Retrieve Metabolic Pathways — pathways • ramen

Retrieves the pathways representing metabolic interactions between species.

By default, returns a concise summary with columns consumed, produced, and n_species. For ConsortiumMetabolismSet objects, n_cons is included as well. Set verbose = TRUE to return the full pathway data including flux statistics, indices, and per-species detail.

The argument type can be used to return only specific types of pathways from a ConsortiumMetabolismSet object:

"all" returns all pathways
"pan-cons" returns pathways present in most consortia
"niche" returns niche pathways specific to few consortia
"core" returns core metabolic pathways shared across most species
"aux" returns auxiliary pathways found in few species

For ConsortiumMetabolismAlignment objects, type selects the pathway subset:

"all" returns the union of all pathways
"shared" (pairwise only) returns pathways shared between query and reference
"unique" (pairwise only) returns pathways unique to query and reference as a list
"consensus" (multiple only) returns consensus network pathways with prevalence

Usage

pathways(object, ...)

# S4 method for class 'ConsortiumMetabolism'
pathways(object, verbose = FALSE)

# S4 method for class 'ConsortiumMetabolismAlignment'
pathways(
  object,
  type = c("all", "shared", "unique", "consensus"),
  verbose = FALSE
)

# S4 method for class 'ConsortiumMetabolismSet'
pathways(
  object,
  type = c("all", "pan-cons", "niche", "core", "aux"),
  quantileCutoff = 0.1,
  verbose = FALSE
)

Arguments

object: A ConsortiumMetabolism, ConsortiumMetabolismSet, or ConsortiumMetabolismAlignment object.
...: Object specific arguments. See methods for details.
verbose: Logical scalar. If FALSE (default), returns a concise summary with columns consumed, produced, and n_species. If TRUE, returns the full pathway data including flux statistics, effective values, and per-species detail.
type: Character scalar giving the type of pathways to output. Note that the four filtered types use two different rulers: "pan-cons" and "niche" rank by the integer per-pathway consortium count and quantile over a uniform integer ruler (seq_len(total_cons)); "core" and "aux" rank by per-pathway species count and quantile over the empirical n_species distribution across pathways.
quantileCutoff: Numeric scalar between 0 and 1 giving the quantile threshold to use for filtering pathways. For "pan-cons" and "core" types, pathways strictly above 1 - quantileCutoff are returned. For "niche" and "aux" types, pathways at or below quantileCutoff are returned (the boundary is included on the lower tail so that ties at the quantile floor are not silently dropped). Defaults to 0.1 (i.e., top/bottom 10 percent).

Value

A data.frame of pathway information. With verbose = FALSE (default): consumed, produced, n_species (and n_cons for CMS objects). With verbose = TRUE: all available columns including flux statistics and indices. For CMA objects, the return depends on type: a data.frame for "all", "shared", and "consensus"; a list of data.frames for "unique".

Methods (by class)

pathways(ConsortiumMetabolism): Get Pathways From a ConsortiumMetabolism Object
pathways(ConsortiumMetabolismAlignment): Get Pathways From a ConsortiumMetabolismAlignment Object
pathways(ConsortiumMetabolismSet): Get Pathways From a ConsortiumMetabolismSet Object

Examples

cm <- synCM("test", n_species = 3, max_met = 5)
pathways(cm)
#> # A tibble: 10 × 3
#>    consumed produced n_species
#>    <chr>    <chr>        <dbl>
#>  1 met4     media            1
#>  2 met4     met5             1
#>  3 met4     met1             1
#>  4 met5     met4             1
#>  5 met5     met1             1
#>  6 met3     met5             1
#>  7 met3     met1             2
#>  8 met3     met4             1
#>  9 met2     met4             1
#> 10 met2     met1             1
pathways(cm, verbose = TRUE)
#> # A tibble: 10 × 14
#>    consumed produced data     n_species  c_sum p_sum c_prob p_prob c_neff p_neff
#>    <chr>    <chr>    <list>       <dbl>  <dbl> <dbl> <list> <list>  <dbl>  <dbl>
#>  1 met4     media    <tibble>         1  0.384 1     <dbl>  <dbl>    1      1   
#>  2 met4     met5     <tibble>         1  1.91  1.34  <dbl>  <dbl>    1      1   
#>  3 met4     met1     <tibble>         1  1.91  3.94  <dbl>  <dbl>    1      1   
#>  4 met5     met4     <tibble>         1  1.34  2.29  <dbl>  <dbl>    1      1   
#>  5 met5     met1     <tibble>         1  1.34  0.790 <dbl>  <dbl>    1      1   
#>  6 met3     met5     <tibble>         1  7.00  1.34  <dbl>  <dbl>    1      1   
#>  7 met3     met1     <tibble>         2 10.2   4.73  <dbl>  <dbl>    1.86   1.57
#>  8 met3     met4     <tibble>         1  3.18  2.29  <dbl>  <dbl>    1      1   
#>  9 met2     met4     <tibble>         1  0.826 2.29  <dbl>  <dbl>    1      1   
#> 10 met2     met1     <tibble>         1  0.826 0.790 <dbl>  <dbl>    1      1   
#> # ℹ 4 more variables: c_eff <dbl>, p_eff <dbl>, c_ind <int>, p_ind <int>

cm1 <- synCM("comm_1", n_species = 3, max_met = 5)
cm2 <- synCM("comm_2", n_species = 4, max_met = 6)
cma <- align(cm1, cm2)
pathways(cma)
#> # A tibble: 0 × 2
#> # ℹ 2 variables: consumed <chr>, produced <chr>
pathways(cma, type = "shared")
#> # A tibble: 0 × 2
#> # ℹ 2 variables: consumed <chr>, produced <chr>
pathways(cma, type = "unique")
#> $query
#> # A tibble: 5 × 2
#>   consumed produced
#>   <chr>    <chr>   
#> 1 met1     media   
#> 2 met2     met1    
#> 3 met1     met2    
#> 4 met1     met3    
#> 5 met2     met5    
#> 
#> $reference
#> # A tibble: 3 × 2
#>   consumed produced
#>   <chr>    <chr>   
#> 1 met4     met1    
#> 2 met1     met4    
#> 3 met5     met4    
#>