diff --git a/notebooks/tourmaline_python_notebook.ipynb b/notebooks/tourmaline_python_notebook.ipynb
index 1a01f87..1eda86e 100644
--- a/notebooks/tourmaline_python_notebook.ipynb
+++ b/notebooks/tourmaline_python_notebook.ipynb
@@ -30,9 +30,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Enabling: qiime2.jupyter\n",
+ "- Writing config: /Users/luke.thompson/miniconda3/envs/qiime2-2023.5/etc/jupyter\n",
+ " - Validating...\n",
+ " qiime2.jupyter \u001b[32mOK\u001b[0m\n"
+ ]
+ }
+ ],
"source": [
"%%bash\n",
"jupyter serverextension enable --py qiime2 --sys-prefix"
@@ -56,7 +67,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -77,7 +88,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -98,7 +109,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -109,7 +120,7 @@
" 'table': '../02-output-%s-%s/00-table-repseqs/table.qza' % (method, filtering),\n",
" 'adiv_vector': '../02-output-%s-%s/03-alpha-diversity/%s_vector.qza' % (method, filtering, adiv_metric),\n",
" 'repseqs_viz': '../02-output-%s-%s/00-table-repseqs/repseqs.qzv' % (method, filtering),\n",
- " 'table_viz': '../02-output-%s-%s/00-table-repseqs/table.qzv' % (method, filtering),\n",
+ " 'table_viz': '../02-output-%s-%s/00-table-repseqs/table_summary.qzv' % (method, filtering),\n",
" 'taxonomy_viz': '../02-output-%s-%s/01-taxonomy/taxonomy.qzv' % (method, filtering),\n",
" 'taxa_bar': '../02-output-%s-%s/01-taxonomy/taxa_barplot.qzv' % (method, filtering),\n",
" 'rooted_tree': '../02-output-%s-%s/02-alignment-tree/rooted_tree.qzv' % (method, filtering),\n",
@@ -135,7 +146,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {
"scrolled": true
},
@@ -162,17 +173,16 @@
"
\n",
" | \n",
" sample_name_water | \n",
- " description | \n",
- " control_status | \n",
- " sample_pairing | \n",
- " sample_pairs | \n",
- " esp_storage_carousel | \n",
- " sample_station | \n",
- " auv_latitude_deg | \n",
- " auv_longitude_deg | \n",
- " archive_bottom | \n",
+ " taxon_id | \n",
+ " scientific_name | \n",
+ " host_subject_id | \n",
+ " physical_specimen_location | \n",
+ " physical_specimen_remaining | \n",
+ " collection_timestamp | \n",
+ " latitude | \n",
+ " longitude | \n",
+ " lat_lon | \n",
" ... | \n",
- " target_gene | \n",
" target_subfragment | \n",
" pcr_primers | \n",
" pcr_primer_names | \n",
@@ -182,6 +192,7 @@
" seq_chemistry | \n",
" run_center | \n",
" run_date | \n",
+ " submitted_to_insdc | \n",
"
\n",
" \n",
" sample_name | \n",
@@ -212,17 +223,16 @@
"
\n",
" SC07.22 | \n",
" SC07 | \n",
- " Lake Erie sample | \n",
- " Lake Erie sample | \n",
- " paired with SC06, SC05 | \n",
- " 15 | \n",
- " SC07 | \n",
- " Field 51-spare | \n",
+ " 1647806 | \n",
+ " lake water metagenome | \n",
+ " SC07.22 | \n",
+ " GLERL | \n",
+ " True | \n",
