Extract statistics from taxatree_models or tax_model output

Source: R/taxatree_models2stats.R

Runs a function e.g. broom::tidy on a list of models, i.e. the output of taxatree_models or tax_model

taxatree_models2stats(data, fun = "auto", ..., .keep_models = FALSE)

tax_models2stats(data, rank = NULL, fun = "auto", ..., .keep_models = FALSE)

Arguments

data

psExtra with attached tax_models or taxatree_models list, or just the list of models

fun

function to assist extraction of stats dataframe from models, or "auto"

...

extra arguments passed to fun

.keep_models

should the models list be kept in the psExtra output?

rank

string naming rank at which tax_model was run (needed if data is a list)

Value

data.frame, attached to psExtra

Functions

  • taxatree_models2stats(): Extract stats from list or psExtra output of taxatree_models

  • tax_models2stats(): Extract stats from list or psExtra output of tax_model

Examples

# This example is an abbreviated excerpt from article on taxon modelling on
# the microViz documentation website

library(dplyr)
data("ibd", package = "microViz")

# We'll keep only the Ulcerative Colitis and Healthy Control samples, to
# simplify the analyses for this example. We'll also remove the Species
# rank information, as most OTUs in this dataset are not assigned to a
# species. We'll also use `tax_fix` to fill any gaps where the Genus is
# unknown, with the family name or whatever higher rank classification is
# known.

phylo <- ibd %>%
  ps_filter(DiseaseState %in% c("UC", "nonIBD")) %>%
  tax_mutate(Species = NULL) %>%
  tax_fix()

# Let's make some sample data variables that are easier to use and compare
# in the statistical modelling ahead. We will convert dichotomous
# categorical variables into similar binary variables (values: 1 for true,
# or 0 for false). We will also scale and center the numeric variable for
# age.

phylo <- phylo %>%
  ps_mutate(
    UC = ifelse(DiseaseState == "UC", yes = 1, no = 0),
    female = ifelse(gender == "female", yes = 1, no = 0),
    antibiotics = ifelse(abx == "abx", yes = 1, no = 0),
    steroids = ifelse(steroids == "steroids", yes = 1, no = 0),
    age_scaled = scale(age, center = TRUE, scale = TRUE)
  )

lm_models <- phylo %>%
  tax_fix() %>%
  tax_prepend_ranks() %>%
  tax_transform("compositional", rank = "Genus") %>%
  tax_filter(min_prevalence = 0.1, use_counts = TRUE) %>%
  taxatree_models(
    type = lm,
    trans = "log2", trans_args = list(zero_replace = "halfmin"),
    ranks = c("Phylum", "Class", "Genus"),
    variables = c("UC", "female", "antibiotics", "steroids", "age_scaled")
  )
#> Proportional min_prevalence given: 0.1 --> min 7/67 samples.
#> 2024-12-16 10:21:43.560688 - modelling at rank: Phylum
#> 2024-12-16 10:21:43.712095 - modelling at rank: Class
#> 2024-12-16 10:21:43.962471 - modelling at rank: Genus

lm_stats <- lm_models %>% taxatree_models2stats()

# inspect the psExtra returned, now with taxatree_stats dataframe added
lm_stats
#> psExtra object - a phyloseq object with extra slots:
#> 
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:         [ 68 taxa and 67 samples ]
#> sample_data() Sample Data:       [ 67 samples by 19 sample variables ]
#> tax_table()   Taxonomy Table:    [ 68 taxa by 6 taxonomic ranks ]
#> 
#> otu_get(counts = TRUE)		 [ 68 taxa and 67 samples ]
#> 
#> psExtra info:
#> tax_agg = "Genus" tax_trans = "compositional" 
#> 
#> taxatree_stats dataframe:
#> 88 taxa at 3 ranks: Phylum, Class, Genus 
#> 5 terms: UC, female, antibiotics, steroids, age_scaled

# inspect the dataframe itself
lm_stats %>% taxatree_stats_get()
#> # A tibble: 440 × 8
#>    term        taxon          rank  formula estimate std.error statistic p.value
#>    <fct>       <chr>          <fct> <chr>      <dbl>     <dbl>     <dbl>   <dbl>
#>  1 UC          P: Firmicutes  Phyl… `P: Fi…   0.0430     0.455    0.0945  0.925 
#>  2 female      P: Firmicutes  Phyl… `P: Fi…  -0.297      0.324   -0.919   0.362 
#>  3 antibiotics P: Firmicutes  Phyl… `P: Fi…  -0.793      0.440   -1.80    0.0763
#>  4 steroids    P: Firmicutes  Phyl… `P: Fi…  -0.747      0.415   -1.80    0.0771
#>  5 age_scaled  P: Firmicutes  Phyl… `P: Fi…   0.144      0.183    0.788   0.434 
#>  6 UC          P: Bacteroide… Phyl… `P: Ba…  -2.58       1.63    -1.58    0.120 
#>  7 female      P: Bacteroide… Phyl… `P: Ba…  -1.01       1.16    -0.870   0.388 
#>  8 antibiotics P: Bacteroide… Phyl… `P: Ba…   1.52       1.58     0.962   0.340 
#>  9 steroids    P: Bacteroide… Phyl… `P: Ba…  -1.67       1.49    -1.12    0.266 
#> 10 age_scaled  P: Bacteroide… Phyl… `P: Ba…  -0.340      0.655   -0.519   0.605 
#> # ℹ 430 more rows

# keep the models on the psExtra object
lm_models %>% taxatree_models2stats(.keep_models = TRUE)
#> psExtra object - a phyloseq object with extra slots:
#> 
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:         [ 68 taxa and 67 samples ]
#> sample_data() Sample Data:       [ 67 samples by 19 sample variables ]
#> tax_table()   Taxonomy Table:    [ 68 taxa by 6 taxonomic ranks ]
#> 
#> otu_get(counts = TRUE)		 [ 68 taxa and 67 samples ]
#> 
#> psExtra info:
#> tax_agg = "Genus" tax_trans = "compositional" 
#> 
#> taxatree_models list:
#> Ranks: Phylum/Class/Genus
#> taxatree_stats dataframe:
#> 88 taxa at 3 ranks: Phylum, Class, Genus 
#> 5 terms: UC, female, antibiotics, steroids, age_scaled

# you can adjust the p values with taxatree_stats_p_adjust

# you can plot the results with taxatree_plots