GGraph: An Efficient Structure-Aware Approach for Iterative Graph Processing

Si, Beibei; Liang, Yuxuan; Zhao, Jin; Liao, Xiaofei; Jin, Hai; Liu, Haikun; Gu, Lin

doi:10.1109/tbdata.2020.3019641

Cited by 10 publications

(5 citation statements)

References 43 publications

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“…Unitigs with a length of >1,000 bp were selected for subsequent taxonomic annotation using Kraken 2 ( 50 ) and a precomputed database ( https://lomanlab.github.io/mockcommunity/mc_databases.html ). The resulting data were processed using the ggraph package for GNU/R ( 51 ).…”

Section: Methodsmentioning

confidence: 99%

Consistent Stool Metagenomic Biomarkers Associated with the Response To Melanoma Immunotherapy

et al. 2023

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Section: Methodsmentioning

confidence: 99%

Consistent Stool Metagenomic Biomarkers Associated with the Response To Melanoma Immunotherapy

et al. 2023

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“…Threshold of significance was defined by P<0.05. Visualization relied on packages venn (62), ggplot2 (63), igraph (64) and ggraph (65). Analysis, figures and html file were generated using R in the RStudio environment.…”

Section: Discussionmentioning

confidence: 99%

Comparative transcriptomics reveals unique patterns of convergence in the evolution of eusociality

Velasque

Yongkai

Liu

et al. 2021

Preprint

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Eusocial insects are characterized by the presence of division of labour between reproductive (i.e. workers) and non-reproductive (i.e. queens) individuals. In eusocial insects, such as wasps, ants, honeybees and termites, such reproductive division of labour is mediated by the use of unique pheromones. In eusocial animals, pheromones control two distinct aspects of the division of labour: reproduction and division of tasks amongst workers. It has been suggested that eusociality arose independently on several occasions from pheromone-mediated co-option of pathways regulating the reproductive life cycle of solitary insects. Transcriptomic analysis has supported the co-option of similar pathways in each independently evolved eusocial insect. Using a solitary insect model, we sought to determine if these similar transcriptomic signals resulted from the co-option of similar pathways or varying pathways subject to convergent evolution. We measured the transcriptomic and physiological response of Drosophila melanogaster to pheromones from bumblebees, honey bees, and termites. In each case we observed a strong physiological response - the reduction of ovary size. However, employing conventional differential gene expression analysis and several forms of network analysis, we were unable to detect any conserved pathway or genes acting as a primer for eusociality. This strongly suggests that pheromone-mediated eusociality is the result of convergent evolution. Wherein a physiological response, such as reduced reproductive capacity, acts as the primer for eusociality and is subsequently refined.

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“…Due to the widespread use of ggplot2 ( Code 2A ), many extension packages have emerged to extend based on ggplot2 with a high capacity of plotting styles, colors, and themes. These packages mainly include ggtern plotting ternary graphs in Code 2B ( Hamilton and Ferry, 2018 ), ggraph plotting network graphs in Code 2C ( Si et al, 2022 ), ggtree plotting evolutionary tree or cladogram in Code 2D ( Xu et al, 2022 ), the ggalluvial package, the ggVennDiagram package (Code 2E), the ggstatsplot package plotting pie chart, and the ggpubr package providing many various themes and colors of output. In addition, the pheatmap and ComplexHeatmap package ( Gu, 2022 ) based on the grid mapping system plots the relative abundance of features in different samples (Code 2F), the VennDiagram package ( Chen and Boutros, 2011 ) could show the number of features in different samples.…”

Section: Preparing Microbiome Data Analysismentioning

confidence: 99%

The best practice for microbiome analysis using R

et al. 2023

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With the gradual maturity of sequencing technology, many microbiome studies have published, driving the emergence and advance of related analysis tools. R language is the widely used platform for microbiome data analysis for powerful functions. However, tens of thousands of R packages and numerous similar analysis tools have brought major challenges for many researchers to explore microbiome data. How to choose suitable, efficient, convenient, and easy-to-learn tools from the numerous R packages has become a problem for many microbiome researchers. We have organized 324 common R packages for microbiome analysis and classified them according to application categories (diversity, difference, biomarker, correlation and network, functional prediction, and others), which could help researchers quickly find relevant R packages for microbiome analysis. Furthermore, we systematically sorted the integrated R packages (phyloseq, microbiome, MicrobiomeAnalystR, Animalcules, microeco, and amplicon) for microbiome analysis, and summarized the advantages and limitations, which will help researchers choose the appropriate tools. Finally, we thoroughly reviewed the R packages for microbiome analysis, summarized most of the common analysis content in the microbiome, and formed the most suitable pipeline for microbiome analysis. This paper is accompanied by hundreds of examples with 10,000 lines codes in GitHub, which can help beginners to learn, also help analysts compare and test different tools. This paper systematically sorts the application of R in microbiome, providing an important theoretical basis and practical reference for the development of better microbiome tools in the future. All the code is available at GitHub.

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GGraph: An Efficient Structure-Aware Approach for Iterative Graph Processing

Cited by 10 publications

References 43 publications

Consistent Stool Metagenomic Biomarkers Associated with the Response To Melanoma Immunotherapy

Consistent Stool Metagenomic Biomarkers Associated with the Response To Melanoma Immunotherapy

Comparative transcriptomics reveals unique patterns of convergence in the evolution of eusociality

The best practice for microbiome analysis using R

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