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| Original file line number | Diff line number | Diff line change |
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| --- | ||
| title: "Modifying the network plot" | ||
| output: rmarkdown::html_vignette | ||
| vignette: > | ||
| %\VignetteIndexEntry{plot_modifications} | ||
| %\VignetteEngine{knitr::rmarkdown} | ||
| %\VignetteEncoding{UTF-8} | ||
| --- | ||
|
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||
| ```{r, include = FALSE} | ||
| # Suppress title check warning | ||
| options(rmarkdown.html_vignette.check_title = FALSE) | ||
| knitr::opts_chunk$set( | ||
| collapse = TRUE, | ||
| comment = "#>" | ||
| ) | ||
| ``` | ||
|
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| ```{r setup, message=FALSE, warning=FALSE} | ||
| library(NetCoMi) | ||
| library(phyloseq) | ||
| ``` | ||
|
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| We construct a network at genus level to demonstrate a variety of modification options for the network plot. The SparCC package is used here because the resulting graph is denser than conditional independence graphs, which is preferable for this tutorial. | ||
|
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| ## Network construction and analysis | ||
|
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| ```{r single_genus_1, fig.height=6, fig.width=6} | ||
| data("amgut2.filt.phy") | ||
| # Agglomerate to genus level | ||
| amgut_genus <- tax_glom(amgut2.filt.phy, taxrank = "Rank6") | ||
| # Rename taxonomic table and make Rank6 (genus) unique | ||
| amgut_genus_renamed <- renameTaxa(amgut_genus, | ||
| pat = "<name>", | ||
| substPat = "<name>_<subst_name>(<subst_R>)", | ||
| numDupli = "Rank6") | ||
| # Network construction and analysis | ||
| net <- netConstruct(amgut_genus_renamed, | ||
| taxRank = "Rank6", | ||
| measure = "sparcc", | ||
| normMethod = "none", | ||
| zeroMethod = "none", | ||
| sparsMethod = "thresh", | ||
| thresh = 0.3, | ||
| dissFunc = "signed", | ||
| verbose = 2, | ||
| seed = 123456) | ||
| netprops <- netAnalyze(net, | ||
| clustMethod = "cluster_fast_greedy", | ||
| gcmHeat = FALSE # Do not plot the GCM heatmap | ||
| ) | ||
| ``` | ||
|
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| ### Simple network plot | ||
|
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| ```{r netplot_mod_simple, fig.height=14, fig.width=15, fig.dpi=200, out.width="100%"} | ||
| plot(netprops) | ||
| ``` | ||
|
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| In the following we will look at the different examples, where the arguments of `plot.microNetProps` are adjusted. | ||
|
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| ### Layout | ||
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| Circular layout: | ||
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| ```{r netplot_mod_layout_circle, fig.height=14, fig.width=15, fig.dpi=200, out.width="100%"} | ||
| plot(netprops, layout = "circle") | ||
| ``` | ||
| Layouts from `igraph`package are also accepted (see `?igraph::layout_`). | ||
|
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| ```{r netplot_mod_layout_with_fr, fig.height=14, fig.width=15, fig.dpi=200, out.width="100%"} | ||
| plot(netprops, layout = "layout_with_fr") | ||
| ``` | ||
|
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| We can also use a layout in matrix form. Here, we generate one in advance using the Fruchterman-Reingold layout algorithm from `igraph` package. | ||
|
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| ```{r netplot_mod_layout_matrix, fig.height=14, fig.width=15, fig.dpi=200, out.width="100%"} | ||
| # Compute layout | ||
| graph <- igraph::graph_from_adjacency_matrix(net$adjaMat1, | ||
| weighted = TRUE) | ||
| set.seed(123456) | ||
| lay_fr <- igraph::layout_with_fr(graph) | ||
| # Row names of the layout matrix must match the node names | ||
| rownames(lay_fr) <- rownames(net$adjaMat1) | ||
| plot(netprops, | ||
| layout = lay_fr) | ||
| ``` | ||
| We can see that the layout is basically the same as before, just rotated. | ||
|
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| ### Repulsion | ||
|
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| Nodes are placed closer together for smaller repulsion values and further apart for higher values. | ||
|
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| ```{r netplot_mod_repulsion1, fig.height=14, fig.width=15, fig.dpi=200, out.width="100%"} | ||
| plot(netprops, repulsion = 1.2) | ||
| ``` | ||
|
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| ```{r netplot_mod_repulsion2, fig.height=14, fig.width=15, fig.dpi=200, out.width="100%"} | ||
| plot(netprops, repulsion = 0.5) | ||
| ``` | ||
|
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| For the rest of the tutorial we will use a repulsion of 0.9. | ||
|
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| ### Shorten labels | ||
|
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| ```{r netplot_mod_shortlabs_simple, fig.height=14, fig.width=15, fig.dpi=200, out.width="100%"} | ||
| plot(netprops, | ||
| repulsion = 0.9, | ||
| shortenLabels = "simple", | ||
| labelLength = 6) | ||
| ``` | ||
|
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| In many cases, it makes sense to use the " intelligent " label shortening, which preserves the last part of the label. This allows you to distinguish for instance between Enterococcus and Enterobacter. With the following label pattern, they would be abbreviated to "Enter'coc" and "Enter'bac", while both would be abbreviated to "Entero" with the "simple" shortening used before. | ||
|
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| ```{r netplot_mod_shortlabs_intelligent, fig.height=14, fig.width=15, fig.dpi=200, out.width="100%"} | ||
| plot(netprops, | ||
| repulsion = 0.9, | ||
| shortenLabels = "intelligent", | ||
| labelPattern = c(5, "'", 3, "'", 3)) | ||
| ``` | ||
|
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| ### Label size and scaling | ||
|
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| By default, labels are scaled according to node size. | ||
|
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| ```{r netplot_mod_labelscale, fig.height=14, fig.width=15, fig.dpi=200, out.width="100%"} | ||
| plot(netprops, | ||
| repulsion = 0.9, | ||
| labelScale = FALSE) | ||
| ``` | ||
|
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| ```{r netplot_mod_labelscale_cex, fig.height=14, fig.width=15, fig.dpi=200, out.width="100%"} | ||
| plot(netprops, | ||
| repulsion = 0.9, | ||
| labelScale = FALSE, | ||
| cexLabels = 1.5) | ||
| ``` | ||
|
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| If the labels overlap, one can play around with the `repulsion` argument to randomly rearrange the node placement until a good solution is found. | ||
|
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| ```{r netplot_mod_labelscale_rep, fig.height=14, fig.width=15, fig.dpi=200, out.width="100%"} | ||
| plot(netprops, | ||
| repulsion = 0.901, | ||
| labelScale = FALSE, | ||
| cexLabels = 1.5) | ||
| ``` | ||
|
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| ### Label font | ||
|
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| Label fonts can be manipulated with the `labelFont' argument. 2 stands for bold, 3 for italic, and so on. | ||
| It is recommended to define the hub label font separately. | ||
|
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| ```{r netplot_mod_label_font, fig.height=14, fig.width=15, fig.dpi=200, out.width="100%"} | ||
| plot(netprops, | ||
| repulsion = 0.901, | ||
| labelScale = FALSE, | ||
| cexLabels = 1.5, | ||
| labelFont = 3, | ||
| hubLabelFont = 2) | ||
| ``` |