2019 SHNW 16S v4 faecal microbiota analyses

Raphael Eisenhofer, July 2020

In [2]:
conda activate qiime2-2020.2

(qiime2-2020.2) 


















In [3]:



    


cd  2019_SHNW_Gut/R1



    















    






(qiime2-2020.2) 









    


























First, let's import the reads into a qiime2 artefact:












In [7]:



    


qiime tools import \
--type SampleData[SequencesWithQuality] \
--input-path ../reads/R1 \
--input-format CasavaOneEightSingleLanePerSampleDirFmt \
--output-path demux-r1.qza



    















    






Imported ../reads/R1 as CasavaOneEightSingleLanePerSampleDirFmt to demux-r1.qza
(qiime2-2020.2) 









    
























In [8]:



    


qiime demux summarize \
--i-data demux-r1.qza \
--o-visualization demux-r1.qzv



    















    






Saved Visualization to: demux-r1.qzv
(qiime2-2020.2) 









    



























-Minimum:   1
-Median:    128215.0
-Mean:  139647.62886597938
-Maximum:   593954
-Total: 13545820















qual.png















Qualities look great. Will choose 150bp as the deblur sequence cutoff. This will let us compare features we find in our studies those found in others. The majority of datasets on QIITA are deblured to 150bp. There's also not a huge amount of information gained in the extra 100bp if we merged R1/R2 to get 250bp (see figure 1 from DOI: 10.1128/AEM.00062-07).












In [9]:



    


qiime deblur denoise-16S \
--i-demultiplexed-seqs demux-r1.qza \
--p-trim-length 150 \
--p-sample-stats \
--o-representative-sequences SHNW-gut-rep-seqs.qza \
--o-table SHNW-gut-table.qza \
--o-stats SHNW-gut-deblur-stats.qza \
--verbose \
--p-jobs-to-start 6



    















    






Saved FeatureTable[Frequency] to: table.qza
Saved FeatureData[Sequence] to: rep-seqs.qza
Saved DeblurStats to: deblur-stats.qza
(qiime2-2020.2) 









    
























In [10]:



    


qiime feature-table tabulate-seqs \
--i-data SHNW-gut-rep-seqs.qza \
--o-visualization SHNW-gut-rep-seqs.qzv



    















    






Saved Visualization to: rep-seqs.qzv
(qiime2-2020.2) 









    
























In [11]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table.qza \
--o-visualization SHNW-gut-table.qzv



    















    






Saved Visualization to: SHNW-gut-table.qzv
(qiime2-2020.2) 









    


























Table summary


Number of samples   92
Number of features  8,483
Total frequency     7,740,835

                    Frequency
Minimum frequency   6.0
1st quartile    50,267.0
Median frequency    77,491.0
3rd quartile    113,561.25
Maximum frequency   349,258.0
Mean frequency  84,139.51086956522





10 samples with lowest # of reads:


K15 10013
E4-EBC4 155
E3-EBC3 154
E6-EBC6 93
E9-EBC9 72
E8-EBC8 43
B12 42
E5-EBC5 23
W15 10
E1-EBC1 6















5 most abundant and prevalent features:


                                    Frequency    # of Samples Observed In
3c44df3672100a011a334b67eea24366    279,556     83
dadb874cf55015dada2a22cc32c1eda9    221,891     82
6234321dd90179936aa89249d23d256d    165,345     78
ac57d463deafd198f817244255e80019    137,292     77
8c6accdc08a5bcbdd15cc07420d92e2e    123,096     81















Assign taxonomy using a naive bayesian classifier against the SILVA 132 v4 database:












In [ ]:



    


qiime feature-classifier classify-sklearn \
--i-reads SHNW-gut-rep-seqs.qza \
--i-classifier silva-132-99-515-806-nb-classifier.qza \
--o-classification SHNW-gut-SILVA-132.qza \
--p-n-jobs 24



    













In [8]:



    


qiime metadata tabulate \
--m-input-file SHNW-gut-SILVA-132.qza \
--o-visualization SHNW-gut-SILVA-132.qzv



    















    






Saved Visualization to: SILVA132.qzv
(qiime2-2020.2) 









    


























Create a phylogenetic tree using SEPP:












In [ ]:



    


qiime fragment-insertion sepp \
--i-representative-sequences SHNW-gut-rep-seqs.qza \
--i-reference-database sepp-refs-silva-128.qza \
--o-tree SHNW-gut-sepp-tree.qza \
--o-placements SHNW-gut-sepp-placements.qza \
--verbose \
--p-threads 24



    















Filter out features that couldn't be inserted on tree:












In [3]:



    


qiime fragment-insertion filter-features \
  --i-table SHNW-gut-table.qza \
  --i-tree SHNW-gut-sepp-tree.qza \
  --o-filtered-table SHNW-gut-filtered_table.qza \
  --o-removed-table SHNW-gut-removed_table.qza \
  --verbose



    















    






Saved FeatureTable[Frequency] to: SHNW-gut-filtered_table.qza
Saved FeatureTable[Frequency] to: SHNW-gut-removed_table.qza
(qiime2-2020.2) 









    
























In [4]:



    


qiime feature-table summarize \
--i-table SHNW-gut-removed_table.qza \
--o-visualization SHNW-gut-removed_table.qzv



    















    






Saved Visualization to: SHNW-gut-removed_table.qzv
(qiime2-2020.2) 









    


























Looks like nothing was removed, so will just use the unfiltered table (table.qza) for subsequent analyses.














Run alpha-rarefaction analysis to see what a good sampling depth will be:












In [6]:



    


qiime diversity alpha-rarefaction \
--i-table SHNW-gut-table.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--o-visualization SHNW-gut-table-rarefaction.qzv \
--p-min-depth 500 \
--p-max-depth 50000



    















    






Saved Visualization to: SHNW-gut-table-rarefaction.qzv
(qiime2-2020.2) 









    


























rarefaction_richness.png















Richness seems to plateau for most samples ~25k reads.












