02 Taxonomy Bridge
Jupyter notebook from the Community Metabolic Ecology via NMDC × Pangenome Integration project.
NB02: File→Sample Bridge and Taxonomy→GTDB Mapping¶
Project: Community Metabolic Ecology via NMDC × Pangenome Integration
Requires: BERDL JupyterHub (Spark — get_spark_session() injected into kernel)
Purpose¶
NB01 confirmed that classifier files (nmdc:dobj-11-*) and metabolomics files (nmdc:dobj-12-*)
share zero file_id overlap — they are different workflow output types. The shared identifier
is the biosample/sample_id (e.g., nmdc:bsm-11-*). This notebook:
- Part 1: Find the
file_id → sample_idbridge innmdc_arkin - Part 2: Build sample inventory — samples with both metagenomics classifier AND metabolomics data
- Part 3: Map NMDC taxon names (centrifuge_gold) → GTDB species (
gtdb_species_clade) - Part 4: Compute bridge quality per sample
- Part 5: Save outputs
Inputs¶
nmdc_arkintables:centrifuge_gold,metabolomics_gold,abiotic_features,study_tablekbase_ke_pangenome:gtdb_species_clade
Outputs¶
data/sample_file_bridge.csv— sample_id ↔ file_id mapping for all omics typesdata/nmdc_sample_inventory.csv— updated with samples having paired omics (replaces NB01 empty file)data/taxon_bridge.tsv— NMDC taxon name → GTDB species clade mappings with confidence tiersdata/bridge_quality.csv— per-sample fraction of community abundance mapped to pangenome
In [1]:
# On BERDL JupyterHub — get_spark_session() is injected into the kernel; no import needed
spark = get_spark_session()
spark
Out[1]:
<pyspark.sql.connect.session.SparkSession at 0x7d052d4fd400>
In [2]:
import os
import re
import pandas as pd
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
PROJECT_DIR = os.path.abspath(os.path.join(os.path.dirname('__file__'), '..'))
DATA_DIR = os.path.join(PROJECT_DIR, 'data')
FIGURES_DIR = os.path.join(PROJECT_DIR, 'figures')
os.makedirs(DATA_DIR, exist_ok=True)
os.makedirs(FIGURES_DIR, exist_ok=True)
print(f'DATA_DIR: {DATA_DIR}')
print(f'FIGURES_DIR: {FIGURES_DIR}')
DATA_DIR: /home/cjneely/repos/BERIL-research-observatory/projects/nmdc_community_metabolic_ecology/data FIGURES_DIR: /home/cjneely/repos/BERIL-research-observatory/projects/nmdc_community_metabolic_ecology/figures
Part 1: Find the file_id → sample_id Bridge¶
NB01 found that classifier tables and metabolomics tables use non-overlapping file_id namespaces.
The NMDC data model links files to biosamples through workflow activities.
We explore candidate tables in nmdc_arkin to find a file_id → sample_id mapping.
In [3]:
# Step 1a: Inspect candidate bridge tables — look for tables that have both file_id and sample_id
# Candidates from schema doc: sample_tokens_v1, taxonomy_dim, taxstring_lookup,
# embedding_metadata, taxonomy_embeddings, trait_unified
candidate_tables = [
'sample_tokens_v1',
'taxonomy_dim',
'taxstring_lookup',
'embedding_metadata',
'taxonomy_embeddings',
'trait_unified',
'biochemical_features',
'biochemical_embeddings',
'abiotic_embeddings',
]
bridge_candidates = [] # tables that have both file_id and sample_id
for tbl in candidate_tables:
try:
schema = spark.sql(f'DESCRIBE nmdc_arkin.{tbl}').toPandas()
cols = set(schema['col_name'].tolist())
has_file = 'file_id' in cols
has_sample = 'sample_id' in cols
n_cols = len(cols)
print(f'{tbl}: {n_cols} cols, file_id={has_file}, sample_id={has_sample}')
if has_file or has_sample:
print(f' -> columns: {sorted(cols)[:12]}')
if has_file and has_sample:
bridge_candidates.append(tbl)
print(f' *** BRIDGE CANDIDATE ***')
except Exception as e:
print(f'{tbl}: ERROR — {e}')
print(f'\nBridge candidates (have both file_id and sample_id): {bridge_candidates}')
sample_tokens_v1: 4 cols, file_id=False, sample_id=True -> columns: ['modality_id', 'sample_id', 'token_id', 'value'] taxonomy_dim: 8 cols, file_id=False, sample_id=False taxstring_lookup: 4 cols, file_id=False, sample_id=False embedding_metadata: 3 cols, file_id=False, sample_id=False taxonomy_embeddings: 19 cols, file_id=False, sample_id=True -> columns: ['coverage_fraction', 'embedding_dim_0', 'embedding_dim_1', 'embedding_dim_10', 'embedding_dim_11', 'embedding_dim_12', 'embedding_dim_13', 'embedding_dim_14', 'embedding_dim_15', 'embedding_dim_2', 'embedding_dim_3', 'embedding_dim_4'] trait_unified: 7 cols, file_id=False, sample_id=False biochemical_features: 21485 cols, file_id=False, sample_id=False biochemical_embeddings: 20 cols, file_id=False, sample_id=False abiotic_embeddings: 19 cols, file_id=False, sample_id=True -> columns: ['coverage_fraction', 'embedding_dim_0', 'embedding_dim_1', 'embedding_dim_10', 'embedding_dim_11', 'embedding_dim_12', 'embedding_dim_13', 'embedding_dim_14', 'embedding_dim_15', 'embedding_dim_2', 'embedding_dim_3', 'embedding_dim_4'] Bridge candidates (have both file_id and sample_id): []
In [4]:
# Step 1b: List all tables in nmdc_arkin to find any additional candidates
all_tables = spark.sql('SHOW TABLES IN nmdc_arkin').toPandas()
print(f'Total tables in nmdc_arkin: {len(all_tables)}')
print(all_tables['tableName'].sort_values().tolist())
Total tables in nmdc_arkin: 63 ['abiotic_embeddings', 'abiotic_features', 'annotation_crossrefs', 'annotation_hierarchies_unified', 'annotation_terms_unified', 'biochemical_embeddings', 'biochemical_features', 'biochemical_features_metadata', 'centrifuge_gold', 'cog_categories', 'cog_hierarchy_flat', 'contig_taxonomy', 'contig_taxonomy_backup', 'covstats_taxonomy_rollup', 'ec_hierarchy_flat', 'ec_hierarchy_graph', 'ec_terms', 'embedding_metadata', 'embeddings_v1', 'go_hierarchy_flat', 'go_hierarchy_graph', 'go_terms', 'gottcha_gold', 'kegg_ko_module', 'kegg_ko_pathway', 'kegg_ko_terms', 'kegg_module_terms', 'kegg_pathway_terms', 'kraken_gold', 'lipidomics_gold', 'metabolomics_gold', 'metacyc_hierarchy_flat', 'metacyc_hierarchy_graph', 'metacyc_pathway_reactions', 'metacyc_pathways', 'metacyc_reaction_ec', 'metatranscriptomics_gold', 'nom_feature_metadata', 'nom_gold', 'nom_matrix_optimized', 'omics_files_table', 'proteomics_gold', 'rhea_crossrefs', 'rhea_reactions', 'sample_file_lookup', 'sample_file_selections', 'sample_tokens_v1', 'study_table', 'taxonomy_dim', 'taxonomy_embeddings', 'taxonomy_family_embeddings', 'taxonomy_features', 'taxonomy_genus_embeddings', 'taxonomy_order_embeddings', 'taxonomy_phylum_embeddings', 'taxstring_lookup', 'trait_embeddings', 'trait_features', 'trait_sources', 'trait_taxonomy_mapping', 'trait_unified', 'unified_embeddings', 'vocab_registry_v1']
