Conversion to count table function with prevalence filter (Extracted from OneNet package)

Usage

get_count_table(
  abund.path = NULL,
  abund.table = NULL,
  sample.id = NULL,
  prev.min,
  verbatim = TRUE,
  msp = NULL
)

Arguments

abund.path: String. Path to the abundance table
abund.table: Dataframe. Abundance table, it should have the bacterial species names as first column
sample.id: String vector. IDs of samples to keep in the final table
prev.min: Numeric. The value is between 0 and 1 and corresponds to the minimal prevalence threshold of bacterial species to keep in the final table
verbatim: Boolean. Controls verbosity
msp: String vector. It indicates bacterial species names, if they are not specified in the abundance table first column

Value

A list containing

data:: the final count table (tibble)
prevalences:: a tibble gathering the prevalence of each bacterial species

Examples

tiny_data <- data.frame(
  msp_name = c("msp_1", "msp_2", "msp_3", "msp_4"),
  SAMPLE1 = c(0, 1.328425e-06, 0, 1.527688e-07),
  SAMPLE2 = c(1.251707e-07, 1.251707e-07, 3.985320e-07, 0),
  SAMPLE3 = c(0, 0, 4.926046e-09, 5.626392e-06),
  SAMPLE4 = c(0, 0, 2.98320e-05, 0)
)
# Applying a prevalence filter of 30% on the new count_table
count_table <- get_count_table(abund.table = tiny_data, sample.id = colnames(tiny_data), prev.min = 0.3)
#> Preprocessing step output for species prevalence>30% : 
#>    -from 4 to 3 species
#>    -from 50% to 41.7% zero values.