├── latex
├── after_body.tex
├── before_body.tex
└── preamble.tex
├── data-backup
├── reroot
│ └── tree_nodes.newick
├── tree_example_april2015
│ ├── pan.png
│ ├── info.png
│ ├── myplot.pdf
│ ├── res_genes.png
│ ├── blocks.csv
│ ├── blocks.txt
│ ├── tree_examples.txt
│ ├── bar.csv
│ ├── res_genes.csv
│ ├── tree.nwk
│ └── info.csv
├── phylopic
│ ├── 67144c22-93c2-4dc0-ba13-9f9dd2d223b9.256.png
│ ├── 7133ab33-cc79-4d7c-9656-48717359abb4.256.png
│ ├── b8aa577b-ac0c-490b-8fae-1ad9a70076c4.256.png
│ ├── c089caae-43ef-4e4e-bf26-973dd4cb65c5.256.png
│ ├── d9af529d-e426-4c7a-922a-562d57a7872e.256.png
│ └── f06b4dd0-094e-4f37-9105-25fdb4eb1b02.256.png
├── tree_boots.nwk
├── microreact
│ └── README.md
├── inode_data.csv
├── tree.nwk
├── tip_data.csv
├── RMI.phy_phyml_tree_rooted_labeled.txt
├── difftax_tab.csv
├── long-branch-example.newick
├── svl.csv
├── HPV58.tree
├── tree.nex
├── anole.tre
├── Tree 30.4.19.nwk
├── HMP_tree
│ ├── hmptree.nwk
│ └── barplot_attr.csv
└── sequence.fasta
├── book-cover.png
├── img
├── codeml.png
├── phyldog.png
├── phylomoji.png
├── phylopic.png
├── apple_emoji.pdf
├── apple_emoji.png
├── blackboard.jpg
├── fan_layout.gif
├── rotate_tree.gif
├── apple_emoji2.png
├── ggtree-plotly.gif
├── ggtree_objects.png
├── rotate_clade.gif
├── treeio-diagram.pdf
├── treeio-diagram.png
├── data-tree-emojim.png
├── ggtree-identify.gif
├── phangorn_example.png
├── phylomoji-ggtree.png
├── ggtree_objects_v2.png
├── phylobase_example.png
├── phylomoji-ggtree-circular.png
├── Screenshot_2019-06-24_ggtree-jupyter.png
└── apple_emoji.svg
├── cache-objs
├── rotl-tree.rds
└── ladderize-example.rds
├── 9781032233574_cover_review.png
├── B-references.Rmd
├── treedata-book.Rproj
├── css
└── style.css
├── ggtree-identity.Rmd
├── .gitignore
├── session-info.Rmd
├── export_pdf.R
├── conflicted.R
├── README.md
├── _bookdown.yml
├── ggtree-comicR.Rmd
├── _output.yaml
├── setup-git-branches.md
├── software-info.Rmd
├── publications.md
├── Makefile
├── software-info.md
├── ggtree-plotly.Rmd
├── setup.R
├── 00_author.Rmd
├── mind-map.R
├── software-link.R
├── book-cover.R
├── Introduction.Rmd
├── hdvips.def
├── 11_ggtree_exts-others.Rmd
├── A-app-fig-tab.Rmd
├── index.Rmd
├── A-app-tools.Rmd
├── others.md
└── 13_ggtree_gallery.Rmd
/latex/after_body.tex:
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1 | \backmatter
2 | \printindex
3 |
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/data-backup/reroot/tree_nodes.newick:
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1 | ((C,D)1,(A,(B,X)3)2,E);
2 |
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/book-cover.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/book-cover.png
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/img/codeml.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/codeml.png
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/img/phyldog.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/phyldog.png
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/img/phylomoji.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/phylomoji.png
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/img/phylopic.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/phylopic.png
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/img/apple_emoji.pdf:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/apple_emoji.pdf
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/img/apple_emoji.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/apple_emoji.png
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/img/blackboard.jpg:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/blackboard.jpg
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/img/fan_layout.gif:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/fan_layout.gif
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/img/rotate_tree.gif:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/rotate_tree.gif
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/img/apple_emoji2.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/apple_emoji2.png
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/img/ggtree-plotly.gif:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/ggtree-plotly.gif
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/img/ggtree_objects.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/ggtree_objects.png
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/img/rotate_clade.gif:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/rotate_clade.gif
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/img/treeio-diagram.pdf:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/treeio-diagram.pdf
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/img/treeio-diagram.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/treeio-diagram.png
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/cache-objs/rotl-tree.rds:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/cache-objs/rotl-tree.rds
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/img/data-tree-emojim.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/data-tree-emojim.png
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/img/ggtree-identify.gif:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/ggtree-identify.gif
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/img/phangorn_example.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/phangorn_example.png
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/img/phylomoji-ggtree.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/phylomoji-ggtree.png
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/img/ggtree_objects_v2.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/ggtree_objects_v2.png
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/img/phylobase_example.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/phylobase_example.png
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/9781032233574_cover_review.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/9781032233574_cover_review.png
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/B-references.Rmd:
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1 | \printindex
2 | \backmatter
3 |
4 | \newpage
5 |
6 | # References {-}
7 |
8 |
9 |
10 |
11 |
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/cache-objs/ladderize-example.rds:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/cache-objs/ladderize-example.rds
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/img/phylomoji-ggtree-circular.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/phylomoji-ggtree-circular.png
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/data-backup/tree_example_april2015/pan.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/data-backup/tree_example_april2015/pan.png
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/data-backup/tree_example_april2015/info.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/data-backup/tree_example_april2015/info.png
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/img/Screenshot_2019-06-24_ggtree-jupyter.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/img/Screenshot_2019-06-24_ggtree-jupyter.png
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/data-backup/tree_example_april2015/myplot.pdf:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/data-backup/tree_example_april2015/myplot.pdf
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/data-backup/tree_example_april2015/res_genes.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/data-backup/tree_example_april2015/res_genes.png
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/data-backup/phylopic/67144c22-93c2-4dc0-ba13-9f9dd2d223b9.256.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/data-backup/phylopic/67144c22-93c2-4dc0-ba13-9f9dd2d223b9.256.png
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/data-backup/phylopic/7133ab33-cc79-4d7c-9656-48717359abb4.256.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/data-backup/phylopic/7133ab33-cc79-4d7c-9656-48717359abb4.256.png
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/data-backup/phylopic/b8aa577b-ac0c-490b-8fae-1ad9a70076c4.256.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/data-backup/phylopic/b8aa577b-ac0c-490b-8fae-1ad9a70076c4.256.png
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/data-backup/phylopic/c089caae-43ef-4e4e-bf26-973dd4cb65c5.256.png:
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https://raw.githubusercontent.com/YuLab-SMU/treedata-book/HEAD/data-backup/phylopic/c089caae-43ef-4e4e-bf26-973dd4cb65c5.256.png
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/data-backup/phylopic/d9af529d-e426-4c7a-922a-562d57a7872e.256.png:
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/data-backup/phylopic/f06b4dd0-094e-4f37-9105-25fdb4eb1b02.256.png:
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/data-backup/tree_boots.nwk:
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1 | (((Rangifer_tarandus:1, Cervus_elaphus:1)Cervidae:1[98], (Bos_taurus:1, Ovis_orientalis:1)Bovidae:1[99])Artiodactyla:1[92], (Suricata_suricatta:2, (Cystophora_cristata:1,Mephitis_mephitis:1)Caniformia:1[98])Carnivora:1[96])Mammalia;
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/data-backup/tree_example_april2015/blocks.csv:
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1 | id,start,stop
30037_HCMC_2009,1,20000
DE0685_HCMC_2001,1.00E+06,1.50E+06
DE1140_HCMC_2002,3.00E+06,3.20E+06
EG0369_HCMC_2007,5.00E+05,8.00E+05
HUE20_HCMC_2009,3.40E+06,3.50E+06
HUE62_HCMC_2010,2.10E+06,2.40E+06
KH14_HCMC_2009,1.80E+06,2.00E+06
KH45_HCMC_2010,9.00E+05,1.50E+06
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/data-backup/tree_example_april2015/blocks.txt:
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1 | id start stop
30037_HCMC_2009 1 20000
DE0685_HCMC_2001 1.00E+06 1.50E+06
DE1140_HCMC_2002 3.00E+06 3.20E+06
EG0369_HCMC_2007 5.00E+05 8.00E+05
HUE20_HCMC_2009 3.40E+06 3.50E+06
HUE62_HCMC_2010 2.10E+06 2.40E+06
KH14_HCMC_2009 1.80E+06 2.00E+06
KH45_HCMC_2010 9.00E+05 1.50E+06
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/treedata-book.Rproj:
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1 | Version: 1.0
2 |
3 | RestoreWorkspace: Default
4 | SaveWorkspace: Default
5 | AlwaysSaveHistory: Default
6 |
7 | EnableCodeIndexing: Yes
8 | UseSpacesForTab: Yes
9 | NumSpacesForTab: 2
10 | Encoding: UTF-8
11 |
12 | RnwWeave: Sweave
13 | LaTeX: pdfLaTeX
14 |
15 | BuildType: Makefile
16 |
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/data-backup/microreact/README.md:
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1 | The `dir` contains the datasets from https://microreact.org/
2 |
3 | + [Candida_auris](https://microreact.org/project/Candidaauris)
4 | The original paper is from [here](https://doi.org/10.1128/mBio.03364-19).
5 | + [Salmonella_Typhi](https://microreact.org/project/styphi)
6 | The original paper is from [here](https://doi.org/10.1038/ng.3281).
7 |
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/css/style.css:
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1 | p.caption {
2 | color: #777;
3 | margin-top: 10px;
4 | }
5 | p code {
6 | white-space: inherit;
7 | }
8 | pre {
9 | word-break: normal;
10 | word-wrap: normal;
11 | }
12 | pre code {
13 | white-space: inherit;
14 | }
15 | p.flushright {
16 | text-align: right;
17 | }
18 | blockquote > p:last-child {
19 | text-align: right;
20 | }
21 | blockquote > p:first-child {
22 | text-align: inherit;
23 | }
24 |
--------------------------------------------------------------------------------
/ggtree-identity.Rmd:
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1 | (ref:ggtreeidentifyscap) Interactive phylogenetic tree using identify() method.
2 |
3 | (ref:ggtreeidentifycap) **Interactive phylogenetic tree using identify() method.** Highlighting, labelling and rotating clades are all supported.
4 |
5 |
6 |
7 | ```{r ggtreeidentify, out.width="100%", fig.cap="(ref:ggtreeidentifycap)", fig.scap="(ref:ggtreeidentifyscap)", echo=FALSE}
8 | knitr::include_graphics("img/ggtree-identify.gif")
9 | ```
10 |
--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
1 | _bookdown_files
2 | treedata.Rmd
3 | *~
4 | .Rproj.user
5 | treedata.log
6 | crc_cache
7 | crc_files
8 | crc.Rmd
9 | crc.html
10 | gh-pages
11 | treedata_cache
12 | treedata_files
13 | pdf
14 | treedata.html
15 | treedata.knit.md
16 | treedata.docx
17 | snapshop-2021-12-09
18 | snapshop-2021-11-16
19 | .Rhistory
20 | treedata.aux
21 | treedata.lof
22 | treedata-lot
23 | treedata.toc
24 | treedata.lot
25 | treedata.idx
26 | treedata.ilg
27 | treedata.ind
28 | *log
29 |
--------------------------------------------------------------------------------
/session-info.Rmd:
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1 |
2 |
3 | # Session Info {#session-info}
4 |
5 | The book was written using R Markdown and was compiled by `r CRANpkg("bookdown")` package. Here is the session information on the system on which this thesis was compiled:
6 |
7 | ```{r sessionInfo, echo=FALSE, cache=FALSE}
8 | ## options(width=90)
9 | devtools::session_info()
10 | if (FALSE) {
11 | sessionInfo() %>% capture.output %>%
12 | `[`(., -c(5:8)) %>% paste('\n') %>% cat
13 | }
14 | ## options(width=80)
15 | ```
16 |
17 |
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/data-backup/inode_data.csv:
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1 | newick_label,vernacularName,infoURL,rank,bootstrap,posterior
2 | Mammalia,Mammals,http://eol.org/pages/1642/overview,class,NA,NA
3 | Carnivora,Carnivores,http://eol.org/pages/7662/overview,order,96,0.89
4 | Caniformia,Dog-like,http://eol.org/pages/2849494/overview,suborder,98,0.93
5 | Bovidae,Bovids,http://eol.org/pages/7687/overview,family,99,0.95
6 | Cervidae,Cervids,http://eol.org/pages/7685/overview,family,98,0.96
7 | Artiodactyla,Even-toed ungulates,http://eol.org/pages/7678/overview,order,92,0.81
8 |
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/latex/before_body.tex:
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1 | % you may need to leave a few empty pages before the dedication page
2 |
3 | %\cleardoublepage\newpage\thispagestyle{empty}\null
4 | %\cleardoublepage\newpage\thispagestyle{empty}\null
5 | %\cleardoublepage\newpage
6 | \thispagestyle{empty}
7 |
8 | \begin{center}
9 | To my son,
10 |
11 | without whom I should have finished this book two years earlier
12 | %\includegraphics{images/dedication.pdf}
13 | \end{center}
14 |
15 | \setlength{\abovedisplayskip}{-5pt}
16 | \setlength{\abovedisplayshortskip}{-5pt}
17 |
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/export_pdf.R:
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1 | rmds <- list.files(pattern = ".Rmd")
2 | rmds <- rmds[rmds != "crc.Rmd"]
3 | rmds <- rmds[rmds != "index.Rmd"]
4 |
5 | system("cat index.Rmd > crc.Rmd")
6 |
7 | for (f in rmds) {
8 | cat("\n\n\n", file = "crc.Rmd", append=TRUE)
9 | cmd <- paste("cat", f, ">> crc.Rmd")
10 | system(cmd)
11 | }
12 |
13 | library(rmarkdown)
14 | library(pagedown)
15 |
16 | render("crc.Rmd", "book_crc")
17 | print("print to pdf")
18 | chrome_print('crc.html', timeout=10000)
19 |
20 |
21 | file.rename("crc.pdf", "docs/treedata.pdf")
22 |
23 | file.remove("crc.Rmd")
24 | file.remove("html.Rmd")
25 |
26 | print("done...")
27 |
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/conflicted.R:
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1 | pacman::p_load(conflicted)
2 |
3 | conflict_prefer("expand", "ggtree")
4 | conflict_prefer("rotate", "ggtree")
5 | conflict_prefer("mask", "treeio")
6 | conflict_prefer("parent", "treeio")
7 | conflict_prefer("ancestor", "treeio")
8 | conflict_prefer("get.data", "treeio")
9 | conflict_prefer("drop.tip", "treeio")
10 | conflict_prefer("read.newick", "treeio")
11 | conflict_prefer("MRCA", "tidytree")
12 |
13 | conflict_prefer("filter", "dplyr")
14 | conflict_prefer("rename", "dplyr")
15 | conflict_prefer("collapse", "dplyr")
16 | conflict_prefer("intersect", "dplyr")
17 | conflict_prefer("union", "dplyr")
18 | conflict_prefer("slice", "dplyr")
19 | conflict_prefer("strsplit", "base")
20 | conflict_prefer("paste", "base")
21 | conflict_prefer("geom_errorbarh", "ggplot2")
22 | conflict_prefer("tax_table", "MicrobiotaProcess")
23 |
24 | conflict_prefer("as.data.frame", "BiocGenerics")
25 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # Data Integration, Manipulation and Visualization of Phylogenetic Trees
2 |
3 |
4 | The book gives comprehensive overviews of phylogenetic tree data integration, manipulation and visualization using a suite of R packages, **tidytree**, **treeio**, **ggtree** and **ggtreeExtra**. The book has been published by [Chapman & Hall/CRC](https://www.routledge.com/Data-Integration-Manipulation-and-Visualization-of-Phylogenetic-Trees/Yu/p/book/9781032233574). You can also find it on [Amazon](https://www.amazon.com/Integration-Manipulation-Visualization-Phylogenetic-Computational-ebook/dp/B0B5NLZR1Z/).
5 |
6 |
7 |
8 |
9 | 
10 |
11 |
12 |
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/_bookdown.yml:
--------------------------------------------------------------------------------
1 | book_filename: "treedata"
2 | chapter_name: "Chapter "
3 | output_dir: gh-pages
4 | repo: https://github.com/YuLab-SMU/treedata-book
5 | delete_merged_file: true
6 | language:
7 | label:
8 | fig: "FIGURE "
9 | tab: "TABLE "
10 | ui:
11 | chapter_name: "Chapter "
12 | edit: "https://github.com/YuLab-SMU/treedata-book/edit/master/%s"
13 | rmd_files: ["index.Rmd",
14 | "00_author.Rmd",
15 | "01_treeio_importing_tree.Rmd",
16 | "02_tidytree.Rmd",
17 | "03_treeio_exporting_tree.Rmd",
18 | "04_ggtree_visualization.Rmd",
19 | "05_ggtree_annotation.Rmd",
20 | "06_ggtree_manipulation.Rmd",
21 | "07_ggtree_tree_with_data.Rmd",
22 | "08_ggtree_with_silhouette.Rmd",
23 | "09_ggtree_tree_objects.Rmd",
24 | "10_ggtree_exts.Rmd",
25 | "11_ggtree_exts-others.Rmd",
26 | "12_ggtree_utilities.Rmd",
27 | "13_ggtree_gallery.Rmd",
28 | "A-app-faq.Rmd",
29 | "A-app-tools.Rmd",
30 | "A-app-fig-tab.Rmd",
31 | "B-references.Rmd"]
32 |
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/ggtree-comicR.Rmd:
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1 | ## Comic (xkcd-like) phylogenetic tree {#commicR}
2 |
3 |
4 | ```{r ggsvg, fig.show='hide'}
5 | library(htmltools)
6 | library(XML)
7 | library(gridSVG)
8 | library(ggplot2)
9 | library(ggtree)
10 | library(comicR)
11 |
12 | p <- ggtree(rtree(30), layout="circular") +
13 | geom_tiplab(aes(label=label), color="purple")
14 | print(p)
15 | svg <- grid.export(name="", res=100)$svg
16 | ```
17 |
18 | (ref:comicRscap) Remove image background.
19 |
20 | (ref:comicRcap) **Remove image background.** Plotting silhouette images on phylogenetic tree without (A) and with (B) background remove.
21 |
22 |
23 | ```{r comicR}
24 | ## need to convert it to png or pdf for pdfbook
25 | tagList(
26 | tags$div(
27 | id = "ggtree_comic",
28 | tags$style("#ggtree_comic text {font-family:Chalkduster;}"),
29 | HTML(saveXML(svg)),
30 | comicR("#ggtree_comic", ff=5)
31 | )
32 | ) # %>% html_print
33 | ```
34 |
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/_output.yaml:
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1 | bookdown::pdf_book:
2 | includes:
3 | in_header: latex/preamble.tex
4 | before_body: latex/before_body.tex
5 | after_body: latex/after_body.tex
6 | number_sections: yes
7 | fig_caption: yes
8 | fig_height: 3.8
9 | fig_width: 6.3
10 | keep_tex: yes
11 | keep_md: yes
12 | pandoc_args: --top-level-division=chapter
13 | dev: "cairo_pdf"
14 | latex_engine: xelatex
15 | citation_package: natbib
16 | template: null
17 | toc_depth: 3
18 | toc_unnumbered: no
19 | toc_appendix: yes
20 | quote_footer: ["\\VA{", "}{}"]
21 | highlight_bw: yes
22 | ui:
23 | chapter_name: "Chapter"
24 | bookdown::epub_book:
25 | stylesheet: css/style.css
26 | bookdown::gitbook:
27 | css: css/style.css
28 | config:
29 | toc:
30 | collapse: none
31 | download: [pdf, epub]
32 | edit: https://github.com/YuLab-SMU/treedata-book/edit/master/%s
33 | sharing:
34 | github: true
35 | facebook: false
36 | bookdown::bs4_book:
37 | dev: "svg"
--------------------------------------------------------------------------------
/data-backup/tree_example_april2015/tree_examples.txt:
--------------------------------------------------------------------------------
1 | # SHIGELLA - R plotting
2 |
3 | # basic strain info
4 | v<-plotTree(tree="tree.nwk",ancestral.reconstruction=F,tip.colour.cex=1,cluster=T,tipColours=c("black","purple2","skyblue2","grey"),lwd=1,infoFile="info.csv",colourNodesBy="location",treeWidth=10,infoWidth=10,infoCols=c("name","location","year"))
5 |
6 | # pan genome heatmap
7 | v<-plotTree(tree="tree.nwk",heatmapData="pan.csv",ancestral.reconstruction=F,tip.colour.cex=1,cluster=T,tipColours=c("black","purple2","skyblue2","grey"),lwd=1,infoFile="info.csv",colourNodesBy="location",treeWidth=5,dataWidth=20,infoCols=NA)
8 |
9 | # curated genes, coloured
10 | v<-plotTree(tree="tree.nwk",heatmapData="res_genes.csv",ancestral.reconstruction=F,tip.colour.cex=1,cluster=F,heatmap.colours=c("white","grey","seagreen3","darkgreen","green","brown","tan","red","orange","pink","magenta","purple","blue","skyblue3","blue","skyblue2"),tipColours=c("black","purple2","skyblue2","grey"),lwd=1,infoFile="info.csv",colourNodesBy="location",treeWidth=10,dataWidth=10,infoCols=c("name","year"),infoWidth=8)
--------------------------------------------------------------------------------
/data-backup/tree.nwk:
--------------------------------------------------------------------------------
1 | (((((((((((Phy000G05U_EMENI:0.451986,((Phy000GDP6_ASPNG:0.1209,Phy003AMS0_602072:0.0987718)0.99985:0.291259,Phy000FJDH_ASPFL:0.360311)0.985965:0.0482813)0.955419:0.0598141,(Phy000FCLK_ASPCL:0.22779,Phy000FQ5O_ASPFU:0.170269)0.99985:0.134498)0.99985:0.173404,(Phy003QBJJ_PENDI:0.183512,Phy003DGO9_PENCH:0.0985641)0.99985:0.603378)0.993843:0.140555,((Phy003BOHC_AJECA:0.911204,(Phy000PFY6_UNCRE:0.311286,Phy000IAZP_COCIM:0.186071)0.99985:0.393614)0.99985:0.291107,(Phy003PHXT_PENMQ:0.0918492,Phy003PVXT_TALSN:0.166689)0.99985:0.659075)0.998568:0.16495)0.99985:0.522657,(((Phy00201Y5_COCHE:0.163381,Phy0020GNV_PYRTR:0.0970357)0.99985:0.245847,Phy0044G80_PHANO:0.283161)0.99985:0.528345,Phy00208KX_MYCGR:1.44102)0.99985:0.366048)0.99985:0.428771,(Phy000HD5X_BOTFU:0.0491427,Phy000ODBJ_SCLSC:0.121984)0.99985:0.751838)0.99985:0.457222,Phy000B0HV_NEUCR:0.990537)0.991353:0.113714,((Phy003BKXA_GIBZE:0.247315,Phy003PZPF_FUSOX:0.080715)0.99985:0.678916,Phy0022J75_CRYPA:0.824262)0.976401:0.106342)0.72129:0.0396991,(Phy0043OCA_COLGM:0.480278,Phy0022OIS_VERA1:0.515005)0.99985:0.302607)0.99985:0.336031,Phy0043W64_36779:0.354123)0.99985:0.522727,Phy000KG2Q_MAGGR:0,Phy00443NV_MAGO7:0);
2 |
--------------------------------------------------------------------------------
/setup-git-branches.md:
--------------------------------------------------------------------------------
1 |
2 | setup git branches:
3 |
4 | ## 把master重命名为all
5 | git checkout master
6 | git branch -m master all
7 | git push -u origin all
8 |
9 | ## 上github把master的default去掉，然后删掉github的master
10 | git push origin --delete master
11 |
12 | ## 重新创建master
13 | git checkout --orphan master
14 |
15 | ## 把不要的文件删掉，只留下源文件和一些必要的文件
16 |
17 | git add .
18 | git commit -m 'source files'
19 | git push -u origin master
20 |
21 |
22 | ## 删掉本地all branch
23 |
24 | git branch -d all
25 |
26 |
27 | ## 重新创建gh-pages
28 | git checkout --orphan gh-pages
29 | touch README.md
30 | git add .
31 | git commit -m 'readme'
32 | git push -u origin gh-pages
33 |
34 | git checkout master
35 | ## 创建gh-pages
36 | mkdir gh-pages
37 | ## 把当前文件夹所有内容，copy 到 gh-pages，等同于这个repo有两个copy
38 |
39 | ## 修改书的html输出到gh-pages文件夹中。
40 | ## 到gh-pages文件夹里，删掉其它的本地branch
41 |
42 | cp -R * gh-pages/
43 | cd gh-pages
44 | git checkout gh-pages
45 | git branch -d master
46 |
47 | ## 到master中删掉gh-pages分支
48 | cd ..
49 | git branch -d gh-pages
50 |
51 |
52 | ## 这样子就有两个分支，分别在两个目录里，当前目录是master入源文件，
53 | ## gh-pages是gh-pages分支，放html输出。
54 |
55 | ## 把gh-pages目录放到master分支的gitignore中。
56 | echo 'gh-pages' >> .gitignore
57 |
58 | ## 然后就可以push到master，以及make生成书，然后到gh-pages里push到github中
59 | ## 这样书就分成了两个分支。
60 |
61 |
--------------------------------------------------------------------------------
/data-backup/tip_data.csv:
--------------------------------------------------------------------------------
1 | Newick_label,vernacularName,imageURL,imageLicense,imageAuthor,infoURL,mass_in_kg,trophic_habit,ncbi_taxid,rank
2 | Rangifer_tarandus,Reindeer,http://media.eol.org/content/2012/06/13/00/48543_orig.jpg,CC-BY-SA,Alexandre Buisse (Nattfodd),http://eol.org/pages/328653/overview,109.09,herbivore,9870,species
3 | Cervus_elaphus,Red deer,http://media.eol.org/content/2014/09/16/00/20239_orig.jpg,CC-BY-SA,Sciadopitys,http://eol.org/pages/328649/overview,240.87,herbivore,9860,species
4 | Bos_taurus,Cattle,https://media.eol.org/content/2014/09/29/06/46535_orig.jpg,CC-BY-SA,Cynthia Sims Parr,http://eol.org/pages/328699/overview,618.64,herbivore,9913,species
5 | Ovis_orientalis,Asiatic mouflon,http://media.eol.org/content/2015/05/20/03/80720_orig.jpg,CC-BY-SA,Jšrg Hempel,http://eol.org/pages/311906/overview,39.1,herbivore,469796,species
6 | Suricata_suricatta,Meerkat,http://media.eol.org/content/2016/08/16/05/67138_orig.jpg,CC-BY-SA,Sara&Joachim,http://eol.org/pages/311580/overview,0.73,carnivore,37032,species
7 | Cystophora_cristata,Hooded seal,http://media.eol.org/content/2013/06/18/07/63362_orig.jpg,CC-BY-SA,"Ecomare, Salko de Wolf",http://eol.org/pages/328632/overview,278.9,omnivore,39293,species
8 | Mephitis_mephitis,Striped skunk,http://media.eol.org/content/2012/06/15/06/75234_orig.jpg,CC-BY,Kevin Bowman,http://eol.org/pages/328593/overview,2.4,omnivore,30548,species
9 |
--------------------------------------------------------------------------------
/software-info.Rmd:
--------------------------------------------------------------------------------
1 |
2 | ## Software information and conventions {-}
3 |
4 | ```{r include=FALSE}
5 | #options(width = 30)
6 |
7 | source('conflicted.R')
8 | source("software-link.R")
9 | ```
10 |
11 | The R and core packages information when compiling this book is as follows:
12 |
13 | ```{r tidy=FALSE, message=TRUE}
14 | R.version.string
15 |
16 | library(treedataverse)
17 | ```
18 |
19 | The `r pkg_treedataverse` is a meta package to make it easy to install and load core packages for processing and visualizing tree with data using the packages described in this book. The installation guide for `r pkg_treedataverse` can be found in FAQ.
20 |
21 | The datasets used in this book have three sources:
22 |
23 | 1. Simulation data
24 | 2. Datasets in the R packages
25 | 3. Data downloaded from the Internet
26 |
27 | In order to make the data downloaded from the Internet more accessible, we packed the data in an R package, `r CRANpkg("TDbook")`, with detailed documentation of the original source, including URL, authors, and citation if the information is available. The `r CRANpkg("TDbook")` is available on CRAN and can be installed using `install.packages("TDbook")`.
28 |
29 |
30 | Package names in this book are formatted as bold text (*e.g.*, `r Biocpkg("ggtree")`), and function names are followed by parentheses (*e.g.*, `treeio::read.beast()`). The double-colon operator (`::`) means accessing an object from a package.
31 |
32 |
--------------------------------------------------------------------------------
/publications.md:
--------------------------------------------------------------------------------
1 | \newpage
2 |
3 | # Publications of the `r Biocpkg("ggtree")` Package Suite
4 |
5 | > Note: ^\*^ Co-corresponding authors
6 |
7 | 1. S Xu, Z Dai, P Guo, X Fu, S Liu, L Zhou, W Tang, T Feng, M Chen, L Zhan, T Wu, E Hu, Y Jiang^\*^, X Bo^\*^, __G Yu__^\*^. ggtreeExtra: Compact visualization of richly annotated phylogenetic data. __*Molecular Biology and Evolution*__. 2021, 38(9):4039-4042.
8 | doi: [10.1093/molbev/msab166](https://doi.org/10.1093/molbev/msab166)
9 | 2. __G Yu__. Using ggtree to visualize data on tree-like structures. __*Current Protocols in Bioinformatics*__, 2020, 69:e96. doi: [10.1002/cpbi.96](https://doi.org/10.1002/cpbi.96)
10 | 3. LG Wang, TTY Lam, S Xu, Z Dai, L Zhou, T Feng, P Guo, CW Dunn, BR Jones, T Bradley, H Zhu, Y Guan, Y Jiang, __G Yu__^\*^. treeio: an R package for phylogenetic tree input and output with richly annotated and associated data. __*Molecular Biology and Evolution*__. 2020, 37(2):599-603.
11 | doi: [10.1093/molbev/msz240](http://dx.doi.org/10.1093/molbev/msz240)
12 | 4. __G Yu__^\*^, TTY Lam, H Zhu, Y Guan^\*^. Two methods for mapping and visualizing associated data on phylogeny using ggtree. __*Molecular Biology and Evolution*__. 2018, 35(2):3041-3043.
13 | doi: [10.1093/molbev/msy194](https://doi.org/10.1093/molbev/msy194)
14 | 5. __G Yu__, DK Smith, H Zhu, Y Guan, TTY Lam^\*^. ggtree: an R package for
15 | visualization and annotation of phylogenetic trees with their covariates and
16 | other associated data. __*Methods in Ecology and Evolution*__. 2017, 8(1):28-36.
