strollur::strollur is an R6 class that stores nucleotide
sequences, abundance, sample and treatment assignments, taxonomic
classifications, trees, asv / otu clusters and various reports. It is
designed to facilitate data analysis across multiple R packages.
Author
Sarah Westcott, swestcot@umich.edu
Public fields
dataRcpp::XPtr pointer to 'Dataset' c++ class. This allows package developers an easy access point to the underlying C++ code with additional functionality.
rawRcpp::RawVector containing the serialized data of the 'Dataset' c++ class. This allows the load and save functions to work with the class.
sequence_treea tree that relates sequences to each other
sample_treea tree that relates samples to each other
Methods
strollur$new()
Create a new strollur::strollur object
Usage
strollur$new(name = "", dataset = NULL)Examples
# to create an empty strollur object, run the following:
data <- strollur::strollur$new("soil")strollur$print()
Print a summary of the strollur::strollur object
Examples
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
miseqstrollur$abundance()
Get the abundance data for sequences, bins, samples, and treatments.
Arguments
typestring containing the type of data you want the number of. Options include: "sequence", "bin", "sample" and "treatment". Default = "sequence".
bin_typestring containing the bin type you would like the abundance data for. Default = "otu".
by_sampleLogical. When by_sample is TRUE, the abundance data will be parsed by sample. Default = FALSE.
Examples
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
# To the total abundance for each sequence
miseq$abundance(type = "sequence") |> head(n = 5)
# To the total abundance for each sequence parsed by sample
miseq$abundance(type = "sequence", by_sample = TRUE) |> head(n = 5)
# To the total abundance for each "otu" bin
miseq$abundance(type = "bin", bin_type = "otu") |> head(n = 5)
# To the total abundance for each "otu" bin parsed by sample
miseq$abundance(type = "bin", bin_type = "otu", by_sample = TRUE) |>
head(n = 5)
# To the total abundance for each "asv" bin
miseq$abundance(type = "bin", bin_type = "asv") |> head(n = 5)
# To the total abundance for each "asv" bin parsed by sample
miseq$abundance(type = "bin", bin_type = "asv", by_sample = TRUE) |>
head(n = 5)
# To the total abundance for each sample
miseq$abundance(type = "sample") |> head(n = 5)
# To the total abundance for each treatment
miseq$abundance(type = "treatment")strollur$add()
Add sequences, reports, trees or resource references
Usage
strollur$add(
table,
type = "sequence",
report_type = NULL,
table_names = list(sequence_name = "sequence_name", sequence = "sequence", comment =
"comment", quality_score = "quality_score", reference_vendor = "vendor",
reference_name = "name", reference_version = "version", reference_usage = "usage",
reference_note = "note", reference_method_url = "method_url",
reference_documentation_url = "documentation_url", reference_parameter = "parameter",
reference_citation = "citation"),
reference = NULL,
verbose = TRUE
)Arguments
tablea data.frame or tree containing the data you wish to add.
typea string containing the type of data. Options include:
sequence,fastq,sample_tree,sequence_tree,resource_referenceandreport.report_typea string containing the type of report you are adding.
table_namesnamed list used to indicate the names of the columns in the table. By default:
table_names <- list(sequence_name = "sequence_name", comment = "comment", sequence = "sequence", quality_score = "quality_score", reference_name = "name", reference_vendor = "vendor", reference_version = "version", reference_usage = "usage", reference_note = "note", reference_documentation_url = "documentation_url", reference_method_url = "method_url", reference_parameter = "parameter", reference_citation = "citation")
In table_names, 'sequence_name' is a string containing the name of the column in 'table' that contains the sequence names. It is used when you are adding FASTA data. Default column name is 'sequence_name'.
In table_names, 'sequence' is a string containing the name of the column in 'table' that contains the sequence nucleotide strings. It is used when you are adding FASTA data. Default column name is 'sequence'.
In table_names, 'comment' is a string containing the name of the column in 'table' that contains the sequence comments. It is used when you are adding FASTA data. Default column name is 'comment'.
In table_names, 'quality_score' is a string containing the name of the column in 'table' that contains the sequence quality scores. It is used when you are adding FASTQ data. Default column name is 'quality_score'.
In table_names, 'reference_vendor' is a string containing the name of the column in 'table' that contains the reference vendor names. It is used when ' you are adding reference data. Default column name is 'vendor'.
In table_names, 'reference_name' is a string containing the name of the ' column in 'table' that contains the reference names. It is used when you are ' adding reference data. Default column name is 'name'.
In table_names, 'reference_version' is a string containing the name of the ' column in 'table' that contains the reference versions. Default column name is 'version'.
In table_names, 'reference_usage' is a string containing the name of the column in 'table' that contains the reference usages. Default column name is 'usage'.
In table_names, 'reference_note' is a string containing the name of the column in 'table' that contains the reference notes. Default column name is 'note'.
In table_names, 'reference_method_url' is a string containing the name of the column in 'table' that contains the reference method urls. Default column name is 'method_url'.
In table_names, 'reference_documentation_url' is a string containing the name of the column in 'table' that contains the reference urls. Default column name is 'documentation_url'.
In table_names, 'reference_parameter' is a string containing the name of the column in 'table' that contains the reference parameters. Default column name is 'parameter'.
In table_names, 'reference_citation' is a string containing the name of the column in 'table' that contains the reference citations. Default column name is 'citation'.
referencea list created by the function new_reference. Optional.
verboselogical, indicating whether or not you want progress messages. Default = TRUE.
Examples
fasta_data <- strollur::read_fasta(fasta =
strollur_example("final.fasta.gz"))
contigs_report <- readRDS(strollur_example("miseq_contigs_report.rds"))
# Create a new empty `strollur::strollur` object named 'example_dataset'
data <- strollur::strollur$new(name = "example_dataset")
data$add(table = fasta_data, type = "sequence")
data$add(
table = contigs_report, type = "report",
report_type = "contigs_report", list(sequence_name = "Name")
)
# To add metadata related to your study
metadata <- readRDS(strollur_example("miseq_metadata.rds"))
data$add(table = metadata, type = "report", report_type = "metadata")
# To add a tree relating to your sequences
tree <- ape::read.tree(strollur_example("final.phylip.tre.gz"))
data$add(table = tree, type = "sequence_tree")
# To add a tree relating to your samples
data <- strollur::strollur$new(name = "example_dataset")
df <-
strollur::read_mothur_shared(strollur_example("final.opti_mcc.shared"))
data$assign(table = df, type = "bin", bin_type = "otu")
tree <- ape::read.tree(strollur_example("final.opti_mcc.jclass.ave.tre"))
data$add(table = tree, type = "sample_tree")
# Create a new empty `strollur::strollur` object named 'example_dataset'
data <- strollur::strollur$new(name = "example_dataset")
# Read FASTQ data into data.frame
fastq_data <-
strollur::read_fastq(fastq = strollur_example("tiny.fastq.gz"))
# Add FASTQ sequence data
data$add(table = fastq_data, type = "fastq")strollur$alignment_length()
Get length of alignment
Examples
data <- strollur::miseq_sop_example()
data$alignment_length()strollur$assign()
Assign sequence abundances, sequence classifications, bins, bin representative sequences, sequence quality data, bin classifications, sample distances or treatments.
Usage
strollur$assign(
table,
type = "bin",
bin_type = "otu",
table_names = list(sequence_name = "sequence_name", quality_score = "quality_score",
abundance = "abundance", sample = "sample", treatment = "treatment", taxonomy =
"taxonomy", level = "level", confidence = "confidence", bin_name = "bin_name"),
reference = NULL,
verbose = TRUE
)Arguments
tablea data.frame containing the data you wish to assign
typea string containing the type of data. Options include:
sequence_abundance,sequence_taxonomy,bin,qualitybin_representative,bin_taxonomy,sample_distanceandtreatment. Default =bin.bin_typestring containing the bin type you would like the number of bins for. Default =
otu.table_namesnamed list used to indicate the names of the columns in the table. By default:
table_names <- list(sequence_name = "sequence_name", abundance = "abundance", sample = "sample", treatment = "treatment", taxonomy = "taxonomy", bin_name = "bin_name")
In table_names,
sequence_nameis a string containing the name of the column in 'table' that contains the sequence names. Default column name is 'sequence_name'.In table_names,
quality_scoreis a string containing the name of the column in 'table' that contains the sequence quality scores. It is used when you are adding quality data. Default column name is 'quality_score'.In table_names,
abundanceis a string containing the name of the column in 'table' that contains the abundances. Default column name is 'abundance'.In table_names,
sampleis a string containing the name of the column in 'table' that contains the samples. Default column name is 'sample'.In table_names,
treatmentis a string containing the name of the column in 'table' that contains the treatment names. Default column name is 'treatment'.In table_names,
taxonomyis a string containing the name of the column in 'table' that contains the classifications. Default column name is 'taxonomy'.In table_names,
levelis a string containing the name of the column in the data.frame that contains the classifications. Default column name is 'level'.In table_names,
confidenceis a string containing the name of the column in the data.frame that contains the classifications confidence scores. Default column name is 'confidence'.In table_names,
bin_nameis a string containing the name of the column in 'table' that contains the bin names. Default column name is 'bin_name'.referencea list created by the function new_reference. Optional.
verboseboolean indicating whether or not you want progress messages. Default = TRUE.
Examples
# create a new empty strollur object named 'example_dataset'
data <- strollur::strollur$new(name = "example_dataset")
# Assign sequence abundances
abundance_by_sample <- strollur::read_mothur_count(strollur_example(
"final.count_table.gz"
))
data$assign(table = abundance_by_sample, type = "sequence_abundance")
# Assign sequence classifications
sequence_classifications <-
strollur::read_mothur_taxonomy(strollur_example( "final.taxonomy.gz" ))
data$assign(table = sequence_classifications, type = "sequence_taxonomy")
# Assigning bins
# read mothur's otu list file into data.frame
otu_data <- strollur::read_mothur_list(list = strollur_example(
"final.opti_mcc.list.gz"
))
# read mothur's asv list file into data.frame
asv_data <- strollur::read_mothur_list(list = strollur_example(
"final.asv.list.gz"
))
# read mothur's phylotype list file into data.frame
phylo_data <- strollur::read_mothur_list(list = strollur_example(
"final.tx.list.gz"
))
# read otu bin representative sequences into a data.frame
bin_reps <- readRDS(strollur_example(
"miseq_representative_sequences.rds"))
# assign 'otu' bins using sequence names
data$assign(table = otu_data, bin_type = "otu")
# assign 'asv' bins using sequence names
data$assign(table = asv_data, bin_type = "asv")
# assign 'phylotype' bins using sequence names
data$assign(table = phylo_data, bin_type = "phylotype")
# assign 'otu' bin representative sequences
data$assign(table = bin_reps, type = "bin_representative")
# To assign abundance only bins
# create a new empty strollur object named 'example_dataset'
data <- strollur::strollur$new(name = "example_dataset")
# read mothur's shared file
otu_data <-
strollur::read_mothur_shared(strollur_example("final.opti_mcc.shared"))
# assign abundance only otus parsed by sample
data$assign(table = otu_data, bin_type = "otu")
# Assigning bin classifications
# read bin taxonomies
otu_data <- strollur::read_mothur_cons_taxonomy(strollur_example(
"final.cons.taxonomy"
))
# assign otu consensus taxonomies
data$assign(
table = otu_data,
type = "bin_taxonomy", bin_type = "otu"
)
# Assign treatments
sample_assignments <- readRDS(
strollur_example("miseq_sample_design.rds"))
data$assign(table = sample_assignments, type = "treatment")
# Assign sample distances
dist_file <- strollur_example("final.opti_mcc.jclass.0.03.column.dist")
sample_dists <- readr::read_table(dist_file,
col_names = FALSE,
show_col_types = FALSE)
data$assign(table = sample_dists, type = "sample_distance")strollur$clear()
Clear data from datasest
Examples
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
miseq
miseq$clear()
miseqstrollur$count()
Find the number of sequences, samples, treatments or bins of a given type
Arguments
typestring containing the type of data you want the number of. Options include: "sequence", "sample", "treatment", "bin", and "resource_reference". Default = "sequence".
bin_typestring containing the bin type you would like the number of bins for. Default = "otu".
samplesvector of strings. samples is only used when 'type' = "sequence" or 'type' = "bin" . samples should contain the names of the samples you want the count for. Default = NULL.
distinctBoolean. distinct is used when 'type' = "sequence" or 'type' = "bin". When 'type' = "sequence" and distinct is TRUE the number of unique sequences is returned. When 'type' = "sequence" and distinct is FALSE the total number of sequences is returned. This can also be combined with samples to find the number of unique sequences found ONLY in a given set of samples, or to find the number of unique sequences in given set of samples that may also be present in other samples. When 'type' = "bin", you can set distinct = TRUE to return the number of bins that ONLY contain sequences from the given samples. When distinct is FALSE the count returned contains bins with sequences from a given samples, but those bins may also contain other samples. Default = FALSE.
Examples
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
# To get the total number of sequences
miseq$count(type = "sequence")
# To get number of unique sequences
miseq$count(type = "sequence", distinct = TRUE)
# To get number of unique sequences from samples 'F3D0' and 'F3D1'
# Note these sequences will be present in both samples but may be
# be present in other samples as well
miseq$count(type = "sequence", samples = c("F3D0", "F3D1"))
# To get number of unique sequences exclusive to samples 'F3D0' and
# 'F3D1'. Note sequences are present in both samples and NOT present in
# any other samples.
miseq$count(type = "sequence",
samples = c("F3D0", "F3D1"), distinct = TRUE )
# To get the number of samples in the dataset
miseq$count(type = "sample")
# To get the number of treatments in the dataset
miseq$count(type = "treatment")
# To get the number of "otu" bins in the dataset
miseq$count(type = "bin", bin_type = "otu")
# To get the number of "asv" bins in the dataset
miseq$count(type = "bin", bin_type = "asv")
# To get the number of "phylotype" bins in the dataset
miseq$count(type = "bin", bin_type = "phylotype")
# To get number of "otu" bins from samples 'F3D0' and 'F3D1'
# Note these bins will have sequences from both samples but there may be
# other samples present as well
miseq$count(
type = "bin", bin_type = "otu", samples = c("F3D0", "F3D1")
)
# To get number of "otu" bins unique to samples 'F3D0' and 'F3D1'
# Note these bins will have sequences from both samples and NO other
# samples will be present in the bins.
miseq$count(
type = "bin", bin_type = "otu",
samples = c("F3D0", "F3D1"), distinct = TRUE
)strollur$get_bin_types()
Get bin table types
Examples
data <- strollur::miseq_sop_example()
data$get_bin_types()strollur$get_version()
Get the version of the strollur object
Examples
data <- strollur::strollur$new("test")
data$get_version()strollur$has_bin_taxonomy()
Determine if your dataset contains bin classifications
Arguments
bin_typestring containing the bin type you would like to check for classifaction data. Default = "otu".
Examples
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
miseq$has_bin_taxonomy(bin_type = "otu")strollur$has_sequence_taxonomy()
Determine if your dataset contains sequence classifications
Examples
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
miseq$has_sequence_taxonomy()strollur$is_equal()
Determine if two strollur objects are equal.
Examples
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
data <- strollur::copy_dataset(miseq)
miseq$is_equal(data)strollur$names()
Get the names of a given type of data
Arguments
typestring containing the type of data you would like. Options include: "dataset", "sequence", "bin", "sample", "treatment", "report". Default = "sequence".
bin_typestring containing the bin type you would like the names for. Default = "otu".
samplesvector of strings. samples is only used when 'type' = "sequence" or 'type' = "bin" . samples should contain the names of the samples you want names for. Default = NULL.
distinctBoolean. distinct is used when 'type' = "sequence" or 'type' = "bin" and the samples parameter is used. The distinct parameter allows you to get the names that present given set of samples. When distinct is TRUE, the names function will return the names that ONLY contain data from the given samples. When distinct is FALSE the data returned contains data from a given samples, but may ALSO contain data from other samples. Default = FALSE.
