From e6af3b9256ffc2a11e11157f25068fc2fb12ce6f Mon Sep 17 00:00:00 2001 From: TuomasBorman Date: Mon, 12 Feb 2024 14:53:04 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20EBI-Meta?= =?UTF-8?q?genomics/MGnifyR@b32a1db25b52409b64ac7e18d8bb10172380bd92=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 404.html | 8 +- LICENSE-text.html | 8 +- articles/MGnifyR.html | 1082 +++++++++++++++ .../figure-html/calculate_diversity-1.png | Bin 0 -> 80317 bytes articles/MGnifyR_files/figure-html/pcoa-1.png | Bin 0 -> 58589 bytes .../figure-html/plot_abundance-1.png | Bin 0 -> 65402 bytes articles/MGnifyR_long.html | 1228 ++++++++++++++++- .../figure-html/calculate_diversity-1.png | Bin 0 -> 71324 bytes .../figure-html/full_metatdata_explore-1.png | Bin 0 -> 39216 bytes .../MGnifyR_long_files/figure-html/pcoa-1.png | Bin 0 -> 78499 bytes .../figure-html/plot_abundance-1.png | Bin 0 -> 33792 bytes articles/index.html | 12 +- authors.html | 12 +- index.html | 8 +- pkgdown.yml | 4 +- .../studies/MGYS00005058_format_json/.RDS | Bin 818 -> 815 bytes reference/MGnifyR-package.html | 8 +- reference/MgnifyClient-accessors.html | 10 +- reference/MgnifyClient.html | 10 +- reference/deprecate.html | 8 +- reference/doQuery.html | 29 +- reference/getData.html | 10 +- reference/getFile.html | 10 +- reference/getMetadata.html | 10 +- reference/getResult.html | 10 +- reference/index.html | 12 +- reference/searchAnalysis.html | 10 +- sitemap.xml | 4 +- 28 files changed, 2328 insertions(+), 165 deletions(-) create mode 100644 articles/MGnifyR.html create mode 100644 articles/MGnifyR_files/figure-html/calculate_diversity-1.png create mode 100644 articles/MGnifyR_files/figure-html/pcoa-1.png create mode 100644 articles/MGnifyR_files/figure-html/plot_abundance-1.png create mode 100644 articles/MGnifyR_long_files/figure-html/calculate_diversity-1.png create mode 100644 articles/MGnifyR_long_files/figure-html/full_metatdata_explore-1.png create mode 100644 articles/MGnifyR_long_files/figure-html/pcoa-1.png create mode 100644 articles/MGnifyR_long_files/figure-html/plot_abundance-1.png diff --git a/404.html b/404.html index 09848e7..36ab3fe 100644 --- a/404.html +++ b/404.html @@ -32,12 +32,15 @@ MGnifyR - 0.99.17 + 0.99.18

Create a client @@ -138,7 +213,28 @@

Create a client +mg +#> An object of class "MgnifyClient" +#> Slot "databaseUrl": +#> [1] "https://www.ebi.ac.uk/metagenomics/api/v1" +#> +#> Slot "authTok": +#> [1] NA +#> +#> Slot "useCache": +#> [1] FALSE +#> +#> Slot "cacheDir": +#> [1] "/__w/MGnifyR/MGnifyR/vignettes/.MGnifyR_cache" +#> +#> Slot "showWarnings": +#> [1] FALSE +#> +#> Slot "clearCache": +#> [1] FALSE +#> +#> Slot "verbose": +#> [1] TRUE

It’s recommended that local caching is enabled with useCache = TRUE. Queries to the MGnify API can be quite slow, particularly when retrieving multipage results for many analyses @@ -202,15 +298,110 @@

Search data
 northpolar <- doQuery(
     mg, "samples", latitude_gte=60.0, experiment_type="amplicon",
-    biome_name="Soil", instrument_platform = "Illumina", max.hits = 10)
-
-northpolar[1:5, ]
+ biome_name="Soil", instrument_platform = "Illumina", max.hits = 10) +
+head(northpolar)
+#>           latitude longitude    biosample accession analysis-completed
+#> SRS518212  78.7857 -103.5513 SAMN02484608 SRS518212         2016-05-04
+#> SRS522877  78.7857 -103.5513 SAMN02484612 SRS522877         2016-05-04
+#> SRS522878  78.7849 -103.5551 SAMN02484613 SRS522878         2016-05-04
+#> SRS522883  78.7839 -103.5574 SAMN02484618 SRS522883         2016-05-04
+#> SRS522884  78.7834 -103.5482 SAMN02484619 SRS522884         2016-05-04
+#> SRS522886  78.7854 -103.5433 SAMN02484621 SRS522886         2016-05-04
+#>                           sample-desc environment-biome sample-name
+#> SRS518212 Keywords: GSC:MIxS MIMS:5.0            tundra       ER-B1
+#> SRS522877 Keywords: GSC:MIxS MIMS:5.0            tundra       ER-I1
+#> SRS522878 Keywords: GSC:MIxS MIMS:5.0            tundra       ER-I2
+#> SRS522883 Keywords: GSC:MIxS MIMS:5.0            tundra       ER-B7
+#> SRS522884 Keywords: GSC:MIxS MIMS:5.0            tundra       ER-B8
+#> SRS522886 Keywords: GSC:MIxS MIMS:5.0            tundra      ER-B10
+#>           sample-alias         last-update investigation type
+#> SRS518212        ER-B1 2024-01-18T21:26:45         metagenome
+#> SRS522877        ER-I1 2024-01-18T21:26:28         metagenome
+#> SRS522878        ER-I2 2024-01-18T21:26:12         metagenome
+#> SRS522883        ER-B7 2024-01-18T21:25:55         metagenome
+#> SRS522884        ER-B8 2024-01-18T21:25:39         metagenome
+#> SRS522886       ER-B10 2024-01-18T21:25:06         metagenome
+#>                                                              project name
+#> SRS518212 A community genomics investigation of fungal adaptation to cold
+#> SRS522877 A community genomics investigation of fungal adaptation to cold
+#> SRS522878 A community genomics investigation of fungal adaptation to cold
+#> SRS522883 A community genomics investigation of fungal adaptation to cold
+#> SRS522884 A community genomics investigation of fungal adaptation to cold
+#> SRS522886 A community genomics investigation of fungal adaptation to cold
+#>           geographic location (longitude) geographic location (depth)
+#> SRS518212                      -103.55135                      0-0.1m
+#> SRS522877                      -103.55135                      0-0.1m
+#> SRS522878                     -103.555133                      0-0.1m
+#> SRS522883                       -103.5574                      0-0.1m
+#> SRS522884                     -103.548183                      0-0.1m
+#> SRS522886                     -103.543267                      0-0.1m
+#>           geographic location (country and/or sea,region) collection date
+#> SRS518212          Canada: Isachsen, Ellef Ringnes Island         2005-08
+#> SRS522877          Canada: Isachsen, Ellef Ringnes Island         2005-08
+#> SRS522878          Canada: Isachsen, Ellef Ringnes Island         2005-08
+#> SRS522883          Canada: Isachsen, Ellef Ringnes Island         2005-08
+#> SRS522884          Canada: Isachsen, Ellef Ringnes Island         2005-08
+#> SRS522886          Canada: Isachsen, Ellef Ringnes Island         2005-08
+#>           environment (biome) environment (feature) environment (material)
+#> SRS518212              tundra            frost boil                   soil
+#> SRS522877              tundra             interboil                   soil
+#> SRS522878              tundra             interboil                   soil
+#> SRS522883              tundra            frost boil                   soil
+#> SRS522884              tundra            frost boil                   soil
+#> SRS522886              tundra            frost boil                   soil
+#>                    environmental package  depth elevation
+#> SRS518212 MIMS.me;MIGS/MIMS/MIMARKS.soil 0-0.1m        41
+#> SRS522877 MIMS.me;MIGS/MIMS/MIMARKS.soil 0-0.1m        41
+#> SRS522878 MIMS.me;MIGS/MIMS/MIMARKS.soil 0-0.1m        40
+#> SRS522883 MIMS.me;MIGS/MIMS/MIMARKS.soil 0-0.1m        32
+#> SRS522884 MIMS.me;MIGS/MIMS/MIMARKS.soil 0-0.1m        30
+#> SRS522886 MIMS.me;MIGS/MIMS/MIMARKS.soil 0-0.1m        40
+#>           miscellaneous parameter geographic location (latitude)
+#> SRS518212                  Boil 1                       78.78565
+#> SRS522877             Interboil 1                       78.78565
+#> SRS522878             Interboil 2                      78.784917
+#> SRS522883                  Boil 7                      78.783933
+#> SRS522884                  Boil 8                      78.783433
+#> SRS522886                 Boil 10                       78.78535
+#>           NCBI sample classification instrument model acc_type
+#> SRS518212                     410658   Illumina MiSeq  samples
+#> SRS522877                     410658   Illumina MiSeq  samples
+#> SRS522878                     410658   Illumina MiSeq  samples
+#> SRS522883                     410658   Illumina MiSeq  samples
+#> SRS522884                     410658   Illumina MiSeq  samples
+#> SRS522886                     410658   Illumina MiSeq  samples
+#>                                         biome      studies    type
+#> SRS518212 root:Environmental:Terrestrial:Soil MGYS00000850 samples
+#> SRS522877 root:Environmental:Terrestrial:Soil MGYS00000850 samples
+#> SRS522878 root:Environmental:Terrestrial:Soil MGYS00000850 samples
+#> SRS522883 root:Environmental:Terrestrial:Soil MGYS00000850 samples
+#> SRS522884 root:Environmental:Terrestrial:Soil MGYS00000850 samples
+#> SRS522886 root:Environmental:Terrestrial:Soil MGYS00000850 samples
+#>           collection-date
+#> SRS518212            <NA>
+#> SRS522877            <NA>
+#> SRS522878            <NA>
+#> SRS522883            <NA>
+#> SRS522884            <NA>
+#> SRS522886      2005-08-01

