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added ploting capacities
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PMA committed Feb 3, 2021
1 parent 916d86f commit 9e5821c
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137 changes: 106 additions & 31 deletions src/met_annot_enhancer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -105,7 +105,7 @@
use_post_taxo = True

# # MS filename extension (a common pattern in all your filenames)
file_extension = '.mzML'
file_extension = '.mzXML'

# # Set True if you want to use rank after taxonomical reweighting for consensus chemical class determination
top_N_chemical_consistency = 30
Expand All @@ -119,17 +119,18 @@


# python met_annot_enhancer.py
job_id = '7f1259a161974b9fa4215b1f2a6dca5e'
gnps_job_path = '/Users/pma/tmp/bafu_ecometabo/'
job_id = '56d01c6ccfe143eca5252017202c8fef'
gnps_job_path = '/Users/pma/tmp/Fred_Legendre/'
project_name = 'sariette_pos'
#isdb_results_path = '/Users/pma/tmp/bafu_ecometabo/GNPS_output/bafu_ecometabo_spectral_match_results.tsv'
metadata_path = '/Users/pma/Documents/190602_DNP_TAXcof_CF.tsv'
output_weighed_ISDB_path = '/Users/pma/tmp/bafu_ecometabo/GNPS_output/bafu_ecometabo_spectral_match_results_repond.tsv'
output_weighed_ISDB_path = '/Users/pma/tmp/Fred_Legendre/GNPS_output_' + project_name + '/' + project_name + '_isdb_repond.tsv'
top_to_output = '3'
ppm_tol = '5'
polarity = 'Pos'
organism_header = 'sample_type'
organism_header = 'ATTRIBUTE_Species'

base_filename = 'GNPS_output'
base_filename = 'GNPS_output_' + project_name
filename_suffix = 'zip'
path_to_folder = os.path.join(gnps_job_path, base_filename)
path_to_file = os.path.join(gnps_job_path, base_filename + "." + filename_suffix)
Expand All @@ -141,8 +142,12 @@
msms_mz_tol = 0.01
min_cos = 0.2
min_peaks = 6
spectral_match_results_filename = 'bafu_ecometabo_spectral_match_results.tsv'
spectral_match_results_filename = project_name + '_spectral_match_results.tsv'
isdb_results_path = os.path.join(path_to_folder,spectral_match_results_filename)
sunburst_chem_filename = project_name + '_chemo_sunburst.html'
sunburst_organisms_filename = project_name + '_organisms_sunburst.html'
sunburst_chem_results_path = os.path.join(path_to_folder,sunburst_chem_filename)
sunburst_organisms_results_path = os.path.join(path_to_folder,sunburst_organisms_filename)



Expand Down Expand Up @@ -193,31 +198,31 @@



job_url_zip = "https://gnps.ucsd.edu/ProteoSAFe/DownloadResult?task="+job_id+"&view=download_cytoscape_data"
# job_url_zip = "https://gnps.ucsd.edu/ProteoSAFe/DownloadResult?task="+job_id+"&view=download_cytoscape_data"

cmd = 'curl -d "" '+job_url_zip+' -o '+path_to_file
subprocess.call(shlex.split(cmd))
# cmd = 'curl -d "" '+job_url_zip+' -o '+path_to_file
# subprocess.call(shlex.split(cmd))

with zipfile.ZipFile(path_to_file, 'r') as zip_ref:
zip_ref.extractall(path_to_folder)
# with zipfile.ZipFile(path_to_file, 'r') as zip_ref:
# zip_ref.extractall(path_to_folder)

# We finally remove the zip file
cmd = 'rm '+ path_to_file
subprocess.call(shlex.split(cmd))
# # We finally remove the zip file
# cmd = 'rm '+ path_to_file
# subprocess.call(shlex.split(cmd))

# %% Spectral matching stage
# # %% Spectral matching stage

# Yes we can !
# # Yes we can !


spectral_lib_matcher.main(query_file_path,
db_file_path,
parent_mz_tol,
msms_mz_tol,
min_cos,
min_peaks,
isdb_results_path
)
# spectral_lib_matcher.main(query_file_path,
# db_file_path,
# parent_mz_tol,
# msms_mz_tol,
# min_cos,
# min_peaks,
# isdb_results_path
# )



Expand Down Expand Up @@ -441,7 +446,7 @@
'Class_cof_DNP', 'Order_cof_DNP', 'Family_cof_DNP', 'Genus_cof_DNP',
'Species_cof_DNP', 'ClassyFy_Status_DNP',
'Kingdom_cf_DNP', 'Superclass_cf_DNP', 'Class_cf_DNP',
'Subclass_cf_DNP', 'Parent_Level_1_cf_DNP']
'Subclass_cf_DNP', 'Parent_Level_1_cf_DNP', 'Biological_Source_DNP', 'Biological_Use_DNP', 'Toxicity_DNP' ]

dt_isdb_results.dropna(subset=['short_inchikey'], inplace=True)
dt_isdb_results = pd.merge(
Expand Down Expand Up @@ -630,9 +635,10 @@
df_tax_lineage_filtered_flat = df_tax_lineage_filtered_flat[cols_to_keep]