17 | doi: [10.1111/2041-210X.12628](https://doi.org/10.1111/2041-210X.12628)
18 |
19 |
--------------------------------------------------------------------------------
/latex/preamble.tex:
--------------------------------------------------------------------------------
1 | \usepackage{booktabs}
2 | \usepackage{longtable}
3 | \usepackage[bf,singlelinecheck=off]{caption}
4 | \captionsetup[table]{labelsep=space}
5 | \captionsetup[figure]{labelsep=space}
6 | \usepackage[scale=.8]{sourcecodepro}
7 |
8 | \usepackage{framed,color}
9 | \definecolor{shadecolor}{RGB}{248,248,248}
10 |
11 | \renewcommand{\textfraction}{0.05}
12 | \renewcommand{\topfraction}{0.8}
13 | \renewcommand{\bottomfraction}{0.8}
14 | \renewcommand{\floatpagefraction}{0.75}
15 |
16 | \renewenvironment{quote}{\begin{VF}}{\end{VF}}
17 | \let\oldhref\href
18 | \renewcommand{\href}[2]{#2\footnote{\url{#1}}}
19 |
20 | \makeatletter
21 | \newenvironment{kframe}{%
22 | \medskip{}
23 | \setlength{\fboxsep}{.8em}
24 | \def\at@end@of@kframe{}%
25 | \ifinner\ifhmode%
26 | \def\at@end@of@kframe{\end{minipage}}%
27 | \begin{minipage}{\columnwidth}%
28 | \fi\fi%
29 | \def\FrameCommand##1{\hskip\@totalleftmargin \hskip-\fboxsep
30 | \colorbox{shadecolor}{##1}\hskip-\fboxsep
31 | % There is no \\@totalrightmargin, so:
32 | \hskip-\linewidth \hskip-\@totalleftmargin \hskip\columnwidth}%
33 | \MakeFramed {\advance\hsize-\width
34 | \@totalleftmargin\z@ \linewidth\hsize
35 | \@setminipage}}%
36 | {\par\unskip\endMakeFramed%
37 | \at@end@of@kframe}
38 | \makeatother
39 |
40 | \renewenvironment{Shaded}{\begin{kframe}}{\end{kframe}}
41 |
42 | \usepackage{makeidx}
43 | \makeindex
44 |
45 | \urlstyle{tt}
46 |
47 | \usepackage{amsthm}
48 | \makeatletter
49 | \def\thm@space@setup{%
50 | \thm@preskip=8pt plus 2pt minus 4pt
51 | \thm@postskip=\thm@preskip
52 | }
53 | \makeatother
54 |
55 | \frontmatter
56 |
--------------------------------------------------------------------------------
/Makefile:
--------------------------------------------------------------------------------
1 |
2 | pdfbook:
3 | sed -i 's/gh-pages/pdf/g' _bookdown.yml;\
4 | sed -i 's/colorlinks: true/colorlinks: false/g' index.Rmd;\
5 | # Rscript -e 'library(bookdown); render_book("index.Rmd", pdf_book(keep_tex=TRUE))'
6 | Rscript -e 'library(bookdown); render_book("index.Rmd", "pdf_book")'
7 |
8 | bs4:
9 | rm -rf gh-pages/libs;\
10 | sed -i 's/pdf/gh-pages/g' _bookdown.yml;\
11 | sed -i 's/colorlinks: false/colorlinks: true/g' index.Rmd;\
12 | Rscript -e 'library(bookdown); render_book("index.Rmd", "bs4_book")';\
13 | sed -i 's/Chalkduster/Comic Sans MS/g' gh-pages/related-tools.html
14 |
15 | gitbook:
16 | rm -rf gh-pages/libs;\
17 | Rscript -e 'library(bookdown); render_book("index.Rmd", "gitbook")';\
18 | sed -i 's/Chalkduster/Comic Sans MS/g' gh-pages/related-tools.html
19 |
20 | ##pdf:
21 | ## pagedjs-cli ./gh-pages/index.html -o treedata-book.pdf
22 |
23 |
24 | latex:
25 | Rscript -e 'rmarkdown::pandoc_convert("treedata.knit.md", to = "latex", output = "treedata.tex", options = "--standalone")'
26 |
27 | epub:
28 | Rscript -e 'library(bookdown); render_book("index.Rmd", "epub_book")'
29 |
30 | word:
31 | Rscript -e 'library(bookdown); render_book("index.Rmd", "word_document2")';\
32 | mv gh-pages/treedata.docx ./treedata.docx
33 |
34 | softwareinfo:
35 | Rscript -e 'rmarkdown::render("software-info.Rmd", rmarkdown::md_document(variant="gfm"))';\
36 | sed -i 's/─//g' software-info.md;\
37 | sed -i 's/✔//g' software-info.md
38 |
39 | clean:
40 | Rscript -e 'bookdown::clean_book()';\
41 | rm -rf _bookdown_files
42 |
43 | cover:
44 | Rscript -e 'source("book-cover.R")'
45 |
46 | largeImg:
47 | ls -lhS gh-pages/treedata_files/figure-html | head
48 |
49 | publish:
50 | cd gh-pages;\
51 | git add .;\
52 | git commit -m 'update';\
53 | git push -u origin gh-pages
54 |
55 |
--------------------------------------------------------------------------------
/software-info.md:
--------------------------------------------------------------------------------
1 | ## Software information and conventions {-}
2 |
3 | The R and core packages information when compiling this book is as
4 | follows:
5 |
6 | ``` r
7 | R.version.string
8 | ```
9 |
10 | ## [1] "R version 4.1.2 (2021-11-01)"
11 |
12 | ``` r
13 | library(treedataverse)
14 | ```
15 |
16 | ## Attaching packages treedataverse 0.0.1
17 |
18 | ## ape 5.5 treeio 1.18.1
19 | ## dplyr 1.0.7 ggtree 3.2.1
20 | ## ggplot2 3.3.5 ggtreeExtra 1.4.1
21 | ## tidytree 0.3.6
22 |
23 | The [**treedataverse**](https://github.com/YuLab-SMU/treedataverse) is a
24 | meta package to make it easy to install and load core packages for
25 | processing and visualizing tree with data using the packages described
26 | in this book. The installation guide for
27 | [**treedataverse**](https://github.com/YuLab-SMU/treedataverse) can be
28 | found in FAQ.
29 |
30 | The datasets used in this book have three sources:
31 |
32 | 1. Simulation data
33 | 2. Datasets in the R packages
34 | 3. Data downloaded from the Internet
35 |
36 | In order to make the data downloaded from the Internet more accessible,
37 | we packed the data in an R package,
38 | [**TDbook**](https://CRAN.R-project.org/package=TDbook), with detailed
39 | documentation of the original source, including URL, authors, and
40 | citation if the information is available. The
41 | [**TDbook**](https://CRAN.R-project.org/package=TDbook) is available on
42 | CRAN and can be installed using `install.packages("TDbook")`.
43 |
44 | Package names in this book are formatted as bold text (*e.g.*,
45 | [**ggtree**](http://bioconductor.org/packages/ggtree)), and function
46 | names are followed by parentheses (*e.g.*, `treeio::read.beast()`). The
47 | double-colon operator (`::`) means accessing an object from a package.
48 |
--------------------------------------------------------------------------------
/data-backup/RMI.phy_phyml_tree_rooted_labeled.txt:
--------------------------------------------------------------------------------
1 | ((Unifoliolatus:0.01356408,Subpinnatus:0.01387845)999:0.03185050,(((Spectabilis:0.00479723,(Australis:0.01185889,(Garcinii:0.01234754,Arabicus:0.00271635)992:0.00554744)714:0.00307324)500:0.00165009,(((Edulis:0.01401936,((Peregrinus:0.00000001,Ornithopodioides:0.00057372)997:0.00414993,(Villosus:0.00261277,(Cytisoides:0.00000004,(Drepanocarpus:0.00000003,Creticus:0.00057519)957:0.00172998)985:0.00261288)790:0.00197741)988:0.00701749)611:0.00226795,(Conjugatus:0.00970402,Maritimus:0.01774284)778:0.00411824)752:0.00305268,(Weilleri:0.00571527,(Eriosolen:0.00172763,(((Arenarius:0.00230230,Collinus:0.00000001)690:0.00057248,(Glaucus:0.00000001,Maroccanus:0.00000001)343:0.00000001)974:0.00230696,((Tenellus:0.00000001,(Emeroides:0.00000004,Kunkelii:0.00057252)272:0.00000001)726:0.00056895,(Campylocladus:0.00000007,(Dumetorum:0.00114880,(Hillebrandii:0.00000001,Spartioides:0.00000001)326:0.00000001)729:0.00114837)677:0.00115265)916:0.00173432)774:0.00057358)965:0.00309324)999:0.00976851)901:0.00385361)944:0.00836931,(((Halophilus:0.01283601,(Hispidus:0.00404270,(Castellanus:0.00000004,(Cruentus:0.00000001,(Palustris:0.00000006,Subbiflorus:0.00000002)71:0.00000005)230:0.00000004)966:0.00219381)999:0.02235103)178:0.00251625,(Pedunculatus:0.00191555,Uliginosus:0.00260807)999:0.00852384)228:0.00594936,(Conimbricensis:0.02624226,(Alpinus:0.00248718,(Mearnsii:0.00285526,(Tenuis:0.01094004,((Gebelia:0.00282885,Filicaulis:0.00166240)997:0.00627934,((Japonicus:0.00224690,(Krylovii:0.00055863,Burttii:0.00055945)557:0.00000005)955:0.00276325,((Corniculatus:0.00460601,Parviflorus:0.00612435)409:0.00178992,((Preslii:0.00000001,Angustissimus:0.00000001)742:0.00116511,(Baorbasii:0.00111793,Glinoides:0.00055855)835:0.00106967)311:0.00055818)52:0.00000003)214:0.00060761)207:0.00000005)304:0.00052635)890:0.00440634)997:0.01099405)668:0.00064045)499:0.00901187)999:0.03185050);
2 |
--------------------------------------------------------------------------------
/ggtree-plotly.Rmd:
--------------------------------------------------------------------------------
1 | ## Convert a `ggtree` object to a `plotly` object {#plotly}
2 |
3 | One way to make a quick interactive phylogenetic tree is using `r Biocpkg("ggtree")` with the `r CRANpkg("plotly")` package. The [ggplotly()](https://plotly-r.com/improving-ggplotly.html) is able to convert `ggtree` object to a `plotly` object. Note that the `r Biocpkg("ggtree")` package also supports interactive manipulation of the phylogenetic tree via the [identify()](#identify) method.
4 |
5 |
6 | ```r
7 | # example from https://twitter.com/drandersgs/status/965996335882059776
8 |
9 | # LOAD LIBS ---------------------------------------------------------------
10 | library(ape)
11 | library(ggtree)
12 | library(plotly)
13 | # CREATE A TREE -------------------------------------------------------------
14 | n_samples <- 20
15 | n_grp <- 4
16 | tree <- ape::rtree(n = n_samples)
17 | # CREATE SOME METADATA ----------------------------------------------------
18 | id <- tree$tip.label
19 | set.seed(42)
20 | grp <- sample(LETTERS[1:n_grp], size = n_samples, replace = T)
21 | dat <- tibble::tibble(id = id,
22 | grp = grp)
23 | # PLOT THE TREE -----------------------------------------------------------
24 | p1 <- ggtree(tree)
25 | metat <- p1$data %>%
26 | dplyr::inner_join(dat, c('label' = 'id'))
27 | p2 <- p1 +
28 | geom_point(data = metat,
29 | aes(x = x,
30 | y = y,
31 | colour = grp,
32 | label = id))
33 | plotly::ggplotly(p2)
34 | ```
35 |
36 |
37 | (ref:ggtreeplotlyscap) Interactive phylogenetic tree by combining ggtree with plotly.
38 |
39 | (ref:ggtreeplotlycap) **Interactive phylogenetic tree by combining ggtree with plotly.**
40 |
41 |
42 |
43 | ```{r ggtreeplotly, fig.cap="(ref:ggtreeplotlycap)", fig.scap="(ref:ggtreeplotlyscap)", echo=FALSE, out.width='100%'}
44 | knitr::include_graphics("img/ggtree-plotly.gif")
45 | ```
46 |
--------------------------------------------------------------------------------
/data-backup/difftax_tab.csv:
--------------------------------------------------------------------------------
1 | f,DIAGNOSIS,pvalue
2 | f__Ruminococcaceae,Healthy,1.51267065035054e-06
3 | c__Clostridia,Healthy,3.05838049063017e-06
4 | o__Clostridiales,Healthy,3.05838049063017e-06
5 | s__un_g__Faecalibacterium,Healthy,4.75087462907852e-06
6 | g__Faecalibacterium,Healthy,4.75329373881338e-06
7 | g__un_f__Ruminococcaceae,Healthy,1.69837255768906e-05
8 | s__un_f__Ruminococcaceae,Healthy,1.69837255768906e-05
9 | o__Coriobacteriales,Healthy,4.25456823627024e-05
10 | f__Coriobacteriaceae,Healthy,4.72583865107421e-05
11 | c__Fusobacteria (class),Tumor,4.76057216365154e-05
12 | f__Fusobacteriaceae,Tumor,4.76057216365154e-05
13 | o__Fusobacteriales,Tumor,4.76057216365154e-05
14 | p__Fusobacteria,Tumor,4.76057216365154e-05
15 | g__Fusobacterium,Tumor,0.000103448683078524
16 | f__Alcaligenaceae,Healthy,0.00012148100023334
17 | f__Lachnospiraceae,Healthy,0.000189840649798373
18 | f__Bacteroidaceae,Healthy,0.000228643348833595
19 | g__Bacteroides_f__Bacteroidaceae,Healthy,0.000228643348833595
20 | g__Sutterella,Healthy,0.000237311074736752
21 | p__Firmicutes,Healthy,0.000252107547230567
22 | s__un_g__Sutterella,Healthy,0.000265692855921201
23 | s__un_g__Fusobacterium,Tumor,0.000410314108015989
24 | s__un_g__Bacteroides,Healthy,0.000520931732075829
25 | o__Burkholderiales,Healthy,0.00116451786808939
26 | c__Epsilonproteobacteria,Tumor,0.00130732690640795
27 | f__Campylobacteraceae,Tumor,0.00130732690640795
28 | o__Campylobacterales,Tumor,0.00130732690640795
29 | g__Collinsella,Healthy,0.00135289251384375
30 | c__Betaproteobacteria,Healthy,0.00171691468792643
31 | s__un_g__Clostridium_g__Clostridium_f__Erysipelotrichaceae,Healthy,0.00198539462978982
32 | f__un_o__Clostridiales,Healthy,0.00199169046802336
33 | g__un_o__Clostridiales,Healthy,0.00199169046802336
34 | s__un_o__Clostridiales,Healthy,0.00199169046802336
35 | g__Campylobacter,Tumor,0.00211127741038884
36 | g__Parabacteroides,Healthy,0.00226737467018806
37 | f__Rikenellaceae,Healthy,0.0024517180612936
38 |
--------------------------------------------------------------------------------
/setup.R:
--------------------------------------------------------------------------------
1 | if (!requireNamespace("pacman", quietly = TRUE)) {
2 | install.packages("pacman")
3 | }
4 |
5 | library(pacman)
6 |
7 | p_load(ape)
8 | p_load(Biostrings)
9 | ## p_load(OutbreakTools)
10 | p_load(igraph)
11 | p_load(phylobase)
12 |
13 | p_load(emojifont)
14 | p_load(ggplot2)
15 | p_load(dplyr)
16 | p_load(kableExtra)
17 | p_load(tidytree)
18 | p_load(treeio)
19 | p_load(ggtree)
20 |
21 | p_load(cowplot)
22 | p_load(patchwork)
23 | p_load(aplot)
24 |
25 | p_load(phytools)
26 | p_load(hexbin)
27 | p_load(TDbook)
28 | p_load(ggimage)
29 | p_load(phyloseq)
30 | p_load(treemap)
31 | p_load(ggtreeExtra)
32 | p_load(ggstar)
33 | p_load(MicrobiotaProcess)
34 | p_load(tanggle)
35 | p_load(gggenes)
36 | p_load(ggtext)
37 | p_load(ggbreak)
38 | p_load(rotl)
39 | p_load(gridSVG)
40 | p_load(data.tree)
41 | p_load(rsvg)
42 |
43 | source("conflicted.R")
44 | source("software-link.R")
45 |
46 | theme_set(theme_grey())
47 |
48 |
49 | badge_version <- function(pkg, color="green") {
50 | v <- packageVersion(pkg)
51 | url <- paste0("https://github.com/YuLab-SMU/", pkg)
52 | badger::badge_custom(pkg, v, color, url)
53 | }
54 |
55 |
56 | ## based on https://bookdown.org/yihui/rmarkdown-cookbook/fig-process.html
57 |
58 | svg2png <- function(path, options) {
59 | if (!grepl('[.]svg$', path)) return(path)
60 |
61 | if (file.size(path)/1000000 < 1.2) {
62 | ## less than 1.2M
63 | return(path)
64 | }
65 |
66 | output <- sub(".svg$", ".png", path)
67 | system2("convert", paste("-density 150", path, output))
68 | file.remove(path)
69 | return(output)
70 | }
71 |
72 |
73 | trim_fig <- function(path, options) {
74 | output <- sub(".png$", "-crop.png", path)
75 | system2("convert", paste("-trim", path, output))
76 | file.remove(path)
77 | return(output)
78 | }
79 |
80 | squote <- function(string) {
81 | if (knitr::is_latex_output()) {
82 | left_quote_mark <- "`"
83 | } else {
84 | left_quote_mark <- "'"
85 | }
86 |
87 | right_quote_mark <- "'"
88 |
89 | fmt <- "%s%s%s"
90 | sprintf(fmt, left_quote_mark, string, right_quote_mark)
91 | }
92 |
--------------------------------------------------------------------------------
/00_author.Rmd:
--------------------------------------------------------------------------------
1 | \newpage
2 | \frontmatter
3 | # About the Author {#author .unnumbered}
4 |
5 | **Guangchuang Yu** (https://yulab-smu.top) is a professor of Bioinformatics and director of the Department of Bioinformatics at Southern Medical University. He earned his Ph.D. from the School of Public Health, The University of Hong Kong. As an active R user, he has authored several R packages, such as `r CRANpkg("aplot")`, `r CRANpkg("badger")`, `r Biocpkg("ChIPseeker")`, `r Biocpkg("clusterProfiler")`, `r Biocpkg("DOSE")`, `r CRANpkg("emojifont")`, `r Biocpkg("enrichplot")`, `r CRANpkg("ggbreak")`, `r CRANpkg("ggfun")`, `r CRANpkg("ggimage")`, `r CRANpkg("ggplotify")`, `r Biocpkg("ggtree")`, `r Biocpkg("GOSemSim")`, `r CRANpkg("hexSticker")`, `r CRANpkg("meme")`, `r Biocpkg("meshes")`, `r CRANpkg("nCov2019")`, `r CRANpkg("plotbb")`, `r Biocpkg("ReactomePA")`, `r CRANpkg("scatterpie")`, `r CRANpkg("seqmagick")`, `r Biocpkg("seqcombo")`, `r CRANpkg("shadowtext")`, `r CRANpkg("tidytree")` and `r Biocpkg("treeio")`. He has supervised post-graduate students to develop a few other packages, including `r Biocpkg("ggmsa")`, `r Biocpkg("ggtreeExtra")`, `r Biocpkg("MicrobiomeProfiler")` and `r Biocpkg("MicrobiotaProcess")`.
6 |
7 |
8 | His research group aims to generate new insights into human health and disease through the development of new software tools and novel analysis of biomedical data. The software package developed by his research group helps biologists analyze data and reveal biological clues hidden in the data.
9 |
10 |
11 | He has published several journal articles, including 5 highly cited papers [@yu2012; @yu_dose_2015; @yu_chipseeker:_2015; @yu_reactomepa_2016; @yu_ggtree:_2017]. The articles have been cited more than 10,000 times. The ggtree [@yu_ggtree:_2017] paper was selected as a feature article to celebrate the 10^th^ anniversary of the launch of _Methods in Ecology and Evolution_^[10th Anniversary Volume 8: Phylogenetic tree visualization with multivariate data: ]. He was one of the 2020 Highly Cited Chinese Researchers (Elsevier-Scopus) in Biomedical Engineering.
12 |
13 |
14 |
--------------------------------------------------------------------------------
/mind-map.R:
--------------------------------------------------------------------------------
1 | x=tibble::tribble(~x, ~y,
2 | 'a', 'a1',
3 | 'a', 'a2',
4 | 'a', 'a3',
5 | 'a', 'a4',
6 | 'a', 'a5',
7 | 'a1', 'b1',
8 | 'a1', 'b2',
9 | 'a1', 'b3',
10 | 'a1', 'b4',
11 | 'a2', 'c1',
12 | 'a2', 'c2',
13 | 'a2', 'c3',
14 | 'a2', 'c4',
15 | 'a3', 'd1',
16 | 'a3', 'd2',
17 | 'a3', 'd3',
18 | 'a4', 'e1',
19 | 'a4', 'e2',
20 | 'a5', 'f1',
21 | 'a5', 'f2',
22 | 'a5', 'f3')
23 |
24 |
25 |
26 | require(treeio)
27 | require(ggtree)
28 |
29 | f = function(n) {
30 | paste(sample(LETTERS,n, replace=T), collapse="")
31 | }
32 |
33 | n = sample(5:8, 7, replace=T)
34 | lab=sapply(n, f)
35 | names(lab) = letters[1:7]
36 | lab
37 |
38 | lab <- c('a' = '项目计划',
39 | 'a1' = '需求讨论',
40 | 'a2' = '讨论解决方案',
41 | 'a3' = '项目设计',
42 | 'a4' = '汇报讨论',
43 | 'a5' = '结果确认',
44 | 'b1' = '现状说明',
45 | 'b2' = '需求分析',
46 | 'b3' = '需求定义',
47 | 'b4' = '明确目标',
48 | 'c1' = '获取基本信息',
49 | 'c2' = '明确需求目标',
50 | 'c3' = '功能范围确认',
51 | 'c4' = '解决方案初步商讨',
52 | 'd1' = '解决方案',
53 | 'd2' = '实施计划',
54 | 'd3' = '原型设计',
55 | 'e1' = '解决方案汇报',
56 | 'e2' = '方案讨论',
57 | 'f1' = '目标明确',
58 | 'f2' = '范围确定',
59 | 'f3' = '方案可靠'
60 | )
61 | y = as.phylo(x)
62 |
63 | require(tidytree)
64 | yy = as_tibble(y) %>% mutate(cat = ifelse(node %in% parent, 1, parent))
65 | yy$cat[rootnode(y)] = 0
66 |
67 | ## http://www.bio-review.com/mind-mapping/
68 | ggtree(as.treedata(yy), ladderize=F, layout='roundrect') +
69 | geom_nodelab(aes(x=x*.95, label=lab[label],
70 | fill=factor(cat)), hjust=1, geom='label') +
71 | geom_tiplab(aes(label=lab[label], fill=factor(cat)), geom='label') +
72 | scale_y_reverse() +
73 | hexpand(.2) + hexpand(.06, -1) +
74 | theme(legend.position = 'none')
75 |
76 |
77 |
--------------------------------------------------------------------------------
/software-link.R:
--------------------------------------------------------------------------------
1 |
2 | pacman::p_load(yulab.utils)
3 |
4 | # pkg name in bold
5 | options("yulab.utils_pkgfmt" = '**%s**')
6 |
7 | pkg_archaeopteryx <- mypkg("Archaeopteryx", "https://sites.google.com/site/cmzmasek/home/software/archaeopteryx")
8 | pkg_astral <- mypkg("ASTRAL", "https://github.com/smirarab/ASTRAL")
9 | pkg_atv <- mypkg("ATV", "http://phylogeny.lirmm.fr/phylo_cgi/one_task.cgi?task_type=atv")
10 | pkg_baseml <- mypkg("BASEML", "http://abacus.gene.ucl.ac.uk/software/paml.html")
11 | pkg_beast <- mypkg("BEAST", "http://beast2.org/")
12 | pkg_codeml <- mypkg("CODEML", "http://abacus.gene.ucl.ac.uk/software/paml.html")
13 | pkg_devout <- mypkg("devout", "https://github.com/coolbutuseless/devout")
14 | pkg_epa <- mypkg("EPA", "http://sco.h-its.org/exelixis/web/software/epa/index.html")
15 | pkg_evolview <- mypkg("EvolView", "https://www.evolgenius.info/evolview/")
16 | pkg_figtree <- mypkg("FigTree", "http://beast.community/figtree")
17 | pkg_hyphy <- mypkg("HyPhy", "https://veg.github.io/hyphy-site/")
18 | pkg_itol <- mypkg("iTOL", "https://itol.embl.de/")
19 | pkg_mega <- mypkg("MEGA", "https://www.megasoftware.net/")
20 | pkg_mothur <- mypkg("mothur", "http://mothur.org/")
21 | pkg_mrbayes <- mypkg("MrBayes", "http://nbisweden.github.io/MrBayes/")
22 | pkg_paml <- mypkg("PAML", "http://abacus.gene.ucl.ac.uk/software/paml.html")
23 | pkg_paup <- mypkg("PAUP\\*", "https://paup.phylosolutions.com/")
24 | pkg_phyldog <- mypkg("PHYLDOG", "http://pbil.univ-lyon1.fr/software/phyldog/")
25 | pkg_phylip <- mypkg("PHYLIP", "https://evolution.genetics.washington.edu/phylip.html")
26 | pkg_phyloch <- mypkg("PHYLOCH", "http://www.christophheibl.de/Rpackages.html")
27 | pkg_phyml <- mypkg("PhyML", "http://www.atgc-montpellier.fr/phyml/")
28 | pkg_pplacer <- mypkg("PPLACER", "http://matsen.fhcrc.org/pplacer/")
29 | pkg_pyrotagger <- mypkg("PyroTagger", "http://pyrotagger.jgi-psf.org/")
30 | pkg_qiime <- mypkg("QIIME", "http://qiime.org/")
31 | pkg_r8s <- mypkg("r8s", "http://loco.biosci.arizona.edu/r8s/")
32 | pkg_raxml <- mypkg("RAxML", "http://evomics.org/learning/phylogenetics/raxml/")
33 | pkg_revbayes <- mypkg("RevBayes", "http://revbayes.github.io/intro.html")
34 | pkg_treedataverse <- mypkg("treedataverse", "https://github.com/YuLab-SMU/treedataverse")
35 | pkg_treedyn <- mypkg("TreeDyn", "http://www.treedyn.org/")
36 | pkg_treeview <- mypkg("TreeView", "http://en.bio-soft.net/tree/TreeView.html")
--------------------------------------------------------------------------------
/data-backup/long-branch-example.newick:
--------------------------------------------------------------------------------
1 | ((((((((((((1:0.0019495867,2:0.0018235031)100:0.0011969927,(3:0.002127256,4:0.0017653282)100:0.0009607227)81:0.0007800209,(5:0.0020609633,6:0.0015594842)100:0.0014696957)99:0.00076205103,(7:0.00103844,8:0.00090185576)100:0.004002927)71:0.00052354374,((((9:0.0005960867,10:0.00029718556)100:0.0021998067,11:0.0017635386)49:0.00032220557,12:0.002093067)100:0.0026289858,((13:0.00030378695,14:0.0004093477)100:0.0020059645,15:0.001714428)100:0.0035134256)88:0.0010869319)47:0.00044896273,((16:0.00089676643,17:0.00091339916)100:0.0012717035,(18:0.00058965845,19:0.00077034876)100:0.0019610838)100:0.003681053)100:0.000986163,((20:0.0017197495,21:0.002026437)100:0.0021111318,(22:0.0014789673,23:0.001172614)100:0.0033353607)100:0.0010296783)75:0.00046172284,((((((24:0.0011399257,25:0.0010135229)100:0.0028160322,26:0.002061178)89:0.00074120885,(27:0.0020872736,28:0.002363772)65:0.00048745636)78:0.00047038822,29:0.0031853558)100:0.0010860511,(((30:0.001801027,31:0.0017156169)50:0.00043764967,(32:0.0019378961,33:0.0018494674)75:0.0005115647)86:0.0008589981,34:0.0031367077)100:0.0010175441)100:0.0011168303,((((35:0.0014192878,36:0.00171504)71:0.00045325398,37:0.0013418088)100:0.0018567514,38:0.0027288173)100:0.0009646732,(39:0.001189804,40:0.0011016381)100:0.0035758137)94:0.00063564494)49:0.00043702923)99:0.00076586666,(((((((41:0.0016199668,42:0.0021672894)98:0.0011483912,(43:0.0020666104,44:0.0020453448)66:0.0008078704)100:0.0008597766,45:0.0020089832)63:0.0004861389,(46:0.001844409,47:0.0015979814)100:0.0008071767)63:0.0004452633,(((48:0.0022112913,49:0.001554472)97:0.0006460188,50:0.0017128587)33:0.00049529294,((51:0.0016750101,52:0.0020238631)97:0.0006743917,(53:0.0019474783,54:0.0021088324)56:0.0005045439)36:0.00047344062)39:0.00039581355)93:0.0006986046,((55:0.0011095047,56:0.0008730135)52:0.00050178525,57:0.0018560977)100:0.00268682)93:0.0006230792,(58:0.00020127192,59:0.0011574014)100:0.0031308904)100:0.0014613273)91:0.00072693784,((((60:0.0008727719,61:0.0013847539)94:0.00042951023,62:0.001326798)35:0.0002063098,63:0.0012339546)46:0.00032965123,((64:0.0016134583,65:0.0015117446)80:0.0006614659,(66:0.0012233815,67:0.0015928008)80:0.0005838249)88:0.0005176356)100:0.0036002458)94:0.0012113276,((((68:0.0024613454,69:0.0004856562)100:0.0017308649,70:0.0029311234)86:0.0007909904,(71:0.00084207306,72:0.0009942316)100:0.0029447847)99:0.0010581666,(73:0.003773614,74:0.002541475)100:0.0030124304)100:0.0012545331)99:0.008205896,(75:0.027509606,76:0.093684964)99:0.00022178098);
2 |
--------------------------------------------------------------------------------
/data-backup/svl.csv:
--------------------------------------------------------------------------------
1 | species,svl
2 | ahli,4.039125443
3 | alayoni,3.815704818
4 | alfaroi,3.526654599
5 | aliniger,4.036556538
6 | allisoni,4.375390078
7 | allogus,4.040138442
8 | altitudinalis,3.842994419
9 | alumina,3.588940722
10 | alutaceus,3.554890814
11 | angusticeps,3.788595498
12 | argenteolus,3.971307363
13 | argillaceus,3.757868992
14 | armouri,4.121684075
15 | bahorucoensis,3.827445029
16 | baleatus,5.05305601
17 | baracoae,5.042779747
18 | barahonae,5.076957937
19 | barbatus,5.003946306
20 | barbouri,3.663931948
21 | bartschi,4.280547466
22 | bremeri,4.113370744
23 | breslini,4.051110808
24 | brevirostris,3.874154977
25 | caudalis,3.911742966
26 | centralis,3.697941213
27 | chamaeleonides,5.04234927
28 | chlorocyanus,4.275448057
29 | christophei,3.884651809
30 | clivicola,3.758726128
31 | coelestinus,4.297965447
32 | confusus,3.938442349
33 | cooki,4.091535064
34 | cristatellus,4.189820018
35 | cupeyalensis,3.462013706
36 | cuvieri,4.875011919
37 | cyanopleurus,3.630161001
38 | cybotes,4.210982161
39 | darlingtoni,4.302036009
40 | distichus,3.928795837
41 | dolichocephalus,3.908550316
42 | equestris,5.113993807
43 | etheridgei,3.657990601
44 | eugenegrahami,4.128504414
45 | evermanni,4.165605231
46 | fowleri,4.288779949
47 | garmani,4.769473237
48 | grahami,4.154274265
49 | guafe,3.87745744
50 | guamuhaya,5.036952602
51 | guazuma,3.76388385
52 | gundlachi,4.18810472
53 | haetianus,4.316542212
54 | hendersoni,3.859834574
55 | homolechis,4.032805981
56 | imias,4.099687407
57 | inexpectatus,3.537438641
58 | insolitus,3.80047052
59 | isolepis,3.657087744
60 | jubar,3.952604971
61 | krugi,3.886500048
62 | lineatopus,4.128611788
63 | longitibialis,4.242103429
64 | loysiana,3.701240244
65 | lucius,4.198914766
66 | luteogularis,5.101085197
67 | macilentus,3.715764783
68 | marcanoi,4.079484699
69 | marron,3.831810283
70 | mestrei,3.987147344
71 | monticola,3.770613039
72 | noblei,5.083472618
73 | occultus,3.663048694
74 | olssoni,3.79389884
75 | opalinus,3.838376465
76 | ophiolepis,3.637962029
77 | oporinus,3.845669505
78 | paternus,3.802961204
79 | placidus,3.773967107
80 | poncensis,3.820377531
81 | porcatus,4.258990989
82 | porcus,5.038034268
83 | pulchellus,3.799022266
84 | pumilis,3.46686027
85 | quadriocellifer,3.901619072
86 | reconditus,4.482606994
87 | ricordii,5.013963084
88 | rubribarbus,4.078469213
89 | sagrei,4.067161768
90 | semilineatus,3.696630586
91 | sheplani,3.682924257
92 | shrevei,3.983003234
93 | singularis,4.057997494
94 | smallwoodi,5.035095592
95 | strahmi,4.274271278
96 | stratulus,3.869880695
97 | valencienni,4.321524401
