Uses seriation methods from seriation::seriate and often dist_calc (depending on if seriation method requires a distance matrix)
ps_seriate(
ps,
method = "OLO_ward",
dist = "bray",
tax_transform = "identity",
add_variable = FALSE,
rank = NA
)phyloseq object
seriation method for ordering samples, from seriation::seriate
distance method for dist_calc (only used if required for particular seriation method!)
transformation to apply before seriation or any distance calculation
add a variable to the sample data indicating seriation order
taxonomic rank to aggregate at, before seriation, NA for no aggregation
phyloseq
library(phyloseq)
data("dietswap", package = "microbiome")
dietswap %>%
sample_data() %>%
head(8)
#> subject sex nationality group sample timepoint
#> Sample-1 byn male AAM DI Sample-1 4
#> Sample-2 nms male AFR HE Sample-2 2
#> Sample-3 olt male AFR HE Sample-3 2
#> Sample-4 pku female AFR HE Sample-4 2
#> Sample-5 qjy female AFR HE Sample-5 2
#> Sample-6 riv female AFR HE Sample-6 2
#> Sample-7 shj female AFR HE Sample-7 2
#> Sample-8 tgx male AFR HE Sample-8 2
#> timepoint.within.group bmi_group
#> Sample-1 1 obese
#> Sample-2 1 lean
#> Sample-3 1 overweight
#> Sample-4 1 obese
#> Sample-5 1 overweight
#> Sample-6 1 obese
#> Sample-7 1 obese
#> Sample-8 1 overweight
dietswap %>%
tax_agg("Genus") %>%
ps_get() %>%
ps_seriate(method = "OLO_ward", dist = "bray") %>%
sample_data() %>%
head(8)
#> subject sex nationality group sample timepoint
#> Sample-125 nmz male AAM HE Sample-125 3
#> Sample-200 jql female AAM DI Sample-200 4
#> Sample-207 nms male AFR ED Sample-207 1
#> Sample-15 shj female AFR HE Sample-15 3
#> Sample-81 fud female AFR DI Sample-81 4
#> Sample-209 pku female AFR ED Sample-209 1
#> Sample-107 byu male AFR HE Sample-107 3
#> Sample-210 qjy female AFR ED Sample-210 1
#> timepoint.within.group bmi_group
#> Sample-125 2 obese
#> Sample-200 1 obese
#> Sample-207 1 lean
#> Sample-15 2 obese
#> Sample-81 1 obese
#> Sample-209 1 obese
#> Sample-107 2 lean
#> Sample-210 1 overweight