Extract all scaffolds within a selection polygon

Finds all scaffolds that fall into a selection polygon highlighted in a plot generated by mmplot.

mmextract(mm, selection, min_length = 0, inverse = FALSE)

Arguments

mm	(required) A dataframe loaded with mmload.
selection	(required) A 2-column dataframe with the x and y coordinates of a selection of points in an mmplot. The column names of the provided dataframe must match column names in mm.
min_length	Filter all scaffolds with a length at or below this value before the extraction. (Default: 0)
inverse	(Logical) If TRUE, then the scaffolds within the selection are instead removed. (Default: FALSE)

Value

A dataframe (tibble) compatible with other mmgenome2 functions.

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)
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.
mmgenome2_extraction
#> # A tibble: 47 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
#>  2 227      190826  65.4         0.348          13.0          6.91          38.5
#>  3 253      296715  70.2         1.06           15.2          7.00          41.1
#>  4 315      218862  71.7         0.366          12.6          7.17          38.8
#>  5 343       27203  57.6         0.235          10.7          5.07          32.0
#>  6 381        6128  67.2         8.11           22.6          5.89          48.7
#>  7 386      164942  68.2         0.359          12.3          6.73          37.2
#>  8 396        7448  72.6         0.329          13.0          7.59          41.0
#>  9 423      100199  68.7         1.40           15.3          6.71          40.0
#> 10 431      446220  72.4         0.409          12.6          6.80          37.9
#> # … with 37 more rows, and 6 more variables: PC1 <dbl>, PC2 <dbl>, PC3 <dbl>,
#> #   geneID <chr>, taxonomy <fct>, rRNA16S <fct>