Specifying an accession parameter will restrict results to just those matching that particular entry, be it a study, sample or run. For example, to retrieve information for study “MGYS00002891”:

-
-study_samples <- doQuery(mg, "studies", accession="MGYS00002891")
-study_samples
+
+study_samples <- doQuery(mg, "studies", accession="MGYS00002891")
+
+study_samples
+#>                 accession  bioproject samples-count is-private
+#> MGYS00002891 MGYS00002891 PRJNA384570            29      FALSE
+#>              secondary-accession             centre-name
+#> MGYS00002891           SRP105345 University of Minnesota
+#>                                                                                             study-abstract
+#> MGYS00002891 Characterization of bacterial communities in marine sediments from Gladstone and Heron Island
+#>                              study-name data-origination         last-update
+#> MGYS00002891 Queensland Marine Sediment        HARVESTED 2019-11-07T16:33:46
+#>              acc_type                                     biomes    type
+#> MGYS00002891  studies root:Environmental:Aquatic:Marine:Sediment studies

Find relevent analyses accessions @@ -232,15 +423,17 @@

Find relevent analysessearchAnalysis(). Following on from our previous search, we have a list of study accessions, so to convert to corresponding analyses we use:

-
+
 analyses_accessions <- searchAnalysis(
-    mg, type="studies", accession = study_samples$accession)
-
-# For demonstrative purpose, take only few samples
-set.seed(595)
-analyses_accessions <- sample(analyses_accessions, 5)
-
-analyses_accessions

+    mg, type="studies", accession = study_samples$accession)
+
+analyses_accessions
+#>  [1] "MGYA00209648" "MGYA00209649" "MGYA00209650" "MGYA00209651" "MGYA00209652"
+#>  [6] "MGYA00209653" "MGYA00209654" "MGYA00209655" "MGYA00209656" "MGYA00209657"
+#> [11] "MGYA00209658" "MGYA00209659" "MGYA00209660" "MGYA00209661" "MGYA00209662"
+#> [16] "MGYA00209663" "MGYA00209664" "MGYA00209665" "MGYA00209666" "MGYA00209667"
+#> [21] "MGYA00209668" "MGYA00209669" "MGYA00209670" "MGYA00209671" "MGYA00209672"
+#> [26] "MGYA00209673" "MGYA00209674" "MGYA00209675" "MGYA00209676"

A useful side effect of the above call is that some attribute metadata for each sample has now been retrieved and stored in the local cache. Thus subsequent API calls for these samples (which will occur @@ -262,9 +455,199 @@

Fetch metadatasample, run and study metadata, which we then filter as required to include only the rows we want.