Examples
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
# To get the name of the dataset
miseq$names(type = "dataset")
# To get the names of the sequences
miseq$names(type = "sequence")
# To get the names of the sequences present sample 'F3D0'
miseq$names(type = "sequence", samples = c("F3D0"))
#' # To get the names of the sequences unique to sample 'F3D0'
miseq$names(type = "sequence", samples = c("F3D0"), distinct = TRUE)
# To get the names of the samples
miseq$names(type = "sample")
# To get the names of the treatments
miseq$names(type = "treatment")
# To get the names of the bins
miseq$names(type = "bin")
# To get the names of the bins that are unique to 'F3D0'
miseq$names(type = "bin", samples = c("F3D0"), distinct = TRUE)
# To get the names of the bins that include sequences from 'F3D0'
miseq$names(type = "bin", samples = c("F3D0"), distinct = FALSE)
# To get the names of the reports
miseq$names(type = "report")strollur$rename()
Rename your strollur object.
Examples
data <- strollur::strollur$new("old_name")
data
data$rename("new_name")
datastrollur$report()
Get a data.frame containing the given report, or the phylo tree requested.
Arguments
typestring containing the type of report you would like. Options include:
fasta,fastq,sequence,quality,sequence_bin_assignment,sequence_taxonomy,sequence_tree,bin_taxonomy,bin_representative,sample_assignment,sample_distance,sample_tree,resource_reference,sequence_scrap,bin_scrap. If you have added custom reports for alignment, contigs_assembly or chimeras, you can get those as well. Default =sequence.bin_typestring containing the bin type you would like a bin_taxonomy report for. Default =
otu.
Examples
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
# To get the FASTA data
miseq$report(type = "fasta") |> head(n = 5)
# To get a report about the FASTA data
miseq$report(type = "sequence") |> head(n = 5)
# To get the sequence bin assignments
miseq$report(type = "sequence_bin_assignment", bin_type = "otu") |>
head(n = 5)
# To get the sample treatment assignments
miseq$report(type = "sample_assignment") |> head(n = 5)
# To get a report about sequence classifications
miseq$report(type = "sequence_taxonomy") |> head(n = 5)
# To get a report about bin classifications for 'otu' data
miseq$report(type = "bin_taxonomy", bin_type = "otu") |> head(n = 5)
# To get the 'otu' bin representative sequences
miseq$report(type = "bin_representative", bin_type = "otu") |>
head(n = 5)
# To get a report about the sequences removed during your analysis:
miseq$report(type = "sequence_scrap")
# To get a report about the "otu" bins removed during your analysis:
miseq$report(type = "bin_scrap", bin_type = "otu")
# To get the metadata associated with your data:
metadata <- miseq$report(type = "metadata") |> head(n = 5)
# To get the resource references associated with your data:
references <- miseq$report(type = "resource_reference")
# To get our custom report containing the contigs assembly data:
miseq$report(type = "contigs_report") |> head(n = 5)
# To get the tree that relates your sequences:
sequence_tree <- miseq$report(type = "sequence_tree")
# To get the tree that relates your sequences:
sample_tree <- miseq$report(type = "sample_tree")
# To get the distances between your samples:
sample_dists <- miseq$report(type = "sample_distance")strollur$summary()
Summarize the sequences data, custom reports, and scrapped data
Arguments
typestring containing the type of data you want the number of. Options include: "sequence", "report" and "scrap". Default = "sequence".
report_typestring containing the report type you would summarized. For example, the miseq_sop_example includes contigs assembly data and can be accessed with report_type = "contigs_report". Default = NULL.
verboselogical indicating whether or not you want progress messages. Default = TRUE.
Examples
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
# To get the summary of your FASTA data
miseq$summary(type = "sequence")
# summarize contigs_report
miseq$summary(type = "report", report_type = "contigs_report")
# remove sample 'F3D0' to produce a scrap report
strollur::xdev_remove_samples(data = miseq, samples = c("F3D0"))
# summarize scrapped data -
# sequences and bins scrapped by removing the sample "F3D0"
miseq$summary(type = "scrap")Examples
## ------------------------------------------------
## Method `strollur$new()`
## ------------------------------------------------
# to create an empty strollur object, run the following:
data <- strollur::strollur$new("soil")
## ------------------------------------------------
## Method `strollur$print()`
## ------------------------------------------------
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
miseq
#> miseq_sop:
#>
#> starts ends nbases ambigs polymers numns numseqs
#> Minimum: 1 375 249 0 3 0 1.00
#> 2.5%-tile: 1 375 252 0 4 0 2850.05
#> 25%-tile: 1 375 252 0 4 0 28491.50
#> Median: 1 375 253 0 4 0 56982.00
#> 75%-tile: 1 375 253 0 5 0 85472.50
#> 97.5%-tile: 1 375 254 0 6 0 111113.95
#> Maximum: 1 375 256 0 6 0 113963.00
#> Mean: 1 375 252 0 4 0 56982.00
#>
#> Number of unique seqs: 2425
#> Total number of seqs: 113963
#>
#> Total number of samples: 19
#> Total number of treatments: 2
#> Total number of otus: 531
#> Total number of otu bin classifications: 531
#> Total number of asvs: 2425
#> Total number of asv bin classifications: 2425
#> Total number of phylotypes: 63
#> Total number of phylotype bin classifications: 63
#> Total number of sequence classifications: 2425
#> Total number of resource references: 2
#> Total number of custom reports: 2
#>
## ------------------------------------------------
## Method `strollur$abundance()`
## ------------------------------------------------
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
# To the total abundance for each sequence
miseq$abundance(type = "sequence") |> head(n = 5)
#> sequence_name abundance
#> 1 M00967_43_000000000-A3JHG_1_1101_10133_8460 620
#> 2 M00967_43_000000000-A3JHG_1_1101_10331_23332 1
#> 3 M00967_43_000000000-A3JHG_1_1101_10382_22128 1
#> 4 M00967_43_000000000-A3JHG_1_1101_11035_15765 1
#> 5 M00967_43_000000000-A3JHG_1_1101_11348_22601 2
# To the total abundance for each sequence parsed by sample
miseq$abundance(type = "sequence", by_sample = TRUE) |> head(n = 5)
#> sequence_name abundance sample treatment
#> 1 M00967_43_000000000-A3JHG_1_1101_10133_8460 32 F3D0 Early
#> 2 M00967_43_000000000-A3JHG_1_1101_10133_8460 127 F3D1 Early
#> 3 M00967_43_000000000-A3JHG_1_1101_10133_8460 1 F3D146 Late
#> 4 M00967_43_000000000-A3JHG_1_1101_10133_8460 1 F3D149 Late
#> 5 M00967_43_000000000-A3JHG_1_1101_10133_8460 1 F3D150 Late
# To the total abundance for each "otu" bin
miseq$abundance(type = "bin", bin_type = "otu") |> head(n = 5)
#> otu_id abundance
#> 1 Otu001 12288
#> 2 Otu002 8892
#> 3 Otu003 7794
#> 4 Otu004 7476
#> 5 Otu005 7450
# To the total abundance for each "otu" bin parsed by sample
miseq$abundance(type = "bin", bin_type = "otu", by_sample = TRUE) |>
head(n = 5)
#> bin_name abundance sample treatment
#> 1 Otu001 499 F3D0 Early
#> 2 Otu001 351 F3D1 Early
#> 3 Otu001 388 F3D141 Late
#> 4 Otu001 244 F3D142 Late
#> 5 Otu001 189 F3D143 Late
# To the total abundance for each "asv" bin
miseq$abundance(type = "bin", bin_type = "asv") |> head(n = 5)
#> asv_id abundance
#> 1 Asv0001 12196
#> 2 Asv0002 8829
#> 3 Asv0003 7698
#> 4 Asv0004 7436
#> 5 Asv0005 7307
# To the total abundance for each "asv" bin parsed by sample
miseq$abundance(type = "bin", bin_type = "asv", by_sample = TRUE) |>
head(n = 5)
#> bin_name abundance sample treatment
#> 1 Asv0001 495 F3D0 Early
#> 2 Asv0001 340 F3D1 Early
#> 3 Asv0001 386 F3D141 Late
#> 4 Asv0001 242 F3D142 Late
#> 5 Asv0001 188 F3D143 Late
# To the total abundance for each sample
miseq$abundance(type = "sample") |> head(n = 5)
#> sample abundance
#> 1 F3D0 6191
#> 2 F3D1 4652
#> 3 F3D141 4656
#> 4 F3D142 2423
#> 5 F3D143 2403
# To the total abundance for each treatment
miseq$abundance(type = "treatment")
#> treatment abundance
#> 1 Early 55634
#> 2 Late 58329
## ------------------------------------------------
## Method `strollur$add()`
## ------------------------------------------------
fasta_data <- strollur::read_fasta(fasta =
strollur_example("final.fasta.gz"))
contigs_report <- readRDS(strollur_example("miseq_contigs_report.rds"))
# Create a new empty `strollur::strollur` object named 'example_dataset'
data <- strollur::strollur$new(name = "example_dataset")
data$add(table = fasta_data, type = "sequence")
#> Added 2425 sequences.
data$add(
table = contigs_report, type = "report",
report_type = "contigs_report", list(sequence_name = "Name")
)
#> Added a contigs_report report.
# To add metadata related to your study
metadata <- readRDS(strollur_example("miseq_metadata.rds"))
data$add(table = metadata, type = "report", report_type = "metadata")
#> Added a metadata report.
# To add a tree relating to your sequences
tree <- ape::read.tree(strollur_example("final.phylip.tre.gz"))
data$add(table = tree, type = "sequence_tree")
# To add a tree relating to your samples
data <- strollur::strollur$new(name = "example_dataset")
df <-
strollur::read_mothur_shared(strollur_example("final.opti_mcc.shared"))
data$assign(table = df, type = "bin", bin_type = "otu")
#> Assigned 531 otu bins.
tree <- ape::read.tree(strollur_example("final.opti_mcc.jclass.ave.tre"))
data$add(table = tree, type = "sample_tree")
# Create a new empty `strollur::strollur` object named 'example_dataset'
data <- strollur::strollur$new(name = "example_dataset")
# Read FASTQ data into data.frame
fastq_data <-
strollur::read_fastq(fastq = strollur_example("tiny.fastq.gz"))
# Add FASTQ sequence data
data$add(table = fastq_data, type = "fastq")
#> Added 3 sequences.
#> Assigned 3 quality scores.
## ------------------------------------------------
## Method `strollur$alignment_length()`
## ------------------------------------------------
data <- strollur::miseq_sop_example()
#> Added 2425 sequences.
#> Assigned 2425 sequence abundances.
#> Assigned 2425 sequence taxonomies.
#> Assigned 531 otu bins.
#> Assigned 2425 asv bins.
#> Assigned 63 phylotype bins.
#> Assigned 19 samples to treatments.
#> Assigned 171 samples distances.
#> Assigned 531 otu bin taxonomies.
#> Assigned 531 otu bin representative sequences.
#> Added a metadata report.
#> Added 2 resource references.
#> Added a contigs_report report.
data$alignment_length()
#> [1] 375
## ------------------------------------------------
## Method `strollur$assign()`
## ------------------------------------------------
# create a new empty strollur object named 'example_dataset'
data <- strollur::strollur$new(name = "example_dataset")
# Assign sequence abundances
abundance_by_sample <- strollur::read_mothur_count(strollur_example(
"final.count_table.gz"
))
data$assign(table = abundance_by_sample, type = "sequence_abundance")
#> Assigned 2425 sequence abundances.
# Assign sequence classifications
sequence_classifications <-
strollur::read_mothur_taxonomy(strollur_example( "final.taxonomy.gz" ))
data$assign(table = sequence_classifications, type = "sequence_taxonomy")
#> Assigned 2425 sequence taxonomies.
# Assigning bins
# read mothur's otu list file into data.frame
otu_data <- strollur::read_mothur_list(list = strollur_example(
"final.opti_mcc.list.gz"
))
# read mothur's asv list file into data.frame
asv_data <- strollur::read_mothur_list(list = strollur_example(
"final.asv.list.gz"
))
# read mothur's phylotype list file into data.frame
phylo_data <- strollur::read_mothur_list(list = strollur_example(
"final.tx.list.gz"
))
# read otu bin representative sequences into a data.frame
bin_reps <- readRDS(strollur_example(
"miseq_representative_sequences.rds"))
# assign 'otu' bins using sequence names
data$assign(table = otu_data, bin_type = "otu")
#> Assigned 531 otu bins.
# assign 'asv' bins using sequence names
data$assign(table = asv_data, bin_type = "asv")
#> Assigned 2425 asv bins.
# assign 'phylotype' bins using sequence names
data$assign(table = phylo_data, bin_type = "phylotype")
#> Assigned 63 phylotype bins.
# assign 'otu' bin representative sequences
data$assign(table = bin_reps, type = "bin_representative")
#> Assigned 531 otu bin representative sequences.
# To assign abundance only bins
# create a new empty strollur object named 'example_dataset'
data <- strollur::strollur$new(name = "example_dataset")
# read mothur's shared file
otu_data <-
strollur::read_mothur_shared(strollur_example("final.opti_mcc.shared"))
# assign abundance only otus parsed by sample
data$assign(table = otu_data, bin_type = "otu")
#> Assigned 531 otu bins.
# Assigning bin classifications
# read bin taxonomies
otu_data <- strollur::read_mothur_cons_taxonomy(strollur_example(
"final.cons.taxonomy"
))
# assign otu consensus taxonomies
data$assign(
table = otu_data,
type = "bin_taxonomy", bin_type = "otu"
)
#> Assigned 531 otu bin taxonomies.
# Assign treatments
sample_assignments <- readRDS(
strollur_example("miseq_sample_design.rds"))
data$assign(table = sample_assignments, type = "treatment")
#> Assigned 19 samples to treatments.
# Assign sample distances
dist_file <- strollur_example("final.opti_mcc.jclass.0.03.column.dist")
sample_dists <- readr::read_table(dist_file,
col_names = FALSE,
show_col_types = FALSE)
data$assign(table = sample_dists, type = "sample_distance")
#> Assigned 171 samples distances.
