2021年11月9日 · Here we describe a new methodology, repeated measures ASCA+ (RM-ASCA+), and show how it can be used to model metabolic changes over time, and compare metabolic changes between groups, in both randomized and non-randomized intervention studies. Tools for both visualization and model validation are discussed.

2017年6月21日 · This paper introduces 2 new methods, ASCA+ and APCA+, that allow, respectively, to extend the use of ASCA and APCA to unbalanced designs using several principles from the theory of general linear models.

2021年11月9日 · Here we describe a new methodology, repeated measures ASCA+ (RM-ASCA+), and show how it can be used to model metabolic changes over time, and compare metabolic changes between groups, in both randomized and non-randomized intervention studies.

Here we describe a new methodology, repeated measures ASCA+ (RM-ASCA+), and show how it can be used to model metabolic changes over time, and compare metabolic changes between groups, in both randomized and non-randomized intervention studies. Tools for both visualization and model validation are discussed.

2023年6月23日 · RM-ASCA+ is a novel analytical framework that takes into account the inter-relatedness of the multiple outcomes and population heterogeneity. Here, the framework is extended to quantify the dynamics in frequently sampled time-series data through the use of linear mixed models with numeric time predictors as the underlying univariate models of ...

2020年12月3日 · In this paper we introduce a new methodology, repeated measures ASCA+ (RM-ASCA+), using repeated measures linear mixed models in conjunction with ASCA+. We show how this method can be used in the analysis of longitudinal multivariate data with unbalanced designs

2020年3月10日 · This paper has its roots in the GLM version of ASCA, called ASCA +, that leads to unbiased estimators of the factors' effects for unbalanced data. It is here extended by replacing GLM by LMM and adapting the methodology. Taking into account the error structure of the data indeed leads to more accurate data modelling and more generalisable results.

方差分析 (ANOVA) 同步成分分析 (ASCA+) 和 Tucker3 张量分解的互补性在设计数据集上得到了证明。 我们展示了如何使用 ASCA+ 来 (a) 识别统计上足够的 Tucker3 模型；(b) 确定统计上重要的三元组，使其更容易解释；(c) 消除不重要的三元组，使可视化和解释更加简单。

2020年12月4日 · Here we describe a new methodology, repeated measures ASCA+ (RM-ASCA+), and show how it can be used to model metabolic changes over time, and compare metabolic changes between groups, in both randomized and non-randomized intervention studies. Tools for both visualization and model validation are discussed.

2023年8月30日 · We show how ASCA+ can be used to (a) identify statistically sufficient Tucker3 models; (b) identify statistically important triads making their interpretation easier; and (c) eliminate non-significant triads making visualization and interpretation simpler.