In [5]:
# Step 1c: Scan ALL tables in nmdc_arkin for file_id OR sample_id columns
# This finds any table not covered by the candidate list above
table_names = all_tables['tableName'].tolist()
file_id_tables = []
sample_id_tables = []
both_id_tables = []
for tbl in table_names:
try:
schema = spark.sql(f'DESCRIBE nmdc_arkin.{tbl}').toPandas()
cols = set(schema['col_name'].tolist())
has_file = 'file_id' in cols
has_sample = 'sample_id' in cols
if has_file:
file_id_tables.append(tbl)
if has_sample:
sample_id_tables.append(tbl)
if has_file and has_sample:
both_id_tables.append(tbl)
except Exception:
pass
print('Tables with file_id:', file_id_tables)
print()
print('Tables with sample_id:', sample_id_tables)
print()
print('Tables with BOTH file_id and sample_id (bridge candidates):', both_id_tables)
Tables with file_id: ['centrifuge_gold', 'covstats_taxonomy_rollup', 'gottcha_gold', 'kraken_gold', 'lipidomics_gold', 'nom_gold', 'omics_files_table', 'proteomics_gold', 'metabolomics_gold', 'metatranscriptomics_gold'] Tables with sample_id: ['sample_tokens_v1', 'contig_taxonomy', 'contig_taxonomy_backup', 'abiotic_embeddings', 'taxonomy_embeddings', 'taxonomy_features', 'trait_embeddings', 'trait_features', 'nom_matrix_optimized', 'omics_files_table', 'sample_file_lookup', 'sample_file_selections', 'abiotic_features'] Tables with BOTH file_id and sample_id (bridge candidates): ['omics_files_table']
In [6]:
# Step 1d: Inspect the bridge table(s) found above
# If both_id_tables is non-empty, show schema and sample rows for each
if both_id_tables:
for tbl in both_id_tables:
print(f'\n=== nmdc_arkin.{tbl} schema ===')
spark.sql(f'DESCRIBE nmdc_arkin.{tbl}').show(30, truncate=False)
n = spark.sql(f'SELECT COUNT(*) as n FROM nmdc_arkin.{tbl}').collect()[0]['n']
print(f'Row count: {n}')
print(f'Sample rows:')
spark.sql(f'SELECT * FROM nmdc_arkin.{tbl} LIMIT 5').show(truncate=False)
else:
print('No table with both file_id and sample_id found in nmdc_arkin.')
print('Will attempt bridge via file_name parsing (Step 1e).')
=== nmdc_arkin.omics_files_table schema === +---------------------+---------+-------+ |col_name |data_type|comment| +---------------------+---------+-------+ |file_id |string |NULL | |file_name |string |NULL | |file_url |string |NULL | |file_size_bytes |double |NULL | |file_type |string |NULL | |file_type_description|string |NULL | |md5_checksum |string |NULL | |omics_processing_id |string |NULL | |omics_processing_name|string |NULL | |workflow_id |string |NULL | |workflow_type |string |NULL | |sample_id |string |NULL | |study_id |string |NULL | +---------------------+---------+-------+ Row count: 385562 Sample rows: +---------------------+-----------------------+-------------------------------------------------+---------------+-------------+--------------------------------------------------------+--------------------------------+---------------------+------------------------------------------------------+-----------+-------------+--------------------+--------------------+ |file_id |file_name |file_url |file_size_bytes|file_type |file_type_description |md5_checksum |omics_processing_id |omics_processing_name |workflow_id|workflow_type|sample_id |study_id | +---------------------+-----------------------+-------------------------------------------------+---------------+-------------+--------------------------------------------------------+--------------------------------+---------------------+------------------------------------------------------+-----------+-------------+--------------------+--------------------+ |nmdc:dobj-11-5gw4sj93|202412_lipid_ref.sqlite|/api/data_object/nmdc%3Adobj-11-5gw4sj93/download|1.01738496E9 |Virus Summary|Tab separated file listing the viruses found by geNomad.|e61b91889283d6fbc055d574fc1e7285|nmdc:dgms-11-vfzh1754|Blanch_Nat_Lip_C_7_AB_M_07_POS_23Jan18_Brandi-WCSH5801|None |NULL |nmdc:bsm-13-cp77j103|nmdc:sty-11-aygzgv51| |nmdc:dobj-11-5gw4sj93|202412_lipid_ref.sqlite|/api/data_object/nmdc%3Adobj-11-5gw4sj93/download|1.01738496E9 |Virus Summary|Tab separated file listing the viruses found by geNomad.|e61b91889283d6fbc055d574fc1e7285|nmdc:dgms-11-vfzh1754|Blanch_Nat_Lip_C_7_AB_M_07_POS_23Jan18_Brandi-WCSH5801|None |NULL |nmdc:bsm-11-qpwvgx11|nmdc:sty-11-dcqce727| |nmdc:dobj-11-5gw4sj93|202412_lipid_ref.sqlite|/api/data_object/nmdc%3Adobj-11-5gw4sj93/download|1.01738496E9 |Virus Summary|Tab separated file listing the viruses found by geNomad.|e61b91889283d6fbc055d574fc1e7285|nmdc:dgms-11-vfzh1754|Blanch_Nat_Lip_C_7_AB_M_07_POS_23Jan18_Brandi-WCSH5801|None |NULL |nmdc:bsm-13-8e1rjf10|nmdc:sty-11-aygzgv51| |nmdc:dobj-11-5gw4sj93|202412_lipid_ref.sqlite|/api/data_object/nmdc%3Adobj-11-5gw4sj93/download|1.01738496E9 |Virus Summary|Tab separated file listing the viruses found by geNomad.|e61b91889283d6fbc055d574fc1e7285|nmdc:dgms-11-vfzh1754|Blanch_Nat_Lip_C_7_AB_M_07_POS_23Jan18_Brandi-WCSH5801|None |NULL |nmdc:bsm-11-whgy3b08|nmdc:sty-11-8ws97026| |nmdc:dobj-11-5gw4sj93|202412_lipid_ref.sqlite|/api/data_object/nmdc%3Adobj-11-5gw4sj93/download|1.01738496E9 |Virus Summary|Tab separated file listing the viruses found by geNomad.|e61b91889283d6fbc055d574fc1e7285|nmdc:dgms-11-vfzh1754|Blanch_Nat_Lip_C_7_AB_M_07_POS_23Jan18_Brandi-WCSH5801|None |NULL |nmdc:bsm-11-fdjmge85|nmdc:sty-11-dcqce727| +---------------------+-----------------------+-------------------------------------------------+---------------+-------------+--------------------------------------------------------+--------------------------------+---------------------+------------------------------------------------------+-----------+-------------+--------------------+--------------------+
In [7]:
# Step 1e: Explore sample_id format in abiotic_features
# to understand what ID format we need to bridge TO
print('=== abiotic_features sample_id examples ===')
abiotic_ids = spark.sql("""
SELECT sample_id
FROM nmdc_arkin.abiotic_features
LIMIT 10
""").toPandas()
print(abiotic_ids['sample_id'].tolist())