## ------------------------------------------------
## Method `strollur$clear()`
## ------------------------------------------------
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
miseq
#> miseq_sop:
#>
#> starts ends nbases ambigs polymers numns numseqs
#> Minimum: 1 375 249 0 3 0 1.00
#> 2.5%-tile: 1 375 252 0 4 0 2850.05
#> 25%-tile: 1 375 252 0 4 0 28491.50
#> Median: 1 375 253 0 4 0 56982.00
#> 75%-tile: 1 375 253 0 5 0 85472.50
#> 97.5%-tile: 1 375 254 0 6 0 111113.95
#> Maximum: 1 375 256 0 6 0 113963.00
#> Mean: 1 375 252 0 4 0 56982.00
#>
#> Number of unique seqs: 2425
#> Total number of seqs: 113963
#>
#> Total number of samples: 19
#> Total number of treatments: 2
#> Total number of otus: 531
#> Total number of otu bin classifications: 531
#> Total number of asvs: 2425
#> Total number of asv bin classifications: 2425
#> Total number of phylotypes: 63
#> Total number of phylotype bin classifications: 63
#> Total number of sequence classifications: 2425
#> Total number of resource references: 2
#> Total number of custom reports: 2
#>
miseq$clear()
miseq
#>
#> Total number of seqs: 0
#>
#>
## ------------------------------------------------
## Method `strollur$count()`
## ------------------------------------------------
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
# To get the total number of sequences
miseq$count(type = "sequence")
#> [1] 113963
# To get number of unique sequences
miseq$count(type = "sequence", distinct = TRUE)
#> [1] 2425
# To get number of unique sequences from samples 'F3D0' and 'F3D1'
# Note these sequences will be present in both samples but may be
# be present in other samples as well
miseq$count(type = "sequence", samples = c("F3D0", "F3D1"))
#> [1] 9385
# To get number of unique sequences exclusive to samples 'F3D0' and
# 'F3D1'. Note sequences are present in both samples and NOT present in
# any other samples.
miseq$count(type = "sequence",
samples = c("F3D0", "F3D1"), distinct = TRUE )
#> [1] 2
# To get the number of samples in the dataset
miseq$count(type = "sample")
#> [1] 19
# To get the number of treatments in the dataset
miseq$count(type = "treatment")
#> [1] 2
# To get the number of "otu" bins in the dataset
miseq$count(type = "bin", bin_type = "otu")
#> [1] 531
# To get the number of "asv" bins in the dataset
miseq$count(type = "bin", bin_type = "asv")
#> [1] 2425
# To get the number of "phylotype" bins in the dataset
miseq$count(type = "bin", bin_type = "phylotype")
#> [1] 63
# To get number of "otu" bins from samples 'F3D0' and 'F3D1'
# Note these bins will have sequences from both samples but there may be
# other samples present as well
miseq$count(
type = "bin", bin_type = "otu", samples = c("F3D0", "F3D1")
)
#> [1] 125
# To get number of "otu" bins unique to samples 'F3D0' and 'F3D1'
# Note these bins will have sequences from both samples and NO other
# samples will be present in the bins.
miseq$count(
type = "bin", bin_type = "otu",
samples = c("F3D0", "F3D1"), distinct = TRUE
)
#> [1] 1
## ------------------------------------------------
## Method `strollur$get_bin_types()`
## ------------------------------------------------
data <- strollur::miseq_sop_example()
#> Added 2425 sequences.
#> Assigned 2425 sequence abundances.
#> Assigned 2425 sequence taxonomies.
#> Assigned 531 otu bins.
#> Assigned 2425 asv bins.
#> Assigned 63 phylotype bins.
#> Assigned 19 samples to treatments.
#> Assigned 171 samples distances.
#> Assigned 531 otu bin taxonomies.
#> Assigned 531 otu bin representative sequences.
#> Added a metadata report.
#> Added 2 resource references.
#> Added a contigs_report report.
data$get_bin_types()
#> [1] "otu" "asv" "phylotype"
## ------------------------------------------------
## Method `strollur$get_version()`
## ------------------------------------------------
data <- strollur::strollur$new("test")
data$get_version()
#> [1] "0.1.2"
## ------------------------------------------------
## Method `strollur$has_bin_taxonomy()`
## ------------------------------------------------
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
miseq$has_bin_taxonomy(bin_type = "otu")
#> [1] TRUE
## ------------------------------------------------
## Method `strollur$has_sequence_taxonomy()`
## ------------------------------------------------
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
miseq$has_sequence_taxonomy()
#> [1] TRUE
## ------------------------------------------------
## Method `strollur$is_equal()`
## ------------------------------------------------
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
data <- strollur::copy_dataset(miseq)
miseq$is_equal(data)
#> → The strollur objects public field 'raw' are not equivalent.
#> [1] FALSE
## ------------------------------------------------
## Method `strollur$names()`
## ------------------------------------------------
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
# To get the name of the dataset
miseq$names(type = "dataset")
#> [1] "miseq_sop"
# To get the names of the sequences
miseq$names(type = "sequence")
#> [1] "M00967_43_000000000-A3JHG_1_1101_10133_8460"
#> [2] "M00967_43_000000000-A3JHG_1_1101_10331_23332"
#> [3] "M00967_43_000000000-A3JHG_1_1101_10382_22128"
#> [4] "M00967_43_000000000-A3JHG_1_1101_11035_15765"
#> [5] "M00967_43_000000000-A3JHG_1_1101_11348_22601"
#> [6] "M00967_43_000000000-A3JHG_1_1101_11371_20625"
#> [7] "M00967_43_000000000-A3JHG_1_1101_11480_8830"
#> [8] "M00967_43_000000000-A3JHG_1_1101_11754_11390"
#> [9] "M00967_43_000000000-A3JHG_1_1101_11983_2593"
#> [10] "M00967_43_000000000-A3JHG_1_1101_12302_23776"
#> [11] "M00967_43_000000000-A3JHG_1_1101_12446_20708"
#> [12] "M00967_43_000000000-A3JHG_1_1101_12732_20169"
#> [13] "M00967_43_000000000-A3JHG_1_1101_12861_15386"
#> [14] "M00967_43_000000000-A3JHG_1_1101_13158_12636"
#> [15] "M00967_43_000000000-A3JHG_1_1101_13609_10025"
#> [16] "M00967_43_000000000-A3JHG_1_1101_13714_14338"
#> [17] "M00967_43_000000000-A3JHG_1_1101_14159_27572"
#> [18] "M00967_43_000000000-A3JHG_1_1101_14364_8401"
#> [19] "M00967_43_000000000-A3JHG_1_1101_14915_26251"
#> [20] "M00967_43_000000000-A3JHG_1_1101_15011_15369"
#> [21] "M00967_43_000000000-A3JHG_1_1101_15463_5782"
#> [22] "M00967_43_000000000-A3JHG_1_1101_15533_5293"
#> [23] "M00967_43_000000000-A3JHG_1_1101_15591_4696"
#> [24] "M00967_43_000000000-A3JHG_1_1101_15683_17782"
#> [25] "M00967_43_000000000-A3JHG_1_1101_16007_27282"
#> [26] "M00967_43_000000000-A3JHG_1_1101_16143_23069"
#> [27] "M00967_43_000000000-A3JHG_1_1101_16182_25089"
#> [28] "M00967_43_000000000-A3JHG_1_1101_16381_21558"
#> [29] "M00967_43_000000000-A3JHG_1_1101_16390_9529"
#> [30] "M00967_43_000000000-A3JHG_1_1101_16914_26242"
#> [31] "M00967_43_000000000-A3JHG_1_1101_17961_23432"
#> [32] "M00967_43_000000000-A3JHG_1_1101_18044_1900"
#> [33] "M00967_43_000000000-A3JHG_1_1101_18106_21085"
#> [34] "M00967_43_000000000-A3JHG_1_1101_18143_13375"
#> [35] "M00967_43_000000000-A3JHG_1_1101_18278_3345"
#> [36] "M00967_43_000000000-A3JHG_1_1101_18346_24737"
#> [37] "M00967_43_000000000-A3JHG_1_1101_18517_10721"
#> [38] "M00967_43_000000000-A3JHG_1_1101_18682_27059"
#> [39] "M00967_43_000000000-A3JHG_1_1101_18693_22396"
#> [40] "M00967_43_000000000-A3JHG_1_1101_18922_4934"
#> [41] "M00967_43_000000000-A3JHG_1_1101_19181_15979"
#> [42] "M00967_43_000000000-A3JHG_1_1101_19534_17052"
#> [43] "M00967_43_000000000-A3JHG_1_1101_20035_18358"
#> [44] "M00967_43_000000000-A3JHG_1_1101_2004_15863"
#> [45] "M00967_43_000000000-A3JHG_1_1101_20262_22075"
#> [46] "M00967_43_000000000-A3JHG_1_1101_20589_8650"
#> [47] "M00967_43_000000000-A3JHG_1_1101_20702_8361"
#> [48] "M00967_43_000000000-A3JHG_1_1101_20815_21052"
#> [49] "M00967_43_000000000-A3JHG_1_1101_21034_7357"
#> [50] "M00967_43_000000000-A3JHG_1_1101_21305_8478"
#> [51] "M00967_43_000000000-A3JHG_1_1101_21616_8560"
#> [52] "M00967_43_000000000-A3JHG_1_1101_22681_5598"
#> [53] "M00967_43_000000000-A3JHG_1_1101_23090_14223"
#> [54] "M00967_43_000000000-A3JHG_1_1101_23238_24359"
#> [55] "M00967_43_000000000-A3JHG_1_1101_23714_7638"
#> [56] "M00967_43_000000000-A3JHG_1_1101_23774_12021"
#> [57] "M00967_43_000000000-A3JHG_1_1101_24095_14123"
#> [58] "M00967_43_000000000-A3JHG_1_1101_24369_12902"
#> [59] "M00967_43_000000000-A3JHG_1_1101_24693_20989"
#> [60] "M00967_43_000000000-A3JHG_1_1101_24952_9272"
#> [61] "M00967_43_000000000-A3JHG_1_1101_25551_18277"
#> [62] "M00967_43_000000000-A3JHG_1_1101_25615_24528"
#> [63] "M00967_43_000000000-A3JHG_1_1101_25878_10062"
#> [64] "M00967_43_000000000-A3JHG_1_1101_26014_7542"
#> [65] "M00967_43_000000000-A3JHG_1_1101_26020_8626"
#> [66] "M00967_43_000000000-A3JHG_1_1101_26204_21938"
#> [67] "M00967_43_000000000-A3JHG_1_1101_26634_17611"
#> [68] "M00967_43_000000000-A3JHG_1_1101_27744_12678"
#> [69] "M00967_43_000000000-A3JHG_1_1101_28416_18742"
#> [70] "M00967_43_000000000-A3JHG_1_1101_3758_10627"
#> [71] "M00967_43_000000000-A3JHG_1_1101_4190_14354"
#> [72] "M00967_43_000000000-A3JHG_1_1101_4346_14940"
#> [73] "M00967_43_000000000-A3JHG_1_1101_5922_6513"
#> [74] "M00967_43_000000000-A3JHG_1_1101_6770_21083"
#> [75] "M00967_43_000000000-A3JHG_1_1101_6836_23417"
#> [76] "M00967_43_000000000-A3JHG_1_1101_6973_14783"
#> [77] "M00967_43_000000000-A3JHG_1_1101_7373_14922"
#> [78] "M00967_43_000000000-A3JHG_1_1101_7971_11346"
#> [79] "M00967_43_000000000-A3JHG_1_1101_8537_7072"
#> [80] "M00967_43_000000000-A3JHG_1_1101_8786_10844"
#> [81] "M00967_43_000000000-A3JHG_1_1101_8868_4602"
#> [82] "M00967_43_000000000-A3JHG_1_1101_8877_3935"
#> [83] "M00967_43_000000000-A3JHG_1_1101_8995_24296"
#> [84] "M00967_43_000000000-A3JHG_1_1101_9065_10633"
#> [85] "M00967_43_000000000-A3JHG_1_1101_9331_5806"
#> [86] "M00967_43_000000000-A3JHG_1_1101_9360_18060"
#> [87] "M00967_43_000000000-A3JHG_1_1101_9553_14094"
#> [88] "M00967_43_000000000-A3JHG_1_1101_9620_19745"
#> [89] "M00967_43_000000000-A3JHG_1_1102_10298_5101"
#> [90] "M00967_43_000000000-A3JHG_1_1102_10390_20786"
#> [91] "M00967_43_000000000-A3JHG_1_1102_11115_19075"
#> [92] "M00967_43_000000000-A3JHG_1_1102_11158_17229"
#> [93] "M00967_43_000000000-A3JHG_1_1102_11222_12600"
#> [94] "M00967_43_000000000-A3JHG_1_1102_11324_6735"
#> [95] "M00967_43_000000000-A3JHG_1_1102_11664_24324"
#> [96] "M00967_43_000000000-A3JHG_1_1102_12417_5953"
#> [97] "M00967_43_000000000-A3JHG_1_1102_12472_26307"
#> [98] "M00967_43_000000000-A3JHG_1_1102_12551_11293"
#> [99] "M00967_43_000000000-A3JHG_1_1102_12689_8055"
#> [100] "M00967_43_000000000-A3JHG_1_1102_12944_14923"
#> [101] "M00967_43_000000000-A3JHG_1_1102_13319_18244"
#> [102] "M00967_43_000000000-A3JHG_1_1102_13343_4567"
#> [103] "M00967_43_000000000-A3JHG_1_1102_14321_10096"