+ " 2018-09-02 17:55 | \n",
" 41.703429 | \n",
" -83.070773 | \n",
- " 68 | \n",
+ " 41.70342935 -83.07077346 | \n",
" ... | \n",
- " 16S rRNA | \n",
" V4 | \n",
" FWD:GTGYCAGCMGCCGCGGTAA; REV:GGACTACNVGGGTWTCTAAT | \n",
" FWD:EMP-16S-515F-Parada; REV:EMP-16S-806R-Apprill | \n",
@@ -232,21 +242,21 @@
" 2x300bp (600 cycles) v3 chemistry | \n",
" University of Michigan Advanced Genomics Core | \n",
" September, 2019 | \n",
+ " True | \n",
"
\n",
" \n",
" SC07.50 | \n",
" SC07 | \n",
- " Lake Erie sample | \n",
- " Lake Erie sample | \n",
- " paired with SC06, SC05 | \n",
- " 15 | \n",
- " SC07 | \n",
- " Field 51-spare | \n",
+ " 1647806 | \n",
+ " lake water metagenome | \n",
+ " SC07.50 | \n",
+ " GLERL | \n",
+ " True | \n",
+ " 2018-09-02 17:55 | \n",
" 41.703429 | \n",
" -83.070773 | \n",
- " 68 | \n",
+ " 41.70342935 -83.07077346 | \n",
" ... | \n",
- " 16S rRNA | \n",
" V4 | \n",
" FWD:GTGYCAGCMGCCGCGGTAA; REV:GGACTACNVGGGTWTCTAAT | \n",
" FWD:EMP-16S-515F-Parada; REV:EMP-16S-806R-Apprill | \n",
@@ -256,21 +266,21 @@
" 2x300bp (600 cycles) v3 chemistry | \n",
" University of Michigan Advanced Genomics Core | \n",
" September, 2019 | \n",
+ " True | \n",
"
\n",
" \n",
" SC13.22 | \n",
" SC13 | \n",
- " Lake Erie sample | \n",
- " Lake Erie sample | \n",
- " paired with SC12, SC11 | \n",
- " 13 | \n",
- " SC13 | \n",
- " Field 44 | \n",
+ " 1647806 | \n",
+ " lake water metagenome | \n",
+ " SC13.22 | \n",
+ " GLERL | \n",
+ " True | \n",
+ " 2018-09-01 02:33 | \n",
" 41.788077 | \n",
" -83.167444 | \n",
- " 164 | \n",
+ " 41.78807732 -83.16744447 | \n",
" ... | \n",
- " 16S rRNA | \n",
" V4 | \n",
" FWD:GTGYCAGCMGCCGCGGTAA; REV:GGACTACNVGGGTWTCTAAT | \n",
" FWD:EMP-16S-515F-Parada; REV:EMP-16S-806R-Apprill | \n",
@@ -280,36 +290,37 @@
" 2x300bp (600 cycles) v3 chemistry | \n",
" University of Michigan Advanced Genomics Core | \n",
" September, 2019 | \n",
+ " True | \n",
"
\n",
" \n",
"\n",
- "3 rows × 53 columns
\n",
+ "3 rows × 61 columns
\n",
""
],
"text/plain": [
- " sample_name_water description control_status \\\n",
- "sample_name \n",
- "SC07.22 SC07 Lake Erie sample Lake Erie sample \n",
- "SC07.50 SC07 Lake Erie sample Lake Erie sample \n",
- "SC13.22 SC13 Lake Erie sample Lake Erie sample \n",
+ " sample_name_water taxon_id scientific_name \\\n",
+ "sample_name \n",
+ "SC07.22 SC07 1647806 lake water metagenome \n",
+ "SC07.50 SC07 1647806 lake water metagenome \n",
+ "SC13.22 SC13 1647806 lake water metagenome \n",
"\n",
- " sample_pairing sample_pairs esp_storage_carousel \\\n",
- "sample_name \n",
- "SC07.22 paired with SC06, SC05 15 SC07 \n",
- "SC07.50 paired with SC06, SC05 15 SC07 \n",
- "SC13.22 paired with SC12, SC11 13 SC13 \n",
+ " host_subject_id physical_specimen_location \\\n",
+ "sample_name \n",
