zero_low_counts
Replace low counts with zero
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, data, min_count = 2, use_total = FALSE,
cols = NULL, other_cols = FALSE, out_names = NULL,
dataset = NULL)Arguments
- obj
A
taxmapobject- data
The name of a table in
obj$data.- 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 = 2would remove singletons.- use_total
If
TRUE, themin_countapplies to the total count for each row (e.g. OTU counts for all samples), rather than each cell in the table. For exampleuse_total = TRUE, min_count = 10would convert all counts of any row to zero if the total for all counts in that row was less than 10.- cols
The columns in
datato 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
TRUEvalues.
- 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
TRUEvalues.
- out_names
The names of count columns in the output. Must be the same length and order as
cols(orunique(groups), ifgroupsis used).- dataset
DEPRECIATED. use "data" instead.
Value
A tibble
See Also
Other calculations: calc_group_mean,
calc_group_median,
calc_group_rsd,
calc_group_stat,
calc_n_samples,
calc_obs_props,
calc_prop_samples,
calc_taxon_abund,
compare_groups,
counts_to_presence,
rarefy_obs
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 = "^(.+)__(.+)$")
# Default use
zero_low_counts(x, "tax_data")
# Use only a subset of columns
zero_low_counts(x, "tax_data", cols = c("700035949", "700097855", "700100489"))
zero_low_counts(x, "tax_data", cols = 4:6)
zero_low_counts(x, "tax_data", cols = startsWith(colnames(x$data$tax_data), "70001"))
# Including all other columns in ouput
zero_low_counts(x, "tax_data", other_cols = TRUE)
# Inlcuding specific columns in output
zero_low_counts(x, "tax_data", cols = c("700035949", "700097855", "700100489"),
other_cols = 2:3)
# Rename output columns
zero_low_counts(x, "tax_data", cols = c("700035949", "700097855", "700100489"),
out_names = c("a", "b", "c"))
# }