98 | vanidicus,3.62620586
99 | vermiculatus,4.802849398
100 | websteri,3.916546094
101 | whitemani,4.097478535
102 |
--------------------------------------------------------------------------------
/book-cover.R:
--------------------------------------------------------------------------------
1 | require(tidyr)
2 | require(ggplot2)
3 | require(ggtree)
4 | require(grid)
5 |
6 | oldwd <- getwd()
7 | setwd("~/github/plotting_tree_with_data/plotTree/tree_example_april2015/")
8 | info <- read.csv("info.csv")
9 | tree <- read.tree("tree.nwk")
10 |
11 | ##tp <- tree$tip.label
12 |
13 | ## set.seed(2018-04-10)
14 | ## tree=rtree(230)
15 | ## tree$tip.label <- tp
16 |
17 | heatmap.colours=c("steelblue","grey","seagreen3","darkgreen","green","brown","tan", "red",
18 | "orange","pink","magenta","purple","blue","skyblue3","blue","skyblue2")
19 | names(heatmap.colours) <- 0:15
20 | heatmapData=read.csv("res_genes.csv", row.names=1)
21 |
22 | rn <- rownames(heatmapData)
23 | heatmapData <- as.data.frame(sapply(heatmapData, as.character))
24 |
25 | rownames(heatmapData) <- rn
26 |
27 | cols <- c(HCMC='black', Hue='purple2', KH='skyblue2')
28 | p <- ggtree(tree, layout='circular', size=.1) %<+% info + theme(legend.position='none') +
29 | # geom_tippoint(aes(color=location), size=.001) + scale_color_manual(values=cols) #+
30 | geom_tiplab2(aes(label=name), align=T, linetype=NA, linesize=.05, size=.5, offset=1, hjust=0.5) #+
31 | # geom_tiplab2(aes(label=year), align=T, linetype=NA, size=.2, offset=3.5, hjust=0.5)
32 |
33 | #p + xlim(-3, NA)
34 |
35 | df <- p$data
36 | df <- df[df$isTip,]
37 | start <- max(df$x) + 2
38 |
39 | dd <- as.data.frame(heatmapData)
40 | ## dd$lab <- rownames(dd)
41 | lab <- df$label[order(df$y)]
42 | dd <- dd[lab, , drop=FALSE]
43 | dd$y <- sort(df$y)
44 | dd$lab <- lab
45 | ## dd <- melt(dd, id=c("lab", "y"))
46 | dd <- gather(dd, variable, value, -c(lab, y))
47 |
48 | i <- which(dd$value == "")
49 | if (length(i) > 0) {
50 | dd$value[i] <- NA
51 | }
52 | width=.5
53 | width <- width * (p$data$x %>% range %>% diff) / ncol(heatmapData)
54 |
55 | V2 <- start + as.numeric(as.factor(dd$variable)) * width
56 |
57 | dd$x <- V2
58 | dd$width <- width
59 |
60 | dd$value[dd$value == 0] = NA
61 |
62 | p2 <- p + geom_tile(data=dd, aes(x, y, fill=value), width=width, inherit.aes=FALSE)
63 |
64 | p2 = p2 + scale_fill_manual(values=heatmap.colours, na.value=NA) #"white")
65 |
66 | setwd(oldwd)
67 | #p2 <- p2+theme_tree()+theme_transparent()
68 | #p2
69 | p3 <- rotate_tree(open_tree(p2, 120), -35)
70 |
71 |
72 | # dev.new(height=297, width=210, unit="mm")
73 | png("book-cover.png", width=210, height=297, units="mm", res=150)
74 | #grid.newpage()
75 |
76 | grid.text("Data Integration, Manipulation and\nVisualization of Phylogenetic Trees",
77 | y=.82, gp=gpar(cex=2.5), just="left", x=.1)
78 |
79 | vp = viewport(x=.5, y=.42, width=.9, height=.8)
80 | pushViewport(vp)
81 | grid.draw(ggplotGrob(p3))
82 |
83 | upViewport()
84 | grid.text("Guangchuang Yu", y=.22, x=.9, just="right", gp=gpar(cex=2))
85 | grid.text("School of Basic Medical Sciences\nSouthern Medical University", y=.15, x=.9, just="right",
86 | gp=gpar(cex=1.5, fontface="italic"))
87 | dev.off()
88 |
89 |
--------------------------------------------------------------------------------
/data-backup/HPV58.tree:
--------------------------------------------------------------------------------
1 | ((((((SC144|FJ385264:0.00136,((PPH58|D90400:0.00027,(SC165|FJ385265:0.00055,SC147|FJ385263:0.00054)18:0.00014)6:0,(SC100|FJ385261:0.00068,LZCC86|EU918765:0.00081)74:0.00027)5:0.00041)4:0.00014,(SC78|FJ385268:0.00029,(QE00190|KY225919:0.00028,((TW00060|KY225918:0,CNZJ3|KC860270:0.00014)95:0.00027,(SC101|FJ385262:0.00041,(SC174|FJ385266:0.00068,SC185|FJ385267:0.00054)7:0)8:0.00027)7:0.00013)7:0.00038)3:0.002)2:0.00041,(ZWE058771|KY225920:0,ZWE047402|KY225921:0.00014)98:0.00163)87:0.00073,(QV00861|HQ537755:0.00163,(QE01538|KY225934:0.00054,((KORK01712|KY225922:0,KORK03505|KY225923:0)100:0.00054,(((JP0221|AB819275:0.00014,QV03554|HQ537754:0.00245)15:0,(JP1352|AB819278:0.00027,(RW791|HQ537753:0.00041,(ZWE052265|KY225924:0,ZWE051406|KY225925:0.00027)97:0.00041)73:0.00014)12:0)45:0.00014,((CNZJ1|KC860269:0.00027,(((KORK00020|KY225926:0.00027,(KORK00038|KY225927:0.00014,KORK00643|KY225928:0.00027)62:0)94:0.00041,(JP0891|AB819277:0.00027,CNZJ2|KC860271:0.00027)32:0)63:0.00014,((KORK00053|KY225929:0,KORK02118|KY225930:0)100:0.00041,(KORK00421|KY225931:0,(JP0302|AB819276:0.00014,KORK00025|KY225932:0.00014)2:0)95:0.00041)29:0)62:0.00014)6:0,(QV15606|HQ537752:0.00014,(36A|KU298920:0.00041,TJ18|GQ472850:0.0019)6:0)0:0)0:0)9:0)61:0.00014)77:0.00026)100:0.0024)71:0.00054,((QV32351|HQ537757:0,QV15563|HQ537756:0.00027)63:0.00014,((AS405|HQ537759:0.00041,(AS347|HQ537760:0.00041,((KORK00613|KY225938:0.00054,(KORK02546|KY225936:0.00068,KORK00008|KY225937:0.00027)29:0)58:0.00014,(((KORK00191|KY225939:0.00014,KORK01374|KY225941:0)86:0.00027,((JP1870|AB819279:0,KORK00034|KY225942:0.00041)63:0.00014,(KORK03823|KY225943:0.00014,KORK00099|KY225944:0.00014)42:0)65:0.00013)64:0,(KORK02277|KY225945:0.00014,(KORK00064|KY225946:0.00014,KORK00043|KY225947:0.00014)34:0)71:0.00014)57:0)76:0.00027)83:0.00027)60:0.00014,((QE00150|KY225948:0.00027,(JPNJ00739|KY225953:0.00081,((THA00468|KY225949:0.00081,KORK02550|KY225950:0)62:0.00014,(KORK00011|KY225951:0,KORK03762|KY225952:0)99:0.00014)18:0)61:0.00014)26:0,(QV00961|HQ537758:0.0019,(QE00470|KY225954:0.00014,QE01132|KY225955:0.00054)93:0.00027)38:0.00013)17:0.00013)10:0)100:0.00225):0.00496,((Z023|HQ537763:0.00082,(BF134|HQ537762:0.00028,BF077|HQ537761:0.00109)87:0.00041)100:0.00342,(((ZWE051089|KY225956:0.00096,ZWE043998|KY225957:0.00027)100:0.00106,(RW937|HQ537764:0.00096,RW754|HQ537765:0.00027)78:0.00031)100:0.00213,(((Z094|HQ537777:0.00042,(ZWE062097|KY225958:0,ZWE050364|KY225959:0)100:0.00067)99:0.00103,((RW792|HQ537775:0.00027,RW644|HQ537776:0)100:0.0007,((QV13816|HQ537774:0.00041,QV34982|HQ537772:0.00027)58:0.00014,(ZWE054176|KY225961:0,(QV03666|HQ537773:0.00136,ZWE044033|KY225963:0.00068)42:0)40:0)86:0.00052)56:0.0005)100:0.00367,((ARGP00138|KY225964:0.00165,(QV03858|HQ537766:0.00178,(QV04732|HQ537767:0.00027,QV03841|HQ537768:0.00027)58:0)100:0.00124)99:0.00079,(ZWE064436|KY225966:0.00073,(ZWE051402|KY225967:0.00044,(RW841|HQ537769:0.00069,(RW697|HQ537770:0.00015,RW63|HQ537771:0.00273)38:0.00014)78:0.00079)64:0.00066)100:0.00169)100:0.00267)98:0.00141)95:0.0013):0.00031)100;
2 |
--------------------------------------------------------------------------------
/data-backup/tree.nex:
--------------------------------------------------------------------------------
1 | #NEXUS
2 | [created by the 10kTree Website - http://10kTrees.fas.harvard.edu]
3 | BEGIN TREES;
4 | translate
5 | 1 Allenopithecus_nigroviridis,
6 | 2 Cercopithecus_mitis,
7 | 3 Cercopithecus_petaurista,
8 | 4 Chlorocebus_sabaeus,
9 | 5 Erythrocebus_patas,
10 | 6 Miopithecus_ogouensis,
11 | 7 Avahi_laniger,
12 | 8 Cheirogaleus_major,
13 | 9 Daubentonia_madagascarensis,
14 | 10 Eulemur_fulvus,
15 | 11 Hapalemur_griseus,
16 | 12 Indri_indri,
17 | 13 Lemur_catta,
18 | 14 Lepilemur_mustelinus,
19 | 15 Microcebus_murinus,
20 | 16 Mirza_zaza,
21 | 17 Propithecus_diadema,
22 | 18 Varecia_variegata,
23 | 19 Alouatta_caraya,
24 | 20 Ateles_geoffroyi,
25 | 21 Lagothrix_lagotricha,
26 | 22 Aotus_trivirgatus,
27 | 23 Callimico_goeldii,
28 | 24 Callithrix_humeralifera,
29 | 25 Cebuella_pygmaea,
30 | 26 Cebus_apella,
31 | 27 Leontopithecus_rosalia,
32 | 28 Saguinus_fuscicollis,
33 | 29 Saimiri_sciureus,
34 | 30 Arctocebus_aureus,
35 | 31 Loris_lydekkerianus,
36 | 32 Nycticebus_coucang,
37 | 33 Perodicticus_potto,
38 | 34 Hoolock_hoolock,
39 | 35 Gorilla_gorilla,
40 | 36 Homo_sapiens,
41 | 37 Hylobates_lar,
42 | 38 Nomascus_concolor,
43 | 39 Pan_troglodytes,
44 | 40 Pongo_pygmaeus,
45 | 41 Symphalangus_syndactylus,
46 | 42 Cacajao_calvus,
47 | 43 Callicebus_moloch,
48 | 44 Chiropotes_chiropotes,
49 | 45 Pithecia_pithecia,
50 | 46 Cercocebus_albigena,
51 | 47 Macaca_fascicularis,
52 | 48 Macaca_nemestrina,
53 | 49 Mandrillus_sphinx,
54 | 50 Papio_papio,
55 | 51 Theropithecus_gelada,
56 | 52 Colobus_polykomos,
57 | 53 Nasalis_larvatus,
58 | 54 Presbytis_rubicunda,
59 | 55 Procolobus_badius,
60 | 56 Pygathrix_nigriceps,
61 | 57 Rhinopithecus_roxellana,
62 | 58 Semnopithecus_schistaceus,
63 | 59 Trachypithecus_cristatus,
64 | 60 Euoticus_elegantulus,
65 | 61 Galago_senegalensis,
66 | 62 Otolemur_crassicaudatus,
67 | 63 Cephalopachus_bancanus,
68 | 64 Tarsius_tarsier,
69 | 65 Carlito_syrichta;
70 | tree consensus_65species = (((((((1:11.894902,(((2:6.429209,3:6.429209):3.418917,(5:8.627245,4:8.627244):1.220881):1.644988,6:11.493113):0.401789):2.980276,(((46:5.304298,49:5.304297):6.046166,(51:5.769164,50:5.769163):5.581300):1.502062,(48:6.877996,47:6.877995):5.974530):2.022653):6.535196,((52:12.537452,55:12.537452):2.897705,((((53:9.634813,56:9.634813):0.839771,57:10.474584):2.310063,(58:11.461377,59:11.461378):1.323270):0.523455,54:13.308102):2.127056):5.975217):8.589626,((34:8.241196,((37:6.598361,41:6.598362):0.749537,38:7.347898):0.893297):11.364750,((35:8.652233,(36:6.175880,39:6.175879):2.476353):6.480222,40:15.132455):4.473491):10.394055):16.811821,(((19:14.760240,(20:9.147391,21:9.147391):5.612849):6.561060,((22:19.487521,(((23:13.232628,(24:4.789811,25:4.789812):8.442815):1.797437,27:15.030065):0.682187,28:15.712252):3.775269):0.605006,(26:18.569905,29:18.569905):1.522623):1.228773):1.411478,(((42:2.274028,44:2.274028):7.368851,45:9.642879):9.753209,43:19.396087):3.336691):24.079043):22.028211,((63:16.314276,65:16.314276):7.983376,64:24.297652):44.542381):4.162986,(((((((15:14.271122,16:14.271123):8.292266,8:22.563389):5.865706,14:28.429095):2.516810,(12:20.909032,(7:16.502600,17:16.502600):4.406432):10.036873):2.318811,((10:15.019595,(11:9.280518,13:9.280517):5.739079):5.523206,18:20.542802):12.721914):17.859349,9:51.124065):11.612487,(((30:18.734550,33:18.734550):15.900706,(31:24.057527,32:24.057528):10.577729):3.364744,((60:5.384501,61:5.384501):7.979917,62:13.364418):24.635583):24.736553):10.266465);
71 | END;
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/Introduction.Rmd:
--------------------------------------------------------------------------------
1 |
2 | # Introduction {-}
3 |
4 | ```{r, eval = !knitr::is_latex_output(), echo=FALSE, results='asis'}
5 | badge_version("tidytree")
6 | badge_version("treeio")
7 | badge_version("ggtree")
8 | badge_version("ggtreeExtra")
9 | ```
10 |
11 | > You can't even begin to understand biology, you can't understand life, unless
12 | > you understand what it's all there for, how it arose - and that means
13 | > evolution.
14 | >
15 | > --- Richard Dawkins
16 |
17 |
18 | ## `r if (!knitr::is_latex_output()) emoji("dart")` Motivation {-}
19 |
20 | The book is meant as a guide for data integration, manipulation and visualization of phylogenetic trees using a suite of R packages, `r CRANpkg("tidytree")`, `r Biocpkg("treeio")` and `r Biocpkg("ggtree")`. Hence, if you are starting to read this book, we assume you have a working knowledge of how to use R and `r CRANpkg("ggplot2")`.
21 |
22 | ## `r if (!knitr::is_latex_output()) emoji("memo")` Citation {-}
23 |
24 | If you use the software suite in published research, please cite the most appropriate paper(s) from this list:
25 |
26 | 1. __G Yu__. Using ggtree to visualize data on tree-like structures. __*Current Protocols in Bioinformatics*__, 2020, 69:e96. doi: [10.1002/cpbi.96](https://doi.org/10.1002/cpbi.96).
27 | 2. LG Wang, TTY Lam, S Xu, Z Dai, L Zhou, T Feng, P Guo, CW Dunn, BR Jones, T Bradley, H Zhu, Y Guan, Y Jiang, __G Yu__^\*^. treeio: an R package for phylogenetic tree input and output with richly annotated and associated data. __*Molecular Biology and Evolution*__. 2020, 37(2):599-603.
28 | doi: [10.1093/molbev/msz240](http://dx.doi.org/10.1093/molbev/msz240).
29 | 3. __G Yu__^\*^, TTY Lam, H Zhu, Y Guan^\*^. Two methods for mapping and visualizing associated data on phylogeny using ggtree. __*Molecular Biology and Evolution*__. 2018, 35(2):3041-3043.
30 | doi: [10.1093/molbev/msy194](https://doi.org/10.1093/molbev/msy194).
31 | 4. __G Yu__, DK Smith, H Zhu, Y Guan, TTY Lam^\*^. ggtree: an R package for
32 | visualization and annotation of phylogenetic trees with their covariates and
33 | other associated data. __*Methods in Ecology and Evolution*__. 2017, 8(1):28-36.
34 | doi: [10.1111/2041-210X.12628](https://doi.org/10.1111/2041-210X.12628).
35 |
36 | ## `r if (!knitr::is_latex_output()) emoji("books")` Book structure {-}
37 |
38 |
39 | + Part 1 (Tree data input, output and manipulation) describes `r Biocpkg("treeio")` package for tree data input and output, and `r CRANpkg("tidytree")` package for tree data manipulation.
40 | + Part 2 (Tree data visualization and annotation) introduces tree visualization and annotation using grammar of graphic syntax implemented in the `r Biocpkg("ggtree")` package. It emphasizes on presenting tree associated data on the tree.
41 | + Part 3 (ggtree extensions) introduces ggtreeExtra for presenting data on circular layout trees and other extensions including MicrobiotaProcess and ggnetworx *etc.*.
42 | + Part 4 (Miscellaneous topics) describes utilities provided by the `r Biocpkg("ggtree")` package suite and presents a set of reproducible examples.
43 |
44 |
45 | ## `r if (!knitr::is_latex_output()) emoji("sparkling_heart")` Want to help? {-}
46 |
47 |
48 | The book’s source code is hosted on GitHub, at . Any feedback on the book is very welcome. Feel free to [open an issue](https://github.com/YuLab-SMU/treedata-book/issues/new) on GitHub or send me a pull request if you notice typos or other issues (I'm not a native English speaker ;) ).
49 |
50 |
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/data-backup/anole.tre:
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1 | ((((((((ahli:0.1308887296,allogus:0.1308887296):0.109078899,rubribarbus:0.2399676286):0.3477240729,imias:0.5876917015):0.1279779191,((((sagrei:0.2576204042,(bremeri:0.1097436524,quadriocellifer:0.1097436524):0.1478767518):0.06150599843,ophiolepis:0.3191264027):0.08721921759,mestrei:0.4063456203):0.1298140501,(((jubar:0.1188659524,homolechis:0.1188659524):0.09052271908,confusus:0.2093886715):0.04215577182,guafe:0.2515444433):0.2846152271):0.1795099503):0.1377237125,((((garmani:0.2000335809,opalinus:0.2000335809):0.01968719882,grahami:0.2197207797):0.2178099139,valencienni:0.4375306936):0.1226128606,(lineatopus:0.4713710622,reconditus:0.4713710622):0.08877249208):0.2932497789):0.06703519523,(((evermanni:0.2135202715,stratulus:0.2135202715):0.3521520586,(((krugi:0.3267560653,pulchellus:0.3267560653):0.1312930371,(gundlachi:0.3864660126,poncensis:0.3864660126):0.0715830898):0.03035078065,(cooki:0.395288192,cristatellus:0.395288192):0.09311169105):0.07727244709):0.1495575755,(((brevirostris:0.2757423466,(caudalis:0.1704974619,marron:0.1704974619):0.1052448847):0.02672749452,websteri:0.3024698411):0.09835748687,distichus:0.400827328):0.3144025776):0.2051986227):0.03488732303,(((barbouri:0.8021085018,(((alumina:0.2681076879,semilineatus:0.2681076879):0.219367178,olssoni:0.4874748658):0.2622236606,(etheridgei:0.5883072151,(fowleri:0.3770938401,insolitus:0.3770938401):0.211213375):0.1613913113):0.05240997539):0.0672038969,((((whitemani:0.3420271265,((haetianus:0.2669834072,breslini:0.2669834072):0.06962183477,((armouri:0.1483909526,cybotes:0.1483909526):0.04416718222,shrevei:0.1925581348):0.1440471072):0.005421884492):0.1066560095,(longitibialis:0.2521253346,strahmi:0.2521253346):0.1965578014):0.09143002532,marcanoi:0.5401131613):0.2505275207,((((((baleatus:0.04173045424,barahonae:0.04173045424):0.05263675531,ricordii:0.09436720956):0.2036021511,eugenegrahami:0.2979693606):0.0851110199,christophei:0.3830803805):0.09095334022,cuvieri:0.4740337207):0.1076385501,(barbatus:0.1467942669,(porcus:0.09310584235,(chamaeleonides:0.07630236186,guamuhaya:0.07630236186):0.01680348049):0.05368842459):0.4348780039):0.2089684112):0.07867171672):0.07597999248,((((((((altitudinalis:0.1748899419,oporinus:0.1748899419):0.09220318062,isolepis:0.2670931225):0.2538920892,(allisoni:0.29602293,porcatus:0.29602293):0.2249622817):0.03703491197,(((argillaceus:0.1142165228,centralis:0.1142165228):0.0249762444,pumilis:0.1391927672):0.2356256274,loysiana:0.3748183946):0.1832017291):0.08522862529,guazuma:0.6432487489):0.04644117492,((placidus:0.1869579579,sheplani:0.1869579579):0.3773659809,(alayoni:0.3793818065,(angusticeps:0.2172126961,paternus:0.2172126961):0.1621691104):0.1849421323):0.125365985):0.07887044542,((alutaceus:0.120861969,inexpectatus:0.120861969):0.4042515809,(((clivicola:0.3359598029,(cupeyalensis:0.08606303065,cyanopleurus:0.08606303065):0.2498967723):0.1189736423,(alfaroi:0.2802339379,macilentus:0.2802339379):0.1746995073):0.0092278683,vanidicus:0.4641613135):0.06095223642):0.2434468193):0.09435314761,(argenteolus:0.6564331946,lucius:0.6564331946):0.2064803223):0.08237887432):0.01002346021):0.04468414858,(((bartschi:0.5247253674,vermiculatus:0.5247253674):0.249459768,((((baracoae:0.05853977536,(noblei:0.02140617522,smallwoodi:0.02140617522):0.03713360014):0.02849164237,luteogularis:0.08703141773):0.017899207,equestris:0.1049306247):0.6297194497,(((monticola:0.6055537678,(bahorucoensis:0.3841100683,(dolichocephalus:0.1509270933,hendersoni:0.1509270933):0.2331829749):0.2214436996):0.03149201716,darlingtoni:0.637045785):0.03288736013,(((aliniger:0.1783542747,singularis:0.1783542747):0.1377057507,chlorocyanus:0.3160600254):0.2135626601,coelestinus:0.5296226856):0.1403104596):0.0647169293):0.0395350609):0.1207482386,occultus:0.8949333739):0.1050666261);
2 |
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/hdvips.def:
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1 | %%
2 | %% This is file `hdvips.def',
3 | %% generated with the docstrip utility.
4 | %%
5 | %% The original source files were:
6 | %%
7 | %% hyperref.dtx (with options: `dvips')
8 | %%
9 | %% File: hyperref.dtx Copyright 1995-2001 Sebastian Rahtz,
10 | %% with portions written by David Carlisle and Heiko Oberdiek,
11 | %% 2001-2012 Heiko Oberdiek.
12 | %%
13 | %% This file is part of the `Hyperref Bundle'.
14 | %% -------------------------------------------
15 | %%
16 | %% This work may be distributed and/or modified under the
17 | %% conditions of the LaTeX Project Public License, either version 1.3
18 | %% of this license or (at your option) any later version.
19 | %% The latest version of this license is in
20 | %% http://www.latex-project.org/lppl.txt
21 | %% and version 1.3 or later is part of all distributions of LaTeX
22 | %% version 2005/12/01 or later.
23 | %%
24 | %% This work has the LPPL maintenance status `maintained'.
25 | %%
26 | %% The Current Maintainer of this work is Heiko Oberdiek.
27 | %%
28 | %% The list of all files belonging to the `Hyperref Bundle' is
29 | %% given in the file `manifest.txt'.
30 | %%
31 | \ProvidesFile{hdvips.def}
32 | [2012/08/13 v6.83a %
33 | Hyperref driver for dvips]
34 | \Hy@VersionCheck{hdvips.def}
35 | \providecommand*{\XR@ext}{pdf}
36 | \let\Hy@raisedlink\ltx@empty
37 | \def\literalps@out#1{\special{ps:SDict begin #1 end}}%
38 | \def\headerps@out#1{\special{! #1}}%
39 | \input{pdfmark.def}%
40 | \ifx\@pdfproducer\relax
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42 | \fi
43 | \providecommand*\@pdfborder{0 0 1}
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60 | /H.V {pdf@hoff pdf@voff null} def%
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63 | currentpoint %
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65 | dup DvipsToPDF 72 add /pdf@hoff exch def %
66 | HyperBorder sub /pdf@llx exch def%
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72 | currentpoint %
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77 | H.L %
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83 | /H.R {%
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104 | \special{papersize=\the\stockwidth,\the\stockheight}%
105 | \fi
106 | \fi
107 | }%
108 | \endgroup
109 | \fi
110 | \Hy@DisableOption{setpagesize}%
111 | }
112 | \endinput
113 | %%
114 | %% End of file `hdvips.def'.
115 |
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1 |
2 |
72 |
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/11_ggtree_exts-others.Rmd:
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1 | \newpage
2 |
3 | # Other ggtree Extensions {#chapter11}
4 |
5 | ```{r include=F}
6 | library(knitr)
7 | opts_chunk$set(message=FALSE, warning=FALSE, eval=TRUE, echo=TRUE, cache=TRUE)
8 | ```
9 |
10 | The `r Biocpkg("ggtree")` package is a general package for visualizing tree structures and associated data. If you have some special requirements that are not directly provided by `r Biocpkg("ggtree")`, you may need to use one of the extension packages built on top of `r Biocpkg("ggtree")`. For example, the `r CRANpkg("RevGadgets")` package for visualizing the output of the `r pkg_revbayes`, the `r Biocpkg("sitePath")` package for visualizing fixation events on phylogenetic pathways, and the `r Biocpkg("enrichplot")` package for visualizing hierarchical structure of the enriched pathways.
11 |
12 |
13 | ```{r revdep}
14 | rp <- BiocManager::repositories()
15 | db <- utils::available.packages(repo=rp)
16 | x <- tools::package_dependencies('ggtree', db=db,
17 | which = c("Depends", "Imports"),
18 | reverse=TRUE)
19 | print(x)
20 | ```
21 |
22 | There are `r length(unlist(x))` packages in CRAN or Bioconductor that depend on or import `r Biocpkg("ggtree")` and several packages on GitHub that extend `r Biocpkg("ggtree")`. Here we briefly introduce some extension packages, including `r Biocpkg("MicrobiotaProcess")` and `r Biocpkg("tanggle")`.
23 |
24 |
25 |
26 | ## Taxonomy Annotation Using MicrobiotaProcess {#MicrobiotaProcess}
27 |
28 | The `r Biocpkg("MicrobiotaProcess")` package provides a LEfSe-like algorithm [@segata_metagenomic_2011] to discover microbiome biomarkers by comparing taxon abundance between different classes. It provides several methods to visualize the analysis result. The `ggdiffclade` is developed based on `r Biocpkg("ggtree")` [@yu_ggtree:_2017]. In addition to the `diff_analysis()` result, it also supports a data frame that contains a hierarchical relationship (*e.g.*, [taxonomy annotation](#MicrobiotaProcess-taxonomy) or KEGG annotation) with another data frame that contains taxa and factor information and/or pvalue. The following example demonstrates how to use data frames (*i.e.*, analysis results) to visualize the differential taxonomy tree. More details can be found on the vignette of the `r Biocpkg("MicrobiotaProcess")` package.
29 |
30 |
31 | (ref:CRCdiffscap) Visualize differential taxonomy clade.
32 |
33 | (ref:CRCdiffcap) **Visualize differential taxonomy clade.**
34 |
35 | ```{r CRCdiffclade, fig.width=7, fig.height=7, message=FALSE, fig.cap="(ref:CRCdiffcap)", fig.scap="(ref:CRCdiffscap)", out.extra='', warning=FALSE, message=FALSE, results='hide'}
36 | library(MicrobiotaProcess)
37 | library(ggplot2)
38 | library(TDbook)
39 |
40 | # load `df_difftax` and `df_difftax_info` from TDbook
41 | taxa <- df_alltax_info
42 | dt <- df_difftax
43 |
44 | ggdiffclade(obj=taxa,
45 | nodedf=dt,
46 | factorName="DIAGNOSIS",
47 | layout="radial",
48 | skpointsize=0.6,
49 | cladetext=2,
50 | linewd=0.2,
51 | taxlevel=3,
52 | # This argument is to remove the branch of unknown taxonomy.
53 | reduce=TRUE) +
54 | scale_fill_manual(values=c("#00AED7", "#009E73"))+
55 | guides(color = guide_legend(keywidth = 0.1, keyheight = 0.6,
56 | order = 3,ncol=1)) +
57 | theme(panel.background=element_rect(fill=NA),
58 | legend.position="right",
59 | plot.margin=margin(0,0,0,0),
60 | legend.spacing.y=unit(0.02, "cm"),
61 | legend.title=element_text(size=7.5),
62 | legend.text=element_text(size=5.5),
63 | legend.box.spacing=unit(0.02,"cm")
64 | )
65 | ```
66 |
67 | The data frame of this example is from the analysis result of `diff_analysis()` using public datasets [@kostic2012genomic]. The `colors` represent the features enriched in the relevant class groups. The size of circle points represents the `-log10(pvalue)`, *i.e.*, a larger point indicates a greater significance. In Figure \@ref(fig:CRCdiffclade), we can find that *Fusobacterium* sequences were enriched in carcinomas, while Firmicutes, Bacteroides, and Clostridiales were greatly reduced in tumors. These results were consistent with the original article [@kostic2012genomic]. The species of *Campylobacter* has been proven to be associated with colorectal cancer [@He289; @wu2013dysbiosis; @amer2017microbiome]. We can find in Figure \@ref(fig:CRCdiffclade) that *Campylobacter* was enriched in tumors, while its relative abundance is lower than *Fusobacterium*.
68 |
69 |
70 | ## Visualizing Phylogenetic Network Using Tanggle
71 |
72 | The `r Biocpkg("tanggle")` package provides functions to display a split network. It extends the `r Biocpkg("ggtree")` package [@yu_ggtree:_2017] to allow the visualization of phylogenetic networks (Figure \@ref(fig:phylonetworx)).
73 |
74 |
75 | (ref:phylonetworxscap) Phylogenetic network.
76 |
77 | (ref:phylonetworxcap) **Phylogenetic network**.
78 |
79 | ```{r phylonetworx, fig.width=7, fig.height=7, message=FALSE, fig.cap="(ref:phylonetworxcap)", fig.scap="(ref:phylonetworxscap)", out.extra='', warning=FALSE}
80 | library(ggplot2)
81 | library(ggtree)
82 | library(tanggle)
83 |
84 | file <- system.file("extdata/trees/woodmouse.nxs", package = "phangorn")
85 | Nnet <- phangorn::read.nexus.networx(file)
86 |
87 | ggsplitnet(Nnet) +
88 | geom_tiplab2(aes(color=label), hjust=-.1)+
89 | geom_nodepoint(color='firebrick', alpha=.4) +
90 | scale_color_manual(values=rainbow(15)) +
91 | theme(legend.position="none") +
92 | ggexpand(.1) + ggexpand(.1, direction=-1)
93 | ```
94 |
95 |
96 | ## Summary {#summary11}
97 |
98 |
99 | The `r Biocpkg("ggtree")` is designed to support the grammar of graphics, allowing users to quickly explore phylogenetic data through visualization. When users have special needs and `r Biocpkg("ggtree")` does not provide them, it is highly recommended to develop extension packages to implement these missing functions. This is a good mechanism, and we also hope that `r Biocpkg("ggtree")` users can become a `r Biocpkg("ggtree")` community. In this way, more functions for special needs can be developed and shared among users. Everyone will benefit from it, and it's exciting that this is happening.
