Overview
The strollur package stores data associated with your microbial DNA analysis. This tutorial will familiarize you with some of the functions available in the strollur package. If you haven’t reviewed the “Getting Started” tuturial, we recommend you start there.
Loading the example dataset
We can 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.To print a summary of the miseq dataset, run the following:
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 metadataAccessing Your Data
The strollur package includes several functions to access your microbial data.
names() - The names function is an extension of base R’s
names function. It allows you to get the name of your dataset or the
names of the sequences, bins, samples, treatments and reports in your
dataset.
count() - The count function allows you to get the
number of sequences, bins, samples and treatments in your dataset.
abundance() - The abundance function can be used to
access the abundance data for sequences, bins, samples, and
treatments.
report() - The report function can be used to access the
various data associated with your dataset.
summary() - The summary function allows you to summarize
sequences, your custom reports, and scrapped data.
names
The names() function allows you to get the names of
sequences, bins, samples, treatments and reports. Let’s take a closer
look at how use it.
To get the name of the dataset, set the type parameter to ‘dataset’:
names(miseq, type = "dataset")
#> [1] "miseq_sop"To get the names of the sequences in your dataset, set the type parameter to ‘sequences’:
all_sequences <- names(
miseq,
type = "sequences",
distinct = FALSE
)
length(all_sequences)
#> [1] 2425
head(all_sequences, n = 5)
#> [1] "M00967_43_000000000-A3JHG_1_2101_16474_12783"
#> [2] "M00967_43_000000000-A3JHG_1_1113_12711_3318"
#> [3] "M00967_43_000000000-A3JHG_1_2108_14707_9807"
#> [4] "M00967_43_000000000-A3JHG_1_1110_4126_16552"
#> [5] "M00967_43_000000000-A3JHG_1_2102_8408_13436"To get the names of the sequences present in sample ‘F3D0’:
include_f3d0 <- names(
miseq,
type = "sequences",
samples = c("F3D0"),
distinct = FALSE
)
length(include_f3d0)
#> [1] 309
head(include_f3d0, n = 5)
#> [1] "M00967_43_000000000-A3JHG_1_2103_25452_6018"
#> [2] "M00967_43_000000000-A3JHG_1_1109_13330_21597"
#> [3] "M00967_43_000000000-A3JHG_1_1110_5315_13833"
#> [4] "M00967_43_000000000-A3JHG_1_2104_26311_10309"
#> [5] "M00967_43_000000000-A3JHG_1_1101_9620_19745"To get the names of the sequences exclusive to sample ‘F3D0’:
exclusive_f3d0 <- names(
miseq,
type = "sequences",
samples = c("F3D0"),
distinct = TRUE
)
length(exclusive_f3d0)
#> [1] 101
head(exclusive_f3d0, n = 5)
#> [1] "M00967_43_000000000-A3JHG_1_2103_25452_6018"
#> [2] "M00967_43_000000000-A3JHG_1_1101_9620_19745"
#> [3] "M00967_43_000000000-A3JHG_1_2109_17345_6668"
#> [4] "M00967_43_000000000-A3JHG_1_1114_14431_2336"
#> [5] "M00967_43_000000000-A3JHG_1_2106_14305_11884"To get the names of the bins in your dataset, you need to specify the bin_type. The miseq example contains 3 bin types: otu, asv and phylotype. Let’s find the bin names for the ‘otu’ bin type.
otu_bins <- names(
miseq,
type = "bins",
bin_type = "otu"
)
length(otu_bins)
#> [1] 531
head(otu_bins, n = 5)
#> [1] "Otu001" "Otu002" "Otu003" "Otu004" "Otu005"To get the names of the ‘otu’ bins that include sequences present in sample ‘F3D0’:
include_f3d0 <- names(
miseq,
type = "bins",
samples = c("F3D0"),
distinct = FALSE
)
length(include_f3d0)
#> [1] 191
head(include_f3d0, n = 5)
#> [1] "Otu001" "Otu002" "Otu003" "Otu004" "Otu005"To get the names of the “otu” bins that are exclusive to sample ‘F3D0’:
exclusive_f3d0 <- names(
miseq,
type = "bins",
samples = c("F3D0"),
distinct = TRUE
)
length(exclusive_f3d0)
#> [1] 14
head(exclusive_f3d0, n = 5)
#> [1] "Otu330" "Otu339" "Otu341" "Otu345" "Otu347"To get the names of the samples
names(miseq, type = "samples")
#> [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
names(miseq, type = "treatments")
#> [1] "Early" "Late"To get the names of the custom reports
names(miseq, type = "reports")
#> [1] "contigs_report"count
The count() function allows you to get the number of
sequences, bins, samples and treatments in your dataset.