+ "SC07.22 SC07.22 GLERL \n",
+ "SC07.50 SC07.50 GLERL \n",
+ "SC13.22 SC13.22 GLERL \n",
"\n",
- " sample_station auv_latitude_deg auv_longitude_deg \\\n",
- "sample_name \n",
- "SC07.22 Field 51-spare 41.703429 -83.070773 \n",
- "SC07.50 Field 51-spare 41.703429 -83.070773 \n",
- "SC13.22 Field 44 41.788077 -83.167444 \n",
+ " physical_specimen_remaining collection_timestamp latitude \\\n",
+ "sample_name \n",
+ "SC07.22 True 2018-09-02 17:55 41.703429 \n",
+ "SC07.50 True 2018-09-02 17:55 41.703429 \n",
+ "SC13.22 True 2018-09-01 02:33 41.788077 \n",
"\n",
- " archive_bottom ... target_gene target_subfragment \\\n",
- "sample_name ... \n",
- "SC07.22 68 ... 16S rRNA V4 \n",
- "SC07.50 68 ... 16S rRNA V4 \n",
- "SC13.22 164 ... 16S rRNA V4 \n",
+ " longitude lat_lon ... target_subfragment \\\n",
+ "sample_name ... \n",
+ "SC07.22 -83.070773 41.70342935 -83.07077346 ... V4 \n",
+ "SC07.50 -83.070773 41.70342935 -83.07077346 ... V4 \n",
+ "SC13.22 -83.167444 41.78807732 -83.16744447 ... V4 \n",
"\n",
" pcr_primers \\\n",
"sample_name \n",
@@ -335,16 +346,22 @@
"SC07.50 MiSeq 2x300bp (600 cycles) v3 chemistry \n",
"SC13.22 MiSeq 2x300bp (600 cycles) v3 chemistry \n",
"\n",
- " run_center run_date \n",
- "sample_name \n",
- "SC07.22 University of Michigan Advanced Genomics Core September, 2019 \n",
- "SC07.50 University of Michigan Advanced Genomics Core September, 2019 \n",
- "SC13.22 University of Michigan Advanced Genomics Core September, 2019 \n",
+ " run_center run_date \\\n",
+ "sample_name \n",
+ "SC07.22 University of Michigan Advanced Genomics Core September, 2019 \n",
+ "SC07.50 University of Michigan Advanced Genomics Core September, 2019 \n",
+ "SC13.22 University of Michigan Advanced Genomics Core September, 2019 \n",
"\n",
- "[3 rows x 53 columns]"
+ " submitted_to_insdc \n",
+ "sample_name \n",
+ "SC07.22 True \n",
+ "SC07.50 True \n",
+ "SC13.22 True \n",
+ "\n",
+ "[3 rows x 61 columns]"
]
},
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -363,7 +380,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -582,7 +599,7 @@
"[301 rows x 7 columns]"
]
},
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -601,7 +618,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {
"scrolled": true
},
@@ -638,28 +655,28 @@
" \n",
" \n",
" \n",
- " b744eae1244325f606575483df0de67e | \n",
- " d__Bacteria; p__Proteobacteria; c__Alphaproteo... | \n",
- " 0.7 | \n",
+ " 01514b1eefaab7c2b75c8a9d0f1c85e6 | \n",
+ " d__Bacteria; p__Cyanobacteria; c__Cyanobacteri... | \n",
+ " 1.0 | \n",
"
\n",
" \n",
- " 9cfa52be027c8fa57d197b21dd7a958c | \n",
- " d__Bacteria; p__Actinobacteriota; c__Actinobac... | \n",
- " 0.6 | \n",
+ " 016a372ed10b8fc27e6f925c235c1dd4 | \n",
+ " d__Bacteria; p__Armatimonadota; c__Fimbriimona... | \n",
