The strollur package stores the data associated with your microbial DNA analysis. This tutorial will explain how to save, load, copy, export, and import your dataset object. If you haven’t reviewed the Getting Started tuturial, we recommend you start there.
Let’s use the miseq_sop_example() function to create a
dataset object from the Miseq SOP Example.
miseq <- 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 531 otu bin taxonomies.
#> Assigned 531 otu bin representative sequences.
#> Added metadata.
#> Added 2 resource references.
#> Added a contigs_report.
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 3 0 2850.08
#> 25%-tile: 1 375 252 0 4 0 28491.75
#> Median: 1 375 252 0 4 0 56982.50
#> 75%-tile: 1 375 253 0 5 0 85473.25
#> 97.5%-tile: 1 375 253 0 6 0 111114.93
#> Maximum: 1 375 256 0 6 0 113963.00
#> Mean: 1 375 252 0 4 0 0.00
#>
#> 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: 1
#> Your dataset includes metadataSaving and Loading
The strollur package has a function to save a dataset object as an
.rds file, save_dataset(), and a function to
create a dataset from an .rds file,
load_dataset(). Let’s use the miseq data object to learn
how to do that.
save_dataset(miseq, file = "miseq_sop.rds")
#> [1] "miseq_sop.rds"
miseq_from_rds <- load_dataset(file = "miseq_sop.rds")
miseq_from_rds
#> 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 3 0 2850.08
#> 25%-tile: 1 375 252 0 4 0 28491.75
#> Median: 1 375 252 0 4 0 56982.50
#> 75%-tile: 1 375 253 0 5 0 85473.25
#> 97.5%-tile: 1 375 253 0 6 0 111114.93
#> Maximum: 1 375 256 0 6 0 113963.00
#> Mean: 1 375 252 0 4 0 0.00
#>
#> 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: 1
#> Your dataset includes metadata
unlink("miseq_sop.rds")We can see that the summaries of miseq and miseq_from_rds are identical. Let’s modify miseq_from_rds to verify they are not referring to the same object. We will add clusters created by mothur using vsearch’s distance-based greedy clustering (dgc) algorithm.
dgc_data <- read_mothur_list(list = strollur_example("final.dgc.list.gz"))
assign(miseq_from_rds, table = dgc_data, bin_type = "dgc")
#> Assigned 361 dgc bins.
#> [1] 361
miseq_from_rds
#> 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 3 0 2850.08
#> 25%-tile: 1 375 252 0 4 0 28491.75
#> Median: 1 375 252 0 4 0 56982.50
#> 75%-tile: 1 375 253 0 5 0 85473.25
#> 97.5%-tile: 1 375 253 0 6 0 111114.93
#> Maximum: 1 375 256 0 6 0 113963.00
#> Mean: 1 375 252 0 4 0 0.00
#>
#> 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 dgcs: 361
#> Total number of dgc bin classifications: 361
#> Total number of sequence classifications: 2425
#> Total number of resource references: 2
#> Total number of custom reports: 1
#> Your dataset includes metadata
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 3 0 2850.08
#> 25%-tile: 1 375 252 0 4 0 28491.75
#> Median: 1 375 252 0 4 0 56982.50
#> 75%-tile: 1 375 253 0 5 0 85473.25
#> 97.5%-tile: 1 375 253 0 6 0 111114.93
#> Maximum: 1 375 256 0 6 0 113963.00
#> Mean: 1 375 252 0 4 0 0.00
#>
#> 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: 1
#> Your dataset includes metadataWe can see from the summary that 361 ‘dgc’ bins were added to miseq_from_rds and not to miseq.
Export and Import
The .rds file is in binary format and is not human readable.
You can use the export_dataset() to see a human readable
form of the raw data stored in the dataset. Let’s export miseq
and look at the table created.
table <- export_dataset(miseq)
str(table)
#> List of 15
#> $ sequence_data :'data.frame': 2425 obs. of 5 variables:
#> ..$ sequence_ids : int [1:2425] 0 1 2 3 4 5 6 7 8 9 ...