To find the total number of sequences in the dataset, run the following:
count(
miseq,
type = "sequences",
distinct = FALSE
)
#> [1] 113963To find the number of unique sequences in the dataset, set ‘distinct’ to TRUE:
count(
miseq,
type = "sequences",
distinct = TRUE
)
#> [1] 2425To get total number of sequences present in sample ‘F3D0’, you can set the samples parameter. Note, these sequences will be present in the sample but may be be present in other samples as well.
To get number of unique sequences exclusive to sample ‘F3D0’:
To get the number of the bins in your dataset, you need to specify the bin_type. The miseq example contains 3 bin types: otu, asv and phylotype. Let’s find the number of bins for the ‘otu’ bin type.
count(
miseq,
type = "bins",
bin_type = "otu"
)
#> [1] 531To get number of otu bins with sequences present in sample ‘F3D0’: Note these bins will have sequences from sample ‘F3D0’ and may contain sequences from other samples as well.
To get number of otu bins exclusive to sample ‘F3D0’: Note these bins will have sequences from sample ‘F3D0’ and NO other samples will be present in the bins.
To get the number of samples in the dataset:
count(miseq, type = "samples")
#> [1] 19To get the number of treatments in the dataset:
count(miseq, type = "treatments")
#> [1] 2abundance
Now that we are familiar with the names() and
count() functions. Let’s learn how we can use the
abundance() function. The abundance function can be used to
access the abundance data for sequences, bins, samples, and treatments.
It returns a data.frame containing the requested abundance data.
To the total abundance for each sequence, set the type = ‘sequences’. This will return a 2 column data.frame containing sequence_names and abundances.
sequence_abundance <- abundance(
miseq,
type = "sequences",
by_sample = FALSE
)
head(sequence_abundance, n = 10)
#> sequence_names abundances
#> 1 M00967_43_000000000-A3JHG_1_2101_16474_12783 1
#> 2 M00967_43_000000000-A3JHG_1_1113_12711_3318 1
#> 3 M00967_43_000000000-A3JHG_1_2108_14707_9807 1
#> 4 M00967_43_000000000-A3JHG_1_1110_4126_16552 1
#> 5 M00967_43_000000000-A3JHG_1_2102_8408_13436 1
#> 6 M00967_43_000000000-A3JHG_1_1107_22580_21773 1
#> 7 M00967_43_000000000-A3JHG_1_1108_14299_17220 191
#> 8 M00967_43_000000000-A3JHG_1_1114_8059_18290 1
#> 9 M00967_43_000000000-A3JHG_1_2112_9811_9982 1
#> 10 M00967_43_000000000-A3JHG_1_2103_25452_6018 1To the abundance for each sequence parsed by sample, set by_sample = TRUE. This will return a 3 or 4 column data.frame containing sequence_names, abundances, samples and treatments (if assigned).
sequence_abundance_by_sample <- abundance(
miseq,
type = "sequences",
by_sample = TRUE
)
head(sequence_abundance_by_sample, n = 10)
#> sequence_names abundances samples treatments
#> 1 M00967_43_000000000-A3JHG_1_2101_16474_12783 1 F3D150 Late
#> 2 M00967_43_000000000-A3JHG_1_1113_12711_3318 1 F3D142 Late
#> 3 M00967_43_000000000-A3JHG_1_2108_14707_9807 1 F3D3 Early
#> 4 M00967_43_000000000-A3JHG_1_1110_4126_16552 1 F3D8 Early
#> 5 M00967_43_000000000-A3JHG_1_2102_8408_13436 1 F3D7 Early
#> 6 M00967_43_000000000-A3JHG_1_1107_22580_21773 1 F3D3 Early
#> 7 M00967_43_000000000-A3JHG_1_1108_14299_17220 22 F3D146 Late
#> 8 M00967_43_000000000-A3JHG_1_1108_14299_17220 19 F3D147 Late
#> 9 M00967_43_000000000-A3JHG_1_1108_14299_17220 12 F3D148 Late
#> 10 M00967_43_000000000-A3JHG_1_1108_14299_17220 9 F3D149 LateTo get the total abundance of the bins in your dataset, you need to specify the bin_type. The miseq example contains 3 bin types: otu, asv and phylotype. Let’s find the abundance data of bins for the otu bin type. This will return a 2 column data.frame containing bin_names and abundances.