# We merge this back with the samplemetadata
# We merge this back with the samplemetadata only if we have an ott.id in the merged df

samples_metadata = pd.merge(merged_df[pd.notnull(merged_df['taxon.ott_id'])], df_tax_lineage_filtered_flat, how='left', left_on='taxon.ott_id', right_on='ott_id' )

samples_metadata = pd.merge(merged_df, df_tax_lineage_filtered_flat, how='left', left_on='taxon.ott_id', right_on='ott_id' )



Expand Down Expand Up @@ -940,7 +946,9 @@
# %%

annot_attr = ['rank_spec', 'score_input', 'inchikey', 'libname', 'InChI_DNP',
'Molecule_Name_DNP', 'Molecule_Formula_DNP', 'Accurate_Mass_DNP', 'matched_kingdom', 'matched_phylum', 'matched_class', 'matched_order',
'Molecule_Name_DNP', 'Molecule_Formula_DNP', 'Accurate_Mass_DNP', 'Biological_Source_DNP', 'Biological_Use_DNP', 'Toxicity_DNP',
'Kingdom_cof_DNP', 'Phylum_cof_DNP', 'Class_cof_DNP', 'Order_cof_DNP', 'Family_cof_DNP' ,'Genus_cof_DNP', 'Species_cof_DNP',
'matched_kingdom', 'matched_phylum', 'matched_class', 'matched_order',
'matched_family', 'matched_genus', 'matched_species', 'score_taxo', 'score_max_consistency', 'Final_score', 'rank_final']

comp_attr = ['component_id', 'Superclass_cf_DNP_consensus', 'freq_Superclass_cf_DNP', 'Class_cf_DNP_consensus',
Expand All @@ -949,7 +957,7 @@

col_to_keep = ['feature_id'] + comp_attr + annot_attr

df4cyto = dt_isdb_results_chem_rew[col_to_keep]
df4cyto_flat = dt_isdb_results_chem_rew[col_to_keep]

# %%

Expand All @@ -959,7 +967,7 @@

# %%

df4cyto = df4cyto.groupby('feature_id').agg(gb_spec)
df4cyto = df4cyto_flat.groupby('feature_id').agg(gb_spec)

# %%
df4cyto.to_csv(output_weighed_ISDB_path, sep='\t')
Expand All @@ -986,3 +994,70 @@

# df4cyto['rank_spec'] = df4cyto['rank_spec'].apply(lambda x: [x])


# %%
# using px express to plot some quick and dirty sunbursts (https://plotly.com/python/sunburst-charts/)
# customize fonts in titles following https://stackoverflow.com/a/57926862
# customize margins following https://stackoverflow.com/a/63162535

import plotly.express as px


fig = px.sunburst(df4cyto_flat, path=['Superclass_cf_DNP_consensus', 'Class_cf_DNP_consensus', 'Subclass_cf_DNP_consensus', 'Parent_Level_1_cf_DNP_consensus'],
)
fig.update_layout(
#font_family="Courier New",
title_font_family="Courier New",
title_font_color="black",
title_font_size=14,
legend_title_font_color="black",
title_text="<b> Overview of the consensus chemical annotions <br> as the superclass, class, subclass and parent_1 level for <br>" + project_name + "</b>",
title_x=0.5
)

fig.update_layout(
title={
'text': "<b> Overview of the consensus chemical annotions <br> as the superclass, class, subclass and parent_1 level for <br>" + '<span style="font-size: 20px;">' + project_name + '</span>' + "</b>",
'y':0.96,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'})

fig.update_layout(margin=dict(l=50, r=50, t=100, b=50)
#,paper_bgcolor="Black"
)

fig.show()
fig.write_html(sunburst_chem_results_path)

# %%

fig = px.sunburst(df4cyto_flat, path=['Kingdom_cof_DNP', 'Phylum_cof_DNP', 'Class_cof_DNP', 'Order_cof_DNP', 'Family_cof_DNP' ,'Genus_cof_DNP', 'Species_cof_DNP'],
)
fig.update_layout(
#font_family="Courier New",
title_font_family="Courier New",
title_font_color="black",
title_font_size=14,
legend_title_font_color="black",
title_text="<b> Overview of the source organisms of the chemical annotation <br> as the kingfom, phylum, class, order, family, genus and species level for <br>" + project_name + "</b>",
title_x=0.5
)

fig.update_layout(
title={
'text': "<b> Overview of the consensus chemical annotions <br> as the superclass, class, subclass and parent_1 level for <br>" + '<span style="font-size: 20px;">' + project_name + '</span>' + "</b>",
'y':0.96,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'})

fig.update_layout(margin=dict(l=50, r=50, t=100, b=50)
#,paper_bgcolor="Black"
)

fig.show()
fig.write_html(sunburst_organisms_results_path)


# %%
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