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Use tracks inside and outside geom_* calls

Source: R/pull_tracks.R
pull_track.Rd

Track selection works like dplyr::pull() and supports unquoted ids and positional arguments. ... can be used to subset the data in dplyr::filter() fashion. pull-prefixed variants return the specified track from a gggenome object. Unprefixed variants work inside geom_* calls.

Usage

feats(.track_id = 1, ..., .ignore = "genes", .geneify = FALSE)

feats0(.track_id = 1, ..., .ignore = NA, .geneify = FALSE)

genes(..., .gene_types = c("CDS", "mRNA", "tRNA", "tmRNA", "ncRNA", "rRNA"))

links(.track_id = 1, ..., .ignore = NULL, .adjacent_only = NULL)

seqs(...)

bins(..., .group = vars())

track(.track_id = 1, ..., .track_type = NULL, .ignore = NULL)

pull_feats(.x, .track_id = 1, ..., .ignore = "genes", .geneify = FALSE)

pull_genes(
  .x,
  ...,
  .gene_types = c("CDS", "mRNA", "tRNA", "tmRNA", "ncRNA", "rRNA")
)

pull_links(.x, .track_id = 1, ..., .ignore = NULL, .adjacent_only = NULL)

pull_seqs(.x, ...)

pull_bins(.x, ..., .group = vars())

# S3 method for class 'gggenomes_layout'
pull_bins(.x, ..., .group = vars())

pull_track(.x, .track_id = 1, ..., .track_type = NULL, .ignore = NULL)

Arguments

.track_id

The track to pull out, either as a literal variable name or as a positive/negative integer giving the position from the left/right.

...

Logical predicates passed on to dplyr::filter. "seqs", "feats", "links". Affects position-based selection.

.ignore

track names to ignore when selecting by position. Default is "genes", if using feats0 this defaults to NA.

.geneify

add dummy type, introns and geom_id column to play nicely with geoms supporting multi-level and spliced gene models.

.gene_types

return only feats of this type (type %in% .gene_types)

.adjacent_only

filter for links connecting direct neighbors (abs(y-yend)==1))

.group

what variables to use in grouping of bins from seqs in addition to y and bin_id. Use this to get additional shared variables from the seqs table into the bins table.

.track_type

restrict to these types of tracks - any combination of "seqs", "feats", "links".

.x

A gggenomes or gggenomes_layout object.

Value

A function that pulls the specified track from a gggenomes object.

A function that pulls the specified track from a gggenomes object.

A function that pulls the specified track from a gggenomes object.

A function that pulls the specified track from a gggenomes object.

A function that pulls the specified track from a gggenomes object.

A function that pulls the specified track from a gggenomes object.

A function that pulls the specified track from a gggenomes object.

Functions

  • feats(): by default pulls out the first feat track not named "genes".

  • feats0(): by default pulls out the first feat track.

  • genes(): pulls out the first feat track (genes), filtering for records with type=="CDS", and adding a dummy gene_id column if missing to play nice with multi-exon geoms.

  • links(): by default pulls out the first link track.

  • seqs(): pulls out the seqs track (there is only one).

  • bins(): pulls out a binwise summary table of the seqs data powering geom_bin_*() calls. The bin table is not a real track, but recomputed on-the-fly.

  • track(): pulls from all tracks in order seqs, feats, links.

Examples


gg <- gggenomes(emale_genes, emale_seqs, emale_tirs, emale_ava)
gg %>% track_info() # info about track ids, positions and types
#> # A tibble: 4 × 4
#> # Groups:   type [3]
#>   id    type      i     n
#>   <chr> <chr> <int> <int>
#> 1 seqs  seqs      1     6
#> 2 genes feats     1   143
#> 3 feats feats     2    12
#> 4 links links     1    38

