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Recipe quality-control metrics

Source: R/recipe_qc.R
recipe_qc.Rd

recipe_qc() summarizes sparsity and sequencing-depth metrics from the count matrix stored in a Recipe or PrepRecipe object. The summary is independent of the centralized statistical model and can therefore be used before model configuration and after recipe preprocessing.

Usage

recipe_qc(rec, group_by = NULL)

Arguments

rec

A Recipe or PrepRecipe object.

group_by

NULL or the name of one sample metadata column used to stratify the QC summary.

Value

A tibble with grouping identifiers and QC metrics.

Details

With group_by = NULL, the result contains one overall row. When a sample metadata column is supplied, the overall row is followed by one row for each observed group. The group_by and group columns are NA in the overall row, so a real group named "all" remains unambiguous.

The returned metrics are:

  • n: number of values in the count matrix.

  • n_zero: number of zero values.

  • pct_zero: percentage of zero values.

  • pct_all_zero: percentage of taxa absent from every sample.

  • pct_singletons: percentage of taxa detected in exactly one sample.

  • pct_doubletons: percentage of taxa detected in exactly two samples.

  • n_samples: number of samples.

  • lib_size_min: minimum sample library size.

  • lib_size_max: maximum sample library size.

  • count_mean: mean of the per-sample mean counts.

  • count_max: mean of the per-sample maximum counts.

Examples

data(metaHIV_phy)

rec <- recipe(metaHIV_phy)

## Overall metrics do not require a statistical model
recipe_qc(rec)
#> # A tibble: 1 × 13
#>   group_by group     n n_zero pct_zero pct_all_zero pct_singletons
#>   <chr>    <chr> <dbl>  <dbl>    <dbl>        <dbl>          <dbl>
#> 1 NA       NA    70356  57632     81.9            0           20.6
#> # ℹ 6 more variables: pct_doubletons <dbl>, n_samples <dbl>,
#> #   lib_size_min <dbl>, lib_size_max <dbl>, count_mean <dbl>, count_max <dbl>

## Add explicit sample-level stratification when useful
recipe_qc(rec, group_by = "RiskGroup2")
#> # A tibble: 4 × 13
#>   group_by   group     n n_zero pct_zero pct_all_zero pct_singletons
#>   <chr>      <chr> <dbl>  <dbl>    <dbl>        <dbl>          <dbl>
#> 1 NA         NA    70356  57632     81.9          0             20.6
#> 2 RiskGroup2 hts   18491  15108     81.7         24.2           22.8
#> 3 RiskGroup2 msm   45100  37019     82.1         16.0           20.2
#> 4 RiskGroup2 pwid   6765   5505     81.4         41.2           16.6
#> # ℹ 6 more variables: pct_doubletons <dbl>, n_samples <dbl>,
#> #   lib_size_min <dbl>, lib_size_max <dbl>, count_mean <dbl>, count_max <dbl>