A Primer on Quantitized Data Analysis and Permutation Testing

Collingridge, David

doi:10.1177/1558689812454457

Cited by 94 publications

(82 citation statements)

References 43 publications

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“…We subtracted the lower multiple‐site dissimilarity value from the higher value and generated the distribution of the dissimilarity differences under null hypothesis. We estimated the significance in the differences using a permutation tests (Collingridge, ).…”

Section: Methodsmentioning

confidence: 99%

Do plant‐based biogeographical regions shape aphyllophoroid fungal communities in Europe?

Ordynets

Heilmann‐Clausen

Savchenko

et al. 2018

Journal of Biogeography

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Aim Aphyllophoroid fungi are associated with plants, either using plants as a resource (as parasites or decomposers) or as symbionts (as mycorrhizal partners). In spite of their strong association with plants, it is unknown how much plant distributions determine their biogeographical patterns compared with environmental factors such as climate and human land use. In this study, our aims are to (1) describe the spatial diversity patterns of aphyllophoroid fungi in Europe and (2) identify the factors shaping these patterns. Location Europe, as well as the adjacent Subarctic to Arctic islands (Greenland, Faroe Islands, Iceland, Svalbard), Palestine and the south‐east coast of the Caspian Sea. Methods We compiled a dataset consisting of 14,030 fruitbody occurrences of 1,491 aphyllophoroid fungal species from 39 geographical areas (17 countries) belonging to eight biogeographical regions. We assessed the differences in fungal species richness and overall diversity and its nestedness and turnover components across biogeographical regions of Europe, as well as between southern and northern Europe (based on geographical latitude of 50° as threshold). We used cluster and ordination analyses to classify the European aphyllophoroid communities biogeographically and evaluated the importance of climate, host‐tree species, topography and human land‐use intensity in explaining biogeographical variation. Results The importance of biogeographical regions in determining European aphyllophoroid fungal communities varies for different diversity components. Species richness and nestedness are best explained by plant‐based biogeographical regions, whereas overall beta diversity and species turnover are driven mostly by variation in climate, and nestedness mostly by tree species occupancy. Beta‐diversity patterns of aphyllophoroid fungi do not differ between southern and northern Europe. Main conclusions At the continental scale, aphyllophoroid fungi are less shaped by historical legacies than vascular plant and animal communities, and trends of overall beta diversity in southern and northern Europe are similar to patterns found for bryophytes.

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Section: Methodsmentioning

confidence: 99%

Do plant‐based biogeographical regions shape aphyllophoroid fungal communities in Europe?

Ordynets

Heilmann‐Clausen

Savchenko

et al. 2018

Journal of Biogeography

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“…However, in most cases, the null distribution of chance is empirically computable by randomly assigning labels to test samples and repeating classification for a number of times. This method, known as a permutation or randomization test, makes it possible to calculate the desired confidence interval of the chance, which consequently can be compared against the achieved classification accuracy using the correct labels (Collingridge, 2013; Fisher et al, 1960; Good, 2006; Mehta et al, 1988) . Recently, for special cases such as SVM, fast analytical estimation of permutation testing has been proposed (Gaonkar and Davatzikos, 2013).…”

Section: Common Machine-learning Pitfalls In Neuroimagingmentioning

confidence: 99%

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

et al. 2017

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Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there are extensive evidences showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.

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“…Table 1 summarizes the themes that were purposefully aligned with the four research questions, and the sub-themes that emerged during the analysis. As the recurrence of emerging themes can indicate their relative importance (Collingridge, 2013), the number of times each theme and sub-theme was mentioned by the counsellors was counted (Table 1). Every counsellor except one expressed the four main themes.…”

Section: Discussionmentioning

confidence: 99%

How does the stigma of problem gambling influence help-seeking, treatment and recovery? a view from the counselling sector

Hing

Nuske

Gainsbury

et al. 2016

International Gambling Studies

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A Primer on Quantitized Data Analysis and Permutation Testing

Cited by 94 publications

References 43 publications

Do plant‐based biogeographical regions shape aphyllophoroid fungal communities in Europe?

Do plant‐based biogeographical regions shape aphyllophoroid fungal communities in Europe?

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

How does the stigma of problem gambling influence help-seeking, treatment and recovery? a view from the counselling sector

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