100 |
101 |
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/data-backup/tree_example_april2015/bar.csv:
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9810267_Madagascar_1998,3
19984123_Mexico_1998,7
20062313_Nepal_2006,8
970044_New,10
IB3277_Pakistan_2002,9
IB3300_Pakistan_2002,3
IB3374_Pakistan_2002,10
IB3488_Pakistan_2003,8
IB3507_Pakistan_2003,4
IB3580_Pakistan_2003,4
IB3599_Pakistan_2003,5
20052631_Peru_2005,2
273_Senegal_1973,4
1567_Senegal_1967,8
36224_Senegal_2003,3
20040880_SriLanka_2004,5
Ss046_Ref_1950,2
54178_Sweden_1945,6
54179_Sweden_1944,6
54181_Sweden_1945,9
54184_Sweden_1945,4
54190_Sweden_1945,6
54210_Sweden_1943,1
54216_Sweden_1946,1
54228_Sweden_1947,3
19904011_unk_1990,5
19910761_unk_1991,1
19911483_unk_1991,1
19920319_unk_1992,5
20003593_unk_2000,1
20010007_unk_2001,3
20011685_unk_2001,3
20021122_unk_2002,8
20040489_unk_2004,2
20041367_unk_2004,4
20071599_unk_2007,9
20081885_unk_2008,7
20051541_Uzbekistan_2005,7
IB1970_Vietnam_2001,2
IB1976_Vietnam_2002,9
IB1980_Vietnam_2002,2
IB1985_Vietnam_2002,1
IB1987_Vietnam_2002,3
IB1990_Vietnam_2003,2
IB1993_Vietnam_2003,3
IB1995_Vietnam_2003,2
IB1997_Vietnam_2003,1
IB2000_Vietnam_2003,10
IB2004_Vietnam_2003,7
IB2008_Vietnam_2003,4
IB2009_Vietnam_2003,3
IB2012_Vietnam_2001,3
IB2013_Vietnam_2001,5
IB2015_Vietnam_2002,3
IB2018_Vietnam_2002,8
IB2024_Vietnam_2002,5
IB2026_Vietnam_2003,1
#N/A,9
10083_HCMC_NA,7
10093_HCMC_NA,9
10102_HCMC_NA,8
10111_HCMC_NA,8
10115_HCMC_NA,4
10134_HCMC_NA,3
10135_HCMC_NA,5
10152_HCMC_NA,1
10159_HCMC_NA,7
10188_HCMC_NA,4
10014_HCMC_NA,5
10263_HCMC_NA,7
10320_HCMC_NA,6
10365_HCMC_NA,8
10021_HCMC_NA,4
10031_HCMC_NA,1
10035_HCMC_NA,2
10060_HCMC_NA,4
10063_HCMC_NA,10
10071_HCMC_NA,1
10073_HCMC_NA,6
--------------------------------------------------------------------------------
/data-backup/tree_example_april2015/res_genes.csv:
--------------------------------------------------------------------------------
1 | id,gyrA,pEK204,CT-X-M-15,pEG356,CTX-M-14,colicinPlasmid1,pMG828-3
KH09_HCMC_2009,4,0,0,0,0,5,0
KH11_HCMC_2009,4,0,0,0,0,5,0
DE0579_HCMC_2001,4,0,0,0,0,5,6
EG0159_HCMC_2007,4,0,0,0,0,5,6
KH54_HCMC_2010,4,0,0,0,0,5,6
EG0392_HCMC_2007,4,0,0,0,0,5,6
EG0372_HCMC_2007,4,0,0,0,0,5,6
KH02_HCMC_2009,4,0,0,0,0,5,6
EG0385_HCMC_2007,4,0,0,0,0,5,6
KH53_HCMC_2010,4,0,0,0,0,5,6
HUE58_HCMC_2010,4,0,0,0,0,5,6
EG0404_HCMC_2007,4,0,0,0,0,5,6
30010_HCMC_2009,4,0,0,0,0,5,6
KH27_HCMC_2009,4,0,0,14,15,5,6
KH20_HCMC_2009,4,0,0,14,15,5,6
KH26_HCMC_2009,4,0,0,14,15,5,6
KH24_HCMC_2009,4,0,0,14,15,5,6
KH15_HCMC_2009,4,0,0,0,0,5,6
KH18_HCMC_2009,4,0,0,0,0,5,6
KH23_HCMC_2009,4,0,0,0,0,5,6
20261_HCMC_2009,4,0,0,0,0,5,6
IB1997_Vietnam_2003,4,0,0,0,0,5,6
IB1987_Vietnam_2002,4,0,0,0,0,5,6
30293_HCMC_2009,4,7,8,14,15,5,0
KH55_HCMC_2010,4,0,0,0,0,5,0
10073_HCMC_2009,4,0,0,0,15,5,0
KH41_HCMC_2009,4,0,0,0,0,5,0
KH37_HCMC_2009,4,0,0,0,0,5,0
KH35_HCMC_2009,4,0,0,0,0,5,0
30450_HCMC_2010,4,0,0,0,0,5,0
DE1150_HCMC_2002,3,0,0,0,0,5,6
DE1336_HCMC_2002,3,0,0,0,0,5,6
DE1209_HCMC_2002,3,0,0,0,0,5,6
IB2024_Vietnam_2002,3,0,0,0,0,5,6
IB2026_Vietnam_2003,3,0,0,0,0,5,6
IB1995_Vietnam_2003,3,0,0,0,0,5,6
EG1015_HCMC_2009,2,7,8,0,0,0,0
10111_HCMC_2009,2,7,8,0,0,5,0
EG0129_HCMC_2007,2,0,0,0,0,5,0
EG0401_HCMC_2007,2,0,0,0,0,5,0
EG0383_HCMC_2007,2,0,0,0,0,5,0
EG0204_HCMC_2008,2,7,8,0,0,5,0
EG0255_HCMC_2008,2,0,0,0,0,5,0
30124_HCMC_2009,2,7,8,0,0,5,0
10115_HCMC_2009,2,7,8,0,0,5,0
10135_HCMC_2009,2,7,8,0,0,5,0
EG1001_HCMC_2008,2,7,8,0,0,5,0
EG1023_HCMC_2009,2,7,8,0,0,5,0
10134_HCMC_2009,2,7,8,0,0,5,0
10152_HCMC_2009,2,7,8,0,0,5,0
KH07_HCMC_2009,2,0,0,14,15,5,0
EG1008_HCMC_2008,2,7,8,0,0,5,0
EG1024_HCMC_2009,2,7,8,0,0,5,0
EG0393_HCMC_2007,2,0,0,0,0,5,0
20037_HCMC_2009,2,7,8,0,0,5,0
EG1004_HCMC_2008,2,7,8,0,0,5,0
KH10_HCMC_2009,2,0,0,0,0,5,0
KH19_HCMC_2009,2,0,0,14,15,5,0
30008_HCMC_2009,2,7,8,0,0,5,0
KH12_HCMC_2009,2,0,0,14,15,5,0
10083_HCMC_2009,2,7,8,0,0,5,0
30054_HCMC_2009,2,7,8,0,0,5,0
KH17_HCMC_2009,2,0,0,0,0,5,0
EG1025_HCMC_2009,2,7,8,0,0,5,0
KH21_HCMC_2009,2,0,0,0,0,5,0
10093_HCMC_2009,2,7,8,0,0,5,0
20070_HCMC_2009,2,7,8,0,0,5,0
30059_HCMC_2009,2,7,8,0,0,5,0
10102_HCMC_2009,2,7,8,0,0,5,0
20021_HCMC_2009,2,7,8,0,0,5,0
EG0430_HCMC_2008,2,7,8,0,0,5,0
EG0357_HCMC_2007,2,0,0,0,0,5,0
10159_HCMC_2009,2,7,8,0,0,5,0
KH30_HCMC_2009,2,0,0,14,15,5,0
20023_HCMC_2009,2,7,8,0,0,5,0
30037_HCMC_2009,2,7,8,0,0,5,0
30071_HCMC_2009,2,7,8,0,0,5,0
EG1026_HCMC_0,2,7,8,0,0,5,0
30100_HCMC_2009,2,7,8,0,0,5,0
20228_HCMC_2009,2,7,8,0,0,5,0
KH25_HCMC_2009,2,0,0,14,15,5,6
30371_HCMC_2009,2,7,8,0,0,5,6
KH05_HCMC_2009,2,0,0,0,0,5,6
KH06_HCMC_2009,2,0,0,14,15,5,6
20006_HCMC_2009,2,7,8,0,0,5,6
KH14_HCMC_2009,2,0,0,14,15,5,6
HUE55_HCMC_2010,2,0,0,0,0,5,6
EG0318_HCMC_2006,2,0,0,0,0,5,6
HUE29_HCMC_2009,2,0,0,0,0,5,6
HUE50_HCMC_2009,2,0,0,14,15,5,6
EG0315_HCMC_2006,2,0,0,0,0,5,6
EG0313_HCMC_2006,2,0,0,0,0,5,6
EG0467_HCMC_2008,2,0,0,0,0,5,6
KH04_HCMC_2009,2,0,0,14,15,5,6
KH43_HCMC_2010,2,7,0,14,15,5,6
EG0375_HCMC_2007,2,0,0,0,0,5,6
EG0365_HCMC_2007,2,0,0,0,0,5,6
EG0362_HCMC_2007,2,0,0,0,0,5,6
EG0369_HCMC_2007,2,0,0,0,0,5,6
30073_HCMC_2009,2,0,0,0,0,5,6
EG0394_HCMC_2007,2,0,0,0,0,5,6
EG0388_HCMC_2007,2,0,0,0,0,5,6
KH16_HCMC_2009,2,0,0,0,0,5,6
EG0395_HCMC_2007,2,7,8,0,0,5,6
EG0390_HCMC_2007,2,7,8,0,0,5,6
KH28_HCMC_2009,2,0,0,0,0,5,6
KH29_HCMC_2009,2,0,0,0,0,5,6
20094_HCMC_2009,2,0,0,0,15,5,6
KH13_HCMC_2009,2,0,0,0,0,5,6
HUE53_HCMC_2010,2,0,0,0,0,5,6
EG0309_HCMC_2006,2,0,0,0,0,5,6
EG0451_HCMC_2008,2,0,0,0,0,5,6
EG0425_HCMC_2008,2,0,0,0,0,5,6
30112_HCMC_2009,2,7,8,0,0,5,6
EG0472_HCMC_2008,2,7,8,0,0,5,6
HUE17_HCMC_2009,2,7,8,0,0,5,6
HUE22_HCMC_2009,2,7,8,0,0,5,6
KH45_HCMC_2010,2,7,8,0,15,5,6
10021_HCMC_2009,2,0,0,0,0,5,0
30366_HCMC_2009,2,0,0,0,0,5,0
KH33_HCMC_2009,2,0,0,0,0,5,0
KH40_HCMC_2009,2,0,0,0,15,5,0
KH32_HCMC_2009,2,0,0,14,15,5,0
KH34_HCMC_2009,2,0,0,14,15,5,0
30003_HCMC_2009,2,0,0,0,0,5,0
KH57_HCMC_2010,2,0,0,0,0,5,0
30451_HCMC_2010,2,0,0,0,0,5,0
HUE46_HCMC_2009,2,0,0,0,0,5,0
EG1017_HCMC_2009,2,7,8,0,0,5,0
EG1014_HCMC_2009,2,7,8,0,0,5,0
EG1018_HCMC_2009,2,7,8,0,0,5,0
EG1022_HCMC_2009,2,7,8,0,0,5,0
EG1019_HCMC_2009,2,7,8,0,0,5,0
10060_HCMC_2009,2,7,8,0,0,5,0
30174_HCMC_2009,2,7,8,0,0,5,0
EG1027_HCMC_0,2,7,8,0,0,5,0
10035_HCMC_2009,2,7,8,0,0,5,0
30162_HCMC_2009,2,7,8,0,0,5,0
10365_HCMC_2010,2,7,8,0,0,5,0
10320_HCMC_2010,2,7,8,0,0,5,0
20343_HCMC_2009,2,7,8,0,0,5,0
30164_HCMC_2009,2,7,8,0,0,5,0
EG1028_HCMC_0,2,7,8,0,0,5,0
EG1016_HCMC_2009,2,7,8,0,0,5,0
30172_HCMC_2009,2,7,8,0,0,5,0
HUE20_HCMC_2009,2,7,8,0,0,5,0
10063_HCMC_2009,2,7,8,0,0,5,0
10014_HCMC_2009,2,7,8,0,0,5,0
EG1020_HCMC_2009,2,7,8,0,0,5,0
EG1021_HCMC_2009,2,7,8,0,0,5,0
HUE19_HCMC_2009,2,7,8,0,0,5,0
20263_HCMC_2009,2,7,8,0,0,5,0
30169_HCMC_2009,2,7,8,0,0,5,0
EG1029_HCMC_0,2,7,8,0,0,5,0
30387_HCMC_2010,2,7,8,0,0,5,0
KH38_HCMC_2009,2,7,8,0,0,5,0
30233_HCMC_2009,2,7,8,0,0,5,0
10188_HCMC_2009,2,7,8,0,0,5,0
10263_HCMC_2009,2,7,8,0,0,5,0
DE0303_HCMC_2000,0,0,0,0,0,5,6
DE0816_HCMC_2001,0,0,0,0,0,5,6
DE0248_HCMC_2000,0,0,0,0,0,5,6
DE0685_HCMC_2001,0,0,0,0,0,5,6
DE0611_HCMC_2001,0,0,0,0,15,5,6
DE0489_HCMC_2000,0,0,0,0,0,5,6
DE0477_HCMC_2000,0,0,0,0,0,5,6
MS0110_HCMC_1996,0,0,0,0,0,5,6
DE0654_HCMC_2001,0,0,0,0,0,5,6
DE0900_HCMC_2001,0,0,0,0,0,5,6
DE0965_HCMC_2001,0,0,0,0,0,5,6
DE1140_HCMC_2002,0,0,0,0,0,5,6
DE0846_HCMC_2001,0,0,0,0,0,5,6
IB2008_Vietnam_2003,0,0,0,0,0,5,6
IB2000_Vietnam_2003,0,0,0,0,0,5,6
DE1318_HCMC_2002,0,0,0,0,0,5,6
MS0128_HCMC_1996,0,0,0,0,0,5,6
DE0127_HCMC_2000,0,0,0,0,0,5,6
DE1256_HCMC_2002,0,0,0,0,0,5,6
DE0115_HCMC_2000,0,0,0,0,0,5,6
DE0427_HCMC_2000,0,0,0,0,0,5,6
DE0295_HCMC_2000,0,0,0,0,0,5,6
IB2004_Vietnam_2003,0,0,0,0,0,5,6
IB1970_Vietnam_2001,0,0,0,0,0,5,6
IB2009_Vietnam_2003,0,0,0,0,0,5,6
IB2015_Vietnam_2002,0,0,0,0,0,5,6
DE0199_HCMC_2000,0,0,0,0,0,5,6
DE1486_HCMC_2002,0,0,0,0,0,5,6
DE1198_HCMC_2002,0,0,0,0,0,5,6
DE0306_HCMC_2000,0,0,0,0,0,5,6
DE1165_HCMC_2002,0,0,0,0,0,5,6
IB2018_Vietnam_2002,0,0,0,0,0,5,6
IB1993_Vietnam_2003,0,0,0,0,0,5,6
IB1990_Vietnam_2003,0,0,0,0,0,5,6
DE1404_HCMC_2002,0,0,0,0,0,5,6
EG0304_HCMC_2006,0,0,0,0,0,5,6
EG0410_HCMC_2007,0,0,0,0,0,5,6
EG0379_HCMC_2007,0,0,0,0,0,5,6
DE1063_HCMC_2002,0,0,0,0,0,5,6
DE1191_HCMC_2002,0,0,0,0,0,5,6
DE1208_HCMC_2002,0,0,0,0,0,5,6
EG0386_HCMC_2007,0,0,0,0,0,5,6
KH42_HCMC_2010,0,0,0,0,15,5,6
HUE25_HCMC_2009,0,0,0,0,0,5,6
EG0373_HCMC_2007,0,7,8,0,0,5,6
HUE26_HCMC_2009,0,0,0,0,0,5,6
HUE47_HCMC_2009,0,0,0,0,0,5,6
HUE16_HCMC_2009,0,0,0,0,0,5,6
HUE23_HCMC_2009,0,0,0,0,0,5,6
HUE21_HCMC_2009,0,0,0,0,0,5,6
HUE67_HCMC_2010,0,0,0,0,0,5,6
HUE64_HCMC_2010,0,0,0,0,0,5,6
HUE27_HCMC_2009,0,0,0,0,0,5,6
HUE24_HCMC_2009,0,0,0,0,0,5,6
HUE48_HCMC_2009,0,0,0,0,0,5,6
HUE60_HCMC_2010,0,0,0,0,0,5,6
HUE57_HCMC_2010,0,0,0,0,0,5,6
HUE68_HCMC_2010,0,0,0,0,0,5,6
HUE62_HCMC_2010,0,0,0,0,0,5,6
HUE43_HCMC_2009,0,0,0,0,0,5,0
HUE33_HCMC_2009,0,0,0,0,15,5,0
10071_HCMC_2009,0,0,0,0,0,5,0
10031_HCMC_2009,0,0,0,0,0,5,0
HUE30_HCMC_2009,0,0,0,0,0,5,0
HUE01_HCMC_2008,0,0,0,0,0,5,0
HUE02_HCMC_2008,0,0,0,0,0,5,0
HUE05_HCMC_2008,0,0,0,0,0,5,0
HUE34_HCMC_2009,0,0,0,0,15,5,0
HUE32_HCMC_2009,0,0,0,0,0,5,0
HUE40_HCMC_2009,0,0,0,0,0,5,0
HUE42_HCMC_2009,0,0,0,0,0,5,0
HUE31_HCMC_2009,0,0,0,0,0,5,0
DE0655_HCMC_2001,0,0,0,0,0,0,0
MS0111_HCMC_1996,0,0,0,0,0,0,0
MS0063_HCMC_1995,0,0,0,0,0,0,0
DE0490_HCMC_2000,4,0,0,0,0,0,0
DE0885_HCMC_2001,4,0,0,0,0,0,0
DE0891_HCMC_2001,4,0,0,0,0,0,0
MS0119_HCMC_1996,0,0,0,0,0,0,0
MS0069_HCMC_1995,0,0,0,0,0,0,0
MS0065_HCMC_1995,0,0,0,0,0,0,0
MS0070_HCMC_1995,0,0,0,0,0,0,0
MS0048_HCMC_1995,0,0,0,0,0,0,0
MS0083_HCMC_1996,0,0,0,0,0,0,0
MS0102_HCMC_1996,0,0,0,0,0,0,0
MS0032_HCMC_1995,0,0,0,0,0,0,0
MS0034_HCMC_1995,0,0,0,0,0,0,0
MS0039_HCMC_1995,0,0,0,0,0,0,0
MS0004_HCMC_1995,0,0,0,0,0,0,0
--------------------------------------------------------------------------------
/A-app-fig-tab.Rmd:
--------------------------------------------------------------------------------
1 | \newpage
2 |
3 |
4 | # Figures and Tables
5 |
6 |
7 |
8 | ```{r facet-geom, echo=FALSE,results='asis'}
9 |
10 | x <- "ggalt\tgeom_dumbbell\tcreates dumbbell charts\n
11 | ggbio\tgeom_alignment\tshows interval data as alignment\n
12 | ggfittext\tgeom_fit_text\tshrinks, grows, or wraps text to fit inside a defined rectangular area\n
13 | gggenes\tgeom_gene_arrow\tdraws genes as arrows\n
14 | ggimage\tgeom_image\tvisualizes image files\n
15 | ggimage\tgeom_phylopic\tqueries image files from the PhyloPic database and visualizes them\n
16 | ggplot2\tgeom_hline\tadds horizontal lines\n
17 | ggplot2\tgeom_jitter\tadds a small amount of random variation to the location of each point\n
18 | ggplot2\tgeom_label\tdraws a rectangle behind the text\n
19 | ggplot2\tgeom_point\tcreates scatterplots\n
20 | ggplot2\tgeom_raster\ta high-performance special case for all the tiles that are the same size\n
21 | ggplot2\tgeom_rect\tdraws rectangle by using the locations of the four corners\n
22 | ggplot2\tgeom_segment\tdraws a straight line between points\n
23 | ggplot2\tgeom_spoke\ta polar parameterization of `geom_segment()'\n
24 | ggplot2\tgeom_text\tadds text to the plot\n
25 | ggplot2\tgeom_tile\tdraws rectangle by using the center of the tile and its size\n
26 | ggplot2\tgeom_vline\tadds vertical lines\n
27 | ggrepel\tgeom_text_repel\tadds text to the plot. The text labels repel away from each other and away from the data points\n
28 | ggrepel\tgeom_label_repel\tdraws a rectangle underneath the text. The text labels repel away from each other and away from the data points\n
29 | ggridges\tgeom_density_ridges\tarranges multiple density plots in a staggered fashion\n
30 | ggridges\tgeom_density_ridges_gradient\tworks just like `geom_density_ridges' except that the `fill' aesthetic can vary along the *x*-axis\n
31 | ggridges\tgeom_ridgeline\tplots the sum of the `y' and `height' aesthetics vs. `x', filling the area between `y' and `y + height' with a color\n
32 | ggridges\tgeom_ridgeline_gradient\tworks just like `geom_ridgeline' except that the `fill' aesthetic can vary along the *x*-axis\n
33 | ggstance\tgeom_barh\thorizontal version of `geom_bar()'\n
34 | ggstance\tgeom_boxploth\thorizontal version of `geom_boxplot()'\n
35 | ggstance\tgeom_crossbarh\thorizontal version of `geom_crossbar()'\n
36 | ggstance\tgeom_errorbarh\thorizontal version of `geom_errorbarh()'\n
37 | ggstance\tgeom_histogramh\thorizontal version of `geom_histogram()'\n
38 | ggstance\tgeom_linerangeh\thorizontal version of `geom_linerange()'\n
39 | ggstance\tgeom_pointrangeh\thorizontal version of `geom_pointrange()'\n
40 | ggstance\tgeom_violinh\thorizontal version of `geom_violin()'\n
41 | ggtree\tgeom_motif\tdraws aligned motifs\n
42 | "
43 |
44 | if (!knitr::is_latex_output()) {
45 | x <- gsub("`", "'", x)
46 | }
47 |
48 | xx <- strsplit(x, "\n\n")[[1]]
49 | y <- strsplit(xx, "\t") %>% do.call("rbind", .)
50 | y <- as.data.frame(y)
51 | colnames(y) <- c("Package", "Geom Layer", "Description")
52 |
53 | require(kableExtra)
54 |
55 | if (knitr::is_latex_output()) {
56 | caption = "Geometric layers that supported by, `geom\\textunderscore facet()'"
57 | } else {
58 | caption = "Geometric layers that supported by `geom_facet()'"
59 | }
60 |
61 |
62 | knitr::kable(y, caption=caption, booktabs = T) %>%
63 | collapse_rows(columns = 1, latex_hline = "major", valign ="top") %>%
64 | kable_styling(latex_options = c("striped", "scale_down", "hold_position"),
65 | bootstrap_options = c("striped", "hover")) %>% landscape
66 | ```
67 |
68 |
69 |
70 | ```{r tree-objects, echo=FALSE,results='asis'}
71 | x <- tibble::tribble(
72 | ~Package, ~Object, ~Description,
73 | "ape", "phylo", "basic phylogenetic tree structure",
74 | "ape", "multiPhylo", "list of phylo objects",
75 | "ade4", "phylog", "tree structure for ecological data",
76 | "phylobase", "phylo4", "S4 version of phylo object",
77 | "phylobase", "phylo4d", "extend phylo4 with trait data",
78 | "phyloseq", "phyloseq", "phylogenetic tree with microbiome data",
79 | "tidytree", "tbl_tree", "phylogenetic tree as a tidy data frame",
80 | "treeio", "treedata", "phylogenetic tree with heterogeneous associated data",
81 | "treeio", "jplace", "treedata object with placement information",
82 | "stats", "hclust", "hierarchical cluster result",
83 | "stats", "dendrogram", "hierarchical clustering or classification/regression tree",
84 | "cluster", "agnes", "agglomerative hierarchical clustering",
85 | "cluster", "diana", "divisive hierarchical clustering",
86 | "cluster", "twins", "agglomerative or divisive (polythetic) hierarchical clustering",
87 | "pvclust", "pvclust", "hierarchical clustering with p-values calculated by multiscale bootstrap resampling",
88 | "igraph", "igraph", "network (currently only tree graph supported)"
89 | )
90 |
91 |
92 | y <- as.data.frame(x)
93 |
94 | require(kableExtra)
95 |
96 |
97 | caption = "Tree-like objects supported by ggtree"
98 |
99 |
100 |
101 | knitr::kable(y, caption=caption, booktabs = T) %>%
102 | collapse_rows(columns = 1, latex_hline = "major", valign ="top") %>%
103 | kable_styling(latex_options = c("striped", "scale_down"),
104 | bootstrap_options = c("striped", "hover")) #%>% landscape
105 |
106 | ```
107 |
108 |
109 |
148 |
149 |
150 | ```{r, child="publications.md"}
151 | ```
152 |
153 |
154 |
155 | ```{r, eval = FALSE, child="session-info.Rmd"}
156 | ```
157 |
158 |
159 |
--------------------------------------------------------------------------------
/index.Rmd:
--------------------------------------------------------------------------------
1 | ---
2 | title: 'Data Integration, Manipulation and Visualization of Phylogenetic Trees'
3 | author: Guangchuang Yu
4 | date: "`r Sys.Date()`"
5 | knit: "bookdown::render_book"
6 | site: bookdown::bookdown_site
7 | header-includes:
8 | - \usepackage{makeidx}
9 | - \makeindex
10 | documentclass: book
11 | classoption: numberinsequence,twoside
12 | bibliography: references.bib
13 | csl: apa.csl
14 | always_allow_html: yes
15 | toc_appendix: yes
16 | toc-depth: 2
17 | link-citations: yes
18 | colorlinks: true
19 | language:
20 | ui:
21 | chapter_name: "Chapter "
22 | lot: yes
23 | lof: yes
24 | fontsize: 11pt
25 | monofont: "Source Code Pro"
26 | monofontoptions: "Scale=0.7"
27 | github-repo: YuLab-SMU/treedata-book
28 | twiter-handle: guangchuangyu
29 | description: "Master ggtree package suite to handle tree with data."
30 | highlight_bw: yes
31 | graphics: yes
32 | #papersize: a4
33 | geometry: "left=35mm,right=35mm,top=25mm,bottom=25mm"
34 | cover-image: book-cover.png
35 | ---
36 |
37 |
38 | ```{r include=FALSE}
39 | source("setup.R")
40 |
41 | options(
42 | htmltools.dir.version = FALSE, formatR.indent = 2, width = 55, digits = 4
43 | )
44 |
45 | options(tinytex.clean = FALSE)
46 |
47 | ## manual setting. the ropenscilabs/icons package will set up everything for you.
48 | ## fontawesome supports
49 | ## htmltools::tagList(rmarkdown::html_dependency_font_awesome())
50 | ## icons::download_fontawesome()
51 |
52 | library(knitr)
53 | #knit_hooks$set(crop = hook_pdfcrop)
54 |
55 | opts_chunk$set(message=FALSE, warning=FALSE, eval=TRUE,
56 | echo=TRUE, cache=TRUE, out.width="98%",
57 | fig.process=svg2png)
58 |
59 | if (knitr::is_latex_output()) {
60 | opts_chunk$set(dev = "cairo_pdf")
61 | } else {
62 | opts_chunk$set(dev = "svg")
63 | }
64 |
65 | ```
66 |
67 |
68 |
69 |
70 |
81 |
82 | \newpage
83 | \frontmatter
84 | # Preface {-}
85 |
86 |
87 | ```{asis, echo=identical(knitr:::pandoc_to(), 'html')}
88 | **Note**: This book has been published by [Chapman & Hall/CRC](https://www.routledge.com/Data-Integration-Manipulation-and-Visualization-of-Phylogenetic-Trees/Yu/p/book/9781032233574). The online version of this book is free to read here (thanks to Chapman & Hall/CRC), and licensed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/).
89 |
90 | ```
91 |
92 |
93 |
94 | I am so excited to have this book published. The book is meant as a guide for data integration, manipulation and visualization of phylogenetic trees using a suite of R packages, `r CRANpkg("tidytree")`, `r Biocpkg("treeio")`, `r Biocpkg("ggtree")` and `r Biocpkg("ggtreeExtra")`. Hence, if you are starting to read this book, we assume you have a working knowledge of how to use R and `r CRANpkg("ggplot2")`.
95 |
96 | The development of the `r Biocpkg("ggtree")` package started during my PhD study at the University of Hong Kong. I joined the State Key Laboratory of Emerging Infectious Diseases (SKLEID) under the supervision of Yi Guan and Tommy Lam. I was asked to provide assistance to modify the newick tree string to incorporate some additional information, such as amino acid substitutions, in the internal node labels of the phylogeny for visualization. I wrote an R script to do it and soon realized that most phylogenetic tree visualization software can only display one type of data through node labels. Basically, we cannot display two data variables at the same time for comparative analysis. In order to produce tree graphs displaying different types of branch/node associated information, such as bootstrap values and substitutions, people mostly relied on post-processing image software. This situation motivates me to develop `r Biocpkg("ggtree")`. First of all, I think a good user interface must fully support the `r CRANpkg("ggplot2")` syntax, which allows us to draw graphs by superimposing layers. In this way, simple graphs are simple, and complex graphs are just a combination of simple layers, which are easy to generate.
97 |
98 | After several years of development, `r Biocpkg("ggtree")` has evolved into a package suite, including `r CRANpkg("tidytree")` for manipulating tree with data using the tidy interface; `r Biocpkg("treeio")` for importing and exporting tree with richly annotated data; `r Biocpkg("ggtree")` for tree visualization and annotation and `r Biocpkg("ggtreeExtra")` for presenting data with a phylogeny side-by-side for a rectangular layout or in outer rings for a circular layout. The `r Biocpkg("ggtree")` is a general tool that supports different types of tree and tree-like structures and can be applied to different disciplines to help researchers presenting and interpreting data in the evolutionary or hierarchical context.
99 |
100 |
101 | ## Structure of the book {-}
102 |
103 |
104 | + Part I (Tree data input, output and manipulation) describes `r Biocpkg("treeio")` package for tree data input and output, and `r CRANpkg("tidytree")` package for tree data manipulation.
105 | + Part II (Tree data visualization and annotation) introduces tree visualization and annotation using the grammar of graphic syntax implemented in the `r Biocpkg("ggtree")` package. It emphasizes presenting tree-associated data on the tree.
106 | + Part III (ggtree extensions) introduces `r Biocpkg("ggtreeExtra")` for presenting data on circular layout trees and other extensions including `r Biocpkg("MicrobiotaProcess")` and `r Biocpkg("tanggle")` *etc*.
107 | + Part IV (Miscellaneous topics) describes utilities provided by the `r Biocpkg("ggtree")` package suite and presents a set of reproducible examples.
108 |
109 |
110 | ```{r, child="software-info.md"}
111 | ```
112 |
113 | ## Acknowledgments {-}
114 |
115 | Many people have contributed to this book with spelling and grammar corrections. I'd particularly like to thank Shuangbin Xu, Lin Li and Xiao Luo for their detailed technical reviews of the book, and Tiao You for designing the front cover of the book.
116 |
117 | Many others have contributed during the development of the `r Biocpkg("ggtree")` package suite. I would like to thank Hadley Wickham, for creating the `r CRANpkg("ggplot2")` package that `r Biocpkg("ggtree")` relies on; Tommy Tsan-Yuk Lam and Yi Guan for being great advisors and supporting the development of `r Biocpkg("ggtree")` during my PhD; Richard Ree for inviting me to catalysis meeting on phylogenetic tree visualization; William Pearson for inviting me to publish a protocol paper of `r Biocpkg("ggtree")` in the *Current Procotols in Bioinformatics* journal; Shuangbin Xu, Yonghe Xia, Justin Silverman, Bradley Jones, Watal M. Iwasaki, Ruizhu Huang, Casey Dunn, Tyler Bradley, Konstantinos Geles, Zebulun Arendsee and many others who have contributed source code or given me feedback; and last, but not least, the members of the `r Biocpkg("ggtree")` mailing list^[], for providing many challenging problems that have helped improve the `r Biocpkg("ggtree")` package suite.
118 |
--------------------------------------------------------------------------------
/data-backup/tree_example_april2015/tree.nwk:
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1 | 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2 |
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/A-app-tools.Rmd:
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1 |
2 | \newpage
3 |
4 |
5 | # Related Tools {#related-tools}
6 |
7 | ## MicrobiotaProcess: Convert Taxonomy Table to a `treedata` Object {#MicrobiotaProcess-taxonomy}
8 |
9 | Taxonomy (genus, family, ...) data are widely used in microbiome or ecology. Hierarchical taxonomies are the tree-like structure that organizes items into subcategories and can be converted to a tree object (see also the [phylog object](#phylog)). The `r Biocpkg("MicrobiotaProcess")` supports converting a `taxonomyTable` object, defined in the `r Biocpkg("phyloseq")` package, to a `treedata` object, and the taxonomic hierarchical relationship can be visualized using `r Biocpkg("ggtree")` (Figure \@ref(fig:TaxaTree)). When there are taxonomy names that are [confused and missing](https://github.com/YuLab-SMU/MicrobiotaProcess/issues/14), the `as.treedata()` method for `taxonomyTable` objects will complete their upper-level taxonomic information automatically.
