• ps_get returns phyloseq

  • info_get returns ps_extra_info object

  • dist_get returns distance matrix (or NULL)

  • ord_get returns ordination object (or NULL)

  • perm_get returns adonis2() permanova model (or NULL)

  • bdisp_get returns results of betadisper() (or NULL)

  • otu_get returns phyloseq otu_table matrix with taxa as columns

  • tt_get returns phyloseq tax_table

  • samdat_tbl returns phyloseq sample_data as a tibble, with sample_names as new first column called .sample_name

ps_get(ps_extra)

dist_get(ps_extra)

ord_get(ps_extra)

info_get(ps_extra)

perm_get(ps_extra)

bdisp_get(ps_extra)

otu_get(data, taxa = NA, samples = NA, counts = FALSE)

tt_get(data)

samdat_tbl(data, sample_names_col = ".sample_name")

Arguments

ps_extra

ps_extra class object

data

phyloseq or ps_extra

taxa

subset of taxa to return, NA for all (default)

samples

subset of samples to return, NA for all (default)

counts

should otu_get ensure it returns counts? if present in object

sample_names_col

name of column where sample_names are put. if NA, return data.frame with rownames (sample_names)

Value

element of ps_extra class object (or NULL)

Examples

data("dietswap", package = "microbiome")
psx <- tax_transform(dietswap, "identity", rank = "Genus")
psx
#> ps_extra object - a list with phyloseq and extras:
#> 
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:         [ 130 taxa and 222 samples ]
#> sample_data() Sample Data:       [ 222 samples by 8 sample variables ]
#> tax_table()   Taxonomy Table:    [ 130 taxa by 3 taxonomic ranks ]
#> 
#> ps_extra info:
#> tax_agg = Genus tax_transform = identity

ps_get(psx)
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:         [ 130 taxa and 222 samples ]
#> sample_data() Sample Data:       [ 222 samples by 8 sample variables ]
#> tax_table()   Taxonomy Table:    [ 130 taxa by 3 taxonomic ranks ]
info_get(psx)
#> ps_extra info:
#> tax_agg = Genus 
#> tax_transform = identity 
#> tax_scale = NA 
#> distMethod = NA 
#> ordMethod = NA 
#> constraints = NA 
#> conditions = NA 

dist_get(psx) # this ps_extra has no dist_calc result
#> NULL
ord_get(psx) # this ps_extra has no ord_calc result
#> NULL
perm_get(psx) # this ps_extra has no dist_permanova result
#> NULL
bdisp_get(psx) # this ps_extra has no dist_bdisp result
#> NULL

# these can be returned from phyloseq objects too
otu_get(psx)[1:6, 1:4]
#> OTU Table:          [4 taxa and 6 samples]
#>                      taxa are columns
#>          Actinomycetaceae Aerococcus Aeromonas Akkermansia
#> Sample-1                0          0         0          18
#> Sample-2                1          0         0          97
#> Sample-3                0          0         0          67
#> Sample-4                1          0         0         256
#> Sample-5                0          0         0          21
#> Sample-6                0          0         0          16
tt_get(psx) %>% head()
#> Taxonomy Table:     [6 taxa by 3 taxonomic ranks]:
#>                              Phylum            Family           
#> Actinomycetaceae             "Actinobacteria"  "Actinobacteria" 
#> Aerococcus                   "Firmicutes"      "Bacilli"        
#> Aeromonas                    "Proteobacteria"  "Proteobacteria" 
#> Akkermansia                  "Verrucomicrobia" "Verrucomicrobia"
#> Alcaligenes faecalis et rel. "Proteobacteria"  "Proteobacteria" 
#> Allistipes et rel.           "Bacteroidetes"   "Bacteroidetes"  
#>                              Genus                         
#> Actinomycetaceae             "Actinomycetaceae"            
#> Aerococcus                   "Aerococcus"                  
#> Aeromonas                    "Aeromonas"                   
#> Akkermansia                  "Akkermansia"                 
#> Alcaligenes faecalis et rel. "Alcaligenes faecalis et rel."
#> Allistipes et rel.           "Allistipes et rel."          
samdat_tbl(psx) %>% head()
#> # A tibble: 6 × 9
#>   .sample_name subject sex    nationality group sample   timep…¹ timep…² bmi_g…³
#>   <chr>        <fct>   <fct>  <fct>       <fct> <chr>      <int>   <int> <fct>  
#> 1 Sample-1     byn     male   AAM         DI    Sample-1       4       1 obese  
#> 2 Sample-2     nms     male   AFR         HE    Sample-2       2       1 lean   
#> 3 Sample-3     olt     male   AFR         HE    Sample-3       2       1 overwe…
#> 4 Sample-4     pku     female AFR         HE    Sample-4       2       1 obese  
#> 5 Sample-5     qjy     female AFR         HE    Sample-5       2       1 overwe…
#> 6 Sample-6     riv     female AFR         HE    Sample-6       2       1 obese  
#> # … with abbreviated variable names ¹​timepoint, ²​timepoint.within.group,
#> #   ³​bmi_group