#> ..$ sequence_names : chr [1:2425] "M00967_43_000000000-A3JHG_1_2101_16474_12783" "M00967_43_000000000-A3JHG_1_1113_12711_3318" "M00967_43_000000000-A3JHG_1_2108_14707_9807" "M00967_43_000000000-A3JHG_1_1110_4126_16552" ...
#> ..$ sequences : chr [1:2425] "TAC--GG-AG-GAT--GCG-A-G-C-G-T-T--AT-C-CGTGAT--TT-A-T-T--GG-GT--TT-A-AA-GG-GT-GC-G-TA-GGC-G-G-A-CA-G-T-T-AA-G-T-"| __truncated__ "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-"| __truncated__ "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-GG-C-G-C-AA-G-T-"| __truncated__ "TAC--GG-AG-GAT--TCA-A-G-C-G-T-T--AT-C-CGG-AT--TT-A-T-T--GG-GT--TT-A-AA-GG-GT-GC-G-TA-GGC-G-G-G-CT-G-T-T-AA-G-T-"| __truncated__ ...
#> ..$ taxonomies : chr [1:2425] "Bacteria(100);\"Bacteroidetes\"(100);\"Bacteroidia\"(99);\"Bacteroidales\"(99);\"Porphyromonadaceae\"(88);\"Por"| __truncated__ "Bacteria(100);Firmicutes(100);Clostridia(100);Clostridiales(100);Clostridiales_unclassified(100);Clostridiales_"| __truncated__ "Bacteria(100);Firmicutes(100);Clostridia(100);Clostridiales(100);Lachnospiraceae(98);Lachnospiraceae_unclassified(98);" "Bacteria(100);\"Bacteroidetes\"(98);\"Bacteroidia\"(97);\"Bacteroidales\"(97);\"Porphyromonadaceae\"(93);\"Porp"| __truncated__ ...
#> ..$ include_sequence: logi [1:2425] TRUE TRUE TRUE TRUE TRUE TRUE ...
#> $ sequence_report :'data.frame': 2425 obs. of 7 variables:
#> ..$ sequence_ids : int [1:2425] 0 1 2 3 4 5 6 7 8 9 ...
#> ..$ starts : int [1:2425] 1 1 1 1 1 1 1 1 1 1 ...
#> ..$ ends : int [1:2425] 375 375 375 375 375 375 375 375 375 375 ...
#> ..$ lengths : int [1:2425] 253 253 253 252 253 252 253 253 253 252 ...
#> ..$ ambigs : int [1:2425] 0 0 0 0 0 0 0 0 0 0 ...
#> ..$ longest_homopolymers: int [1:2425] 4 5 4 4 5 5 5 4 5 4 ...
#> ..$ num_ns : int [1:2425] 0 0 0 0 0 0 0 0 0 0 ...
#> $ sequence_abundance_table :'data.frame': 5539 obs. of 4 variables:
#> ..$ sequence_ids: int [1:5539] 0 1 2 3 4 5 6 6 6 6 ...
#> ..$ abundances : num [1:5539] 1 1 1 1 1 1 22 19 12 9 ...
#> ..$ samples : chr [1:5539] "F3D150" "F3D142" "F3D3" "F3D8" ...
#> ..$ treatments : chr [1:5539] "Late" "Late" "Early" "Early" ...
#> $ otu_bin_data :'data.frame': 531 obs. of 5 variables:
#> ..$ bin_ids : int [1:531] 0 1 2 3 4 5 6 7 8 9 ...
#> ..$ bin_names : chr [1:531] "Otu001" "Otu002" "Otu003" "Otu004" ...
#> ..$ abundances : num [1:531] 12288 8892 7794 7476 7450 ...
