Account for missing values by dropping features, samples or using imputation.
remove_missing_values(
tomic,
value_var = NULL,
missing_val_method = "drop_samples",
missing_value_types = c("NA", "NaN", "Inf"),
verbose = FALSE
)Either a tidy_omic or triple_omic object
An abundance value to use with hclust
Approach to remove missing values:
Drop features with missing values
Drop samples which are missing all features, then drop features
Impute missing values
Types of values to treat as missing. One or more of "NA", "NaN" and "Inf"
extra reporting messages
A tomic object where missing values have been accounted
for.
remove_missing_values(brauer_2008_triple)
#> $features
#> # A tibble: 460 × 4
#> name BP MF systematic_name
#> <chr> <chr> <chr> <chr>
#> 1 YOL029C biological process unknown mole… YOL029C
#> 2 SCW11 cytokinesis, completion of separation gluc… YGL028C
#> 3 YHR036W biological process unknown mole… YHR036W
#> 4 BGL2 cell wall organization and biogenesis gluc… YGR282C
#> 5 ACT1 cell wall organization and biogenesis* stru… YFL039C
#> 6 FKH1 pseudohyphal growth* tran… YIL131C
#> 7 HOC1 cell wall mannoprotein biosynthesis* tran… YJR075W
#> 8 CSN12 adaptation to pheromone during conjugation wit… mole… YJR084W
#> 9 YAL046C biological process unknown mole… YAL046C
#> 10 SLG1 cell wall organization and biogenesis* tran… YOR008C
#> # ℹ 450 more rows
#>
#> $samples
#> # A tibble: 36 × 3
#> sample nutrient DR
#> <chr> <chr> <dbl>
#> 1 G0.05 G 0.05
#> 2 G0.1 G 0.1
#> 3 G0.15 G 0.15
#> 4 G0.2 G 0.2
#> 5 G0.25 G 0.25
#> 6 G0.3 G 0.3
#> 7 N0.05 N 0.05
#> 8 N0.1 N 0.1
#> 9 N0.15 N 0.15
#> 10 N0.2 N 0.2
#> # ℹ 26 more rows
#>
#> $measurements
#> # A tibble: 16,560 × 3
#> name sample expression
#> <chr> <chr> <dbl>
#> 1 YOL029C G0.05 -0.22
#> 2 SCW11 G0.05 -0.67
#> 3 YHR036W G0.05 -0.91
#> 4 BGL2 G0.05 -0.08
#> 5 ACT1 G0.05 -0.04
#> 6 FKH1 G0.05 -0.57
#> 7 HOC1 G0.05 -0.42
#> 8 CSN12 G0.05 -0.49
#> 9 YAL046C G0.05 0.05
#> 10 SLG1 G0.05 -0.06
#> # ℹ 16,550 more rows
#>
#> $design
#> $design$features
#> # A tibble: 4 × 2
#> variable type
#> <chr> <chr>
#> 1 name feature_primary_key
#> 2 systematic_name character
#> 3 BP character
#> 4 MF character
#>
#> $design$samples
#> # A tibble: 3 × 2
#> variable type
#> <chr> <chr>
#> 1 sample sample_primary_key
#> 2 nutrient character
#> 3 DR numeric
#>
#> $design$measurements
#> # A tibble: 3 × 2
#> variable type
#> <chr> <chr>
#> 1 name feature_primary_key
#> 2 sample sample_primary_key
#> 3 expression numeric
#>
#> $design$feature_pk
#> [1] "name"
#>
#> $design$sample_pk
#> [1] "sample"
#>
#>
#> attr(,"class")
#> [1] "triple_omic" "tomic" "general"