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- __builtin__.tuple(__builtin__.object)
-
- ComputeScoreParams
- WeederRunner
- cmonkey.scoring.ScoringFunctionBase
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- MotifScoringFunctionBase
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- MemeScoringFunction
- WeederScoringFunction
class ComputeScoreParams(__builtin__.tuple) |
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ComputeScoreParams(iteration, cluster, feature_ids, seqs, used_seqs, meme_runner, min_cluster_rows, max_cluster_rows, num_motifs, previous_motif_infos, outdir, num_iterations, debug)
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- Method resolution order:
- ComputeScoreParams
- __builtin__.tuple
- __builtin__.object
Methods defined here:
- __getnewargs__(self)
- Return self as a plain tuple. Used by copy and pickle.
- __getstate__(self)
- Exclude the OrderedDict from pickling
- __repr__(self)
- Return a nicely formatted representation string
- _asdict(self)
- Return a new OrderedDict which maps field names to their values
- _replace(_self, **kwds)
- Return a new ComputeScoreParams object replacing specified fields with new values
Class methods defined here:
- _make(cls, iterable, new=<built-in method __new__ of type object>, len=<built-in function len>) from __builtin__.type
- Make a new ComputeScoreParams object from a sequence or iterable
Static methods defined here:
- __new__(_cls, iteration, cluster, feature_ids, seqs, used_seqs, meme_runner, min_cluster_rows, max_cluster_rows, num_motifs, previous_motif_infos, outdir, num_iterations, debug)
- Create new instance of ComputeScoreParams(iteration, cluster, feature_ids, seqs, used_seqs, meme_runner, min_cluster_rows, max_cluster_rows, num_motifs, previous_motif_infos, outdir, num_iterations, debug)
Data descriptors defined here:
- __dict__
- Return a new OrderedDict which maps field names to their values
- cluster
- Alias for field number 1
- debug
- Alias for field number 12
- feature_ids
- Alias for field number 2
- iteration
- Alias for field number 0
- max_cluster_rows
- Alias for field number 7
- meme_runner
- Alias for field number 5
- min_cluster_rows
- Alias for field number 6
- num_iterations
- Alias for field number 11
- num_motifs
- Alias for field number 8
- outdir
- Alias for field number 10
- previous_motif_infos
- Alias for field number 9
- seqs
- Alias for field number 3
- used_seqs
- Alias for field number 4
Data and other attributes defined here:
- _fields = ('iteration', 'cluster', 'feature_ids', 'seqs', 'used_seqs', 'meme_runner', 'min_cluster_rows', 'max_cluster_rows', 'num_motifs', 'previous_motif_infos', 'outdir', 'num_iterations', 'debug')
Methods inherited from __builtin__.tuple:
- __add__(...)
- x.__add__(y) <==> x+y
- __contains__(...)
- x.__contains__(y) <==> y in x
- __eq__(...)
- x.__eq__(y) <==> x==y
- __ge__(...)
- x.__ge__(y) <==> x>=y
- __getattribute__(...)
- x.__getattribute__('name') <==> x.name
- __getitem__(...)
- x.__getitem__(y) <==> x[y]
- __getslice__(...)
- x.__getslice__(i, j) <==> x[i:j]
Use of negative indices is not supported.
- __gt__(...)
- x.__gt__(y) <==> x>y
- __hash__(...)
- x.__hash__() <==> hash(x)
- __iter__(...)
- x.__iter__() <==> iter(x)
- __le__(...)
- x.__le__(y) <==> x<=y
- __len__(...)
- x.__len__() <==> len(x)
- __lt__(...)
- x.__lt__(y) <==> x<y
- __mul__(...)
- x.__mul__(n) <==> x*n
- __ne__(...)
- x.__ne__(y) <==> x!=y
- __rmul__(...)
- x.__rmul__(n) <==> n*x
- __sizeof__(...)
- T.__sizeof__() -- size of T in memory, in bytes
- count(...)