In [5]:



    






    















    






SILVA132.qza      qual.png             silva-132-99-515-806-nb-classifier.qza
deblur-stats.qza  rep-seqs.qza         table.qza
deblur.log        rep-seqs.qzv         table.qzv
demux-r1.qza      sepp-placements.qza
demux-r1.qzv      sepp-tree.qza
(qiime2-2020.2) 









    


























Remove the soil samples from the table -- we don't need them for further analyses:












In [9]:



    


qiime feature-table filter-samples \
--i-table SHNW-gut-table.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--p-where "SampleType IN ('Soil', 'Control')" \
--p-exclude-ids \
--o-filtered-table SHNW-gut-table-no-soil.qza



    















    






Saved FeatureTable[Frequency] to: SHNW-gut-table-no-soil.qza
(qiime2-2020.2) 









    


























Remove samples with low #s of reads (<25,129):












In [10]:



    


qiime feature-table filter-samples \
--i-table SHNW-gut-table-no-soil.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--p-min-frequency 25129 \
--o-filtered-table SHNW-gut-table-no-soil-at-least-25129-reads.qza



    















    






Saved FeatureTable[Frequency] to: SHNW-gut-table-no-soil-at-least-25129-reads.qza
(qiime2-2020.2) 









    
























In [11]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-no-soil-at-least-25129-reads.qza \
--o-visualization SHNW-gut-table-no-soil-at-least-25129-reads.qzv



    















    






Saved Visualization to: SHNW-gut-table-no-soil-at-least-25129-reads.qzv
(qiime2-2020.2) 









    


























Samples removed:


Ksoil   57671
Bsoil   44302
Wsoil   33548
M10b    11363
K15 10013
E4-EBC4 155
E3-EBC3 154
E6-EBC6 93
E9-EBC9 72
E8-EBC8 43
B12 42
E5-EBC5 23
W15 10
E1-EBC1 6















Create taxa barplots per individual sample:












In [12]:



    


qiime taxa barplot \
--i-table SHNW-gut-table-no-soil-at-least-25129-reads.qza \
--i-taxonomy SILVA132.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--o-visualization SHNW-gut-table-no-soil-at-least-25129-reads-BARPLOT-PER-SAMPLE.qzv



    















    






Saved Visualization to: SHNW-gut-table-no-soil-at-least-25129-reads-BARPLOT-PER-SAMPLE.qzv
(qiime2-2020.2) 









    


























Remove outlier samples, joey samples, multiple samples from the same individual, and contaminated sample from table (see SI Figures 1 and 2):
Outlier:


    

  • B10
    • 

  • B11
    • 

  • B13
    • 

  • B14
    • 

  • B15
    • 

  • B16
    • 

  • B17
    • 



Joey:


    

  • M6
    • 

  • M14
    • 



Same individuals sampled from multiple days samples:


    

  • M1b
    • 

  • M1c
    • 

  • M3b
    • 

  • M4b
    • 

  • M9b
    • 



Contaminated sample:


    

  • M7b
    • 














In [ ]:



    






    













In [13]:



    


qiime feature-table filter-samples \
--i-table SHNW-gut-table-no-soil-at-least-25129-reads.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--p-where  "name IN ('B10', 'B11', 'B13', 'B14', 'B15', 'B16', 'B17', 'M7b', 'M6', 'M14', 'M1b', 'M1c', 'M3b', 'M4b', 'M9b')" \
--p-exclude-ids \
--o-filtered-table SHNW-gut-table-final.qza



    















    






Saved FeatureTable[Frequency] to: SHNW-gut-table-final.qza
(qiime2-2020.2) 









    
























In [14]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final.qza \
--o-visualization SHNW-gut-table-final.qzv



    















    






Saved Visualization to: SHNW-gut-table-final.qzv
(qiime2-2020.2) 









    


























Sample with lowest number of reads is now W7 (36,346)














Create bar plots on final filtered table:












In [24]:



    


qiime taxa barplot \
--i-table SHNW-gut-table-final.qza \
--i-taxonomy SILVA132.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--o-visualization SHNW-gut-table-final-BARPLOT-PER-SAMPLE.qzv



    















    






Saved Visualization to: table-final-BARPLOT-PER-SAMPLE.qzv
(qiime2-2020.2) 









    


























Collapse samples by population, then make barplots












In [25]:



    


qiime feature-table group \
--i-table SHNW-gut-table-final.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--m-metadata-column Population \
--p-mode mean-ceiling \
--p-axis sample \
--o-grouped-table SHNW-gut-table-final-POPULATION.qza



    















    






Saved FeatureTable[Frequency] to: table-final-POPULATION.qza
(qiime2-2020.2) 









    
























In [26]:



    


qiime taxa barplot \
--i-table SHNW-gut-table-final-POPULATION.qza \
--i-taxonomy SILVA132.qza \
--m-metadata-file SHNW_2019_Gut_Metadata_PPN.txt \
--o-visualization SHNW-gut-table-final-POPULATION-BARPLOT.qzv



    















    






Saved Visualization to: table-final-POPULATION-BARPLOT.qzv
(qiime2-2020.2) 









    


























Core diversity analyses using 36,346 as the rarefaction depth on the final filtered table:












In [3]:



    


qiime diversity core-metrics-phylogenetic \
--i-table SHNW-gut-table-final.qza \
--i-phylogeny SHNW-gut-sepp-tree.qza \
--p-sampling-depth 36346 \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--output-dir SHNW-gut-Core-metrics-final-filtered-table-36346



    















    






Saved FeatureTable[Frequency] to: SHNW-gut-Core-metrics-final-filtered-table-36346/rarefied_table.qza
Saved SampleData[AlphaDiversity] % Properties('phylogenetic') to: SHNW-gut-Core-metrics-final-filtered-table-36346/faith_pd_vector.qza
Saved SampleData[AlphaDiversity] to: SHNW-gut-Core-metrics-final-filtered-table-36346/observed_otus_vector.qza
Saved SampleData[AlphaDiversity] to: SHNW-gut-Core-metrics-final-filtered-table-36346/shannon_vector.qza
Saved SampleData[AlphaDiversity] to: SHNW-gut-Core-metrics-final-filtered-table-36346/evenness_vector.qza
Saved DistanceMatrix % Properties('phylogenetic') to: SHNW-gut-Core-metrics-final-filtered-table-36346/unweighted_unifrac_distance_matrix.qza
Saved DistanceMatrix % Properties('phylogenetic') to: SHNW-gut-Core-metrics-final-filtered-table-36346/weighted_unifrac_distance_matrix.qza
Saved DistanceMatrix to: SHNW-gut-Core-metrics-final-filtered-table-36346/jaccard_distance_matrix.qza
Saved DistanceMatrix to: SHNW-gut-Core-metrics-final-filtered-table-36346/bray_curtis_distance_matrix.qza
Saved PCoAResults to: SHNW-gut-Core-metrics-final-filtered-table-36346/unweighted_unifrac_pcoa_results.qza
Saved PCoAResults to: SHNW-gut-Core-metrics-final-filtered-table-36346/weighted_unifrac_pcoa_results.qza
Saved PCoAResults to: SHNW-gut-Core-metrics-final-filtered-table-36346/jaccard_pcoa_results.qza
Saved PCoAResults to: SHNW-gut-Core-metrics-final-filtered-table-36346/bray_curtis_pcoa_results.qza
Saved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/unweighted_unifrac_emperor.qzv
Saved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/weighted_unifrac_emperor.qzv
Saved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/jaccard_emperor.qzv
Saved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/bray_curtis_emperor.qzv
(qiime2-2020.2) 