# get first feat track that isn't "genes" (all equivalent)
gg %>% pull_feats() # easiest
#> # A tibble: 12 × 20
#>        y     x  xend bin_id      seq_id start   end file_id strand type  feat_id
#>    <int> <dbl> <dbl> <chr>       <chr>  <int> <int> <chr>   <chr>  <chr> <chr>  
#>  1     2     0   473 BVI_008A    BVI_0…     1   473 emales… +      repe… BVI_00…
#>  2     2 26820 26347 BVI_008A    BVI_0… 26348 26820 emales… -      repe… BVI_00…
#>  3     3     0   488 BVI_069     BVI_0…     1   488 emales… +      repe… BVI_06…
#>  4     3 26808 26320 BVI_069     BVI_0… 26321 26808 emales… -      repe… BVI_06…
#>  5     4     0  1081 Cflag_017B  Cflag…     1  1081 emales… +      repe… Cflag_…
#>  6     4 21311 20230 Cflag_017B  Cflag… 20231 21311 emales… -      repe… Cflag_…
#>  7     5     0   319 E4-10_086   E4-10…     1   319 emales… +      repe… E4-10_…
#>  8     5 20642 20323 E4-10_086   E4-10… 20324 20642 emales… -      repe… E4-10_…
#>  9     1     0   458 E4-10_112   E4-10…     1   458 emales… +      repe… E4-10_…
#> 10     1 26856 26398 E4-10_112   E4-10… 26399 26856 emales… -      repe… E4-10_…
#> 11     6     0   454 RCC970_016B RCC97…     1   454 emales… +      repe… RCC970…
#> 12     6 20152 19698 RCC970_016B RCC97… 19699 20152 emales… -      repe… RCC970…
#> # ℹ 9 more variables: introns <list>, parent_ids <list>, source <chr>,
#> #   score <chr>, phase <int>, name <chr>, width <chr>, geom_id <chr>,
#> #   .marginal <lgl>
gg %>% pull_feats(feats) # by id
#> # A tibble: 12 × 20
#>        y     x  xend bin_id      seq_id start   end file_id strand type  feat_id
#>    <int> <dbl> <dbl> <chr>       <chr>  <int> <int> <chr>   <chr>  <chr> <chr>  
#>  1     2     0   473 BVI_008A    BVI_0…     1   473 emales… +      repe… BVI_00…
#>  2     2 26820 26347 BVI_008A    BVI_0… 26348 26820 emales… -      repe… BVI_00…
#>  3     3     0   488 BVI_069     BVI_0…     1   488 emales… +      repe… BVI_06…
#>  4     3 26808 26320 BVI_069     BVI_0… 26321 26808 emales… -      repe… BVI_06…
#>  5     4     0  1081 Cflag_017B  Cflag…     1  1081 emales… +      repe… Cflag_…
#>  6     4 21311 20230 Cflag_017B  Cflag… 20231 21311 emales… -      repe… Cflag_…
#>  7     5     0   319 E4-10_086   E4-10…     1   319 emales… +      repe… E4-10_…
#>  8     5 20642 20323 E4-10_086   E4-10… 20324 20642 emales… -      repe… E4-10_…
#>  9     1     0   458 E4-10_112   E4-10…     1   458 emales… +      repe… E4-10_…
#> 10     1 26856 26398 E4-10_112   E4-10… 26399 26856 emales… -      repe… E4-10_…
#> 11     6     0   454 RCC970_016B RCC97…     1   454 emales… +      repe… RCC970…
#> 12     6 20152 19698 RCC970_016B RCC97… 19699 20152 emales… -      repe… RCC970…
#> # ℹ 9 more variables: introns <list>, parent_ids <list>, source <chr>,
#> #   score <chr>, phase <int>, name <chr>, width <chr>, geom_id <chr>,
#> #   .marginal <lgl>
gg %>% pull_feats(1) # by position
#> # A tibble: 12 × 20
#>        y     x  xend bin_id      seq_id start   end file_id strand type  feat_id
#>    <int> <dbl> <dbl> <chr>       <chr>  <int> <int> <chr>   <chr>  <chr> <chr>  
#>  1     2     0   473 BVI_008A    BVI_0…     1   473 emales… +      repe… BVI_00…
#>  2     2 26820 26347 BVI_008A    BVI_0… 26348 26820 emales… -      repe… BVI_00…
#>  3     3     0   488 BVI_069     BVI_0…     1   488 emales… +      repe… BVI_06…
#>  4     3 26808 26320 BVI_069     BVI_0… 26321 26808 emales… -      repe… BVI_06…
#>  5     4     0  1081 Cflag_017B  Cflag…     1  1081 emales… +      repe… Cflag_…
#>  6     4 21311 20230 Cflag_017B  Cflag… 20231 21311 emales… -      repe… Cflag_…
#>  7     5     0   319 E4-10_086   E4-10…     1   319 emales… +      repe… E4-10_…
#>  8     5 20642 20323 E4-10_086   E4-10… 20324 20642 emales… -      repe… E4-10_…
#>  9     1     0   458 E4-10_112   E4-10…     1   458 emales… +      repe… E4-10_…
#> 10     1 26856 26398 E4-10_112   E4-10… 26399 26856 emales… -      repe… E4-10_…
#> 11     6     0   454 RCC970_016B RCC97…     1   454 emales… +      repe… RCC970…
#> 12     6 20152 19698 RCC970_016B RCC97… 19699 20152 emales… -      repe… RCC970…
#> # ℹ 9 more variables: introns <list>, parent_ids <list>, source <chr>,
#> #   score <chr>, phase <int>, name <chr>, width <chr>, geom_id <chr>,
#> #   .marginal <lgl>
gg %>% pull_feats(2, .ignore = NULL) # default .ignore="genes"
#> # A tibble: 12 × 20
#>        y     x  xend bin_id      seq_id start   end file_id strand type  feat_id
#>    <int> <dbl> <dbl> <chr>       <chr>  <int> <int> <chr>   <chr>  <chr> <chr>  
#>  1     2     0   473 BVI_008A    BVI_0…     1   473 emales… +      repe… BVI_00…
#>  2     2 26820 26347 BVI_008A    BVI_0… 26348 26820 emales… -      repe… BVI_00…
#>  3     3     0   488 BVI_069     BVI_0…     1   488 emales… +      repe… BVI_06…
#>  4     3 26808 26320 BVI_069     BVI_0… 26321 26808 emales… -      repe… BVI_06…
#>  5     4     0  1081 Cflag_017B  Cflag…     1  1081 emales… +      repe… Cflag_…
#>  6     4 21311 20230 Cflag_017B  Cflag… 20231 21311 emales… -      repe… Cflag_…
#>  7     5     0   319 E4-10_086   E4-10…     1   319 emales… +      repe… E4-10_…
#>  8     5 20642 20323 E4-10_086   E4-10… 20324 20642 emales… -      repe… E4-10_…
#>  9     1     0   458 E4-10_112   E4-10…     1   458 emales… +      repe… E4-10_…
#> 10     1 26856 26398 E4-10_112   E4-10… 26399 26856 emales… -      repe… E4-10_…
#> 11     6     0   454 RCC970_016B RCC97…     1   454 emales… +      repe… RCC970…
#> 12     6 20152 19698 RCC970_016B RCC97… 19699 20152 emales… -      repe… RCC970…
#> # ℹ 9 more variables: introns <list>, parent_ids <list>, source <chr>,
#> #   score <chr>, phase <int>, name <chr>, width <chr>, geom_id <chr>,
#> #   .marginal <lgl>