# Also check study_table: do study_ids appear in sample_ids?
print('\nStudy_id examples from study_table:')
study_ids = spark.sql("""
SELECT study_id FROM nmdc_arkin.study_table LIMIT 5
""").toPandas()
print(study_ids['study_id'].tolist())
# Check if sample_id in abiotic_features starts with 'nmdc:bsm-'
bsm_count = spark.sql("""
SELECT COUNT(*) as n
FROM nmdc_arkin.abiotic_features
WHERE sample_id LIKE 'nmdc:bsm-%'
""").collect()[0]['n']
print(f'\nabiotic_features rows where sample_id starts with nmdc:bsm-: {bsm_count}')
=== abiotic_features sample_id examples === ['nmdc:bsm-11-042nd237', 'nmdc:bsm-11-622k6044', 'nmdc:bsm-11-65a4xw75', 'nmdc:bsm-11-93mc8g67', 'nmdc:bsm-11-cpekyy11', 'nmdc:bsm-11-cwey4y35', 'nmdc:bsm-11-efm5hh51', 'nmdc:bsm-11-frgt4x11', 'nmdc:bsm-11-gk0czc37', 'nmdc:bsm-11-nq2tfm26'] Study_id examples from study_table: ['nmdc:sty-11-8fb6t785', 'nmdc:sty-11-33fbta56', 'nmdc:sty-11-aygzgv51', 'nmdc:sty-11-34xj1150', 'nmdc:sty-11-076c9980'] abiotic_features rows where sample_id starts with nmdc:bsm-: 13847
In [8]:
# Step 1f: Check if the sample_tokens_v1 table has more columns than documented
# and whether it contains file_id or a file reference field
try:
print('=== sample_tokens_v1 schema ===')
spark.sql('DESCRIBE nmdc_arkin.sample_tokens_v1').show(40, truncate=False)
n = spark.sql('SELECT COUNT(*) as n FROM nmdc_arkin.sample_tokens_v1').collect()[0]['n']
print(f'Row count: {n}')
print('Sample rows:')
spark.sql('SELECT * FROM nmdc_arkin.sample_tokens_v1 LIMIT 3').show(truncate=False)
except Exception as e:
print(f'sample_tokens_v1 error: {e}')
# Also check embeddings_v1 for sample_id format
try:
print('\n=== embeddings_v1 schema ===')
spark.sql('DESCRIBE nmdc_arkin.embeddings_v1').show(20, truncate=False)
n = spark.sql('SELECT COUNT(*) as n FROM nmdc_arkin.embeddings_v1').collect()[0]['n']
print(f'Row count: {n}')
# Show non-vector columns (sample_id etc.)
emb_schema = spark.sql('DESCRIBE nmdc_arkin.embeddings_v1').toPandas()
str_cols = emb_schema[emb_schema['data_type'] == 'string']['col_name'].tolist()
if str_cols:
cols_sql = ', '.join([f'`{c}`' for c in str_cols[:6]])
spark.sql(f'SELECT {cols_sql} FROM nmdc_arkin.embeddings_v1 LIMIT 5').show(truncate=False)
except Exception as e:
print(f'embeddings_v1 error: {e}')
=== sample_tokens_v1 schema === +-----------+-------------+-------+ |col_name |data_type |comment| +-----------+-------------+-------+ |sample_id |string |NULL | |token_id |array<bigint>|NULL | |modality_id|array<bigint>|NULL | |value |array<double>|NULL | +-----------+-------------+-------+ Row count: 5316 Sample rows: +--------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ |sample_id |token_id |modality_id |value | +--------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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|nmdc:bsm-11-3a6fv767|[4193, 5160, 432, 6305, 3675, 4718, 4581, 7642, 1970, 266, 1118, 494, 1472, 7036, 9327, 666, 5201, 1878, 2980, 594, 9705, 1201, 8041, 4043, 3347, 5238, 3178, 5631, 2200, 8427, 3543, 1882, 2122, 1930, 1402, 4155, 9762, 7796, 2454, 8901, 5929, 780, 743, 135, 8014, 4550, 4445, 4156, 906, 7133, 2000, 457, 937, 1112, 3113, 5662, 7714, 2169, 2058, 8086, 5776, 822, 3043, 8028, 7092, 4551, 1111, 1464, 1860, 7272, 8230, 4387, 3712, 2127, 246, 7936, 4044, 2431, 1380, 3243, 3042, 3457, 2559, 7301, 1623, 6046, 8593, 1278, 5263, 1079, 3430, 7649, 6553, 164, 6025, 4925, 8284, 3756, 4515, 5519, 1639, 8302, 3440, 9756, 2789, 9294, 3512, 10434, 3688, 5518, 3027, 3099, 8195, 5496, 4926, 473, 1445, 7313, 6082, 3074, 7571, 954, 9647, 2706, 5590, 7384, 4232, 5870, 3706, 3453, 5070, 3988, 4845, 835, 304, 8339, 9257, 5102, 8557, 2386, 1164, 1027, 626, 2850, 5022, 439, 3689, 1516, 2500, 4549, 9321, 7093, 6019, 936, 498, 3203, 2102, 5663, 3636, 407, 2131, 4489, 7907, 2737, 5249, 3374, 995, 3057, 3947, 4746, 9260, 7610, 2657, 4194, 5474, 7220, 1483, 5140, 1932, 1202, 8603, 8241, 7980, 9227, 2223, 4392, 5645, 9156, 1778, 4051, 4635, 1325, 2782, 2746, 2175, 5018, 2351, 1520, 4771, 7276, 9673, 10360, 2016, 6096, 4033, 4517, 6515, 7482, 7682, 5691, 3087, 2691, 8635, 3266, 2002, 9128, 7311, 4970, 8965, 4688, 2591, 459, 4124, 1542, 7241, 1051, 3149, 8376, 2206, 4058, 3726, 4179, 8624, 2586, 5340, 5539, 3649, 8382, 8819, 8521, 1010, 1151, 6347, 705, 3671, 8357, 5509, 3874, 1397, 9918, 1109, 4531, 8370, 6663, 2202, 6276, 3321, 541, 704, 10330, 1572, 5338, 4524, 1375, 350, 1436, 3108, 5999, 8359, 2366, 1787, 8541, 8209, 2840, 8552, 7109, 6240, 7145, 2696, 3447, 7749, 212, 4685, 9429, 7369, 6147, 3391, 1317, 2043, 5694, 3473, 2028, 6490, 2553, 3463, 5177, 2563, 5371, 2289, 5056, 3761, 3796, 578, 4422, 3045, 9230, 9687, 3111, 1815, 8383, 2622, 7694, 148, 2812, 2369, 5520, 7219, 2390, 3465, 4918, 7149, 3672, 8290, 314, 876, 9685, 2035, 1864, 698, 2854, 2512, 5255, 6152, 8033, 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=== embeddings_v1 schema === +--------+---------+-------+ |col_name|data_type|comment| +--------+---------+-------+ |dim_0 |float |NULL | |dim_1 |float |NULL | |dim_2 |float |NULL | |dim_3 |float |NULL | |dim_4 |float |NULL | |dim_5 |float |NULL | |dim_6 |float |NULL | |dim_7 |float |NULL | |dim_8 |float |NULL | |dim_9 |float |NULL | |dim_10 |float |NULL | |dim_11 |float |NULL | |dim_12 |float |NULL | |dim_13 |float |NULL | |dim_14 |float |NULL | |dim_15 |float |NULL | |dim_16 |float |NULL | |dim_17 |float |NULL | |dim_18 |float |NULL | |dim_19 |float |NULL | +--------+---------+-------+ only showing top 20 rows Row count: 5316 +--------------------+ |__index_level_0__ | +--------------------+ |nmdc:bsm-11-3a6fv767| |nmdc:bsm-11-2hve6455| |nmdc:bsm-11-s8ktn043| |nmdc:bsm-12-8496kx05| |nmdc:bsm-11-x1g8z726| +--------------------+
In [9]:
# Step 1g: Confirm the bridge table and check coverage via Spark SQL.
# NOTE: Do NOT convert omics_files_table to pandas and back to Spark —
# spark.createDataFrame(pandas_df) fails when the DataFrame has PyArrow-backed
# columns (ChunkedArray), which is always the case after .toPandas() on
# Spark Connect. Use omics_files_table directly in SQL for all subsequent joins.
if both_id_tables:
bridge_tbl_name = f'nmdc_arkin.{both_id_tables[0]}'
print(f'Bridge table: {bridge_tbl_name}')
# Coverage check: how many centrifuge / metabolomics file_ids are bridged to a sample_id?
clf_coverage = spark.sql(f"""
SELECT
COUNT(DISTINCT c.file_id) as clf_files_total,
COUNT(DISTINCT CASE WHEN b.sample_id IS NOT NULL THEN c.file_id END) as clf_files_bridged
FROM (SELECT DISTINCT file_id FROM nmdc_arkin.centrifuge_gold) c
LEFT JOIN {bridge_tbl_name} b ON c.file_id = b.file_id
""").toPandas()
met_coverage = spark.sql(f"""
SELECT
COUNT(DISTINCT m.file_id) as met_files_total,
COUNT(DISTINCT CASE WHEN b.sample_id IS NOT NULL THEN m.file_id END) as met_files_bridged
FROM (SELECT DISTINCT file_id FROM nmdc_arkin.metabolomics_gold) m
LEFT JOIN {bridge_tbl_name} b ON m.file_id = b.file_id
""").toPandas()
print('Classifier coverage:')
print(clf_coverage.to_string())
print('Metabolomics coverage:')
print(met_coverage.to_string())
file_bridge_found = True
else:
print('No direct bridge table found. Attempting file_name-based bridge...')
spark.sql("""
SELECT DISTINCT file_id, file_name FROM nmdc_arkin.centrifuge_gold LIMIT 5
""").show(truncate=False)
spark.sql("""
SELECT DISTINCT file_id, file_name FROM nmdc_arkin.metabolomics_gold LIMIT 5
""").show(truncate=False)
file_bridge_found = False
bridge_tbl_name = None
Bridge table: nmdc_arkin.omics_files_table Classifier coverage: clf_files_total clf_files_bridged 0 3577 3577 Metabolomics coverage: met_files_total met_files_bridged 0 2460 2460
In [10]:
# Step 1h: If no direct bridge was found, check nmdc_ncbi_biosamples for a cross-reference.
if not file_bridge_found:
print('Exploring nmdc_ncbi_biosamples for file → sample links...')
try:
ncbi_tables = spark.sql('SHOW TABLES IN nmdc_ncbi_biosamples').toPandas()
print('Tables in nmdc_ncbi_biosamples:', ncbi_tables['tableName'].tolist())
try:
spark.sql('DESCRIBE nmdc_ncbi_biosamples.biosamples_ids').show(20, truncate=False)
spark.sql('SELECT * FROM nmdc_ncbi_biosamples.biosamples_ids LIMIT 5').show(truncate=False)
except Exception as e:
print(f'biosamples_ids: {e}')
try:
link_schema = spark.sql('DESCRIBE nmdc_ncbi_biosamples.biosamples_links').toPandas()
print('\nbiosamples_links columns:', link_schema['col_name'].tolist())
spark.sql('SELECT * FROM nmdc_ncbi_biosamples.biosamples_links LIMIT 5').show(truncate=False)
except Exception as e:
print(f'biosamples_links: {e}')
except Exception as e:
print(f'nmdc_ncbi_biosamples exploration error: {e}')
else:
print(f'Bridge found: {bridge_tbl_name}. Skipping nmdc_ncbi_biosamples.')
Bridge found: nmdc_arkin.omics_files_table. Skipping nmdc_ncbi_biosamples.
Part 2: Sample Inventory — Samples with Both Omics Data Types¶
Using the bridge found in Part 1, identify sample_id values that have both
a metagenomics classifier file (centrifuge_gold) AND a metabolomics file (metabolomics_gold).
In [11]:
# Build sample overlap via the bridge
# Use omics_files_table directly in SQL — no pandas→Spark roundtrip.
# spark.createDataFrame(pandas_df) fails with ChunkedArray error when pandas_df
# was produced by .toPandas() on a Spark Connect DataFrame (PyArrow-backed columns).
if file_bridge_found and bridge_tbl_name:
# Samples with metagenomics (centrifuge) classifier data
clf_samples = spark.sql(f"""
SELECT DISTINCT b.sample_id
FROM (SELECT DISTINCT file_id FROM nmdc_arkin.centrifuge_gold) c
JOIN {bridge_tbl_name} b ON c.file_id = b.file_id
WHERE b.sample_id IS NOT NULL
""").toPandas()
# Samples with metabolomics data
met_samples = spark.sql(f"""
SELECT DISTINCT b.sample_id
FROM (SELECT DISTINCT file_id FROM nmdc_arkin.metabolomics_gold) m
JOIN {bridge_tbl_name} b ON m.file_id = b.file_id
WHERE b.sample_id IS NOT NULL
""").toPandas()
# Samples with BOTH
clf_set = set(clf_samples['sample_id'].tolist())
met_set = set(met_samples['sample_id'].tolist())
overlap_samples = clf_set & met_set
print(f'Samples with classifier data (centrifuge): {len(clf_set)}')
print(f'Samples with metabolomics data: {len(met_set)}')
print(f'Samples with BOTH (overlap): {len(overlap_samples)}')
overlap_sample_ids = list(overlap_samples)
else:
print('ERROR: No file→sample bridge available. Cannot compute sample overlap.')
