TaxaNorm_Normalization: Function to run TaxaNorm algorithm

In TaxaNorm: Feature-Wise Normalization for Microbiome Sequencing Data

Function to run TaxaNorm algorithm

Description

Function to run TaxaNorm algorithm

Usage

TaxaNorm_Normalization(
  data,
  depth = NULL,
  group = NULL,
  meta.data = NULL,
  filter.cell.num = 10,
  filter.taxa.count = 0,
  random = FALSE,
  ncores = NULL
)

Arguments

data	(Required) Input data; should be either a phyloseq object or a count matrix
depth	sequencing depth if pre-calculated. It should be a vector with the same length and order as the column of the count data
group	condition variables if samples are from multiple groups; should be correpsond to the column of the count data. default is NULL, where no grouping is considered
meta.data	meta data for Taxa
filter.cell.num	taxa with "filter.cell.num" in more than the value provided will be filtered
filter.taxa.count	"filter.taxa.count" samples will be removed before testing. default is keep taxa appear in at least 10 samples within each group
random	calculate randomized normal quantile residual
ncores	whether multiple cores is used for parallel computing; default is max(1, detectCores() - 1)

Value

a TaxaNorm Object containing the normalized count values and accessory information

Examples

data("TaxaNorm_Example_Input", package = "TaxaNorm")
Normalized_Data <- TaxaNorm_Normalization(data= TaxaNorm_Example_Input,
                                         depth = NULL,
                                         group = sample_data(TaxaNorm_Example_Input)$body_site,
                                         meta.data = NULL,
                                         filter.cell.num = 10,
                                         filter.taxa.count = 0,
                                         random = FALSE,
                                         ncores = 1)

TaxaNorm documentation built on June 24, 2024, 5:15 p.m.