TASSELpy.net.maizegenetics.analysis.association package

Submodules

TASSELpy.net.maizegenetics.analysis.association.FixedEffectLMPlugin module

class TASSELpy.net.maizegenetics.analysis.association.FixedEffectLMPlugin.FixedEffectLMPlugin(*args, **kwargs)

Bases: TASSELpy.net.maizegenetics.plugindef.AbstractPlugin.AbstractPlugin

Methods

castTo(pyType)	Casts this object to another java/python type
clone(*args)	Creates and returns a copy of this object
equals(*args)	Indicates whether some other object is “equal to” this one
getArray(size)	Gets an empty wrapped java array that can accept the type of the wrapped
getCitation(*args)	Returns Citation for this plugin
getClass(*args)	Returns the runtime class of this Object.
getDblArray(rows[, cols])	Gets an empty wrapped java array that can accept the type of other wrapped java arrays: i.e.
getInputs(*args)	Gets list of inputs
getParameter(*args)	Returns a parameter value for given parameter key
hashCode(*args)	Returns a hash code vlaue for the object
isInteractive(*args)	If interactive == true, the plugin will create dialgos and panels
performFunction(*args)	Performs the function of this plugin
pluginDescription(*args)	Returns description of the plugin
processData(*args)	For the new Generic plugin parameter design, performFunction() will automatically call this.
receiveInput(*args)	Sets up this plugin to receive input from another plugin
setMaxP(*args)	Sets the maximum value of the p-value to keep
setNumberOfPermutations(*args)	Sets the number of permutations to perform
setOutputFile(*args)	Sets the output file
setParameter(*args)	Sets parameter value
setParameters(*args)	Allows self-describing Plugins to use args to set parameters specific
setPermute(*args)	Sets whether to perform permutations
setRandomizer(*args)	Sets the randomizer seed so that permutation results are reproducible for testing.
setRestrictOutput(*args)	Set the output to be restricted (or not)
toString(*args)	Returns a string representation of the object
wrap_existing_array(arr_instance)	Wraps a java array of this class’s type

__init__(*args, **kwargs)

Creates a new instance of FixedEffectLMPlugin

Signature:

FixedEffectLMPlugin (Frame parentFrame, boolean isInteractive)

Parameters:

• parentFrame (Frame) – A frame, can be null
• isInteractive (boolean) – Whether in an interactive session

setMaxP(*args)

Sets the maximum value of the p-value to keep

Signature:setMaxP (double value) Parameters:value (double) – the maximum p-value

setNumberOfPermutations(*args)

Sets the number of permutations to perform

Signature:setNumberOfPermutations (int numberOfPermutations) Parameters:numberOfPermutations (int) – the number of permutations to perform

setOutputFile(*args)

Sets the output file

Signature:setOutputFile (String filename) Parameters:filename (String) – the name of the filename

setPermute(*args)

Sets whether to perform permutations

Signature:setPermute (boolean permute) Parameters:permute (boolean) – Whether to perform permutations

setRandomizer(*args)

Sets the randomizer seed so that permutation results are reproducible for testing. The same seed will reproduce the same sequence of pseudo-random numbers

Signature:setRandomizer (long seed) Parameters:seed (long) – the seed

setRestrictOutput(*args)

Set the output to be restricted (or not)

Signature:setRestrictOutput (boolean restrict) Parameters:restrict (boolean) – whether to restrict the output

class TASSELpy.net.maizegenetics.analysis.association.FixedEffectLMPlugin.easy_GLM(outputFile=None, restrictOutput=False, maxP=None, permute=False, numberOfPermutations=1000, seed=None)

Bases: TASSELpy.net.maizegenetics.analysis.association.FixedEffectLMPlugin.FixedEffectLMPlugin

Subclass of FixedEffectLMPlugin that allows for easy running of GLMs

Methods

addCovariate(covar, name[, comment])	Adds a covariate to the analysis
addMarkers(markers, name[, comment])	Adds markers to the analysis
addPhenotype(pheno, name[, comment])	Adds a Phenotype object to the analysis
castTo(pyType)	Casts this object to another java/python type
clone(*args)	Creates and returns a copy of this object
equals(*args)	Indicates whether some other object is “equal to” this one
getArray(size)	Gets an empty wrapped java array that can accept the type of the wrapped
getCitation(*args)	Returns Citation for this plugin
getClass(*args)	Returns the runtime class of this Object.
getDblArray(rows[, cols])	Gets an empty wrapped java array that can accept the type of other wrapped java arrays: i.e.
getInputs(*args)	Gets list of inputs
getParameter(*args)	Returns a parameter value for given parameter key
hashCode(*args)	Returns a hash code vlaue for the object
isInteractive(*args)	If interactive == true, the plugin will create dialgos and panels
performFunction(*args)	Performs the function of this plugin
pluginDescription(*args)	Returns description of the plugin
processData(*args)	For the new Generic plugin parameter design, performFunction() will automatically call this.
receiveInput(*args)	Sets up this plugin to receive input from another plugin
run_glm(phenotypes, markers[, covariates])	Runs the GLM and returns the results as a TableReport
setMaxP(*args)	Sets the maximum value of the p-value to keep
setNumberOfPermutations(*args)	Sets the number of permutations to perform
setOutputFile(*args)	Sets the output file
setParameter(*args)	Sets parameter value
setParameters(*args)	Allows self-describing Plugins to use args to set parameters specific
setPermute(*args)	Sets whether to perform permutations
setRandomizer(*args)	Sets the randomizer seed so that permutation results are reproducible for testing.
setRestrictOutput(*args)	Set the output to be restricted (or not)
toString(*args)	Returns a string representation of the object
wrap_existing_array(arr_instance)	Wraps a java array of this class’s type

__init__(outputFile=None, restrictOutput=False, maxP=None, permute=False, numberOfPermutations=1000, seed=None)

Instantiates a GLM analysis

Parameters:

outputFile : str, optional

A file for outputting results

restrictOutput : boolean, optional

Whether to restrict output

maxP : double, optional

A maximum p-value

permute : boolean, optional

Whether to run permutations

numberOfPermutations : int, optional

Number of permutations to perform (if performing permutations)

seed : int, optional

Random seed if reproducible permutations desired

addCovariate(covar, name, comment=None)

Adds a covariate to the analysis

Parameters:

covar : Phenotype object

Phenotype object consisting of 1 or more covariates

name : str

The name to give to this covariate datum

comment : str, optional

A comment on the covariate. If None, will be the same as name

Raises:

TypeError:

if covariate is not a Phenotype or name is not a string

addMarkers(markers, name, comment=None)

Adds markers to the analysis

Parameters:

markers : GenotypeTable object

Genotype table givng the genetic markers

name : str

The name to give to this set of markers

comment : str, optional

A comment on the markers. If None, will be the same as name

Raises:

TypeError

If markers is not a GenotypeTable or name not a string

addPhenotype(pheno, name, comment=None)

Adds a Phenotype object to the analysis

Parameters:

pheno : Phenotype object

Phenotype object consisting of 1 or more traits to analyze

name : str

The name to give to this Phenotype datum

comment : str, optional

A comment on the Phenotype. If None, will be the same as name

Raises:

TypeError:

if pheno is not a Phenotype or name is not a string

run_glm(phenotypes, markers, covariates=None)

Runs the GLM and returns the results as a TableReport

Parameters:

phenotypes : iterable of strings

The names of the phenotypes you want to run

markers : str

The name of the marker set you want to use

covariates : iterable of strings, optional

The names of the covariates you want to use

Returns:

TableReport of marker effects, TableReport of allelic effects

Raises:

KeyError

If names not present

Module contents