clf_set = set()
met_set = set()
overlap_sample_ids = []
Samples with classifier data (centrifuge): 6361 Samples with metabolomics data: 1148 Samples with BOTH (overlap): 221
In [12]:
# Merge ecosystem metadata from study_table for overlap samples
# abiotic_features uses sample_id — join directly
if overlap_sample_ids:
# Load abiotic features for overlap samples
abiotic_df = spark.sql('SELECT * FROM nmdc_arkin.abiotic_features').toPandas()
abiotic_overlap = abiotic_df[abiotic_df['sample_id'].isin(overlap_sample_ids)].copy()
print(f'Abiotic features rows for overlap samples: {len(abiotic_overlap)}')
# Study table for ecosystem categorization
# study_id format: nmdc:sty-11-XXXXXXXX
# sample_id format: nmdc:bsm-11-XXXXXXXX (if biosample prefix)
# Link via file_bridge_df: get file_id → study linkage from file_name
# file_name for classifier: 'nmdc_wfrbt-11-krmkys65.1_kraken2_report.tsv'
# Study linkage must come through study_table
# For now, check what sample_id values look like
if overlap_sample_ids:
print(f'\nFirst 10 overlap sample_ids: {overlap_sample_ids[:10]}')
else:
print('No overlap samples — skipping ecosystem metadata merge.')
abiotic_overlap = pd.DataFrame()
Abiotic features rows for overlap samples: 221 First 10 overlap sample_ids: ['nmdc:bsm-11-rk62cv25', 'nmdc:bsm-11-q72ew015', 'nmdc:bsm-11-62fxv990', 'nmdc:bsm-11-j9v5kc93', 'nmdc:bsm-13-c0xjg970', 'nmdc:bsm-11-frgt4x11', 'nmdc:bsm-11-011z7z70', 'nmdc:bsm-11-ce1sg012', 'nmdc:bsm-11-h49t7j41', 'nmdc:bsm-13-amdcp906']
In [13]:
# Build per-sample file inventory with omics type labels.
# Use omics_files_table directly in SQL subqueries — no temp views needed.
if overlap_sample_ids and file_bridge_found and bridge_tbl_name:
# Overlap sample_ids as a SQL IN list (safe: IDs are nmdc:bsm-11-* strings)
# For large lists use a subquery approach instead of IN clause
# Here we use the SQL INTERSECT approach to keep it in Spark:
clf_file_df = spark.sql(f"""
SELECT DISTINCT b.sample_id, c.file_id as clf_file_id, c.file_name as clf_file_name
FROM (SELECT DISTINCT file_id, file_name FROM nmdc_arkin.centrifuge_gold) c
JOIN {bridge_tbl_name} b ON c.file_id = b.file_id
WHERE b.sample_id IN (
SELECT b2.sample_id FROM {bridge_tbl_name} b2
JOIN (SELECT DISTINCT file_id FROM nmdc_arkin.centrifuge_gold) c2 ON b2.file_id = c2.file_id
INTERSECT
SELECT b3.sample_id FROM {bridge_tbl_name} b3
JOIN (SELECT DISTINCT file_id FROM nmdc_arkin.metabolomics_gold) m3 ON b3.file_id = m3.file_id
)
""").toPandas()
met_file_df = spark.sql(f"""
SELECT DISTINCT b.sample_id, m.file_id as met_file_id, m.file_name as met_file_name
FROM (SELECT DISTINCT file_id, file_name FROM nmdc_arkin.metabolomics_gold) m
JOIN {bridge_tbl_name} b ON m.file_id = b.file_id
WHERE b.sample_id IN (
SELECT b2.sample_id FROM {bridge_tbl_name} b2
JOIN (SELECT DISTINCT file_id FROM nmdc_arkin.centrifuge_gold) c2 ON b2.file_id = c2.file_id
INTERSECT
SELECT b3.sample_id FROM {bridge_tbl_name} b3
JOIN (SELECT DISTINCT file_id FROM nmdc_arkin.metabolomics_gold) m3 ON b3.file_id = m3.file_id
)
""").toPandas()
inventory = clf_file_df.merge(met_file_df, on='sample_id', how='inner')
print(f'Inventory rows (sample × clf_file × met_file): {len(inventory)}')
print(f'Unique samples in inventory: {inventory["sample_id"].nunique()}')
print(inventory.head(5).to_string())
else:
print('Cannot build inventory — no bridge or no overlap samples.')
inventory = pd.DataFrame()
clf_file_df = pd.DataFrame()
met_file_df = pd.DataFrame()
Inventory rows (sample × clf_file × met_file): 646
Unique samples in inventory: 221
sample_id clf_file_id clf_file_name met_file_id met_file_name
0 nmdc:bsm-11-ahfq0n74 nmdc:dobj-11-ggpy8r50 nmdc_wfrbt-11-mcj39k52.1_centrifuge_report.tsv nmdc:dobj-12-716d9s20 20220119_JGI_MD_507130_BioS_final1_QE-139_HILICZ_USHXG01825_POS_MSMS_5RM_BESC-13-Corv-RM_2_Rg70to1050-CE102040-root-S1_Run922.csv
1 nmdc:bsm-11-ahfq0n74 nmdc:dobj-11-ggpy8r50 nmdc_wfrbt-11-mcj39k52.1_centrifuge_report.tsv nmdc:dobj-12-591x8e41 20220211_JGI_MD_507130_BioS_final1_IDX_C18_USDAY59443_POS_MSMS_5RM_BESC-13-Corv-RM_2_Rg80to1200-CE102040-root-S1_Run935.csv
2 nmdc:bsm-11-ahfq0n74 nmdc:dobj-11-ggpy8r50 nmdc_wfrbt-11-mcj39k52.1_centrifuge_report.tsv nmdc:dobj-12-mk76tz88 20220211_JGI_MD_507130_BioS_final1_IDX_C18_USDAY59443_NEG_MSMS_5RM_BESC-13-Corv-RM_2_Rg80to1200-CE102040-root-S1_Run936.csv
3 nmdc:bsm-11-ahfq0n74 nmdc:dobj-11-ggpy8r50 nmdc_wfrbt-11-mcj39k52.1_centrifuge_report.tsv nmdc:dobj-12-2nvkxm21 20220119_JGI_MD_507130_BioS_final1_QE-139_HILICZ_USHXG01825_NEG_MSMS_5RM_BESC-13-Corv-RM_2_Rg70to1050-CE102040-root-S1_Run923.csv
4 nmdc:bsm-11-v0hqhp22 nmdc:dobj-11-6qgzvr76 nmdc_wfrbt-11-11v82153.1_centrifuge_report.tsv nmdc:dobj-12-jr6te653 20220119_JGI_MD_507130_BioS_final1_QE-139_HILICZ_USHXG01825_POS_MSMS_17RZM_BESC-234-Corv-RZM_1_Rg70to1050-CE102040-rhizo-S1_Run344.csv
Part 3: NMDC Taxon Names → GTDB Species Clade¶
Map species-rank taxon names from centrifuge_gold to GTDB species clades
in kbase_ke_pangenome.gtdb_species_clade using normalized name matching.
