Add taxon prevalences to the taxon table

add_prevalence() calculates taxon prevalences (overall or per condition) and adds it to the taxa table under the column name "prevalence". Prevalence can be expressed as the number of samples where a taxon occurs or the ratio of samples where a taxon occurs and the total amount of samples.

Usage

add_prevalence(
  ta,
  condition = NULL,
  relative = FALSE,
  fisher_test = FALSE,
  ...
)

Arguments

ta

A tidytacos object.

condition

A categorical variable (string).

relative

Whether to use relative occurrences.

fisher_test

Whether to perform a fisher test and add the p-values of the test to the taxa table.

...

Arguments passed on to stats::fisher.test

x

either a two-dimensional contingency table in matrix form, or a factor object.

y

a factor object; ignored if x is a matrix.

workspace

an integer specifying the size of the workspace used in the network algorithm. In units of 4 bytes. Only used for non-simulated p-values larger than \(2 \times 2\) tables. Since R version 3.5.0, this also increases the internal stack size which allows larger problems to be solved, however sometimes needing hours. In such cases, simulate.p.values=TRUE may be more reasonable.

hybrid

a logical. Only used for larger than \(2 \times 2\) tables, in which cases it indicates whether the exact probabilities (default) or a hybrid approximation thereof should be computed.

hybridPars

a numeric vector of length 3, by default describing “Cochran's conditions” for the validity of the chi-squared approximation, see ‘Details’.

control

a list with named components for low level algorithm control. At present the only one used is "mult", a positive integer \(\ge 2\) with default 30 used only for larger than \(2 \times 2\) tables. This says how many times as much space should be allocated to paths as to keys: see file fexact.c in the sources of this package.

or

the hypothesized odds ratio. Only used in the \(2 \times 2\) case.

alternative

indicates the alternative hypothesis and must be one of "two.sided", "greater" or "less". You can specify just the initial letter. Only used in the \(2 \times 2\) case.

conf.int

logical indicating if a confidence interval for the odds ratio in a \(2 \times 2\) table should be computed (and returned).

conf.level

confidence level for the returned confidence interval. Only used in the \(2 \times 2\) case and if conf.int = TRUE.

simulate.p.value

a logical indicating whether to compute p-values by Monte Carlo simulation, in larger than \(2 \times 2\) tables.

B

an integer specifying the number of replicates used in the Monte Carlo test.

Value

A tidytacos object.

Details

If 'condition' is specified, the prevalences will be calculated separately for each group defined by the condition variable. This variable should be present in the sample table.

If condition is specified, differential prevalence testing can be performed by setting the fisher_test argument. Options are F (default) or T. When set to T, significance of differential prevalence will be added to the taxa table under column name fisher_p.

Condition should be a categorical variable present in the samples table. Supply condition as a string.

See also

Other taxa-modifiers: add_mean_rel_abundance(), add_taxon_name(), add_taxon_name_color()

Examples

# add prevalences of all taxa
urtp <- urt %>% add_prevalence()
urtp$taxa %>% dplyr::select(taxon_id, prevalence)
#> # A tibble: 1,957 × 2
#>    taxon_id prevalence
#>    <chr>         <int>
#>  1 t1               86
#>  2 t2              207
#>  3 t3                7
#>  4 t4                5
#>  5 t5               16
#>  6 t6               22
#>  7 t7              155
#>  8 t8               25
#>  9 t9               24
#> 10 t10               1
#> # ℹ 1,947 more rows

# add prevalences and fisher test for location
urtpf <- urt %>%
  add_prevalence(condition="location", fisher_test=TRUE, relative=TRUE)
urtpf$taxa %>%
  dplyr::select(taxon_id, prevalence_in_N, prevalence_in_NF, fisher_p)
#> # A tibble: 1,957 × 4
#>    taxon_id prevalence_in_N prevalence_in_NF fisher_p
#>    <chr>              <dbl>            <dbl>    <dbl>
#>  1 t1                0.326           0.443    0.0885 
#>  2 t2                0.988           0.931    0.0437 
#>  3 t3                0.0233          0.0382   0.706  
#>  4 t4                0.0233          0.0229   1      
#>  5 t5                0.0581          0.0840   0.599  
#>  6 t6                0.128           0.0840   0.359  
#>  7 t7                0.814           0.649    0.0120 
#>  8 t8                0.0465          0.160    0.00933
#>  9 t9                0.0349          0.160    0.00354
#> 10 t10               0               0.00763  1      
#> # ℹ 1,947 more rows