-
Notifications
You must be signed in to change notification settings - Fork 0
/
README.md
270 lines (207 loc) · 10.7 KB
/
README.md
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
# dendroNetwork <img src="man/figures/dendroNetwork_hexsticker.png" align="right" height="200" style="float:right; height:200px;"/>
<!-- badges: start -->
[](https://www.repostatus.org/#active)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://github.com/ropensci/dendroNetwork/actions/workflows/R-CMD-check.yaml)
[](https://github.com/ropensci/software-review/issues/627)
[](https://ropensci.r-universe.dev)
[](https://CRAN.R-project.org/package=dendroNetwork)
[](https://cranlogs.r-pkg.org/badges/grand-total/dendroNetwork)
[](https://zenodo.org/doi/10.5281/zenodo.10636310)
<!-- badges: end -->
dendroNetwork is a package to create dendrochronological networks for
gaining insight into provenance or other patterns based on the
statistical relations between tree ring curves. The code and the
functions are based on several published papers (Visser 2021b, 2021a;
Visser and Vorst 2022).
The package is written for dendrochronologists and have a general
knowledge on the discipline and used jargon. There is an excellent
website for the introduction of using R in dendrochronology:
<https://opendendro.org/r/>. The basics of dendrochronology can be found
in handbooks (Cook and Kariukstis 1990; Speer 2010) or on
<https://www.dendrohub.com/>.
## Installation
This package depends on
[RCy3](https://www.bioconductor.org/packages/release/bioc/html/RCy3.html),
which is part of Bioconductor. Therefore it is recommended to install
RCy3 first using:
``` r
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("RCy3")
```
The functionality of RCy3 depends on the installation of Cytoscape.
Cytoscape is needed for visualising the networks. This open source
software is platform independent and provides easy visual access to
complex networks and the attributes of both nodes and edges in a network
(see the
[Cytoscape-website](https://cytoscape.org/what_is_cytoscape.html) for
more information). It is therefore recommended to install Cytoscape as
well. Please follow the download and installation instructions for your
operating system: <https://cytoscape.org/>.
You can install the latest version of dendroNetwork from
[CRAN](https://cran.r-project.org/web/packages/dendroNetwork) or the
development version from
[R-universe](https://ropensci.r-universe.dev/dendroNetwork) by running
one of the commands below:
``` r
# CRAN
install.packages("dendroNetwork")
# R-Universe
install.packages("dendroNetwork", repos = "https://ropensci.r-universe.dev")
```
## Usage
The package aims to make the creation of dendrochronological
(provenance) networks as easy as possible. To be able to make use of all
options, it is assumed that Cytoscape (Shannon et al. 2003)is installed
(<https://cytoscape.org/>). Some data is included in this package,
namely the Roman data published by Hollstein (Hollstein 1980).
The first steps are visualized in the flowchart below, including
community detection using either (or both) the Girvan-Newman algorithm
(Girvan and Newman 2002) and Clique Percolation Method (Palla et al.
2005) for all clique sizes. Both methods are explained very well in the
papers, and on wikipedia for both
[CPM](https://en.wikipedia.org/wiki/Clique_percolation_method) and the
[Girvan-Newman
algorithm](https://en.wikipedia.org/wiki/Girvan%E2%80%93Newman_algorithm).
<!-- -->
``` r
library(dendroNetwork)
data(hol_rom) # 1
sim_table_hol <- sim_table(hol_rom) # 2
g_hol <- dendro_network(sim_table_hol) # 3
g_hol_gn <- gn_names(g_hol) # 4
g_hol_cpm <- clique_community_names(g_hol, k=3) # 4
hol_com_cpm_all <- find_all_cpm_com(g_hol) # 5
plot(g_hol) # plotting the graph in R
```
<!-- -->
``` r
plot(g_hol, vertex.color="deepskyblue", vertex.size=15, vertex.frame.color="gray",
vertex.label.color="darkslategrey", vertex.label.cex=0.8, vertex.label.dist=2) # better readable version
```
<!-- -->
### Visualization in Cytoscape
After creating the network in R, it is possible to visualize the network
using Cytoscape. The main advantage is that visualisation in Cytoscape
is more easy, intuitive and visual. In addition, it is very easy to
automate workflows in Cytoscape with R (using
[RCy3](https://bioconductor.org/packages/release/bioc/html/RCy3.html)).
For this purpose we need to start Cytoscape firstly. After Cytoscape has
completely loaded, the next steps can be taken.
1. The network can now be loaded in Cytoscape for further
visualisation:
`cyto_create_graph(g_hol, CPM_table = hol_com_cpm_all, GN_table = g_hol_gn)`
2. Styles for visualisation can now be generated. However, Cytoscape
comes with a lot of default styles that can be confusing. Therefore
it is recommended to use: `cyto_clean_styles()` once in a session.
3. To visualize the styles for CPM with only k=3:
`cyto_create_cpm_style(g_hol, k=3, com_k = g_hol_cpm)`
- This can be repeated for all possible clique sizes. To find the
maximum clique size in a network, please use:
`igraph::clique_num(g_hol)`.
- To automate this:
`for (i in 3:igraph::clique_num(g_hol)) { cyto_create_cpm_style(g_hol, k=i, com_k = g_hol_cpm)}`.