-
-analyses_metadata <- getMetadata(mg, analyses_accessions)
-head(analyses_metadata)
+
+analyses_metadata <- getMetadata(mg, analyses_accessions)
+
+head(analyses_metadata)
+#>              analysis_analysis-status analysis_pipeline-version
+#> MGYA00209648                completed                       4.1
+#> MGYA00209649                completed                       4.1
+#> MGYA00209650                completed                       4.1
+#> MGYA00209651                completed                       4.1
+#> MGYA00209652                completed                       4.1
+#> MGYA00209653                completed                       4.1
+#>              analysis_experiment-type analysis_accession analysis_is-private
+#> MGYA00209648                 amplicon       MGYA00209648               FALSE
+#> MGYA00209649                 amplicon       MGYA00209649               FALSE
+#> MGYA00209650                 amplicon       MGYA00209650               FALSE
+#> MGYA00209651                 amplicon       MGYA00209651               FALSE
+#> MGYA00209652                 amplicon       MGYA00209652               FALSE
+#> MGYA00209653                 amplicon       MGYA00209653               FALSE
+#>              analysis_complete-time analysis_instrument-platform
+#> MGYA00209648    2018-09-06T00:00:00                     ILLUMINA
+#> MGYA00209649    2018-09-06T00:00:00                     ILLUMINA
+#> MGYA00209650    2018-09-06T00:00:00                     ILLUMINA
+#> MGYA00209651    2018-09-06T00:00:00                     ILLUMINA
+#> MGYA00209652    2018-09-06T00:00:00                     ILLUMINA
+#> MGYA00209653    2018-09-06T00:00:00                     ILLUMINA
+#>              analysis_instrument-model analysis_Submitted nucleotide sequences
+#> MGYA00209648       Illumina HiSeq 2500                                 1441694
+#> MGYA00209649       Illumina HiSeq 2500                                  650265
+#> MGYA00209650       Illumina HiSeq 2500                                 1207289
+#> MGYA00209651       Illumina HiSeq 2500                                  469703
+#> MGYA00209652       Illumina HiSeq 2500                                  606584
+#> MGYA00209653       Illumina HiSeq 2500                                  692146
+#>              analysis_Nucleotide sequences after format-specific filtering
+#> MGYA00209648                                                       1441359
+#> MGYA00209649                                                        650108
+#> MGYA00209650                                                       1206954
+#> MGYA00209651                                                        469585
+#> MGYA00209652                                                        606429
+#> MGYA00209653                                                        691971
+#>              analysis_Nucleotide sequences after length filtering
+#> MGYA00209648                                              1272787
+#> MGYA00209649                                               578060
+#> MGYA00209650                                              1090737
+#> MGYA00209651                                               419171
+#> MGYA00209652                                               536462
+#> MGYA00209653                                               623965
+#>              analysis_Nucleotide sequences after undetermined bases filtering
+#> MGYA00209648                                                          1272787
+#> MGYA00209649                                                           578060
+#> MGYA00209650                                                          1090737
+#> MGYA00209651                                                           419171
+#> MGYA00209652                                                           536462
+#> MGYA00209653                                                           623965
+#>              analysis_Reads with predicted CDS
+#> MGYA00209648                             22713
+#> MGYA00209649                             11079
+#> MGYA00209650                             19717
+#> MGYA00209651                              7586
+#> MGYA00209652                             11348
+#> MGYA00209653                             25746
+#>              analysis_Reads with predicted RNA
+#> MGYA00209648                           1243457
+#> MGYA00209649                            564004
+#> MGYA00209650                           1065641
+#> MGYA00209651                            410161
+#> MGYA00209652                            523120
+#> MGYA00209653                            595537
+#>              analysis_Reads with InterProScan match analysis_Predicted CDS
+#> MGYA00209648                                     54                  22807
+#> MGYA00209649                                     44                  11215
+#> MGYA00209650                                    372                  19758
+#> MGYA00209651                                     34                   7604
+#> MGYA00209652                                     41                  11356
+#> MGYA00209653                                    280                  25977
+#>              analysis_Predicted CDS with InterProScan match
+#> MGYA00209648                                             55
+#> MGYA00209649                                             44
+#> MGYA00209650                                            373
+#> MGYA00209651                                             35
+#> MGYA00209652                                             41
+#> MGYA00209653                                            280
+#>              analysis_Total InterProScan matches analysis_acc_type
+#> MGYA00209648                                  97     analysis-jobs
+#> MGYA00209649                                  58     analysis-jobs
+#> MGYA00209650                                 589     analysis-jobs
+#> MGYA00209651                                  58     analysis-jobs
+#> MGYA00209652                                  47     analysis-jobs
+#> MGYA00209653                                 485     analysis-jobs
+#>              study_attributes.accession study_attributes.bioproject
+#> MGYA00209648               MGYS00002891                 PRJNA384570
+#> MGYA00209649               MGYS00002891                 PRJNA384570
+#> MGYA00209650               MGYS00002891                 PRJNA384570
+#> MGYA00209651               MGYS00002891                 PRJNA384570
+#> MGYA00209652               MGYS00002891                 PRJNA384570
+#> MGYA00209653               MGYS00002891                 PRJNA384570
+#>              study_attributes.samples-count study_attributes.is-private
+#> MGYA00209648                             29                       FALSE
+#> MGYA00209649                             29                       FALSE
+#> MGYA00209650                             29                       FALSE
+#> MGYA00209651                             29                       FALSE
+#> MGYA00209652                             29                       FALSE
+#> MGYA00209653                             29                       FALSE
+#>              study_attributes.secondary-accession study_attributes.centre-name
+#> MGYA00209648                            SRP105345      University of Minnesota
+#> MGYA00209649                            SRP105345      University of Minnesota
+#> MGYA00209650                            SRP105345      University of Minnesota
+#> MGYA00209651                            SRP105345      University of Minnesota
+#> MGYA00209652                            SRP105345      University of Minnesota
+#> MGYA00209653                            SRP105345      University of Minnesota
+#>                                                                            study_attributes.study-abstract
+#> MGYA00209648 Characterization of bacterial communities in marine sediments from Gladstone and Heron Island
+#> MGYA00209649 Characterization of bacterial communities in marine sediments from Gladstone and Heron Island
+#> MGYA00209650 Characterization of bacterial communities in marine sediments from Gladstone and Heron Island
+#> MGYA00209651 Characterization of bacterial communities in marine sediments from Gladstone and Heron Island
+#> MGYA00209652 Characterization of bacterial communities in marine sediments from Gladstone and Heron Island
+#> MGYA00209653 Characterization of bacterial communities in marine sediments from Gladstone and Heron Island
+#>              study_attributes.study-name study_attributes.data-origination
+#> MGYA00209648  Queensland Marine Sediment                         HARVESTED
+#> MGYA00209649  Queensland Marine Sediment                         HARVESTED
+#> MGYA00209650  Queensland Marine Sediment                         HARVESTED
+#> MGYA00209651  Queensland Marine Sediment                         HARVESTED
+#> MGYA00209652  Queensland Marine Sediment                         HARVESTED
+#> MGYA00209653  Queensland Marine Sediment                         HARVESTED
+#>              study_attributes.last-update study_accession study_acc_type
+#> MGYA00209648          2019-11-07T16:33:46    MGYS00002891        studies
+#> MGYA00209649          2019-11-07T16:33:46    MGYS00002891        studies
+#> MGYA00209650          2019-11-07T16:33:46    MGYS00002891        studies
+#> MGYA00209651          2019-11-07T16:33:46    MGYS00002891        studies
+#> MGYA00209652          2019-11-07T16:33:46    MGYS00002891        studies
+#> MGYA00209653          2019-11-07T16:33:46    MGYS00002891        studies
+#>              sample_latitude sample_biosample sample_longitude sample_accession
+#> MGYA00209648         -23.749     SAMN06842047         151.3654       SRS2151215
+#> MGYA00209649        -23.7692     SAMN06842069         151.3167       SRS2151190
+#> MGYA00209650        -23.6158     SAMN06842067         152.1597       SRS2151193
+#> MGYA00209651        -23.7692     SAMN06842071         151.3167       SRS2151189
+#> MGYA00209652        -23.4369     SAMN06842064         151.9813       SRS2151195
+#> MGYA00209653        -23.6158     SAMN06842065         152.1597       SRS2151196
+#>              sample_analysis-completed
+#> MGYA00209648                2018-09-06
+#> MGYA00209649                2018-09-06
+#> MGYA00209650                2018-09-06
+#> MGYA00209651                2018-09-06
+#> MGYA00209652                2018-09-06
+#> MGYA00209653                2018-09-06
+#>                                    sample_geo-loc-name sample_sample-desc
+#> MGYA00209648                     Australia: Queensland          composite
+#> MGYA00209649                     Australia: Queensland               core
+#> MGYA00209650 Australia: Queensland, Great Barrier Reef               core
+#> MGYA00209651                     Australia: Queensland               core
+#> MGYA00209652 Australia: Queensland, Great Barrier Reef               core
+#> MGYA00209653 Australia: Queensland, Great Barrier Reef               core
+#>                sample_sample-name  sample_sample-alias  sample_last-update
+#> MGYA00209648  Facing island box 2  Facing island box 2 2018-09-06T01:18:52
+#> MGYA00209649 Gladstone Harbour 7A Gladstone Harbour 7A 2018-09-06T01:18:52
+#> MGYA00209650      Fitzroy reef 2C      Fitzroy reef 2C 2018-09-06T01:18:52
+#> MGYA00209651 Gladstone Harbour 7C Gladstone Harbour 7C 2018-09-06T01:18:52
+#> MGYA00209652      Heron Island 4D      Heron Island 4D 2018-09-06T01:18:52
+#> MGYA00209653      Fitzroy reef 2A      Fitzroy reef 2A 2018-09-06T01:18:52
+#>              sample_geographic location (longitude)
+#> MGYA00209648                              151.36536
+#> MGYA00209649                              151.31674
+#> MGYA00209650                              152.15974
+#> MGYA00209651                              151.31674
+#> MGYA00209652                              151.98132
+#> MGYA00209653                              152.15974
+#>              sample_geographic location (country and/or sea,region)
+#> MGYA00209648                                  Australia: Queensland
+#> MGYA00209649                                  Australia: Queensland
+#> MGYA00209650              Australia: Queensland, Great Barrier Reef
+#> MGYA00209651                                  Australia: Queensland
+#> MGYA00209652              Australia: Queensland, Great Barrier Reef
+#> MGYA00209653              Australia: Queensland, Great Barrier Reef
+#>              sample_geographic location (latitude) sample_instrument model
+#> MGYA00209648                            -23.749048     Illumina HiSeq 2500
+#> MGYA00209649                            -23.769222     Illumina HiSeq 2500
+#> MGYA00209650                            -23.615824     Illumina HiSeq 2500
+#> MGYA00209651                            -23.769222     Illumina HiSeq 2500
+#> MGYA00209652                            -23.436857     Illumina HiSeq 2500
+#> MGYA00209653                            -23.615824     Illumina HiSeq 2500
+#>              sample_acc_type run_accession
+#> MGYA00209648         samples    SRR5483782
+#> MGYA00209649         samples    SRR5483760
+#> MGYA00209650         samples    SRR5483762
+#> MGYA00209651         samples    SRR5483758
+#> MGYA00209652         samples    SRR5483765
+#> MGYA00209653         samples    SRR5483764
+#>                                            biome_string sample_depth
+#> MGYA00209648 root:Environmental:Aquatic:Marine:Sediment         <NA>
+#> MGYA00209649 root:Environmental:Aquatic:Marine:Sediment          0.0
+#> MGYA00209650 root:Environmental:Aquatic:Marine:Sediment         20.0
+#> MGYA00209651 root:Environmental:Aquatic:Marine:Sediment         20.0
+#> MGYA00209652 root:Environmental:Aquatic:Marine:Sediment         30.0
+#> MGYA00209653 root:Environmental:Aquatic:Marine:Sediment          0.0

The resulting data.frame has columns with names prefixed with their source type. For example, “sample_xxx” columns correspond to metadata gleaned from querying an accession’s sample entry. MGnify @@ -284,11 +667,12 @@

Fetch metadataAll sample data from MGnifyR is initially retrieved as type character, and it’s up to the user to make sure ostensibly numeric entries are converted properly.

-
+
 known_depths <- analyses_metadata[
     !is.na(as.numeric(analyses_metadata$sample_depth)), ]
 # How many are left?
-dim(known_depths)

+dim(known_depths)
+#> [1] 26 49

Fetch microbiome data @@ -314,28 +698,54 @@

Amplicon sequencinggetResult() method returns the dataset as a TreeSE by default. See other output types from the function documentation.

-
-tse <- getResult(mg, accession = analyses_accessions, get.func = FALSE)
-tse
+
+tse <- getResult(mg, accession = analyses_accessions, get.func = FALSE)
+
+tse
+#> class: TreeSummarizedExperiment 
+#> dim: 3689 29 
+#> metadata(0):
+#> assays(1): counts
+#> rownames(3689): 92640 251937 ... 233398 265506
+#> rowData names(8): Kingdom Phylum ... Species taxonomy1
+#> colnames(29): MGYA00209651 MGYA00209670 ... MGYA00209657 MGYA00209667
+#> colData names(49): analysis_experiment.type analysis_pipeline.version
+#>   ... biome_string sample_depth
+#> reducedDimNames(0):
+#> mainExpName: NULL
+#> altExpNames(0):
+#> rowLinks: NULL
+#> rowTree: NULL
+#> colLinks: NULL
+#> colTree: NULL

TreeSE object is uniquely positioned to support SummarizedExperiment-based microbiome data manipulation and visualization. Moreover, it enables access to miaverse tools. For example, we can estimate diversity of samples.

-
+
 tse <- estimateDiversity(tse, index = "shannon")
 
 library(scater)
+#> Loading required package: scuttle
+#> Loading required package: ggplot2
 
 plotColData(tse, "shannon", x = "sample_geo.loc.name")

-
+

+
 library(miaViz)
+#> Loading required package: ggraph
 
-plotAbundance(tse)

+plotAbundance(tse[!is.na( rowData(tse)[["Kingdom"]] ), ], rank = "Kingdom")

+

 
If needed, TreeSE can be converted to
 phyloseq.

