TaxaNorm_Normalization {TaxaNorm}

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)