#> ..$ taxonomies : chr [1:531] "Bacteria(100);\"Bacteroidetes\"(100);\"Bacteroidia\"(100);\"Bacteroidales\"(100);\"Porphyromonadaceae\"(100);\""| __truncated__ "Bacteria(100);\"Bacteroidetes\"(100);\"Bacteroidia\"(100);\"Bacteroidales\"(100);\"Porphyromonadaceae\"(100);\""| __truncated__ "Bacteria(100);\"Bacteroidetes\"(100);\"Bacteroidia\"(100);\"Bacteroidales\"(100);\"Porphyromonadaceae\"(100);\""| __truncated__ "Bacteria(100);\"Bacteroidetes\"(100);\"Bacteroidia\"(100);\"Bacteroidales\"(100);\"Porphyromonadaceae\"(100);Barnesiella(100);" ...
#> ..$ include_bin: logi [1:531] TRUE TRUE TRUE TRUE TRUE TRUE ...
#> $ otu_sequence_bin_assignments :'data.frame': 2425 obs. of 2 variables:
#> ..$ bin_ids : int [1:2425] 16 28 47 2 232 5 58 10 29 11 ...
#> ..$ sequence_ids: int [1:2425] 0 1 2 3 4 5 6 7 8 9 ...
#> $ otu_bin_representative_sequences :'data.frame': 531 obs. of 2 variables:
#> ..$ bin_ids : int [1:531] 0 1 2 3 4 5 6 7 8 9 ...
#> ..$ sequence_ids: int [1:531] 6 13 17 39 45 49 53 54 60 63 ...
#> $ asv_bin_data :'data.frame': 2425 obs. of 5 variables:
#> ..$ bin_ids : int [1:2425] 0 1 2 3 4 5 6 7 8 9 ...
#> ..$ bin_names : chr [1:2425] "Asv0001" "Asv0002" "Asv0003" "Asv0004" ...
#> ..$ abundances : num [1:2425] 12196 8829 7698 7436 7307 ...
#> ..$ taxonomies : chr [1:2425] "Bacteria(100);\"Bacteroidetes\"(100);\"Bacteroidia\"(100);\"Bacteroidales\"(100);\"Porphyromonadaceae\"(100);\""| __truncated__ "Bacteria(100);\"Bacteroidetes\"(100);\"Bacteroidia\"(100);\"Bacteroidales\"(100);\"Porphyromonadaceae\"(100);\""| __truncated__ "Bacteria(100);\"Bacteroidetes\"(100);\"Bacteroidia\"(100);\"Bacteroidales\"(100);\"Porphyromonadaceae\"(100);\""| __truncated__ "Bacteria(100);\"Bacteroidetes\"(100);\"Bacteroidia\"(100);\"Bacteroidales\"(100);\"Porphyromonadaceae\"(100);Barnesiella(100);" ...
#> ..$ include_bin: logi [1:2425] TRUE TRUE TRUE TRUE TRUE TRUE ...
#> $ asv_sequence_bin_assignments :'data.frame': 2425 obs. of 2 variables:
#> ..$ bin_ids : int [1:2425] 1374 1112 1888 853 1506 537 72 1334 2287 1560 ...
#> ..$ sequence_ids: int [1:2425] 0 1 2 3 4 5 6 7 8 9 ...
#> $ phylotype_bin_data :'data.frame': 63 obs. of 5 variables:
#> ..$ bin_ids : int [1:63] 0 1 2 3 4 5 6 7 8 9 ...
#> ..$ bin_names : chr [1:63] "Phylo01" "Phylo02" "Phylo03" "Phylo04" ...
#> ..$ abundances : num [1:63] 21639 53147 2805 1773 5337 ...
#> ..$ taxonomies : chr [1:63] "Bacteria(100);Firmicutes(100);Clostridia(100);Clostridiales(100);Lachnospiraceae(100);Lachnospiraceae_unclassified(100);" "Bacteria(100);\"Bacteroidetes\"(100);\"Bacteroidia\"(100);\"Bacteroidales\"(100);\"Porphyromonadaceae\"(100);\""| __truncated__ "Bacteria(100);Firmicutes(100);Clostridia(100);Clostridiales(100);Ruminococcaceae(100);Ruminococcaceae_unclassified(100);" "Bacteria(100);Firmicutes(100);Clostridia(100);Clostridiales(100);Clostridiales_unclassified(100);Clostridiales_"| __truncated__ ...