-
+
 pseq <- makePhyloseqFromTreeSE(tse)
-pseq

+pseq
+#> phyloseq-class experiment-level object
+#> otu_table()   OTU Table:         [ 3689 taxa and 29 samples ]
+#> sample_data() Sample Data:       [ 29 samples by 50 sample variables ]
+#> tax_table()   Taxonomy Table:    [ 3689 taxa by 7 taxonomic ranks ]

Metagenomics @@ -359,17 +769,17 @@

Metagenomics
+
 soil <- searchAnalysis(mg, "studies", "MGYS00001447")
 human <- searchAnalysis(mg, "studies", "MGYS00001442")
 marine <- searchAnalysis(mg, "studies", "MGYS00001282")
 
 # Combine analyses
-all_accessions <- c(soil, human, marine)
-# Subset the accessions by taking 5 random analyses
-set.seed(74)
-all_accessions <- sample(all_accessions, 5)
-all_accessions

+all_accessions <- c(soil, human, marine)

+
+head(all_accessions)
+#> [1] "MGYA00097621" "MGYA00097622" "MGYA00097623" "MGYA00097624" "MGYA00097625"
+#> [6] "MGYA00097626"

The first step with this new accession list is, as previously, to retrieve the associated metadata using getMetadata(), and as seen with the doQuery() results, the returned @@ -377,11 +787,347 @@

Metagenomics
+
 full_metadata <- getMetadata(mg, all_accessions)

-
+
 colnames(full_metadata)
-head(full_metadata)