# get "seqs" track (always track #1)
gg %>% pull_seqs()
#> # A tibble: 6 × 12
#> # Groups:   bin_id [6]
#>       y     x  xend strand seq_id   bin_id length bin_offset start   end file_id
#>   <int> <dbl> <dbl> <chr>  <chr>    <chr>   <int>      <dbl> <dbl> <int> <chr>  
#> 1     6     0 20152 +      RCC970_… RCC97…  20152          0     1 20152 emales 
#> 2     5     0 20642 +      E4-10_0… E4-10…  20642          0     1 20642 emales 
#> 3     4     0 21311 +      Cflag_0… Cflag…  21311          0     1 21311 emales 
#> 4     3     0 26808 +      BVI_069  BVI_0…  26808          0     1 26808 emales 
#> 5     2     0 26820 +      BVI_008A BVI_0…  26820          0     1 26820 emales 
#> 6     1     0 26856 +      E4-10_1… E4-10…  26856          0     1 26856 emales 
#> # ℹ 1 more variable: seq_desc <chr>

# plot integrated transposons and GC content for some viral genomes
gg <- gggenomes(seqs = emale_seqs, feats = list(emale_ngaros, GC = emale_gc))
gg + geom_seq() +
  geom_feat(color = "skyblue") + # defaults to data=feats()
  geom_line(aes(x, y + score - .6, group = y), data = feats(GC), color = "gray60")