    
























In [4]:



    


for i in SHNW-gut-Core-metrics-final-filtered-table-36346/*vector.qza; do qiime diversity alpha-group-significance --i-alpha-diversity $i --m-metadata-file SHNW_2019_Gut_Metadata.txt --o-visualization ${i/.qza/.qzv}; done



    















    






Saved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/evenness_vector.qzv
Saved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/faith_pd_vector.qzv
Saved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/observed_otus_vector.qzv
Saved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/shannon_vector.qzv
(qiime2-2020.2) 









    


























PERMANOVA tests for microbial composition differences:












In [5]:



    


qiime diversity beta-group-significance \
--i-distance-matrix SHNW-gut-Core-metrics-final-filtered-table-36346/unweighted_unifrac_distance_matrix.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--m-metadata-column Captive \
--o-visualization SHNW-gut-Core-metrics-final-filtered-table-36346/unweighted_unifrac_PERMANOVA.qzv \
--p-method permanova \
--p-pairwise



    















    






Saved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/unweighted_unifrac_PERMANOVA.qzv
(qiime2-2020.2) 









    
























In [6]:



    


qiime diversity beta-group-significance \
--i-distance-matrix SHNW-gut-Core-metrics-final-filtered-table-36346/weighted_unifrac_distance_matrix.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--m-metadata-column Captive \
--o-visualization SHNW-gut-Core-metrics-final-filtered-table-36346/weighted_unifrac_PERMANOVA.qzv \
--p-method permanova \
--p-pairwise



    















    






Saved Visualization to: SHNW-gut-Core-metrics-final-filtered-table-36346/weighted_unifrac_PERMANOVA.qzv
(qiime2-2020.2) 









    


























ANCOM analysis to identify differentially abundant features:














Remove low abundance/prevalence features












In [36]:



    


qiime feature-table filter-features \
--i-table SHNW-gut-table-final.qza \
--o-filtered-table SHNW-gut-table-final-FILTERED-ANCOM-ms8-mf500.qza \
--p-min-samples 8 \
--p-min-frequency 500



    















    






Saved FeatureTable[Frequency] to: table-final-FILTERED-ANCOM-ms8-mf200.qza
(qiime2-2020.2) 









    
























In [37]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final-FILTERED-ANCOM-ms8-mf500.qza \
--o-visualization SHNW-gut-table-final-FILTERED-ANCOM-ms8-mf500.qzv



    















    






Saved Visualization to: table-final-FILTERED-ANCOM-ms8-mf200.qzv
(qiime2-2020.2) 









    
























In [38]:



    


qiime composition add-pseudocount \
--i-table SHNW-gut-table-final-FILTERED-ANCOM-ms8-mf500.qza \
--o-composition-table SHNW-gut-table-final-FILTERED-ANCOM-ms8-mf500-pseudo.qza



    















    






Saved FeatureTable[Composition] to: table-final-FILTERED-ANCOM-ms8-mf200-pseudo.qza
(qiime2-2020.2) 









    
























In [39]:



    


qiime composition ancom \
--i-table SHNW-gut-table-final-FILTERED-ANCOM-ms8-mf500-pseudo.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--m-metadata-column Captive \
--o-visualization SHNW-gut-table-final-FILTERED-ANCOM-ms8-mf500-pseudo-CAPTIVE.qzv



    















    






Saved Visualization to: table-final-FILTERED-ANCOM-ms8-mf200-pseudo-CAPTIVE.qzv
(qiime2-2020.2) 









    


























ANCOM collapsed at family level (i.e. not at the feature level)












In [11]:



    


qiime taxa collapse \
--i-table SHNW-gut-table-final.qza \
--i-taxonomy SHNW-gut-SILVA-132.qza \
--p-level 5 \
--o-collapsed-table SHNW-gut-table-final-FAMILY.qza



    















    






Saved FeatureTable[Frequency] to: SHNW-gut-table-final-FAMILY.qza
(qiime2-2020.2) 









    
























In [13]:



    


qiime feature-table filter-features \
--i-table SHNW-gut-table-final-FAMILY.qza \
--o-filtered-table SHNW-gut-table-final-FAMILY-FILTERED-ANCOM-ms8-mf500.qza \
--p-min-samples 8 \
--p-min-frequency 500



    















    






Saved FeatureTable[Frequency] to: SHNW-gut-table-final-FAMILY-FILTERED-ANCOM-ms8-mf500.qza
(qiime2-2020.2) 









    
























In [15]:



    


qiime composition add-pseudocount \
--i-table SHNW-gut-table-final-FAMILY-FILTERED-ANCOM-ms8-mf500.qza \
--o-composition-table SHNW-gut-table-final-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo.qza



    















    






Saved FeatureTable[Composition] to: SHNW-gut-table-final-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo.qza
(qiime2-2020.2) 









    
























In [17]:



    


qiime composition ancom \
--i-table SHNW-gut-table-final-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--m-metadata-column Captive \
--o-visualization SHNW-gut-table-final-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo-CAPTIVE.qzv



    















    






Saved Visualization to: SHNW-gut-table-final-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo-CAPTIVE.qzv
(qiime2-2020.2) 









    


























Unique feature analysis:














Separate main table to population-specific tables












In [37]:



    


qiime feature-table filter-samples \
--i-table SHNW-gut-table-final.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--p-where "[Captive]='Yes'" \
--o-filtered-table SHNW-gut-table-final-CAPTIVEonly.qza



    















    






Saved FeatureTable[Frequency] to: table-final-CAPTIVEonly.qza
(qiime2-2020.2) 









    
























In [38]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final-CAPTIVEonly.qza \
--o-visualization SHNW-gut-table-final-CAPTIVEonly.qzv



    















    






Saved Visualization to: table-final-CAPTIVEonly.qzv
(qiime2-2020.2) 









    
























In [39]:



    


qiime feature-table filter-samples \
--i-table SHNW-gut-table-final.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--p-where "[Captive]='No'" \
--o-filtered-table SHNW-gut-table-final-WILDonly.qza



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly.qza
(qiime2-2020.2) 









    
























In [40]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final-WILDonly.qza \
--o-visualization SHNW-gut-table-final-WILDonly.qzv



    















    






Saved Visualization to: table-final-WILDonly.qzv
(qiime2-2020.2) 









    


























A foray into cross-sample contamination














Hard to get exact read numbers per ASV per sample, so export to ye old school biom format:












In [4]:



    


qiime tools export \
--input-path table-final-CAPTIVEonly.qza \
--output-path table-final-CAPTIVEonly.biom



    















    






Exported table-final-CAPTIVEonly.qza as BIOMV210DirFmt to directory table-final-CAPTIVEonly.biom
(qiime2-2020.2) 









    
























In [6]:



    


biom convert -i table-final-CAPTIVEonly.biom/feature-table.biom -o table-final-CAPTIVEonly.biom/feature-table.tsv --to-tsv



    















    






(qiime2-2020.2) 









    


























Pull out the EBCs to check if these abundant features spilled over into them as would be expected if cross-sample contamination was happening.












In [7]:



    


qiime feature-table filter-samples \
--i-table SHNW-gut-table.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--p-where "SampleType IN ('Control')" \
--o-filtered-table SHNW-gut-table-EBCs-ONLY.qza



    















    






Saved FeatureTable[Frequency] to: SHNW-gut-table-EBCs-ONLY.qza
(qiime2-2020.2) 









    
























In [8]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-EBCs-ONLY.qza \
--o-visualization SHNW-gut-table-EBCs-ONLY.qzv



    















    






Saved Visualization to: SHNW-gut-table-EBCs-ONLY.qzv
(qiime2-2020.2) 









    


























Looks like we do get spillover of the most abundant ASVs into the EBCs, as we would expect under the scenario that greater abundance = greater chance of cross-contamination.














Remove features with fewer than 200 reads (0.00015% of total table frequency) and found in fewer than 3 samples. Rationale for 200 reads is to help mitigate effect of cross-well sample contamination.
The most abundant feature in the wild-only table is 3c44df3672100a011a334b67eea24366, with a frequency of 278,742 (total table frequency 5,026,870) or 5.5% of total wild-only table abundance.
This feature is found in the captive-only table with a frequency of only 131 (total table frequency 1,292,572) or 0.0001% of total captive-only table abundance.
At least present in 3 samples because I'm interested in populations here.












In [41]:



    


qiime feature-table filter-features \
--i-table SHNW-gut-table-final-CAPTIVEonly.qza \
--o-filtered-table SHNW-gut-table-final-CAPTIVEonly-ms3-mf200.qza \
--p-min-samples 3 \
--p-min-frequency 200



    















    






Saved FeatureTable[Frequency] to: table-final-CAPTIVEonly-ms3-mf200.qza
(qiime2-2020.2) 









    
























In [42]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final-CAPTIVEonly-ms3-mf200.qza \
--o-visualization SHNW-gut-table-final-CAPTIVEonly-ms3-mf200.qzv



    















    






Saved Visualization to: table-final-CAPTIVEonly-ms3-mf200.qzv
(qiime2-2020.2) 









    


























pre-filter:


Number of features  2,618
Total frequency     1,292,572



post-filter:


Number of features  550
Total frequency     1,107,746















Filtering the wild-only table. Using a min-frequency of 750 to keep it the same filter % as the captive only table, as the wild-only table has a greater total frequency..












In [45]:



    


qiime feature-table filter-features \
--i-table SHNW-gut-table-final-WILDonly.qza \
--o-filtered-table SHNW-gut-table-final-WILDonly-ms3-mf750.qza \
--p-min-samples 3 \
--p-min-frequency 750



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly-ms3-mf750.qza
(qiime2-2020.2) 









    
























In [46]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final-WILDonly-ms3-mf750.qza \
--o-visualization SHNW-gut-table-final-WILDonly-ms3-mf750.qzv



    















    






Saved Visualization to: table-final-WILDonly-ms3-mf750.qzv
(qiime2-2020.2) 









    


























pre-filter


Number of features  3,096
Total frequency     5,026,870



post-filter


Number of features  642
Total frequency     4,729,528















Filter captive/wild-specific features from tables to look at total abundance of captive/wild-specific features












In [49]:



    


qiime feature-table filter-features \
--i-table SHNW-gut-table-final-WILDonly.qza \
--m-metadata-file WILD-only.txt \
--o-filtered-table SHNW-gut-table-final-WILDonly-WILD-SPECIFIC.qza



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly-WILD-SPECIFIC.qza
(qiime2-2020.2) 









    
























In [50]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final-WILDonly-WILD-SPECIFIC.qza \
--o-visualization SHNW-gut-table-final-WILDonly-WILD-SPECIFIC.qzv



    















    






Saved Visualization to: table-final-WILDonly-WILD-SPECIFIC.qzv
(qiime2-2020.2) 









    


























Total frequency in wild-only table = 4,729,528
Total frequency of wild-specific features = 3,370,268
71.3%












In [51]:



    


qiime feature-table filter-features \
--i-table SHNW-gut-table-final-CAPTIVEonly.qza \
--m-metadata-file CAPTIVE-only.txt \
--o-filtered-table SHNW-gut-table-final-CAPTIVEonly-CAPTIVE-SPECIFIC.qza



    















    






Saved FeatureTable[Frequency] to: table-final-CAPTIVEonly-CAPTIVE-SPECIFIC.qza
(qiime2-2020.2) 









    
























In [52]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final-CAPTIVEonly-CAPTIVE-SPECIFIC.qza \
--o-visualization SHNW-gut-table-final-CAPTIVEonly-CAPTIVE-SPECIFIC.qzv



    















    






Saved Visualization to: table-final-CAPTIVEonly-CAPTIVE-SPECIFIC.qzv
(qiime2-2020.2) 









    


























Total frequency in captive table = 1,107,746
Total frequency of captive-specific features = 816,701
73.7%














Core microbiome analysis:












In [3]:



    


qiime feature-table core-features \
--i-table SHNW-gut-table-final-WILDonly-ms3-mf750.qza \
--o-visualization SHNW-gut-table-final-WILDonly-ms3-mf750-CORE-FEATURES.qzv



    















    






