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Subset taxa using a taxa control or distribution based method

Source: R/controls.R
subset_taxa_tax_control.Rd

lifecycle-experimental

There is 3 main methods : discard taxa (i) using a control taxa (e.g. truffle root tips), (ii) using a mixture models to detect bimodality in pseudo-abundance distribution or (iii) using a minimum difference threshold pseudo-abundance. Each cutoff is defined at the sample level.

Usage

subset_taxa_tax_control(
  physeq,
  taxa_distri,
  method = "mean",
  min_diff_for_cutoff = NULL
)

Arguments

physeq

(required): a phyloseq-class object obtained using the phyloseq package.

taxa_distri

(required) a vector of length equal to the number of samples with the number of sequences per sample for the taxa control

method

(default: "mean") a method to calculate the cut-off value. There are 6 available methods:

  1. cutoff_seq: discard taxa with less than the number of sequence than taxa control,

  2. cutoff_mixt: using mixture models,

  3. cutoff_diff: using a minimum difference threshold (need the argument min_diff_for_cutoff)

  4. min: the minimum of the three firsts methods

  5. max: the maximum of the three firsts methods

  6. mean: the mean of the three firsts methods

min_diff_for_cutoff

(int) argument for method cutoff_diff. Required if method is cutoff_diff, min, max or mean

Value

A new phyloseq-class object.

Author

Adrien Taudière

Examples


subset_taxa_tax_control(data_fungi,
  as.numeric(data_fungi@otu_table[, 300]),
  min_diff_for_cutoff = 2
)
#> number of iterations= 11 
#> number of iterations= 27 
#> number of iterations= 18 
#> number of iterations= 15 
#> number of iterations= 4 
#> number of iterations= 28 
#> Error in stats::uniroot(f = f, lower = 1, upper = 1000) : 
#>   f.upper = f(upper) is NA
#> Warning: NAs introduced by coercion
#> number of iterations= 23 
#> number of iterations= 10 
#> number of iterations= 10 
#> number of iterations= 9 
#> number of iterations= 15 
#> number of iterations= 6 
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#> number of iterations= 41 
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#> number of iterations= 9 
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#> number of iterations= 8 
#> number of iterations= 12 
#> number of iterations= 18 
#> number of iterations= 5 
#> number of iterations= 15 
#> number of iterations= 22 
#> One of the variances is going to zero;  trying new starting values.
#> One of the variances is going to zero;  trying new starting values.
#> One of the variances is going to zero;  trying new starting values.
#> One of the variances is going to zero;  trying new starting values.
#> One of the variances is going to zero;  trying new starting values.
#> number of iterations= 7 
#> number of iterations= 11 
#> number of iterations= 16 
#> number of iterations= 5 
#> number of iterations= 9 
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#> number of iterations= 4 
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#> number of iterations= 2 
#> number of iterations= 8 
#> number of iterations= 13 
#> number of iterations= 8 
#> Error in stats::uniroot(f = f, lower = 1, upper = 1000) : 
#>   f.upper = f(upper) is NA
#> Warning: NAs introduced by coercion
#> number of iterations= 11 
#> number of iterations= 4 
#> number of iterations= 15 
#> number of iterations= 8 
#> number of iterations= 6 
#> number of iterations= 13 
#> number of iterations= 5 
#> number of iterations= 15 
#> number of iterations= 17 
#> One of the variances is going to zero;  trying new starting values.
#> number of iterations= 6 
#> number of iterations= 18 
#> Error in stats::uniroot(f = f, lower = 1, upper = 1000) : 
#>   f.upper = f(upper) is NA
#> Warning: NAs introduced by coercion
#> number of iterations= 6 
#> number of iterations= 8 
#> number of iterations= 11 
#> number of iterations= 7 
#> number of iterations= 8 
#> number of iterations= 17 
#> number of iterations= 10 
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#> number of iterations= 4 
#> number of iterations= 3 
#> Warning: no non-missing arguments to min; returning Inf
#> number of iterations= 5 
#> number of iterations= 6 
#> number of iterations= 4 
#> number of iterations= 22 
#> number of iterations= 6 
#> number of iterations= 3 
#> number of iterations= 8 
#> number of iterations= 9 
#> One of the variances is going to zero;  trying new starting values.
#> One of the variances is going to zero;  trying new starting values.
#> number of iterations= 4 
#> number of iterations= 13 
#> One of the variances is going to zero;  trying new starting values.
#> number of iterations= 14 
#> number of iterations= 13 
#> number of iterations= 8 
#> number of iterations= 5 
#> number of iterations= 4 
#> number of iterations= 6 
#> Error in stats::uniroot(f = f, lower = 1, upper = 1000) : 
#>   f.upper = f(upper) is NA
#> Warning: NAs introduced by coercion
#> number of iterations= 18 
#> number of iterations= 18 
#> number of iterations= 9 
#> number of iterations= 8 
#> number of iterations= 7 
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#> number of iterations= 14 
#> number of iterations= 27 
#> number of iterations= 7 
#> Warning: no non-missing arguments to min; returning Inf
#> Warning: no non-missing arguments to min; returning Inf
#> number of iterations= 8 
#> Warning: no non-missing arguments to min; returning Inf
#> number of iterations= 9 
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#> number of iterations= 12 
#> Warning: no non-missing arguments to min; returning Inf
#> number of iterations= 11 
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#> number of iterations= 5 
#> number of iterations= 7 
#> number of iterations= 5 
#> One of the variances is going to zero;  trying new starting values.
#> number of iterations= 3 
#> number of iterations= 17 
#> number of iterations= 9 
#> The filtering processes discard 65 taxa and 36813 sequences
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:         [ 1355 taxa and 185 samples ]
#> sample_data() Sample Data:       [ 185 samples by 7 sample variables ]
#> tax_table()   Taxonomy Table:    [ 1355 taxa by 12 taxonomic ranks ]
#> refseq()      DNAStringSet:      [ 1355 reference sequences ]