cooccurNet: an R package for co-occurrence network construction and analysis

Zou, Yuanqiang; Wu, Zhiqiang; Deng, Lizong; Wu, Aiping; Wu, Fan; Li, Kenli; Jiang, Taijiao; Peng, Yousong

doi:10.1093/bioinformatics/btx062

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…Moreover, it also provides information on the spatial effects of environmental conditions and trophic and non-trophic interactions [196]. Those networks are generally built with similarity measures-based methods, such as Spearman or Pearson correlations [186,191,195,[197][198][199], dissimilarity measures, for example, Bray-Curtis [200], probabilistic models, for instance, co-occur [201,202] or other methods, such as the wTO [126] and EcoSim [203].…”

Section: Co-occurrence Networkmentioning

confidence: 99%

Construction, comparison and evolution of networks in life sciences and other disciplines

Gysi

Nowick

2020

J. R. Soc. Interface.

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Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.

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Section: Co-occurrence Networkmentioning

confidence: 99%

Construction, comparison and evolution of networks in life sciences and other disciplines

Gysi

Nowick

2020

J. R. Soc. Interface.

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“…Co-occurrence is a method for tracking cooperative genomic interactions as a major force underlying virus evolution. Existing co-occurrence network construction tools such as cooccurNet [24] consider pairs of nucleotides or amino acids for analysis and apply filters on the significance of the cooccurrence of genes. The distance in a co-occurrence network counts for the relatedness of genes.…”

Section: Applicationsmentioning

confidence: 99%

“…), for if not, then there would be c 0 > c 1 in which r c0 i (I) ∈ (I \ X J ) ∪ {r 1 i (X J )}, a contradiction. From this, the right hand side of equation (24) becomes…”

Section: Appendixmentioning

confidence: 99%

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AWLCO: All-Window Length Co-Occurrence

Sobel,

Bertram,

Ding

et al. 2020

Preprint

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Analyzing patterns in a sequence of events has applications in text analysis, computer programming, and genomics research. In this paper, we consider the all-window-length analysis model which analyzes a sequence of events with respect to windows of all lengths. We study the exact co-occurrence counting problem for the all-window-length analysis model. Our first algorithm is an offline algorithm that counts all-window-length co-occurrences by performing multiple passes over a sequence and computing single-window-length co-occurrences. This algorithm has the time complexity O(n) for each window length and thus a total complexity of O(n 2 ) and the space complexity O(|I|) for a sequence of size n and an itemset of size |I|. We propose AWLCO, an online algorithm that computes all-window-length co-occurrences in a single pass with the expected time complexity of O(n) and space complexity of O( n|I|). Following this, we generalize our use case to patterns in which we propose an algorithm that computes all-window-length co-occurrence with expected time complexity O(n|I|) and space complexity O( n|I| + emax|I|), where emax is the length of the largest pattern.2012 ACM Subject Classification CCS → Theory of computation, Design and analysis of algorithms → Streaming, sublinear and near linear time algorithms

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“…Co-occurrence patterns have been successfully applied to the analysis of microbial species in a variety of environments and have great advantages in revealing specific but less obvious relationships in complex community datasets (Li et al, 2015a;Zou et al, 2017;Han et al, 2020;Li et al, 2021;Zhang et al, 2021a). In this study, the summer and winter networks only had one independent module.…”

Section: The Ecological Patterns and Network Of Dnra Bacterial Commun...mentioning

confidence: 99%

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cooccurNet: an R package for co-occurrence network construction and analysis

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 8 publications

References 9 publications

Construction, comparison and evolution of networks in life sciences and other disciplines

Construction, comparison and evolution of networks in life sciences and other disciplines

AWLCO: All-Window Length Co-Occurrence

Data_Sheet_1.docx

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