Saved Visualization to: table-final-WILDonly-ms3-mf750-CORE-FEATURES.qzv
(qiime2-2020.2) 









    
























In [4]:



    


qiime feature-table core-features \
--i-table SHNW-gut-table-final-CAPTIVEonly-ms3-mf200.qza \
--o-visualization SHNW-gut-table-final-CAPTIVEonly-ms3-mf20-CORE-FEATURES.qzv



    















    






Saved Visualization to: table-final-CAPTIVEonly-ms3-mf20-CORE-FEATURES.qzv
(qiime2-2020.2) 









    


























Merge filtered tables to look for core microbiome accross all wombats (i.e. captive and wild)












In [19]:



    


qiime feature-table merge \
--i-tables SHNW-gut-table-final-CAPTIVEonly-ms3-mf200.qza SHNW-gut-table-final-WILDonly-ms3-mf750.qza \
--o-merged-table SHNW-gut-table-final-FILTERED-MERGED.qza



    















    






Saved FeatureTable[Frequency] to: SHNW-gut-table-final-FILTERED-MERGED.qza
(qiime2-2020.2) 









    
























In [20]:



    


qiime feature-table core-features \
--i-table SHNW-gut-table-final-FILTERED-MERGED.qza \
--o-visualization SHNW-gut-table-final-FILTERED-MERGED-CORE-FEATURES.qzv



    















    






Saved Visualization to: SHNW-gut-table-final-FILTERED-MERGED-CORE-FEATURES.qzv
(qiime2-2020.2) 









    


























Pull out a list of features from each of the core microbiota tables to filter the main tables from (to work out abundance of the core microbiota):












In [ ]:



    


for i in *.tsv; do awk '{print $1}' $i > ${i/.tsv/-FEATURES.tsv}; done



    













In [24]:



    


for i in *.tsv; do sed -i'' 's/Feature/feature id/g' $i; done



    















    






(qiime2-2020.2) 









    
























In [25]:



    


for i in *ALL*.tsv; do qiime feature-table filter-features --i-table SHNW-gut-table-final-FILTERED-MERGED.qza --m-metadata-file $i --o-filtered-table ${i/.tsv/.qza}; done



    















    






Saved FeatureTable[Frequency] to: core-features-ALL-0.900-FEATURES.qza
Saved FeatureTable[Frequency] to: core-features-ALL-1.000-FEATURES.qza
(qiime2-2020.2) 









    
























In [26]:



    


for i in *captive*.tsv; do qiime feature-table filter-features --i-table SHNW-gut-table-final-CAPTIVEonly-ms3-mf200.qza --m-metadata-file $i --o-filtered-table ${i/.tsv/.qza}; done



    















    






Saved FeatureTable[Frequency] to: core-features-captive-0.900-FEATURES.qza
Saved FeatureTable[Frequency] to: core-features-captive-1.000-FEATURES.qza
(qiime2-2020.2) 









    
























In [27]:



    


for i in *WILD*.tsv; do qiime feature-table filter-features --i-table SHNW-gut-table-final-WILDonly-ms3-mf750.qza --m-metadata-file $i --o-filtered-table ${i/.tsv/.qza}; done



    















    






Saved FeatureTable[Frequency] to: core-features-WILD-0.900-FEATURES.qza
Saved FeatureTable[Frequency] to: core-features-WILD-1.000-FEATURES.qza
(qiime2-2020.2) 









    
























In [28]:



    


for i in *FEATURE*.qza; do qiime feature-table summarize --i-table $i --o-visualization ${i/.qza/.qzv}; done



    















    






Saved Visualization to: core-features-ALL-0.900-FEATURES.qzv
Saved Visualization to: core-features-ALL-1.000-FEATURES.qzv
Saved Visualization to: core-features-WILD-0.900-FEATURES.qzv
Saved Visualization to: core-features-WILD-1.000-FEATURES.qzv
Saved Visualization to: core-features-captive-0.900-FEATURES.qzv
Saved Visualization to: core-features-captive-1.000-FEATURES.qzv
(qiime2-2020.2) 









    


























Wild sample analyses












In [11]:



    


qiime diversity core-metrics-phylogenetic \
--i-table SHNW-gut-table-final-WILDonly.qza \
--i-phylogeny SHNW-gut-sepp-tree.qza \
--p-sampling-depth 36346 \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--output-dir SHNW-gut-Core-metrics-final-filtered-table-36346-WILDonly



    















    






Saved FeatureTable[Frequency] to: Core-metrics-final-filtered-table-36346-WILDonly/rarefied_table.qza
Saved SampleData[AlphaDiversity] % Properties('phylogenetic') to: Core-metrics-final-filtered-table-36346-WILDonly/faith_pd_vector.qza
Saved SampleData[AlphaDiversity] to: Core-metrics-final-filtered-table-36346-WILDonly/observed_otus_vector.qza
Saved SampleData[AlphaDiversity] to: Core-metrics-final-filtered-table-36346-WILDonly/shannon_vector.qza
Saved SampleData[AlphaDiversity] to: Core-metrics-final-filtered-table-36346-WILDonly/evenness_vector.qza
Saved DistanceMatrix % Properties('phylogenetic') to: Core-metrics-final-filtered-table-36346-WILDonly/unweighted_unifrac_distance_matrix.qza
Saved DistanceMatrix % Properties('phylogenetic') to: Core-metrics-final-filtered-table-36346-WILDonly/weighted_unifrac_distance_matrix.qza
Saved DistanceMatrix to: Core-metrics-final-filtered-table-36346-WILDonly/jaccard_distance_matrix.qza
Saved DistanceMatrix to: Core-metrics-final-filtered-table-36346-WILDonly/bray_curtis_distance_matrix.qza
Saved PCoAResults to: Core-metrics-final-filtered-table-36346-WILDonly/unweighted_unifrac_pcoa_results.qza
Saved PCoAResults to: Core-metrics-final-filtered-table-36346-WILDonly/weighted_unifrac_pcoa_results.qza
Saved PCoAResults to: Core-metrics-final-filtered-table-36346-WILDonly/jaccard_pcoa_results.qza
Saved PCoAResults to: Core-metrics-final-filtered-table-36346-WILDonly/bray_curtis_pcoa_results.qza
Saved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/unweighted_unifrac_emperor.qzv
Saved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/weighted_unifrac_emperor.qzv
Saved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/jaccard_emperor.qzv
Saved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/bray_curtis_emperor.qzv
(qiime2-2020.2) 









    
























In [12]:



    


for i in SHNW-gut-Core-metrics-final-filtered-table-36346-WILDonly/*vector.qza; do qiime diversity alpha-group-significance --i-alpha-diversity $i --m-metadata-file SHNW_2019_Gut_Metadata.txt --o-visualization ${i/.qza/.qzv}; done



    















    






Saved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/evenness_vector.qzv
Saved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/faith_pd_vector.qzv
Saved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/observed_otus_vector.qzv
Saved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/shannon_vector.qzv
(qiime2-2020.2) 









    


























PERMANOVA tests for microbial composition differences:












In [13]:



    


qiime diversity beta-group-significance \
--i-distance-matrix SHNW-gut-Core-metrics-final-filtered-table-36346-WILDonly/unweighted_unifrac_distance_matrix.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--m-metadata-column Population \
--o-visualization SHNW-gut-Core-metrics-final-filtered-table-36346-WILDonly/unweighted_unifrac_PERMANOVA.qzv \
--p-method permanova \
--p-pairwise



    















    






Saved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/unweighted_unifrac_PERMANOVA.qzv
(qiime2-2020.2) 









    
























In [14]:



    


qiime diversity beta-group-significance \
--i-distance-matrix SHNW-gut-Core-metrics-final-filtered-table-36346-WILDonly/weighted_unifrac_distance_matrix.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--m-metadata-column Population \
--o-visualization SHNW-gut-Core-metrics-final-filtered-table-36346-WILDonly/weighted_unifrac_PERMANOVA.qzv \
--p-method permanova \
--p-pairwise



    















    






Saved Visualization to: Core-metrics-final-filtered-table-36346-WILDonly/weighted_unifrac_PERMANOVA.qzv
(qiime2-2020.2) 









    


























ANCOM analysis to identify differentially abundant features:














Remove low abundance/prevalence features












In [32]:



    


qiime feature-table filter-features \
--i-table SHNW-gut-table-final-WILDonly.qza \
--o-filtered-table SHNW-gut-table-final-WILDonly-FILTERED-ANCOM-ms8-mf500.qza \
--p-min-samples 8 \
--p-min-frequency 500



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly-FILTERED-ANCOM-ms8-mf2000.qza
(qiime2-2020.2) 









    
























In [33]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final-WILDonly-FILTERED-ANCOM-ms8-mf500.qza \
--o-visualization SHNW-gut-table-final-WILDonly-FILTERED-ANCOM-ms8-mf500.qzv



    















    






Saved Visualization to: table-final-WILDonly-FILTERED-ANCOM-ms8-mf2000.qzv
(qiime2-2020.2) 









    
























In [34]:



    


qiime composition add-pseudocount \
--i-table SHNW-gut-table-final-WILDonly-FILTERED-ANCOM-ms8-mf500.qza \
--o-composition-table SHNW-gut-table-final-WILDonly-FILTERED-ANCOM-ms8-mf500-pseudo.qza



    















    






Saved FeatureTable[Composition] to: table-final-WILDonly-FILTERED-ANCOM-ms8-mf2000-pseudo.qza
(qiime2-2020.2) 









    
























In [35]:



    


qiime composition ancom \
--i-table SHNW-gut-table-final-WILDonly-FILTERED-ANCOM-ms8-mf500-pseudo.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--m-metadata-column Population \
--o-visualization SHNW-gut-table-final-WILDonly-FILTERED-ANCOM-ms8-mf500-pseudo-POPULATION.qzv



    















    






Saved Visualization to: table-final-WILDonly-FILTERED-ANCOM-ms8-mf2000-pseudo-POPULATION.qzv
(qiime2-2020.2) 









    


























ANCOM collapsed at family level (i.e. not at the feature level)












In [29]:



    


qiime taxa collapse \
--i-table SHNW-gut-table-final-WILDonly.qza \
--i-taxonomy SHNW-gut-SILVA-132.qza \
--p-level 5 \
--o-collapsed-table SHNW-gut-table-final-WILDonly-FAMILY.qza



    















    






Saved FeatureTable[Frequency] to: SHNW-gut-table-final-WILDonly-FAMILY.qza
(qiime2-2020.2) 









    
























In [30]:



    


qiime feature-table filter-features \
--i-table SHNW-gut-table-final-WILDonly-FAMILY.qza \
--o-filtered-table SHNW-gut-table-final-WILDonly-FAMILY-FILTERED-ANCOM-ms8-mf500.qza \
--p-min-samples 8 \
--p-min-frequency 500



    















    






Saved FeatureTable[Frequency] to: SHNW-gut-table-final-WILDonly-FAMILY-FILTERED-ANCOM-ms8-mf500.qza
(qiime2-2020.2) 









    
























In [31]:



    


qiime composition add-pseudocount \
--i-table SHNW-gut-table-final-WILDonly-FAMILY-FILTERED-ANCOM-ms8-mf500.qza \
--o-composition-table SHNW-gut-table-final-WILDonly-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo.qza



    















    






Saved FeatureTable[Composition] to: SHNW-gut-table-final-WILDonly-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo.qza
(qiime2-2020.2) 









    
























In [32]:



    


qiime composition ancom \
--i-table SHNW-gut-table-final-WILDonly-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--m-metadata-column Population \
--o-visualization SHNW-gut-table-final-WILDonly-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo-POPULATION.qzv



    















    






Saved Visualization to: SHNW-gut-table-final-WILDonly-FAMILY-FILTERED-ANCOM-ms8-mf500-pseudo-POPULATION.qzv
(qiime2-2020.2) 









    


























Split samples into population-specific tables:












In [5]:



    


qiime feature-table filter-samples \
--i-table SHNW-gut-table-final-WILDonly.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--p-where "[Population]='Wonga'" \
--o-filtered-table SHNW-gut-table-final-WILDonly-Wonga.qza



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly-Wonga.qza
(qiime2-2020.2) 









    
























In [8]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final-WILDonly-Wonga.qza \
--o-visualization SHNW-gut-table-final-WILDonly-Wonga.qzv



    















    






Saved Visualization to: table-final-WILDonly-Wonga.qzv
(qiime2-2020.2) 









    
























In [6]:



    


qiime feature-table filter-samples \
--i-table SHNW-gut-table-final-WILDonly.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--p-where "[Population]='Kooloola'" \
--o-filtered-table SHNW-gut-table-final-WILDonly-Kooloola.qza



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly-Kooloola.qza
(qiime2-2020.2) 









    
























In [9]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final-WILDonly-Kooloola.qza \
--o-visualization SHNW-gut-table-final-WILDonly-Kooloola.qzv



    















    






Saved Visualization to: table-final-WILDonly-Kooloola.qzv
(qiime2-2020.2) 









    
























In [7]:



    


qiime feature-table filter-samples \
--i-table SHNW-gut-table-final-WILDonly.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--p-where "[Population]='Brookfield'" \
--o-filtered-table SHNW-gut-table-final-WILDonly-Brookfield.qza



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly-Brookfield.qza
(qiime2-2020.2) 









    
























In [10]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final-WILDonly-Brookfield.qza \
--o-visualization SHNW-gut-table-final-WILDonly-Brookfield.qzv



    















    






Saved Visualization to: table-final-WILDonly-Brookfield.qzv
(qiime2-2020.2) 









    


























Again, filter features from each table with a per-table frequency of < 0.00015%, and fewer than 3 samples.












