Dataset Information

Comparison of dimension reduction methods on fatty acids food source study.

ABSTRACT: Serum fatty acids (FAs) exist in the four lipid fractions of triglycerides (TGs), phospholipids (PLs), cholesteryl esters (CEs) and free fatty acids (FFAs). Total fatty acids (TFAs) indicate the sum of FAs in them. In this study, four statistical analysis methods, which are independent component analysis (ICA), factor analysis, common principal component analysis (CPCA) and principal component analysis (PCA), were conducted to uncover food sources of FAs among the four lipid fractions (CE, FFA, and TG + PL). Among the methods, ICA provided the most suggestive results. To distinguish the animal fat intake from endogenous fatty acids, FFA variables in ICA and factor analysis were studied. ICA provided more distinct suggestions of FA food sources (endogenous, plant oil intake, animal fat intake, and fish oil intake) than factor analysis. Moreover, ICA was discovered as a new approach to distinguish animal FAs from endogenous FAs, which will have an impact on epidemiological studies. In addition, the correlation coefficients between a published dataset of food FA compositions and the loading values obtained in the present ICA study suggested specific foods as serum FA sources. In conclusion, we found that ICA is a useful tool to uncover food sources of serum FAs.

SUBMITTER: Chen Y

PROVIDER: S-EPMC8455623 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundThe most popular methods for significance analysis on microarray data are well suited to find genes differentially expressed across predefined categories. However, identification of features that correlate with continuous dependent variables is more difficult using these methods, and long lists of significant genes returned are not easily probed for co-regulations and dependencies. Dimension reduction methods are much used in the microarray literature for classification or for obtaining low-dimensional representations of data sets. These methods have an additional interpretation strength that is often not fully exploited when expression data are analysed. In addition, significance analysis may be performed directly on the model parameters to find genes that are important for any number of categorical or continuous responses. We introduce a general scheme for analysis of expression data that combines significance testing with the interpretative advantages of the dimension reduction methods. This approach is applicable both for explorative analysis and for classification and regression problems.ResultsThree public data sets are analysed. One is used for classification, one contains spiked-in transcripts of known concentrations, and one represents a regression problem with several measured responses. Model-based significance analysis is performed using a modified version of Hotelling's T2-test, and a false discovery rate significance level is estimated by resampling. Our results show that underlying biological phenomena and unknown relationships in the data can be detected by a simple visual interpretation of the model parameters. It is also found that measured phenotypic responses may model the expression data more accurately than if the design-parameters are used as input. For the classification data, our method finds much the same genes as the standard methods, in addition to some extra which are shown to be biologically relevant. The list of spiked-in genes is also reproduced with high accuracy.ConclusionThe dimension reduction methods are versatile tools that may also be used for significance testing. Visual inspection of model components is useful for interpretation, and the methodology is the same whether the goal is classification, prediction of responses, feature selection or exploration of a data set. The presented framework is conceptually and algorithmically simple, and a Matlab toolbox (Mathworks Inc, USA) is supplemented.

Project description:Fatty acids are essential to macroinvertebrate growth and reproduction and can indicate food web structure and nutritional quality of basal resources. However, broad-scale examinations of how catchment land cover and associated stressors affect the proportions of fatty acids (FAs) in stream food webs are few.Here, we: (1) examine relationships among proportions of FAs among benthic periphyton and macroinvertebrate collector/gatherers, shredders, and predators; and (2) test if relationships between periphytic and macroinvertebrate FAs were altered due to the intensity of urban development in catchments.Proportions of the ?20-C eicosapentaenoic acid (EPA 20:5?3), arachidonic acid (ARA 20:4?6), and docosahexaenoic acid (22:6?3) indicated collector/gatherers had a diet richer in periphyton than in shredders, which had significantly lower proportions of these FAs. Collector/gatherers were in turn likely to be high-quality sources of ?3 and ? 20-C FAs for predators, which also had significantly greater EPA and ARA proportions than those in shredders. Linoleic (18:2?6) and ?-linolenic acid (18:3?3) comprised the greatest proportions of FAs in shredders, which suggested a diet dominated by leaf litter and associated hyphomycetes.As catchment urbanisation increased, proportions of total ?3 FAs and EPA in periphyton were significantly greater. This pattern also was seen through macroinvertebrate consumers and predators, given that proportions of these FAs in macroinvertebrates also were significantly correlated with factors associated with catchment urbanisation. The significant increase in total ?3 FAs and EPA proportions within shredders indicated that periphyton growth, and their FAs, increased on leaf litter, probably due to greater nutrient concentrations associated with catchment urbanisation. Proportions of total ?6 FAs in biota were not significantly correlated with factors associated with urban development, which could indicate that they were of sufficient abundance for consumers regardless of urban intensity or possible changes in their sources.Our study provides an informative first step that identified notable differences in proportions of FAs among macroinvertebrates in urban streams and an increase in proportions of total ?3 FAs and EPA in periphyton, consumers, and predators as catchment urbanisation increases. Identifying how FA relationships within food webs change in response to catchment alterations and stressors could inform land use and management decisions by linking environmental changes to measures important to ecosystem outcomes.

Dataset Information

Comparison of dimension reduction methods on fatty acids food source study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets