pytransit.analysis package¶
Submodules¶
pytransit.analysis.base module¶
- class pytransit.analysis.base.AnalysisGUI[source]¶
Bases:
object
- class pytransit.analysis.base.AnalysisMethod(short_name, long_name, short_desc, long_desc, output, annotation_path, wxobj=None)[source]¶
Bases:
object
Basic class for analysis methods. Inherited by SingleMethod and ComparisonMethod.
- 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).
- class pytransit.analysis.base.InfoIcon(panel, flag, bmp=None, tooltip='')[source]¶
Bases:
wx._core.StaticBitmap
- 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, excluded_conditions=[], included_conditions=[], nterm=0.0, cterm=0.0)[source]¶
Bases:
pytransit.analysis.base.AnalysisMethod
Class to be inherited by analysis methods that compare essentiality between multiple conditions (e.g Anova).
- filter_wigs_by_conditions(data, conditions, covariates=[], interactions=[], excluded_conditions=[], included_conditions=[])[source]¶
Filters conditions that are excluded/included. ([[Wigdata]], [Condition], [[Covar]], [Condition], [Condition]) -> Tuple([[Wigdata]], [Condition])
- filter_wigs_by_conditions2(data, conditions, conditionsList, covariates=[], interactions=[])[source]¶
Filters conditions that are excluded/included. ([[Wigdata]], [Condition], [[Covar]], [Condition], [Condition]) -> Tuple([[Wigdata]], [Condition])
- filter_wigs_by_conditions3(data, fileNames, conditionNames, included_cond, excluded_cond, conditions, covariates=[], interactions=[])[source]¶
Filters conditions that are excluded/included; also extract cond, covar, and interaction labels conditionNames: based on original Conditions column in metadata conditions: user might have specified an alternative column to analyze (list of labels parallel to wigs)
- 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).
- 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).
- 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]¶
Bases:
object
pytransit.analysis.binomial module¶
- 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¶
- 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¶
- 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¶
- 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¶
- 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¶
- 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¶
- class pytransit.analysis.resampling.ResamplingMethod(ctrldata, expdata, annotation_path, output_file, normalization='TTR', samples=10000, adaptive=False, doHistogram=False, includeZeros=False, pseudocount=1, replicates='Sum', LOESS=False, ignoreCodon=True, NTerminus=0.0, CTerminus=0.0, ctrl_lib_str='', exp_lib_str='', winz=False, wxobj=None, Z=False, diffStrains=False, annotation_path_exp='', combinedWigParams=None)[source]¶
Bases:
pytransit.analysis.base.DualConditionMethod
resampling
- filter_wigs_by_conditions(data, conditions, included_conditions)[source]¶
Filters conditions from wig to ctrl, exp conditions only ([[Wigdata]], [ConditionCtrl, ConditionExp]) -> Tuple([[Wigdata]], [Condition])
pytransit.analysis.tn5gaps module¶
- 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¶
- class pytransit.analysis.anova.AnovaMethod(combined_wig, metadata, annotation, normalization, output_file, excluded_conditions=[], included_conditions=[], nterm=0.0, cterm=0.0, PC=1, winz=False, refs=[])[source]¶
Bases:
pytransit.analysis.base.MultiConditionMethod
anova
- 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]
- 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
- 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
- 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