In [41]:



    


qiime feature-table filter-features \
--i-table SHNW-gut-table-final-WILDonly-Brookfield.qza \
--o-filtered-table SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147.qza \
--p-min-samples 3 \
--p-min-frequency 147



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly-Brookfield-ms3-mf147.qza
(qiime2-2020.2) 









    
























In [42]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147.qza \
--o-visualization SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147.qzv



    















    






Saved Visualization to: table-final-WILDonly-Brookfield-ms3-mf147.qzv
(qiime2-2020.2) 









    


























pre-filter


Number of samples   9
Number of features  1,897
Total frequency     980,523



post-filter


Number of samples   9
Number of features  498
Total frequency     928,796













In [43]:



    


qiime feature-table filter-features \
--i-table SHNW-gut-table-final-WILDonly-Kooloola.qza \
--o-filtered-table SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245.qza \
--p-min-samples 3 \
--p-min-frequency 245



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly-Kooloola-ms3-mf245.qza
(qiime2-2020.2) 









    
























In [44]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245.qza \
--o-visualization SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245.qzv



    















    






Saved Visualization to: table-final-WILDonly-Kooloola-ms3-mf245.qzv
(qiime2-2020.2) 









    


























pre-filter


Number of samples   20
Number of features  2,281
Total frequency     1,635,208



post-filter


Number of samples   20
Number of features  587
Total frequency     1,548,744













In [45]:



    


qiime feature-table filter-features \
--i-table SHNW-gut-table-final-WILDonly-Wonga.qza \
--o-filtered-table SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361.qza \
--p-min-samples 3 \
--p-min-frequency 361



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly-Wonga-ms3-mf361.qza
(qiime2-2020.2) 









    
























In [46]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361.qza \
--o-visualization SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361.qzv



    















    






Saved Visualization to: table-final-WILDonly-Wonga-ms3-mf361.qzv
(qiime2-2020.2) 









    


























pre-filter


Number of samples   22
Number of features  2,393
Total frequency     2,411,139



post-filter


Number of samples   22
Number of features  553
Total frequency     2,288,425













In [19]:



    


qiime feature-table core-features \
--i-table SHNW-gut-table-no-soil-at-least-25129-reads-outliers-removed-WILDonly.qza \
--o-visualization SHNW-gut-table-no-soil-at-least-25129-reads-outliers-removed-WILDonly-CORE-FEATURES.qzv



    















    






Saved Visualization to: table-no-soil-at-least-25129-reads-outliers-removed-WILDonly-CORE-FEATURES.qzv
(qiime2-2020.2) 









    


























Filter population-specific features from tables to look at total abundance of population-specific features












In [5]:



    


qiime feature-table filter-features \
--i-table SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361.qza \
--m-metadata-file Wonga-only.txt \
--o-filtered-table SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361-WONGA-SPECIFIC.qza



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly-Wonga-ms3-mf361-WONGA-SPECIFIC.qza
(qiime2-2020.2) 









    
























In [6]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361-WONGA-SPECIFIC.qza \
--o-visualization SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361-WONGA-SPECIFIC.qzv



    















    






Saved Visualization to: table-final-WILDonly-Wonga-ms3-mf361-WONGA-SPECIFIC.qzv
(qiime2-2020.2) 









    


























Total frequency in Wonga table = 2,288,425
Total frequency of Wonga-specific features = 249,847
10.9%












In [9]:



    


qiime feature-table filter-features \
--i-table SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245.qza \
--m-metadata-file Kooloola-only.txt \
--o-filtered-table SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245-KOOLOOLA-SPECIFIC.qza



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly-Kooloola-ms3-mf245-KOOLOOLA-SPECIFIC.qza
(qiime2-2020.2) 









    
























In [10]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245-KOOLOOLA-SPECIFIC.qza \
--o-visualization SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245-KOOLOOLA-SPECIFIC.qzv



    















    






Saved Visualization to: table-final-WILDonly-Kooloola-ms3-mf245-KOOLOOLA-SPECIFIC.qzv
(qiime2-2020.2) 









    


























Total frequency in Kooloola table = 1,548,744
Total frequency of Kooloola-specific features = 117,404
7.6%












In [11]:



    


qiime feature-table filter-features \
--i-table SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147.qza \
--m-metadata-file Brookfield-only.txt \
--o-filtered-table SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147-BROOKFIELD-SPECIFIC.qza



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly-Brookfield-ms3-mf147-BROOKFIELD-SPECIFIC.qza
(qiime2-2020.2) 









    
























In [12]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147-BROOKFIELD-SPECIFIC.qza \
--o-visualization SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147-BROOKFIELD-SPECIFIC.qzv



    















    






Saved Visualization to: table-final-WILDonly-Brookfield-ms3-mf147-BROOKFIELD-SPECIFIC.qzv
(qiime2-2020.2) 









    


























Total frequency in Brookfield table = 928,796
Total frequency of Brookfield-specific features = 37,753
4%














Brookfield AND Kooloola specific features:












In [35]:



    


qiime feature-table merge \
--i-tables SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147.qza table-final-WILDonly-Kooloola-ms3-mf245.qza \
--o-merged-table SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola.qza



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly-Brookfield-AND-Kooloola.qza
(qiime2-2020.2) 









    
























In [36]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola.qza \
--o-visualization SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola.qzv



    















    






Saved Visualization to: table-final-WILDonly-Brookfield-AND-Kooloola.qzv
(qiime2-2020.2) 









    
























In [37]:



    


qiime feature-table filter-features \
--i-table SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola.qza \
--m-metadata-file Kooloola-and-Brookfield-only.txt \
--o-filtered-table SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola-SPECIFIC.qza