bin_abundance <- abundance(
miseq,
type = "bins",
bin_type = "otu",
by_sample = FALSE
)
head(bin_abundance, n = 10)
#> otu_id abundance
#> 1 Otu001 12288
#> 2 Otu002 8892
#> 3 Otu003 7794
#> 4 Otu004 7476
#> 5 Otu005 7450
#> 6 Otu006 6621
#> 7 Otu007 6304
#> 8 Otu008 5337
#> 9 Otu009 3606
#> 10 Otu010 3061To the abundance for each bin parsed by sample, set by_sample = TRUE. This will return a 3 or 4 column data.frame containing bin_names, abundances, samples and treatments (if assigned).
bin_abundance_by_sample <- abundance(
miseq,
type = "bins",
bin_type = "otu",
by_sample = TRUE
)
head(bin_abundance_by_sample, n = 10)
#> bin_names abundances samples treatments
#> 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
#> 6 Otu001 346 F3D144 Late
#> 7 Otu001 566 F3D145 Late
#> 8 Otu001 270 F3D146 Late
#> 9 Otu001 1316 F3D147 Late
#> 10 Otu001 750 F3D148 LateTo access the distribution of sequences across the samples in your dataset, set the type = ‘samples’. This will return a 2 column data.frame containing sample names and abundances.
abundance(
miseq,
type = "samples"
)
#> samples abundances
#> 1 F3D0 6191
#> 2 F3D1 4652
#> 3 F3D141 4656
#> 4 F3D142 2423
#> 5 F3D143 2403
#> 6 F3D144 3449
#> 7 F3D145 5532
#> 8 F3D146 3831
#> 9 F3D147 12430
#> 10 F3D148 9465
#> 11 F3D149 10014
#> 12 F3D150 4126
#> 13 F3D2 15686
#> 14 F3D3 5199
#> 15 F3D5 3469
#> 16 F3D6 6394
#> 17 F3D7 4055
#> 18 F3D8 4253
#> 19 F3D9 5735To access the distribution of sequences across the treatments in your dataset, set the type = ‘treatments’. This will return a 2 column data.frame containing treatment names and abundances.
abundance(miseq, type = "treatments")
#> treatments abundances
#> 1 Early 55634
#> 2 Late 58329report
The report() function allows you access you to access
FASTA sequences, sequence and classification reports, bin assignments,
sample assignments, metadata, sequence data reports, custom reports,
resource references and scrapped data reports. It returns a data.frame
containing the requested report data. Let’s look at some examples
together.
To get a report containing the sequences FASTA data, set the type = “fasta”. This will be a 2 or 3 column data.frame containing sequence names, sequence nucleotide strings, and comments (if provided).
