Perform a basic ordination analysis and return an objectized result.
Examples
data("global_patterns", package = "microbiomedataset")
x <- run_ordination(global_patterns, method = "PCoA")
class(x)
#> [1] "microbiome_ordination"
#> attr(,"package")
#> [1] "microbiomedataset"
head(x@sample_coord)
#> Axis.1 Axis.2 sample_id Primer Final_Barcode
#> 1 -0.12667533 -0.015709044 CL3 ILBC_01 AACGCA
#> 2 -0.14940705 -0.007137804 CC1 ILBC_02 AACTCG
#> 3 -0.10429782 -0.053710244 SV1 ILBC_03 AACTGT
#> 4 0.28543181 0.025755609 M31Fcsw ILBC_04 AAGAGA
#> 5 0.24415649 0.023606010 M11Fcsw ILBC_05 AAGCTG
#> 6 -0.09259285 -0.327628427 M31Plmr ILBC_07 AATCGT
#> Barcode_truncated_plus_T Barcode_full_length SampleType
#> 1 TGCGTT CTAGCGTGCGT Soil
#> 2 CGAGTT CATCGACGAGT Soil
#> 3 ACAGTT GTACGCACAGT Soil
#> 4 TCTCTT TCGACATCTCT Feces
#> 5 CAGCTT CGACTGCAGCT Feces
#> 6 ACGATT CGAGTCACGAT Skin
#> Description class
#> 1 Calhoun South Carolina Pine soil, pH 4.9 Subject
#> 2 Cedar Creek Minnesota, grassland, pH 6.1 Subject
#> 3 Sevilleta new Mexico, desert scrub, pH 8.3 Subject
#> 4 M3, Day 1, fecal swab, whole body study Subject
#> 5 M1, Day 1, fecal swab, whole body study Subject
#> 6 M3, Day 1, right palm, whole body study Subject