Approach (following enigma_contamination_functional_potential NB02):
- Normalize genus name from NCBI species name (remove 's__' prefix, lowercase)
- Match at species level first (exact GTDB species name match)
- Fall back to genus-level match for unresolved taxa
- Report confidence tier:
species_exact,genus_proxy,unmapped
In [14]:
# Get all unique species-rank taxa from centrifuge_gold
# Using all files (not just overlap) to build a comprehensive bridge table
# centrifuge_gold: rank='species', label=taxon name, abundance=relative abundance
centrifuge_species = spark.sql("""
SELECT label as taxon_name, COUNT(DISTINCT file_id) as n_files,
AVG(abundance) as mean_abundance
FROM nmdc_arkin.centrifuge_gold
WHERE LOWER(rank) = 'species'
AND label IS NOT NULL
AND label != ''
GROUP BY label
ORDER BY n_files DESC
""").toPandas()
print(f'Unique species-rank taxa in centrifuge_gold: {len(centrifuge_species)}')
print('Most common taxa:')
print(centrifuge_species.head(10).to_string())
Unique species-rank taxa in centrifuge_gold: 4894
Most common taxa:
taxon_name n_files mean_abundance
0 Bradyrhizobium sp. ORS 278 3576 0.000740
1 Burkholderia cenocepacia 3575 0.000111
2 Rhodopseudomonas palustris 3575 0.012858
3 Pseudomonas fluorescens 3575 0.001186
4 Streptomyces albus 3575 0.000081
5 Bradyrhizobium sp. BTAi1 3575 0.004004
6 Stutzerimonas stutzeri 3575 0.000270
7 Ralstonia solanacearum 3574 0.000176
8 Nocardia cyriacigeorgica 3574 0.000206
9 Rhizorhabdus wittichii 3574 0.000295
In [15]:
# Normalization functions for name matching
def norm_species(x: str) -> str:
"""Normalize NCBI species name for GTDB matching.
NCBI: 'Candidatus Methylotenera versatilis' -> 'methylotenera_versatilis'
GTDB: 's__Methylotenera_versatilis' -> 'methylotenera_versatilis'
"""
if pd.isna(x) or not str(x).strip():
return ''
x = str(x).strip().lower()
# Remove GTDB prefix if present
x = re.sub(r'^s__', '', x)
# Remove 'candidatus' prefix
x = re.sub(r'^candidatus\s+', '', x)
# Replace spaces and special chars with underscore
x = re.sub(r'[^a-z0-9]+', '_', x)
return x.strip('_')
def norm_genus(x: str) -> str:
"""Extract and normalize genus from species name."""
if pd.isna(x) or not str(x).strip():
return ''
x = str(x).strip().lower()
x = re.sub(r'^s__', '', x)
x = re.sub(r'^g__', '', x)
x = re.sub(r'^candidatus\s+', '', x)
# Take first word as genus
parts = re.split(r'[^a-z0-9]', x)
genus = parts[0] if parts else ''
return re.sub(r'[^a-z0-9]', '_', genus).strip('_')
# Prepare centrifuge taxa with normalized names
centrifuge_species['species_norm'] = centrifuge_species['taxon_name'].map(norm_species)
centrifuge_species['genus_norm'] = centrifuge_species['taxon_name'].map(norm_genus)
centrifuge_species = centrifuge_species[centrifuge_species['species_norm'] != '']
print(f'Taxa with non-empty normalized name: {len(centrifuge_species)}')
print(centrifuge_species[['taxon_name', 'species_norm', 'genus_norm']].head(10).to_string())
Taxa with non-empty normalized name: 4894
taxon_name species_norm genus_norm
0 Bradyrhizobium sp. ORS 278 bradyrhizobium_sp_ors_278 bradyrhizobium
1 Burkholderia cenocepacia burkholderia_cenocepacia burkholderia
2 Rhodopseudomonas palustris rhodopseudomonas_palustris rhodopseudomonas
3 Pseudomonas fluorescens pseudomonas_fluorescens pseudomonas
4 Streptomyces albus streptomyces_albus streptomyces
5 Bradyrhizobium sp. BTAi1 bradyrhizobium_sp_btai1 bradyrhizobium
6 Stutzerimonas stutzeri stutzerimonas_stutzeri stutzerimonas
7 Ralstonia solanacearum ralstonia_solanacearum ralstonia
8 Nocardia cyriacigeorgica nocardia_cyriacigeorgica nocardia
9 Rhizorhabdus wittichii rhizorhabdus_wittichii rhizorhabdus
In [16]:
# Load GTDB species clades from pangenome
# GTDB species names look like: 's__Methylotenera_versatilis'
gtdb_species = spark.sql("""
SELECT DISTINCT gtdb_species_clade_id, GTDB_species
FROM kbase_ke_pangenome.gtdb_species_clade
""").toPandas()
print(f'GTDB species clades: {len(gtdb_species)}')
print('Sample GTDB species names:')
print(gtdb_species['GTDB_species'].head(10).tolist())
# Build normalized species and genus columns for GTDB
gtdb_species['species_norm'] = gtdb_species['GTDB_species'].map(norm_species)
gtdb_species['genus_norm'] = gtdb_species['GTDB_species'].map(norm_genus)
gtdb_species = gtdb_species[gtdb_species['species_norm'] != ''].drop_duplicates()
print(f'\nGTDB species with non-empty normalized name: {len(gtdb_species)}')
print(gtdb_species[['GTDB_species', 'species_norm', 'genus_norm']].head(5).to_string())
GTDB species clades: 27690
Sample GTDB species names:
['s__Rhizobium_phaseoli', 's__CAIPMZ01_sp903861715', 's__Pseudomonas_nitroreducens', 's__Leuconostoc_gelidum', 's__Prevotella_nigrescens', 's__Tractidigestivibacter_sp000752675', 's__Ellagibacter_isourolithinifaciens', 's__Limimorpha_sp900769945', 's__Succinivibrio_sp003456415', 's__VUNA01_sp002299625']
GTDB species with non-empty normalized name: 27690
GTDB_species species_norm genus_norm
0 s__Rhizobium_phaseoli rhizobium_phaseoli rhizobium
1 s__CAIPMZ01_sp903861715 caipmz01_sp903861715 caipmz01
2 s__Pseudomonas_nitroreducens pseudomonas_nitroreducens pseudomonas
3 s__Leuconostoc_gelidum leuconostoc_gelidum leuconostoc
4 s__Prevotella_nigrescens prevotella_nigrescens prevotella
In [17]:
# Step 3a: Species-level exact match (normalized NCBI species name == normalized GTDB species name)
species_exact = centrifuge_species.merge(
gtdb_species[['gtdb_species_clade_id', 'GTDB_species', 'species_norm']],
on='species_norm',
how='left'
)
species_exact['mapping_tier'] = species_exact['gtdb_species_clade_id'].apply(
lambda v: 'species_exact' if pd.notna(v) else 'unmapped'
)
mapped_species = species_exact[species_exact['mapping_tier'] == 'species_exact']
still_unmapped = species_exact[species_exact['mapping_tier'] == 'unmapped']
print(f'Species-exact matches: {mapped_species["taxon_name"].nunique()}')
print(f'Unmapped after species-exact: {still_unmapped["taxon_name"].nunique()}')
print(f'Species-exact match rate: {mapped_species["taxon_name"].nunique() / len(centrifuge_species):.1%}')
Species-exact matches: 1375 Unmapped after species-exact: 3519 Species-exact match rate: 28.1%
In [18]:
# Step 3b: Genus-level fallback for unmapped taxa
# Match on normalized genus; if one GTDB clade per genus, use it (species proxy)
# If multiple clades per genus, record as multi_clade_ambiguous
# Count GTDB species per genus
gtdb_genus_counts = (
gtdb_species.groupby('genus_norm')['gtdb_species_clade_id']
.nunique()
.rename('n_gtdb_clades_for_genus')
.reset_index()
)
# Best representative GTDB clade per genus (use first alphabetically when ambiguous)
gtdb_genus_rep = (
gtdb_species.sort_values(['genus_norm', 'GTDB_species'])
.drop_duplicates(subset=['genus_norm'])
[['genus_norm', 'gtdb_species_clade_id', 'GTDB_species']]
.rename(columns={'GTDB_species': 'GTDB_species_representative'})
)
genus_fallback = still_unmapped[['taxon_name', 'species_norm', 'genus_norm', 'n_files', 'mean_abundance']].merge(
gtdb_genus_rep,
on='genus_norm',
how='left'
).merge(
gtdb_genus_counts,
on='genus_norm',
how='left'
)
genus_fallback['n_gtdb_clades_for_genus'] = genus_fallback['n_gtdb_clades_for_genus'].fillna(0).astype(int)
genus_fallback['mapping_tier'] = genus_fallback.apply(
lambda row: (
'genus_proxy_unique' if row['n_gtdb_clades_for_genus'] == 1
else 'genus_proxy_ambiguous' if row['n_gtdb_clades_for_genus'] > 1
else 'unmapped'
),
axis=1
)
print('Genus fallback mapping tier distribution:')
print(genus_fallback['mapping_tier'].value_counts().to_string())
Genus fallback mapping tier distribution: mapping_tier unmapped 2022 genus_proxy_ambiguous 1352 genus_proxy_unique 145
In [19]:
# Step 3c: Combine species_exact and genus_fallback into the full bridge table
# Standardize columns for concatenation
species_exact_out = mapped_species[['taxon_name', 'species_norm', 'genus_norm',
'gtdb_species_clade_id', 'GTDB_species',
'n_files', 'mean_abundance', 'mapping_tier']].copy()
species_exact_out['n_gtdb_clades_for_genus'] = 1
species_exact_out['GTDB_species_representative'] = species_exact_out['GTDB_species']
genus_fallback_out = genus_fallback.copy()
genus_fallback_out['GTDB_species'] = genus_fallback_out.get('GTDB_species_representative',
pd.Series([''] * len(genus_fallback)))
# Keep only unmapped from genus_fallback (species-exact already captured)
genus_mapped = genus_fallback_out[genus_fallback_out['mapping_tier'] != 'unmapped'].copy()
truly_unmapped = genus_fallback_out[genus_fallback_out['mapping_tier'] == 'unmapped'].copy()
bridge = pd.concat([
species_exact_out[['taxon_name', 'species_norm', 'genus_norm',
'gtdb_species_clade_id', 'GTDB_species',
'n_files', 'mean_abundance', 'mapping_tier', 'n_gtdb_clades_for_genus']],
genus_mapped[['taxon_name', 'species_norm', 'genus_norm',
'gtdb_species_clade_id', 'GTDB_species_representative',
'n_files', 'mean_abundance', 'mapping_tier', 'n_gtdb_clades_for_genus']].rename(
columns={'GTDB_species_representative': 'GTDB_species'}),
truly_unmapped[['taxon_name', 'species_norm', 'genus_norm',
'n_files', 'mean_abundance', 'mapping_tier', 'n_gtdb_clades_for_genus']],
], ignore_index=True)
print(f'Total bridge rows: {len(bridge)}')
print('Mapping tier distribution:')
print(bridge['mapping_tier'].value_counts().to_string())
mapped_frac = bridge[bridge['mapping_tier'] != 'unmapped']['taxon_name'].nunique() / len(bridge)
print(f'\nOverall taxon mapping rate: {mapped_frac:.1%}')
Total bridge rows: 4894 Mapping tier distribution: mapping_tier unmapped 2022 species_exact 1375 genus_proxy_ambiguous 1352 genus_proxy_unique 145 Overall taxon mapping rate: 58.7%
Part 4: Bridge Quality Per Sample¶
For each overlap sample (samples with both classifier and metabolomics data),
compute the fraction of community abundance that has been mapped to a GTDB clade.
Quality metric: bridge_coverage = Σ(abundance_i) for mapped taxa / Σ(abundance_all_taxa)
Flag samples below 30% bridge coverage for exclusion.
In [20]:
# Compute bridge quality per classifier file.
# Use omics_files_table in SQL subqueries to filter to overlap samples —
# avoids spark.createDataFrame() which fails on PyArrow ChunkedArray columns.
if overlap_sample_ids and file_bridge_found and bridge_tbl_name and not clf_file_df.empty:
# Subquery: classifier files that belong to overlap samples
# (samples with BOTH centrifuge AND metabolomics, determined by SQL INTERSECT)
clf_data = spark.sql(f"""
SELECT c.file_id, c.label as taxon_name, c.abundance
FROM nmdc_arkin.centrifuge_gold c
JOIN {bridge_tbl_name} b ON c.file_id = b.file_id
WHERE b.sample_id IN (
SELECT b2.sample_id FROM {bridge_tbl_name} b2
JOIN (SELECT DISTINCT file_id FROM nmdc_arkin.centrifuge_gold) c2 ON b2.file_id = c2.file_id
INTERSECT
SELECT b3.sample_id FROM {bridge_tbl_name} b3
JOIN (SELECT DISTINCT file_id FROM nmdc_arkin.metabolomics_gold) m3 ON b3.file_id = m3.file_id
)
AND LOWER(c.rank) = 'species'
AND c.label IS NOT NULL AND c.label != ''
AND c.abundance > 0
""").toPandas()
print(f'Species-rank rows for overlap files: {len(clf_data)}')
print(f'Files in clf_data: {clf_data["file_id"].nunique()}')
if len(clf_data) > 0:
# Join with bridge to mark mapped vs unmapped taxa
mapped_taxa = set(bridge[bridge['mapping_tier'] != 'unmapped']['taxon_name'].tolist())
clf_data['is_mapped'] = clf_data['taxon_name'].isin(mapped_taxa)
# Compute bridge coverage per file
bridge_quality_file = clf_data.groupby('file_id').agg(
total_abundance=('abundance', 'sum'),
mapped_abundance=('abundance', lambda x: x[clf_data.loc[x.index, 'is_mapped']].sum()),
n_taxa=('taxon_name', 'nunique'),
n_mapped_taxa=('taxon_name', lambda x: x[clf_data.loc[x.index, 'is_mapped']].nunique())
).reset_index()
bridge_quality_file['bridge_coverage'] = (
bridge_quality_file['mapped_abundance'] / bridge_quality_file['total_abundance']
).fillna(0)
# Map file_id → sample_id via clf_file_df (already in pandas)
bridge_quality_file = bridge_quality_file.merge(
clf_file_df[['sample_id', 'clf_file_id']].rename(columns={'clf_file_id': 'file_id'}),
on='file_id', how='left'
)
print(f'\nBridge quality summary:')
print(bridge_quality_file[['file_id', 'sample_id', 'bridge_coverage',
'n_taxa', 'n_mapped_taxa']].describe().to_string())
print(f'\nFiles with >30% bridge coverage: '
f'{(bridge_quality_file["bridge_coverage"] >= 0.30).sum()} / {len(bridge_quality_file)}')
else:
print('No species-rank rows returned for overlap files.')