#> ..$ include_bin: logi [1:63] TRUE TRUE TRUE TRUE TRUE TRUE ...
#> $ phylotype_sequence_bin_assignments:'data.frame': 2425 obs. of 2 variables:
#> ..$ bin_ids : int [1:2425] 1 3 0 1 3 1 0 1 10 1 ...
#> ..$ sequence_ids: int [1:2425] 0 1 2 3 4 5 6 7 8 9 ...
#> $ references :'data.frame': 2 obs. of 5 variables:
#> ..$ reference_names : chr [1:2] "trainset9_032012.pds.zip" "silva.v4.fasta"
#> ..$ reference_versions: chr [1:2] "NA" "1.38.1"
#> ..$ reference_usages : chr [1:2] "classification by mothur" "alignment"
#> ..$ reference_notes : chr [1:2] "NA" "custom reference created by trimming silva.bacteria.fasta to the V4 region"
#> ..$ reference_urls : chr [1:2] "https://mothur.s3.us-east-2.amazonaws.com/wiki/trainset9_032012.pds.zip" "https://mothur.org/wiki/silva_reference_files/"
#> $ metadata :'data.frame': 19 obs. of 2 variables:
#> ..$ sample : chr [1:19] "F3D0" "F3D1" "F3D141" "F3D142" ...
#> ..$ days_post_wean: num [1:19] 0 1 141 142 143 144 145 146 147 148 ...
#> $ contigs_report :'data.frame': 2425 obs. of 8 variables:
#> ..$ Name : chr [1:2425] "M00967_43_000000000-A3JHG_1_2101_16474_12783" "M00967_43_000000000-A3JHG_1_1113_12711_3318" "M00967_43_000000000-A3JHG_1_2108_14707_9807" "M00967_43_000000000-A3JHG_1_1110_4126_16552" ...
#> ..$ Length : num [1:2425] 253 253 253 252 253 252 253 253 253 252 ...
#> ..$ Overlap_Length : num [1:2425] 250 249 249 249 249 250 249 249 249 249 ...
#> ..$ Overlap_Start : num [1:2425] 2 2 2 2 2 1 2 3 2 2 ...
#> ..$ Overlap_End : num [1:2425] 252 251 251 251 251 251 251 252 251 251 ...
#> ..$ MisMatches : num [1:2425] 19 0 0 4 0 8 0 11 0 18 ...
#> ..$ Num_Ns : num [1:2425] 0 0 0 0 0 0 0 0 0 0 ...
#> ..$ Expected_Errors: num [1:2425] 0.29461 0.00183 0.00197 0.0563 0.0026 ...
#> ..- attr(*, "sequence_name")= chr "Name"
#> $ sequence_tree :List of 4
#> ..$ edge : int [1:4848, 1:2] 2426 2427 2427 2426 2428 2429 2429 2428 2430 2431 ...
#> ..$ edge.length: num [1:4848] NaN 0.00395 0.00395 0 0.00198 ...
#> ..$ Nnode : int 2424
#> ..$ tip.label : chr [1:2425] "M00967_43_000000000-A3JHG_1_1114_15727_25995" "M00967_43_000000000-A3JHG_1_2109_19976_22044" "M00967_43_000000000-A3JHG_1_1102_9244_9305" "M00967_43_000000000-A3JHG_1_2101_14159_9619" ...
#> ..- attr(*, "class")= chr "phylo"
#> ..- attr(*, "order")= chr "cladewise"
#> $ sample_tree :List of 5
#> ..$ edge : int [1:36, 1:2] 20 21 22 23 24 25 26 27 27 26 ...
#> ..$ edge.length: num [1:36] 0.03913 0.01741 0.02565 0.00379 0.01732 ...
#> ..$ Nnode : int 18
#> ..$ tip.label : chr [1:19] "F3D9" "F3D8" "F3D6" "F3D5" ...
