Correction methods for ties in rank correlations

Amerise, Ilaria Lucrezia; Tarsitano, Agostino

doi:10.1080/02664763.2015.1043870

Cited by 19 publications

(15 citation statements)

References 19 publications

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“…In this setting, Spearman will overestimate correlations when nearly all abundances are either zero or some integer close to zero [ 24 , 25 ]. As an intermediate step, Spearman assigns a rank value to each location, and locations with equal abundance receive the same rank.…”

Section: Resultsmentioning

confidence: 99%

Using null models to infer microbial co-occurrence networks

2017

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Although microbial communities are ubiquitous in nature, relatively little is known about the structural and functional roles of their constituent organisms’ underlying interactions. A common approach to study such questions begins with extracting a network of statistically significant pairwise co-occurrences from a matrix of observed operational taxonomic unit (OTU) abundances across sites. The structure of this network is assumed to encode information about ecological interactions and processes, resistance to perturbation, and the identity of keystone species. However, common methods for identifying these pairwise interactions can contaminate the network with spurious patterns that obscure true ecological signals. Here, we describe this problem in detail and develop a solution that incorporates null models to distinguish ecological signals from statistical noise. We apply these methods to the initial OTU abundance matrix and to the extracted network. We demonstrate this approach by applying it to a large soil microbiome data set and show that many previously reported patterns for these data are statistical artifacts. In contrast, we find the frequency of three-way interactions among microbial OTUs to be highly statistically significant. These results demonstrate the importance of using appropriate null models when studying observational microbiome data, and suggest that extracting and characterizing three-way interactions among OTUs is a promising direction for unraveling the structure and function of microbial ecosystems.

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Section: Resultsmentioning

confidence: 99%

Using null models to infer microbial co-occurrence networks

2017

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“…In this setting, Spearman will overestimate correlations when nearly all abundances are either zero or some integer close to zero [24,25]. As an intermediate step, Spearman assigns a rank value to each location, and locations with equal abundance receive the same rank.…”

Section: Sectionmentioning

confidence: 99%

Using null models to infer microbial co-occurrence networks

Connor

Barberán

Clauset

2016

Preprint

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Although microbial communities are ubiquitous in nature, relatively little is known about the structural and functional roles of their constituent organisms' underlying interactions. A common approach to study such questions begins with extracting a network of statistically significant pairwise co-occurrences from a matrix of observed operational taxonomic unit (OTU) abundances across sites. The structure of this network is assumed to encode information about ecological interactions and processes, resistance to perturbation, and the identity of keystone species. However, common methods for identifying these pairwise interactions can contaminate the network with spurious patterns that obscure true ecological signals. Here, we describe this problem in detail and develop a solution that incorporates null models to distinguish ecological signals from statistical noise. We apply these methods to the initial OTU abundance matrix and to the extracted network. We demonstrate this approach by applying it to a large soil microbiome data set and show that many previously reported patterns for these data are statistical artifacts. In contrast, we find the frequency of three-way interactions among microbial OTUs to be highly statistically significant. These results demonstrate the importance of using appropriate null models when studying observational microbiome data, and suggest that extracting and characterizing three-way interactions among OTUs is a promising direction for unraveling the structure and function of microbial ecosystems.

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“…Spearman ρ was performed on movement duration of the lower and upper limbs, excluding ratings where 0 movement was reported in all observations (in order to limit the number of ties). Max-min method was used to take into account the remaining ties (Amerise and Tarsitano, 2015). The reliability between the three observations was ρ = 0.65 in mean for the lower limbs (range: 0.57 to 0.77, all p < 0.05) and ρ = 0.80 in mean for the upper limbs (range: 0.77 to 0.86, all p < 0.05).…”

Section: Movements Of Children During the Ant-cmentioning

confidence: 99%

Body Mobility and Attention Networks in 6- to 7-Year-Old Children

et al. 2021

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Learning in 6- to 7-year-old children is strongly influenced by three functions of attention: alertness, orienting, and executive control. These functions share a close relationship with body mobility, such as the posture adopted or a request to stay still during tasks. The aim of this study (ClinicalTrials.gov) was to analyze the influence of body posture (standing versus sitting) and the influence of these imposed postures compared to a free body mobility on attention functions in 6- to 7-year-old children. Twenty-one children (11 girls) with a mean age of 6.7±0.6years performed the Attention Network Test for Children in three-body mobility conditions: sitting still, standing still, and free to move. Three attentional scores were calculated which would separately reflect performance of alertness, orienting, and executive control. Overall, no difference in alertness performance was found between the three bodily mobility conditions. In addition, our results suggest a general poor orienting performance in children, whatever the body mobility condition, which might be related to their young age. Finally, children improved their executive control performance when they stood still, probably due to an improvement in arousal and mental state.

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Correction methods for ties in rank correlations

Cited by 19 publications

References 19 publications

Using null models to infer microbial co-occurrence networks

Using null models to infer microbial co-occurrence networks

Using null models to infer microbial co-occurrence networks

Body Mobility and Attention Networks in 6- to 7-Year-Old Children

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