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WIP: replace calc-weighted-overlap with mgmanysearch #19

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WIP: replace calc-weighted-overlap with mgmanysearch #19

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115d35a
replace calc-weighted-overlap with mgmanysearch
ctb Feb 6, 2024
08c51fa
bump up requirements for sourmash containment plugin
ctb Feb 11, 2024
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7 changes: 4 additions & 3 deletions Snakefile
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Original file line number Diff line number Diff line change
Expand Up @@ -371,13 +371,14 @@ rule metag_x_genomes_prefetch:
input:
genomes = expand("sketches/genomes/{n}.sig.zip", n=GENOME_NAMES),
metag="sketches/metag/{metag}.sig.zip",
bin = "scripts/calc-weighted-overlap.py",
output:
"outputs/prefetch/{metag}.x.genomes.{k}.csv",
threads: 1
shell: """
{input.bin} -k {wildcards.k} --genomes {input.genomes} \
--metagenomes {input.metag} -o {output}
sourmash scripts mgmanysearch -k {wildcards.k} \
--queries {input.genomes} \
--against {input.metag} \
-o {output}
"""

rule metag_x_genomes_prefetch_summary:
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3 changes: 3 additions & 0 deletions requirements.txt
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Original file line number Diff line number Diff line change
@@ -1,3 +1,6 @@
sourmash_plugin_containment_search>=0.4.3

# mkdocs stuff
pymdown-extensions
mkdocs-material
mkdocs-material-extensions
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122 changes: 0 additions & 122 deletions scripts/calc-weighted-overlap.py
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8 changes: 4 additions & 4 deletions scripts/summarize-weighted-overlap.py
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Expand Up @@ -31,7 +31,7 @@ def main():
sample_dfs = {}
for sample in samples:
sample_df = df[df['match_name'] == sample]
sample_df = sample_df[['query_name', 'f_unique_weighted']]
sample_df = sample_df[['query_name', 'f_match_weighted']]
sample_dfs[sample] = sample_df

# go through and merge, retaining the column named `containment`;
Expand All @@ -40,14 +40,14 @@ def main():
# do the first sample:
sample = samples.pop(0)
combined_df = sample_dfs[sample]
combined_df.rename(columns={'f_unique_weighted': sample}, inplace=True)
combined_df.rename(columns={'f_match_weighted': sample}, inplace=True)

# and then... the rest!
while samples:
sample = samples.pop(0)
sample_df = sample_dfs[sample]
sample_df = sample_df[['query_name', 'f_unique_weighted']]
sample_df.rename(columns={'f_unique_weighted': sample},
sample_df = sample_df[['query_name', 'f_match_weighted']]
sample_df.rename(columns={'f_match_weighted': sample},
inplace=True)
combined_df = combined_df.merge(sample_df, on='query_name',
how='outer')
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