Find connections of a subset of scaffolds in a larger set of scaffolds

Source: R/mmexpand_network.R

Searches for all connections between the given set of scaffolds and the scaffolds in mm. The given set of scaffolds is often a subset of mm, but can also be a manually selected set of scaffolds from which to find connections in mm.

mmexpand_network(
  mm,
  scaffolds,
  network,
  min_connections = 2,
  include_connections = "direct"
)

Arguments

mm

(required) A dataframe loaded with mmload in which to find connections from scaffolds.
scaffolds

(required) The scaffolds from which to find connections in mm. Must be a vector of scaffold names or a dataframe with scaffold names in the first column.
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 before the extraction. (Default: 2)
include_connections

The connections to include. One of the following:

"direct"

Extract only scaffolds from mm that are directly connected to any of the scaffolds in scaffolds. (default)

"all"

Same as "direct", except that any further connections to connected scaffolds are also included, continuing until no further connections are found.

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)
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,
  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>
mmgenome2_extraction_expanded <- mmexpand_network(
  mm = mmgenome2,
  scaffolds = mmgenome2_extraction,
  network = paired_ends,
  min_connections = 10,
  include_connections = "direct"
)

#> The provided 47 scaffolds had 23 additional direct connections to other scaffolds in mm.
#> Total: 70 scaffolds (or 1.92% of the scaffolds in mm, weighted by length).
mmgenome2_extraction_expanded

#> # A tibble: 70 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 60 more rows, and 6 more variables: PC1 <dbl>, PC2 <dbl>, PC3 <dbl>,
#> #   geneID <chr>, taxonomy <fct>, rRNA16S <fct>