10 |
11 |
12 |
13 | (ref:TaxaToTreescap) Convert a `taxonomyTable` object to a `treedata` object.
14 |
15 | (ref:TaxaToTreecap) **Convert a `taxonomyTable` object to a `treedata` object.**
16 |
17 | ```{r TaxaTree, fig.widht=8, fig.height=8, fig.cap="(ref:TaxaToTreecap)", fig.scap="ref:TaxaToTreescap"}
18 | library(MicrobiotaProcess)
19 | library(ggtree)
20 |
21 | # The original kostic2012crc is a MPSE object
22 | data(kostic2012crc)
23 |
24 | taxa <- tax_table(kostic2012crc)
25 | #The rownames (usually is OTUs or other features ) of the taxa will be
26 | # served as the tip labels if include.rownames = TRUE
27 | tree <- as.treedata(taxa, include.rownames=TRUE)
28 | # Or extract the taxa tree (treedata) with mp_extract_tree, because the
29 | # taxonomy information is stored as treedata in the MPSE class (kostic2012crc).
30 | # tree <- kostic2012crc %>% mp_extract_tree()
31 |
32 | ggtree(tree, layout="circular", size=0.2) +
33 | geom_tiplab(size=1)
34 | ```
35 |
36 | ## rtol: An R Interface to Open Tree API {#rtol}
37 |
38 | The `r CRANpkg("rtol")` [@michonneau_rotl:_2016] is an R package to interact with the Open Tree of Life data APIs. Users can use it to query phylogenetic trees and visualize the trees with `r Biocpkg("ggtree")` to explore species relationships (Figure \@ref(fig:rotlSubtree)).
39 |
40 | (ref:rotlSubtreescap) Get an induced subtree from the big Open Tree.
41 |
42 | (ref:rotlSubtreecap) **Get an induced subtree from the big Open Tree.**
43 |
44 |
45 |
46 | ```{r rotlSubtree, fig.width=8, fig.height=6, fig.cap="(ref:rotlSubtreecap)", fig.scap="(ref:rotlSubtreescap)", out.width='100%'}
47 | ## example from: https://github.com/ropensci/rotl
48 | library(rotl)
49 | apes <- c("Pongo", "Pan", "Gorilla", "Hoolock", "Homo")
50 | (resolved_names <- tnrs_match_names(apes))
51 | tr <- tol_induced_subtree(ott_ids = ott_id(resolved_names))
52 | ggtree(tr) + geom_tiplab() + xlim(NA, 5)
53 | ```
54 |
55 |
56 |
57 | ```{r, eval = !knitr::is_latex_output(), child="ggtree-plotly.Rmd"}
58 | ```
59 |
60 |
61 | ```{r, eval = !knitr::is_latex_output(), child="ggtree-comicR.Rmd"}
62 | ```
63 |
64 |
65 | ## Print ASCII-art Rooted Tree {#ascii-tree}
66 |
67 | ```{r asciiTree, comment=NA}
68 | library(data.tree)
69 | tree <- rtree(10)
70 | d <- as.data.frame(as.Node(tree))
71 | names(d) <- NULL
72 | print(d, row.names=FALSE)
73 | ```
74 |
75 | It is neat to print ASCII-art of the phylogeny. Sometimes, we don't want to plot the tree, but just take a glance at the tree structure without leaving the focus from the R console. However, it is not a good idea to print the whole tree as ASCII text if the tree is large. Sometimes, we just want to look at a specific portion of the tree and its immediate relatives. In this scenario, we can use `treeio::tree_subset()` function (see [session 2.4](subsetting-tree-with-data)) to extract selected portion of a tree. Then we can print ASCII-art of the tree subset to explore the evolutionary relationship of the species of our interest in the R console.
76 |
77 | The `r Biocpkg("ggtree")` supports parsing tip labels as emoji to create [phylomoji](#phylomoji). With the `r CRANpkg("data.tree")` and `r CRANpkg("emojifont")` packages, we can also print phylomoji as ASCII text (Figure \@ref(fig:emojidatatree)).
78 |
79 |
80 |
81 | ```{r asciiTreeEmoji, comment=NA, eval=FALSE}
82 | library(data.tree)
83 | library(emojifont)
84 |
85 | tt <- '((snail,mushroom),(((sunflower,evergreen_tree),leaves),green_salad));'
86 | tree <- read.tree(text = tt)
87 | tree$tip.label <- emoji(tree$tip.label)
88 | d <- as.data.frame(as.Node(tree))
89 | names(d) <- NULL
90 | print(d, row.names=FALSE)
91 | ```
92 |
93 |
94 | (ref:emojidatatreescap) Print phylomoji as ASCII text.
95 |
96 | (ref:emojidatatreecap) **Print phylomoji as ASCII text.**
97 |
98 |
99 |
100 | ```{r emojidatatree, echo=FALSE, fig.cap="(ref:emojidatatreecap)", fig.scap="(ref:emojidatatreescap)", out.width='100%'}
101 | knitr::include_graphics("img/data-tree-emojim.png")
102 | ```
103 |
104 | Another way to print ASCII-art of phylogeny is to use the `ascii()` device defined in the `r pkg_devout` package. Here is an example:
105 |
106 | ```{r devout-ascii, comment=NA}
107 | library(devout)
108 | ascii(width=80)
109 | ggtree(rtree(5))
110 | invisible(dev.off())
111 | ```
112 |
113 |
114 | ## Zoom in on the Selected Portion {#facet-zoom}
115 |
116 |
117 | In addition to using `viewClade()` function, users can use the `r CRANpkg("ggforce")` package to zoom in on a selected clade (Figure \@ref(fig:facetZoom)).
118 |
119 |
120 | (ref:facetZoomscap) Zoom in on a selected clade.
121 |
122 | (ref:facetZoomcap) **Zoom in on a selected clade.**
123 |
124 |
125 | ```{r facetZoom, fig.width=6, fig.height=4, fig.cap="(ref:facetZoomcap)", fig.scap="(ref:facetZoomscap)"}
126 | set.seed(2019-08-05)
127 | x <- rtree(30)
128 | nn <- tidytree::offspring(x, 43, self_include=TRUE)
129 | ggtree(x) + ggforce::facet_zoom(xy = node %in% nn)
130 | ```
131 |
132 |
133 | ## Tips for Using `ggtree` with `ggimage` {#ggimage-tips}
134 |
135 | The `r Biocpkg("ggtree")` supports annotating a tree with silhouette images via the `r CRANpkg("ggimage")` package. The `r CRANpkg("ggimage")` provides the grammar of graphic syntax to work with image files. It allows processing images on the fly via the `image_fun` parameter, which accepts a function to process `magick-image` objects (Figure \@ref(fig:ggimagebg)). The `r CRANpkg("magick")` package provides several functions, and these functions can be combined to perform a particular task.
136 |
137 | ### Example 1: Remove background of images {#ggimage-rm-image-bg}
138 |
139 | (ref:ggimagebgscap) Remove image background.
140 |
141 | (ref:ggimagebgcap) **Remove image background.** Plotting silhouette images on a phylogenetic tree with background not removed (A) and removed (B).
142 |
143 |
144 | ```{r ggimagebg, fig.width=8, fig.height=4, fig.cap="(ref:ggimagebgcap)", fig.scap="(ref:ggimagebgscap)", out.width='100%'}
145 | library(ggimage)
146 |
147 | imgdir <- system.file("extdata/frogs", package = "TDbook")
148 |
149 | set.seed(1982)
150 | x <- rtree(5)
151 | p <- ggtree(x) + theme_grey()
152 | p1 <- p + geom_nodelab(image=paste0(imgdir, "/frog.jpg"),
153 | geom="image", size=.12) +
154 | ggtitle("original image")
155 | p2 <- p + geom_nodelab(image=paste0(imgdir, "/frog.jpg"),
156 | geom="image", size=.12,
157 | image_fun= function(.) magick::image_transparent(., "white")) +
158 | ggtitle("image with background removed")
159 | plot_grid(p1, p2, ncol=2)
160 | ```
161 |
162 | ### Example 2: Plot tree on a background image {#ggimage-bgimage}
163 |
164 | The `geom_bgimage()` adds a layer of the image and puts the layer to the bottom of the layer stack. It is a normal layer and doesn't change the structure of the output `ggtree` object. Users can add annotation layers without the background image layer (Figure \@ref(fig:bgimage)).
165 |
166 | (ref:bgimagescap) Use an image file as a tree background.
167 |
168 | (ref:bgimagecap) **Use an image file as a tree background.**
169 |
170 |
171 | ```{r bgimage, fig.width=6, fig.height=4, fig.cap="(ref:bgimagecap)", fig.scap="(ref:bgimagescap)"}
172 | ggtree(rtree(20), size=1.5, color="white") +
173 | geom_bgimage('img/blackboard.jpg') +
174 | geom_tiplab(color="white", size=5, family='xkcd')
175 | ```
176 |
177 |
178 | ## Run ggtree in Jupyter Notebook
179 |
180 | If you have [Jupyter notebook](https://jupyter.org/) installed on your system, you can install [IRkernel](https://irkernel.github.io/) with the following command in R:
181 |
182 | ```r
183 | install.packages("IRkernel")
184 | IRkernel::installspec()
185 | ```
186 |
187 | Then you can use `r Biocpkg("ggtree")` and other R packages in the Jupyter notebook (Figure \@ref(fig:jupyter)). Here is a screenshot of recreating Figure \@ref(fig:phylomoji1) in the Jupyter notebook.
188 |
189 | (ref:jupyterscap) ggtree in Jupyter notebook.
190 |
191 | (ref:jupytercap) **ggtree in Jupyter notebook.** Running ggtree in Jupyter notebook via R kernel.
192 |
193 |
194 | ```{r jupyter, echo=F, fig.cap="(ref:jupytercap)", fig.scap="(ref:jupyterscap)", out.width='100%'}
195 | ## htmltools::includeHTML("img/ggtree_jupyter.html")
196 | knitr::include_graphics("img/Screenshot_2019-06-24_ggtree-jupyter.png")
197 | ```
198 |
199 |
200 |
201 |
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/others.md:
--------------------------------------------------------------------------------
1 |
2 |
3 |
4 | ## to do
5 | https://github.com/YuLab-SMU/ggtree/pull/434
6 | https://github.com/YuLab-SMU/ggtree/issues/367
7 |
8 | ggtree + deeptime can do something like this, .
9 |
10 | + [ introduce align argument in geom_hilight #431 ](https://github.com/YuLab-SMU/ggtree/pull/431)
11 | + [geom_facet() support discrete scale](https://github.com/YuLab-SMU/ggtree/issues/351)
12 | + [geom_cladelab: the devel version of geom_cladelabel to support aes mapping](https://github.com/YuLab-SMU/ggtree/pull/342)
13 | + [add horizontal parameter in geom_cladelabel](https://github.com/YuLab-SMU/ggtree/pull/343)
14 | + [branch size can be grandually changed](https://github.com/YuLab-SMU/ggtree/pull/349)
15 | + [add offset.label and label to geom_treescale](https://github.com/YuLab-SMU/ggtree/pull/360)
16 | + [fix extendto argument for geom_highlight when layout="inward_circular" or "dendrogram"](379)
17 | + [added new default tip.order to ggdensitree that is more similar to DensiTree's](382)
18 | + [update stat method of tree layout](385)
19 | + [ remove inhibit.size and use continuous replace](https://github.com/YuLab-SMU/ggtree/pull/387)
20 | + [The optimization of internal function of geom_tiplab](https://github.com/YuLab-SMU/ggtree/pull/392)
21 | + [optimize the angle of text in geom_cladelab and don't inherit the global aes in geom_cladelab and geom_taxalink](https://github.com/YuLab-SMU/ggtree/pull/396)
22 | + [fix ape layout branch.length](https://github.com/YuLab-SMU/ggtree/pull/403)
23 | + [update geom_tiplab for better compatibility with geom_nodelab](https://github.com/YuLab-SMU/ggtree/pull/406)
24 | + [new geom_nodelab and geom_tiplab](https://github.com/YuLab-SMU/ggtree/pull/408)
25 | + [Update geom_range.R to fix issue #306](https://github.com/YuLab-SMU/ggtree/pull/410)
26 | + [optimizing geom_range to support the mapping of color, size.](https://github.com/YuLab-SMU/ggtree/pull/411)
27 | + [Add branch.y (and branch.x = branch) in calculate_branch_mid() for positioning on branches in unrooted layouts](https://github.com/YuLab-SMU/ggtree/pull/412)
28 | + [add branch.x and branch.y for unrooted layout](https://github.com/YuLab-SMU/ggtree/pull/414)
29 | + [fixed fontface of aes](https://github.com/YuLab-SMU/ggtree/pull/418)
30 | + [add td_mutate and geom_hilight, geom_cladelab supporting fuction data](https://github.com/YuLab-SMU/ggtree/pull/421)
31 | + [fix the issue of data argument of geom_tiplab](https://github.com/YuLab-SMU/ggtree/pull/426)
32 | + [options to control the line type of radial](https://github.com/YuLab-SMU/ggtree/pull/427)
33 | + [ data argument of geom_facet supporting function #430 ](https://github.com/YuLab-SMU/ggtree/pull/430)
34 | + [add `select`, `filter`, `mutate`, `left_join`, `unnest`, `pull` and `rename` verbs for `treedata` object](https://github.com/YuLab-SMU/tidytree/pull/19)
35 |
36 |
37 |
38 |
39 |
40 |
41 | geom_zoom_clade
42 |
43 | ggtree for pvclust
44 |
45 |
46 | + [ggtree - updating a tree view](https://mp.weixin.qq.com/s/csZUfzoluTkXp9DxYR7w6g)
47 |
48 |
49 | ----------------
50 |
51 |
52 | 写一段ggtree画思维导图的例子。
53 |
54 | x = tribble(~x, ~y,
55 | 'a', 'b',
56 | a', 'c')
57 | ggtree(as.phylo(x), layout='roundrect') + geom_label(aes(label=label))
58 |
59 | 还可以把原来的label当做是个指代，替换成真正的label，可以用ggtext支持富文本。
60 |
61 |
62 |
63 | + [Regarding "read.beast" function](https://github.com/YuLab-SMU/treeio/issues/43)
64 | + [update method to parse phyloxml format file](https://github.com/YuLab-SMU/treeio/pull/44)
65 | + [Adding read.treeqza to parse tree qza file from output of qiime2](46)
66 |
67 |
68 | + [fix drop.tip when all tips removed](https://github.com/YuLab-SMU/treeio/pull/65)
69 | + [fix the add_pseudo_nodelabel when the node.label is not null in beast file](https://github.com/YuLab-SMU/treeio/pull/64)
70 | + [fixed bug when some node labels are blank in read.beast](https://github.com/YuLab-SMU/treeio/pull/63)
71 | + [to fix issues about additional parameters of drop.tip](https://github.com/YuLab-SMU/treeio/pull/62)
72 | + [As.phylo as.treedata](https://github.com/YuLab-SMU/treeio/pull/61)
73 | + [Optimization read.mcmctree](https://github.com/YuLab-SMU/treeio/pull/60)
74 | + [add read.mcmctree function](https://github.com/YuLab-SMU/treeio/pull/58)
75 | + [fix read.beast](https://github.com/YuLab-SMU/treeio/pull/56)
76 | + [add new as.phylo method for chronos class](https://github.com/YuLab-SMU/treeio/pull/54)
77 | + [fix bug for parsing MrBayes output](https://github.com/YuLab-SMU/treeio/pull/53)
78 | + [update read.fasta](https://github.com/YuLab-SMU/treeio/pull/52)
79 | + [update read.nhx to parse large tree file](https://github.com/YuLab-SMU/treeio/pull/51)
80 | + [Added read.beast.tree and exported write_beast_newick](https://github.com/YuLab-SMU/treeio/pull/50)
81 | + [update read.beast](https://github.com/YuLab-SMU/treeio/pull/48)
82 |
83 |
84 |
85 |
86 | ## tidytree
87 |
88 | + [给进化树加分支](https://mp.weixin.qq.com/s/OBb9sqR9IuEfAzDqEBr6Uw)
89 |
90 |
91 | ## treeio
92 |
93 | + [树变图，图变树？](https://mp.weixin.qq.com/s/Uhx3l3lKQS88OJ4SHntkNg)
94 | + [treeio: 进化树基础类和方法](https://mp.weixin.qq.com/s/uhxCqbnssgP_GHVHqANbaQ)
95 | + [进化树改名](https://mp.weixin.qq.com/s/-v1vklrKRwkEJ0amjVJjJQ)
96 | + [treeio: Phylogenetic data integration](https://mp.weixin.qq.com/s/vLcoF2yMpOa4VzO2nxhZEw)
97 | + [Subsetting Phylogenetic Trees](https://mp.weixin.qq.com/s/-wpzjkpHLGL8jEzNTSivJQ)
98 | + [treeio支持解析MEGA的输出](https://mp.weixin.qq.com/s/Ikp3hq8OBOsdkQY390grqA)
99 |
100 |
101 |
102 | ## ggtree
103 |
104 |
105 | + [Ask me anything about ggtree](https://mp.weixin.qq.com/s/IzSC1GnNPUwKdehhz-U8fQ)
106 | + [当年ggtree发布文，为整合数据而生！](https://mp.weixin.qq.com/s/4g2dPmm5ycTvbube4ujlgQ)
107 | + [ggtree无根树及注释](https://mp.weixin.qq.com/s/Jij3xQhvgsuZBH_g45MvvQ)
108 | + [无根树中加入枝长图例](https://mp.weixin.qq.com/s/kvCuKjHA9qZ6qL-7R855dg)
109 | + [ggtree画根分支](https://mp.weixin.qq.com/s/ObJlBkOTKIYjYpHTww6cIw)
110 | + [phylip树格式](https://mp.weixin.qq.com/s/NcS-yOsRxHJZStiZ7SPw4g)
111 | + [ggtree版本的plotTree](https://mp.weixin.qq.com/s/JM7m7fQSxW7SdRVyGhuxTA)
112 | + [align genomic features with phylogenetic tree](https://mp.weixin.qq.com/s/3j9qg0qpMUsxpp_QkTm9fA)
113 | + [ggjoy facet with ggtree](https://mp.weixin.qq.com/s/iTJzXJRHD3z_rq9OHZJ_4Q)
114 | + [reproducible logo generated by ggtree](https://mp.weixin.qq.com/s/Tp0ydzaInr80S6oVlEsBCA)
115 | + [用ggtree重现Figtree的示例进化树](https://mp.weixin.qq.com/s/3Fc83au6gV5p6ZdlzlAC2w)
116 | + [ggtree for microbiome data](https://mp.weixin.qq.com/s/KScSppwajYsuHuf1w3bQTQ)
117 | + [ggtree for outbreak data ](https://mp.weixin.qq.com/s/eo_lrVctJ3X3OCdAQqK9Dw)
118 | + [ggtree支持phylog对象](https://mp.weixin.qq.com/s/7MFS_OUVB5GkKCnHSGzUGA)
119 | + [ggtree画层次聚类](https://mp.weixin.qq.com/s/tLaboUnsm2iA20PWLuf6yg)
120 | + [用户数据注释进化树](https://mp.weixin.qq.com/s/ClTWsdyIYyHeNN0enwlglw)
121 | + [改变outgroup的枝长](https://mp.weixin.qq.com/s/d2sLLmuMTXLZNfQbWtKUxg)
122 | + [使用自己的数据来给进化树上色](https://mp.weixin.qq.com/s/8ryU-3HjMvE7RFgo4rQ3Ew)
123 | + [bootstrap分段标记](https://mp.weixin.qq.com/s/7dq1br8LCY5jAtQDm1bXPg)
124 | + [ggtree中标记树分支？](https://mp.weixin.qq.com/s/nlAGbHMJ2tBEaaBxCQC13A)
125 | + [xlim_tree: set x axis limits for only Tree panel](https://mp.weixin.qq.com/s/eOSLVtLC0KM61DVQjpCcig)
126 | + [reverse time scale](https://mp.weixin.qq.com/s/JdjWAIFfGDKBzYQOabzKaw)
127 | + [vertical dendrogram in ggtree](https://mp.weixin.qq.com/s/2sV5wGux37ytBZulB3p3JA)
128 | + [中空的环形树](https://mp.weixin.qq.com/s/7W6Z7wVQZEPR1A5P-sMHZA)
129 | + [可视化操作树的拓扑结构](https://mp.weixin.qq.com/s/SbAyY4WzB7hNRbID48NIgg)
130 | + [ggtree - updating a tree view](https://mp.weixin.qq.com/s/csZUfzoluTkXp9DxYR7w6g)
131 | + [facet_plot: 加图层到特定的分面上](https://mp.weixin.qq.com/s/hY38gr2x8AqRaTh4C7mrnA)
132 | + [facet_plot：加图层到特定分面，方法二](https://mp.weixin.qq.com/s/PnbasfW4HKILuZNdrLVX_g)
133 | + [facet_plot: 关联数据和进化树的通用方法](https://mp.weixin.qq.com/s/FlrnY9GeV5fHa6EZpZhTJA)
134 | + [facet_plot更改panel label](https://mp.weixin.qq.com/s/RC9TsEZRjflIZE15xpa0sg)
135 | + [同时让N颗树关联同一个数据集](https://mp.weixin.qq.com/s/LTK1tDPLEk8_BWEVB9kReQ)
136 | + [漫画版的进化树你见过吗？](https://mp.weixin.qq.com/s/P7yUFLwW4OLGBrPw05iQ_A)
137 | + [取子集画图](https://mp.weixin.qq.com/s/JXpakSKqPPRHhcyQQVdoGA)
138 | + [Y叔不想养蛙，只想养你！](https://mp.weixin.qq.com/s/S5K9HwgCC2LtUJsnKBx0Yg)
139 | + [用图片注释进化树](https://mp.weixin.qq.com/s/BV-8HtiZC-XSHUVAwiS7Vw)
140 | + [当生物女博士遇到小学二年级语文作业](https://mp.weixin.qq.com/s/BJjy28aru_l40v-jdPnSPA)
141 | + [identify: 交互式操作进化树](https://mp.weixin.qq.com/s/PIns29a9pwrUSK6kWpUBIw)
142 | + [plotly: 交互式探索进化树](https://mp.weixin.qq.com/s/a0XHr8Vfr49tEBYZWoRBxA)
143 | + [ggnetworx：让ggtree支持phylogenetic networks](https://mp.weixin.qq.com/s/YytLpKKkTqqpcrJJLFOhjg)
144 | + [ggtree买家秀](https://mp.weixin.qq.com/s/W9UI9doKKeq8YhbcA0N-_A)
145 | + [ggplot2 - 更改分面的相对宽度](https://mp.weixin.qq.com/s/72UHjAQTRXuHzoeWUc-ccA)
146 | + [听说你想画好几颗树在一起](https://mp.weixin.qq.com/s/Hx9fI-JaMN7gY_vElQiQKg)
147 | + [倒不如画一些渐变色的线条吧](https://mp.weixin.qq.com/s/0WGS6b11F1Ul0ZkceKYOvQ)
148 | + [python的世界里没有这么好的工具！](https://mp.weixin.qq.com/s/zops2EyAdJsLtBcGcxa3ZQ)
149 | + [风儿啊，它吹乱了我的头发](https://mp.weixin.qq.com/s/iUUyXtJG3Fj3uPfvfHmdQA)
150 | + [ggtree在线书](https://mp.weixin.qq.com/s/NwYXAxhAQNW0Xmq21FY2nw)
151 | + [进化树和基因组结构](https://mp.weixin.qq.com/s/rm6x7-sWnvQ9qRn26eP2nQ)
152 | + [ggtree所支持的各种树的布局](https://mp.weixin.qq.com/s/E0MfgCFdMxJyrVqpmwNUgA)
153 | + [进化树上随意选几条边来上色](https://mp.weixin.qq.com/s/TssW_2exj54YYrgHro3dtg)
154 | + [进化树上随意选几条边，换线条类型！](https://mp.weixin.qq.com/s/FcigGbaRR2RKTTza7fWx1w)
155 | + [听说你想在系统发育树上随意加个点？](https://mp.weixin.qq.com/s/L1eNG-bGXQmHu3spQoINRg)
156 | + [这么多年了，终于有个故事可以讲](https://mp.weixin.qq.com/s/okDgKJgV8wr5xDMufOEihw)
157 | + [对ggplot2指定的分面，任性调整xlim和分面宽度！](https://mp.weixin.qq.com/s/ClGhvr_sJi-6-SN8hgRUyg)
158 | + [给不同的分面分别设置x label？](https://mp.weixin.qq.com/s/sIoXMP000HJGEgAalTCrWg)
159 | + [度量生物多样性](https://mp.weixin.qq.com/s/ntgUi0-AZOu5nbZHvSS9aQ)
160 | + [进化树与序列的距离矩阵](https://mp.weixin.qq.com/s/Vb9MBt419sO69UOPfEb0UQ)
161 | + [画两颗面对面的树，还要分别加注释！](https://mp.weixin.qq.com/s/jevqlyaf7c7zudQCAkylJQ)
162 | + [对进化树的分支长度做数据变换](https://mp.weixin.qq.com/s/rUgOI2uMjE_lK4ou9x2hUw)
163 | + [一个legend伪装成三个，服气！](https://mp.weixin.qq.com/s/OcTP0mU3LPraza-7EHmy2w)
164 | + [听说你想给线条画个边框？](https://mp.weixin.qq.com/s/Cq6e_ZBUgyZtxJOHiXkpZA)
165 | + [纯文本的进化树](https://mp.weixin.qq.com/s/inI7RtaFAg3g9oH3iwMLwg)
166 | + [用ggtree画思维导图，小白也能学会！](https://mp.weixin.qq.com/s/NArmAQJp5OPt_SEVGmGv8g)
167 |
168 |
169 |
170 | ### ggtreeExtra
171 |
172 | + [用图层叠加方法绘制环形进化树](https://mp.weixin.qq.com/s/Il8yZqUoBVCvND7U7HKxxA)
173 | + [用ggtreeExtra给进化树加热图](https://mp.weixin.qq.com/s/rhv7xLgrY1TlpfbC_riAvQ)
174 | ### 直播
175 |
176 | + [webinar录播 (2017-10-24)：plotting tree + data](https://mp.weixin.qq.com/s/YuUOztQg3nUnhdvyg8asvg)
177 | + [ggtree直播PPT第一部分](https://mp.weixin.qq.com/s/3yEN-8oUck2WPmNQ368qYA)
178 | + [ggtree直播PPT第二部分](https://mp.weixin.qq.com/s/7-YhGzzu_tVAwySrYOKGFg)
179 |
180 |
--------------------------------------------------------------------------------
/data-backup/HMP_tree/hmptree.nwk:
--------------------------------------------------------------------------------