+#>  [1] "analysis_experiment-type"                                        
+#>  [2] "analysis_pipeline-version"                                       
+#>  [3] "analysis_analysis-status"                                        
+#>  [4] "analysis_accession"                                              
+#>  [5] "analysis_is-private"                                             
+#>  [6] "analysis_complete-time"                                          
+#>  [7] "analysis_instrument-platform"                                    
+#>  [8] "analysis_instrument-model"                                       
+#>  [9] "analysis_Submitted nucleotide sequences"                         
+#> [10] "analysis_Nucleotide sequences after format-specific filtering"   
+#> [11] "analysis_Nucleotide sequences after length filtering"            
+#> [12] "analysis_Nucleotide sequences after undetermined bases filtering"
+#> [13] "analysis_Reads with predicted CDS"                               
+#> [14] "analysis_Reads with predicted RNA"                               
+#> [15] "analysis_Reads with InterProScan match"                          
+#> [16] "analysis_Predicted CDS"                                          
+#> [17] "analysis_Predicted CDS with InterProScan match"                  
+#> [18] "analysis_Total InterProScan matches"                             
+#> [19] "analysis_acc_type"                                               
+#> [20] "study_attributes.accession"                                      
+#> [21] "study_attributes.bioproject"                                     
+#> [22] "study_attributes.samples-count"                                  
+#> [23] "study_attributes.is-private"                                     
+#> [24] "study_attributes.secondary-accession"                            
+#> [25] "study_attributes.centre-name"                                    
+#> [26] "study_attributes.study-abstract"                                 
+#> [27] "study_attributes.study-name"                                     
+#> [28] "study_attributes.data-origination"                               
+#> [29] "study_attributes.last-update"                                    
+#> [30] "study_accession"                                                 
+#> [31] "study_acc_type"                                                  
+#> [32] "sample_latitude"                                                 
+#> [33] "sample_longitude"                                                
+#> [34] "sample_biosample"                                                
+#> [35] "sample_accession"                                                
+#> [36] "sample_analysis-completed"                                       
+#> [37] "sample_geo-loc-name"                                             
+#> [38] "sample_sample-desc"                                              
+#> [39] "sample_environment-biome"                                        
+#> [40] "sample_environment-feature"                                      
+#> [41] "sample_environment-material"                                     
+#> [42] "sample_sample-name"                                              
+#> [43] "sample_sample-alias"                                             
+#> [44] "sample_last-update"                                              
+#> [45] "sample_investigation type"                                       
+#> [46] "sample_project name"                                             
+#> [47] "sample_geographic location (depth)"                              
+#> [48] "sample_collection date"                                          
+#> [49] "sample_sequencing method"                                        
+#> [50] "sample_geographic location (elevation)"                          
+#> [51] "sample_NCBI sample classification"                               
+#> [52] "sample_instrument model"                                         
+#> [53] "sample_ENA checklist"                                            
+#> [54] "sample_sediment environmental package"                           
+#> [55] "sample_acc_type"                                                 
+#> [56] "run_accession"                                                   
+#> [57] "biome_string"                                                    
+#> [58] "sample_collection-date"                                          
+#> [59] "sample_host-tax-id"                                              
+#> [60] "sample_species"                                                  
+#> [61] "sample_geographic location (longitude)"                          
+#> [62] "sample_geographic location (country and/or sea,region)"          
+#> [63] "sample_environment (biome)"                                      
+#> [64] "sample_environment (feature)"                                    
+#> [65] "sample_environment (material)"                                   
+#> [66] "sample_environmental package"                                    
+#> [67] "sample_host taxid"                                               
+#> [68] "sample_geographic location (latitude)"                           
+#> [69] "sample_host scientific name"                                     
+#> [70] "sample_human gut environmental package"
+head(full_metadata)
+#>              analysis_experiment-type analysis_pipeline-version
+#> MGYA00097631              metagenomic                       3.0
+#> MGYA00097632              metagenomic                       3.0
+#> MGYA00097633              metagenomic                       3.0
+#> MGYA00097634              metagenomic                       3.0
+#> MGYA00097635              metagenomic                       3.0
+#> MGYA00097636              metagenomic                       3.0
+#>              analysis_analysis-status analysis_accession analysis_is-private
+#> MGYA00097631                completed       MGYA00097631               FALSE
+#> MGYA00097632                completed       MGYA00097632               FALSE
+#> MGYA00097633                completed       MGYA00097633               FALSE
+#> MGYA00097634                completed       MGYA00097634               FALSE
+#> MGYA00097635                completed       MGYA00097635               FALSE
+#> MGYA00097636                completed       MGYA00097636               FALSE
+#>              analysis_complete-time analysis_instrument-platform
+#> MGYA00097631    2017-02-10T00:00:00                     ILLUMINA
+#> MGYA00097632    2017-02-10T00:00:00                     ILLUMINA
+#> MGYA00097633    2017-02-10T00:00:00                     ILLUMINA
+#> MGYA00097634    2017-02-10T00:00:00                     ILLUMINA
+#> MGYA00097635    2017-02-10T00:00:00                     ILLUMINA
+#> MGYA00097636    2017-02-10T00:00:00                     ILLUMINA
+#>              analysis_instrument-model analysis_Submitted nucleotide sequences
+#> MGYA00097631       Illumina HiSeq 4000                                14263292
+#> MGYA00097632       Illumina HiSeq 4000                                14017313
+#> MGYA00097633       Illumina HiSeq 4000                                17493693
+#> MGYA00097634       Illumina HiSeq 4000                                12803734
+#> MGYA00097635       Illumina HiSeq 4000                                19918196
+#> MGYA00097636       Illumina HiSeq 4000                                11768981
+#>              analysis_Nucleotide sequences after format-specific filtering
+#> MGYA00097631                                                      14221062
+#> MGYA00097632                                                      14007866
+#> MGYA00097633                                                      17476376
+#> MGYA00097634                                                      12794042
+#> MGYA00097635                                                      19901713
+#> MGYA00097636                                                      11759662
+#>              analysis_Nucleotide sequences after length filtering
+#> MGYA00097631                                             13131530
+#> MGYA00097632                                             13251661
+#> MGYA00097633                                             16306229
+#> MGYA00097634                                             12081026
+#> MGYA00097635                                             18776052
+#> MGYA00097636                                             11124185
+#>              analysis_Nucleotide sequences after undetermined bases filtering
+#> MGYA00097631                                                         13131432
+#> MGYA00097632                                                         13251540
+#> MGYA00097633                                                         16306111
+#> MGYA00097634                                                         12080915
+#> MGYA00097635                                                         18775871
+#> MGYA00097636                                                         11124089
+#>              analysis_Reads with predicted CDS
+#> MGYA00097631                          12706029
+#> MGYA00097632                          12854908
+#> MGYA00097633                          15781126
+#> MGYA00097634                          11692069
+#> MGYA00097635                          18174796
+#> MGYA00097636                          10767961
+#>              analysis_Reads with predicted RNA
+#> MGYA00097631                             16190
+#> MGYA00097632                             15055
+#> MGYA00097633                             20933
+#> MGYA00097634                             14064
+#> MGYA00097635                             23868
+#> MGYA00097636                             15644
+#>              analysis_Reads with InterProScan match analysis_Predicted CDS
+#> MGYA00097631                                4564209               12799168
+#> MGYA00097632                                4905147               12958349
+#> MGYA00097633                                5734721               15898933
+#> MGYA00097634                                4303972               11775442
+#> MGYA00097635                                6944293               18316341
+#> MGYA00097636                                4064336               10844621
+#>              analysis_Predicted CDS with InterProScan match
+#> MGYA00097631                                        4567359
+#> MGYA00097632                                        4909395
+#> MGYA00097633                                        5738555
+#> MGYA00097634                                        4306951
+#> MGYA00097635                                        6950012
+#> MGYA00097636                                        4067254
+#>              analysis_Total InterProScan matches analysis_acc_type
+#> MGYA00097631                             7258924     analysis-jobs
+#> MGYA00097632                             7837311     analysis-jobs
+#> MGYA00097633                             9124572     analysis-jobs
+#> MGYA00097634                             6849131     analysis-jobs
+#> MGYA00097635                            11109826     analysis-jobs
+#> MGYA00097636                             6486426     analysis-jobs
+#>              study_attributes.accession study_attributes.bioproject
+#> MGYA00097631               MGYS00001447                  PRJEB19235
+#> MGYA00097632               MGYS00001447                  PRJEB19235
+#> MGYA00097633               MGYS00001447                  PRJEB19235
+#> MGYA00097634               MGYS00001447                  PRJEB19235
+#> MGYA00097635               MGYS00001447                  PRJEB19235
+#> MGYA00097636               MGYS00001447                  PRJEB19235
+#>              study_attributes.samples-count study_attributes.is-private
+#> MGYA00097631                             38                       FALSE
+#> MGYA00097632                             38                       FALSE
+#> MGYA00097633                             38                       FALSE
+#> MGYA00097634                             38                       FALSE
+#> MGYA00097635                             38                       FALSE
+#> MGYA00097636                             38                       FALSE
+#>              study_attributes.secondary-accession study_attributes.centre-name
+#> MGYA00097631                            ERP021219           University of York
+#> MGYA00097632                            ERP021219           University of York
+#> MGYA00097633                            ERP021219           University of York
+#> MGYA00097634                            ERP021219           University of York
+#> MGYA00097635                            ERP021219           University of York
+#> MGYA00097636                            ERP021219           University of York
+#>                                                                     study_attributes.study-abstract
+#> MGYA00097631 Samples were collected from natural and realigned sites, and during Summer and Winter.
+#> MGYA00097632 Samples were collected from natural and realigned sites, and during Summer and Winter.
+#> MGYA00097633 Samples were collected from natural and realigned sites, and during Summer and Winter.
+#> MGYA00097634 Samples were collected from natural and realigned sites, and during Summer and Winter.
+#> MGYA00097635 Samples were collected from natural and realigned sites, and during Summer and Winter.
+#> MGYA00097636 Samples were collected from natural and realigned sites, and during Summer and Winter.
+#>                                    study_attributes.study-name
+#> MGYA00097631 Samples from salt marshes in the south of England
+#> MGYA00097632 Samples from salt marshes in the south of England
+#> MGYA00097633 Samples from salt marshes in the south of England
+#> MGYA00097634 Samples from salt marshes in the south of England
+#> MGYA00097635 Samples from salt marshes in the south of England
+#> MGYA00097636 Samples from salt marshes in the south of England
+#>              study_attributes.data-origination study_attributes.last-update
+#> MGYA00097631                         SUBMITTED          2017-02-10T11:32:47
+#> MGYA00097632                         SUBMITTED          2017-02-10T11:32:47
+#> MGYA00097633                         SUBMITTED          2017-02-10T11:32:47
+#> MGYA00097634                         SUBMITTED          2017-02-10T11:32:47
+#> MGYA00097635                         SUBMITTED          2017-02-10T11:32:47
+#> MGYA00097636                         SUBMITTED          2017-02-10T11:32:47
+#>              study_accession study_acc_type sample_latitude sample_longitude
+#> MGYA00097631    MGYS00001447        studies         51.7972            0.921
+#> MGYA00097632    MGYS00001447        studies         51.7972            0.921
+#> MGYA00097633    MGYS00001447        studies         51.7865           0.8609
+#> MGYA00097634    MGYS00001447        studies         51.7865           0.8609
+#> MGYA00097635    MGYS00001447        studies            <NA>             <NA>
+#> MGYA00097636    MGYS00001447        studies         51.7968           0.9218
+#>              sample_biosample sample_accession sample_analysis-completed
+#> MGYA00097631    SAMEA56090668       ERS1512902                2017-02-10
+#> MGYA00097632    SAMEA56092168       ERS1512904                2017-02-10
+#> MGYA00097633    SAMEA56099668       ERS1512914                2017-02-10
+#> MGYA00097634    SAMEA56095168       ERS1512908                2017-02-10
+#> MGYA00097635             <NA>             <NA>                      <NA>
+#> MGYA00097636    SAMEA56094418       ERS1512907                2017-02-10
+#>              sample_geo-loc-name                          sample_sample-desc
+#> MGYA00097631      United Kingdom   Natural salt marsh, Mersea Island, Winter
+#> MGYA00097632      United Kingdom   Natural salt marsh, Mersea Island, Winter
+#> MGYA00097633      United Kingdom    Natural salt marsh, Abbotts Hall, Winter
+#> MGYA00097634      United Kingdom    Natural salt marsh, Abbotts Hall, Summer
+#> MGYA00097635                <NA>                                        <NA>
+#> MGYA00097636      United Kingdom Realigned salt marsh, Mersea Island, Winter
+#>              sample_environment-biome sample_environment-feature
+#> MGYA00097631  marine salt marsh biome                  sea shore
+#> MGYA00097632  marine salt marsh biome                  sea shore
+#> MGYA00097633  marine salt marsh biome                  sea shore
+#> MGYA00097634  marine salt marsh biome                  sea shore
+#> MGYA00097635                     <NA>                       <NA>
+#> MGYA00097636  marine salt marsh biome                  sea shore
+#>              sample_environment-material
+#> MGYA00097631                    sediment
+#> MGYA00097632                    sediment
+#> MGYA00097633                    sediment
+#> MGYA00097634                    sediment
+#> MGYA00097635                        <NA>
+#> MGYA00097636                    sediment
+#>                                       sample_sample-name sample_sample-alias
+#> MGYA00097631   Natural salt marsh, Mersea Island, Winter           W MINM 1A
+#> MGYA00097632   Natural salt marsh, Mersea Island, Winter           W MINM 3A
+#> MGYA00097633    Natural salt marsh, Abbotts Hall, Winter           W AHNM 2A
+#> MGYA00097634    Natural salt marsh, Abbotts Hall, Summer           S AHNM 1A
+#> MGYA00097635                                        <NA>                <NA>
+#> MGYA00097636 Realigned salt marsh, Mersea Island, Winter           W MIRM 3A
+#>               sample_last-update sample_investigation type  sample_project name
+#> MGYA00097631 2017-02-10T11:32:50                metagenome Southern Saltmarshes
+#> MGYA00097632 2017-02-10T11:32:50                metagenome Southern Saltmarshes
+#> MGYA00097633 2017-02-10T11:32:50                metagenome Southern Saltmarshes
+#> MGYA00097634 2017-02-10T11:32:50                metagenome Southern Saltmarshes
+#> MGYA00097635                <NA>                      <NA>                 <NA>
+#> MGYA00097636 2017-02-10T11:32:50                metagenome Southern Saltmarshes
+#>              sample_geographic location (depth) sample_collection date
+#> MGYA00097631                               0.05             2015-02-24
+#> MGYA00097632                               0.05             2015-02-24
+#> MGYA00097633                               0.05             2015-03-28
+#> MGYA00097634                               0.05             2014-07-03
+#> MGYA00097635                               <NA>                   <NA>
+#> MGYA00097636                               0.05             2015-02-24
+#>              sample_sequencing method sample_geographic location (elevation)
+#> MGYA00097631           Illumina HiSeq                                    0.3
+#> MGYA00097632           Illumina HiSeq                                    0.3
+#> MGYA00097633           Illumina HiSeq                                      2
+#> MGYA00097634           Illumina HiSeq                                      2
+#> MGYA00097635                     <NA>                                   <NA>
+#> MGYA00097636           Illumina HiSeq                                      1
+#>              sample_NCBI sample classification sample_instrument model
+#> MGYA00097631                            749907     Illumina HiSeq 4000
+#> MGYA00097632                            749907     Illumina HiSeq 4000
+#> MGYA00097633                            749907     Illumina HiSeq 4000
+#> MGYA00097634                            749907     Illumina HiSeq 4000
+#> MGYA00097635                              <NA>                    <NA>
+#> MGYA00097636                            749907     Illumina HiSeq 4000
+#>                       sample_ENA checklist
+#> MGYA00097631 GSC MIxS sediment (ERC000021)
+#> MGYA00097632 GSC MIxS sediment (ERC000021)
+#> MGYA00097633 GSC MIxS sediment (ERC000021)
+#> MGYA00097634 GSC MIxS sediment (ERC000021)
+#> MGYA00097635                          <NA>
+#> MGYA00097636 GSC MIxS sediment (ERC000021)
+#>              sample_sediment environmental package sample_acc_type
+#> MGYA00097631                              sediment         samples
+#> MGYA00097632                              sediment         samples
+#> MGYA00097633                              sediment         samples
+#> MGYA00097634                              sediment         samples
+#> MGYA00097635                                  <NA>            <NA>
+#> MGYA00097636                              sediment         samples
+#>              run_accession                        biome_string
+#> MGYA00097631    ERR1811630 root:Environmental:Terrestrial:Soil
+#> MGYA00097632    ERR1811632 root:Environmental:Terrestrial:Soil
+#> MGYA00097633    ERR1811642 root:Environmental:Terrestrial:Soil
+#> MGYA00097634    ERR1811636 root:Environmental:Terrestrial:Soil
+#> MGYA00097635    ERR1811628                                <NA>
+#> MGYA00097636    ERR1811635 root:Environmental:Terrestrial:Soil
+#>              sample_collection-date sample_host-tax-id sample_species
+#> MGYA00097631                   <NA>               <NA>           <NA>
+#> MGYA00097632                   <NA>               <NA>           <NA>
+#> MGYA00097633                   <NA>               <NA>           <NA>
+#> MGYA00097634                   <NA>               <NA>           <NA>
+#> MGYA00097635                   <NA>               <NA>           <NA>
+#> MGYA00097636                   <NA>               <NA>           <NA>
+#>              sample_geographic location (longitude)
+#> MGYA00097631                                   <NA>
+#> MGYA00097632                                   <NA>
+#> MGYA00097633                                   <NA>
+#> MGYA00097634                                   <NA>
+#> MGYA00097635                                   <NA>
+#> MGYA00097636                                   <NA>
+#>              sample_geographic location (country and/or sea,region)
+#> MGYA00097631                                                   <NA>
+#> MGYA00097632                                                   <NA>
+#> MGYA00097633                                                   <NA>
+#> MGYA00097634                                                   <NA>
+#> MGYA00097635                                                   <NA>
+#> MGYA00097636                                                   <NA>
+#>              sample_environment (biome) sample_environment (feature)
+#> MGYA00097631                       <NA>                         <NA>
+#> MGYA00097632                       <NA>                         <NA>
+#> MGYA00097633                       <NA>                         <NA>
+#> MGYA00097634                       <NA>                         <NA>
+#> MGYA00097635                       <NA>                         <NA>
+#> MGYA00097636                       <NA>                         <NA>
+#>              sample_environment (material) sample_environmental package
+#> MGYA00097631                          <NA>                         <NA>
+#> MGYA00097632                          <NA>                         <NA>
+#> MGYA00097633                          <NA>                         <NA>
+#> MGYA00097634                          <NA>                         <NA>
+#> MGYA00097635                          <NA>                         <NA>
+#> MGYA00097636                          <NA>                         <NA>
+#>              sample_host taxid sample_geographic location (latitude)
+#> MGYA00097631              <NA>                                  <NA>
+#> MGYA00097632              <NA>                                  <NA>
+#> MGYA00097633              <NA>                                  <NA>
+#> MGYA00097634              <NA>                                  <NA>
+#> MGYA00097635              <NA>                                  <NA>
+#> MGYA00097636              <NA>                                  <NA>
+#>              sample_host scientific name sample_human gut environmental package
+#> MGYA00097631                        <NA>                                   <NA>
+#> MGYA00097632                        <NA>                                   <NA>
+#> MGYA00097633                        <NA>                                   <NA>
+#> MGYA00097634                        <NA>                                   <NA>
+#> MGYA00097635                        <NA>                                   <NA>
+#> MGYA00097636                        <NA>                                   <NA>