#> [104] "M00967_43_000000000-A3JHG_1_1102_14586_9284"
#> [105] "M00967_43_000000000-A3JHG_1_1102_14615_8833"
#> [106] "M00967_43_000000000-A3JHG_1_1102_14900_7576"
#> [107] "M00967_43_000000000-A3JHG_1_1102_14905_6542"
#> [108] "M00967_43_000000000-A3JHG_1_1102_14928_26203"
#> [109] "M00967_43_000000000-A3JHG_1_1102_14993_16910"
#> [110] "M00967_43_000000000-A3JHG_1_1102_15058_11924"
#> [111] "M00967_43_000000000-A3JHG_1_1102_15350_5623"
#> [112] "M00967_43_000000000-A3JHG_1_1102_15600_19124"
#> [113] "M00967_43_000000000-A3JHG_1_1102_15632_13472"
#> [114] "M00967_43_000000000-A3JHG_1_1102_16785_23687"
#> [115] "M00967_43_000000000-A3JHG_1_1102_16964_23052"
#> [116] "M00967_43_000000000-A3JHG_1_1102_17763_13764"
#> [117] "M00967_43_000000000-A3JHG_1_1102_17902_26485"
#> [118] "M00967_43_000000000-A3JHG_1_1102_18244_26058"
#> [119] "M00967_43_000000000-A3JHG_1_1102_18640_14309"
#> [120] "M00967_43_000000000-A3JHG_1_1102_18759_22050"
#> [121] "M00967_43_000000000-A3JHG_1_1102_19038_11166"
#> [122] "M00967_43_000000000-A3JHG_1_1102_19130_22074"
#> [123] "M00967_43_000000000-A3JHG_1_1102_19172_4834"
#> [124] "M00967_43_000000000-A3JHG_1_1102_19764_24086"
#> [125] "M00967_43_000000000-A3JHG_1_1102_20639_13713"
#> [126] "M00967_43_000000000-A3JHG_1_1102_20738_4913"
#> [127] "M00967_43_000000000-A3JHG_1_1102_20961_15950"
#> [128] "M00967_43_000000000-A3JHG_1_1102_2114_15227"
#> [129] "M00967_43_000000000-A3JHG_1_1102_21170_25493"
#> [130] "M00967_43_000000000-A3JHG_1_1102_21598_16589"
#> [131] "M00967_43_000000000-A3JHG_1_1102_21808_10504"
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#> [2310] "M00967_43_000000000-A3JHG_1_2113_15802_4098"
#> [2311] "M00967_43_000000000-A3JHG_1_2113_16554_11081"
#> [2312] "M00967_43_000000000-A3JHG_1_2113_16796_6663"
#> [2313] "M00967_43_000000000-A3JHG_1_2113_16978_21215"
#> [2314] "M00967_43_000000000-A3JHG_1_2113_17952_9366"
#> [2315] "M00967_43_000000000-A3JHG_1_2113_18085_11993"
#> [2316] "M00967_43_000000000-A3JHG_1_2113_18276_27021"
#> [2317] "M00967_43_000000000-A3JHG_1_2113_18397_7516"
#> [2318] "M00967_43_000000000-A3JHG_1_2113_18844_7914"
#> [2319] "M00967_43_000000000-A3JHG_1_2113_19864_14092"
#> [2320] "M00967_43_000000000-A3JHG_1_2113_20030_3857"
#> [2321] "M00967_43_000000000-A3JHG_1_2113_20184_16464"
#> [2322] "M00967_43_000000000-A3JHG_1_2113_20319_11310"
#> [2323] "M00967_43_000000000-A3JHG_1_2113_20783_5301"
#> [2324] "M00967_43_000000000-A3JHG_1_2113_20973_10900"
#> [2325] "M00967_43_000000000-A3JHG_1_2113_21028_10433"
#> [2326] "M00967_43_000000000-A3JHG_1_2113_21308_17042"
#> [2327] "M00967_43_000000000-A3JHG_1_2113_21352_15666"
#> [2328] "M00967_43_000000000-A3JHG_1_2113_21647_20109"
#> [2329] "M00967_43_000000000-A3JHG_1_2113_21662_22858"
#> [2330] "M00967_43_000000000-A3JHG_1_2113_21823_22689"
#> [2331] "M00967_43_000000000-A3JHG_1_2113_21899_25122"
#> [2332] "M00967_43_000000000-A3JHG_1_2113_22451_22884"
#> [2333] "M00967_43_000000000-A3JHG_1_2113_23168_9750"
#> [2334] "M00967_43_000000000-A3JHG_1_2113_23466_13467"
#> [2335] "M00967_43_000000000-A3JHG_1_2113_23687_14507"
#> [2336] "M00967_43_000000000-A3JHG_1_2113_23848_13201"
#> [2337] "M00967_43_000000000-A3JHG_1_2113_24485_10653"
#> [2338] "M00967_43_000000000-A3JHG_1_2113_24996_25271"
#> [2339] "M00967_43_000000000-A3JHG_1_2113_25427_14638"
#> [2340] "M00967_43_000000000-A3JHG_1_2113_25465_9646"
#> [2341] "M00967_43_000000000-A3JHG_1_2113_26675_15471"
#> [2342] "M00967_43_000000000-A3JHG_1_2113_27238_12024"
#> [2343] "M00967_43_000000000-A3JHG_1_2113_27299_8066"
#> [2344] "M00967_43_000000000-A3JHG_1_2113_28097_13665"
#> [2345] "M00967_43_000000000-A3JHG_1_2113_29656_14155"
#> [2346] "M00967_43_000000000-A3JHG_1_2113_4057_19587"
#> [2347] "M00967_43_000000000-A3JHG_1_2113_4525_9290"
#> [2348] "M00967_43_000000000-A3JHG_1_2113_5333_12504"
#> [2349] "M00967_43_000000000-A3JHG_1_2113_5393_13669"
#> [2350] "M00967_43_000000000-A3JHG_1_2113_5453_21104"
#> [2351] "M00967_43_000000000-A3JHG_1_2113_6545_8894"
#> [2352] "M00967_43_000000000-A3JHG_1_2113_6721_20524"
#> [2353] "M00967_43_000000000-A3JHG_1_2113_7021_18070"
#> [2354] "M00967_43_000000000-A3JHG_1_2113_7083_22842"
#> [2355] "M00967_43_000000000-A3JHG_1_2113_7165_8175"
#> [2356] "M00967_43_000000000-A3JHG_1_2113_7343_26333"
#> [2357] "M00967_43_000000000-A3JHG_1_2113_7430_24285"
#> [2358] "M00967_43_000000000-A3JHG_1_2113_8012_12345"
#> [2359] "M00967_43_000000000-A3JHG_1_2113_8502_12671"
#> [2360] "M00967_43_000000000-A3JHG_1_2113_9492_17806"
#> [2361] "M00967_43_000000000-A3JHG_1_2113_9583_13925"
#> [2362] "M00967_43_000000000-A3JHG_1_2113_9708_9804"
#> [2363] "M00967_43_000000000-A3JHG_1_2114_10056_13609"
#> [2364] "M00967_43_000000000-A3JHG_1_2114_10492_14599"
#> [2365] "M00967_43_000000000-A3JHG_1_2114_10524_26952"
#> [2366] "M00967_43_000000000-A3JHG_1_2114_11245_12909"
#> [2367] "M00967_43_000000000-A3JHG_1_2114_11892_8192"
#> [2368] "M00967_43_000000000-A3JHG_1_2114_12364_4557"
#> [2369] "M00967_43_000000000-A3JHG_1_2114_12423_13578"
#> [2370] "M00967_43_000000000-A3JHG_1_2114_12634_10967"
#> [2371] "M00967_43_000000000-A3JHG_1_2114_12741_23025"
#> [2372] "M00967_43_000000000-A3JHG_1_2114_13345_25809"
#> [2373] "M00967_43_000000000-A3JHG_1_2114_13884_15208"
#> [2374] "M00967_43_000000000-A3JHG_1_2114_14201_8661"
#> [2375] "M00967_43_000000000-A3JHG_1_2114_14273_14388"
#> [2376] "M00967_43_000000000-A3JHG_1_2114_14906_9268"
#> [2377] "M00967_43_000000000-A3JHG_1_2114_15073_13278"
#> [2378] "M00967_43_000000000-A3JHG_1_2114_15082_26647"
#> [2379] "M00967_43_000000000-A3JHG_1_2114_15143_1651"
#> [2380] "M00967_43_000000000-A3JHG_1_2114_16137_10353"
#> [2381] "M00967_43_000000000-A3JHG_1_2114_16268_21350"
#> [2382] "M00967_43_000000000-A3JHG_1_2114_16615_9462"
#> [2383] "M00967_43_000000000-A3JHG_1_2114_16946_3813"
#> [2384] "M00967_43_000000000-A3JHG_1_2114_17062_26446"
#> [2385] "M00967_43_000000000-A3JHG_1_2114_17435_3730"
#> [2386] "M00967_43_000000000-A3JHG_1_2114_17684_11765"
#> [2387] "M00967_43_000000000-A3JHG_1_2114_18085_25907"
#> [2388] "M00967_43_000000000-A3JHG_1_2114_18278_17470"
#> [2389] "M00967_43_000000000-A3JHG_1_2114_18507_11303"
#> [2390] "M00967_43_000000000-A3JHG_1_2114_18555_17761"
#> [2391] "M00967_43_000000000-A3JHG_1_2114_18715_18416"
#> [2392] "M00967_43_000000000-A3JHG_1_2114_18928_10051"
#> [2393] "M00967_43_000000000-A3JHG_1_2114_19251_6041"
#> [2394] "M00967_43_000000000-A3JHG_1_2114_19661_25608"
#> [2395] "M00967_43_000000000-A3JHG_1_2114_20048_11075"
#> [2396] "M00967_43_000000000-A3JHG_1_2114_20048_7664"
#> [2397] "M00967_43_000000000-A3JHG_1_2114_20216_27116"
#> [2398] "M00967_43_000000000-A3JHG_1_2114_20314_10028"
#> [2399] "M00967_43_000000000-A3JHG_1_2114_20868_11538"
#> [2400] "M00967_43_000000000-A3JHG_1_2114_20876_7767"
#> [2401] "M00967_43_000000000-A3JHG_1_2114_20979_14273"
#> [2402] "M00967_43_000000000-A3JHG_1_2114_21674_11680"
#> [2403] "M00967_43_000000000-A3JHG_1_2114_22468_4333"
#> [2404] "M00967_43_000000000-A3JHG_1_2114_22474_15335"
#> [2405] "M00967_43_000000000-A3JHG_1_2114_24274_20575"
#> [2406] "M00967_43_000000000-A3JHG_1_2114_24618_11376"
#> [2407] "M00967_43_000000000-A3JHG_1_2114_24635_10372"
#> [2408] "M00967_43_000000000-A3JHG_1_2114_24873_12639"
#> [2409] "M00967_43_000000000-A3JHG_1_2114_25121_9113"
#> [2410] "M00967_43_000000000-A3JHG_1_2114_26629_16356"
#> [2411] "M00967_43_000000000-A3JHG_1_2114_27537_17929"
#> [2412] "M00967_43_000000000-A3JHG_1_2114_28476_17852"
#> [2413] "M00967_43_000000000-A3JHG_1_2114_4298_19710"
#> [2414] "M00967_43_000000000-A3JHG_1_2114_4499_18914"
#> [2415] "M00967_43_000000000-A3JHG_1_2114_4609_11919"
#> [2416] "M00967_43_000000000-A3JHG_1_2114_4725_7023"
#> [2417] "M00967_43_000000000-A3JHG_1_2114_4913_11992"
#> [2418] "M00967_43_000000000-A3JHG_1_2114_5448_14619"
#> [2419] "M00967_43_000000000-A3JHG_1_2114_5702_10845"
#> [2420] "M00967_43_000000000-A3JHG_1_2114_6259_8307"
#> [2421] "M00967_43_000000000-A3JHG_1_2114_6705_14284"
#> [2422] "M00967_43_000000000-A3JHG_1_2114_7386_6808"
#> [2423] "M00967_43_000000000-A3JHG_1_2114_7946_17808"
#> [2424] "M00967_43_000000000-A3JHG_1_2114_8962_26729"
#> [2425] "M00967_43_000000000-A3JHG_1_2114_9526_8096"
# To get the names of the sequences present sample 'F3D0'
miseq$names(type = "sequence", samples = c("F3D0"))
#> [1] "M00967_43_000000000-A3JHG_1_1101_10133_8460"
#> [2] "M00967_43_000000000-A3JHG_1_1101_10331_23332"
#> [3] "M00967_43_000000000-A3JHG_1_1101_11035_15765"
#> [4] "M00967_43_000000000-A3JHG_1_1101_14364_8401"
#> [5] "M00967_43_000000000-A3JHG_1_1101_15591_4696"
#> [6] "M00967_43_000000000-A3JHG_1_1101_18143_13375"
#> [7] "M00967_43_000000000-A3JHG_1_1101_18278_3345"
#> [8] "M00967_43_000000000-A3JHG_1_1101_18346_24737"
#> [9] "M00967_43_000000000-A3JHG_1_1101_18922_4934"
#> [10] "M00967_43_000000000-A3JHG_1_1101_19534_17052"
#> [11] "M00967_43_000000000-A3JHG_1_1101_21616_8560"
#> [12] "M00967_43_000000000-A3JHG_1_1101_23238_24359"
#> [13] "M00967_43_000000000-A3JHG_1_1101_6836_23417"
#> [14] "M00967_43_000000000-A3JHG_1_1101_8868_4602"
#> [15] "M00967_43_000000000-A3JHG_1_1101_9331_5806"
#> [16] "M00967_43_000000000-A3JHG_1_1101_9553_14094"
#> [17] "M00967_43_000000000-A3JHG_1_1101_9620_19745"
#> [18] "M00967_43_000000000-A3JHG_1_1102_11115_19075"
#> [19] "M00967_43_000000000-A3JHG_1_1102_14905_6542"
#> [20] "M00967_43_000000000-A3JHG_1_1102_15058_11924"
#> [21] "M00967_43_000000000-A3JHG_1_1102_16785_23687"
#> [22] "M00967_43_000000000-A3JHG_1_1102_17763_13764"
#> [23] "M00967_43_000000000-A3JHG_1_1102_2114_15227"
#> [24] "M00967_43_000000000-A3JHG_1_1103_13364_5496"
#> [25] "M00967_43_000000000-A3JHG_1_1103_13966_3813"
#> [26] "M00967_43_000000000-A3JHG_1_1103_14518_16099"
#> [27] "M00967_43_000000000-A3JHG_1_1103_19870_21567"
#> [28] "M00967_43_000000000-A3JHG_1_1103_21051_5371"
#> [29] "M00967_43_000000000-A3JHG_1_1103_22490_21890"
#> [30] "M00967_43_000000000-A3JHG_1_1103_23102_6138"
#> [31] "M00967_43_000000000-A3JHG_1_1103_24429_8227"
#> [32] "M00967_43_000000000-A3JHG_1_1103_25888_23585"
#> [33] "M00967_43_000000000-A3JHG_1_1103_3864_17599"
#> [34] "M00967_43_000000000-A3JHG_1_1103_5171_14027"
#> [35] "M00967_43_000000000-A3JHG_1_1103_5754_14689"
#> [36] "M00967_43_000000000-A3JHG_1_1103_7049_5487"
#> [37] "M00967_43_000000000-A3JHG_1_1103_8900_10891"
#> [38] "M00967_43_000000000-A3JHG_1_1103_9123_12021"
#> [39] "M00967_43_000000000-A3JHG_1_1104_10834_8009"
#> [40] "M00967_43_000000000-A3JHG_1_1104_14252_27883"
#> [41] "M00967_43_000000000-A3JHG_1_1104_17583_11048"
#> [42] "M00967_43_000000000-A3JHG_1_1104_19441_6353"
#> [43] "M00967_43_000000000-A3JHG_1_1104_25962_6708"
#> [44] "M00967_43_000000000-A3JHG_1_1105_11964_4686"
#> [45] "M00967_43_000000000-A3JHG_1_1105_12761_22353"
