has been proven effective in other populations, issues related to implementation of and compliance with some intervention components may have led to the observed lack of intervention effect.

scholarly journals The Gut Microbiota of Healthy Aged Chinese Is Similar to That of the Healthy Young

mSphere ◽  
2017 ◽  
Vol 2 (5) ◽  
Author(s):  
Gaorui Bian ◽  
Gregory B. Gloor ◽  
Aihua Gong ◽  
Changsheng Jia ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Gut Microbiota ◽  
Large Scale ◽  
Compositional Data ◽  
Healthy Lifestyle ◽  
Compositional Data Analysis ◽  
Surprising Result ◽  
Microbiota Composition ◽  
Cross Sectional ◽  
Age Cohorts

ABSTRACT We report the large-scale use of compositional data analysis to establish a baseline microbiota composition in an extremely healthy cohort of the Chinese population. This baseline will serve for comparison for future cohorts with chronic or acute disease. In addition to the expected difference in the microbiota of children and adults, we found that the microbiota of the elderly in this population was similar in almost all respects to that of healthy people in the same population who are scores of years younger. We speculate that this similarity is a consequence of an active healthy lifestyle and diet, although cause and effect cannot be ascribed in this (or any other) cross-sectional design. One surprising result was that the gut microbiota of persons in their 20s was distinct from those of other age cohorts, and this result was replicated, suggesting that it is a reproducible finding and distinct from those of other populations. The microbiota of the aged is variously described as being more or less diverse than that of younger cohorts, but the comparison groups used and the definitions of the aged population differ between experiments. The differences are often described by null hypothesis statistical tests, which are notoriously irreproducible when dealing with large multivariate samples. We collected and examined the gut microbiota of a cross-sectional cohort of more than 1,000 very healthy Chinese individuals who spanned ages from 3 to over 100 years. The analysis of 16S rRNA gene sequencing results used a compositional data analysis paradigm coupled with measures of effect size, where ordination, differential abundance, and correlation can be explored and analyzed in a unified and reproducible framework. Our analysis showed several surprising results compared to other cohorts. First, the overall microbiota composition of the healthy aged group was similar to that of people decades younger. Second, the major differences between groups in the gut microbiota profiles were found before age 20. Third, the gut microbiota differed little between individuals from the ages of 30 to >100. Fourth, the gut microbiota of males appeared to be more variable than that of females. Taken together, the present findings suggest that the microbiota of the healthy aged in this cross-sectional study differ little from that of the healthy young in the same population, although the minor variations that do exist depend upon the comparison cohort. IMPORTANCE We report the large-scale use of compositional data analysis to establish a baseline microbiota composition in an extremely healthy cohort of the Chinese population. This baseline will serve for comparison for future cohorts with chronic or acute disease. In addition to the expected difference in the microbiota of children and adults, we found that the microbiota of the elderly in this population was similar in almost all respects to that of healthy people in the same population who are scores of years younger. We speculate that this similarity is a consequence of an active healthy lifestyle and diet, although cause and effect cannot be ascribed in this (or any other) cross-sectional design. One surprising result was that the gut microbiota of persons in their 20s was distinct from those of other age cohorts, and this result was replicated, suggesting that it is a reproducible finding and distinct from those of other populations.

Compositional Data Analysis (CoDA) as a tool to study the (paleo)ecology of coccolithophores from coastal-neritic settings off central Portugal

2015 ◽  
Vol 319 ◽  
pp. 134-146 ◽  
Author(s):  
Catarina Guerreiro ◽  
Mário Cachão ◽  
Vera Pawlowsky-Glahn ◽  
Anabela Oliveira ◽  
Aurora Rodrigues
Keyword(s):  
Data Analysis ◽  
Compositional Data ◽  
Compositional Data Analysis ◽  
Central Portugal

scholarly journals Compositional data analysis and zeros in micro data

2000 ◽  
Vol 32 (8) ◽  
pp. 953-959 ◽  
Author(s):  
Jane M. Fry ◽  
Tim R. L. Fry ◽  
Keith R. McLaren
Keyword(s):  
Data Analysis ◽  
Compositional Data ◽  
Compositional Data Analysis ◽  
Micro Data

Surface–bottom relationships in the Gulf of Salerno (Tyrrhenian Sea) over the last 34 kyr: Compositional data analysis of palaeontological proxies and geochemical evidence

Geobios ◽  
2009 ◽  
Vol 42 (5) ◽  
pp. 561-579 ◽  
Author(s):  
Valentino Di Donato ◽  
Paola Esposito ◽  
Vittorio Garilli ◽  
Debora Naimo ◽  
Giuseppe Buccheri ◽  
...  
Keyword(s):  
Data Analysis ◽  
Compositional Data ◽  
Compositional Data Analysis ◽  
Tyrrhenian Sea ◽  
Geochemical Evidence

scholarly journals Associations between sedentary time, physical activity and bone health among older people using compositional data analysis

PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0206013 ◽  
Author(s):  
Irene Rodríguez-Gómez ◽  
Asier Mañas ◽  
José Losa-Reyna ◽  
Leocadio Rodríguez-Mañas ◽  
Sebastien F. M. Chastin ◽  
...  
Keyword(s):  
Physical Activity ◽  
Data Analysis ◽  
Older People ◽  
Bone Health ◽  
Sedentary Time ◽  
Compositional Data ◽  
Compositional Data Analysis

Individual categorisation of glucose profiles using compositional data analysis

2018 ◽  
Vol 28 (12) ◽  
pp. 3550-3567 ◽  
Author(s):  
Lyvia Biagi ◽  
Arthur Bertachi ◽  
Marga Giménez ◽  
Ignacio Conget ◽  
Jorge Bondia ◽  
...  
Keyword(s):  
Data Analysis ◽  
Compositional Data ◽  
Glucose Monitoring ◽  
Glucose Variability ◽  
Compositional Data Analysis ◽  
Relative Information ◽  
Glucose Profiles ◽  
Patient Groups ◽  
The Times

The aim of this study was to apply a methodology based on compositional data analysis (CoDA) to categorise glucose profiles obtained from continuous glucose monitoring systems. The methodology proposed considers complete daily glucose profiles obtained from six patients with type 1 diabetes (T1D) who had their glucose monitored for eight weeks. The glucose profiles were distributed into the time spent in six different ranges. The time in one day is finite and limited to 24 h, and the times spent in each of these different ranges are co-dependent and carry only relative information; therefore, CoDA is applied to these profiles. A K-means algorithm was applied to the coordinates obtained from the CoDA to obtain different patterns of days for each patient. Groups of days with relatively high time in the hypo and/or hyperglycaemic ranges and with different glucose variability were observed. Using CoDA of time in different ranges, individual glucose profiles were categorised into groups of days, which can be used by physicians to detect the different conditions of patients and personalise patient's insulin therapy according to each group. This approach can be useful to assist physicians and patients in managing the day-to-day variability that hinders glycaemic control.

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