본문으로 건너뛰기
cccskills
무료GitHub에 공개

bioconductor-tanggle

Offers functions for plotting split (or implicit) networks (unrooted, undirected) and explicit networks (rooted, directed) with reticulations extending. 'ggtree' and using functions from 'ape' and 'phangorn'. It extends the 'ggtree' package [@Yu2017] to allow the visualization of phylogenetic networks using the 'ggplot2' syntax. It offers an alternative to the plot functions already available in 'ape' Paradis and Schliep (2019) <doi:10.1093/bioinformatics/bty633> and 'phangorn' Schliep (2011) <d

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

실행 점수 미검증

설치 방법 보기

포함된 파일(1)

  • SKILL.md4.4 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

tanggle

Workflows

Standard Workflow

Visualize unrooted, undirected split networks (such as consensus networks or neighbor-nets) from Nexus files.

library(tanggle)
library(phangorn)
library(ggtree)

fdir <- system.file("extdata/trees", package = "phangorn")
Nnet <- phangorn::read.nexus.networx(file.path(fdir,"woodmouse.nxs"))

p <- ggsplitnet(Nnet) + geom_tiplab2()
p <- p + xlim(-0.019, .003) + ylim(-.01, .012)

Input: A Nexus file containing split network data, read as a networx object; Output: A ggplot object representing the unrooted split network with tip labels.

Explicit Networks

Visualize explicit, rooted phylogenetic networks with reticulations from extended Newick format.

library(tanggle)
library(ape)

z <- read.evonet(text = "((1,((2,(3,(4)Y#H1)g)e,(((Y#H1,5)h,6)f)X#H2)c)a, ((X#H2,7)d,8)b)r;")
p <- ggevonet(z, layout = "slanted") + geom_tiplab() + geom_nodelab()

Input: An extended Newick format string; Output: A ggplot object representing a rooted phylogenetic network with reticulations.

When to Use

  • Visualizing unrooted, undirected split networks (consensus networks or neighbor-nets) using ggsplitnet.
  • Visualizing explicit, rooted phylogenetic networks with reticulations using ggevonet.
  • Customizing phylogenetic network plots with ggplot2 layers such as geom_tiplab2 and geom_nodepoint.

When NOT to Use

  • For standard, non-reticulated phylogenetic trees, use ggtree directly as it is optimized for simple tree structures.
  • For plotting networks without a phylogenetic context (e.g., protein-protein interaction networks), use ggraph or igraph.

Data Requirements

  • Split networks must be represented as a networx object, typically read via phangorn::read.nexus.networx.
  • Explicit networks must be represented as an evonet object, typically read via ape::read.evonet.

Key Parameters

  • layout ("rectangular"): Layout style for explicit networks in ggevonet (can be "rectangular" or "slanted").
  • col ("blue"): Color parameter inside geom_tiplab2 or geom_nodepoint to customize labels and nodes.
  • font (4): Font style parameter in geom_tiplab2.
  • hjust (-0.15): Horizontal adjustment for tip labels in geom_tiplab2.

Best Practices

  • Adjust plot limits using xlim and ylim to ensure long tip labels are fully visible and readable.
  • Use geom_tiplab2 for split networks to properly align labels along the network perimeter.
  • Minimize reticulation line crossings in complex explicit networks using minimize_overlap.

Common Pitfalls

  • Truncated tip labels: Labels extending beyond the default plot margins. Fix by adding xlim and ylim limits to the ggplot object.
  • Overlapping reticulation lines: Hard-to-read explicit networks. Fix by applying minimize_overlap to the network object before plotting.

Alternatives

  • ape: For basic plotting of evonet objects without ggplot2 syntax.
  • phangorn: For basic plotting of networx split networks without ggplot2 integration.

Citations

  • Yu, Guangchuang, David Smith, Huachen Zhu, Yi Guan, and Tommy Tsan-Yuk Lam. 2017. "Ggtree: An R Package for Visualization and Annotation of Phylogenetic Trees with Their Covariates and Other Associated Data." Methods in Ecology and Evolution 8 (1): 28–36.
  • Cardona, Gabriel, Francesc Rosselló, and Gabriel Valiente. 2008. "Extended Newick: It Is Time for a Standard Representation of Phylogenetic Networks." BMC Bioinformatics 9 (1): 532.

References

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bioMate-AI/biomate-bioconductor-kb7932026년 6월 21일 업데이트

This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bioMate-AI/biomate-bioconductor-kb7932026년 6월 21일 업데이트

This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bioMate-AI/biomate-bioconductor-kb7932026년 6월 21일 업데이트

apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bioMate-AI/biomate-bioconductor-kb7932026년 6월 21일 업데이트

ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bioMate-AI/biomate-bioconductor-kb7932026년 6월 21일 업데이트

AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bioMate-AI/biomate-bioconductor-kb7932026년 6월 21일 업데이트

bioMate-AI의 스킬 모두 보기

이 스킬의 문제 신고