#> [46] "M00967_43_000000000-A3JHG_1_1105_13657_20414"
#> [47] "M00967_43_000000000-A3JHG_1_1105_14547_12843"
#> [48] "M00967_43_000000000-A3JHG_1_1105_14835_21938"
#> [49] "M00967_43_000000000-A3JHG_1_1105_18475_22342"
#> [50] "M00967_43_000000000-A3JHG_1_1105_18566_14540"
#> [51] "M00967_43_000000000-A3JHG_1_1105_22508_16792"
#> [52] "M00967_43_000000000-A3JHG_1_1105_23025_12058"
#> [53] "M00967_43_000000000-A3JHG_1_1105_23612_18072"
#> [54] "M00967_43_000000000-A3JHG_1_1105_25421_15745"
#> [55] "M00967_43_000000000-A3JHG_1_1105_25554_16982"
#> [56] "M00967_43_000000000-A3JHG_1_1105_26683_22024"
#> [57] "M00967_43_000000000-A3JHG_1_1105_5158_15329"
#> [58] "M00967_43_000000000-A3JHG_1_1105_9179_20196"
#> [59] "M00967_43_000000000-A3JHG_1_1106_11240_22282"
#> [60] "M00967_43_000000000-A3JHG_1_1106_12231_13452"
#> [61] "M00967_43_000000000-A3JHG_1_1106_20287_11044"
#> [62] "M00967_43_000000000-A3JHG_1_1106_21375_27109"
#> [63] "M00967_43_000000000-A3JHG_1_1106_26087_11342"
#> [64] "M00967_43_000000000-A3JHG_1_1106_26888_10548"
#> [65] "M00967_43_000000000-A3JHG_1_1106_9558_4409"
#> [66] "M00967_43_000000000-A3JHG_1_1107_10661_18652"
#> [67] "M00967_43_000000000-A3JHG_1_1107_12904_20713"
#> [68] "M00967_43_000000000-A3JHG_1_1107_13192_19802"
#> [69] "M00967_43_000000000-A3JHG_1_1107_14548_18902"
#> [70] "M00967_43_000000000-A3JHG_1_1107_15750_18592"
#> [71] "M00967_43_000000000-A3JHG_1_1107_18594_19714"
#> [72] "M00967_43_000000000-A3JHG_1_1107_18662_2044"
#> [73] "M00967_43_000000000-A3JHG_1_1107_19005_21895"
#> [74] "M00967_43_000000000-A3JHG_1_1107_19088_18601"
#> [75] "M00967_43_000000000-A3JHG_1_1107_19562_20695"
#> [76] "M00967_43_000000000-A3JHG_1_1107_21038_8569"
#> [77] "M00967_43_000000000-A3JHG_1_1107_24854_21229"
#> [78] "M00967_43_000000000-A3JHG_1_1107_6417_21653"
#> [79] "M00967_43_000000000-A3JHG_1_1107_7945_19118"
#> [80] "M00967_43_000000000-A3JHG_1_1108_11048_4561"
#> [81] "M00967_43_000000000-A3JHG_1_1108_12037_3053"
#> [82] "M00967_43_000000000-A3JHG_1_1108_15903_3841"
#> [83] "M00967_43_000000000-A3JHG_1_1108_18362_23908"
#> [84] "M00967_43_000000000-A3JHG_1_1108_18996_9436"
#> [85] "M00967_43_000000000-A3JHG_1_1108_19065_7362"
#> [86] "M00967_43_000000000-A3JHG_1_1108_4844_10914"
#> [87] "M00967_43_000000000-A3JHG_1_1109_12679_21321"
#> [88] "M00967_43_000000000-A3JHG_1_1109_13330_21597"
#> [89] "M00967_43_000000000-A3JHG_1_1109_14359_12484"
#> [90] "M00967_43_000000000-A3JHG_1_1109_15029_15883"
#> [91] "M00967_43_000000000-A3JHG_1_1109_20401_24219"
#> [92] "M00967_43_000000000-A3JHG_1_1109_20716_17379"
#> [93] "M00967_43_000000000-A3JHG_1_1109_22380_20532"
#> [94] "M00967_43_000000000-A3JHG_1_1109_24274_5733"
#> [95] "M00967_43_000000000-A3JHG_1_1109_26108_17578"
#> [96] "M00967_43_000000000-A3JHG_1_1109_7971_24259"
#> [97] "M00967_43_000000000-A3JHG_1_1109_8014_5010"
#> [98] "M00967_43_000000000-A3JHG_1_1109_9629_6769"
#> [99] "M00967_43_000000000-A3JHG_1_1110_12295_19699"
#> [100] "M00967_43_000000000-A3JHG_1_1110_13033_21990"
#> [101] "M00967_43_000000000-A3JHG_1_1110_13719_21639"
#> [102] "M00967_43_000000000-A3JHG_1_1110_14443_28339"
#> [103] "M00967_43_000000000-A3JHG_1_1110_14717_26485"
#> [104] "M00967_43_000000000-A3JHG_1_1110_15399_7509"
#> [105] "M00967_43_000000000-A3JHG_1_1110_17220_22884"
#> [106] "M00967_43_000000000-A3JHG_1_1110_18313_3175"
#> [107] "M00967_43_000000000-A3JHG_1_1110_21542_3012"
#> [108] "M00967_43_000000000-A3JHG_1_1110_5315_13833"
#> [109] "M00967_43_000000000-A3JHG_1_1110_6952_6984"
#> [110] "M00967_43_000000000-A3JHG_1_1111_11386_7041"
#> [111] "M00967_43_000000000-A3JHG_1_1111_13795_8174"
#> [112] "M00967_43_000000000-A3JHG_1_1111_17002_7999"
#> [113] "M00967_43_000000000-A3JHG_1_1111_17819_12937"
#> [114] "M00967_43_000000000-A3JHG_1_1111_20588_10279"
#> [115] "M00967_43_000000000-A3JHG_1_1111_21761_7263"
#> [116] "M00967_43_000000000-A3JHG_1_1111_22910_7741"
#> [117] "M00967_43_000000000-A3JHG_1_1111_4893_9943"
#> [118] "M00967_43_000000000-A3JHG_1_1111_7157_8555"
#> [119] "M00967_43_000000000-A3JHG_1_1111_8373_10700"
#> [120] "M00967_43_000000000-A3JHG_1_1111_8697_7063"
#> [121] "M00967_43_000000000-A3JHG_1_1111_9294_7319"
#> [122] "M00967_43_000000000-A3JHG_1_1111_9420_7291"
#> [123] "M00967_43_000000000-A3JHG_1_1112_10024_21424"
#> [124] "M00967_43_000000000-A3JHG_1_1112_10194_20788"
#> [125] "M00967_43_000000000-A3JHG_1_1112_11192_11762"
#> [126] "M00967_43_000000000-A3JHG_1_1112_11343_21537"
#> [127] "M00967_43_000000000-A3JHG_1_1112_12715_8487"
#> [128] "M00967_43_000000000-A3JHG_1_1112_12844_3769"
#> [129] "M00967_43_000000000-A3JHG_1_1112_13142_18436"
#> [130] "M00967_43_000000000-A3JHG_1_1112_14780_8078"
#> [131] "M00967_43_000000000-A3JHG_1_1112_17696_15281"
#> [132] "M00967_43_000000000-A3JHG_1_1112_18848_19266"
#> [133] "M00967_43_000000000-A3JHG_1_1112_19376_17765"
#> [134] "M00967_43_000000000-A3JHG_1_1112_20127_11122"
#> [135] "M00967_43_000000000-A3JHG_1_1112_20884_13058"
#> [136] "M00967_43_000000000-A3JHG_1_1112_24118_19062"
#> [137] "M00967_43_000000000-A3JHG_1_1112_24606_18511"
#> [138] "M00967_43_000000000-A3JHG_1_1112_25719_18946"
#> [139] "M00967_43_000000000-A3JHG_1_1112_6862_18037"
#> [140] "M00967_43_000000000-A3JHG_1_1112_7075_7100"
#> [141] "M00967_43_000000000-A3JHG_1_1112_8821_13863"
#> [142] "M00967_43_000000000-A3JHG_1_1113_11294_24024"
#> [143] "M00967_43_000000000-A3JHG_1_1113_13777_28036"
#> [144] "M00967_43_000000000-A3JHG_1_1113_16913_23641"
#> [145] "M00967_43_000000000-A3JHG_1_1113_18037_24127"
#> [146] "M00967_43_000000000-A3JHG_1_1113_18548_4292"
#> [147] "M00967_43_000000000-A3JHG_1_1113_18644_24962"
#> [148] "M00967_43_000000000-A3JHG_1_1113_18958_10702"
#> [149] "M00967_43_000000000-A3JHG_1_1113_19532_4899"
#> [150] "M00967_43_000000000-A3JHG_1_1113_20770_14973"
#> [151] "M00967_43_000000000-A3JHG_1_1113_22254_26020"
#> [152] "M00967_43_000000000-A3JHG_1_1113_5336_24219"
#> [153] "M00967_43_000000000-A3JHG_1_1113_8139_17897"
#> [154] "M00967_43_000000000-A3JHG_1_1114_11769_3401"
#> [155] "M00967_43_000000000-A3JHG_1_1114_12471_18325"
#> [156] "M00967_43_000000000-A3JHG_1_1114_13397_2211"
#> [157] "M00967_43_000000000-A3JHG_1_1114_14280_26421"
#> [158] "M00967_43_000000000-A3JHG_1_1114_14431_2336"
#> [159] "M00967_43_000000000-A3JHG_1_1114_19875_9916"
#> [160] "M00967_43_000000000-A3JHG_1_1114_21222_13060"
#> [161] "M00967_43_000000000-A3JHG_1_1114_21515_19829"
#> [162] "M00967_43_000000000-A3JHG_1_1114_22681_21130"
#> [163] "M00967_43_000000000-A3JHG_1_1114_22804_12324"
#> [164] "M00967_43_000000000-A3JHG_1_1114_23055_10307"
#> [165] "M00967_43_000000000-A3JHG_1_1114_25590_23883"
#> [166] "M00967_43_000000000-A3JHG_1_1114_25962_10907"
#> [167] "M00967_43_000000000-A3JHG_1_1114_26968_12650"
#> [168] "M00967_43_000000000-A3JHG_1_1114_3131_16050"
#> [169] "M00967_43_000000000-A3JHG_1_1114_5902_6841"
#> [170] "M00967_43_000000000-A3JHG_1_1114_8676_16145"
#> [171] "M00967_43_000000000-A3JHG_1_2101_13105_15429"
#> [172] "M00967_43_000000000-A3JHG_1_2101_13595_15201"
#> [173] "M00967_43_000000000-A3JHG_1_2101_15190_13450"
#> [174] "M00967_43_000000000-A3JHG_1_2101_16955_14005"
#> [175] "M00967_43_000000000-A3JHG_1_2101_21575_17061"
#> [176] "M00967_43_000000000-A3JHG_1_2101_22400_13416"
#> [177] "M00967_43_000000000-A3JHG_1_2101_22942_18205"
#> [178] "M00967_43_000000000-A3JHG_1_2101_27541_14282"
#> [179] "M00967_43_000000000-A3JHG_1_2101_6377_14009"
#> [180] "M00967_43_000000000-A3JHG_1_2102_12263_16584"
#> [181] "M00967_43_000000000-A3JHG_1_2102_12696_27241"
#> [182] "M00967_43_000000000-A3JHG_1_2102_15356_3054"
#> [183] "M00967_43_000000000-A3JHG_1_2102_15542_26928"
#> [184] "M00967_43_000000000-A3JHG_1_2102_15692_18713"
#> [185] "M00967_43_000000000-A3JHG_1_2102_15728_25058"
#> [186] "M00967_43_000000000-A3JHG_1_2102_17019_10818"
#> [187] "M00967_43_000000000-A3JHG_1_2102_27520_15637"
#> [188] "M00967_43_000000000-A3JHG_1_2102_6705_15205"
#> [189] "M00967_43_000000000-A3JHG_1_2103_10155_24625"
#> [190] "M00967_43_000000000-A3JHG_1_2103_11533_13455"
#> [191] "M00967_43_000000000-A3JHG_1_2103_11635_15432"
#> [192] "M00967_43_000000000-A3JHG_1_2103_12830_18492"
#> [193] "M00967_43_000000000-A3JHG_1_2103_15275_25227"
#> [194] "M00967_43_000000000-A3JHG_1_2103_17221_27256"
#> [195] "M00967_43_000000000-A3JHG_1_2103_20332_24524"
#> [196] "M00967_43_000000000-A3JHG_1_2103_21597_21834"
#> [197] "M00967_43_000000000-A3JHG_1_2103_22029_15973"
#> [198] "M00967_43_000000000-A3JHG_1_2103_22514_6355"
#> [199] "M00967_43_000000000-A3JHG_1_2103_25452_6018"
#> [200] "M00967_43_000000000-A3JHG_1_2103_25809_24518"
#> [201] "M00967_43_000000000-A3JHG_1_2104_12407_10383"
#> [202] "M00967_43_000000000-A3JHG_1_2104_12505_3424"
#> [203] "M00967_43_000000000-A3JHG_1_2104_12560_11750"
#> [204] "M00967_43_000000000-A3JHG_1_2104_13679_3191"
#> [205] "M00967_43_000000000-A3JHG_1_2104_15263_18242"
#> [206] "M00967_43_000000000-A3JHG_1_2104_17426_10838"
#> [207] "M00967_43_000000000-A3JHG_1_2104_20685_7431"
#> [208] "M00967_43_000000000-A3JHG_1_2104_22645_18534"
#> [209] "M00967_43_000000000-A3JHG_1_2104_25418_11257"
#> [210] "M00967_43_000000000-A3JHG_1_2104_26311_10309"
#> [211] "M00967_43_000000000-A3JHG_1_2105_10725_6078"
#> [212] "M00967_43_000000000-A3JHG_1_2105_12878_11098"
#> [213] "M00967_43_000000000-A3JHG_1_2105_17320_11249"
#> [214] "M00967_43_000000000-A3JHG_1_2105_17957_13444"
#> [215] "M00967_43_000000000-A3JHG_1_2105_20695_7710"
#> [216] "M00967_43_000000000-A3JHG_1_2105_26628_10360"
#> [217] "M00967_43_000000000-A3JHG_1_2105_8155_16465"
#> [218] "M00967_43_000000000-A3JHG_1_2106_10758_21336"
#> [219] "M00967_43_000000000-A3JHG_1_2106_11005_4817"
#> [220] "M00967_43_000000000-A3JHG_1_2106_12340_14438"
#> [221] "M00967_43_000000000-A3JHG_1_2106_12968_23126"
#> [222] "M00967_43_000000000-A3JHG_1_2106_14236_13388"
#> [223] "M00967_43_000000000-A3JHG_1_2106_14305_11884"
#> [224] "M00967_43_000000000-A3JHG_1_2106_17516_3721"
#> [225] "M00967_43_000000000-A3JHG_1_2106_19750_8249"
#> [226] "M00967_43_000000000-A3JHG_1_2106_20954_22445"
#> [227] "M00967_43_000000000-A3JHG_1_2106_23199_7043"
#> [228] "M00967_43_000000000-A3JHG_1_2106_25519_17332"
#> [229] "M00967_43_000000000-A3JHG_1_2106_27480_14501"
#> [230] "M00967_43_000000000-A3JHG_1_2106_8736_3855"
#> [231] "M00967_43_000000000-A3JHG_1_2107_11601_20781"
#> [232] "M00967_43_000000000-A3JHG_1_2107_12056_5129"
#> [233] "M00967_43_000000000-A3JHG_1_2107_12502_13322"
#> [234] "M00967_43_000000000-A3JHG_1_2107_18723_18535"
#> [235] "M00967_43_000000000-A3JHG_1_2107_25731_13155"
#> [236] "M00967_43_000000000-A3JHG_1_2107_26555_14955"
#> [237] "M00967_43_000000000-A3JHG_1_2107_26817_14749"
#> [238] "M00967_43_000000000-A3JHG_1_2107_6956_16009"