+ " 1.0 | \n",
"
\n",
" \n",
- " 3f456a18968cea5c5e549e281f0f5808 | \n",
- " d__Bacteria; p__Actinobacteriota; c__Actinobac... | \n",
- " 0.9 | \n",
+ " 022ad8e97714db1259070a7dd5786652 | \n",
+ " d__Bacteria; p__Bdellovibrionota; c__Oligoflex... | \n",
+ " 1.0 | \n",
"
\n",
" \n",
- " 251fb9e771085e0267a4f87cf841ce2f | \n",
- " d__Bacteria; p__Cyanobacteria; c__Cyanobacteri... | \n",
+ " 03ccb07b8bd33e46bab0f1dee0f00afe | \n",
+ " d__Bacteria; p__Bacteroidota; c__Bacteroidia; ... | \n",
" 1.0 | \n",
"
\n",
" \n",
- " 01514b1eefaab7c2b75c8a9d0f1c85e6 | \n",
- " d__Bacteria; p__Cyanobacteria; c__Cyanobacteri... | \n",
+ " 048ace022a16d6cdb62afebfdae6c6fc | \n",
+ " d__Bacteria; p__Planctomycetota; c__Planctomyc... | \n",
" 1.0 | \n",
"
\n",
" \n",
@@ -668,28 +685,28 @@
" ... | \n",
"
\n",
" \n",
- " 1a27c63f6fee3c9b10ea7224d75f70b4 | \n",
- " d__Bacteria; p__Verrucomicrobiota; c__Verrucom... | \n",
- " 0.6 | \n",
+ " fbaea78fa0fc859a3fa28d26dd4ef605 | \n",
+ " d__Bacteria; p__Proteobacteria; c__Alphaproteo... | \n",
+ " 1.0 | \n",
"
\n",
" \n",
- " 6208bbf143fc29e81ad75b876a9a24ab | \n",
+ " fe40dc40772d7d5f26a992b3f0d27f0b | \n",
" d__Bacteria; p__Cyanobacteria; c__Cyanobacteri... | \n",
- " 0.6 | \n",
+ " 1.0 | \n",
"
\n",
" \n",
- " e2b82a3e451a8e43e175bbeb3a56a713 | \n",
- " d__Bacteria; p__Verrucomicrobiota; c__Verrucom... | \n",
+ " fe7b5b769375b837bb6d4bfa6dee234f | \n",
+ " d__Bacteria; p__Bacteroidota; c__Bacteroidia; ... | \n",
" 1.0 | \n",
"
\n",
" \n",
- " 2753d01dfea1ef6cea361b4ed91cc256 | \n",
- " d__Bacteria; p__Planctomycetota; c__Planctomyc... | \n",
- " 0.9 | \n",
+ " fed24691259ad53bae4ae128168a1870 | \n",
+ " d__Bacteria; p__Proteobacteria; c__Alphaproteo... | \n",
+ " 1.0 | \n",
"
\n",
" \n",
- " 3b7badb7274fad25197a014df653c15e | \n",
- " d__Bacteria; p__Verrucomicrobiota; c__Verrucom... | \n",
+ " ffd660f96ba6668b2d8d86fc4e150862 | \n",
+ " d__Bacteria; p__Bacteroidota; c__Bacteroidia; ... | \n",
" 0.6 | \n",
"
\n",
" \n",
@@ -700,36 +717,36 @@
"text/plain": [
" Taxon \\\n",
"Feature ID \n",
- "b744eae1244325f606575483df0de67e d__Bacteria; p__Proteobacteria; c__Alphaproteo... \n",
- "9cfa52be027c8fa57d197b21dd7a958c d__Bacteria; p__Actinobacteriota; c__Actinobac... \n",
- "3f456a18968cea5c5e549e281f0f5808 d__Bacteria; p__Actinobacteriota; c__Actinobac... \n",
- "251fb9e771085e0267a4f87cf841ce2f d__Bacteria; p__Cyanobacteria; c__Cyanobacteri... \n",
"01514b1eefaab7c2b75c8a9d0f1c85e6 d__Bacteria; p__Cyanobacteria; c__Cyanobacteri... \n",
+ "016a372ed10b8fc27e6f925c235c1dd4 d__Bacteria; p__Armatimonadota; c__Fimbriimona... \n",
+ "022ad8e97714db1259070a7dd5786652 d__Bacteria; p__Bdellovibrionota; c__Oligoflex... \n",
+ "03ccb07b8bd33e46bab0f1dee0f00afe d__Bacteria; p__Bacteroidota; c__Bacteroidia; ... \n",