bridge_quality_file = pd.DataFrame()
else:
print('No overlap samples or bridge — skipping bridge quality computation.')
bridge_quality_file = pd.DataFrame()
Species-rank rows for overlap files: 126905
Files in clf_data: 220
Bridge quality summary:
bridge_coverage n_taxa n_mapped_taxa
count 220.000000 220.000000 220.00000
mean 0.935370 576.840909 512.70000
std 0.076238 350.603729 313.98018
min 0.314354 3.000000 1.00000
25% 0.899178 234.000000 200.75000
50% 0.960668 669.000000 597.00000
75% 0.986040 859.750000 764.00000
max 1.000000 1470.000000 1303.00000
Files with >30% bridge coverage: 220 / 220
In [21]:
# Summarize bridge quality by coverage threshold
if len(bridge_quality_file) > 0:
print('Bridge coverage distribution:')
for threshold in [0.10, 0.20, 0.30, 0.50, 0.70]:
n = (bridge_quality_file['bridge_coverage'] >= threshold).sum()
print(f' >= {threshold:.0%}: {n} / {len(bridge_quality_file)} files '
f'({n/len(bridge_quality_file):.1%})')
# Flag samples
BRIDGE_THRESHOLD = 0.30
bridge_quality_file['passes_bridge_qc'] = bridge_quality_file['bridge_coverage'] >= BRIDGE_THRESHOLD
n_pass = bridge_quality_file['passes_bridge_qc'].sum()
print(f'\nSamples passing QC (>={BRIDGE_THRESHOLD:.0%}): {n_pass}')
print(f'Samples failing QC (<{BRIDGE_THRESHOLD:.0%}): {len(bridge_quality_file) - n_pass}')
Bridge coverage distribution: >= 10%: 220 / 220 files (100.0%) >= 20%: 220 / 220 files (100.0%) >= 30%: 220 / 220 files (100.0%) >= 50%: 219 / 220 files (99.5%) >= 70%: 218 / 220 files (99.1%) Samples passing QC (>=30%): 220 Samples failing QC (<30%): 0
Part 5: Save Outputs and Figures¶
In [22]:
# Save outputs
bridge_path = os.path.join(DATA_DIR, 'taxon_bridge.tsv')
bridge.to_csv(bridge_path, sep='\t', index=False)
print(f'Saved: data/taxon_bridge.tsv ({len(bridge)} rows)')
if len(bridge_quality_file) > 0:
bq_path = os.path.join(DATA_DIR, 'bridge_quality.csv')
bridge_quality_file.to_csv(bq_path, index=False)
print(f'Saved: data/bridge_quality.csv ({len(bridge_quality_file)} rows)')
if not inventory.empty:
inv_path = os.path.join(DATA_DIR, 'nmdc_sample_inventory.csv')
inventory.to_csv(inv_path, index=False)
print(f'Saved: data/nmdc_sample_inventory.csv ({len(inventory)} rows, '
f'{inventory["sample_id"].nunique()} unique samples)')
else:
print('WARNING: No overlap samples found — nmdc_sample_inventory.csv not updated.')
Saved: data/taxon_bridge.tsv (4894 rows) Saved: data/bridge_quality.csv (220 rows) Saved: data/nmdc_sample_inventory.csv (646 rows, 221 unique samples)
In [23]:
# Figure: Bridge quality distribution and mapping tier breakdown
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
fig.suptitle('Taxonomy Bridge Quality', fontsize=14)
# Panel 1: Bridge coverage histogram
ax = axes[0]
if len(bridge_quality_file) > 0:
ax.hist(bridge_quality_file['bridge_coverage'], bins=30,
color='#4C9BE8', edgecolor='white')
ax.axvline(0.30, color='red', linestyle='--', label='30% threshold')
ax.set_xlabel('Bridge coverage (fraction of abundance mapped)')
ax.set_ylabel('Number of samples')
ax.set_title('Per-sample bridge coverage')
ax.legend()
else:
ax.text(0.5, 0.5, 'No bridge quality data', ha='center', va='center',
transform=ax.transAxes)
# Panel 2: Mapping tier breakdown (n taxa in each tier, weighted by n_files)
ax2 = axes[1]
if len(bridge) > 0:
tier_counts = bridge['mapping_tier'].value_counts()
colors = {
'species_exact': '#2ecc71',
'genus_proxy_unique': '#f39c12',
'genus_proxy_ambiguous': '#e67e22',
'unmapped': '#e74c3c'
}
bar_colors = [colors.get(t, '#95a5a6') for t in tier_counts.index]
ax2.bar(range(len(tier_counts)), tier_counts.values,
color=bar_colors, edgecolor='white')
ax2.set_xticks(range(len(tier_counts)))
ax2.set_xticklabels(tier_counts.index, rotation=30, ha='right', fontsize=9)
ax2.set_ylabel('Number of unique taxa')
ax2.set_title('Taxon mapping tier distribution')
for i, val in enumerate(tier_counts.values):
ax2.text(i, val + 0.5, str(val), ha='center', fontsize=9)
else:
ax2.text(0.5, 0.5, 'No bridge data', ha='center', va='center', transform=ax2.transAxes)
plt.tight_layout()
fig_path = os.path.join(FIGURES_DIR, 'bridge_quality_distribution.png')
plt.savefig(fig_path, dpi=150, bbox_inches='tight')
plt.show()
print(f'Saved: figures/bridge_quality_distribution.png')
Saved: figures/bridge_quality_distribution.png
Summary and Decisions for NB03¶
| Question | Finding |
|---|---|
| file→sample bridge table | nmdc_arkin.omics_files_table (has both file_id and sample_id) |
| Samples with both classifier + metabolomics | 221 (via sample bridge) |
| Species-exact GTDB matches | 1,375 of 4,894 centrifuge taxa (28.1%) |
| Genus-proxy unique matches | 145 |
| Genus-proxy ambiguous | 1,352 |
| Overall taxon mapping rate | 58.7% of centrifuge taxa (88.9% of rows, 93.5% of abundance) |
| Samples passing 30% bridge QC | 220 / 220 (100%) |
| Classifier used for bridge | centrifuge_gold (61.3% species-rank, best from NB01) |
Decision for NB03:
Use all 220 samples passing 30% bridge QC.
Include all three mapping tiers (species_exact, genus_proxy_unique, genus_proxy_ambiguous) in community weighting.
For genus_proxy_ambiguous taxa (which map to multiple GTDB clades), select one representative clade via alphabetical tiebreaking on gtdb_species_clade_id (sort_values + drop_duplicates).
This decision is documented in NB03 cell-17 with sensitivity notes in REPORT.md Limitations.