hd_umap() runs a UMAP analysis on the provided data. If data contain missing values,
the function imputes them using the k-nearest neighbors algorithm (k = 5). The number of components
to be calculated is defined by the user. The function returns a tibble with the UMAP results.
Arguments
- dat
An HDAnalyzeR object or a dataset in wide format and sample ID as its first column.
- by_sample
If TRUE, points represent samples. If FALSE, points represent features. Default is TRUE.
- impute
If TRUE, missing values are imputed using the k-nearest neighbors algorithm (k = 5). Default is TRUE.
- components
The number of components to be calculated. Default is 10.
- seed
The seed to be used in the UMAP analysis. Default is 123.
Examples
# Create the HDAnalyzeR object providing the data and metadata
hd_object <- hd_initialize(example_data, example_metadata)
# Run the UMAP analysis
hd_umap(hd_object, components = 2, by_sample = TRUE, seed = 123)
#> $umap_res
#> # A tibble: 586 × 3
#> DAid UMAP1 UMAP2
#> <chr> <dbl> <dbl>
#> 1 DA00001 -2.62 0.384
#> 2 DA00002 1.99 1.73
#> 3 DA00003 -1.69 -1.87
#> 4 DA00004 -2.17 -1.74
#> 5 DA00005 -2.83 0.315
#> 6 DA00006 1.54 -0.733
#> 7 DA00007 -3.49 -0.886
#> 8 DA00008 1.54 -1.08
#> 9 DA00009 -0.655 0.278
#> 10 DA00010 1.93 1.53
#> # ℹ 576 more rows
#>
#> $by_sample
#> [1] TRUE
#>
#> attr(,"class")
#> [1] "hd_umap"
# Run the UMAP analysis by feature
hd_umap(hd_object, components = 2, by_sample = FALSE, seed = 123)
#> $umap_res
#> # A tibble: 100 × 3
#> Assay UMAP1 UMAP2
#> <chr> <dbl> <dbl>
#> 1 AARSD1 3.50 2.25
#> 2 ABL1 3.19 0.255
#> 3 ACAA1 1.90 -0.679
#> 4 ACAN -0.488 -1.08
#> 5 ACE2 0.330 -0.343
#> 6 ACOX1 -1.36 -1.87
#> 7 ACP5 0.289 -0.847
#> 8 ACP6 0.836 0.0397
#> 9 ACTA2 1.45 1.07
#> 10 ACTN4 -0.954 -1.50
#> # ℹ 90 more rows
#>
#> $by_sample
#> [1] FALSE
#>
#> attr(,"class")
#> [1] "hd_umap"