1 | ((t640427121:0.62306,t637000163:0.86427)1.00:1.20714,((t637000327:0.43426,(t649990026:0.44157,t645951869:0.42069)0.82:0.13553)1.00:2.28724,((((((t646206254:0.25238,(t645951859:0.05372,t646206275:0.05447)1.00:0.20326)1.00:0.22069,(t645951804:0.43651,(t647000254:0.08827,t645951848:0.09514)1.00:0.49290)0.76:0.12629)Fusobacterium1.00:0.57061,(t646311956:0.94182,(t647000268:0.25552,(t644736384:0.05250,t645951860:0.04949)1.00:0.25915)1.00:0.26943)1.00:0.55191)1.00:1.20558,((((t645951833:0.45158,t649989928:0.49364)1.00:0.86254,(t649989971:0.77561,(t648276753:0.08735,(t647000331:0.04084,t643886074:0.03151)1.00:0.07723)Veillonella1.00:0.87508)0.85:0.20223)1.00:0.34970,(t646311901:1.24020,(t650377958:0.64183,(t645951802:0.45313,(t642979367:0.31026,(t649989996:0.13520,(t645058738:0.18190,t645951808:0.12394)0.98:0.06557)1.00:0.46726)Selenomonas1.00:0.13201)1.00:0.24998)1.00:0.42320)0.79:0.12649)1.00:0.64941,(((((t645058834:0.12489,t649989952:0.13364)1.00:1.27427,(t650377974:0.24803,(t637000284:0.21116,(t646862342:0.17560,((t645058825:0.11274,(t643886010:0.08795,t637000281:0.10708)1.00:0.04396)0.89:0.02581,(t646564572:0.16321,(t643886039:0.11753,t642555160:0.09339)1.00:0.05215)1.00:0.05109)1.00:0.04368)1.00:0.08360)1.00:0.15227)Staphylococcus1.00:0.76048)0.95:0.25974,(t646311941:0.73682,((t647000207:1.06835,((t645951817:0.23965,t647533172:0.24700)1.00:0.39312,t643886188:0.99035)0.97:0.16723)1.00:0.19148,((((t644736382:0.05582,(t639633028:0.01316,t643886139:0.00679)1.00:0.06408)1.00:0.59390,(((t643886217:0.37955,t650377954:0.41989)0.97:0.06199,(t641228495:0.11149,(t650716045:0.08682,t647533176:0.08356)0.94:0.03880)1.00:0.17549)1.00:0.10128,(t648276682:0.48918,t645058712:0.17334)0.46:0.06868)1.00:0.76878)1.00:0.19034,(((t647533194:0.66861,(t639633027:0.48736,t644736381:0.42525)0.25:0.08973)0.25:0.05962,(((t649989969:0.16765,(t638341112:0.21999,t643886050:0.17986)0.95:0.04552)1.00:0.11993,(t647533178:0.31353,t646206276:0.30158)1.00:0.09813)1.00:0.37331,(t643886109:0.17390,(t649633064:0.14957,t641522636:0.17750)1.00:0.12022)1.00:1.02166)1.00:0.11589)1.00:0.10672,(t648276684:0.31905,t643886045:0.37635)1.00:0.29763)0.25:0.08750)Lactobacillus1.00:0.40974,((t650377929:0.26419,(t647533156:0.27068,t647533146:0.27655)0.97:0.07084)1.00:0.22589,(t640069315:0.62191,(((t637000302:0.04157,(t649990016:0.02274,t651053073:0.02748)0.68:0.02378)1.00:0.18880,(t646564575:0.25367,((t638341202:0.22691,(t648276747:0.08769,t650377975:0.04808)1.00:0.18943)1.00:0.08425,(t648276732:0.05642,t641736171:0.06627)1.00:0.12518)0.66:0.04343)0.91:0.04846)1.00:0.08064,(t648276749:0.33677,((((t649990007:0.02984,t649990011:0.06508)1.00:0.03108,(t648028055:0.06246,(t648276734:0.02455,(t649990014:0.01580,t648276739:0.03819)1.00:0.01296)1.00:0.02508)1.00:0.03783)1.00:0.10028,(t650716089:0.09784,t649990003:0.09781)1.00:0.07427)0.99:0.03355,(t649990001:0.18358,(t649990004:0.09027,t640753055:0.12204)0.94:0.04117)0.99:0.04208)1.00:0.10884)0.89:0.05668)Streptococcus1.00:0.45364)1.00:0.54881)0.66:0.10830)1.00:0.15718)1.00:0.40753)1.00:0.23244)1.00:0.79854,(((t646206251:0.54983,t643886110:0.61644)1.00:0.61319,(t643886103:0.68403,(t647000218:0.69258,t649989999:0.37936)1.00:0.66872)1.00:0.62461)0.91:0.23036,(t637000332:1.42246,t646311946:1.12938)1.00:1.33031)0.99:0.33694)1.00:0.63232,(((((t648276710:0.73586,(t649989982:0.48914,t647000289:0.54665)1.00:0.25699)1.00:0.47470,(((t645058761:0.07756,t651324009:0.13259)Anaerococcus1.00:0.32698,(t643886145:0.38805,t643886055:0.32650)1.00:0.17811)1.00:0.89120,(t641380421:1.14288,t648276674:0.87140)0.94:0.24806)1.00:0.23400)1.00:0.47471,t645058705:2.31829)0.95:0.18894,(((t648276712:0.28770,t647000290:0.29998)1.00:0.46439,(t642979371:0.37921,(t640069308:0.28255,t641736113:0.51731)1.00:0.12697)0.97:0.15256)1.00:0.65624,(t637000077:0.63998,t643692016:0.63175)1.00:0.66995)1.00:0.21158)0.49:0.11264,(((t643886181:1.04955,(t650377925:0.74496,(((((t642979369:0.37442,((t640963057:0.22659,(t642791604:0.21513,t640963025:0.26241)1.00:0.09125)1.00:0.13482,(t643886116:0.39599,((t642979359:0.23361,t641736197:0.20069)1.00:0.10447,(t641736133:0.28228,t640963046:0.19463)Dorea0.99:0.06006)1.00:0.09792)0.60:0.04989)Ruminococcus0.86:0.06459)1.00:0.17849,(t643886107:0.54690,(t643886146:0.48773,(t640963024:0.39829,t643886199:0.42881)0.43:0.09687)1.00:0.11526)1.00:0.13359)1.00:0.09840,((t643886104:1.30009,t649989950:0.71542)1.00:0.25959,(t649989922:0.48403,((t641380428:0.19283,t643886112:0.24361)1.00:0.17973,(t648028018:0.22906,t645951835:0.20343)1.00:0.16395)0.99:0.08667)1.00:0.13977)Clostridium1.00:0.19948)0.92:0.05894,(((t645951834:0.70309,(t642979337:0.42615,t644736366:0.44103)1.00:0.20028)0.99:0.14220,((t644736367:0.46986,(t642979356:0.22225,t643886006:0.20446)Roseburia1.00:0.16571)1.00:0.13546,t643886070:1.03339)1.00:0.14903)0.24:0.06889,t640963022:0.79399)0.20:0.05298)1.00:0.08365,(t641380422:0.78877,(t643886203:0.56913,t641736227:0.68083)1.00:0.14367)1.00:0.12104)0.97:0.07985)1.00:0.13824)1.00:0.71173,((((t640963014:1.43820,(t643886206:0.85890,t641736271:0.76972)0.99:0.13558)0.28:0.08445,(t641380427:0.59471,t650377966:0.87211)1.00:0.29480)1.00:0.11825,(t643886143:0.47083,t645951831:0.45539)1.00:0.87717)0.94:0.08006,(t650377938:0.88859,t649633094:0.80911)1.00:0.27472)1.00:0.44590)1.00:0.35200,(t641736193:1.29702,(t649989993:0.77072,:0.52965)1.00:0.66328)Eubacterium1.00:0.33542)0.81:0.11757)1.00:0.26344)1.00:0.17478)0.99:0.22121)0.99:0.22203,(t645951855:0.85506,t647000302:0.62721)1.00:1.79347)0.95:0.12970,(((((((t649989980:0.44052,(t646311928:0.30197,(t643348515:0.29154,((t641736189:0.07804,(t639633010:0.06256,(t642979313:0.02455,t642979312:0.02065)1.00:0.04651)0.89:0.02633)1.00:0.07669,(t642979361:0.16891,(t643886040:0.16533,(t651053005:0.05719,t642555107:0.04800)1.00:0.10646)1.00:0.07163)0.19:0.03981)1.00:0.07782)Bifidobacterium1.00:0.09572)1.00:0.15045)1.00:0.99317,(t648028043:0.79096,(t640963058:0.53569,(t643886015:0.22340,(t649989905:0.02479,t649989901:0.03171)1.00:0.18047)1.00:0.35723)Actinomyces0.62:0.10256)1.00:0.16599)0.99:0.11386,(t644736390:0.52702,(t642555133:0.36312,(t649633093:0.12636,t645058800:0.11675)1.00:0.44380)1.00:0.15632)1.00:0.34427)0.99:0.14775,(t641522641:0.59182,(t642979306:0.44711,((t643692019:0.48635,t647000231:0.38762)0.99:0.08924,(t648276634:0.38218,(t648276633:0.36355,((t643886058:0.09939,(t645058719:0.01224,t643886084:0.01137)1.00:0.06277)1.00:0.09133,(t643692018:0.13765,t643886057:0.14215)1.00:0.06077)1.00:0.21220)1.00:0.11135)1.00:0.15171)0.28:0.06082)Corynebacterium1.00:0.33701)1.00:0.39785)0.71:0.15358,(t651053058:0.52830,t649633084:0.56550)1.00:0.32539)1.00:1.85856,((t645951837:0.55683,(t644736346:0.56842,(t644736358:0.14675,t650377943:0.26040)1.00:0.23770)0.89:0.12932)1.00:0.30101,((t640612206:0.26559,(t642979321:0.12573,t642979320:0.10309)1.00:0.21297)1.00:0.34617,((t644736327:0.24170,t643886019:0.21960)1.00:0.25083,(t648028047:0.31060,t643886063:0.66789)1.00:0.11596)1.00:0.25201)1.00:0.48659)1.00:1.06483)1.00:0.63684,((t640069321:3.07389,(t640753047:1.82952,t637000092:2.64294)0.79:0.17326)0.86:0.17192,((((t637000271:0.90984,((t645058865:0.55603,(t639279312:0.47208,t641522638:0.54130)1.00:0.15964)1.00:0.24275,((t640069327:0.34903,t639633048:0.31147)1.00:0.54194,t648028010:0.94428)0.77:0.10475)1.00:0.17230)1.00:0.67617,((((t643886193:0.21583,(t647533210:0.24836,t649989956:0.19210)0.16:0.04382)1.00:0.11587,(t643886208:0.25511,(t647000283:0.15188,((t643886150:0.02189,t645058748:0.03897)1.00:0.03584,((t643886198:0.02189,t643886194:0.03018)1.00:0.07738,(t643886151:0.03095,(t647000284:0.01736,(t649633075:0.04336,(t641228498:0.02255,t647533186:0.03199)0.80:0.00819)1.00:0.01841)1.00:0.03098)1.00:0.06066)0.97:0.02113)1.00:0.11898)Neisseria1.00:0.12313)0.58:0.04778)1.00:0.82985,(t649990021:1.28919,(t649989970:0.76755,(t641228489:0.68974,(t637000046:0.45744,(t648028033:0.29669,t646206250:0.56508)1.00:0.21135)0.60:0.11121)0.97:0.11702)0.79:0.09869)1.00:0.18159)1.00:0.26088,(t642555169:1.19932,((((t643886024:0.30159,(t640753048:0.19241,(t651053056:0.22624,((t649633041:0.08649,((t643348560:0.00642,t646311937:0.00303)1.00:0.09381,t642979368:0.11613)0.84:0.02239)1.00:0.03586,(((t646862322:0.00605)Escherichia:0.00000,((t641522650:0.02230,t638341196:0.11900)1.00:0.00695,t640427143:0.01101)0.92:0.01075)0.82:0.01536,t643692022:0.03992)1.00:0.07254)1.00:0.12056)0.99:0.06453)1.00:0.09181)1.00:0.26445,((t641736156:0.17684,(t643886008:0.21958,t647533181:0.15327)0.60:0.04229)1.00:0.16278,(t640753001:0.25735,(t637000127:0.22907,((t637000203:0.10224,t647000288:0.09674)1.00:0.10841,((t649989906:0.05089,(t644736324:0.05395,t648028005:0.07584)0.91:0.02238)1.00:0.12402,(t651324039:0.08635,t650377944:0.13190)Haemophilus1.00:0.10250)1.00:0.05068)1.00:0.05656)0.35:0.04382)1.00:0.08047)1.00:0.40025)1.00:0.65600,((t638341158:0.20534,(t637000221:0.14730,t650377963:0.14842)1.00:0.14042)1.00:0.47929,((t646206271:0.10752,((t650377901:0.09491,t646206269:0.10747)1.00:0.06303,(t646206268:0.09860,t646206270:0.10625)1.00:0.05730)0.66:0.03487)Acinetobacter1.00:0.47243,(t646564552:0.49830,(t645951810:0.35249,(t637000226:0.03139,t637000227:0.03298)1.00:0.36282)0.57:0.10347)1.00:0.38615)1.00:0.66564)0.11:0.13932)1.00:0.22480,t645951819:1.42192)0.72:0.10714)0.54:0.14593)1.00:0.92506)1.00:0.75294,((t637000095:0.51681,(t649989917:0.53898,t642979316:0.60272)0.97:0.14948)1.00:1.38324,(t651053029:1.16977,(((t643886009:0.06077,t645058720:0.03647)1.00:0.23775,(t640753010:0.20079,t640753009:0.19765)1.00:0.10310)1.00:0.18727,(t645951857:0.34592,t640753011:0.40921)1.00:0.15998)Campylobacter1.00:0.67270)1.00:1.68116)0.08:0.13022)1.00:0.33470,(((t643886113:0.38687,(t642979305:0.05235,t649989920:0.04871)1.00:0.27483)1.00:0.52426,((t650377904:0.28945,t641736205:0.19327)1.00:0.71790,(t649633078:0.78521,((((t645951840:0.63372,(t648276716:0.30892,(((t649989986:0.19496,t643886200:0.25794)0.98:0.05732,((t648861012:0.07642,t649989989:0.10016)1.00:0.18949,(t645951825:0.33423,((t649989987:0.10015,(t648028051:0.08147,t645951836:0.10696)0.71:0.01992)1.00:0.07452,(t648276715:0.23810,(t648276713:0.17513,t647000292:0.08733)1.00:0.16206)0.99:0.06486)1.00:0.13555)0.99:0.05663)0.16:0.04370)1.00:0.06497,(t649989988:0.19822,(t647000293:0.12894,t647000295:0.14334)1.00:0.17101)0.78:0.04980)1.00:0.08817)1.00:0.26393)Prevotella1.00:0.24393,(((t645058788:0.12426,((t640963023:0.04145,(t642979319:0.05529,(t647000215:0.03155,t651324012:0.01315)1.00:0.02808)1.00:0.01776)0.98:0.01819,t637000026:0.04422)1.00:0.07121)1.00:0.04571,((t643886111:0.03342,t642791621:0.03498)1.00:0.07882,((t649989912:0.03891,t641736196:0.03700)1.00:0.05264,(t641380447:0.06685,t649633012:0.11346)0.99:0.02194)1.00:0.05685)1.00:0.05907)1.00:0.11533,((t642979370:0.00899,t640753008:0.01095)1.00:0.14185,(t643886197:0.11996,(t642979351:0.13831,(t649633013:0.16743,t642791613:0.09293)0.20:0.02952)0.94:0.02920)1.00:0.11861)1.00:0.09617)Bacteroides1.00:0.11562)1.00:0.24050,(t640753039:0.13352,(t640963016:0.03471,t642979358:0.02476)1.00:0.10827)1.00:0.23309)0.99:0.15469,(t642555148:0.39212,(t643886148:0.52057,(t649989985:0.02086,t643886142:0.03850)1.00:0.75598)1.00:0.27610)Porphyromonas1.00:0.27541)1.00:0.35830)0.49:0.16069)1.00:0.60588)1.00:1.04257,(t642555104:1.87557,t650633000:2.10387)0.95:0.37071)0.77:0.31073)0.99:0.21945)1.00:0.23723)0.83:0.13130)0.80:0.16410):1.20714):1.00000;
2 |
--------------------------------------------------------------------------------
/data-backup/HMP_tree/barplot_attr.csv:
--------------------------------------------------------------------------------
1 | ID,Sites,HigherAbundance
2 | t637000026,Stool (prevalence),0.1322531
3 | t637000046,Vagina (prevalence),0.035
4 | t637000077,Stool (prevalence),0.035
5 | t637000092,Stool (prevalence),0.035
6 | t637000095,Stool (prevalence),0.035
7 | t637000127,Plaque (prevalence),0.035
8 | t637000163,Stool (prevalence),0.035
9 | t637000203,Tongue (prevalence),0.035
10 | t637000221,Vagina (prevalence),0.035
11 | t637000226,Stool (prevalence),0.035
12 | t637000227,Stool (prevalence),0.035
13 | t637000271,Stool (prevalence),0.035
14 | t637000281,Skin (prevalence),1.7287928
15 | t637000284,Stool (prevalence),0.035
16 | t637000302,Tongue (prevalence),0.035
17 | t637000327,Plaque (prevalence),0.0384153
18 | t637000332,Vagina (prevalence),0.0789712
19 | t638341112,Stool (prevalence),0.035
20 | t638341158,Stool (prevalence),0.035
21 | t638341196,Stool (prevalence),0.035
22 | t638341202,Stool (prevalence),0.035
23 | t639279312,Stool (prevalence),0.035
24 | t639633010,Stool (prevalence),0.0418075
25 | t639633027,Stool (prevalence),0.035
26 | t639633028,Stool (prevalence),0.035
27 | t639633048,Stool (prevalence),0.035
28 | t640069308,Stool (prevalence),0.035
29 | t640069315,Stool (prevalence),0.035
30 | t640069321,Tongue (prevalence),0.035
31 | t640069327,Stool (prevalence),0.035
32 | t640427121,Stool (prevalence),0.035
33 | t640427143,Stool (prevalence),0.035
34 | t640612206,Stool (prevalence),0.035
35 | t640753001,Stool (prevalence),0.035
36 | t640753008,Stool (prevalence),1.31244295
37 | t640753009,Tongue (prevalence),0.4131946
38 | t640753010,Plaque (prevalence),0.035
39 | t640753011,Stool (prevalence),0.035
40 | t640753039,Stool (prevalence),0.0596358
41 | t640753047,Stool (prevalence),0.035
42 | t640753048,Stool (prevalence),0.035
43 | t640753055,Plaque (prevalence),0.0598542
44 | t640963014,Stool (prevalence),0.035
45 | t640963016,Stool (prevalence),0.54832785
46 | t640963022,Stool (prevalence),0.0390922
47 | t640963023,Stool (prevalence),0.5207622
48 | t640963024,Stool (prevalence),0.035
49 | t640963025,Stool (prevalence),0.1643698
50 | t640963046,Stool (prevalence),0.035
51 | t640963057,Stool (prevalence),0.035
52 | t640963058,Tongue (prevalence),0.13555815
53 | t641228489,Stool (prevalence),0.035
54 | t641228495,Stool (prevalence),0.035
55 | t641228498,Plaque (prevalence),0.035
56 | t641380421,Tongue (prevalence),0.035
57 | t641380422,Stool (prevalence),0.035
58 | t641380427,Stool (prevalence),0.035
59 | t641380428,Stool (prevalence),0.035
60 | t641380447,Stool (prevalence),0.21505365
61 | t641522636,Stool (prevalence),0.035
62 | t641522638,Stool (prevalence),0.035
63 | t641522641,Stool (prevalence),0.035
64 | t641522650,Stool (prevalence),0.035
65 | t641736113,Stool (prevalence),0.035
66 | t641736133,Stool (prevalence),0.035
67 | t641736156,Stool (prevalence),0.035
68 | t641736171,Stool (prevalence),0.035
69 | t641736189,Stool (prevalence),0.035
70 | t641736193,Stool (prevalence),0.035
71 | t641736196,Stool (prevalence),0.69204345
72 | t641736197,Stool (prevalence),0.035
73 | t641736205,Stool (prevalence),1.0281943
74 | t641736227,Stool (prevalence),0.035
75 | t641736271,Stool (prevalence),0.035
76 | t642555104,Stool (prevalence),0.1756692
77 | t642555107,Stool (prevalence),0.035
78 | t642555133,Stool (prevalence),0.035
79 | t642555148,Tongue (prevalence),0.035
80 | t642555160,Stool (prevalence),0.035
81 | t642555169,Stool (prevalence),0.035
82 | t642791604,Stool (prevalence),0.0409892
83 | t642791613,Stool (prevalence),0.08686685
84 | t642791621,Stool (prevalence),0.0675612
85 | t642979305,Plaque (prevalence),0.34347635
86 | t642979306,Skin (prevalence),0.035
87 | t642979312,Stool (prevalence),0.035
88 | t642979313,Stool (prevalence),0.035
89 | t642979316,Stool (prevalence),0.035
90 | t642979319,Stool (prevalence),0.3462326
91 | t642979320,Stool (prevalence),0.035
92 | t642979321,Stool (prevalence),0.035
93 | t642979351,Stool (prevalence),0.1613409
94 | t642979356,Stool (prevalence),0.1223558
95 | t642979358,Stool (prevalence),0.07141785
96 | t642979359,Stool (prevalence),0.035
97 | t642979361,Stool (prevalence),0.035
98 | t642979367,Plaque (prevalence),0.035
99 | t642979368,Stool (prevalence),0.035
100 | t642979369,Stool (prevalence),0.035
101 | t642979370,Stool (prevalence),0.03755955
102 | t642979371,Stool (prevalence),0.035
103 | t643348515,Stool (prevalence),0.035
104 | t643348560,Stool (prevalence),0.035
105 | t643692016,Stool (prevalence),0.035
106 | t643692018,Stool (prevalence),0.035
107 | t643692019,Skin (prevalence),1.7536435
108 | t643692022,Stool (prevalence),0.035
109 | t643886006,Stool (prevalence),0.11314555
110 | t643886008,Cheek (prevalence),0.035
111 | t643886009,Plaque (prevalence),0.035
112 | t643886010,Skin (prevalence),0.0881097
113 | t643886015,Plaque (prevalence),0.035
114 | t643886019,Plaque (prevalence),0.035
115 | t643886024,Stool (prevalence),0.035
116 | t643886039,Skin (prevalence),0.035
117 | t643886040,Stool (prevalence),0.035
118 | t643886045,Stool (prevalence),0.035
119 | t643886050,Vagina (prevalence),0.035
120 | t643886055,Vagina (prevalence),0.035
121 | t643886057,Stool (prevalence),0.035
122 | t643886058,Nose (prevalence),2.1912912
123 | t643886063,Vagina (prevalence),0.035
124 | t643886070,Plaque (prevalence),0.035
125 | t643886074,Tongue (prevalence),0.33143285
126 | t643886084,Nose (prevalence),0.1159837
127 | t643886103,Stool (prevalence),0.035
128 | t643886104,Tongue (prevalence),0.1089571
129 | t643886107,Stool (prevalence),0.035
130 | t643886109,Stool (prevalence),0.035
131 | t643886110,Stool (prevalence),0.035
132 | t643886111,Stool (prevalence),0.31685255
133 | t643886112,Stool (prevalence),0.035
134 | t643886113,Plaque (prevalence),0.303856
135 | t643886116,Stool (prevalence),0.03798865
136 | t643886139,Stool (prevalence),0.035
137 | t643886142,Stool (prevalence),0.035
138 | t643886143,Stool (prevalence),0.035
139 | t643886145,Stool (prevalence),0.035
140 | t643886146,Stool (prevalence),0.035
141 | t643886148,Plaque (prevalence),0.0749924
142 | t643886150,Plaque (prevalence),0.7126903
143 | t643886151,Cheek (prevalence),0.035
144 | t643886181,Plaque (prevalence),0.035
145 | t643886188,Plaque (prevalence),0.0708568
146 | t643886193,Plaque (prevalence),0.1497888
147 | t643886194,Tongue (prevalence),0.2289952
148 | t643886197,Stool (prevalence),0.035
149 | t643886198,Tongue (prevalence),0.8942367
150 | t643886199,Stool (prevalence),0.035
151 | t643886200,Stool (prevalence),1.60445565
152 | t643886203,Stool (prevalence),0.035
153 | t643886206,Stool (prevalence),0.035
154 | t643886208,Plaque (prevalence),0.05481735
155 | t643886217,Vagina (prevalence),3.4955655
156 | t644736324,Plaque (prevalence),0.27294925
157 | t644736327,Tongue (prevalence),0.0719936
158 | t644736346,Plaque (prevalence),0.035
159 | t644736358,Stool (prevalence),0.035
160 | t644736366,Stool (prevalence),0.1200542
161 | t644736367,Stool (prevalence),0.59744125
162 | t644736381,Stool (prevalence),0.035
163 | t644736382,Stool (prevalence),0.035
164 | t644736384,Plaque (prevalence),0.1122373
165 | t644736390,Skin (prevalence),0.035
166 | t645058705,Cheek (prevalence),0.035
167 | t645058712,Vagina (prevalence),3.3628546
168 | t645058719,Nose (prevalence),0.07586565
169 | t645058720,Plaque (prevalence),0.0618765
170 | t645058738,Plaque (prevalence),0.035
171 | t645058748,Plaque (prevalence),0.33075665
172 | t645058761,Stool (prevalence),0.035
173 | t645058788,Stool (prevalence),0.035
174 | t645058800,Tongue (prevalence),0.6114507
175 | t645058825,Skin (prevalence),0.035
176 | t645058834,Cheek (prevalence),0.5328302
177 | t645058865,Stool (prevalence),0.035
178 | t645951802,Plaque (prevalence),0.035
179 | t645951804,Stool (prevalence),0.035
180 | t645951808,Plaque (prevalence),0.15132985
181 | t645951810,Skin (prevalence),0.035
182 | t645951817,Tongue (prevalence),0.0713286
183 | t645951819,Plaque (prevalence),0.13372415
184 | t645951825,Stool (prevalence),0.035
185 | t645951831,Stool (prevalence),0.4400445
186 | t645951833,Stool (prevalence),0.44330825
187 | t645951834,Stool (prevalence),0.0668115
188 | t645951835,Stool (prevalence),0.035
189 | t645951836,Tongue (prevalence),0.05664155
190 | t645951837,Plaque (prevalence),0.035
191 | t645951840,Plaque (prevalence),0.0402612
192 | t645951848,Tongue (prevalence),0.03630445
193 | t645951855,Stool (prevalence),0.035
194 | t645951857,Plaque (prevalence),0.11065775
195 | t645951859,Stool (prevalence),0.035
196 | t645951860,Plaque (prevalence),0.08560475
197 | t645951869,Plaque (prevalence),0.03768975
198 | t646206250,Stool (prevalence),0.035
199 | t646206251,Stool (prevalence),0.035
200 | t646206254,Stool (prevalence),0.035
201 | t646206268,Skin (prevalence),0.035
202 | t646206269,Stool (prevalence),0.035
203 | t646206270,Skin (prevalence),0.035
204 | t646206271,Stool (prevalence),0.035
205 | t646206275,Stool (prevalence),0.035
206 | t646206276,Vagina (prevalence),0.035
207 | t646311901,Stool (prevalence),0.035
208 | t646311928,Vagina (prevalence),0.6981674
209 | t646311937,Stool (prevalence),0.035
210 | t646311941,Stool (prevalence),0.035
211 | t646311946,Stool (prevalence),0.035
212 | t646311956,Stool (prevalence),0.035
213 | t646564552,Nose (prevalence),0.17719975
214 | t646564572,Stool (prevalence),0.035
215 | t646564575,Plaque (prevalence),0.035
216 | t646862322,Stool (prevalence),0.035
217 | t646862342,Nose (prevalence),0.9083788
218 | t647000207,Stool (prevalence),0.035
219 | t647000215,Stool (prevalence),0.3949099
220 | t647000218,Plaque (prevalence),0.035
221 | t647000231,Stool (prevalence),0.035
222 | t647000254,Plaque (prevalence),0.22168965
223 | t647000268,Plaque (prevalence),0.035
224 | t647000283,Plaque (prevalence),0.30211825
225 | t647000284,Plaque (prevalence),0.035
226 | t647000288,Stool (prevalence),0.035
227 | t647000289,Skin (prevalence),0.035
228 | t647000290,Stool (prevalence),0.035
229 | t647000292,Stool (prevalence),0.035
230 | t647000293,Nose (prevalence),0.035
231 | t647000295,Vagina (prevalence),0.035
232 | t647000302,Stool (prevalence),0.035
233 | t647000331,Plaque (prevalence),0.4522273
234 | t647533146,Stool (prevalence),0.035
235 | t647533156,Stool (prevalence),0.035
236 | t647533172,Cheek (prevalence),0.1436659
237 | t647533176,Vagina (prevalence),3.4997018
238 | t647533178,Stool (prevalence),0.035
239 | t647533181,Stool (prevalence),0.035
240 | t647533186,Plaque (prevalence),0.035
241 | t647533194,Stool (prevalence),0.035
242 | t647533210,Tongue (prevalence),0.035
243 | t648028005,Plaque (prevalence),0.035
244 | t648028010,Stool (prevalence),0.035
245 | t648028018,Stool (prevalence),0.035
246 | t648028033,Stool (prevalence),0.035
247 | t648028043,Stool (prevalence),0.035
248 | t648028047,Plaque (prevalence),0.035
249 | t648028051,Tongue (prevalence),0.7506632
250 | t648028055,Cheek (prevalence),0.035
251 | t648276633,Skin (prevalence),0.035
252 | t648276634,Plaque (prevalence),1.32123915
253 | t648276674,Skin (prevalence),0.31030615
254 | t648276682,Vagina (prevalence),3.3675978
255 | t648276684,Stool (prevalence),0.035
256 | t648276710,Stool (prevalence),0.035
257 | t648276712,Tongue (prevalence),0.035
258 | t648276713,Tongue (prevalence),0.035
259 | t648276715,Vagina (prevalence),0.035
260 | t648276716,Plaque (prevalence),0.035
261 | t648276732,Stool (prevalence),0.035
262 | t648276734,Cheek (prevalence),2.75962645
263 | t648276739,Cheek (prevalence),0.109263
264 | t648276747,Stool (prevalence),0.035
265 | t648276749,Stool (prevalence),0.035
266 | t648276753,Tongue (prevalence),0.50017695
267 | t648861012,Plaque (prevalence),0.0831061
268 | t649633012,Stool (prevalence),0.035
269 | t649633013,Stool (prevalence),0.035
270 | t649633041,Stool (prevalence),0.035
271 | t649633064,Stool (prevalence),0.035
272 | t649633075,Plaque (prevalence),0.035
273 | t649633078,Stool (prevalence),0.15575735
274 | t649633084,Stool (prevalence),0.035
275 | t649633093,Plaque (prevalence),1.4408919
276 | t649633094,Stool (prevalence),0.035
277 | t649989901,Plaque (prevalence),0.2938411
278 | t649989905,Plaque (prevalence),0.3467912
279 | t649989906,Plaque (prevalence),0.1525657
280 | t649989912,Stool (prevalence),0.30093665
281 | t649989917,Stool (prevalence),0.035
282 | t649989920,Plaque (prevalence),0.25156775
283 | t649989922,Stool (prevalence),0.035
284 | t649989928,Vagina (prevalence),0.035
285 | t649989950,Plaque (prevalence),0.05172825
286 | t649989952,Plaque (prevalence),0.04427815
287 | t649989956,Plaque (prevalence),0.035
288 | t649989969,Stool (prevalence),0.035
289 | t649989970,Plaque (prevalence),0.39373915
290 | t649989971,Tongue (prevalence),0.16614045
291 | t649989980,Stool (prevalence),0.035
292 | t649989982,Skin (prevalence),0.03697995
293 | t649989985,Stool (prevalence),0.035
294 | t649989986,Plaque (prevalence),0.035
295 | t649989987,Plaque (prevalence),0.035
296 | t649989988,Plaque (prevalence),0.035
297 | t649989989,Tongue (prevalence),0.1198344
298 | t649989993,Stool (prevalence),0.035
299 | t649989996,Plaque (prevalence),0.035
300 | t649989999,Tongue (prevalence),0.09095345
301 | t649990001,Plaque (prevalence),0.035
302 | t649990003,Tongue (prevalence),0.3424806
303 | t649990004,Plaque (prevalence),0.07084315
304 | t649990007,Tongue (prevalence),0.27167315
305 | t649990011,Tongue (prevalence),0.0372757
306 | t649990014,Plaque (prevalence),0.47115705
307 | t649990016,Cheek (prevalence),0.27710235
308 | t649990021,Stool (prevalence),0.1999088
309 | t649990026,Stool (prevalence),0.035
310 | t650377901,Skin (prevalence),0.035
311 | t650377904,Stool (prevalence),0.2573032
312 | t650377925,Stool (prevalence),0.035
313 | t650377929,Stool (prevalence),0.035
314 | t650377938,Stool (prevalence),0.2355206
315 | t650377943,Stool (prevalence),0.035
316 | t650377944,Cheek (prevalence),0.4919257
317 | t650377954,Stool (prevalence),0.035
318 | t650377958,Stool (prevalence),0.035
319 | t650377963,Stool (prevalence),0.035
320 | t650377966,Stool (prevalence),0.33431195
321 | t650377974,Stool (prevalence),0.035
322 | t650377975,Stool (prevalence),0.035
323 | t650633000,Stool (prevalence),0.035
324 | t650716045,Stool (prevalence),0.035
325 | t650716089,Tongue (prevalence),0.7958867
326 | t651053005,Stool (prevalence),0.035
327 | t651053029,Stool (prevalence),0.035
328 | t651053056,Stool (prevalence),0.035
329 | t651053058,Skin (prevalence),3.47356625
330 | t651053073,Tongue (prevalence),0.49160055
331 | t651324009,Skin (prevalence),0.035