fasta_report <- report(
miseq,
type = "fasta"
)
head(fasta_report, n = 5)
#> sequence_names
#> 1 M00967_43_000000000-A3JHG_1_2101_16474_12783
#> 2 M00967_43_000000000-A3JHG_1_1113_12711_3318
#> 3 M00967_43_000000000-A3JHG_1_2108_14707_9807
#> 4 M00967_43_000000000-A3JHG_1_1110_4126_16552
#> 5 M00967_43_000000000-A3JHG_1_2102_8408_13436
#> sequences
#> 1 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-C-A-G-C-G-G--TA-A-AA-TT-G-A-GA-GG--CT-C-AA-C-C-T-C-T-T-C--CC-G-C-CGTT-GAAAC-TG-A-TTGTC-TTGA-GT-GG-GC-GA-G-A---AG-T-A-TGTGGAATGCGTGGTGT-AGCGGT-GAAATGCATAG-AT-A-TC-AC-GC-AG-AACTCCGAT-TGCGAAGGCA------GCATA-CCG-G-CG-CC-C-A-ACTGACG-CTGA-AGCA-CGAAA-GCG-TGGGT-ATC-GAACAGG
#> 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-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-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-GG-C-G-C-AA-G-T-C-T-G-A-A-G--TG-A-AA-GC-C-C-GT-GG--CT-C-AA-C-C-G-C-G-G-A-ACC-G-C-TTTG-GAAAC-TG-C-GAGGC-TGGA-GT-GC-TG-GA-G-A---GG-T-A-AGCGGAATTCCTGGTGT-AGCGGT-GAAATGCGTAG-AT-A-TC-AG-GA-GG-AACACCGGT-GGCGAAGGCG------GCTTA-CTG-G-AC-AG-T-G-ACTGACG-TTGA-GGCT-CGAAA-GCG-TGGGG-AGC-GAACAGG
#> 4 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-C-A-G-C-G-G--TC-A-AA-TG-T-C-GG-GG--CT-C-AA-C-C-C-C-G-G-C--CT-G-C-CGTT-GAAAC-TG-G-CGGCC-TCGA-GT-GG-GC-GA-G-A---AG-T-A-TGCGGAATGCGTGGTGT-AGCGGT-GAAATGCATAG-AT-A-TC-AC-GC-AG-AACTCCGAT-TGCGAAGGCA------GCATA-CCG-G-CG-CC-C-T-ACTGACG-CTGA-GGCA-CGAAA-GCG-TGGGT-ATC-GAACAGG
#> 5 TAC--GG-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-AG-T-G-C-AA-G-T-C-A-G-A-A-G--TG-A-AA-GC-C-C-AA-GG--CT-C-AA-C---C-A-TGG-G-ACT-G-C-TTTT-GAAAC-TG-T-ACAGC-TAGA-TT-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-AATGACG-CTGA-GGCT-CGAAA-GCG-TGGGG-AGC-AAACAGGIf you want to take a closer look at the sequences, you can request a report containing the sequence names, starts, ends, lengths, ambiguous bases, longest homopolymers, and number of N’s. To get a sequence report, set type = “sequences”.
sequence_report <- report(
miseq,
type = "sequences"
)
head(sequence_report, n = 5)
#> id start end length ambig
#> 1 M00967_43_000000000-A3JHG_1_2101_16474_12783 1 375 253 0
#> 2 M00967_43_000000000-A3JHG_1_1113_12711_3318 1 375 253 0
#> 3 M00967_43_000000000-A3JHG_1_2108_14707_9807 1 375 253 0
#> 4 M00967_43_000000000-A3JHG_1_1110_4126_16552 1 375 252 0
#> 5 M00967_43_000000000-A3JHG_1_2102_8408_13436 1 375 253 0
#> longest_homopolymer num_n
#> 1 4 0
#> 2 5 0
#> 3 4 0
#> 4 4 0
#> 5 5 0The report function also allows you to access classification reports about your dataset. There are 2 types of classification reports: sequence_taxonomy and bin_taxonomy. To get a report about the sequence classifications, set type = “sequence_taxonomy”. This will create a 3 or 4 column report containing sequence names, taxonomic levels, taxons and confidence scores(if provided).
sequence_classification <- report(
miseq,
type = "sequence_taxonomy"
)
head(sequence_classification, n = 10)
#> sequence_names levels
#> 1 M00967_43_000000000-A3JHG_1_2101_16474_12783 1
#> 2 M00967_43_000000000-A3JHG_1_2101_16474_12783 2
#> 3 M00967_43_000000000-A3JHG_1_2101_16474_12783 3
#> 4 M00967_43_000000000-A3JHG_1_2101_16474_12783 4
#> 5 M00967_43_000000000-A3JHG_1_2101_16474_12783 5
#> 6 M00967_43_000000000-A3JHG_1_2101_16474_12783 6
#> 7 M00967_43_000000000-A3JHG_1_1113_12711_3318 1
#> 8 M00967_43_000000000-A3JHG_1_1113_12711_3318 2
#> 9 M00967_43_000000000-A3JHG_1_1113_12711_3318 3
#> 10 M00967_43_000000000-A3JHG_1_1113_12711_3318 4
#> taxonomies confidences
#> 1 Bacteria 100
#> 2 "Bacteroidetes" 100
#> 3 "Bacteroidia" 99
#> 4 "Bacteroidales" 99
#> 5 "Porphyromonadaceae" 88
#> 6 "Porphyromonadaceae"_unclassified 88
#> 7 Bacteria 100
#> 8 Firmicutes 100
#> 9 Clostridia 100
#> 10 Clostridiales 100To get the consensus taxonomies assigned to the bins in your dataset, you need to specify the bin_type. The miseq example contains 3 bin types: otu, asv and phylotype. Let’s find the consensus taxonomies of bins for the otu bin type.
