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added plugin to plugin_setup
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@dpear
dpear committed Mar 1, 2022
1 parent c6d87a4 commit 0e41ea8
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126 changes: 125 additions & 1 deletion q2_katharoseq/plugin_setup.py
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from qiime2.plugin import (Plugin, Citations, Str, Int,
MetadataColumn, Categorical, Numeric, Choices)
from q2_types.feature_table import (FeatureTable, Frequency)
from . import read_count_threshold, estimating_biomass
from . import read_count_threshold, estimating_biomass, biomass_plot
import q2_katharoseq
from q2_katharoseq._type import EstimatedBiomass
from q2_katharoseq._format import EstimatedBiomassFmt, EstimatedBiomassDirFmt
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)


plugin.visualizers.register_function(
function=read_count_threshold,
inputs={
'table': FeatureTable[Frequency]
},
parameters={
'control': Str % Choices(['zymobiomics', 'classic', 'atcc']),
'threshold': Int,
'positive_control_value': Str,
'positive_control_column': MetadataColumn[Categorical],
'cell_count_column': MetadataColumn[Numeric]
},
input_descriptions={
'table': (
'A FeatureTable collapsed to the genus level (level 6) that '
'contains the control samples.'
),
},
parameter_descriptions={
'control': (
'The type of positive control used.'
),
'threshold': (
'Threshold to use in calculating minimum frequency. '
'Must be int in [0,100].'
),
'positive_control_value': (
'The value in the control column that demarks which samples are '
'the positive controls.'
),
'positive_control_column': (
'The column in the sample metadata that describes which samples '
'are and are not controls.'
),
},
name='Methods for the application of the KatharoSeq protocol',
description='KatharoSeq is high-throughput protocol combining laboratory '
'and bioinformatic methods that can differentiate a true '
'positive signal in samples with as few as 50 to 500 cells.',
citations=[citations['minich2018']]
)


plugin.methods.register_function(
function=estimating_biomass,
inputs={},
parameters={'total_reads': MetadataColumn[Numeric],
'control_cell_extraction': MetadataColumn[Numeric],
'positive_control_column': MetadataColumn[Categorical],
'positive_control_value': Str,
'extraction_mass_g': MetadataColumn[Numeric],
'min_total_reads': Int,
'pcr_template_vol': Int,
'dna_extract_vol': Int},
outputs=[('estimated_biomass', EstimatedBiomass)],
input_descriptions={},
parameter_descriptions={
'total_reads': 'The total sum of the reads or ASVs for each sample.',
'control_cell_extraction': (
'The estimated number of cells or genomes used as input to your '
'library prep. One may typically estimate this by determining the '
'total number of cells from a stock solution used to make '
'standard titrations. Each titration will have an estimated '
'number of microbial cells put into the extraction. The final '
'estimate will depend on the elution volume and the final volume '
'used into the library prep (e.g. 16S PCR).'),
'positive_control_column': (
'The column in the sample metadata that describes which samples '
'are and are not controls.'),
'positive_control_value': (
'The value in the control column that demarks which samples are '
'the positive controls.'),
'extraction_mass_g': (
'The column in the sample metadata that describes the sample '
'(e.g. stool, tissue, soil, etc) mass (in grams - typically '
'converted from mg)'),
'min_total_reads': 'The minimum threshold to apply.',
'pcr_template_vol': (
'The volume of DNA used as template in the '
'library prep (PCR reaction)'),
'dna_extract_vol': (
'The final elution volume used during DNA extraction')},
output_descriptions={
'estimated_biomass': (
'A dataframe containing the details on estimated biomass')
},
name='Estimate the biomass of samples using KatharoSeq controls.',
description='Estimate the biomass of samples using KatharoSeq controls.',
citations=[]
)

plugin.visualizers.register_function(
function=biomass_plot,
inputs={},
parameters={'total_reads': MetadataColumn[Numeric],
'control_cell_extraction': MetadataColumn[Numeric],
'positive_control_column': MetadataColumn[Categorical],
'positive_control_value': Str,
'min_total_reads': Int
},
input_descriptions={},
parameter_descriptions={
'total_reads': 'The total sum of the reads or ASVs for each sample.',
'control_cell_extraction': (
'The estimated number of cells or genomes used as input to your '
'library prep. One may typically estimate this by determining the '
'total number of cells from a stock solution used to make '
'standard titrations. Each titration will have an estimated '
'number of microbial cells put into the extraction. The final '
'estimate will depend on the elution volume and the final volume '
'used into the library prep (e.g. 16S PCR).'),
'positive_control_column': (
'The column in the sample metadata that describes which samples '
'are and are not controls.'),
'positive_control_value': (
'The value in the control column that demarks which samples are '
'the positive controls.'),
'min_total_reads': 'The minimum threshold to apply.',
},
name='Plot the results of estimating_biomass.',
description='Plot the results of estimating_biomass.',
citations=[]
)

importlib.import_module('q2_katharoseq._transformer')

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