+ "048ace022a16d6cdb62afebfdae6c6fc d__Bacteria; p__Planctomycetota; c__Planctomyc... \n",
"... ... \n",
- "1a27c63f6fee3c9b10ea7224d75f70b4 d__Bacteria; p__Verrucomicrobiota; c__Verrucom... \n",
- "6208bbf143fc29e81ad75b876a9a24ab d__Bacteria; p__Cyanobacteria; c__Cyanobacteri... \n",
- "e2b82a3e451a8e43e175bbeb3a56a713 d__Bacteria; p__Verrucomicrobiota; c__Verrucom... \n",
- "2753d01dfea1ef6cea361b4ed91cc256 d__Bacteria; p__Planctomycetota; c__Planctomyc... \n",
- "3b7badb7274fad25197a014df653c15e d__Bacteria; p__Verrucomicrobiota; c__Verrucom... \n",
+ "fbaea78fa0fc859a3fa28d26dd4ef605 d__Bacteria; p__Proteobacteria; c__Alphaproteo... \n",
+ "fe40dc40772d7d5f26a992b3f0d27f0b d__Bacteria; p__Cyanobacteria; c__Cyanobacteri... \n",
+ "fe7b5b769375b837bb6d4bfa6dee234f d__Bacteria; p__Bacteroidota; c__Bacteroidia; ... \n",
+ "fed24691259ad53bae4ae128168a1870 d__Bacteria; p__Proteobacteria; c__Alphaproteo... \n",
+ "ffd660f96ba6668b2d8d86fc4e150862 d__Bacteria; p__Bacteroidota; c__Bacteroidia; ... \n",
"\n",
" Consensus \n",
"Feature ID \n",
- "b744eae1244325f606575483df0de67e 0.7 \n",
- "9cfa52be027c8fa57d197b21dd7a958c 0.6 \n",
- "3f456a18968cea5c5e549e281f0f5808 0.9 \n",
- "251fb9e771085e0267a4f87cf841ce2f 1.0 \n",
"01514b1eefaab7c2b75c8a9d0f1c85e6 1.0 \n",
+ "016a372ed10b8fc27e6f925c235c1dd4 1.0 \n",
+ "022ad8e97714db1259070a7dd5786652 1.0 \n",
+ "03ccb07b8bd33e46bab0f1dee0f00afe 1.0 \n",
+ "048ace022a16d6cdb62afebfdae6c6fc 1.0 \n",
"... ... \n",
- "1a27c63f6fee3c9b10ea7224d75f70b4 0.6 \n",
- "6208bbf143fc29e81ad75b876a9a24ab 0.6 \n",
- "e2b82a3e451a8e43e175bbeb3a56a713 1.0 \n",
- "2753d01dfea1ef6cea361b4ed91cc256 0.9 \n",
- "3b7badb7274fad25197a014df653c15e 0.6 \n",
+ "fbaea78fa0fc859a3fa28d26dd4ef605 1.0 \n",
+ "fe40dc40772d7d5f26a992b3f0d27f0b 1.0 \n",
+ "fe7b5b769375b837bb6d4bfa6dee234f 1.0 \n",
+ "fed24691259ad53bae4ae128168a1870 1.0 \n",
+ "ffd660f96ba6668b2d8d86fc4e150862 0.6 \n",
"\n",
"[301 rows x 2 columns]"
]
},
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -749,7 +766,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -773,116 +790,629 @@
" \n",
" \n",
" | \n",
- " Taxon | \n",
- " Consensus | \n",
- "
\n",
- " \n",
- " Feature ID | \n",
- " | \n",
- " | \n",
+ " b744eae1244325f606575483df0de67e | \n",
+ " 9cfa52be027c8fa57d197b21dd7a958c | \n",
+ " 3f456a18968cea5c5e549e281f0f5808 | \n",
+ " 251fb9e771085e0267a4f87cf841ce2f | \n",
+ " 01514b1eefaab7c2b75c8a9d0f1c85e6 | \n",
+ " 3eeb4a6278652363d350b5391ebeb470 | \n",
+ " 8a06c5836b61ad5476c93f2b7d2b9311 | \n",
+ " 3b26ecfcd2d6fcb7e7a3493e5788c1bc | \n",
+ " 6b94959874b693c3304e68c57cefd287 | \n",
+ " 4e2d0baae4086a5f74e74e70bb12bf03 | \n",
+ " ... | \n",
+ " 3e834f7ea643d4a772d7e4196781ea7d | \n",
