Compare coverage profiles — mmplot_cov

Plots all coverage profiles in the data for comparison. This is useful for identifying scaffold patterns across multiple coverage profiles and spot potential contaminants.

mmplot_cov_profiles(
  mm,
  normalise = FALSE,
  alpha = 0.6,
  color_by = NULL,
  color_vector = c("blue", "green", "red"),
  color_scale_log10 = FALSE,
  y_scale_log10 = FALSE,
  plot_lines = TRUE,
  interactive_plot = FALSE,
  mm_normalise = NULL,
  mm_normalise_index = NULL
)

Arguments

mm	(required) A dataframe loaded with `mmload`.
normalise	(Logical) Normalises each coverage column in `mm` to its highest value and multiplies by 100. (Default: `TRUE`)
alpha	The transparancy of the scaffold points and lines, where 0 is invisible and 1 is opaque. (Default: `0.6`)
color_by	Color the scaffolds by a variable in `mm`. (Default: `NULL`)
color_vector	The colors from which to generate a color gradient when `color_by` is set and the variable is continuous. Any number of colors can be used. (Default: `c("blue", "green", "red")`)
color_scale_log10	(Logical) Log10-scale the color gradient when `color_by` is set and the variable is continuous. (Default: `FALSE`)
y_scale_log10	(Logical) Log10-scale the y axis. (Default: `FALSE`)
plot_lines	(Logical) Connect scaffolds with lines. (Default: `TRUE`)
interactive_plot	(Logical) Return an interactive `plotly` plot or not. (Default: `FALSE`)
mm_normalise	A dataframe loaded with `mmload` to normalise by.
mm_normalise_index	A coverage column to serve as an index for normalisation.

Value

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

Examples

library(mmgenome2)
data(mmgenome2)
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,
  min_length = 3000,
  selection = selection,
  inverse = FALSE
)
#> 47 scaffolds (or 1.33% of the scaffolds in mm, weighted by length) remain after 97238 of 97285 scaffolds have been filtered.
mmplot_cov_profiles(mmgenome2_extraction,
  color_by = "taxonomy",
  normalise = FALSE
)