pytransit.analysis package¶
Submodules¶
pytransit.analysis.base module¶
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class
pytransit.analysis.base.
AnalysisGUI
[source]¶
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class
pytransit.analysis.base.
AnalysisMethod
(short_name, long_name, short_desc, long_desc, output, annotation_path, wxobj=None)[source]¶ Basic class for analysis methods. Inherited by SingleMethod and ComparisonMethod.
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class
pytransit.analysis.base.
DualConditionMethod
(short_name, long_name, short_desc, long_desc, ctrldata, expdata, annotation_path, output, normalization, replicates='Sum', LOESS=False, ignoreCodon=True, NTerminus=0.0, CTerminus=0.0, wxobj=None)[source]¶ Bases:
pytransit.analysis.base.AnalysisMethod
Class to be inherited by analysis methods that determine changes in essentiality between two conditions (e.g. Resampling, DEHMM).
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exception
pytransit.analysis.base.
InvalidArgumentException
(message)[source]¶ Bases:
exceptions.Exception
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class
pytransit.analysis.base.
MultiConditionMethod
(short_name, long_name, short_desc, long_desc, combined_wig, metadata, annotation_path, output, normalization=None, LOESS=False, ignoreCodon=True, wxobj=None)[source]¶ Bases:
pytransit.analysis.base.AnalysisMethod
Class to be inherited by analysis methods that compare essentiality between multiple conditions (e.g Anova).
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class
pytransit.analysis.base.
QuadConditionMethod
(short_name, long_name, short_desc, long_desc, ctrldataA, ctrldataB, expdataA, expdataB, annotation_path, output, normalization, replicates='Sum', LOESS=False, ignoreCodon=True, NTerminus=0.0, CTerminus=0.0, wxobj=None)[source]¶ Bases:
pytransit.analysis.base.AnalysisMethod
Class to be inherited by analysis methods that determine changes in essentiality between four conditions (e.g. GI).
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class
pytransit.analysis.base.
SingleConditionMethod
(short_name, long_name, short_desc, long_desc, ctrldata, annotation_path, output, replicates='Sum', normalization=None, LOESS=False, ignoreCodon=True, NTerminus=0.0, CTerminus=0.0, wxobj=None)[source]¶ Bases:
pytransit.analysis.base.AnalysisMethod
Class to be inherited by analysis methods that determine essentiality in a single condition (e.g. Gumbel, Binomial, HMM).
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class
pytransit.analysis.base.
TransitAnalysis
(sn, ln, short_desc, long_desc, tn, method_class=<class pytransit.analysis.base.AnalysisMethod>, gui_class=<class pytransit.analysis.base.AnalysisGUI>, filetypes=[<class pytransit.analysis.base.TransitFile>])[source]¶
pytransit.analysis.binomial module¶
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class
pytransit.analysis.binomial.
BinomialMethod
(ctrldata, annotation_path, output_file, samples=10000, burnin=500, replicates='Sum', normalization=None, LOESS=False, ignoreCodon=True, NTerminus=0.0, CTerminus=0.0, pi0=0.5, pi1=0.5, M0=1.0, M1=1.0, a0=10.0, a1=10.0, b0=1.0, b1=1.0, alpha_w=0.5, beta_w=0.5, wxobj=None)[source]¶ Bases:
pytransit.analysis.base.SingleConditionMethod
binomial
pytransit.analysis.example module¶
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class
pytransit.analysis.example.
ExampleMethod
(ctrldata, annotation_path, output_file, replicates='Sum', normalization=None, LOESS=False, ignoreCodon=True, NTerminus=0.0, CTerminus=0.0, wxobj=None)[source]¶ Bases:
pytransit.analysis.base.SingleConditionMethod
Example
pytransit.analysis.griffin module¶
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class
pytransit.analysis.griffin.
GriffinMethod
(ctrldata, annotation_path, output_file, minread=1, replicates='Sum', normalization=None, LOESS=False, ignoreCodon=True, NTerminus=0.0, CTerminus=0.0, wxobj=None)[source]¶ Bases:
pytransit.analysis.base.SingleConditionMethod
griffin
pytransit.analysis.gumbel module¶
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class
pytransit.analysis.gumbel.