bin_classification <- report(
miseq,
type = "bin_taxonomy",
bin_type = "otu"
)
head(bin_classification, n = 10)
#> bin_names levels taxonomies confidences
#> 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
#> 6 Otu001 6 "Porphyromonadaceae"_unclassified 100
#> 7 Otu002 1 Bacteria 100
#> 8 Otu002 2 "Bacteroidetes" 100
#> 9 Otu002 3 "Bacteroidia" 100
#> 10 Otu002 4 "Bacteroidales" 100To get the sequence bin assignments for the otu bins, run the following:
sequence_bin_assignments <- report(
miseq,
type = "sequence_bin_assignments",
bin_type = "otu"
)
head(sequence_bin_assignments, n = 10)
#> otu_id seq_id
#> 1 Otu001 M00967_43_000000000-A3JHG_1_1111_20933_6700
#> 2 Otu001 M00967_43_000000000-A3JHG_1_1113_17095_9759
#> 3 Otu001 M00967_43_000000000-A3JHG_1_1114_22144_24942
#> 4 Otu001 M00967_43_000000000-A3JHG_1_1112_5981_8948
#> 5 Otu001 M00967_43_000000000-A3JHG_1_2106_5509_18056
#> 6 Otu001 M00967_43_000000000-A3JHG_1_1112_18411_17052
#> 7 Otu001 M00967_43_000000000-A3JHG_1_1101_20262_22075
#> 8 Otu001 M00967_43_000000000-A3JHG_1_1114_13556_18457
#> 9 Otu001 M00967_43_000000000-A3JHG_1_2114_12634_10967
#> 10 Otu001 M00967_43_000000000-A3JHG_1_1102_18640_14309To get the sample treatment assignments, set type = “sample_assignments”.
sample_treatment_assignments <- report(
miseq,
type = "sample_assignments"
)
head(sample_treatment_assignments, n = 5)
#> samples treatments
#> 1 F3D0 Early
#> 2 F3D1 Early
#> 3 F3D141 Late
#> 4 F3D142 Late
#> 5 F3D143 LateIf you assigned bin representative sequences, you can access the report by setting type = “bin_representatives”. The miseq example includes bin representatives for the otu bins. Let’s take a look:
otu_bin_representatives <- report(
miseq,
type = "bin_representatives",
bin_type = "otu"
)
head(otu_bin_representatives, n = 5)
#> otu_names representative_names
#> 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_sequences
#> 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-AAACAGGThe miseq example contains a custum report. To access the custom reports, first let’s find the names.
names(miseq, type = "reports")
#> [1] "contigs_report"To access the custom contigs assembly report, set type = “contigs_report”.
contigs_assembly_report <- report(
miseq,
type = "contigs_report"
)
head(contigs_assembly_report, n = 5)
#> Name Length Overlap_Length
#> 1 M00967_43_000000000-A3JHG_1_2101_16474_12783 253 250
#> 2 M00967_43_000000000-A3JHG_1_1113_12711_3318 253 249
#> 3 M00967_43_000000000-A3JHG_1_2108_14707_9807 253 249
#> 4 M00967_43_000000000-A3JHG_1_1110_4126_16552 252 249
#> 5 M00967_43_000000000-A3JHG_1_2102_8408_13436 253 249
#> Overlap_Start Overlap_End MisMatches Num_Ns Expected_Errors
#> 1 2 252 19 0 0.29461400
#> 2 2 251 0 0 0.00183396
#> 3 2 251 0 0 0.00196774
#> 4 2 251 4 0 0.05629750
#> 5 2 251 0 0 0.00259554To get the metadata associated with your dataset, set type = “metadata”.
metadata <- report(
miseq,
type = "metadata"
)
head(metadata, n = 5)
#> sample days_post_wean
#> 1 F3D0 0
#> 2 F3D1 1
#> 3 F3D141 141
#> 4 F3D142 142
#> 5 F3D143 143To get the resource references associated with your dataset, set type = “references”.