 
From full_metadata we get an idea of the type of data
 we’re dealing with, and can extract useul information such as sequencing
 platform, source biome, etc. The next code snippet tallies a few of
@@ -390,15 +1136,23 @@ 
Metagenomics
+
 # Load ggplot2 
 library(ggplot2)
 
 #Distribution of sample source material:
 table(full_metadata$`sample_environment-material`)
+#> 
+#> sediment    stool    water 
+#>       27       38      412
 
 #What sequencing machine(s) were used?
 table(full_metadata$`sample_instrument model`)
+#> 
+#>   Illumina HiScanSQ Illumina HiSeq 2000 Illumina HiSeq 2500 Illumina HiSeq 4000 
+#>                  38                 352                   2                  25 
+#>         NextSeq 500 
+#>                  60
 
 # Boxplot of raw read counts:
 ggplot(
@@ -406,7 +1160,9 @@ 
Metagenomicsas.numeric(`analysis_Submitted nucleotide sequences`)))) +
     geom_boxplot(aes(group=study_accession)) +
     theme_bw() +
-    ylab("log(submitted reads)")

+    ylab("log(submitted reads)")
+#> Warning: Removed 1 rows containing non-finite values (`stat_boxplot()`).

+

Again, we can fetch the data by calling getResult(). bulk.dl=TRUE has the potential to significantly speed up data retrieval. MGnify makes its functional results available in two @@ -437,9 +1193,25 @@

Metagenomics
-mae <- getResult(mg, all_accessions, bulk.dl = TRUE)
-mae
+
+mae <- getResult(mg, all_accessions, bulk.dl = TRUE)
+
+mae
+#> A MultiAssayExperiment object of 4 listed
+#>  experiments with user-defined names and respective classes.
+#>  Containing an ExperimentList class object of length 4:
+#>  [1] microbiota: TreeSummarizedExperiment with 32401 rows and 487 columns
+#>  [2] go-slim: TreeSummarizedExperiment with 116 rows and 487 columns
+#>  [3] go-terms: TreeSummarizedExperiment with 2640 rows and 487 columns
+#>  [4] interpro-identifiers: TreeSummarizedExperiment with 15818 rows and 487 columns
+#> Functionality:
+#>  experiments() - obtain the ExperimentList instance
+#>  colData() - the primary/phenotype DataFrame
+#>  sampleMap() - the sample coordination DataFrame
+#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
+#>  *Format() - convert into a long or wide DataFrame
+#>  assays() - convert ExperimentList to a SimpleList of matrices
+#>  exportClass() - save data to flat files

For metagenomic samples, the result is MultiAssayExperiment (MAE) which links multiple TreeSE objects into one dataset. These TreeSE objects include taxonomic @@ -447,11 +1219,28 @@

Metagenomics
-mae[[2]]
+
+mae[[2]]
+#> class: TreeSummarizedExperiment 
+#> dim: 116 487 
+#> metadata(0):
+#> assays(1): counts
+#> rownames(116): GO:0000015 GO:0000150 ... GO:1902494 GO:1990204
+#> rowData names(10): description category ... Genus Species
+#> colnames(487): MGYA00083332 MGYA00083120 ... MGYA00097653 MGYA00097655
+#> colData names(70): analysis_analysis.status analysis_pipeline.version
+#>   ... sample_host.scientific.name
+#>   sample_human.gut.environmental.package
+#> reducedDimNames(0):
+#> mainExpName: NULL
+#> altExpNames(0):
+#> rowLinks: NULL
+#> rowTree: NULL
+#> colLinks: NULL
+#> colTree: NULL

We can perform principal component analysis to microbial profiling data by utilizing miaverse tools.

-
+
 # Apply relative transformation
 mae[[1]] <- transformAssay(mae[[1]], method = "relabundance")
 # Perform PCoA
@@ -460,9 +1249,59 @@ 
Metagenomics= vegan::vegdist, method = "bray")
 # Plot
 plotReducedDim(mae[[1]], "MDS", colour_by = "sample_environment.feature")

+
+

Fetch raw files +

+

While getResult() can be utilized to retrieve microbial +profiling data, getData() can be used more flexibly to +retrieve any kind of data from the database. It returns data as simple +data.frame or list format.

+
+kegg <- getData(
+    mg, type = "kegg-modules", accession = "MGYA00642773",
+    accession.type = "analyses")
+
+head(kegg)
+#>   document.id         type     id attributes.accession attributes.completeness
+#> 1           1 kegg-modules M00001               M00001                     100
+#> 2           2 kegg-modules M00002               M00002                     100
+#> 3           3 kegg-modules M00003               M00003                     100
+#> 4           4 kegg-modules M00004               M00004                     100
+#> 5           5 kegg-modules M00005               M00005                     100
+#> 6           6 kegg-modules M00006               M00006                     100
+#>                                                      attributes.description
+#> 1 Pathway modules; Carbohydrate metabolism; Central carbohydrate metabolism
+#> 2 Pathway modules; Carbohydrate metabolism; Central carbohydrate metabolism
+#> 3 Pathway modules; Carbohydrate metabolism; Central carbohydrate metabolism
+#> 4 Pathway modules; Carbohydrate metabolism; Central carbohydrate metabolism
+#> 5 Pathway modules; Carbohydrate metabolism; Central carbohydrate metabolism
+#> 6 Pathway modules; Carbohydrate metabolism; Central carbohydrate metabolism
+#>                                                         attributes.name
+#> 1             Glycolysis (Embden-Meyerhof pathway), glucose => pyruvate
+#> 2              Glycolysis, core module involving three-carbon compounds
+#> 3                          Gluconeogenesis, oxaloacetate => fructose-6P
+#> 4                   Pentose phosphate pathway (Pentose phosphate cycle)
+#> 5                                  PRPP biosynthesis, ribose 5P => PRPP
+#> 6 Pentose phosphate pathway, oxidative phase, glucose 6P => ribulose 5P
+#>                                                                              links.self
+#> 1 https://www.ebi.ac.uk/metagenomics/api/v1/annotations/kegg-modules/M00001?format=json
+#> 2 https://www.ebi.ac.uk/metagenomics/api/v1/annotations/kegg-modules/M00002?format=json
+#> 3 https://www.ebi.ac.uk/metagenomics/api/v1/annotations/kegg-modules/M00003?format=json
+#> 4 https://www.ebi.ac.uk/metagenomics/api/v1/annotations/kegg-modules/M00004?format=json
+#> 5 https://www.ebi.ac.uk/metagenomics/api/v1/annotations/kegg-modules/M00005?format=json
+#> 6 https://www.ebi.ac.uk/metagenomics/api/v1/annotations/kegg-modules/M00006?format=json
+#>         ..JSON    accession
+#> 1 kegg-mod.... MGYA00642773
+#> 2 kegg-mod.... MGYA00642773
+#> 3 kegg-mod.... MGYA00642773
+#> 4 kegg-mod.... MGYA00642773
+#> 5 kegg-mod.... MGYA00642773
+#> 6 kegg-mod.... MGYA00642773
+
+