332 | t651324012,Stool (prevalence),1.12855925
333 | t651324039,Tongue (prevalence),1.75505365
334 |
--------------------------------------------------------------------------------
/data-backup/tree_example_april2015/info.csv:
--------------------------------------------------------------------------------
1 | id,location,name,country,year,
CS14_Brazil_2001,other,CS14,Brazil,2001,
CS2_Brazil_1997,other,CS2,Brazil,1997,
CS20_Brazil_2002,other,CS20,Brazil,2002,
CS6_Brazil_2000,other,CS6,Brazil,2000,
CS7_Brazil_2000,other,CS7,Brazil,2000,
373_Cameroun_1973,other,373,Cameroun,1973,
32222_Cuba_2003,other,32222,Cuba,2003,
54185_Denmark_1945,other,54185,Denmark,1945,
20061758_Dominican,other,20061758,Dominican,,
20051272_Egypt_2005,other,20051272,Egypt,2005,
20060018_Egypt_2006,other,20060018,Egypt,2006,
20061309_Egypt_2006,other,20061309,Egypt,2006,
20062087_Egypt_Tunisia_2006,other,20062087,Egypt,Tunisia,2006
259_France_1959,other,259,France,1959,
476_France_1976,other,476,France,1976,
658_France_1958,other,658,France,1958,
1166_France_1966,other,1166,France,1966,
1167_France_1967,other,1167,France,1967,
1173_France_1973,other,1173,France,1973,
1263_France_1963,other,1263,France,1963,
1265_France_1965,other,1265,France,1965,
1267_France_1967,other,1267,France,1967,
1274_France_1974,other,1274,France,1974,
1460_France_1960,other,1460,France,1960,
1461_France_1961,other,1461,France,1961,
1761_France_1961,other,1761,France,1961,
2073_France_1973,other,2073,France,1973,
2574_France_1974,other,2574,France,1974,
4374_France_1974,other,4374,France,1974,
4474_France_1974,other,4474,France,1974,
8883_France_1983,other,8883,France,1983,
54213_France_1945,other,54213,France,1945,
998911_France_1999,other,998911,France,1999,
IB48279_France_NA,other,IB48279,France,NA,
pWR105_France_NA,other,pWR105,France,NA,
2225_French,other,2225,French,,
988743_French,other,988743,French,,
10014_HCMC_2009,HCMC,10014,HCMC,2009,
10021_HCMC_2009,HCMC,10021,HCMC,2009,
10031_HCMC_2009,HCMC,10031,HCMC,2009,
10035_HCMC_2009,HCMC,10035,HCMC,2009,
10060_HCMC_2009,HCMC,10060,HCMC,2009,
10063_HCMC_2009,HCMC,10063,HCMC,2009,
10071_HCMC_2009,HCMC,10071,HCMC,2009,
10073_HCMC_2009,HCMC,10073,HCMC,2009,
10083_HCMC_2009,HCMC,10083,HCMC,2009,
10093_HCMC_2009,HCMC,10093,HCMC,2009,
10102_HCMC_2009,HCMC,10102,HCMC,2009,
10111_HCMC_2009,HCMC,10111,HCMC,2009,
10115_HCMC_2009,HCMC,10115,HCMC,2009,
10134_HCMC_2009,HCMC,10134,HCMC,2009,
10135_HCMC_2009,HCMC,10135,HCMC,2009,
10152_HCMC_2009,HCMC,10152,HCMC,2009,
10159_HCMC_2009,HCMC,10159,HCMC,2009,
10188_HCMC_2009,HCMC,10188,HCMC,2009,
10263_HCMC_2009,HCMC,10263,HCMC,2009,
10320_HCMC_2010,HCMC,10320,HCMC,2010,
10365_HCMC_2010,HCMC,10365,HCMC,2010,
20006_HCMC_2009,HCMC,20006,HCMC,2009,
20021_HCMC_2009,HCMC,20021,HCMC,2009,
20023_HCMC_2009,HCMC,20023,HCMC,2009,
20037_HCMC_2009,HCMC,20037,HCMC,2009,
20070_HCMC_2009,HCMC,20070,HCMC,2009,
20094_HCMC_2009,HCMC,20094,HCMC,2009,
20228_HCMC_2009,HCMC,20228,HCMC,2009,
20261_HCMC_2009,HCMC,20261,HCMC,2009,
20263_HCMC_2009,HCMC,20263,HCMC,2009,
20343_HCMC_2009,HCMC,20343,HCMC,2009,
30003_HCMC_2009,HCMC,30003,HCMC,2009,
30008_HCMC_2009,HCMC,30008,HCMC,2009,
30010_HCMC_2009,HCMC,30010,HCMC,2009,
30037_HCMC_2009,HCMC,30037,HCMC,2009,
30054_HCMC_2009,HCMC,30054,HCMC,2009,
30059_HCMC_2009,HCMC,30059,HCMC,2009,
30071_HCMC_2009,HCMC,30071,HCMC,2009,
30073_HCMC_2009,HCMC,30073,HCMC,2009,
30100_HCMC_2009,HCMC,30100,HCMC,2009,
30112_HCMC_2009,HCMC,30112,HCMC,2009,
30124_HCMC_2009,HCMC,30124,HCMC,2009,
30162_HCMC_2009,HCMC,30162,HCMC,2009,
30164_HCMC_2009,HCMC,30164,HCMC,2009,
30169_HCMC_2009,HCMC,30169,HCMC,2009,
30172_HCMC_2009,HCMC,30172,HCMC,2009,
30174_HCMC_2009,HCMC,30174,HCMC,2009,
30233_HCMC_2009,HCMC,30233,HCMC,2009,
30293_HCMC_2009,HCMC,30293,HCMC,2009,
30366_HCMC_2009,HCMC,30366,HCMC,2009,
30371_HCMC_2009,HCMC,30371,HCMC,2009,
30387_HCMC_2010,HCMC,30387,HCMC,2010,
30450_HCMC_2010,HCMC,30450,HCMC,2010,
30451_HCMC_2010,HCMC,30451,HCMC,2010,
DE0115_HCMC_2000,HCMC,DE0115,HCMC,2000,
DE0127_HCMC_2000,HCMC,DE0127,HCMC,2000,
DE0199_HCMC_2000,HCMC,DE0199,HCMC,2000,
DE0248_HCMC_2000,HCMC,DE0248,HCMC,2000,
DE0295_HCMC_2000,HCMC,DE0295,HCMC,2000,
DE0303_HCMC_2000,HCMC,DE0303,HCMC,2000,
DE0306_HCMC_2000,HCMC,DE0306,HCMC,2000,
DE0330_HCMC_2000,HCMC,DE0330,HCMC,2000,
DE0427_HCMC_2000,HCMC,DE0427,HCMC,2000,
DE0477_HCMC_2000,HCMC,DE0477,HCMC,2000,
DE0489_HCMC_2000,HCMC,DE0489,HCMC,2000,
DE0490_HCMC_2000,HCMC,DE0490,HCMC,2000,
DE0579_HCMC_2001,HCMC,DE0579,HCMC,2001,
DE0611_HCMC_2001,HCMC,DE0611,HCMC,2001,
DE0654_HCMC_2001,HCMC,DE0654,HCMC,2001,
DE0655_HCMC_2001,HCMC,DE0655,HCMC,2001,
DE0685_HCMC_2001,HCMC,DE0685,HCMC,2001,
DE0816_HCMC_2001,HCMC,DE0816,HCMC,2001,
DE0846_HCMC_2001,HCMC,DE0846,HCMC,2001,
DE0885_HCMC_2001,HCMC,DE0885,HCMC,2001,
DE0891_HCMC_2001,HCMC,DE0891,HCMC,2001,
DE0900_HCMC_2001,HCMC,DE0900,HCMC,2001,
DE0965_HCMC_2001,HCMC,DE0965,HCMC,2001,
DE1063_HCMC_2002,HCMC,DE1063,HCMC,2002,
DE1140_HCMC_2002,HCMC,DE1140,HCMC,2002,
DE1150_HCMC_2002,HCMC,DE1150,HCMC,2002,
DE1165_HCMC_2002,HCMC,DE1165,HCMC,2002,
DE1191_HCMC_2002,HCMC,DE1191,HCMC,2002,
DE1198_HCMC_2002,HCMC,DE1198,HCMC,2002,
DE1208_HCMC_2002,HCMC,DE1208,HCMC,2002,
DE1209_HCMC_2002,HCMC,DE1209,HCMC,2002,
DE1256_HCMC_2002,HCMC,DE1256,HCMC,2002,
DE1318_HCMC_2002,HCMC,DE1318,HCMC,2002,
DE1336_HCMC_2002,HCMC,DE1336,HCMC,2002,
DE1404_HCMC_2002,HCMC,DE1404,HCMC,2002,
DE1486_HCMC_2002,HCMC,DE1486,HCMC,2002,
EG0129_HCMC_2007,HCMC,EG0129,HCMC,2007,
EG0159_HCMC_2007,HCMC,EG0159,HCMC,2007,
EG0204_HCMC_2008,HCMC,EG0204,HCMC,2008,
EG0255_HCMC_2008,HCMC,EG0255,HCMC,2008,
EG0304_HCMC_2006,HCMC,EG0304,HCMC,2006,
EG0309_HCMC_2006,HCMC,EG0309,HCMC,2006,
EG0313_HCMC_2006,HCMC,EG0313,HCMC,2006,
EG0315_HCMC_2006,HCMC,EG0315,HCMC,2006,
EG0318_HCMC_2006,HCMC,EG0318,HCMC,2006,
EG0352_HCMC_2007,HCMC,EG0352,HCMC,2007,
EG0357_HCMC_2007,HCMC,EG0357,HCMC,2007,
EG0362_HCMC_2007,HCMC,EG0362,HCMC,2007,
EG0365_HCMC_2007,HCMC,EG0365,HCMC,2007,
EG0369_HCMC_2007,HCMC,EG0369,HCMC,2007,
EG0372_HCMC_2007,HCMC,EG0372,HCMC,2007,
EG0373_HCMC_2007,HCMC,EG0373,HCMC,2007,
EG0375_HCMC_2007,HCMC,EG0375,HCMC,2007,
EG0379_HCMC_2007,HCMC,EG0379,HCMC,2007,
EG0383_HCMC_2007,HCMC,EG0383,HCMC,2007,
EG0385_HCMC_2007,HCMC,EG0385,HCMC,2007,
EG0386_HCMC_2007,HCMC,EG0386,HCMC,2007,
EG0388_HCMC_2007,HCMC,EG0388,HCMC,2007,
EG0390_HCMC_2007,HCMC,EG0390,HCMC,2007,
EG0392_HCMC_2007,HCMC,EG0392,HCMC,2007,
EG0393_HCMC_2007,HCMC,EG0393,HCMC,2007,
EG0394_HCMC_2007,HCMC,EG0394,HCMC,2007,
EG0395_HCMC_2007,HCMC,EG0395,HCMC,2007,
EG0401_HCMC_2007,HCMC,EG0401,HCMC,2007,
EG0404_HCMC_2007,HCMC,EG0404,HCMC,2007,
EG0410_HCMC_2007,HCMC,EG0410,HCMC,2007,
EG0425_HCMC_2008,HCMC,EG0425,HCMC,2008,
EG0430_HCMC_2008,HCMC,EG0430,HCMC,2008,
EG0451_HCMC_2008,HCMC,EG0451,HCMC,2008,
EG0467_HCMC_2008,HCMC,EG0467,HCMC,2008,
EG0472_HCMC_2008,HCMC,EG0472,HCMC,2008,
EG1001_HCMC_2008,HCMC,EG1001,HCMC,2008,
EG1004_HCMC_2008,HCMC,EG1004,HCMC,2008,
EG1008_HCMC_2008,HCMC,EG1008,HCMC,2008,
EG1014_HCMC_2009,HCMC,EG1014,HCMC,2009,
EG1015_HCMC_2009,HCMC,EG1015,HCMC,2009,
EG1016_HCMC_2009,HCMC,EG1016,HCMC,2009,
EG1017_HCMC_2009,HCMC,EG1017,HCMC,2009,
EG1018_HCMC_2009,HCMC,EG1018,HCMC,2009,
EG1019_HCMC_2009,HCMC,EG1019,HCMC,2009,
EG1020_HCMC_2009,HCMC,EG1020,HCMC,2009,
EG1021_HCMC_2009,HCMC,EG1021,HCMC,2009,
EG1022_HCMC_2009,HCMC,EG1022,HCMC,2009,
EG1023_HCMC_2009,HCMC,EG1023,HCMC,2009,
EG1024_HCMC_2009,HCMC,EG1024,HCMC,2009,
EG1025_HCMC_2009,HCMC,EG1025,HCMC,2009,
EG1026_HCMC_0,HCMC,EG1026,HCMC,0,
EG1027_HCMC_0,HCMC,EG1027,HCMC,0,
EG1028_HCMC_0,HCMC,EG1028,HCMC,0,
EG1029_HCMC_0,HCMC,EG1029,HCMC,0,
HUE01_HCMC_2008,Hue,HUE01,HCMC,2008,
HUE02_HCMC_2008,Hue,HUE02,HCMC,2008,
HUE05_HCMC_2008,Hue,HUE05,HCMC,2008,
HUE16_HCMC_2009,Hue,HUE16,HCMC,2009,
HUE17_HCMC_2009,Hue,HUE17,HCMC,2009,
HUE19_HCMC_2009,Hue,HUE19,HCMC,2009,
HUE20_HCMC_2009,Hue,HUE20,HCMC,2009,
HUE21_HCMC_2009,Hue,HUE21,HCMC,2009,
HUE22_HCMC_2009,Hue,HUE22,HCMC,2009,
HUE23_HCMC_2009,Hue,HUE23,HCMC,2009,
HUE24_HCMC_2009,Hue,HUE24,HCMC,2009,
HUE25_HCMC_2009,Hue,HUE25,HCMC,2009,
HUE26_HCMC_2009,Hue,HUE26,HCMC,2009,
HUE27_HCMC_2009,Hue,HUE27,HCMC,2009,
HUE29_HCMC_2009,Hue,HUE29,HCMC,2009,
HUE30_HCMC_2009,Hue,HUE30,HCMC,2009,
HUE31_HCMC_2009,Hue,HUE31,HCMC,2009,
HUE32_HCMC_2009,Hue,HUE32,HCMC,2009,
HUE33_HCMC_2009,Hue,HUE33,HCMC,2009,
HUE34_HCMC_2009,Hue,HUE34,HCMC,2009,
HUE40_HCMC_2009,Hue,HUE40,HCMC,2009,
HUE42_HCMC_2009,Hue,HUE42,HCMC,2009,
HUE43_HCMC_2009,Hue,HUE43,HCMC,2009,
HUE46_HCMC_2009,Hue,HUE46,HCMC,2009,
HUE47_HCMC_2009,Hue,HUE47,HCMC,2009,
HUE48_HCMC_2009,Hue,HUE48,HCMC,2009,
HUE50_HCMC_2009,Hue,HUE50,HCMC,2009,
HUE53_HCMC_2010,Hue,HUE53,HCMC,2010,
HUE55_HCMC_2010,Hue,HUE55,HCMC,2010,
HUE57_HCMC_2010,Hue,HUE57,HCMC,2010,
HUE58_HCMC_2010,Hue,HUE58,HCMC,2010,
HUE60_HCMC_2010,Hue,HUE60,HCMC,2010,
HUE62_HCMC_2010,Hue,HUE62,HCMC,2010,
HUE64_HCMC_2010,Hue,HUE64,HCMC,2010,
HUE67_HCMC_2010,Hue,HUE67,HCMC,2010,
HUE68_HCMC_2010,Hue,HUE68,HCMC,2010,
KH02_HCMC_2009,KH,KH02,HCMC,2009,
KH04_HCMC_2009,KH,KH04,HCMC,2009,
KH05_HCMC_2009,KH,KH05,HCMC,2009,
KH06_HCMC_2009,KH,KH06,HCMC,2009,
KH07_HCMC_2009,KH,KH07,HCMC,2009,
KH09_HCMC_2009,KH,KH09,HCMC,2009,
KH10_HCMC_2009,KH,KH10,HCMC,2009,
KH11_HCMC_2009,KH,KH11,HCMC,2009,
KH12_HCMC_2009,KH,KH12,HCMC,2009,
KH13_HCMC_2009,KH,KH13,HCMC,2009,
KH14_HCMC_2009,KH,KH14,HCMC,2009,
KH15_HCMC_2009,KH,KH15,HCMC,2009,
KH16_HCMC_2009,KH,KH16,HCMC,2009,
KH17_HCMC_2009,KH,KH17,HCMC,2009,
KH18_HCMC_2009,KH,KH18,HCMC,2009,
KH19_HCMC_2009,KH,KH19,HCMC,2009,
KH20_HCMC_2009,KH,KH20,HCMC,2009,
KH21_HCMC_2009,KH,KH21,HCMC,2009,
KH23_HCMC_2009,KH,KH23,HCMC,2009,
KH24_HCMC_2009,KH,KH24,HCMC,2009,
KH25_HCMC_2009,KH,KH25,HCMC,2009,
KH26_HCMC_2009,KH,KH26,HCMC,2009,
KH27_HCMC_2009,KH,KH27,HCMC,2009,
KH28_HCMC_2009,KH,KH28,HCMC,2009,
KH29_HCMC_2009,KH,KH29,HCMC,2009,
KH30_HCMC_2009,KH,KH30,HCMC,2009,
KH32_HCMC_2009,KH,KH32,HCMC,2009,
KH33_HCMC_2009,KH,KH33,HCMC,2009,
KH34_HCMC_2009,KH,KH34,HCMC,2009,
KH35_HCMC_2009,KH,KH35,HCMC,2009,
KH37_HCMC_2009,KH,KH37,HCMC,2009,
KH38_HCMC_2009,KH,KH38,HCMC,2009,
KH40_HCMC_2009,KH,KH40,HCMC,2009,
KH41_HCMC_2009,KH,KH41,HCMC,2009,
KH42_HCMC_2010,KH,KH42,HCMC,2010,
KH43_HCMC_2010,KH,KH43,HCMC,2010,
KH45_HCMC_2010,KH,KH45,HCMC,2010,
KH53_HCMC_2010,KH,KH53,HCMC,2010,
KH54_HCMC_2010,KH,KH54,HCMC,2010,
KH55_HCMC_2010,KH,KH55,HCMC,2010,
KH57_HCMC_2010,KH,KH57,HCMC,2010,
MS0004_HCMC_1995,HCMC,MS0004,HCMC,1995,
MS0011_HCMC_1995,HCMC,MS0011,HCMC,1995,
MS0032_HCMC_1995,HCMC,MS0032,HCMC,1995,
MS0034_HCMC_1995,HCMC,MS0034,HCMC,1995,
MS0035_HCMC_1995,HCMC,MS0035,HCMC,1995,
MS0039_HCMC_1995,HCMC,MS0039,HCMC,1995,
MS0042_HCMC_1995,HCMC,MS0042,HCMC,1995,
MS0043_HCMC_1995,HCMC,MS0043,HCMC,1995,
MS0048_HCMC_1995,HCMC,MS0048,HCMC,1995,
MS0063_HCMC_1995,HCMC,MS0063,HCMC,1995,
MS0065_HCMC_1995,HCMC,MS0065,HCMC,1995,
MS0069_HCMC_1995,HCMC,MS0069,HCMC,1995,
MS0070_HCMC_1995,HCMC,MS0070,HCMC,1995,
MS0080_HCMC_1995,HCMC,MS0080,HCMC,1995,
MS0083_HCMC_1996,HCMC,MS0083,HCMC,1996,
MS0094_HCMC_1996,HCMC,MS0094,HCMC,1996,
MS0102_HCMC_1996,HCMC,MS0102,HCMC,1996,
MS0110_HCMC_1996,HCMC,MS0110,HCMC,1996,
MS0111_HCMC_1996,HCMC,MS0111,HCMC,1996,
MS0119_HCMC_1996,HCMC,MS0119,HCMC,1996,
MS0122_HCMC_1996,HCMC,MS0122,HCMC,1996,
MS0127_HCMC_1996,HCMC,MS0127,HCMC,1996,
MS0128_HCMC_1996,HCMC,MS0128,HCMC,1996,
20031275_Iran_2003,other,20031275,Iran,2003,
31382_Israel_2003,other,31382,Israel,2003,
20040924_Kenya_Egypt_2004,other,20040924,Kenya,Egypt,2004
IB1_Korea_2003,other,IB1,Korea,2003,
IB10_Korea_2003,other,IB10,Korea,2003,
IB2_Korea_2003,other,IB2,Korea,2003,
IB2493_Korea_NA,other,IB2493,Korea,NA,
IB3_Korea_2003,other,IB3,Korea,2003,
IB681_Korea_1991,other,IB681,Korea,1991,
IB683_Korea_1994,other,IB683,Korea,1994,
IB687_Korea_1998,other,IB687,Korea,1998,
IB690_Korea_2000,other,IB690,Korea,2000,
IB691_Korea_1999,other,IB691,Korea,1999,
IB694_Korea_1979,other,IB694,Korea,1979,
IB695_Korea_1983,other,IB695,Korea,1983,
IB696_Korea_1980,other,IB696,Korea,1980,
IB697_Korea_1982,other,IB697,Korea,1982,
IB698_Korea_1983,other,IB698,Korea,1983,
IB713_Korea_1981,other,IB713,Korea,1981,
IB716_Korea_1981,other,IB716,Korea,1981,
IB717_Korea_1982,other,IB717,Korea,1982,
IB739_Korea_1985,other,IB739,Korea,1985,
IB748_Korea_1987,other,IB748,Korea,1987,
53G_Korea_2000,other,53G,Korea,2000,
5827_Madagascar_2000,other,5827,Madagascar,2000,
989560_Madagascar_1998,other,989560,Madagascar,1998,
9810267_Madagascar_1998,other,9810267,Madagascar,1998,
19984123_Mexico_1998,other,19984123,Mexico,1998,
20062313_Nepal_2006,other,20062313,Nepal,2006,
970044_New,other,970044,New,,
IB3277_Pakistan_2002,other,IB3277,Pakistan,2002,
IB3300_Pakistan_2002,other,IB3300,Pakistan,2002,
IB3374_Pakistan_2002,other,IB3374,Pakistan,2002,
IB3488_Pakistan_2003,other,IB3488,Pakistan,2003,
IB3507_Pakistan_2003,other,IB3507,Pakistan,2003,
IB3580_Pakistan_2003,other,IB3580,Pakistan,2003,
IB3599_Pakistan_2003,other,IB3599,Pakistan,2003,
20052631_Peru_2005,other,20052631,Peru,2005,
273_Senegal_1973,other,273,Senegal,1973,
1567_Senegal_1967,other,1567,Senegal,1967,
36224_Senegal_2003,other,36224,Senegal,2003,
20040880_SriLanka_2004,other,20040880,SriLanka,2004,
Ss046_Ref_1950,other,Ss046,Ref,1950,
54178_Sweden_1945,other,54178,Sweden,1945,
54179_Sweden_1944,other,54179,Sweden,1944,
54181_Sweden_1945,other,54181,Sweden,1945,
54184_Sweden_1945,other,54184,Sweden,1945,
54190_Sweden_1945,other,54190,Sweden,1945,
54210_Sweden_1943,other,54210,Sweden,1943,
54216_Sweden_1946,other,54216,Sweden,1946,
54228_Sweden_1947,other,54228,Sweden,1947,
19904011_unk_1990,other,19904011,unk,1990,
19910761_unk_1991,other,19910761,unk,1991,
19911483_unk_1991,other,19911483,unk,1991,
19920319_unk_1992,other,19920319,unk,1992,
20003593_unk_2000,other,20003593,unk,2000,
20010007_unk_2001,other,20010007,unk,2001,
20011685_unk_2001,other,20011685,unk,2001,
20021122_unk_2002,other,20021122,unk,2002,
20040489_unk_2004,other,20040489,unk,2004,
20041367_unk_2004,other,20041367,unk,2004,
20071599_unk_2007,other,20071599,unk,2007,
20081885_unk_2008,other,20081885,unk,2008,
20051541_Uzbekistan_2005,other,20051541,Uzbekistan,2005,
IB1970_Vietnam_2001,VN,IB1970,Vietnam,2001,
IB1976_Vietnam_2002,VN,IB1976,Vietnam,2002,
IB1980_Vietnam_2002,VN,IB1980,Vietnam,2002,
IB1985_Vietnam_2002,VN,IB1985,Vietnam,2002,
IB1987_Vietnam_2002,VN,IB1987,Vietnam,2002,
IB1990_Vietnam_2003,VN,IB1990,Vietnam,2003,
IB1993_Vietnam_2003,VN,IB1993,Vietnam,2003,
IB1995_Vietnam_2003,VN,IB1995,Vietnam,2003,
IB1997_Vietnam_2003,VN,IB1997,Vietnam,2003,
IB2000_Vietnam_2003,VN,IB2000,Vietnam,2003,
IB2004_Vietnam_2003,VN,IB2004,Vietnam,2003,
IB2008_Vietnam_2003,VN,IB2008,Vietnam,2003,
IB2009_Vietnam_2003,VN,IB2009,Vietnam,2003,
IB2012_Vietnam_2001,VN,IB2012,Vietnam,2001,
IB2013_Vietnam_2001,VN,IB2013,Vietnam,2001,
IB2015_Vietnam_2002,VN,IB2015,Vietnam,2002,
IB2018_Vietnam_2002,VN,IB2018,Vietnam,2002,
IB2024_Vietnam_2002,VN,IB2024,Vietnam,2002,
IB2026_Vietnam_2003,VN,IB2026,Vietnam,2003,
#N/A,other,#N/A,,,
10083_HCMC_NA,other,10083,HCMC,NA,
10093_HCMC_NA,other,10093,HCMC,NA,
10102_HCMC_NA,other,10102,HCMC,NA,
10111_HCMC_NA,other,10111,HCMC,NA,
10115_HCMC_NA,other,10115,HCMC,NA,
10134_HCMC_NA,other,10134,HCMC,NA,
10135_HCMC_NA,other,10135,HCMC,NA,
10152_HCMC_NA,other,10152,HCMC,NA,
10159_HCMC_NA,other,10159,HCMC,NA,
10188_HCMC_NA,other,10188,HCMC,NA,
10014_HCMC_NA,other,10014,HCMC,NA,
10263_HCMC_NA,other,10263,HCMC,NA,
10320_HCMC_NA,other,10320,HCMC,NA,
10365_HCMC_NA,other,10365,HCMC,NA,
10021_HCMC_NA,other,10021,HCMC,NA,
10031_HCMC_NA,other,10031,HCMC,NA,
10035_HCMC_NA,other,10035,HCMC,NA,
10060_HCMC_NA,other,10060,HCMC,NA,
10063_HCMC_NA,other,10063,HCMC,NA,
10071_HCMC_NA,other,10071,HCMC,NA,
10073_HCMC_NA,other,10073,HCMC,NA,
--------------------------------------------------------------------------------
/data-backup/sequence.fasta:
--------------------------------------------------------------------------------
1 | >Phy000B0HV_NEUCR
2 | M-----GIGSATLG-----------------------------------SRIPTPVLVARAVVSSSDGK-----DC--VA
3 | NPNLCEKP-VGGSQLTVPIVLGLW----------------RNMKKLAAEEAHDPHKSLDFGLDENM-----------GKA
4 | KGRNMAG------EKDGNGSRFHAHQMSMDMNLSSPYLLPPDAH-GSQSSLNSLARTL-NPQDDPFRPVTQYTASDAASV
5 | KSMP-R-----GTD-----------R-------GPGG------PFRGPPPRQGSMP-RSPEPTHA---RPGNG----PRP
6 | PRI-SVQD------------P---SSNA-TS-D--NE-TS----------------------D----------------S
7 | ERTLT-GSPRELHAATHK-------------------DGVKPPA-SPSQPISPANP-AV---------------------
8 | >Phy000FCLK_ASPCL
9 | --------------------------------------------------------------------------------
10 | -------------------------------------------------------------------------------M
11 | -------------REAEKGNPMHAKGMSLDIV-PSPYLLPPGLH-GSRESLHSLSRSV-IGDDDKYRHATSFL-GDNASV
12 | RSQP-R---G-YHDDAMTYSR-SQ-S------K-VS------M--R-DDMNQGLLQ------------NAQRMSR--SSP
13 | PL-YNTPPDGGSVHSPVGQD------------------------------------------R-----------------
14 | GQDSG-LQLNLPRSLSPVHI-----------------PGFNGSR-GPSPV-----P-TS-PE------GNDDKLPS----
15 | >Phy000FJDH_ASPFL
16 | MH---YHHRHQT--HQDIHMV-VRSPP-RRPDI-VPRHRLP------YLV-PEPPTFVKRDSDPS--------QTCSAGD
17 | TSSKCEKPTSTTTTTTLPVVLGAVVPILCAV-IVLIYLHRRNVRKLRSEDANDKHRSLDFGLDLEP-TG----GGNA--M
18 | R-------Q----TEKSNGSYNHNKGISLDIG-PSPYLLPPGLH-GSRDSLHSLSRSI--GGDDKYRHATSFL-GDNASV
19 | RSQS-R---G-AQDDAPSFTG-SA-R------K-AA------L---GDDMKQGLLG------------NAQRMSR--SSP
20 | PL-YISPGEDGA-HVQVDPI------------------------------------------A-----------------
21 | QPDHG-FQFELPRSPSPVLI-----------------PGAPSTK-ESITP-----TNNV-DK------------------
22 | >Phy000FQ5O_ASPFU
23 | MH---HHHQHLHFPRHGIHLA-VRSPP-RRPDI-VPRDRVP------LLVGTEDPTLVKRVPSTSTTSTA--STRCPEGD
24 | TSSACEKYTNSSSTTTLPIVLGAVIPIVCAI-IVLFYLHRRNVKKLRQEDANDKHKSLDFGLDLEP-RA----GSKP--M
25 | -------------TQAEKGSNMHSKGMSLDIG-QSPYLLPPGLH-GSRESLHSLSRSI-IGDDDKYRHASSFL-GDNASV
26 | RSQP-R---G-FHDETSAFSR-SQ-S------K-AS------L--RGDDMNQGLLQ------------NAQRMSR--SSP
27 | PL-YNAPSDGGSSHSPRGQG------------------------------------------N-----------------
28 | GQDMG-LQLNLPRSLSPVHI-----------------PGVNGSR-GTSPA-----P-GGHAD------GSEDISSS----
29 | >Phy000G05U_EMENI
30 | MH---RHQQHQH--RHGKYLG-ARFAP-VEPAL-MPRNRPP------YLLMPEAPTLVKREPMPTTDSGR--VETCSPGD
31 | NSARCEKNTSTASNTTLPVVLGAVIPIVCAI-IVLIFLHRRNVKKLRNEDANDKHKSLDFGMDLAP-SG----GRSG--M
32 | Q-------E---------KGSHHMKGISLDIG-PSPYMLPPSIR-GSKDSLNSLPRTI-LADDDKYRHAHTYFSTDAQSI
33 | RSQR-R-----VHDDAASVAG-ST-R------R-GA------F---GDEMNQGLLG------------NAQRISR--SSP
34 | PL-YNPPEPTAGRAQ----P------------------------------------------Q-----------------
35 | VQDAG-FELSLPRSPSPVHV-----------------SGLTSIN-ESTTE-----TGRE-AN------------------
36 | >Phy000GDP6_ASPNG
37 | M--------------------------------------------------RETPTLARREPLPSTDSSS------ASSS
38 | TASSGTKPTSTLTTTTLPVVLGAVVPIVIAI-GILLYLHRRNVKKLRNEDANDKHKSLDFGLDLAP-TN----GAVP--M
39 | Q-------Q----AEKTDRNAAHNKGISLDIG-PSPYLLPPGLH-NSRESLASMSRSIGDGDDDKYRHVGSFL-GDNSSL
40 | RSHS-R---G-PHDDAASFTG-ST-R------R-AA------L---GDDMNQGLLR------------NAQRMSR--SSP
41 | PL-YKTSSGDRNVQSPASSD------------------------------------------H-----------------
42 | EHDHG-FQLDLPRSPSPVHV-----------------PGMAISE-PHT-------TSNE-VG------FAGDHAVTETSA
43 | >Phy000HD5X_BOTFU
44 | MADHQRLANIVRLARRV----P--LAE-AAAED-IGNIASIL------KMSLPDPVLMVRSATTSAAASS---STCAADD
45 | TSAACEKP-VGPSAYTLPVILGIVIPVGGAI-ILFTILQRRYMKKAREEDLNDPTKNMDFGMGRIS-R--T------AGG
46 | ESG-I--S---N-FDDEKGGAVRTRQMSLDLGGKSPYLLPPELH-NSRESLHSLSRTI-HSNEDPYRPVHEAV---GGSI
47 | RSKQ-------GRNGSSIMTESSA-A--------PSK-----MYDAGSPDGQGLLS------------NAAAMSR--TTP
48 | PSTGSSPP------------P---KSNS--I-----P-P-----------------------------------------
49 | -ANMP-AEPKQAESPQNVARKGL--------------PGNFRPQ-DRFPTAMPVPM-PYP-------------DRESYAG
50 | >Phy000IAZP_COCIM
51 | MA------------RHTYRDP--SRLV-SRALA-IPVERSI------ILTALEPPSLVKRNPADAASSSSVPTKTCGPDD
52 | TTGVCTRPVNSTTTLTLPIVLGAVIPLTCAF-IAFFFLHRRHVKKLRLEDANDKHKSLDFGLDFVP-SG----SNNNRRG
53 | NGGNG-P------SMAEKSTRRGGHGVSMDLTLNSPYLLPPGLH-GSHESIHSLSRSL-HGEDDKYRHASAFPTGDSGSI
54 | RSCS-PSFKRGGDD-ASSHNSPSS-K------Y-PY----------GDDMNQHLLK------------NAQRMSR--SPP
55 | AI-ELDPIESDLGHPPHHA--------------------T----------------------A-----VSASE------S
56 | GNTTF-HGRSELTVPTAVSS-----------------HGDRSSS-SSSER-----DDSV-LR---------KS-------
57 | >Phy000KG2Q_MAGGR
58 | MVGVTVHEGEYHLGSRM----P--VMA-RDAST-PAL-QIAA------DGPGFFKRLVARQSSDD----------CVNGE
59 | PSNLCEKP-VTSQTLALPIALGVTIPLVALV-VMLIWLHRKNVRRQRQEDANDPHKSLDFGLDMGP------------GK
60 | RKS-K--L---F-GGEKLGGGPHNRQISMDMNLSSPYLLPPNMQ-NSRESIHSLAKTL--HNEDPYRHITQYNASDAGSL
61 | RSYK-A---G-GMD-------------------RPIG-----PKITVPTSRKGSLQATSPTSTIGSVPPRYEASQ---DD
62 | YV-KPPPP------------A---ALK---S-P--TQ-DS----------------------TPYPDDKSGP-------L
63 | ATVMP-SVP-EIQEPKPASLSK----E-SS---QAPS---LAAV-PPSSPLTISAP-EI---------------------
64 | >Phy000ODBJ_SCLSC
65 | MEDHQRLANIVRLARRV----P--LAE-AAAED-IDNIASIL------KMAVPDRVIMGRSSTTTSSTSS---STCAADD
66 | TSAACEKP-IGPSAYTLPVILGIVIPVAGAI-ILFTILQRRYTKRAREEDANDPTKNMDFGMGRIS-R--T------AGG
67 | ESS-I--S---N-FDDEKGDSGRPRQMSLDLGGKSPYLLPPELQ-DSRESLHSLSKTI-HQNEDPYRPVHEAV--GAASI
68 | RSKQ-------GRNGSSILSASTV-A--------PSG-----MNDTGSPDGQGLLS------------NAAAMSR--TTP
69 | PTAGFNPP------------P---RSNS--I-----P-P-----------------------------------------
70 | -AKMP-EEPRQSP-EQNVDKKGP--------------PGNFRPQ-NGFSSTRSIPM-PFL-------------DWESYAG
71 | >Phy000PFY6_UNCRE
72 | MA------------RHAFQPA--SGLV-PRALA-IPLDRSI------LLTSLDHPSHVKRSPAATASSSAAATTSCGPND
73 | TTGICTRPVSSTTTMTLPIVLGAAIPITCAI-IAFFFLHRRHVKKLRLEDANDKHKSLDFGLDFVP-SG----SNNNKRG
74 | NGGNG-G------LMGEKSTRQRAHGVSLDLTMGNPYLLPPVSM-GSHESIHSLSKSL-HGGDDKYRHAAAFPSSENRF-
75 | --------------------------------------------------RHSVLQ------------PTNPLA---SEP
76 | RS-PLSPPGRNELTKLKQQ--------------------L----------------------------------------
77 | ------------------------------------------------DK-----EQSV-LR---------KS-------
78 | >Phy00201Y5_COCHE
79 | M-------------------------------------------------------------------------------
80 | --------------------CATTVPVVGIA-VVLAFLHRRNKQKLREEDQRDKYKSNDWGMEGVIPK---------TSK
81 | KGG-P-EM---S-ISEKEISGGHDRGLSIETG--SPYILPPGLH-GSRESFHSLSRST-HDPHDPYGPVAFLR--DDQSL
82 | RSH----GPY-KGETNSVYT--A-SS-------SGT---------KKEGLQAGLLQ------------NAQRMST--SAP
83 | VR-GESLS------------P---DSTR--SPD--SK-FAEAGIPLSPLNPRYEPEAPA-----AAPAPAPA-------P
84 | AHAAP-VASKPTDVP-TI----------------------SIPE-PQVTEKQV---------------------------
85 | >Phy00208KX_MYCGR
86 | MY---IPRA------------EDS-----------R-VQRMV------DGAAAGLRIVARSL------A-------ERAE
87 | SNSKEDTPNDRMKVQNIGIALGVIIPIGGAI-IVLTYLHRRHVKRQRVEDMNDPHKSLDFGLEGLG-SMPPQAPKKSRRG
88 | KKGPE-MIV-TDFGGPTAHPSKRGHGMSLDLGVPSPYLLPAGLQ-GSKESIHSMSRN--YDEHDPYRSVAMM--RPSGET
89 | DRF---R----GDDKGSVYSMSTG-N------R-SA------L--PQD--RASLIA------------NARPMS---ITP
90 | SK-RSDPATSHPSTPADVSP------------------------------------------R-----------------
91 | DSHSPISRTRSPLAKLSVDE-----------------TAIAEKQLEPLPS-----P-PTVPE------VALMMPPP-RKS
