R/taxatree_stats_p_adjust.R
taxatree_stats_p_adjust.RdApply a p value adjustment method from stats::p.adjust.methods,
such as false-discovery rate adjustment with "BH",
or more conservative family-wise error rate controlling methods such as "holm" or "bonferroni".
taxatree_stats_p_adjust(
data,
method,
grouping = "rank",
p = "p.value",
new_var = NULL
)psExtra with taxatree_stats dataframe, or just the dataframe
any method from stats::p.adjust.methods
defines grouping of p-values into families for adjustment, see details.
name of variable containing p values for adjustment
name of new variable created for adjusted p values (automatically inferred by default)
psExtra with dataframe of statistics, or just the data.frame
Define how to group the p values for adjustment with the grouping argument.
The default is to adjust the p values in groups at each taxonomic rank,
but you could also adjust per "taxon" or per "term".
Or even group by a combination of rank and term with c("rank", "term").
You should specify the name of the new variable containing the adjusted
p values in the new_var argument. If left as NULL the new variable name will
be created by pasting together p.adj, the method, and the grouping variable(s)
separated by ".".
# This example is an abbreviated excerpt from article on taxon modelling on
# the microViz documentation website
library(corncob)
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)
)
bb_models <- phylo %>%
tax_fix() %>%
tax_prepend_ranks() %>%
tax_filter(min_prevalence = 0.3) %>%
taxatree_models(
type = corncob::bbdml,
ranks = c("Phylum", "Class", "Order"),
variables = c("UC", "female", "antibiotics", "steroids", "age_scaled")
)
#> Proportional min_prevalence given: 0.3 --> min 21/67 samples.
#> 2024-11-18 14:50:21.219984 - modelling at rank: Phylum
#> 2024-11-18 14:50:21.469337 - modelling at rank: Class
#> 2024-11-18 14:50:21.95153 - modelling at rank: Order
bb_stats <- bb_models %>%
taxatree_models2stats(param = "mu") %>%
taxatree_stats_p_adjust(method = "BH", grouping = "rank")
bb_stats
#> psExtra object - a phyloseq object with extra slots:
#>
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 105 taxa and 67 samples ]
#> sample_data() Sample Data: [ 67 samples by 19 sample variables ]
#> tax_table() Taxonomy Table: [ 105 taxa by 6 taxonomic ranks ]
#>
#>
#> taxatree_stats dataframe:
#> 19 taxa at 3 ranks: Phylum, Class, Order
#> 5 terms: UC, female, antibiotics, steroids, age_scaled
bb_stats %>% taxatree_stats_get()
#> # A tibble: 95 × 10
#> # Groups: rank [3]
#> term taxon rank formula parameter estimate std.error t.statistic p.value
#> <fct> <chr> <fct> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 UC P: F… Phyl… `P: Fi… mu -0.375 0.434 -0.864 0.392
#> 2 female P: F… Phyl… `P: Fi… mu 0.0227 0.309 0.0734 0.942
#> 3 antibio… P: F… Phyl… `P: Fi… mu -0.781 0.478 -1.63 0.108
#> 4 steroids P: F… Phyl… `P: Fi… mu -0.162 0.426 -0.380 0.705
#> 5 age_sca… P: F… Phyl… `P: Fi… mu 0.294 0.188 1.56 0.124
#> 6 UC P: B… Phyl… `P: Ba… mu -0.814 0.558 -1.46 0.150
#> 7 female P: B… Phyl… `P: Ba… mu -0.559 0.349 -1.60 0.115
#> 8 antibio… P: B… Phyl… `P: Ba… mu -0.116 0.706 -0.164 0.871
#> 9 steroids P: B… Phyl… `P: Ba… mu -0.130 0.656 -0.197 0.844
#> 10 age_sca… P: B… Phyl… `P: Ba… mu -0.0967 0.264 -0.366 0.716
#> # ℹ 85 more rows
#> # ℹ 1 more variable: p.adj.BH.rank <dbl>
# you can also directly modify the dataframe,
# and choose a different variable name
bb_stats %>%
taxatree_stats_get() %>%
taxatree_stats_p_adjust(
method = "holm", grouping = "taxon", new_var = "p_adj_holm"
)
#> # A tibble: 95 × 11
#> # Groups: taxon [19]
#> term taxon rank formula parameter estimate std.error t.statistic p.value
#> <fct> <chr> <fct> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 UC P: F… Phyl… `P: Fi… mu -0.375 0.434 -0.864 0.392
#> 2 female P: F… Phyl… `P: Fi… mu 0.0227 0.309 0.0734 0.942
#> 3 antibio… P: F… Phyl… `P: Fi… mu -0.781 0.478 -1.63 0.108
#> 4 steroids P: F… Phyl… `P: Fi… mu -0.162 0.426 -0.380 0.705
#> 5 age_sca… P: F… Phyl… `P: Fi… mu 0.294 0.188 1.56 0.124
#> 6 UC P: B… Phyl… `P: Ba… mu -0.814 0.558 -1.46 0.150
#> 7 female P: B… Phyl… `P: Ba… mu -0.559 0.349 -1.60 0.115
#> 8 antibio… P: B… Phyl… `P: Ba… mu -0.116 0.706 -0.164 0.871
#> 9 steroids P: B… Phyl… `P: Ba… mu -0.130 0.656 -0.197 0.844
#> 10 age_sca… P: B… Phyl… `P: Ba… mu -0.0967 0.264 -0.366 0.716
#> # ℹ 85 more rows
#> # ℹ 2 more variables: p.adj.BH.rank <dbl>, p_adj_holm <dbl>
# see all available adjustment methods
stats::p.adjust.methods
#> [1] "holm" "hochberg" "hommel" "bonferroni" "BH"
#> [6] "BY" "fdr" "none"