#> [239] "M00967_43_000000000-A3JHG_1_2107_8240_20277"
#> [240] "M00967_43_000000000-A3JHG_1_2108_18470_11561"
#> [241] "M00967_43_000000000-A3JHG_1_2108_19309_25091"
#> [242] "M00967_43_000000000-A3JHG_1_2108_19840_26409"
#> [243] "M00967_43_000000000-A3JHG_1_2108_20440_11777"
#> [244] "M00967_43_000000000-A3JHG_1_2108_21552_11279"
#> [245] "M00967_43_000000000-A3JHG_1_2108_22213_11738"
#> [246] "M00967_43_000000000-A3JHG_1_2108_22507_11051"
#> [247] "M00967_43_000000000-A3JHG_1_2108_24812_22312"
#> [248] "M00967_43_000000000-A3JHG_1_2108_25681_15613"
#> [249] "M00967_43_000000000-A3JHG_1_2108_27907_12796"
#> [250] "M00967_43_000000000-A3JHG_1_2108_4168_17142"
#> [251] "M00967_43_000000000-A3JHG_1_2108_5077_12621"
#> [252] "M00967_43_000000000-A3JHG_1_2108_6318_9727"
#> [253] "M00967_43_000000000-A3JHG_1_2108_7674_17990"
#> [254] "M00967_43_000000000-A3JHG_1_2108_8189_19210"
#> [255] "M00967_43_000000000-A3JHG_1_2108_8220_23281"
#> [256] "M00967_43_000000000-A3JHG_1_2109_13206_18588"
#> [257] "M00967_43_000000000-A3JHG_1_2109_17345_6668"
#> [258] "M00967_43_000000000-A3JHG_1_2109_21549_9949"
#> [259] "M00967_43_000000000-A3JHG_1_2109_9943_10858"
#> [260] "M00967_43_000000000-A3JHG_1_2110_10107_20150"
#> [261] "M00967_43_000000000-A3JHG_1_2110_10606_15947"
#> [262] "M00967_43_000000000-A3JHG_1_2110_16815_14755"
#> [263] "M00967_43_000000000-A3JHG_1_2110_18978_25958"
#> [264] "M00967_43_000000000-A3JHG_1_2110_20054_3531"
#> [265] "M00967_43_000000000-A3JHG_1_2110_21191_16122"
#> [266] "M00967_43_000000000-A3JHG_1_2110_21271_27854"
#> [267] "M00967_43_000000000-A3JHG_1_2110_23478_21034"
#> [268] "M00967_43_000000000-A3JHG_1_2110_23584_15948"
#> [269] "M00967_43_000000000-A3JHG_1_2110_27497_19046"
#> [270] "M00967_43_000000000-A3JHG_1_2110_4035_19768"
#> [271] "M00967_43_000000000-A3JHG_1_2110_6116_13104"
#> [272] "M00967_43_000000000-A3JHG_1_2110_8957_20034"
#> [273] "M00967_43_000000000-A3JHG_1_2110_9069_16610"
#> [274] "M00967_43_000000000-A3JHG_1_2110_9142_17889"
#> [275] "M00967_43_000000000-A3JHG_1_2111_10373_11883"
#> [276] "M00967_43_000000000-A3JHG_1_2111_13741_6972"
#> [277] "M00967_43_000000000-A3JHG_1_2111_14452_16768"
#> [278] "M00967_43_000000000-A3JHG_1_2111_15748_5366"
#> [279] "M00967_43_000000000-A3JHG_1_2111_16376_7553"
#> [280] "M00967_43_000000000-A3JHG_1_2111_17223_18856"
#> [281] "M00967_43_000000000-A3JHG_1_2111_22278_7396"
#> [282] "M00967_43_000000000-A3JHG_1_2111_29227_17392"
#> [283] "M00967_43_000000000-A3JHG_1_2111_5664_6671"
#> [284] "M00967_43_000000000-A3JHG_1_2111_6782_8678"
#> [285] "M00967_43_000000000-A3JHG_1_2111_6887_10490"
#> [286] "M00967_43_000000000-A3JHG_1_2111_8444_24070"
#> [287] "M00967_43_000000000-A3JHG_1_2112_12596_22917"
#> [288] "M00967_43_000000000-A3JHG_1_2112_15899_17037"
#> [289] "M00967_43_000000000-A3JHG_1_2112_19543_24449"
#> [290] "M00967_43_000000000-A3JHG_1_2112_21752_23067"
#> [291] "M00967_43_000000000-A3JHG_1_2112_23618_22699"
#> [292] "M00967_43_000000000-A3JHG_1_2112_26066_19401"
#> [293] "M00967_43_000000000-A3JHG_1_2112_26773_14017"
#> [294] "M00967_43_000000000-A3JHG_1_2112_6716_23007"
#> [295] "M00967_43_000000000-A3JHG_1_2112_8202_24000"
#> [296] "M00967_43_000000000-A3JHG_1_2113_12406_27672"
#> [297] "M00967_43_000000000-A3JHG_1_2113_14487_9130"
#> [298] "M00967_43_000000000-A3JHG_1_2113_18397_7516"
#> [299] "M00967_43_000000000-A3JHG_1_2113_20030_3857"
#> [300] "M00967_43_000000000-A3JHG_1_2113_21028_10433"
#> [301] "M00967_43_000000000-A3JHG_1_2113_21352_15666"
#> [302] "M00967_43_000000000-A3JHG_1_2113_23466_13467"
#> [303] "M00967_43_000000000-A3JHG_1_2113_7021_18070"
#> [304] "M00967_43_000000000-A3JHG_1_2113_9708_9804"
#> [305] "M00967_43_000000000-A3JHG_1_2114_10492_14599"
#> [306] "M00967_43_000000000-A3JHG_1_2114_16946_3813"
#> [307] "M00967_43_000000000-A3JHG_1_2114_27537_17929"
#> [308] "M00967_43_000000000-A3JHG_1_2114_4298_19710"
#> [309] "M00967_43_000000000-A3JHG_1_2114_6259_8307"
#' # To get the names of the sequences unique to sample 'F3D0'
miseq$names(type = "sequence", samples = c("F3D0"), distinct = TRUE)
#> [1] "M00967_43_000000000-A3JHG_1_1101_10331_23332"
#> [2] "M00967_43_000000000-A3JHG_1_1101_11035_15765"
#> [3] "M00967_43_000000000-A3JHG_1_1101_14364_8401"
#> [4] "M00967_43_000000000-A3JHG_1_1101_18346_24737"
#> [5] "M00967_43_000000000-A3JHG_1_1101_9620_19745"
#> [6] "M00967_43_000000000-A3JHG_1_1102_11115_19075"
#> [7] "M00967_43_000000000-A3JHG_1_1102_14905_6542"
#> [8] "M00967_43_000000000-A3JHG_1_1102_15058_11924"
#> [9] "M00967_43_000000000-A3JHG_1_1102_16785_23687"
#> [10] "M00967_43_000000000-A3JHG_1_1102_2114_15227"
#> [11] "M00967_43_000000000-A3JHG_1_1103_13966_3813"
#> [12] "M00967_43_000000000-A3JHG_1_1103_22490_21890"
#> [13] "M00967_43_000000000-A3JHG_1_1103_3864_17599"
#> [14] "M00967_43_000000000-A3JHG_1_1104_10834_8009"
#> [15] "M00967_43_000000000-A3JHG_1_1105_11964_4686"
#> [16] "M00967_43_000000000-A3JHG_1_1105_14547_12843"
#> [17] "M00967_43_000000000-A3JHG_1_1105_9179_20196"
#> [18] "M00967_43_000000000-A3JHG_1_1106_11240_22282"
#> [19] "M00967_43_000000000-A3JHG_1_1106_12231_13452"
#> [20] "M00967_43_000000000-A3JHG_1_1106_26087_11342"
#> [21] "M00967_43_000000000-A3JHG_1_1106_26888_10548"
#> [22] "M00967_43_000000000-A3JHG_1_1106_9558_4409"
#> [23] "M00967_43_000000000-A3JHG_1_1107_18662_2044"
#> [24] "M00967_43_000000000-A3JHG_1_1107_21038_8569"
#> [25] "M00967_43_000000000-A3JHG_1_1108_12037_3053"
#> [26] "M00967_43_000000000-A3JHG_1_1108_18996_9436"
#> [27] "M00967_43_000000000-A3JHG_1_1108_19065_7362"
#> [28] "M00967_43_000000000-A3JHG_1_1109_14359_12484"
#> [29] "M00967_43_000000000-A3JHG_1_1109_24274_5733"
#> [30] "M00967_43_000000000-A3JHG_1_1109_7971_24259"
#> [31] "M00967_43_000000000-A3JHG_1_1109_8014_5010"
#> [32] "M00967_43_000000000-A3JHG_1_1110_13719_21639"
#> [33] "M00967_43_000000000-A3JHG_1_1110_14443_28339"
#> [34] "M00967_43_000000000-A3JHG_1_1111_17002_7999"
#> [35] "M00967_43_000000000-A3JHG_1_1111_8373_10700"
#> [36] "M00967_43_000000000-A3JHG_1_1112_10194_20788"
#> [37] "M00967_43_000000000-A3JHG_1_1112_11343_21537"
#> [38] "M00967_43_000000000-A3JHG_1_1112_12844_3769"
#> [39] "M00967_43_000000000-A3JHG_1_1112_20884_13058"
#> [40] "M00967_43_000000000-A3JHG_1_1113_16913_23641"
#> [41] "M00967_43_000000000-A3JHG_1_1113_18548_4292"
#> [42] "M00967_43_000000000-A3JHG_1_1113_18958_10702"
#> [43] "M00967_43_000000000-A3JHG_1_1114_12471_18325"
#> [44] "M00967_43_000000000-A3JHG_1_1114_13397_2211"
#> [45] "M00967_43_000000000-A3JHG_1_1114_14431_2336"
#> [46] "M00967_43_000000000-A3JHG_1_1114_21222_13060"
#> [47] "M00967_43_000000000-A3JHG_1_1114_22804_12324"
#> [48] "M00967_43_000000000-A3JHG_1_1114_23055_10307"
#> [49] "M00967_43_000000000-A3JHG_1_1114_26968_12650"
#> [50] "M00967_43_000000000-A3JHG_1_1114_3131_16050"
#> [51] "M00967_43_000000000-A3JHG_1_1114_5902_6841"
#> [52] "M00967_43_000000000-A3JHG_1_1114_8676_16145"
#> [53] "M00967_43_000000000-A3JHG_1_2101_16955_14005"
#> [54] "M00967_43_000000000-A3JHG_1_2101_22942_18205"
#> [55] "M00967_43_000000000-A3JHG_1_2102_12263_16584"
#> [56] "M00967_43_000000000-A3JHG_1_2102_12696_27241"
#> [57] "M00967_43_000000000-A3JHG_1_2102_17019_10818"
#> [58] "M00967_43_000000000-A3JHG_1_2102_6705_15205"
#> [59] "M00967_43_000000000-A3JHG_1_2103_17221_27256"
#> [60] "M00967_43_000000000-A3JHG_1_2103_22029_15973"
#> [61] "M00967_43_000000000-A3JHG_1_2103_22514_6355"
#> [62] "M00967_43_000000000-A3JHG_1_2103_25452_6018"
#> [63] "M00967_43_000000000-A3JHG_1_2104_12505_3424"
#> [64] "M00967_43_000000000-A3JHG_1_2105_12878_11098"
#> [65] "M00967_43_000000000-A3JHG_1_2105_17320_11249"
#> [66] "M00967_43_000000000-A3JHG_1_2105_26628_10360"
#> [67] "M00967_43_000000000-A3JHG_1_2105_8155_16465"
#> [68] "M00967_43_000000000-A3JHG_1_2106_10758_21336"
#> [69] "M00967_43_000000000-A3JHG_1_2106_14305_11884"
#> [70] "M00967_43_000000000-A3JHG_1_2106_19750_8249"
#> [71] "M00967_43_000000000-A3JHG_1_2106_27480_14501"
#> [72] "M00967_43_000000000-A3JHG_1_2107_25731_13155"
#> [73] "M00967_43_000000000-A3JHG_1_2107_26555_14955"
#> [74] "M00967_43_000000000-A3JHG_1_2107_26817_14749"
#> [75] "M00967_43_000000000-A3JHG_1_2107_8240_20277"
#> [76] "M00967_43_000000000-A3JHG_1_2108_19840_26409"
#> [77] "M00967_43_000000000-A3JHG_1_2108_5077_12621"
#> [78] "M00967_43_000000000-A3JHG_1_2108_6318_9727"
#> [79] "M00967_43_000000000-A3JHG_1_2108_8220_23281"
#> [80] "M00967_43_000000000-A3JHG_1_2109_17345_6668"
#> [81] "M00967_43_000000000-A3JHG_1_2109_21549_9949"
#> [82] "M00967_43_000000000-A3JHG_1_2110_10107_20150"
#> [83] "M00967_43_000000000-A3JHG_1_2110_10606_15947"
#> [84] "M00967_43_000000000-A3JHG_1_2110_16815_14755"
#> [85] "M00967_43_000000000-A3JHG_1_2110_18978_25958"
#> [86] "M00967_43_000000000-A3JHG_1_2110_20054_3531"
#> [87] "M00967_43_000000000-A3JHG_1_2110_23478_21034"
#> [88] "M00967_43_000000000-A3JHG_1_2110_9069_16610"
#> [89] "M00967_43_000000000-A3JHG_1_2111_17223_18856"
#> [90] "M00967_43_000000000-A3JHG_1_2111_22278_7396"
#> [91] "M00967_43_000000000-A3JHG_1_2111_29227_17392"
#> [92] "M00967_43_000000000-A3JHG_1_2111_6782_8678"
#> [93] "M00967_43_000000000-A3JHG_1_2111_6887_10490"
#> [94] "M00967_43_000000000-A3JHG_1_2111_8444_24070"
#> [95] "M00967_43_000000000-A3JHG_1_2112_15899_17037"
#> [96] "M00967_43_000000000-A3JHG_1_2112_26066_19401"
#> [97] "M00967_43_000000000-A3JHG_1_2113_12406_27672"
#> [98] "M00967_43_000000000-A3JHG_1_2113_18397_7516"
#> [99] "M00967_43_000000000-A3JHG_1_2113_9708_9804"
#> [100] "M00967_43_000000000-A3JHG_1_2114_27537_17929"
#> [101] "M00967_43_000000000-A3JHG_1_2114_4298_19710"
# To get the names of the samples
miseq$names(type = "sample")
#> [1] "F3D0" "F3D1" "F3D141" "F3D142" "F3D143" "F3D144" "F3D145" "F3D146"
#> [9] "F3D147" "F3D148" "F3D149" "F3D150" "F3D2" "F3D3" "F3D5" "F3D6"
#> [17] "F3D7" "F3D8" "F3D9"
# To get the names of the treatments
miseq$names(type = "treatment")
#> [1] "Early" "Late"
# To get the names of the bins
miseq$names(type = "bin")
#> [1] "Otu001" "Otu002" "Otu003" "Otu004" "Otu005" "Otu006" "Otu007" "Otu008"
#> [9] "Otu009" "Otu010" "Otu011" "Otu012" "Otu013" "Otu014" "Otu015" "Otu016"
#> [17] "Otu017" "Otu018" "Otu019" "Otu020" "Otu021" "Otu022" "Otu023" "Otu024"
#> [25] "Otu025" "Otu026" "Otu027" "Otu028" "Otu029" "Otu030" "Otu031" "Otu032"
#> [33] "Otu033" "Otu034" "Otu035" "Otu036" "Otu037" "Otu038" "Otu039" "Otu040"
#> [41] "Otu041" "Otu042" "Otu043" "Otu044" "Otu045" "Otu046" "Otu047" "Otu048"
#> [49] "Otu049" "Otu050" "Otu051" "Otu052" "Otu053" "Otu054" "Otu055" "Otu056"
#> [57] "Otu057" "Otu058" "Otu059" "Otu060" "Otu061" "Otu062" "Otu063" "Otu064"
#> [65] "Otu065" "Otu066" "Otu067" "Otu068" "Otu069" "Otu070" "Otu071" "Otu072"