#> ..$ root.edge : num 0.221
#> ..- attr(*, "class")= chr "phylo"
#> ..- attr(*, "order")= chr "cladewise"
#> - attr(*, "strollur_version")= chr "1.0.0"
#> - attr(*, "dataset_name")= chr "miseq_sop"Similarly to load_dataset(), you can use the
import_dataset() function to create a new dataset object
from the exported table.
miseq_import <- import_dataset(table = table)
#> Added 2425 sequences.
#> Assigned 2425 sequence taxonomies.
#> Assigned 2425 sequence abundances.
#> Assigned 531 otu bins.
#> Assigned 531 otu bin representative sequences.
#> Assigned 531 otu bin taxonomies.
#> Assigned 2425 asv bins.
#> Assigned 2425 asv bin taxonomies.
#> Assigned 63 phylotype bins.
#> Assigned 63 phylotype bin taxonomies.
#> Added metadata.
#> Added 2 resource references.
#> Added a contigs_report.
miseq_import
#> 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 3 0 2850.08
#> 25%-tile: 1 375 252 0 4 0 28491.75
#> Median: 1 375 252 0 4 0 56982.50
#> 75%-tile: 1 375 253 0 5 0 85473.25
#> 97.5%-tile: 1 375 253 0 6 0 111114.93
#> Maximum: 1 375 256 0 6 0 113963.00
#> Mean: 1 375 252 0 4 0 0.00
#>
#> 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: 1
#> Your dataset includes metadataAgain, we can see that the summary of miseq_import is identical to the summary of miseq.
Copy
Lastly, you can make a deep copy of your dataset using the
copy_dataset() function. Note, if you use an assignment
operator to copy it’s a shallow copy. The dataset object is an R6 object
to keep the memory usage low. First let’s learn how to use the
copy_dataset() function, then we will take a closer look at
how deep and shallow copying differ.
miseq_deep_copy <- copy_dataset(miseq)
miseq_shallow_copy <- miseqLet’s add the dgc_data to miseq_shallow_copy and then compare miseq, miseq_deep_copy, and mise_shallow_copy.
assign(miseq_shallow_copy, table = dgc_data, bin_type = "dgc")
#> Assigned 361 dgc bins.
#> [1] 361
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 3 0 2850.08
#> 25%-tile: 1 375 252 0 4 0 28491.75
#> Median: 1 375 252 0 4 0 56982.50
#> 75%-tile: 1 375 253 0 5 0 85473.25
#> 97.5%-tile: 1 375 253 0 6 0 111114.93
#> Maximum: 1 375 256 0 6 0 113963.00
#> Mean: 1 375 252 0 4 0 0.00
#>
#> 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 dgcs: 361
#> Total number of dgc bin classifications: 361
#> Total number of sequence classifications: 2425
#> Total number of resource references: 2
#> Total number of custom reports: 1
#> Your dataset includes metadata
miseq_shallow_copy
#> 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 3 0 2850.08
#> 25%-tile: 1 375 252 0 4 0 28491.75
#> Median: 1 375 252 0 4 0 56982.50
#> 75%-tile: 1 375 253 0 5 0 85473.25
#> 97.5%-tile: 1 375 253 0 6 0 111114.93
#> Maximum: 1 375 256 0 6 0 113963.00
#> Mean: 1 375 252 0 4 0 0.00
#>
#> 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 dgcs: 361
#> Total number of dgc bin classifications: 361
#> Total number of sequence classifications: 2425
#> Total number of resource references: 2
#> Total number of custom reports: 1
#> Your dataset includes metadata
miseq_deep_copy
#> 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 3 0 2850.08
#> 25%-tile: 1 375 252 0 4 0 28491.75
#> Median: 1 375 252 0 4 0 56982.50
#> 75%-tile: 1 375 253 0 5 0 85473.25
#> 97.5%-tile: 1 375 253 0 6 0 111114.93
#> Maximum: 1 375 256 0 6 0 113963.00
#> Mean: 1 375 252 0 4 0 0.00
#>
#> 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 custom reports: 1
#> Your dataset includes metadataYou can see from the summaries that the dgc_data was added to both miseq and miseq_shallow_copy because they actually reference the same object, but miseq_deep_copy was not modified.