GumbelMethod
(ctrldata, annotation_path, output_file, samples=10000, burnin=500, trim=1, minread=1, replicates='Sum', normalization=None, LOESS=False, ignoreCodon=True, NTerminus=0.0, CTerminus=0.0, wxobj=None)[source]¶ Bases:
pytransit.analysis.base.SingleConditionMethod
Gumbel
pytransit.analysis.hmm module¶
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class
pytransit.analysis.hmm.
HMMMethod
(ctrldata, annotation_path, output_file, replicates='Mean', normalization=None, LOESS=False, ignoreCodon=True, NTerminus=0.0, CTerminus=0.0, wxobj=None)[source]¶ Bases:
pytransit.analysis.base.SingleConditionMethod
HMM
pytransit.analysis.rankproduct module¶
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class
pytransit.analysis.rankproduct.
RankProductMethod
(ctrldata, expdata, annotation_path, output_file, normalization='TTR', samples=10000, adaptive=False, doHistogram=False, replicates='Sum', LOESS=False, ignoreCodon=True, NTerminus=0.0, CTerminus=0.0, wxobj=None)[source]¶ Bases:
pytransit.analysis.base.DualConditionMethod
rankproduct
pytransit.analysis.resampling module¶
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class
pytransit.analysis.resampling.
ResamplingMethod
(ctrldata, expdata, annotation_path, output_file, normalization='TTR', samples=10000, adaptive=False, doHistogram=False, includeZeros=False, pseudocount=0.0, replicates='Sum', LOESS=False, ignoreCodon=True, NTerminus=0.0, CTerminus=0.0, ctrl_lib_str='', exp_lib_str='', wxobj=None)[source]¶ Bases:
pytransit.analysis.base.DualConditionMethod
resampling
pytransit.analysis.tn5gaps module¶
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class
pytransit.analysis.tn5gaps.
Tn5GapsMethod
(ctrldata, annotation_path, output_file, replicates='Sum', normalization=None, LOESS=False, ignoreCodon=True, minread=1, NTerminus=0.0, CTerminus=0.0, wxobj=None)[source]¶ Bases:
pytransit.analysis.base.SingleConditionMethod
Example
pytransit.analysis.anova module¶
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class
pytransit.analysis.anova.
AnovaMethod
(combined_wig, metadata, annotation, normalization, output_file, ignored_conditions=set([]))[source]¶ Bases:
pytransit.analysis.base.MultiConditionMethod
anova
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filter_by_conditions_blacklist
(data, conditions, ignored_conditions)[source]¶ Filters out wigfiles, with ignored conditions. ([[Wigdata]], [Condition]) -> Tuple([[Wigdata]], [Condition])
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group_by_condition
(wigList, conditions)[source]¶ Returns array of datasets, where each dataset corresponds to one condition. ([[Wigdata]], [Condition]) -> [[DataForCondition]] Wigdata :: [Number] Condition :: String DataForCondition :: [Number]
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means_by_condition_for_gene
(sites, conditions, data)[source]¶ Returns a dictionary of {Condition: Mean} for each condition. ([Site], [Condition]) -> {Condition: Number} Site :: Number Condition :: String
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means_by_rv
(data, RvSiteindexesMap, genes, conditions)[source]¶ Returns Dictionary of mean values by condition ([[Wigdata]], {Rv: SiteIndex}, [Gene], [Condition]) -> {Rv: {Condition: Number}} Wigdata :: [Number] SiteIndex :: Number Gene :: {start, end, rv, gene, strand} Condition :: String
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read_samples_metadata
(metadata_file)[source]¶ Filename -> ConditionMap ConditionMap :: {Filename: Condition}
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run_anova
(data, genes, MeansByRv, RvSiteindexesMap, conditions)[source]¶ Runs Anova (grouping data by condition) and returns p and q values ([[Wigdata]], [Gene], {Rv: {Condition: Mean}}, {Rv: [SiteIndex]}, [Condition]) -> Tuple([Number], [Number]) Wigdata :: [Number] Gene :: {start, end, rv, gene, strand} Mean :: Number SiteIndex: Integer Condition :: String
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