+ " f1b355a5bba01224d7fb8deca6d96b41 | \n",
+ " 752a0b05b0f77cb31c05a65f4e322487 | \n",
+ " 264dab11766c188cdbdb62a233fa738f | \n",
+ " 98e3d05c0e8980b343dddd744adc4260 | \n",
+ " 1a27c63f6fee3c9b10ea7224d75f70b4 | \n",
+ " 6208bbf143fc29e81ad75b876a9a24ab | \n",
+ " e2b82a3e451a8e43e175bbeb3a56a713 | \n",
+ " 2753d01dfea1ef6cea361b4ed91cc256 | \n",
+ " 3b7badb7274fad25197a014df653c15e | \n",
"
\n",
" \n",
" \n",
" \n",
- " b744eae1244325f606575483df0de67e | \n",
- " d__Bacteria; p__Proteobacteria; c__Alphaproteo... | \n",
- " 0.7 | \n",
+ " SC07.22 | \n",
+ " 100.0 | \n",
+ " 100.0 | \n",
+ " 74.0 | \n",
+ " 0.0 | \n",
+ " 6.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 18.0 | \n",
+ " 0.0 | \n",
+ " ... | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
"
\n",
" \n",
- " 9cfa52be027c8fa57d197b21dd7a958c | \n",
- " d__Bacteria; p__Actinobacteriota; c__Actinobac... | \n",
- " 0.6 | \n",
+ " SC07.50 | \n",
+ " 8.0 | \n",
+ " 12.0 | \n",
+ " 8.0 | \n",
+ " 0.0 | \n",
+ " 16.0 | \n",
+ " 0.0 | \n",
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"
\n",
" \n",
- " 3f456a18968cea5c5e549e281f0f5808 | \n",
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"
\n",
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"
\n",
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"
\n",
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\n",
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\n",
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\n",
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- " 6208bbf143fc29e81ad75b876a9a24ab | \n",
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"
\n",
" \n",
- " e2b82a3e451a8e43e175bbeb3a56a713 | \n",
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\n",
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- " 2753d01dfea1ef6cea361b4ed91cc256 | \n",
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\n",
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\n",
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"
\n",
" \n",
"\n",
- "301 rows × 2 columns
\n",
+ "16 rows × 301 columns
\n",
""
],
"text/plain": [
- " Taxon \\\n",
- "Feature ID \n",
- "b744eae1244325f606575483df0de67e d__Bacteria; p__Proteobacteria; c__Alphaproteo... \n",
- "9cfa52be027c8fa57d197b21dd7a958c d__Bacteria; p__Actinobacteriota; c__Actinobac... \n",
- "3f456a18968cea5c5e549e281f0f5808 d__Bacteria; p__Actinobacteriota; c__Actinobac... \n",
- "251fb9e771085e0267a4f87cf841ce2f d__Bacteria; p__Cyanobacteria; c__Cyanobacteri... \n",
- "01514b1eefaab7c2b75c8a9d0f1c85e6 d__Bacteria; p__Cyanobacteria; c__Cyanobacteri... \n",
- "... ... \n",
- "1a27c63f6fee3c9b10ea7224d75f70b4 d__Bacteria; p__Verrucomicrobiota; c__Verrucom... \n",
- "6208bbf143fc29e81ad75b876a9a24ab d__Bacteria; p__Cyanobacteria; c__Cyanobacteri... \n",
