metacoder (version 0.2.0)

zero_low_counts: Replace low counts with zero

Description

For a given table in a taxmap object, convert all counts below a minimum number to zero. This is useful for effectively removing "singletons", "doubletons", or other low abundance counts.

Usage

zero_low_counts(obj, dataset, min_count = 2, use_total = FALSE,
  cols = NULL, other_cols = FALSE, out_names = NULL)

Arguments

obj

A taxmap object

dataset

The name of a table in obj that contains counts.

min_count

The minimum number of counts needed for a count to remain unchanged. Any could less than this will be converted to a zero. For example, min_count = 2 would remove singletons.

use_total

If TRUE, the min_count applies to the total count for each row (e.g. OTU counts for all samples), rather than each cell in the table. For example use_total = TRUE, min_count = 10 would convert all counts of any row to zero if the total for all counts in that row was less than 10.

cols

The names/indexes of columns in dataset 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.

other_cols

Preserve in the output non-target columns present in the input data. New columns with proportions will always be on the end. The "taxon_id" column will always be preserved in the front. Takes one of the following inputs:

TRUE/FALSE:

All non-target columns will be preserved or not.

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

If supplied, rename the output proportion columns. Must be the same length as cold.

Value

A tibble

See Also

Other calculations: calc_n_samples, calc_obs_props, calc_taxon_abund, compare_groups, rarefy_obs

Examples

# NOT RUN {
# Parse dataset for examples
x = parse_tax_data(hmp_otus, class_cols = "lineage", class_sep = ";",
                   class_key = c(tax_rank = "info", 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"))

# }