Network plot of mate-pair or paired-end connected scaffold reads

Source: R/mmnetwork.R

Plots all connected scaffolds in a network plot. Scaffolds can then be highlighted and extracted using the locator and selection features.

mmnetwork(
  mm,
  network,
  min_connections = 1,
  color_by = NULL,
  color_scale_log10 = FALSE,
  locator = FALSE,
  selection = NULL,
  highlight_labels = NULL,
  highlight_color = "darkred",
  links_scale = 1,
  scaffold_labels = FALSE,
  print_nolinks = FALSE,
  seed = 0
)

Arguments

mm

(required) A dataframe loaded with mmload.
network

(required) Paired-end or mate-pair connections between scaffolds in long format. The first and second columns must contain all connected scaffold pairs and the third column the number of connections.
min_connections

Filter all scaffold pairs with equal to or less than this number of connections. (Default: 2)
color_by

Color the scaffolds by a variable in mm. (Default: NULL)
color_scale_log10

(Logical) Log10-scale the color gradient when color_by is set and the variable is continuous (Default: FALSE)
locator

(Logical) When TRUE, left-clicks in the plot are captured and the exact x/y-coordinates of the mouse clicks are captured and returned. These coordinates can be used to highlight a selection of scaffolds in the plot, and to extract all scaffolds within the selection. (Default: FALSE)
selection

A 2-column dataframe with the x and y coordinates of points with which to draw a polygon onto the plot to highlight a selected region. A selection can be obtained by using the locator feature (by locator = TRUE). (Default: NULL)
highlight_labels

A dataframe or vector of scaffold names whose labels to highlight in the plot (colored by highlight_color). (Default: NULL)
highlight_color

The color with which to highlight the scaffold labels defined by highlight_labels. (Default: "darkred")
links_scale

A factor to scale the sizes of the links plotted between scaffolds. (Default: 1)
scaffold_labels

Add labels with the scaffold names of all scaffolds.
print_nolinks

(Logical) Print the names of all scaffolds with no links to other scaffolds to the console. (Default: FALSE)
seed

Network plots are based on Random Number Generation, and this is used to set a specific random seed (with set.seed) allowing for reproducible network plots. Set to NULL to generate a unique network plot with each run of mmnetwork. (Default: 0)

Value

A ggplot object. Note that mmgenome2 hides all warnings produced by ggplot objects.

Using the locator with mmnetwork

Unlike a plot generated by mmplot, the coordinates of points in each dimension in a mmnetwork plot is not contained within the provided dataframe mm itself. As such mmnetwork does not have a corresponding extract function like mmextract is for mmplot. Instead, a subset of mm containing all scaffolds within the selection polygon is available in the returned ggplot object with plot$data_in_selection.

Author

Kasper Skytte Andersen ksa@bio.aau.dk

Soren M. Karst smk@bio.aau.dk

Mads Albertsen MadsAlbertsen85@gmail.com

Examples

library(mmgenome2)
data(mmgenome2)
data(paired_ends)
mmgenome2

#> # A tibble: 97,285 x 13
#>    scaffold length    gc cov_C13.11.14 cov_C13.11.25 cov_C13.12.03 cov_C14.01.09
#>    <chr>     <dbl> <dbl>         <dbl>         <dbl>         <dbl>         <dbl>
#>  1 1          8264  57.8       1.44             53.6         0             0.066
#>  2 2          1027  57.0       0.625            24.2         0             0    
#>  3 3          1665  55.9      13.5             434.          0.166         0.177
#>  4 4          9056  35.9       0.01             23.4         0             0    
#>  5 5          3343  64.0       3.20             16.4         0             0    
#>  6 6         98207  39.1       0.00966          24.5         3.29          9.85 
#>  7 7          6480  63.0       2.61             19.2         1.46         12.3  
#>  8 8         15790  61.7       2.78             21.2         1.62         10.3  
#>  9 9          1403  70.4      85.1             192.          0             0    
#> 10 10         2018  70.2      50.3             101.          0             0    
#> # … with 97,275 more rows, and 6 more variables: PC1 <dbl>, PC2 <dbl>,
#> #   PC3 <dbl>, geneID <chr>, taxonomy <fct>, rRNA16S <fct>
selection <- data.frame(
  cov_C13.11.25 = c(7.2, 16.2, 25.2, 23.3, 10.1),
  cov_C14.01.09 = c(47, 77, 52.8, 29.5, 22.1)
)
mmgenome2_extraction <- mmextract(mmgenome2, selection = selection)

#> 61 scaffolds (or 1.34% of the scaffolds in mm, weighted by length) remain after 97224 of 97285 scaffolds have been filtered.
mmgenome2_extraction

#> # A tibble: 61 x 13
#>    scaffold length    gc cov_C13.11.14 cov_C13.11.25 cov_C13.12.03 cov_C14.01.09
#>    <chr>     <dbl> <dbl>         <dbl>         <dbl>         <dbl>         <dbl>
#>  1 180       22093  74.0         0.298          12.0          7.34          37.9
#>  2 181        2970  68.0         0.34           13.9          7.37          38.6
#>  3 227      190826  65.4         0.348          13.0          6.91          38.5
#>  4 253      296715  70.2         1.06           15.2          7.00          41.1
#>  5 315      218862  71.7         0.366          12.6          7.17          38.8
#>  6 343       27203  57.6         0.235          10.7          5.07          32.0
#>  7 381        6128  67.2         8.11           22.6          5.89          48.7
#>  8 386      164942  68.2         0.359          12.3          6.73          37.2
#>  9 396        7448  72.6         0.329          13.0          7.59          41.0
#> 10 423      100199  68.7         1.40           15.3          6.71          40.0
#> # … with 51 more rows, and 6 more variables: PC1 <dbl>, PC2 <dbl>, PC3 <dbl>,
#> #   geneID <chr>, taxonomy <fct>, rRNA16S <fct>
p <- mmnetwork(mmgenome2_extraction,
  network = paired_ends,
  min_connections = 1,
  color_by = "taxonomy"
)
p