- "e2b82a3e451a8e43e175bbeb3a56a713 d__Bacteria; p__Verrucomicrobiota; c__Verrucom... \n",
- "2753d01dfea1ef6cea361b4ed91cc256 d__Bacteria; p__Planctomycetota; c__Planctomyc... \n",
- "3b7badb7274fad25197a014df653c15e d__Bacteria; p__Verrucomicrobiota; c__Verrucom... \n",
+ " b744eae1244325f606575483df0de67e 9cfa52be027c8fa57d197b21dd7a958c \\\n",
+ "SC07.22 100.0 100.0 \n",
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+ "SC56.22 146.0 22.0 \n",
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"\n",
- " Consensus \n",
- "Feature ID \n",
- "b744eae1244325f606575483df0de67e 0.7 \n",
- "9cfa52be027c8fa57d197b21dd7a958c 0.6 \n",
- "3f456a18968cea5c5e549e281f0f5808 0.9 \n",
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- "01514b1eefaab7c2b75c8a9d0f1c85e6 1.0 \n",
- "... ... \n",
- "1a27c63f6fee3c9b10ea7224d75f70b4 0.6 \n",
- "6208bbf143fc29e81ad75b876a9a24ab 0.6 \n",
- "e2b82a3e451a8e43e175bbeb3a56a713 1.0 \n",
- "2753d01dfea1ef6cea361b4ed91cc256 0.9 \n",
- "3b7badb7274fad25197a014df653c15e 0.6 \n",
+ " 3f456a18968cea5c5e549e281f0f5808 251fb9e771085e0267a4f87cf841ce2f \\\n",
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"\n",
- "[301 rows x 2 columns]"
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+ "\n",
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+ "SC16.22 0.0 0.0 \n",
+ "SC16.50 0.0 0.0 \n",
+ "SC18.22 0.0 0.0 \n",
+ "SC18.50 0.0 0.0 \n",
+ "SC51.22 0.0 0.0 \n",
+ "SC51.50 0.0 0.0 \n",
+ "SC53.22 0.0 0.0 \n",
+ "SC53.50 0.0 0.0 \n",
+ "SC54.22 0.0 0.0 \n",
+ "SC54.50 2.0 0.0 \n",
+ "SC56.22 0.0 0.0 \n",
+ "SC56.50 0.0 2.0 \n",
+ "\n",
+ " 3b7badb7274fad25197a014df653c15e \n",
+ "SC07.22 0.0 \n",
+ "SC07.50 0.0 \n",
+ "SC13.22 0.0 \n",
+ "SC13.50 0.0 \n",
+ "SC16.22 0.0 \n",
+ "SC16.50 0.0 \n",
+ "SC18.22 0.0 \n",
+ "SC18.50 0.0 \n",
+ "SC51.22 0.0 \n",
+ "SC51.50 0.0 \n",
+ "SC53.22 0.0 \n",
+ "SC53.50 0.0 \n",
+ "SC54.22 0.0 \n",
+ "SC54.50 0.0 \n",
+ "SC56.22 0.0 \n",
+ "SC56.50 2.0 \n",
+ "\n",
+ "[16 rows x 301 columns]"
]
},
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table = Artifact.load(inputs['table'])\n",
- "df_table = taxonomy.view(view_type=pd.DataFrame)\n",
+ "df_table = table.view(view_type=pd.DataFrame)\n",
"df_table"
]
},
@@ -902,44 +1432,32 @@
},
{
"cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "scrolled": false
- },
+ "execution_count": 9,
+ "metadata": {},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/luke.thompson/miniconda3/envs/qiime2-2021.2/lib/python3.6/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n"
- ]
- },
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
+ "image/png": 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",
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