4. To visualize the styles using the Girvan-Newman algorithm (GN):
`cyto_create_gn_style(g_hol)` This would look something like this in
Cytoscape:
<figure>
<img src="man/figures/g_hol_GN.png"
alt="The network of Roman sitechronologies with the Girvan-Newman communities visualized." />
<figcaption aria-hidden="true">The network of Roman sitechronologies
with the Girvan-Newman communities visualized.</figcaption>
</figure>
## Usage for large datasets
When using larger datasets of tree-ring series, calculating the table
with similarities can take a lot of time, but finding communities even
more. It is therefore recommended to use of parallel computing for
Clique Percolation:
`clique_community_names_par(network, k=3, n_core = 4)`. This reduces the
amount of time significantly. For most datasets
`clique_community_names()` is sufficiently fast and for smaller datasets
`clique_community_names_par()` can even be slower due to the
parallelisation. Therefore, the function `clique_community_names()`
should be used initially and if this is very slow, start using
`clique_community_names_par()`. See the separate
[vignette](https://docs.ropensci.org/dendroNetwork/articles/large_datasets_communities.html)
for that.
## Citation
If you use this software, please cite this using:
Visser, R. (2024). dendroNetwork: a R-package to create
dendrochronological provenance networks (Version 0.5.4) \[Computer
software\]. <https://zenodo.org/doi/10.5281/zenodo.10636310>
## Acknowledgements
This package reuses and adapts the CliquePercolationMethod-R package
developed by Angelo Salatino (The Open University). Source code:
<https://github.com/angelosalatino/CliquePercolationMethod-R>
This package reuses and adapts the function cor.with.limit.R() developed
by Andy Bunn (Western Washington University), but the new function is
optimized and also outputs the number of overlapping rings. Source code:
<https://github.com/AndyBunn/dplR/blob/master/R/rwi.stats.running.R>.
## References
<div id="refs" class="references csl-bib-body hanging-indent"
line-spacing="2">
<div id="ref-cook1990" class="csl-entry">
Cook, ER and Kariukstis, LA. 1990. *Methods of dendrochronology.
Applications in the environmental sciences*. Dordrecht: Kluwer Academic
Publishers.
</div>
<div id="ref-girvan2002" class="csl-entry">
Girvan, M and Newman, MEJ. 2002 Community structure in social and
biological networks. *Proceedings of the National Academy of Sciences of
the United States of America* 99(12): 7821–7826. DOI:
https://doi.org/[10.1073/pnas.122653799](https://doi.org/10.1073/pnas.122653799).
</div>
<div id="ref-hollstein1980" class="csl-entry">
Hollstein, E. 1980. *Mitteleuropäische eichenchronologie. Trierer
dendrochronologische forschungen zur archäologie und kunstgeschichte.*
Trierer grabungen und forschungen 11. Mainz am Rhein: Verlag Philipp von
Zabern.
</div>
<div id="ref-palla2005" class="csl-entry">
Palla, G, Derenyi, I, Farkas, I and Vicsek, T. 2005 Uncovering the
overlapping community structure of complex networks in nature and
society. *Nature* 435(7043): 814–818. DOI:
https://doi.org/[10.1038/nature03607](https://doi.org/10.1038/nature03607).
</div>
<div id="ref-shannon2003" class="csl-entry">
Shannon, P, Markiel, A, Ozier, O, Baliga, NS, Wang, JT, Ramage, D, Amin,
N, Schwikowski, B and Ideker, T. 2003 Cytoscape: A software environment
for integrated models of biomolecular interaction networks. *Genome
Research* 13(11): 2498–2504. DOI:
https://doi.org/[10.1101/gr.1239303](https://doi.org/10.1101/gr.1239303).
</div>
<div id="ref-speer2010" class="csl-entry">
Speer, JH. 2010. *Fundamentals of tree ring research*. Tucson:
University of Arizona Press.
</div>
<div id="ref-visser2021b" class="csl-entry">
Visser, RM. 2021a Dendrochronological Provenance Patterns. Network
Analysis of Tree-Ring Material Reveals Spatial and Economic Relations of
Roman Timber in the Continental North-Western Provinces. *Journal of
Computer Applications in Archaeology* 4(1): 230253. DOI:
https://doi.org/[10.5334/jcaa.79](https://doi.org/10.5334/jcaa.79).
</div>
<div id="ref-visser2021a" class="csl-entry">
Visser, RM. 2021b On the similarity of tree-ring patterns: Assessing the
influence of semi-synchronous growth changes on the
Gleichläufigkeitskoeffizient for big tree-ring data sets. *Archaeometry*
63(1): 204–215. DOI:
https://doi.org/[10.1111/arcm.12600](https://doi.org/10.1111/arcm.12600).
</div>
<div id="ref-visser2022" class="csl-entry">
Visser, RM and Vorst, Y. 2022 Connecting Ships: Using
Dendrochronological Network Analysis to Determine the Wood Provenance of
Roman-Period River Barges Found in the Lower Rhine Region and Visualise
Wood Use Patterns. *International Journal of Wood Culture* 3(1-3):
123–151. DOI:
https://doi.org/[10.1163/27723194-bja10014](https://doi.org/10.1163/27723194-bja10014).
</div>
</div>