Fetch sequence files

Finally, we can use searchFile() and @@ -481,18 +1320,115 @@

Fetch sequence files
-#Find list of available downloads, and filter for 
+
+# Find list of available downloads
 dl_urls <- searchFile(
     mg, full_metadata$analysis_accession, type = "analyses")

-
-target_urls <- dl_urls[
+
+# Filter table
+target_urls <- dl_urls[
     dl_urls$attributes.description.label == "Predicted CDS with annotation",]
-head(target_urls)

+head(target_urls)
+#>         type                                           id
+#> 3   analyses ERR1811630_MERGED_FASTQ_CDS_annotated.faa.gz
+#> 24  analyses ERR1811632_MERGED_FASTQ_CDS_annotated.faa.gz
+#> 45  analyses ERR1811642_MERGED_FASTQ_CDS_annotated.faa.gz
+#> 68  analyses ERR1811636_MERGED_FASTQ_CDS_annotated.faa.gz
+#> 90  analyses ERR1811628_MERGED_FASTQ_CDS_annotated.faa.gz
+#> 115 analyses ERR1811635_MERGED_FASTQ_CDS_annotated.faa.gz
+#>                                 attributes.alias attributes.file.format.name
+#> 3   ERR1811630_MERGED_FASTQ_CDS_annotated.faa.gz                       FASTA
+#> 24  ERR1811632_MERGED_FASTQ_CDS_annotated.faa.gz                       FASTA
+#> 45  ERR1811642_MERGED_FASTQ_CDS_annotated.faa.gz                       FASTA
+#> 68  ERR1811636_MERGED_FASTQ_CDS_annotated.faa.gz                       FASTA
+#> 90  ERR1811628_MERGED_FASTQ_CDS_annotated.faa.gz                       FASTA
+#> 115 ERR1811635_MERGED_FASTQ_CDS_annotated.faa.gz                       FASTA
+#>     attributes.file.format.extension attributes.file.format.compression
+#> 3                              fasta                               TRUE
+#> 24                             fasta                               TRUE
+#> 45                             fasta                               TRUE
+#> 68                             fasta                               TRUE
+#> 90                             fasta                               TRUE
+#> 115                            fasta                               TRUE
+#>      attributes.description.label
+#> 3   Predicted CDS with annotation
+#> 24  Predicted CDS with annotation
+#> 45  Predicted CDS with annotation
+#> 68  Predicted CDS with annotation
+#> 90  Predicted CDS with annotation
+#> 115 Predicted CDS with annotation
+#>                         attributes.description.description
+#> 3   Predicted coding sequences with InterPro match (FASTA)
+#> 24  Predicted coding sequences with InterPro match (FASTA)
+#> 45  Predicted coding sequences with InterPro match (FASTA)
+#> 68  Predicted coding sequences with InterPro match (FASTA)
+#> 90  Predicted coding sequences with InterPro match (FASTA)
+#> 115 Predicted coding sequences with InterPro match (FASTA)
+#>     attributes.group.type attributes.file.checksum.checksum
+#> 3           Sequence data                                  
+#> 24          Sequence data                                  
+#> 45          Sequence data                                  
+#> 68          Sequence data                                  
+#> 90          Sequence data                                  
+#> 115         Sequence data                                  
+#>     attributes.file.checksum.checksum.algorithm
+#> 3                                              
+#> 24                                             
+#> 45                                             
+#> 68                                             
+#> 90                                             
+#> 115                                            
+#>     relationships.pipeline.data.type relationships.pipeline.data.id
+#> 3                          pipelines                            3.0
+#> 24                         pipelines                            3.0
+#> 45                         pipelines                            3.0
+#> 68                         pipelines                            3.0
+#> 90                         pipelines                            3.0
+#> 115                        pipelines                            3.0
+#>                                          relationships.pipeline.related
+#> 3   https://www.ebi.ac.uk/metagenomics/api/v1/pipelines/3.0?format=json
+#> 24  https://www.ebi.ac.uk/metagenomics/api/v1/pipelines/3.0?format=json
+#> 45  https://www.ebi.ac.uk/metagenomics/api/v1/pipelines/3.0?format=json
+#> 68  https://www.ebi.ac.uk/metagenomics/api/v1/pipelines/3.0?format=json
+#> 90  https://www.ebi.ac.uk/metagenomics/api/v1/pipelines/3.0?format=json
+#> 115 https://www.ebi.ac.uk/metagenomics/api/v1/pipelines/3.0?format=json
+#>                                                                                                          download_url
+#> 3   https://www.ebi.ac.uk/metagenomics/api/v1/analyses/MGYA00097631/file/ERR1811630_MERGED_FASTQ_CDS_annotated.faa.gz
+#> 24  https://www.ebi.ac.uk/metagenomics/api/v1/analyses/MGYA00097632/file/ERR1811632_MERGED_FASTQ_CDS_annotated.faa.gz
+#> 45  https://www.ebi.ac.uk/metagenomics/api/v1/analyses/MGYA00097633/file/ERR1811642_MERGED_FASTQ_CDS_annotated.faa.gz
+#> 68  https://www.ebi.ac.uk/metagenomics/api/v1/analyses/MGYA00097634/file/ERR1811636_MERGED_FASTQ_CDS_annotated.faa.gz
+#> 90  https://www.ebi.ac.uk/metagenomics/api/v1/analyses/MGYA00097635/file/ERR1811628_MERGED_FASTQ_CDS_annotated.faa.gz
+#> 115 https://www.ebi.ac.uk/metagenomics/api/v1/analyses/MGYA00097636/file/ERR1811635_MERGED_FASTQ_CDS_annotated.faa.gz
+#>        accession
+#> 3   MGYA00097631
+#> 24  MGYA00097632
+#> 45  MGYA00097633
+#> 68  MGYA00097634
+#> 90  MGYA00097635
+#> 115 MGYA00097636

 
To list the types of available files, and guide the filtering,
 something like the following might be useful.

-
-table(dl_urls$attributes.description.label)

+
+table(dl_urls$attributes.description.label)
+#> 
+#>                 Complete GO annotation                     GO slim annotation 
+#>                                    478                                    478 
+#>                       InterPro matches OTUs, counts and taxonomic assignments 
+#>                                    483                                   1434 
+#>                      Phylogenetic tree          Predicted CDS with annotation 
+#>                                    478                                    477 
+#>       Predicted CDS without annotation       Predicted ORF without annotation 
+#>                                    480                                    488 
+#>                        Predicted tRNAs             Processed nucleotide reads 
+#>                                    478                                    508 
+#>        Processed reads with annotation              Processed reads with pCDS 
+#>                                    477                                    508 
+#>     Processed reads without annotation                Reads encoding 16S rRNA 
+#>                                    488                                    478 
+#>                Reads encoding 23S rRNA                 Reads encoding 5S rRNA 
+#>                                    478                                    478 
+#>            Taxa abundance distribution 
+#>                                    475

 
Unlike other MGnifyR functions,
 searchFile() is not limited to analyses, and
 by specifying accession_type other results types may be
@@ -500,11 +1436,46 @@ 
Fetch sequence files
-# FIXME
-genome_urls <- searchFile(mg, "MGYS00005650", type = "genomes")

-
-genome_urls[c("id","attributes.file.format.name","download_url")]
+
+genome_urls <- searchFile(mg, "MGYG000433953", type = "genomes")
+
+genome_urls[c("id","attributes.file.format.name","download_url")]
+#>                                   id attributes.file.format.name
+#> 1                     core_genes.txt                         TAB
+#> 2          gene_presence_absence.csv                         CSV
+#> 3         gene_presence_absence.Rtab                         TSV
+#> 4                       mashtree.nwk               Newick format
+#> 5                  MGYG000433953.faa                       FASTA
+#> 6                  MGYG000433953.fna                       FASTA
+#> 7              MGYG000433953.fna.fai                         FAI
+#> 8                  MGYG000433953.gff                         GFF
+#> 9    MGYG000433953_amrfinderplus.tsv                         TSV
+#> 10 MGYG000433953_crisprcasfinder.gff                         GFF
+#> 11 MGYG000433953_crisprcasfinder.tsv                         TSV
+#> 12          MGYG000433953_eggNOG.tsv                         TSV
+#> 13    MGYG000433953_InterProScan.tsv                         TSV
+#> 14        MGYG000433953_mobilome.gff                         GFF
+#> 15         MGYG000433953_rRNAs.fasta                       FASTA
+#> 16         MGYG000433953_sanntis.gff                         GFF
+#> 17                    pan-genome.fna                       FASTA
+#>                                                                                                   download_url
+#> 1                     https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000433953/downloads/core_genes.txt
+#> 2          https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000433953/downloads/gene_presence_absence.csv
+#> 3         https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000433953/downloads/gene_presence_absence.Rtab
+#> 4                       https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000433953/downloads/mashtree.nwk
+#> 5                  https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000433953/downloads/MGYG000433953.faa
+#> 6                  https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000433953/downloads/MGYG000433953.fna
+#> 7              https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000433953/downloads/MGYG000433953.fna.fai
+#> 8                  https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000433953/downloads/MGYG000433953.gff
+#> 9    https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000433953/downloads/MGYG000433953_amrfinderplus.tsv
+#> 10 https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000433953/downloads/MGYG000433953_crisprcasfinder.gff
+#> 11 https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000433953/downloads/MGYG000433953_crisprcasfinder.tsv
+#> 12          https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000433953/downloads/MGYG000433953_eggNOG.tsv
+#> 13    https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000433953/downloads/MGYG000433953_InterProScan.tsv
+#> 14        https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000433953/downloads/MGYG000433953_mobilome.gff
+#> 15         https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000433953/downloads/MGYG000433953_rRNAs.fasta
+#> 16         https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000433953/downloads/MGYG000433953_sanntis.gff
+#> 17                    https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000433953/downloads/pan-genome.fna

Having found the a set of target urls, the final step is to use getFile() to actually retrieve the file. Unlike other functions, this only works with a single url location at once, so each @@ -519,18 +1490,21 @@

Fetch sequence filesgetFile() is to retrieve the file specified in the parameter url, save it to disk, and return the filepath it was saved to.

