Use one or more variables in the sample_data to identify and remove duplicate samples (leaving one sample per group).
methods:
method = "readcount" keeps the one sample in each duplicate group with the highest total number of reads (phyloseq::sample_sums)
method = "first" keeps the first sample in each duplicate group encountered in the row order of the sample_data
method = "last" keeps the last sample in each duplicate group encountered in the row order of the sample_data
method = "random" keeps a random sample from each duplicate group (set.seed for reproducibility)
More than one "duplicate" sample can be kept per group by setting n samples > 1.
ps_dedupe(
ps,
vars,
method = "readcount",
verbose = TRUE,
n = 1,
.keep_group_var = FALSE,
.keep_readcount = FALSE,
.message_IDs = FALSE,
.label_only = FALSE,
.keep_all_taxa = FALSE
)phyloseq object
names of variables, whose (combined) levels identify groups from which only 1 sample is desired
keep sample with max "readcount" or the "first" or "last" or "random" samples encountered in given sample_data order for each duplicate group
message about number of groups, and number of samples dropped?
number of 'duplicates' to keep per group, defaults to 1
keep grouping variable .GROUP. in phyloseq object?
keep readcount variable .READCOUNT. in phyloseq object?
message sample names of dropped variables?
if TRUE, the samples will NOT be filtered, just labelled with a new logical variable .KEEP_SAMPLE.
keep all taxa after removing duplicates? If FALSE, the default, taxa are removed if they never occur in any of the retained samples
phyloseq object
What happens when duplicated samples have exactly equal readcounts in method = "readcount"? The first encountered maximum is kept (in sample_data row order, like method = "first")
ps_filter for filtering samples by sample_data variables
data("dietswap", package = "microbiome")
dietswap
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 130 taxa and 222 samples ]
#> sample_data() Sample Data: [ 222 samples by 8 sample variables ]
#> tax_table() Taxonomy Table: [ 130 taxa by 3 taxonomic ranks ]
# let's pretend the dietswap data contains technical replicates from each subject
# we want to keep only one of them
ps_dedupe(dietswap, vars = "subject", method = "readcount", verbose = TRUE)
#> 38 groups: with 4 to 6 samples each
#> Dropped 184 samples.
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 120 taxa and 38 samples ]
#> sample_data() Sample Data: [ 38 samples by 8 sample variables ]
#> tax_table() Taxonomy Table: [ 120 taxa by 3 taxonomic ranks ]
# contrived example to show identifying "duplicates" via the interaction of multiple columns
ps1 <- ps_dedupe(
ps = dietswap, method = "readcount", verbose = TRUE,
vars = c("timepoint", "group", "bmi_group")
)
#> 18 groups: with 8 to 15 samples each
#> Dropped 204 samples.
phyloseq::sample_data(ps1)
#> subject sex nationality group sample timepoint
#> Sample-2 nms male AFR HE Sample-2 2
#> Sample-3 olt male AFR HE Sample-3 2
#> Sample-11 olt male AFR HE Sample-11 3
#> Sample-12 pku female AFR HE Sample-12 3
#> Sample-17 ufm male AFR HE Sample-17 3
#> Sample-20 pku female AFR DI Sample-20 4
#> Sample-25 ufm male AFR DI Sample-25 4
#> Sample-28 pku female AFR DI Sample-28 5
#> Sample-38 nmz male AAM ED Sample-38 1
#> Sample-46 mni female AAM ED Sample-46 6
#> Sample-54 dwc male AFR ED Sample-54 6
#> Sample-77 zaq female AFR DI Sample-77 4
#> Sample-101 zaq female AFR DI Sample-101 5
#> Sample-143 kpp male AAM DI Sample-143 5
#> Sample-156 gtd male AAM HE Sample-156 2
#> Sample-208 olt male AFR ED Sample-208 1
#> Sample-214 ufm male AFR ED Sample-214 1
#> Sample-217 pku female AFR ED Sample-217 6
#> timepoint.within.group bmi_group
#> Sample-2 1 lean
#> Sample-3 1 overweight
#> Sample-11 2 overweight
#> Sample-12 2 obese
#> Sample-17 2 lean
#> Sample-20 1 obese
#> Sample-25 1 lean
#> Sample-28 2 obese
#> Sample-38 1 obese
#> Sample-46 2 overweight
#> Sample-54 2 lean
#> Sample-77 1 overweight
#> Sample-101 2 overweight
#> Sample-143 2 lean
#> Sample-156 1 obese
#> Sample-208 1 overweight
#> Sample-214 1 lean
#> Sample-217 2 obese
ps2 <- ps_dedupe(
ps = dietswap, method = "first", verbose = TRUE,
vars = c("timepoint", "group", "bmi_group")
)
#> 18 groups: with 8 to 15 samples each
#> Dropped 204 samples.
phyloseq::sample_data(ps2)
#> 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-10 nms male AFR HE Sample-10 3
#> Sample-11 olt male AFR HE Sample-11 3
#> Sample-12 pku female AFR HE Sample-12 3
#> Sample-18 nms male AFR DI Sample-18 4
#> Sample-19 olt male AFR DI Sample-19 4
#> Sample-26 nms male AFR DI Sample-26 5
#> Sample-27 olt male AFR DI Sample-27 5
#> Sample-28 pku female AFR DI Sample-28 5
#> Sample-32 azh female AAM ED Sample-32 1
#> Sample-35 kpp male AAM ED Sample-35 1
#> Sample-36 lot female AAM ED Sample-36 1
#> Sample-41 azh female AAM ED Sample-41 6
#> Sample-44 kpp male AAM ED Sample-44 6
#> Sample-45 lot female AAM ED Sample-45 6
#> timepoint.within.group bmi_group
#> Sample-1 1 obese
#> Sample-2 1 lean
#> Sample-3 1 overweight
#> Sample-4 1 obese
#> Sample-10 2 lean
#> Sample-11 2 overweight
#> Sample-12 2 obese
#> Sample-18 1 lean
#> Sample-19 1 overweight
#> Sample-26 2 lean
#> Sample-27 2 overweight
#> Sample-28 2 obese
#> Sample-32 1 overweight
#> Sample-35 1 lean
#> Sample-36 1 obese
#> Sample-41 2 overweight
#> Sample-44 2 lean
#> Sample-45 2 obese