#> [73] "Otu073" "Otu074" "Otu075" "Otu076" "Otu077" "Otu078" "Otu079" "Otu080"
#> [81] "Otu081" "Otu082" "Otu083" "Otu084" "Otu085" "Otu086" "Otu087" "Otu088"
#> [89] "Otu089" "Otu090" "Otu091" "Otu092" "Otu093" "Otu094" "Otu095" "Otu096"
#> [97] "Otu097" "Otu098" "Otu099" "Otu100" "Otu101" "Otu102" "Otu103" "Otu104"
#> [105] "Otu105" "Otu106" "Otu107" "Otu108" "Otu109" "Otu110" "Otu111" "Otu112"
#> [113] "Otu113" "Otu114" "Otu115" "Otu116" "Otu117" "Otu118" "Otu119" "Otu120"
#> [121] "Otu121" "Otu122" "Otu123" "Otu124" "Otu125" "Otu126" "Otu127" "Otu128"
#> [129] "Otu129" "Otu130" "Otu131" "Otu132" "Otu133" "Otu134" "Otu135" "Otu136"
#> [137] "Otu137" "Otu138" "Otu139" "Otu140" "Otu141" "Otu142" "Otu143" "Otu144"
#> [145] "Otu145" "Otu146" "Otu147" "Otu148" "Otu149" "Otu150" "Otu151" "Otu152"
#> [153] "Otu153" "Otu154" "Otu155" "Otu156" "Otu157" "Otu158" "Otu159" "Otu160"
#> [161] "Otu161" "Otu162" "Otu163" "Otu164" "Otu165" "Otu166" "Otu167" "Otu168"
#> [169] "Otu169" "Otu170" "Otu171" "Otu172" "Otu173" "Otu174" "Otu175" "Otu176"
#> [177] "Otu177" "Otu178" "Otu179" "Otu180" "Otu181" "Otu182" "Otu183" "Otu184"
#> [185] "Otu185" "Otu186" "Otu187" "Otu188" "Otu189" "Otu190" "Otu191" "Otu192"
#> [193] "Otu193" "Otu194" "Otu195" "Otu196" "Otu197" "Otu198" "Otu199" "Otu200"
#> [201] "Otu201" "Otu202" "Otu203" "Otu204" "Otu205" "Otu206" "Otu207" "Otu208"
#> [209] "Otu209" "Otu210" "Otu211" "Otu212" "Otu213" "Otu214" "Otu215" "Otu216"
#> [217] "Otu217" "Otu218" "Otu219" "Otu220" "Otu221" "Otu222" "Otu223" "Otu224"
#> [225] "Otu225" "Otu226" "Otu227" "Otu228" "Otu229" "Otu230" "Otu231" "Otu232"
#> [233] "Otu233" "Otu234" "Otu235" "Otu236" "Otu237" "Otu238" "Otu239" "Otu240"
#> [241] "Otu241" "Otu242" "Otu243" "Otu244" "Otu245" "Otu246" "Otu247" "Otu248"
#> [249] "Otu249" "Otu250" "Otu251" "Otu252" "Otu253" "Otu254" "Otu255" "Otu256"
#> [257] "Otu257" "Otu258" "Otu259" "Otu260" "Otu261" "Otu262" "Otu263" "Otu264"
#> [265] "Otu265" "Otu266" "Otu267" "Otu268" "Otu269" "Otu270" "Otu271" "Otu272"
#> [273] "Otu273" "Otu274" "Otu275" "Otu276" "Otu277" "Otu278" "Otu279" "Otu280"
#> [281] "Otu281" "Otu282" "Otu283" "Otu284" "Otu285" "Otu286" "Otu287" "Otu288"
#> [289] "Otu289" "Otu290" "Otu291" "Otu292" "Otu293" "Otu294" "Otu295" "Otu296"
#> [297] "Otu297" "Otu298" "Otu299" "Otu300" "Otu301" "Otu302" "Otu303" "Otu304"
#> [305] "Otu305" "Otu306" "Otu307" "Otu308" "Otu309" "Otu310" "Otu311" "Otu312"
#> [313] "Otu313" "Otu314" "Otu315" "Otu316" "Otu317" "Otu318" "Otu319" "Otu320"
#> [321] "Otu321" "Otu322" "Otu323" "Otu324" "Otu325" "Otu326" "Otu327" "Otu328"
#> [329] "Otu329" "Otu330" "Otu331" "Otu332" "Otu333" "Otu334" "Otu335" "Otu336"
#> [337] "Otu337" "Otu338" "Otu339" "Otu340" "Otu341" "Otu342" "Otu343" "Otu344"
#> [345] "Otu345" "Otu346" "Otu347" "Otu348" "Otu349" "Otu350" "Otu351" "Otu352"
#> [353] "Otu353" "Otu354" "Otu355" "Otu356" "Otu357" "Otu358" "Otu359" "Otu360"
#> [361] "Otu361" "Otu362" "Otu363" "Otu364" "Otu365" "Otu366" "Otu367" "Otu368"
#> [369] "Otu369" "Otu370" "Otu371" "Otu372" "Otu373" "Otu374" "Otu375" "Otu376"
#> [377] "Otu377" "Otu378" "Otu379" "Otu380" "Otu381" "Otu382" "Otu383" "Otu384"
#> [385] "Otu385" "Otu386" "Otu387" "Otu388" "Otu389" "Otu390" "Otu391" "Otu392"
#> [393] "Otu393" "Otu394" "Otu395" "Otu396" "Otu397" "Otu398" "Otu399" "Otu400"
#> [401] "Otu401" "Otu402" "Otu403" "Otu404" "Otu405" "Otu406" "Otu407" "Otu408"
#> [409] "Otu409" "Otu410" "Otu411" "Otu412" "Otu413" "Otu414" "Otu415" "Otu416"
#> [417] "Otu417" "Otu418" "Otu419" "Otu420" "Otu421" "Otu422" "Otu423" "Otu424"
#> [425] "Otu425" "Otu426" "Otu427" "Otu428" "Otu429" "Otu430" "Otu431" "Otu432"
#> [433] "Otu433" "Otu434" "Otu435" "Otu436" "Otu437" "Otu438" "Otu439" "Otu440"
#> [441] "Otu441" "Otu442" "Otu443" "Otu444" "Otu445" "Otu446" "Otu447" "Otu448"
#> [449] "Otu449" "Otu450" "Otu451" "Otu452" "Otu453" "Otu454" "Otu455" "Otu456"
#> [457] "Otu457" "Otu458" "Otu459" "Otu460" "Otu461" "Otu462" "Otu463" "Otu464"
#> [465] "Otu465" "Otu466" "Otu467" "Otu468" "Otu469" "Otu470" "Otu471" "Otu472"
#> [473] "Otu473" "Otu474" "Otu475" "Otu476" "Otu477" "Otu478" "Otu479" "Otu480"
#> [481] "Otu481" "Otu482" "Otu483" "Otu484" "Otu485" "Otu486" "Otu487" "Otu488"
#> [489] "Otu489" "Otu490" "Otu491" "Otu492" "Otu493" "Otu494" "Otu495" "Otu496"
#> [497] "Otu497" "Otu498" "Otu499" "Otu500" "Otu501" "Otu502" "Otu503" "Otu504"
#> [505] "Otu505" "Otu506" "Otu507" "Otu508" "Otu509" "Otu510" "Otu511" "Otu512"
#> [513] "Otu513" "Otu514" "Otu515" "Otu516" "Otu517" "Otu518" "Otu519" "Otu520"
#> [521] "Otu521" "Otu522" "Otu523" "Otu524" "Otu525" "Otu526" "Otu527" "Otu528"
#> [529] "Otu529" "Otu530" "Otu531"
# To get the names of the bins that are unique to 'F3D0'
miseq$names(type = "bin", samples = c("F3D0"), distinct = TRUE)
#> [1] "Otu330" "Otu339" "Otu341" "Otu345" "Otu347" "Otu354" "Otu364" "Otu431"
#> [9] "Otu466" "Otu469" "Otu470" "Otu491" "Otu493" "Otu529"
# To get the names of the bins that include sequences from 'F3D0'
miseq$names(type = "bin", samples = c("F3D0"), distinct = FALSE)
#> [1] "Otu001" "Otu002" "Otu003" "Otu004" "Otu005" "Otu006" "Otu007" "Otu008"
#> [9] "Otu009" "Otu010" "Otu011" "Otu012" "Otu013" "Otu014" "Otu015" "Otu016"
#> [17] "Otu017" "Otu018" "Otu019" "Otu020" "Otu021" "Otu022" "Otu023" "Otu024"
#> [25] "Otu025" "Otu026" "Otu027" "Otu028" "Otu029" "Otu030" "Otu031" "Otu032"
#> [33] "Otu033" "Otu034" "Otu035" "Otu036" "Otu037" "Otu038" "Otu039" "Otu040"
#> [41] "Otu041" "Otu042" "Otu043" "Otu044" "Otu045" "Otu046" "Otu047" "Otu048"
#> [49] "Otu049" "Otu050" "Otu051" "Otu052" "Otu053" "Otu054" "Otu056" "Otu057"
#> [57] "Otu060" "Otu061" "Otu062" "Otu063" "Otu064" "Otu065" "Otu066" "Otu067"
#> [65] "Otu068" "Otu069" "Otu070" "Otu071" "Otu072" "Otu073" "Otu074" "Otu075"
#> [73] "Otu076" "Otu078" "Otu079" "Otu082" "Otu083" "Otu084" "Otu085" "Otu087"
#> [81] "Otu089" "Otu090" "Otu091" "Otu092" "Otu093" "Otu094" "Otu095" "Otu096"
#> [89] "Otu097" "Otu098" "Otu101" "Otu102" "Otu103" "Otu104" "Otu105" "Otu106"
#> [97] "Otu107" "Otu108" "Otu109" "Otu111" "Otu112" "Otu113" "Otu114" "Otu115"
#> [105] "Otu117" "Otu118" "Otu119" "Otu120" "Otu122" "Otu123" "Otu124" "Otu125"
#> [113] "Otu127" "Otu129" "Otu130" "Otu132" "Otu136" "Otu137" "Otu138" "Otu141"
#> [121] "Otu143" "Otu144" "Otu145" "Otu146" "Otu147" "Otu148" "Otu150" "Otu151"
#> [129] "Otu154" "Otu156" "Otu157" "Otu160" "Otu161" "Otu162" "Otu163" "Otu164"
#> [137] "Otu166" "Otu168" "Otu169" "Otu170" "Otu172" "Otu174" "Otu175" "Otu176"
#> [145] "Otu178" "Otu182" "Otu183" "Otu186" "Otu187" "Otu188" "Otu190" "Otu196"
#> [153] "Otu197" "Otu202" "Otu203" "Otu206" "Otu207" "Otu208" "Otu211" "Otu215"
#> [161] "Otu216" "Otu220" "Otu222" "Otu224" "Otu225" "Otu227" "Otu237" "Otu243"
#> [169] "Otu256" "Otu259" "Otu275" "Otu277" "Otu287" "Otu290" "Otu300" "Otu310"
#> [177] "Otu312" "Otu330" "Otu339" "Otu341" "Otu345" "Otu347" "Otu354" "Otu364"
#> [185] "Otu431" "Otu466" "Otu469" "Otu470" "Otu491" "Otu493" "Otu529"
# To get the names of the reports
miseq$names(type = "report")
#> [1] "contigs_report" "metadata"
## ------------------------------------------------
## Method `strollur$rename()`
## ------------------------------------------------
data <- strollur::strollur$new("old_name")
data
#> old_name:
#>
#>
#> Total number of seqs: 0
#>
#>
data$rename("new_name")
data
#> new_name:
#>
#>
#> Total number of seqs: 0
#>
#>
## ------------------------------------------------
## Method `strollur$report()`
## ------------------------------------------------
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
# To get the FASTA data
miseq$report(type = "fasta") |> head(n = 5)
#> sequence_name
#> 1 M00967_43_000000000-A3JHG_1_1101_10133_8460
#> 2 M00967_43_000000000-A3JHG_1_1101_10331_23332
#> 3 M00967_43_000000000-A3JHG_1_1101_10382_22128
#> 4 M00967_43_000000000-A3JHG_1_1101_11035_15765
#> 5 M00967_43_000000000-A3JHG_1_1101_11348_22601
#> sequence
#> 1 TAC--GT-AG-GGG--GCA-A-G-C-G-T-T--AT-C-CGG-AT--TT-A-C-T--GG-GT--GT-A-AA-GG-GA-GC-G-TA-GGC-G-G-C-CA-T-G-C-AA-G-T-C-A-G-A-A-G--TG-A-AA-AC-C-C-GG-GG--CT-C-AA-C---C-C-TGG-G-AGT-G-C-TTTT-GAAAC-TG-T-GCGGC-TAGA-GT-GT-CG-GA-G-G---GG-T-A-AGTGGAATTCCTAGTGT-AGCGGT-GAAATGCGTAG-AT-A-TT-AG-GA-GG-AACACCAGT-GGCGAAGGCG------GCTTA-CTG-G-AC-GA-T-G-ACTGACG-CTGA-GGCT-CGAAA-GCG-TGGGG-AGC-AAACAGG
#> 2 TAC--GG-AG-GAT--GCG-A-G-C-G-T-T--AT-C-CGG-AT--TT-A-T-T--GG-GT--TT-A-AA-GG-GA-GC-G-CA-GGC-G-G-C-AT-G-G-C-AA-G-T-C-A-G-A-T-G--TG-A-AA-GC-C-C-GG-GG--CT-C-AA-C-C-C-C-G-G-G-ACT-G-C-ATTT-GAAAC-TG-C-CAGGC-TAGA-GT-GT-CG-GA-G-A---GG-C-A-AGTGGAATTCCTAGTGT-AGCGGT-GAAATGCGTAG-AT-A-TT-AG-GA-GG-AACACCAGT-GGCGGAGGCG------GCTTA-CTG-G-AC-GG-T-C-ACTGACG-CTGA-GGCT-CGAAA-GCG-TGGGG-AGC-AAACAGG
#> 3 TAC--GT-AG-GTA--GCA-A-G-C-G-T-T--GT-C-CGG-AT--TT-A-C-T--GG-GT--GT-A-AA-GG-GC-GT-G-TA-GCC-G-G-G-CT-T-A-C-AA-G-T-C-A-G-A-T-G--TG-A-AA-TC-C-G-GG-GG--CT-C-AA-C-C-C-C-C-G-A-ACT-G-C-ATTT-GAAAC-TG-T-AGGTC-TTGA-GT-AT-CG-GA-G-A---GG-C-A-GGCGGAATTCCTAGTGT-AGCGGT-GAAATGCGTAG-AT-A-TT-AG-GA-GG-AACACCAGT-GGCGAAGGCG------GCCTG-CTG-G-AC-GA-C-A-ACTGACG-GTGA-GGCG-CGAAA-GCG-TGGGG-AGC-AAACAGG
#> 4 TAC--GT-AG-GGG--GCA-A-G-C-G-T-T--AT-C-CGG-AT--TT-A-C-T--GG-GT--GT-A-AA-GG-GG-GC-G-TA-GAC-G-G-C-AG-T-G-C-AA-G-T-C-T-G-A-A-G--TG-A-AA-GG-C-G-GG-GG--CC-C-AA-C-C-C-C-C-G-A-ACT-G-C-TTTG-GAAAC-TG-T-GCTGC-TGGA-GT-GC-AG-GA-G-A---GG-C-A-AGTGGAATTCCTAGTGT-AGCGGT-GAAATGCGTAG-AT-A-TT-AG-GA-GG-AACACCAG--GGCGAAGGCG------GCTTG-CTG-G-AC-TG-T-A-ACTGACG-TTGA-GGCT-CGAAA-GCG-TGGGG-AGC-AAACAGG
#> 5 TAC--GG-AG-GAT--GCG-A-G-C-G-T-T--AT-C-CGG-AT--TC-A-C-T--GG-GT--GT-A-AA-GG-GA-GC-G-TA-GAC-G-G-C-CA-T-G-C-AA-G-C-C-A-G-G-G-G--TG-A-AA-GC-C-C-GG-GG--CC-C-AA-C-C-C-C-G-G-G-ACT-G-C-CCTT-GGAAC-TG-C-ATGGC-TGGA-GT-GC-GG-GA-G-G---GG-C-A-GGCGGAATTCCTGGTGT-AGCGGT-GAAATGCGTAG-AT-A-TC-AG-GA-GG-AACACCGGC-GGCGAAGGCG------GCCTG-CTG-G-AC-CG-C-G-ACTGACG-TTGA-GGCT-CGAAA-GCG-TGGGG-AGC-GAACAGG
# To get a report about the FASTA data
miseq$report(type = "sequence") |> head(n = 5)
#> sequence_name start end length ambig
#> 1 M00967_43_000000000-A3JHG_1_1101_10133_8460 1 375 253 0
#> 2 M00967_43_000000000-A3JHG_1_1101_10331_23332 1 375 253 0
#> 3 M00967_43_000000000-A3JHG_1_1101_10382_22128 1 375 253 0
#> 4 M00967_43_000000000-A3JHG_1_1101_11035_15765 1 375 252 0
#> 5 M00967_43_000000000-A3JHG_1_1101_11348_22601 1 375 253 0
#> longest_homopolymer num_n
#> 1 5 0
#> 2 4 0
#> 3 5 0
#> 4 5 0
#> 5 4 0
# To get the sequence bin assignments
miseq$report(type = "sequence_bin_assignment", bin_type = "otu") |>
head(n = 5)
#> otu_id seq_id
#> 1 Otu001 M00967_43_000000000-A3JHG_1_1101_12302_23776
#> 2 Otu001 M00967_43_000000000-A3JHG_1_1101_20262_22075
#> 3 Otu001 M00967_43_000000000-A3JHG_1_1102_18640_14309
#> 4 Otu001 M00967_43_000000000-A3JHG_1_1102_20738_4913