-
+
 # Just select a single file from the target_urls list for demonstration.
 
 # Default behavior - use local cache.
-cached_location = getFile(mg, target_urls$download_url[[1]])
+cached_location1 = getFile(mg, target_urls$download_url[[1]])
 
 # Specifying a file
-specified_location <- getFile(
+cached_location2 <- getFile(
     mg, target_urls$download_url[[1]])
 
-# Where are the files?
-c(cached_location,specified_location)

+cached_location <- c(cached_location1, cached_location2)
+
+# Where are the files?
+cached_location
+#> [1] "/tmp/RtmpeKYwQ1/file4c1937cbdfd5" "/tmp/RtmpeKYwQ1/file4c19a3b3548"

A second download option is available, which allows built-in parsing of the file. If we know ahead of time what processing will be performed, it may be possible to integrate it into a function, pass this function @@ -555,7 +1529,7 @@

Fetch sequence files
+
 library(Biostrings)
 
 # Simple function to a count of unique sequences matching PFAM amoC/mmoC motif
@@ -566,14 +1540,120 @@ 
Fetch sequence files}

 
Having defined the function, it just remains to include it in the
 call to getFile().

-
+
 # Just download a single accession for demonstration, specifying a read_function
 amoC_seq_counts <- getFile(
-    mg, target_urls$download_url[[1]], read_func = getAmoCseqs)
-
-amoC_seq_counts

-
+    mg, target_urls$download_url[[1]], read_func = getAmoCseqs)

+
+amoC_seq_counts
+#> [1] "/tmp/RtmpeKYwQ1/file4c1941f3c62"

+
+sessionInfo()
+#> R Under development (unstable) (2024-02-07 r85873)
+#> Platform: x86_64-pc-linux-gnu
+#> Running under: Ubuntu 22.04.3 LTS
+#> 
+#> Matrix products: default
+#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
+#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
+#> 
+#> locale:
+#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
+#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
+#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
+#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
+#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
+#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
+#> 
+#> time zone: UTC
+#> tzcode source: system (glibc)
+#> 
+#> attached base packages:
+#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
+#> [8] base     
+#> 
+#> other attached packages:
+#>  [1] miaViz_1.11.0                   ggraph_2.1.0                   
+#>  [3] scater_1.31.2                   ggplot2_3.4.4                  
+#>  [5] scuttle_1.13.0                  MGnifyR_0.99.18                
+#>  [7] biomformat_1.31.0               mia_1.11.1                     
+#>  [9] MultiAssayExperiment_1.29.1     TreeSummarizedExperiment_2.11.0
+#> [11] Biostrings_2.71.2               XVector_0.43.1                 
+#> [13] SingleCellExperiment_1.25.0     SummarizedExperiment_1.33.3    
+#> [15] Biobase_2.63.0                  GenomicRanges_1.55.2           
+#> [17] GenomeInfoDb_1.39.6             IRanges_2.37.1                 
+#> [19] S4Vectors_0.41.3                BiocGenerics_0.49.1            
+#> [21] MatrixGenerics_1.15.0           matrixStats_1.2.0              
+#> [23] knitr_1.45                      BiocStyle_2.31.0               
+#> 
+#> loaded via a namespace (and not attached):
+#>   [1] jsonlite_1.8.8              tidyjson_0.3.2             
+#>   [3] magrittr_2.0.3              ggbeeswarm_0.7.2           
+#>   [5] farver_2.1.1                rmarkdown_2.25             
+#>   [7] fs_1.6.3                    zlibbioc_1.49.0            
+#>   [9] ragg_1.2.7                  vctrs_0.6.5                
+#>  [11] multtest_2.59.0             memoise_2.0.1              
+#>  [13] DelayedMatrixStats_1.25.1   RCurl_1.98-1.14            
+#>  [15] ggtree_3.11.0               BiocBaseUtils_1.5.0        
+#>  [17] htmltools_0.5.7             S4Arrays_1.3.3             
+#>  [19] BiocNeighbors_1.21.2        Rhdf5lib_1.25.1            
+#>  [21] gridGraphics_0.5-1          SparseArray_1.3.3          
+#>  [23] rhdf5_2.47.3                sass_0.4.8                 
+#>  [25] bslib_0.6.1                 desc_1.4.3                 
+#>  [27] plyr_1.8.9                  DECIPHER_2.31.1            
+#>  [29] cachem_1.0.8                igraph_2.0.1.1             
+#>  [31] iterators_1.0.14            lifecycle_1.0.4            
+#>  [33] pkgconfig_2.0.3             rsvd_1.0.5                 
+#>  [35] Matrix_1.6-5                R6_2.5.1                   
+#>  [37] fastmap_1.1.1               GenomeInfoDbData_1.2.11    
+#>  [39] aplot_0.2.2                 digest_0.6.34              
+#>  [41] ggnewscale_0.4.10           colorspace_2.1-0           
+#>  [43] patchwork_1.2.0             irlba_2.3.5.1              
+#>  [45] textshaping_0.3.7           RSQLite_2.3.5              
+#>  [47] vegan_2.6-4                 beachmat_2.19.1            
+#>  [49] labeling_0.4.3              fansi_1.0.6                
+#>  [51] urltools_1.7.3              polyclip_1.10-6            
+#>  [53] httr_1.4.7                  abind_1.4-5                
+#>  [55] mgcv_1.9-1                  compiler_4.4.0             
+#>  [57] withr_3.0.0                 bit64_4.0.5                
+#>  [59] BiocParallel_1.37.0         viridis_0.6.5              
+#>  [61] DBI_1.2.1                   highr_0.10                 
+#>  [63] ggforce_0.4.1               MASS_7.3-60.2              
+#>  [65] DelayedArray_0.29.1         bluster_1.13.0             
+#>  [67] permute_0.9-7               tools_4.4.0                
+#>  [69] vipor_0.4.7                 beeswarm_0.4.0             
+#>  [71] ape_5.7-1                   glue_1.7.0                 
+#>  [73] nlme_3.1-164                rhdf5filters_1.15.2        
+#>  [75] grid_4.4.0                  ade4_1.7-22                
+#>  [77] cluster_2.1.6               reshape2_1.4.4             
+#>  [79] generics_0.1.3              gtable_0.3.4               
+#>  [81] tidyr_1.3.1                 data.table_1.15.0          
+#>  [83] tidygraph_1.3.1             BiocSingular_1.19.0        
+#>  [85] ScaledMatrix_1.11.0         utf8_1.2.4                 
+#>  [87] foreach_1.5.2               ggrepel_0.9.5              
+#>  [89] pillar_1.9.0                stringr_1.5.1              
+#>  [91] yulab.utils_0.1.4           splines_4.4.0              
+#>  [93] tweenr_2.0.2                dplyr_1.1.4                
+#>  [95] treeio_1.27.0               lattice_0.22-5             
+#>  [97] survival_3.5-7              bit_4.0.5                  
+#>  [99] tidyselect_1.2.0            DirichletMultinomial_1.45.0
+#> [101] gridExtra_2.3               bookdown_0.37              
+#> [103] phyloseq_1.47.0             xfun_0.42                  
+#> [105] graphlayouts_1.1.0          stringi_1.8.3              
+#> [107] ggfun_0.1.4                 lazyeval_0.2.2             
+#> [109] yaml_2.3.8                  evaluate_0.23              
+#> [111] codetools_0.2-19            tibble_3.2.1               
+#> [113] BiocManager_1.30.22         ggplotify_0.1.2            
+#> [115] cli_3.6.2                   systemfonts_1.0.5          
+#> [117] munsell_0.5.0               jquerylib_0.1.4            
+#> [119] Rcpp_1.0.12                 triebeard_0.4.1            
+#> [121] parallel_4.4.0              pkgdown_2.0.7              
+#> [123] assertthat_0.2.1            blob_1.2.4                 
+#> [125] sparseMatrixStats_1.15.0    bitops_1.0-7               
+#> [127] decontam_1.23.0             viridisLite_0.4.2          
+#> [129] tidytree_0.4.6              scales_1.3.0               
+#> [131] purrr_1.0.2                 crayon_1.5.2               
+#> [133] rlang_1.1.3

 
 
   
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