calc_obs_props: Calculate proportions from observation counts

In metacoder: Tools for Parsing, Manipulating, and Graphing Taxonomic Abundance Data

Calculate proportions from observation counts

Description

For a given table in a taxmap object, convert one or more columns containing counts to proportions. This is meant to be used with counts associated with observations (e.g. OTUs), as opposed to counts that have already been summed per taxon.

Usage

calc_obs_props(
  obj,
  data,
  cols = NULL,
  groups = NULL,
  other_cols = FALSE,
  out_names = NULL,
  dataset = NULL
)

Arguments

obj	A taxmap object
data	The name of a table in obj$data.
cols	The columns in data to use. By default, all numeric columns are used. Takes one of the following inputs: TRUE/FALSE: All/No columns will used. Character vector: The names of columns to use Numeric vector: The indexes of columns to use Vector of TRUE/FALSE of length equal to the number of columns: Use the columns corresponding to TRUE values.
groups	Group multiple columns per treatment/group. This should be a vector of group IDs (e.g. character, integer) the same length as cols that defines which samples go in which group. When used, there will be one column in the output for each unique value in groups.
other_cols	Preserve in the output non-target columns present in the input data. New columns will always be on the end. The "taxon_id" column will be preserved in the front. Takes one of the following inputs: NULL: No columns will be added back, not even the taxon id column. TRUE/FALSE: All/None of the non-target columns will be preserved. Character vector: The names of columns to preserve Numeric vector: The indexes of columns to preserve Vector of TRUE/FALSE of length equal to the number of columns: Preserve the columns corresponding to TRUE values.
out_names	The names of count columns in the output. Must be the same length and order as cols (or unique(groups), if groups is used).
dataset	DEPRECIATED. use "data" instead.

Value

A tibble

See Also

Other calculations: calc_diff_abund_deseq2(), calc_group_mean(), calc_group_median(), calc_group_rsd(), calc_group_stat(), calc_n_samples(), calc_prop_samples(), calc_taxon_abund(), compare_groups(), counts_to_presence(), rarefy_obs(), zero_low_counts()

Examples

## Not run: 
# Parse data for examples
x = parse_tax_data(hmp_otus, class_cols = "lineage", class_sep = ";",
                   class_key = c(tax_rank = "taxon_rank", tax_name = "taxon_name"),
                   class_regex = "^(.+)__(.+)$")
                   
# Calculate proportions for all numeric columns
calc_obs_props(x, "tax_data")

# Calculate proportions for a subset of columns
calc_obs_props(x, "tax_data", cols = c("700035949", "700097855", "700100489"))
calc_obs_props(x, "tax_data", cols = 4:6)
calc_obs_props(x, "tax_data", cols = startsWith(colnames(x$data$tax_data), "70001"))

# Including all other columns in ouput
calc_obs_props(x, "tax_data", other_cols = TRUE)

# Inlcuding specific columns in output
calc_obs_props(x, "tax_data", cols = c("700035949", "700097855", "700100489"),
               other_cols = 2:3)
               
# Rename output columns
calc_obs_props(x, "tax_data", cols = c("700035949", "700097855", "700100489"),
               out_names = c("a", "b", "c"))
               
# Get proportions for groups of samples
calc_obs_props(x, "tax_data", groups = hmp_samples$sex)
calc_obs_props(x, "tax_data", groups = hmp_samples$sex,
               out_names = c("Women", "Men"))


## End(Not run)

metacoder documentation built on April 4, 2023, 9:08 a.m.