add_pca_loadings.RdAdd Principal Components Analysis Loadings to a tidy or triple omics dataset.
add_pca_loadings(
tomic,
value_var = NULL,
center_rows = TRUE,
npcs = NULL,
missing_val_method = "drop_samples"
)Either a tidy_omic or triple_omic object
An abundance value to use with hclust
center rows before performing PCA
number of principal component loadings to add to samples (default is number of samples)
Approach to remove missing values:
Drop features with missing values
Drop samples which are missing all features, then drop features
Impute missing values
A tomic object with principal components added to samples.
add_pca_loadings(brauer_2008_triple, npcs = 5)
#> 40 features dropped due to missing values
#> $features
#> # A tibble: 460 × 4
#> name BP MF systematic_name
#> <chr> <chr> <chr> <chr>
#> 1 YOL029C biological process unknown molecular function unk… YOL029C
#> 2 SCW11 cytokinesis, completion of s… glucan 1,3-beta-glucos… YGL028C
#> 3 YHR036W biological process unknown molecular function unk… YHR036W
#> 4 BGL2 cell wall organization and b… glucan 1,3-beta-glucos… YGR282C
#> 5 ACT1 cell wall organization and b… structural constituent… YFL039C
#> 6 FKH1 pseudohyphal growth* transcription factor a… YIL131C
#> 7 HOC1 cell wall mannoprotein biosy… transferase activity, … YJR075W
#> 8 CSN12 adaptation to pheromone duri… molecular function unk… YJR084W
#> 9 YAL046C biological process unknown molecular function unk… YAL046C
#> 10 SLG1 cell wall organization and b… transmembrane receptor… YOR008C
#> # … with 450 more rows
#>
#> $samples
#> # A tibble: 36 × 8
#> sample nutrient DR PC1 PC2 PC3 PC4 PC5
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 G0.05 G 0.05 -0.252 0.00514 0.317 0.0234 -0.166
#> 2 G0.1 G 0.1 -0.169 -0.0478 0.272 -0.104 -0.172
#> 3 G0.15 G 0.15 -0.177 -0.0824 0.272 -0.0165 -0.193
#> 4 G0.2 G 0.2 -0.153 -0.109 0.257 -0.00839 -0.174
#> 5 G0.25 G 0.25 -0.0111 -0.177 0.179 0.00250 -0.163
#> 6 G0.3 G 0.3 0.0561 -0.213 0.154 0.0247 -0.154
#> 7 N0.05 N 0.05 -0.380 0.153 -0.175 0.427 -0.135
#> 8 N0.1 N 0.1 -0.284 0.0849 -0.255 0.323 -0.109
#> 9 N0.15 N 0.15 -0.0754 0.0482 -0.320 0.0141 -0.0906
#> 10 N0.2 N 0.2 -0.0200 -0.0235 -0.311 0.0379 -0.0873
#> # … with 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
#> # … with 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: 9 × 2
#> variable type
#> <chr> <chr>
#> 1 sample sample_primary_key
#> 2 nutrient character
#> 3 DR numeric
#> 4 PC1 numeric
#> 5 PC2 numeric
#> 6 PC3 numeric
#> 7 PC4 numeric
#> 8 PC5 numeric
#> 9 sample 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"