report(
miseq,
type = "references"
)
#> reference_names reference_versions reference_usages
#> 1 trainset9_032012.pds.zip NA classification by mothur
#> 2 silva.v4.fasta 1.38.1 alignment
#> reference_notes
#> 1 NA
#> 2 custom reference created by trimming silva.bacteria.fasta to the V4 region
#> reference_urls
#> 1 https://mothur.s3.us-east-2.amazonaws.com/wiki/trainset9_032012.pds.zip
#> 2 https://mothur.org/wiki/silva_reference_files/Lastly, if sequences or bins have been removed over the course of your analysis you can see a report about the scrapped data by setting type = “sequence_scrap” or “bin_scrap”. The miseq example does not include scrapped data, but let’s take a look at how to access it.
summary
The summary() function allows you to summarize
sequences, custom reports, and scrapped data.
To get a summary for our custom reports, first let’s find the report names.
names(miseq, type = "reports")
#> [1] "contigs_report"You can see we have a custom report so let get the summary by setting report_type = “contigs_report”.
summary(
miseq,
type = "reports",
report_type = "contigs_report"
)
#> Expected_Errors Length MisMatches Num_Ns Overlap_End
#> Minimum: 0.0000452496 250.0000 0.000000 0 248.0000
#> 2.5%-tile: 0.0016101600 252.0000 0.000000 0 251.0000
#> 25%-tile: 0.0028177700 252.0000 0.000000 0 251.0000
#> Median: 0.0062948698 252.0000 2.000000 0 251.0000
#> 75%-tile: 0.0264780000 253.0000 4.000000 0 251.0000
#> 97.5%-tile: 1.1412299871 253.0000 76.000000 0 252.0000
#> Maximum: 3.0126200000 270.0000 120.000000 0 256.0000
#> Mean: 0.0984788569 252.5128 7.534147 0 251.0762
#> Overlap_Length Overlap_Start
#> Minimum: 232.0000 0.000000
#> 2.5%-tile: 248.0000 1.000000
#> 25%-tile: 249.0000 2.000000
#> Median: 249.0000 2.000000
#> 75%-tile: 249.0000 2.000000
#> 97.5%-tile: 251.0000 3.000000
#> Maximum: 255.0000 22.000000
#> Mean: 249.2136 1.862692
#> Expected_Errors Length MisMatches Num_Ns Overlap_End
#> Minimum: 0.0000452496 250.0000 0.000000 0 248.0000
#> 2.5%-tile: 0.0016101600 252.0000 0.000000 0 251.0000
#> 25%-tile: 0.0028177700 252.0000 0.000000 0 251.0000
#> Median: 0.0062948698 252.0000 2.000000 0 251.0000
#> 75%-tile: 0.0264780000 253.0000 4.000000 0 251.0000
#> 97.5%-tile: 1.1412299871 253.0000 76.000000 0 252.0000
#> Maximum: 3.0126200000 270.0000 120.000000 0 256.0000
#> Mean: 0.0984788569 252.5128 7.534147 0 251.0762
#> Overlap_Length Overlap_Start
#> Minimum: 232.0000 0.000000
#> 2.5%-tile: 248.0000 1.000000
#> 25%-tile: 249.0000 2.000000
#> Median: 249.0000 2.000000
#> 75%-tile: 249.0000 2.000000
#> 97.5%-tile: 251.0000 3.000000
#> Maximum: 255.0000 22.000000
#> Mean: 249.2136 1.862692Lastly, if sequences or bins have been removed over the course of your analysis you can see a summary about the scrapped data by setting type = “scrap”. The miseq example does not include scrapped data, but let’s take a look at how to access it.
summary(miseq, type = "scrap")
#> data frame with 0 columns and 0 rows
#> data frame with 0 columns and 0 rowsSample Trees and Sequence Trees
strollur allows you to include trees relating the sequences and samples within your dataset. You can access and plot them using the following functions.
miseq_sequence_tree <- miseq$get_sequence_tree()
miseq_sample_tree <- miseq$get_sample_tree()
#| fig.alt: >
#| Plot of Miseq_SOP's sample relationship tree
par(bg = "white")
ape::plot.phylo(miseq_sample_tree,
no.margin = TRUE,
cex = 0.5, edge.color = "maroon", tip.color = "navy"
)
Thanks for following along. Next, find out how to import your own data using the links below: