Wrapper for applying base scale function to phyloseq otu_table
tax_scale(data, center = TRUE, scale = TRUE, do = NA, keep_counts = TRUE)phyloseq or psExtra or otu_table
if TRUE: center each taxon by subtracting its mean
if TRUE, divide each centred taxon by its standard deviation (or divide by RMS if not centred!)
alternative argument that overrides center and scale options! takes "both", "scale", "center" or "neither"
if TRUE, retain the original count data in psExtra counts slot
data("dietswap", package = "microbiome")
ps <- dietswap
ps %>%
otu_get() %>%
.[1:6, 1:6]
#> OTU Table: [6 taxa and 6 samples]
#> taxa are columns
#> Actinomycetaceae Aerococcus Aeromonas Akkermansia
#> Sample-1 0 0 0 18
#> Sample-2 1 0 0 97
#> Sample-3 0 0 0 67
#> Sample-4 1 0 0 256
#> Sample-5 0 0 0 21
#> Sample-6 0 0 0 16
#> Alcaligenes faecalis et rel. Allistipes et rel.
#> Sample-1 1 336
#> Sample-2 2 63
#> Sample-3 3 36
#> Sample-4 2 96
#> Sample-5 2 49
#> Sample-6 2 17
# standard use (mean center and SD scale)
tax_scale(ps) %>%
otu_get() %>%
.[1:6, 1:6] # Aerococcus is NaN as standard deviation = 0 (0 prevalence)
#> OTU Table: [6 taxa and 6 samples]
#> taxa are columns
#> Actinomycetaceae Aerococcus Aeromonas Akkermansia
#> Sample-1 0 0 0 18
#> Sample-2 1 0 0 97
#> Sample-3 0 0 0 67
#> Sample-4 1 0 0 256
#> Sample-5 0 0 0 21
#> Sample-6 0 0 0 16
#> Alcaligenes faecalis et rel. Allistipes et rel.
#> Sample-1 1 336
#> Sample-2 2 63
#> Sample-3 3 36
#> Sample-4 2 96
#> Sample-5 2 49
#> Sample-6 2 17
# RMS scale only (directly on otu_table)
otu_get(ps) %>%
tax_scale(center = FALSE) %>%
.[1:6, 1:6] # Aerococcus is NaN as standard deviation = 0 (0 prevalence)
#> OTU Table: [6 taxa and 6 samples]
#> taxa are columns
#> Actinomycetaceae Aerococcus Aeromonas Akkermansia
#> Sample-1 0.000000 NaN 0 0.05741261
#> Sample-2 3.716517 NaN 0 0.30939020
#> Sample-3 0.000000 NaN 0 0.21370251
#> Sample-4 3.716517 NaN 0 0.81653496
#> Sample-5 0.000000 NaN 0 0.06698138
#> Sample-6 0.000000 NaN 0 0.05103343
#> Alcaligenes faecalis et rel. Allistipes et rel.
#> Sample-1 0.05065049 0.83097699
#> Sample-2 0.10130097 0.15580819
#> Sample-3 0.15195146 0.08903325
#> Sample-4 0.10130097 0.23742200
#> Sample-5 0.10130097 0.12118414
#> Sample-6 0.10130097 0.04204348
# example using alternative `do` argument (to center only, no scaling)
tax_scale(ps, do = "center") %>%
otu_get() %>%
.[1:6, 1:6]
#> OTU Table: [6 taxa and 6 samples]
#> taxa are columns
#> Actinomycetaceae Aerococcus Aeromonas Akkermansia
#> Sample-1 0 0 0 18
#> Sample-2 1 0 0 97
#> Sample-3 0 0 0 67
#> Sample-4 1 0 0 256
#> Sample-5 0 0 0 21
#> Sample-6 0 0 0 16
#> Alcaligenes faecalis et rel. Allistipes et rel.
#> Sample-1 1 336
#> Sample-2 2 63
#> Sample-3 3 36
#> Sample-4 2 96
#> Sample-5 2 49
#> Sample-6 2 17
# preserves existing info
tax_transform(ps, "compositional", rank = "Genus") %>% tax_scale()
#> psExtra object - a phyloseq object with extra slots:
#>
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 130 taxa and 222 samples ]
#> sample_data() Sample Data: [ 222 samples by 8 sample variables ]
#> tax_table() Taxonomy Table: [ 130 taxa by 3 taxonomic ranks ]
#>
#> otu_get(counts = TRUE) [ 130 taxa and 222 samples ]
#>
#> psExtra info:
#> tax_agg = "Genus" tax_trans = "compositional" tax_scale = "centered&scaled"
# drops other psExtra objects previously calculated with unscaled data
psxDist <- tax_agg(ps, "Genus") %>% dist_calc()
psxDist
#> psExtra object - a phyloseq object with extra slots:
#>
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 130 taxa and 222 samples ]
#> sample_data() Sample Data: [ 222 samples by 8 sample variables ]
#> tax_table() Taxonomy Table: [ 130 taxa by 3 taxonomic ranks ]
#>
#> psExtra info:
#> tax_agg = "Genus"
#>
#> bray distance matrix of size 222
#> 0.7639533 0.7851213 0.6680796 0.7699252 0.80507 ...
psxDist %>% tax_scale()
#> psExtra object - a phyloseq object with extra slots:
#>
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 130 taxa and 222 samples ]
#> sample_data() Sample Data: [ 222 samples by 8 sample variables ]
#> tax_table() Taxonomy Table: [ 130 taxa by 3 taxonomic ranks ]
#>
#> otu_get(counts = TRUE) [ 130 taxa and 222 samples ]
#>
#> psExtra info:
#> tax_agg = "Genus" tax_scale = "centered&scaled"
#>
#> distance matrix of size 222
#> 0.7639533 0.7851213 0.6680796 0.7699252 0.80507 ...
tax_scale(psxDist) %>% info_get()
#> psExtra info:
#> tax_agg = "Genus" tax_scale = "centered&scaled"