92 | >Phy0020GNV_PYRTR
93 | MP---HSHHLHHMRHQL----R--HDN-QLGSP-ITGSKTMH------VFERATRVLVARAESS-----------C-TND
94 | SDPGCTKP---TQVPTMAIALAVIVPIVGVS-IVLCFLHRRNKRKLAEEDSKDQYKSNDWGMEGVA-K---------TNK
95 | KKR-P-EM---S-LSEKDAGGGHDRGLSIEAG--SPYILPVGLH-GSRESFHSLSRSQ-HDPHDPYGPVAFLK--DDQST
96 | RGSSVRGGPY-RNETGSVYT--T-SS-------SGT---------RKEGLQAGLLQ------------NAQRMST--SNP
97 | VR-GDSLS------------P---VSTS--SPD--TK-FPDPGIPLSPLNPRFENQSPI-----SPPAASPS-------P
98 | S-------IKPNSVP-TI----------------------SIPE-PGVTEKQV---------------------------
99 | >Phy0022J75_CRYPA
100 | MDEMLARRNGHLMGPRI----P--IGR-RVAAV--AE-DTSV------EASTPPSHVVGRSSSSTSDASSST-ATCSSSS
101 | ASNTCEKP--TSTSIAGEISIGIAVPMAIIFICVLIYFHRRNLKRQAAEDRDPHHRSLDFGLGDTS-S----------GK
102 | SKR-K-SM-----LGLGGEKSKHPRGLSIDMNLSSPYLLPEHVQ-GSRESMNSLAKTL-HQADDPYRPITKYM-SETGSV
103 | GSLE-K---N-GRYTPSVMTASTK-RVSRQSYANPM------SPALQQPLRQNSYP-KSPLTPSAA--------------
104 | ----SSVT------------A---VETDIST-P--TAAKE----------------------PTVPEDGPMPPPQC---D
105 | LPPLP-VVP-EIRQPAPVAQRGAA----REPVMQEHEEELDLPD-FSNNSKRESAD-EL---------------------
106 | >Phy0022OIS_VERA1
107 | MAATAFNGNGYRMGSRI----H--VRT-AEPTHEDAA-L----------LRSPGPVIAARKEC---------------DP
108 | DHPDCEAPAVKPQTLI--IALSVVIPIVAIM-SILYYLHRRGIKKQRMEEASDPTMSLDFGINDDK-M---------GRG
109 | GKRKS-VF---R-EKMLNLDPKHRAQVSMDMNLSSPYLLPPALQ-GSKQSLHSLARNL-HDDDDPYRPVNQYG-SEVGSI
110 | RSFRPEK--E-GRAGSSVYTGSTE-R-------GSSL------HSRTHPPRQNSLP-KPPPLT-A---DPFATPTGARTP
111 | QLETSPIS------------P---TGGS--------L-PH----------------------AIIPEIGTVSYAEDFDDS
112 | NRNLP-HVP-DVTQPAPVAQRDARRVSSGASQSSWNEPAAQFPD-PAAHQVHNAAP-TL---------------------
113 | >Phy003AMS0_602072
114 | M--------------------------------------------------AETPTLARREPLPSTESSS-------SSS
115 | SSSSETKPTSTLTTTTLPVVLGAVIPVVIAI-AILLYLHRRNVKKLRNEDANDKHKSLDFGLDLAP-T-----GAKP--M
116 | Q-------Q----AEKLDRNAAHNKGVSLDIG-PSPYLLPPGLH-NSRESLSSLSRSIGDGDDDKYRHVGSFL-GDNASL
117 | RSHS-R---G-PQDDASSFTG-ST-R------R-GA------L---GDDMNQGLLR------------NAQRMSR--SSP
118 | PL-YTIPSGDRNVQSPASSD------------------------------------------H-----------------
119 | ERDPG-FQLDLPRSPSPVHV-----------------PGMTISE-PTNSM-----TSNE-PE------FSGVHANTENSA
120 | >Phy003BKXA_GIBZE
121 | MGLTHYH--DQ----------R--ADIGQGASS-ISQ-KMAS------SSSHIFRRLARRENC----------------K
122 | DDNSCAQS-SVSNS--------LVLPIVVAI-I--------NMKKQMLEDAHDPHKSLDFGLGDEG-G---------AKK
123 | SAR-R-SI---FMGGGEKTLAHKPSQLSMDMNLSSPYLLPPGLQ-ESRESLNSLAKSLGNDNQDPYQYVAAITQSETGSL
124 | RSFNPK---D-SHSRNTKFNSPRN---------SGKP-----GSLKMPPSRMNSLP-ETPVSATESRVDPFGTPKM--PA
125 | PA-HPAKS------------P---FDS---E-KDAFH-PA----------------------PIVPEIGVVSD-------
126 | FDEKN-AVP-SVQQPPIARSKT----------------------------------------------------------
127 | >Phy003BOHC_AJECA
128 | -----------------------------------------------MQIPPPPPTLARRHVVPK---------------
129 | TPPEDARD----LLVMLPLPLYPYIPLTIAI-LVLVFLHRRHIRKLRSEDANDKHKSLDFGLDVVP-SG------NKKRG
130 | RGRKG-G-MEMTTADAEKSVRRNDRGLSMDITMTSPYLLPPALN-GSHDSLHSLSRSV-HADDDRYRTATAFSAGDNSSM
131 | RSFT-SNLKP-FPDDSVSFTGMSS-R------H-AP------P---GDEMHANLLR------------NAQRMSR--ASP
132 | PP-GTATHSIGSSQSHRSPPR---KLTT--PT------PN----------------------I-----VS----------
133 | DRSGI-HSPD---------------------------------------R-----SLAP-KSISTPGSELRKS-------
134 | >Phy003DGO9_PENCH
135 | MP---HAHH-------AGLVMRNH----VRRDV-IPPHRLPFLVPSTSSIATELPSLVARAE-----------AS-----
136 | TTVTGEKPTSNLTTTVLPVVLGAGVPILCAI-VVLIVLHRRHVKKLLREDAMDKHKSLDFGMDTVG-PA----TRRK--G
137 | P------------GMPPMSEPTHTKGLSLDVG---PYLMPPGLK-NSPESLRSMSI-----DDDKYRPATA-------SI
138 | RSYP-R---------GSRFEG-------------------------ADDGNSGLLQ------------NAQRMSR--SSP
139 | PL-YSSPIESHGRSLDQHND------------------------------------------Y-----------------
140 | L-----GEVPGVTHPPAAQQ-----------------PGMAIGS-PNANRIPSPEP-LP--------------HLDSSLG
141 | >Phy003PHXT_PENMQ
142 | MS--HRHGMHHHVRRHI------PEDP-VQLES-VPLEPAP--------TISEAPSVIRRTSSATST--------C----
143 | TGSSCETTSSSNLVNTLPVVLGVVIPVVLAI-AVLLFLHRRHVRKLRQEDANDKHKSLDFGMEVVR-AG----GGK----
144 | -------------ANPEMGEKPHKHGMSLDI-ISSPYLLPPGLH-GSKESLRSLSKVI-SPDDDKYRLGLAAQ-SDTASL
145 | RSYR-SHPRM-GQDDASSFRG-ST-R------H-GP------L---PDDMNQGLLQ------------NASRMSR--SPP
146 | VD-ATSPLSVNHTIHEEQFD------------------------------------------H-----------------
147 | PRTVG-NQSPIRQAESPPMA--------------------KSPK-NHVSP-----DHSG-QG---------DE-------
148 | >Phy003PVXT_TALSN
149 | -M--PHRHGIHHVHRRN------AENL-IKLES-LPLKPAP--------TISEPPSVVRRASSETST--------C----
150 | SGASCEKSSSSGLVNTLPVVLGVVIPVVAAI-IVLLILHRRHVRKLRQEDANDKHKSLDFGMEVVR-AG----GGN----
151 | ---P---------KQPEMGEKPHKHGMSLDI-IGSPYLLPPGLH-NSKESLRSLTKVI-SVEDDKYRVAAQ---SDTASL
152 | RSHR-T---M-GNDDASSFGG-ST-R------H-GP------I---PDDMNQGLLQ------------NASRMSR--SPP
153 | VD-ASSPLSVSQTIHEEPFD------------------------------------------H-----------------
154 | SNAMR-NQSQNHQAVDS-HM-----------------PPEDLPK-NHSSP-----APSG-PG---------DE-------
155 | >Phy003PZPF_FUSOX
156 | MGIAHYE--GARLR-------P--RTNIEDVSS-ASQ-NGVA------LSSSIFRRLVTRENC----------------Q
157 | DTDSCAAA-SANTNLVVPIVVAIVVPIVLIA-IFLYYLHRKNMKRQMLEDANDPHKSLDFGLDGA--G---------GKK
158 | SAR-R-SL---FMGGGEKGLNHKPSQLSMDMNLSSPYLLPPGLQ-ESRESLNSLAKSLGNDNQDPYHP------------
159 | ----------------------RN---------SGKP-----GSMKMPPSRMNSLP-ETPVSATDSKVDPFGTPKA--PA
160 | PT-HQPNS------------H---FDE-----KDGFQ-PT----------------------AIIPEIGVVSD-------
161 | FDEKR-DGA-SVQPPPAVRSKT----------------------------------------------------------
162 | >Phy003QBJJ_PENDI
163 | MS---HAHH-------AGLVMRNH----VRRDV-IPANRVPIFVPS-LSVATQLPTLVARSE------------S-----
164 | EPTSGPKATSNLATTVFPIVFGAGIPIFCAL-IILVVLHRRQVKKLVREDAMDKHKSLDFGLDTVG-PA----TRRK--G
165 | A-------K----GMPPMSEHNHTKGLSLDVG---PYLLPPGLQ-HSTDSLRSMSI-----DDGKYRPATA-------SI
166 | RSNS-R---------NSKYGG-------------------------TDDGNSGLLQ------------NAQRIPR--SSP
167 | PL-CSPIEPRARSPLNQHDD------------------------------------------Y-----------------
168 | I-----GQVPEVTHPPAVHQ-----------------PGMAIGS-PNTNRIPSPEP-LP--------------HVDSSSG
169 | >Phy0043OCA_COLGM
170 | MASASFSANGYVMGSRI----P--IRD-VNPINMTPT-PASP-------IRIASRIIGARDE------------QC--TG
171 | SATLCEKP-VDPASLTLPITLGVTIPIVGAL-FLLYYFHRRNMRRQAQEDATDPNRGLDFGLGDAP-I--D------KGG
172 | KKRKS-LM---FREKGMGIETNKQRQLSMDMNLSSPYLLPPGLQ-SSRESLNSLARTL-HNEADPYRPVYASS--DAGSI
173 | YTKT-------TSR-----------R-------GSSMTGRTTMTQNTLPPRQTSLP-RPPPAT-A---DPLGASR--SGS
174 | PSL-PPTS------------P---AIR---S-P--LV-AE----------------------PVIPQIETVP-------S
175 | GSSLP-QIP-DVPEPEPVAQRGL--------------PGNSRPS-PGHPTILEARE-PE---------------------
176 | >Phy0043W64_36779
177 | M-AGVAEAGSYRMSGRI----P--IVR-RNASG-VEA-LDVP-------QPDQTRPLVARESID-----------C-TGE
178 | NANLCEKP-YGANSLGVPIALGVAIPIVALL-GVVFWLHRRNIKKQRSEEANDPHKSLDFGLGDGS-R--G------SKG
179 | GKRKS-AF---FGGGGAEKASHRNNQLSMDMNLSSPYLLPPSAQAGSRESLHSLARTL-HGNEDPYSPVYQ--QSDARSM
180 | RSTK-K---G-SRDD-------YN---------GPSG-----PGLSVPPSRKSSFP-TSPTSPVTSIPPRYEASK---DE
181 | VT-PPPPA------------HSPGQAN---F-P--LN-DT----------------------SPYPNDHQLDA------H
182 | GVSMP-AVP-ELQEPAQAKMPS-------------SP---RFPL-P----------------------------------
183 | >Phy00443NV_MAGO7
184 | MVGVTVHEGEYHLGSRM----P--VMA-RDAST-PAL-QIAA------DGPGFFKRLVARQSSDD----------CVNGE
185 | PSNLCEKP-VTSQTLALPIALGVTIPLVALV-VMLIWLHRKNVRRQRQEDANDPHKSLDFGLDMGP------------GK
186 | RKS-K--L---F-GGEKLGGGPHNRQISMDMNLSSPYLLPPNMQ-NSRESIHSLAKTL--HNEDPYRHITQYNASDAGSL
187 | RSYK-A---G-GMD-------------------RPIG-----PKITVPTSRKGSLQATSPTSTIGSVPPRYEASQ---DD
188 | YV-KPPPP------------A---ALK---S-P--TQ-DS----------------------TPYPDDKSGP-------L
189 | ATVMP-SVP-EIQEPKPASLSK----E-SS---QAPS---LAAV-PPSSPLTISAP-EI--------------------A
190 | >Phy0044G80_PHANO
191 | M-------------HHL----R--RDA-QMAAS-TSATHTL--------VDRASRVLVARTT-------------C-TND
192 | SDPGCTKP---TQVPTIAIALAAIVPVVGLL-IVLVFLHRRNQKKLAAEDAKDKYKSMDFGMGGAG-K---------KNK
193 | -GG-P-EM---SITEKDIRGGAHSRGISLEGG--NPYILPVGLH-GSRESFHSLSRSQ-NDPHDPYRPVTFLR-NDNQSI
194 | RSQS-RG--Y-GHDNGSLYTTRTMSS-------GGT---------QRNRMGDGLLN------------NAQRMST--SRP
195 | MR-SESLS------------P---DSTT--SPD--VK-FPEQNIALSPLNPRFEGEPLAMPATELPHSRTPP-------S
196 | A-------SSPPNVP-II----------------------AVPA-PAAAKPEI---------------------------
197 |
--------------------------------------------------------------------------------
/13_ggtree_gallery.Rmd:
--------------------------------------------------------------------------------
1 | \newpage
2 |
3 | # Gallery of Reproducible Examples {#chapter13}
4 |
5 |
6 |
7 |
8 | ## Visualizing pairwise nucleotide sequence distance with a phylogenetic tree {#hpv58}
9 |
10 |
11 | This example reproduces figure 1 of [@chen_ancient_2017]. It extracts accession numbers from tip labels of the HPV58 tree and calculates pairwise nucleotide sequence distances. The distance matrix is visualized as dot and line plots. This example demonstrates the ability to add multiple layers to a specific panel. As illustrated in Figure \@ref(fig:jv2017), the `geom_facet()` function displays sequence distances as a dot plot and then adds a layer of line plot to the same panel, *i.e.*, sequence distance. In addition, the tree in `geom_facet()` can be fully annotated with multiple layers (clade labels, bootstrap support values, *etc.*). The source code is modified from the supplemental file of [@yu_two_2018].
12 |
13 | ```{r message=FALSE}
14 | library(TDbook)
15 | library(tibble)
16 | library(tidyr)
17 | library(Biostrings)
18 | library(treeio)
19 | library(ggplot2)
20 | library(ggtree)
21 |
22 | # loaded from TDbook package
23 | tree <- tree_HPV58
24 |
25 | clade <- c(A3 = 92, A1 = 94, A2 = 108, B1 = 156,
26 | B2 = 159, C = 163, D1 = 173, D2 = 176)
27 | tree <- groupClade(tree, clade)
28 | cols <- c(A1 = "#EC762F", A2 = "#CA6629", A3 = "#894418", B1 = "#0923FA",
29 | B2 = "#020D87", C = "#000000", D1 = "#9ACD32",D2 = "#08630A")
30 |
31 | ## visualize the tree with tip labels and tree scale
32 | p <- ggtree(tree, aes(color = group), ladderize = FALSE) %>%
33 | rotate(rootnode(tree)) +
34 | geom_tiplab(aes(label = paste0("italic('", label, "')")),
35 | parse = TRUE, size = 2.5) +
36 | geom_treescale(x = 0, y = 1, width = 0.002) +
37 | scale_color_manual(values = c(cols, "black"),
38 | na.value = "black", name = "Lineage",
39 | breaks = c("A1", "A2", "A3", "B1", "B2", "C", "D1", "D2")) +
40 | guides(color = guide_legend(override.aes = list(size = 5, shape = 15))) +
41 | theme_tree2(legend.position = c(.1, .88))
42 | ## Optional
43 | ## add labels for monophyletic (A, C and D) and paraphyletic (B) groups
44 | dat <- tibble(node = c(94, 108, 131, 92, 156, 159, 163, 173, 176,172),
45 | name = c("A1", "A2", "A3", "A", "B1",
46 | "B2", "C", "D1", "D2", "D"),
47 | offset = c(0.003, 0.003, 0.003, 0.00315, 0.003,
48 | 0.003, 0.0031, 0.003, 0.003, 0.00315),
49 | offset.text = c(-.001, -.001, -.001, 0.0002, -.001,
50 | -.001, 0.0002, -.001, -.001, 0.0002),
51 | barsize = c(1.2, 1.2, 1.2, 2, 1.2, 1.2, 3.2, 1.2, 1.2, 2),
52 | extend = list(c(0, 0.5), 0.5, c(0.5, 0), 0, c(0, 0.5),
53 | c(0.5, 0), 0, c(0, 0.5), c(0.5, 0), 0)
54 | ) %>%
55 | dplyr::group_split(barsize)
56 |
57 | p <- p +
58 | geom_cladelab(
59 | data = dat[[1]],
60 | mapping = aes(
61 | node = node,
62 | label = name,
63 | color = group,
64 | offset = offset,
65 | offset.text = offset.text,
66 | extend = extend
67 | ),
68 | barsize = 1.2,
69 | fontface = 3,
70 | align = TRUE
71 | ) +
72 | geom_cladelab(
73 | data = dat[[2]],
74 | mapping = aes(
75 | node = node,
76 | label = name,
77 | offset = offset,
78 | offset.text =offset.text,
79 | extend = extend
80 | ),
81 | barcolor = "darkgrey",
82 | textcolor = "darkgrey",
83 | barsize = 2,
84 | fontsize = 5,
85 | fontface = 3,
86 | align = TRUE
87 | ) +
88 | geom_cladelab(
89 | data = dat[[3]],
90 | mapping = aes(
91 | node = node,
92 | label = name,
93 | offset = offset,
94 | offset.text = offset.text,
95 | extend = extend
96 | ),
97 | barcolor = "darkgrey",
98 | textcolor = "darkgrey",
99 | barsize = 3.2,
100 | fontsize = 5,
101 | fontface = 3,
102 | align = TRUE
103 | ) +
104 | geom_strip(65, 71, "italic(B)", color = "darkgrey",
105 | offset = 0.00315, align = TRUE, offset.text = 0.0002,
106 | barsize = 2, fontsize = 5, parse = TRUE)
107 |
108 | ## Optional
109 | ## display support values
110 | p <- p + geom_nodelab(aes(subset = (node == 92), label = "*"),
111 | color = "black", nudge_x = -.001, nudge_y = 1) +
112 | geom_nodelab(aes(subset = (node == 155), label = "*"),
113 | color = "black", nudge_x = -.0003, nudge_y = -1) +
114 | geom_nodelab(aes(subset = (node == 158), label = "95/92/1.00"),
115 | color = "black", nudge_x = -0.0001,
116 | nudge_y = -1, hjust = 1) +
117 | geom_nodelab(aes(subset = (node == 162), label = "98/97/1.00"),
118 | color = "black", nudge_x = -0.0001,
119 | nudge_y = -1, hjust = 1) +
120 | geom_nodelab(aes(subset = (node == 172), label = "*"),
121 | color = "black", nudge_x = -.0003, nudge_y = -1)
122 | ```
123 |
124 | ```{r eval=F}
125 | ## extract accession numbers from tip labels
126 | tl <- tree$tip.label
127 | acc <- sub("\\w+\\|", "", tl)
128 | names(tl) <- acc
129 |
130 | ## read sequences from GenBank directly into R
131 | ## and convert the object to DNAStringSet
132 | tipseq <- ape::read.GenBank(acc) %>% as.character %>%
133 | lapply(., paste0, collapse = "") %>% unlist %>%
134 | DNAStringSet
135 | ## align the sequences using muscle
136 | tipseq_aln <- muscle::muscle(tipseq)
137 | tipseq_aln <- DNAStringSet(tipseq_aln)
138 | ```
139 |
140 | ```{r echo=F}
141 | ## extract accession numbers from tip labels
142 | tl <- tree$tip.label
143 | acc <- sub("\\w+\\|", "", tl)
144 | names(tl) <- acc
145 |
146 | ## writeXStringSet(tipseq_aln, file = "data/HPV58_aln.fas")
147 | #tipseq_aln <- readDNAStringSet("data/HPV58_aln.fas")
148 | tipseq_aln <- TDbook::dna_HPV58_aln %>%
149 | as.character %>%
150 | lapply(., paste0, collapse = "") %>%
151 | unlist() %>%
152 | Biostrings::DNAStringSet()
153 | ```
154 |
155 |
156 | (ref:jv2017scap) Phylogeny of HPV58 complete genomes with dot and line plots of pairwise nucleotide sequence distances.
157 |
158 | (ref:jv2017cap) **Phylogeny of HPV58 complete genomes with dot and line plots of pairwise nucleotide sequence distances**.
159 |
160 |
161 | ```{r jv2017, fig.width=12, fig.height=12, fig.cap="(ref:jv2017cap)", fig.scap="(ref:jv2017scap)", warning=FALSE, out.width='100%'}
162 | ## calculate pairwise hamming distances among sequences
163 | tipseq_dist <- stringDist(tipseq_aln, method = "hamming")
164 |
165 | ## calculate the percentage of differences
166 | tipseq_d <- as.matrix(tipseq_dist) / width(tipseq_aln[1]) * 100
167 |
168 | ## convert the matrix to a tidy data frame for facet_plot
169 | dd <- as_tibble(tipseq_d)
170 | dd$seq1 <- rownames(tipseq_d)
171 | td <- gather(dd,seq2, dist, -seq1)
172 | td$seq1 <- tl[td$seq1]
173 | td$seq2 <- tl[td$seq2]
174 |
175 | g <- p$data$group
176 | names(g) <- p$data$label
177 | td$clade <- g[td$seq2]
178 |
179 | ## visualize the sequence differences using dot plot and line plot
180 | ## and align the sequence difference plot to the tree using facet_plot
181 | p2 <- p + geom_facet(panel = "Sequence Distance",
182 | data = td, geom = geom_point, alpha = .6,
183 | mapping = aes(x = dist, color = clade, shape = clade)) +
184 | geom_facet(panel = "Sequence Distance",
185 | data = td, geom = geom_path, alpha = .6,
186 | mapping=aes(x = dist, group = seq2, color = clade)) +
187 | scale_shape_manual(values = 1:8, guide = FALSE)
188 |
189 | print(p2)
190 | ```
191 |
192 | ## Displaying Different Symbolic Points for Bootstrap Values. {#symbolic-bootstrap}
193 |
194 | We can cut the bootstrap values into several intervals, *e.g.*, to indicate whether the clade is of high, moderate, or low support. Then we can use these intervals as categorical variables to set different colors or shapes of symbolic points to indicate the bootstrap values belong to which category (Figure \@ref(fig:bpinterval)).
195 |
196 | (ref:bpintervalscap) Partitioning bootstrap values.
197 |
198 | (ref:bpintervalcap) **Partitioning bootstrap values**. Bootstrap values were divided into three categories and this information was used to color circle points.
199 |
200 | ```{r include = FALSE}
201 | ## phytools also have a read.newick function
202 | read.newick <- treeio::read.newick
203 | ```
204 |
205 | ```{r bpinterval, fig.width=7.5, fig.height=8.6, fig.cap="(ref:bpintervalcap)", fig.scap="(ref:bpintervalscap)", out.width='100%'}
206 | library(treeio)
207 | library(ggplot2)
208 | library(ggtree)
209 | library(TDbook)
210 |
211 | tree <- read.newick(text=text_RMI_tree, node.label = "support")
212 | root <- rootnode(tree)
213 | ggtree(tree, color="black", size=1.5, linetype=1, right=TRUE) +
214 | geom_tiplab(size=4.5, hjust = -0.060, fontface="bold") + xlim(0, 0.09) +
215 | geom_point2(aes(subset=!isTip & node != root,
216 | fill=cut(support, c(0, 700, 900, 1000))),
217 | shape=21, size=4) +
218 | theme_tree(legend.position=c(0.2, 0.2)) +
219 | scale_fill_manual(values=c("white", "grey", "black"), guide='legend',
220 | name='Bootstrap Percentage(BP)',
221 | breaks=c('(900,1e+03]', '(700,900]', '(0,700]'),
222 | labels=expression(BP>=90,70 <= BP * " < 90", BP < 70))
223 | ```
224 |
225 |
226 | ## Highlighting Different Groups {#phylo-grouping}
227 |
228 |
229 | This example reproduces Figure 1 of [@larsen_identification_2019]. It used `groupOTU()` to add grouping information of chicken CTLDcps. The branch line type and color are defined based on this grouping information. Two groups of CTLDcps are highlighted in different background colors using `geom_hilight` (red for Group II and green for Group V). The avian-specific expansion of Group V with the subgroups of A and B- are labeled using `geom_cladelab` (Figure \@ref(fig:treeLarsen)).
230 |
231 |
232 |
233 | (ref:treeLarsenscap) Phylogenetic tree of CTLDcps.
234 |
235 | (ref:treeLarsencap) **Phylogenetic tree of CTLDcps**. Using different background colors, line types and colors, and clade labels to distinguish groups.
236 |
237 |
238 |
239 |
240 | ```{r treeLarsen, fig.cap="(ref:treeLarsencap)", fig.scap="(ref:treeLarsenscap)", fig.width=7.5, fig.height=6.3, out.width='100%'}
241 | library(TDbook)
242 | mytree <- tree_treenwk_30.4.19
243 |
244 | # Define nodes for coloring later on
245 | tiplab <- mytree$tip.label
246 | cls <- tiplab[grep("^ch", tiplab)]
247 | labeltree <- groupOTU(mytree, cls)
248 |
249 | p <- ggtree(labeltree, aes(color=group, linetype=group), layout="circular") +
250 | scale_color_manual(values = c("#efad29", "#63bbd4")) +
251 | geom_nodepoint(color="black", size=0.1) +
252 | geom_tiplab(size=2, color="black")
253 |
254 | p2 <- flip(p, 136, 110) %>%
255 | flip(141, 145) %>%
256 | rotate(141) %>%
257 | rotate(142) %>%
258 | rotate(160) %>%
259 | rotate(164) %>%
260 | rotate(131)
261 |
262 | ### Group V and II coloring
263 | dat <- data.frame(
264 | node = c(110, 88, 156,136),
265 | fill = c("#229f8a", "#229f8a", "#229f8a", "#f9311f")
266 | )
267 | p3 <- p2 +
268 | geom_hilight(
269 | data = dat,
270 | mapping = aes(
271 | node = node,
272 | fill = I(fill)
273 | ),
274 | alpha = 0.2,
275 | extendto = 1.4
276 | )
277 |
278 | ### Putting on a label on the avian specific expansion
279 | p4 <- p3 +
280 | geom_cladelab(
281 | node = 113,
282 | label = "Avian-specific expansion",
283 | align = TRUE,
284 | angle = -35,
285 | offset.text = 0.05,
286 | hjust = "center",
287 | fontsize = 2,
288 | offset = .2,
289 | barsize = .2
290 | )
291 |
292 | ### Adding the bootstrap values with subset used to remove all bootstraps < 50
293 | p5 <- p4 +
294 | geom_nodelab(
295 | mapping = aes(
296 | x = branch,
297 | label = label,
298 | subset = !is.na(as.numeric(label)) & as.numeric(label) > 50
299 | ),
300 | size = 2,
301 | color = "black",
302 | nudge_y = 0.6
303 | )
304 |
305 | ### Putting labels on the subgroups
306 | p6 <- p5 +
307 | geom_cladelab(
308 | data = data.frame(
309 | node = c(114, 121),
310 | name = c("Subgroup A", "Subgroup B")
311 | ),
312 | mapping = aes(
313 | node = node,
314 | label = name
315 | ),
316 | align = TRUE,
317 | offset = .05,
318 | offset.text = .03,
319 | hjust = "center",
320 | barsize = .2,
321 | fontsize = 2,
322 | angle = "auto",
323 | horizontal = FALSE
324 | ) +
325 | theme(
326 | legend.position = "none",
327 | plot.margin = grid::unit(c(-15, -15, -15, -15), "mm")
328 | )
329 | print(p6)
330 | ```
331 |
332 | ## Phylogenetic Tree with Genome Locus Structure {#genome-locus}
333 |
334 | The `geom_motif()` is defined in `r Biocpkg("ggtree")` and it is a wrapper layer of the `gggenes::geom_gene_arrow()`. The `geom_motif()` can automatically adjust genomic alignment by selective gene (via the `on` parameter) and can label genes via the `label` parameter. In the following example, we use `example_genes` dataset provided by `r CRANpkg("gggenes")`. As the dataset only provides genomic coordination of a set of genes, a phylogeny for the genomes needs to be constructed first. We calculate Jaccard similarity based on the ratio of overlapping genes among genomes and correspondingly determine genome distance. The BioNJ algorithm was applied to construct the tree. Then we can use `geom_facet()` to visualize the tree with the genomic structures (Figure \@ref(fig:gggenes)).
335 |
336 |
337 | (ref:gggenesscap) Genomic features with a phylogenetic tree.
338 |
339 | (ref:gggenescap) **Genomic features with a phylogenetic tree.**
340 |
341 |
342 | ```{r gggenes, fig.width=9, fig.height=4, fig.cap="(ref:gggenescap)", fig.scap="(ref:gggenesscap)", out.width='100%'}
343 | library(dplyr)
344 | library(ggplot2)
345 | library(gggenes)
346 | library(ggtree)
347 |
348 | get_genes <- function(data, genome) {
349 | filter(data, molecule == genome) %>% pull(gene)
350 | }
351 |
352 | g <- unique(example_genes[,1])
353 | n <- length(g)
354 | d <- matrix(nrow = n, ncol = n)
355 | rownames(d) <- colnames(d) <- g
356 | genes <- lapply(g, get_genes, data = example_genes)
357 |
358 | for (i in 1:n) {
359 | for (j in 1:i) {
360 | jaccard_sim <- length(intersect(genes[[i]], genes[[j]])) /
361 | length(union(genes[[i]], genes[[j]]))
362 | d[j, i] <- d[i, j] <- 1 - jaccard_sim
363 | }
364 | }
365 |
366 | tree <- ape::bionj(d)
367 |
368 | p <- ggtree(tree, branch.length='none') +
369 | geom_tiplab() + xlim_tree(5.5) +
370 | geom_facet(mapping = aes(xmin = start, xmax = end, fill = gene),
371 | data = example_genes, geom = geom_motif, panel = 'Alignment',
372 | on = 'genE', label = 'gene', align = 'left') +
373 | scale_fill_brewer(palette = "Set3") +
374 | scale_x_continuous(expand=c(0,0)) +
375 | theme(strip.text=element_blank(),
376 | panel.spacing=unit(0, 'cm'))
377 |
378 | facet_widths(p, widths=c(1,2))
379 | ```
380 |
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