Normalize OTU table using samples depth — normalize_prop

This function implement the method proposed by McKnight et al. 2018 (doi:10.5061/dryad.tn8qs35 )

Usage

normalize_prop_pq(physeq, base_log = 2, constante = 10000, digits = 4)

Arguments

physeq: (required): a phyloseq-class object obtained using the phyloseq package.
base_log: (integer, default 2) the base for log-transformation. If set to NULL or NA, no log-transformation is compute after normalization.
constante: a constante to multiply the otu_table values
digits: (default = 2) integer indicating the number of decimal places to be used (see ?round for more information)

Value

A new phyloseq-class object with otu_table count normalize and log transformed (if base_log is an integer)

Author

Adrien Taudière

Examples

taxa_sums(data_fungi_mini)
#>   ASV7   ASV8  ASV12  ASV18  ASV25  ASV26  ASV27  ASV29  ASV32  ASV34  ASV35 
#>  47410  46857  38237  27368  21522  20660  20584  19661  16933  15804  15298 
#>  ASV41  ASV42  ASV46  ASV47  ASV48  ASV49  ASV50  ASV53  ASV54  ASV58  ASV59 
#>  12922  12270  11053  10878  10784  10215  10178  10038   9895   9172   9045 
#>  ASV61  ASV62  ASV63  ASV64  ASV67  ASV68  ASV71  ASV72  ASV75  ASV77  ASV82 
#>   8935   8922   8872   8849   8521   8417   8171   8167   7842   7818   7356 
#>  ASV83  ASV85  ASV91  ASV93  ASV94  ASV99 ASV100 ASV101 ASV104 ASV105 ASV107 
#>   7328   7145   6319   5886   5842   5699   5675   5649   5566   5467   5158 
#> ASV108 
#>   5137 
data_f_norm <- normalize_prop_pq(data_fungi_mini)
taxa_sums(data_f_norm)
#>     ASV7     ASV8    ASV12    ASV18    ASV25    ASV26    ASV27    ASV29 
#> 414.6046 644.1998 249.0031 259.4578 114.5518 118.3969  39.8368 103.1480 
#>    ASV32    ASV34    ASV35    ASV41    ASV42    ASV46    ASV47    ASV48 
#>  82.3404 372.7509  80.0378  78.9897  83.1868  93.3887  32.1618  25.6481 
#>    ASV49    ASV50    ASV53    ASV54    ASV58    ASV59    ASV61    ASV62 
#>  45.3490  23.2659  40.4996  13.2874  63.4629  51.1092  39.3335  61.9517 
#>    ASV63    ASV64    ASV67    ASV68    ASV71    ASV72    ASV75    ASV77 
#>  20.9074  30.8507  48.5672  12.9911 380.9520  53.6068  30.4545  15.5597 
#>    ASV82    ASV83    ASV85    ASV91    ASV93    ASV94    ASV99   ASV100 
#>  22.2811 177.5237  68.8903  71.8991  15.5826 234.0406  33.1109  19.7700 
#>   ASV101   ASV104   ASV105   ASV107   ASV108 
#>  43.5813  49.3524  27.2813 100.0606  11.9978 
ggplot(data.frame(
  "norm" = scale(taxa_sums(data_f_norm)),
  "raw" = scale(taxa_sums(data_fungi_mini)),
  "name_otu" = taxa_names(data_f_norm)
)) +
  geom_point(aes(x = raw, y = norm))


data_f_norm <- normalize_prop_pq(data_fungi_mini, base_log = NULL)