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly-Brookfield-AND-Kooloola-SPECIFIC.qza
(qiime2-2020.2) 









    
























In [38]:



    


qiime feature-table summarize \
--i-table SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola-SPECIFIC.qza \
--o-visualization SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola-SPECIFIC.qzv



    















    






Saved Visualization to: table-final-WILDonly-Brookfield-AND-Kooloola-SPECIFIC.qzv
(qiime2-2020.2) 









    


























Total frequency in Brookfield AND Kooloola table = 2,477,540
Total frequency of Brookfield AND Kooloola-specific features = 346,106
14%














Make taxa barplots of these population-specific microbes:












In [41]:



    


qiime feature-table group \
--i-table SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola-SPECIFIC.qza \
--m-metadata-file SHNW_2019_Gut_Metadata_habitat.txt \
--m-metadata-column Habitat \
--p-mode mean-ceiling \
--p-axis sample \
--o-grouped-table SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola-SPECIFIC-COLLAPSED.qza



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly-Brookfield-AND-Kooloola-SPECIFIC-COLLAPSED.qza
(qiime2-2020.2) 









    
























In [42]:



    


qiime feature-table group \
--i-table SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147-BROOKFIELD-SPECIFIC.qza \
--m-metadata-file SHNW_2019_Gut_Metadata_habitat.txt \
--m-metadata-column Population \
--p-mode mean-ceiling \
--p-axis sample \
--o-grouped-table SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147-BROOKFIELD-SPECIFIC-COLLAPSED.qza



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly-Brookfield-ms3-mf147-BROOKFIELD-SPECIFIC-COLLAPSED.qza
(qiime2-2020.2) 









    
























In [43]:



    


qiime feature-table group \
--i-table SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245-KOOLOOLA-SPECIFIC.qza \
--m-metadata-file SHNW_2019_Gut_Metadata_habitat.txt \
--m-metadata-column Population \
--p-mode mean-ceiling \
--p-axis sample \
--o-grouped-table SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245-KOOLOOLA-SPECIFIC-COLLASPED.qza



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly-Kooloola-ms3-mf245-KOOLOOLA-SPECIFIC-COLLASPED.qza
(qiime2-2020.2) 









    
























In [44]:



    


qiime feature-table group \
--i-table SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361-WONGA-SPECIFIC.qza \
--m-metadata-file SHNW_2019_Gut_Metadata_habitat.txt \
--m-metadata-column Population \
--p-mode mean-ceiling \
--p-axis sample \
--o-grouped-table SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361-WONGA-SPECIFIC-COLLAPSED.qza



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly-Wonga-ms3-mf361-WONGA-SPECIFIC-COLLAPSED.qza
(qiime2-2020.2) 









    
























In [47]:



    


qiime feature-table merge \
--i-tables SHNW-gut-table-final-WILDonly-Wonga-ms3-mf361-WONGA-SPECIFIC-COLLAPSED.qza SHNW-gut-table-final-WILDonly-Kooloola-ms3-mf245-KOOLOOLA-SPECIFIC-COLLASPED.qza SHNW-gut-table-final-WILDonly-Brookfield-ms3-mf147-BROOKFIELD-SPECIFIC-COLLAPSED.qza SHNW-gut-table-final-WILDonly-Brookfield-AND-Kooloola-SPECIFIC-COLLAPSED.qza \
--o-merged-table SHNW-gut-table-final-WILDonly-POPULATION-SPECIFIC-COLLASPED.qza



    















    






Saved FeatureTable[Frequency] to: table-final-WILDonly-POPULATION-SPECIFIC-COLLASPED.qza
(qiime2-2020.2) 









    
























In [48]:



    


qiime taxa barplot \
--i-table SHNW-gut-table-final-WILDonly-POPULATION-SPECIFIC-COLLASPED.qza \
--i-taxonomy SILVA132.qza \
--m-metadata-file SHNW_2019_Gut_Metadata_PPN.txt \
--o-visualization SHNW-gut-table-final-WILDonly-POPULATION-SPECIFIC-COLLASPED-BARPLOT.qzv



    















    






Saved Visualization to: table-final-WILDonly-POPULATION-SPECIFIC-COLLASPED-BARPLOT.qzv
(qiime2-2020.2) 









    
























In [ ]:



    






    















SourceTracker2 analysis -- is soil the source of some gut microbes from wild SHNWs?












In [ ]:



    


conda activate st2



    















Filter intial table; remove samples with <25,000 reads ('M10b', 'K15', 'B12', 'W15'), outlier samples ('B10', 'B11', 'B13', 'B14', 'B15', 'B16', 'B17', 'M7b', 'M6', 'M14'), and captive samples (Mx).













In [4]:



    


qiime feature-table filter-samples \
--i-table SHNW-gut-table.qza \
--m-metadata-file SHNW_2019_Gut_Metadata.txt \
--p-where  "name IN ('B10', 'B11', 'B13', 'B14', 'B15', 'B16', 'B17', 'M7b', 'M6', 'M14', 'M10b', 'K15', 'B12', 'W15', 'M1a', 'M1b', 'M1c', 'M2', 'M3a', 'M3b', 'M4a', 'M4b', 'M5', 'M6', 'M7a', 'M7b', 'M8', 'M9a', 'M9b', 'M10a', 'M10b', 'M11', 'M12', 'M13', 'M14')" \
--p-exclude-ids \
--o-filtered-table SHNW-gut-table-for-st2.qza



    















    






Saved FeatureTable[Frequency] to: table-for-st2.qza
(qiime2-2020.2) 









    
























In [5]:



    


qiime feature-table summarize \
--i-table tSHNW-gut-able-for-st2.qza \
--o-visualization SHNW-gut-table-for-st2.qzv



    















    






Saved Visualization to: table-for-st2.qzv
(qiime2-2020.2) 









    
























In [6]:



    


qiime tools export \
--input-path SHNW-gut-table-for-st2.qza \
--output-path SHNW-gut-table-for-st2.biom



    















    






Exported table-for-st2.qza as BIOMV210DirFmt to directory table-for-st2.biom
(qiime2-2020.2) 









    
























In [ ]:



    


sourcetracker2 gibbs \
-i SHNW-gut-table-for-st2.biom/feature-table.biom \
-m SHNW_2019_Gut_ST.txt \
-o ST-analysis



    
















Answer: no, very minimal <0.0001 % coming from soils