#> 5 Otu001 M00967_43_000000000-A3JHG_1_1102_6774_6343
# To get the sample treatment assignments
miseq$report(type = "sample_assignment") |> head(n = 5)
#> sample treatment
#> 1 F3D0 Early
#> 2 F3D1 Early
#> 3 F3D141 Late
#> 4 F3D142 Late
#> 5 F3D143 Late
# To get a report about sequence classifications
miseq$report(type = "sequence_taxonomy") |> head(n = 5)
#> sequence_name level taxonomy confidence
#> 1 M00967_43_000000000-A3JHG_1_1101_10133_8460 1 Bacteria 100
#> 2 M00967_43_000000000-A3JHG_1_1101_10133_8460 2 Firmicutes 100
#> 3 M00967_43_000000000-A3JHG_1_1101_10133_8460 3 Clostridia 100
#> 4 M00967_43_000000000-A3JHG_1_1101_10133_8460 4 Clostridiales 100
#> 5 M00967_43_000000000-A3JHG_1_1101_10133_8460 5 Lachnospiraceae 85
# To get a report about bin classifications for 'otu' data
miseq$report(type = "bin_taxonomy", bin_type = "otu") |> head(n = 5)
#> bin_name level taxonomy confidence
#> 1 Otu001 1 Bacteria 100
#> 2 Otu001 2 "Bacteroidetes" 100
#> 3 Otu001 3 "Bacteroidia" 100
#> 4 Otu001 4 "Bacteroidales" 100
#> 5 Otu001 5 "Porphyromonadaceae" 100
# To get the 'otu' bin representative sequences
miseq$report(type = "bin_representative", bin_type = "otu") |>
head(n = 5)
#> otu_names representative_name
#> 1 Otu001 M00967_43_000000000-A3JHG_1_1108_14299_17220
#> 2 Otu002 M00967_43_000000000-A3JHG_1_1106_22705_6123
#> 3 Otu003 M00967_43_000000000-A3JHG_1_1101_15533_5293
#> 4 Otu004 M00967_43_000000000-A3JHG_1_1105_25642_17588
#> 5 Otu005 M00967_43_000000000-A3JHG_1_2102_7041_13746
#> representative_sequence
#> 1 TAC--GT-AG-GGG--GCA-A-G-C-G-T-T--AT-C-CGG-AT--TT-A-C-T--GG-GT--GT-A-AA-GG-GA-GC-G-TA-GAC-G-G-C-TG-T-G-C-AA-G-T-C-T-G-A-A-G--TG-A-AA-TG-C-C-GG-GG--CT-C-AA-C-C-C-C-G-G-A-ACT-G-C-TTTG-GAAAC-TG-T-ACAGC-TAGA-GT-GC-AG-GA-G-G---GG-T-G-AGCGGAATTCCTAGTGT-AGCGGT-GAAATGCGTAG-AT-A-TT-AG-GA-GG-AACACCGGT-GGCGAAGGCG------GCTCA-CTG-G-AC-TG-T-A-ACTGACG-TTGA-GGCT-CGAAA-GCG-TGGGG-AGC-AAACAGG
#> 2 TAC--GT-AG-GGG--GCA-A-G-C-G-T-T--AT-C-CGG-AT--TT-A-C-T--GG-GT--GT-A-AA-GG-GA-GC-G-CA-GAC-G-G-C-TG-T-G-C-AA-G-T-C-T-G-G-A-G--TG-A-AA-GG-C-G-GG-GG--CC-C-AA-C-C-C-C-C-G-G-ACT-G-C-TCTG-GAAAC-TG-T-AAAGC-TGGA-GT-GC-AG-GA-G-A---GG-T-A-AGCGGAATTCCTAGTGT-AGCGGT-GAAATGCGTAG-AT-A-TT-AG-GA-GG-AACACCAGT-GGCGAAGGCG------GCTTA-CTG-G-AC-TG-C-A-ACTGACG-TTGA-GGCT-CGAAA-GCG-TGGGT-ATC-GAACAGG
#> 3 TAC--GG-AG-GAT--GCG-A-G-C-G-T-T--AT-C-CGG-AT--TT-A-C-T--GG-GT--GT-A-AA-GG-GA-GC-G-TA-GAC-G-G-C-GA-T-G-C-AA-G-T-C-T-G-A-A-G--TG-A-AA-GG-C-G-GG-GG--CC-C-AA-C-C-C-C-C-G-G-ACT-G-C-TTTG-GAAAC-TG-T-ATAGC-TGGA-GT-GC-AG-GA-G-A---GG-T-A-AGTGGAATTCCTAGTGT-AGCGGT-GAAATGCGTAG-AT-A-TT-AG-GA-GG-AACACCAGT-GGCGAAGGCG------GCTTA-CTG-G-AC-TG-T-A-ACTGACG-TTGA-GGCT-CGAAA-GCG-TGGGG-AGC-AAACAGG
#> 4 TAC--GT-AG-GTG--GCA-A-G-C-G-T-T--AT-C-CGG-AT--TT-A-C-T--GG-GT--GT-A-AA-GG-GC-GT-G-TA-GGC-G-G-G-AC-T-G-C-AA-G-T-C-A-G-A-T-G--TG-A-AA-CC-C-A-TG-GG--CT-C-AA-C-C-C-A-T-G-G-CCT-G-C-ATTT-GAAAC-TG-T-AGTTC-TTGA-GT-GA-TG-GA-G-A---GG-C-A-GGCGGAATTCCGTGTGT-AGCGGT-GAAATGCGTAG-AT-A-TA-CG-GA-GG-AACACCAGT-GGCGAAGGCG------GCCTG-CTG-G-AC-AT-T-A-ACTGACG-CTGA-GGCG-CGAAA-GCG-TGGGG-AGC-AAACAGG
#> 5 TAC--GT-AG-GGG--GCG-A-G-C-G-T-T--AT-C-CGG-AT--TC-A-T-T--GG-GC--GT-A-AA-GC-GC-GC-G-CA-GGC-G-G-A-CT-C-A-T-AA-G-C-G-G-A-G-C-C--TT-T-AA-TC-T-T-GG-GG--CT-T-AA-C-C-T-C-A-A-G-T-C-G-G-GCCC-CGAAC-TG-T-GAGTC-TCGA-GT-GT-GG-TA-G-G---GG-A-A-GGCGGAATTCCCGGTGT-AGCGGT-GGAATGCGCAG-AT-A-TC-GG-GA-AG-AACACCGAT-GGCGAAGGCA------GCCTT-CTG-G-GC-CA-T-C-ACTGACG-CTGA-GGCG-CGAAA-GCT-AGGGG-AGC-AAACAGG
# To get a report about the sequences removed during your analysis:
miseq$report(type = "sequence_scrap")
#> data frame with 0 columns and 0 rows
# To get a report about the "otu" bins removed during your analysis:
miseq$report(type = "bin_scrap", bin_type = "otu")
#> data frame with 0 columns and 0 rows
# To get the metadata associated with your data:
metadata <- miseq$report(type = "metadata") |> head(n = 5)
# To get the resource references associated with your data:
references <- miseq$report(type = "resource_reference")
# To get our custom report containing the contigs assembly data:
miseq$report(type = "contigs_report") |> head(n = 5)
#> Name Length Overlap_Length
#> 1 M00967_43_000000000-A3JHG_1_1101_10133_8460 253 249
#> 2 M00967_43_000000000-A3JHG_1_1101_10331_23332 253 249
#> 3 M00967_43_000000000-A3JHG_1_1101_10382_22128 253 249
#> 4 M00967_43_000000000-A3JHG_1_1101_11035_15765 252 250
#> 5 M00967_43_000000000-A3JHG_1_1101_11348_22601 253 249
#> Overlap_Start Overlap_End MisMatches Num_Ns Expected_Errors
#> 1 2 251 0 0 0.00207161
#> 2 2 251 0 0 0.00230661
#> 3 2 251 2 0 0.01134540
#> 4 1 251 19 0 0.08720880
#> 5 2 251 2 0 0.00674431
# To get the tree that relates your sequences:
sequence_tree <- miseq$report(type = "sequence_tree")
# To get the tree that relates your sequences:
sample_tree <- miseq$report(type = "sample_tree")
# To get the distances between your samples:
sample_dists <- miseq$report(type = "sample_distance")
## ------------------------------------------------
## Method `strollur$summary()`
## ------------------------------------------------
miseq <- strollur::load_dataset(strollur_example("miseq_sop.rds"))
# To get the summary of your FASTA data
miseq$summary(type = "sequence")
#> starts ends nbases ambigs polymers numns numseqs
#> Minimum: 1 375 249.0000 0 3.000000 0 1.00
#> 2.5%-tile: 1 375 252.0000 0 4.000000 0 2850.05
#> 25%-tile: 1 375 252.0000 0 4.000000 0 28491.50
#> Median: 1 375 253.0000 0 4.000000 0 56982.00
#> 75%-tile: 1 375 253.0000 0 5.000000 0 85472.50
#> 97.5%-tile: 1 375 254.0000 0 6.000000 0 111113.95
#> Maximum: 1 375 256.0000 0 6.000000 0 113963.00
#> Mean: 1 375 252.7406 0 4.496082 0 56982.00
#> starts ends nbases ambigs polymers numns numseqs
#> Minimum: 1 375 249.0000 0 3.000000 0 1.00
#> 2.5%-tile: 1 375 252.0000 0 4.000000 0 2850.05
#> 25%-tile: 1 375 252.0000 0 4.000000 0 28491.50
#> Median: 1 375 253.0000 0 4.000000 0 56982.00
#> 75%-tile: 1 375 253.0000 0 5.000000 0 85472.50
#> 97.5%-tile: 1 375 254.0000 0 6.000000 0 111113.95
#> Maximum: 1 375 256.0000 0 6.000000 0 113963.00
#> Mean: 1 375 252.7406 0 4.496082 0 56982.00
#> starts ends nbases ambigs polymers numns numseqs
#> Minimum: 1 375 249.0000 0 3.000000 0 1.00
#> 2.5%-tile: 1 375 252.0000 0 4.000000 0 2850.05
#> 25%-tile: 1 375 252.0000 0 4.000000 0 28491.50
#> Median: 1 375 253.0000 0 4.000000 0 56982.00
#> 75%-tile: 1 375 253.0000 0 5.000000 0 85472.50
#> 97.5%-tile: 1 375 254.0000 0 6.000000 0 111113.95
#> Maximum: 1 375 256.0000 0 6.000000 0 113963.00
#> Mean: 1 375 252.7406 0 4.496082 0 56982.00
# summarize contigs_report
miseq$summary(type = "report", report_type = "contigs_report")
#> Length Overlap_Length Overlap_Start Overlap_End MisMatches Num_Ns
#> Minimum: 250.0000 232.0000 0.000000 248.0000 0.000000 0
#> 2.5%-tile: 252.0000 246.0000 1.000000 250.0000 0.000000 0
#> 25%-tile: 252.0000 249.0000 2.000000 251.0000 0.000000 0
#> Median: 253.0000 249.0000 2.000000 251.0000 1.000000 0
#> 75%-tile: 253.0000 250.0000 2.000000 251.0000 5.000000 0
#> 97.5%-tile: 254.0000 251.0000 4.000000 253.0000 26.000000 0
#> Maximum: 270.0000 255.0000 22.000000 256.0000 120.000000 0
#> Mean: 252.7575 249.1501 2.005361 251.1555 5.162474 0
#> Expected_Errors
#> Minimum: 0.0000452496
#> 2.5%-tile: 0.0010211820
#> 25%-tile: 0.0022619900
#> Median: 0.0092259500
#> 75%-tile: 0.0558268000
#> 97.5%-tile: 0.4933484000
#> Maximum: 3.0126200000
#> Mean: 0.0738509482
#> Length Overlap_Length Overlap_Start Overlap_End MisMatches Num_Ns
#> Minimum: 250.0000 232.0000 0.000000 248.0000 0.000000 0
#> 2.5%-tile: 252.0000 246.0000 1.000000 250.0000 0.000000 0
#> 25%-tile: 252.0000 249.0000 2.000000 251.0000 0.000000 0
#> Median: 253.0000 249.0000 2.000000 251.0000 1.000000 0
#> 75%-tile: 253.0000 250.0000 2.000000 251.0000 5.000000 0
#> 97.5%-tile: 254.0000 251.0000 4.000000 253.0000 26.000000 0
#> Maximum: 270.0000 255.0000 22.000000 256.0000 120.000000 0
#> Mean: 252.7575 249.1501 2.005361 251.1555 5.162474 0
#> Expected_Errors
#> Minimum: 0.0000452496
#> 2.5%-tile: 0.0010211820
#> 25%-tile: 0.0022619900
#> Median: 0.0092259500
#> 75%-tile: 0.0558268000
#> 97.5%-tile: 0.4933484000
#> Maximum: 3.0126200000
#> Mean: 0.0738509482
#> Length Overlap_Length Overlap_Start Overlap_End MisMatches Num_Ns
#> Minimum: 250.0000 232.0000 0.000000 248.0000 0.000000 0
#> 2.5%-tile: 252.0000 246.0000 1.000000 250.0000 0.000000 0
#> 25%-tile: 252.0000 249.0000 2.000000 251.0000 0.000000 0
#> Median: 253.0000 249.0000 2.000000 251.0000 1.000000 0
#> 75%-tile: 253.0000 250.0000 2.000000 251.0000 5.000000 0
#> 97.5%-tile: 254.0000 251.0000 4.000000 253.0000 26.000000 0
#> Maximum: 270.0000 255.0000 22.000000 256.0000 120.000000 0
#> Mean: 252.7575 249.1501 2.005361 251.1555 5.162474 0
#> Expected_Errors
#> Minimum: 0.0000452496
#> 2.5%-tile: 0.0010211820
#> 25%-tile: 0.0022619900
#> Median: 0.0092259500
#> 75%-tile: 0.0558268000
#> 97.5%-tile: 0.4933484000
#> Maximum: 3.0126200000
#> Mean: 0.0738509482
# remove sample 'F3D0' to produce a scrap report
strollur::xdev_remove_samples(data = miseq, samples = c("F3D0"))
#> miseq_sop:
#>
#> starts ends nbases ambigs polymers numns numseqs
#> Minimum: 1 375 249 0 3 0 1.00
#> 2.5%-tile: 1 375 252 0 4 0 2695.28
#> 25%-tile: 1 375 252 0 4 0 26943.75
#> Median: 1 375 253 0 4 0 53886.50
#> 75%-tile: 1 375 253 0 5 0 80829.25
#> 97.5%-tile: 1 375 254 0 6 0 105077.72
#> Maximum: 1 375 256 0 6 0 107772.00
#> Mean: 1 375 252 0 4 0 53886.50
#>
#> scrap_summary:
#> type trash_code unique total
#> 1 sequence remove_samples 101 109
#> 2 otu remove_samples 14 14
#> 3 asv remove_samples 101 109
#> 4 phylotype remove_samples 2 2
#>
#> Number of unique seqs: 2324
#> Total number of seqs: 107772
#>
#> Total number of samples: 18
#> Total number of treatments: 2
#> Total number of otus: 517
#> Total number of otu bin classifications: 517
#> Total number of asvs: 2324
#> Total number of asv bin classifications: 2324
#> Total number of phylotypes: 61
#> Total number of phylotype bin classifications: 61
#> Total number of sequence classifications: 2324
#> Total number of resource references: 2
#> Total number of custom reports: 2
#>
# summarize scrapped data -
# sequences and bins scrapped by removing the sample "F3D0"
miseq$summary(type = "scrap")
#> type trash_code unique total
#> 1 sequence remove_samples 101 109
#> 2 otu remove_samples 14 14
#> 3 asv remove_samples 101 109
#> 4 phylotype remove_samples 2 2
#> type trash_code unique total
#> 1 sequence remove_samples 101 109
#> 2 otu remove_samples 14 14
#> 3 asv remove_samples 101 109
#> 4 phylotype remove_samples 2 2
#> type trash_code unique total
#> 1 sequence remove_samples 101 109
#> 2 otu remove_samples 14 14
#> 3 asv remove_samples 101 109
#> 4 phylotype remove_samples 2 2