- T.count(value) -> integer -- return number of occurrences of value
- index(...)
- T.index(value, [start, [stop]]) -> integer -- return first index of value.
Raises ValueError if the value is not present.
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class MemeScoringFunction(MotifScoringFunctionBase) |
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Scoring function for motifs
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- Method resolution order:
- MemeScoringFunction
- MotifScoringFunctionBase
- cmonkey.scoring.ScoringFunctionBase
Methods defined here:
- __init__(self, organism, membership, ratios, config_params=None)
- creates a ScoringFunction
- initialize(self, args)
- process additional parameters
- meme_runner(self)
- returns the MEME runner object
Methods inherited from MotifScoringFunctionBase:
- compute(self, iteration_result, ref_matrix=None)
- override base class compute() method, behavior is more complicated,
since it nests Motif and MEME runs
- compute_force(self, iteration_result, ref_matrix=None)
- override base class compute() method, behavior is more complicated,
since it nests Motif and MEME runs
- compute_pvalues(self, iteration_result, num_motifs, force)
- Compute motif scores.
The result is a dictionary from cluster -> (feature_id, pvalue)
containing a sparse gene-to-pvalue mapping for each cluster
In order to influence the sequences
that go into meme, the user can specify a list of sequence filter
functions that have the signature
(seqs, feature_ids, distance) -> seqs
These filters are applied in the order they appear in the list.
- last_cached(self)
- motif_in_iteration(self, i)
- TODO: change to an id that is not called 'MEME'
- run_logs(self)
Methods inherited from cmonkey.scoring.ScoringFunctionBase:
- check_requirements(self)
- Give the scoring module an opportunity to check whether the
requirements to run are all met
- do_compute(self, iteration_result, ref_matrix=None)
- gene_names(self)
- returns the gene names
- num_clusters(self)
- returns the number of clusters
- pickle_path(self)
- returns the function-specific pickle-path
- rows_for_cluster(self, cluster)
- returns the rows for the specified cluster
- run_in_iteration(self, i)
- scaling(self, iteration)
- returns the quantile normalization scaling for the specified iteration
- set_score_means(self, iteration_result, matrix)
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class MotifScoringFunctionBase(cmonkey.scoring.ScoringFunctionBase) |
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Base class for motif scoring functions that use MEME
This class of scoring function has 2 schedules:
1. run_in_iteration(i) is the normal schedule
2. motif_in_iteration(i) determines when the motifing tools is run
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Methods defined here:
- __init__(self, id, organism, membership, ratios, seqtype, config_params=None)
- creates a ScoringFunction
- compute(self, iteration_result, ref_matrix=None)
- override base class compute() method, behavior is more complicated,
since it nests Motif and MEME runs
- compute_force(self, iteration_result, ref_matrix=None)
- override base class compute() method, behavior is more complicated,
since it nests Motif and MEME runs
- compute_pvalues(self, iteration_result, num_motifs, force)
- Compute motif scores.
The result is a dictionary from cluster -> (feature_id, pvalue)
containing a sparse gene-to-pvalue mapping for each cluster
In order to influence the sequences
that go into meme, the user can specify a list of sequence filter
functions that have the signature
(seqs, feature_ids, distance) -> seqs
These filters are applied in the order they appear in the list.
- last_cached(self)
- motif_in_iteration(self, i)
- TODO: change to an id that is not called 'MEME'
- run_logs(self)
Methods inherited from cmonkey.scoring.ScoringFunctionBase:
- check_requirements(self)
- Give the scoring module an opportunity to check whether the
requirements to run are all met
- do_compute(self, iteration_result, ref_matrix=None)
- gene_names(self)
- returns the gene names
- num_clusters(self)
- returns the number of clusters
- pickle_path(self)
- returns the function-specific pickle-path
- rows_for_cluster(self, cluster)
- returns the rows for the specified cluster
- run_in_iteration(self, i)
- scaling(self, iteration)
- returns the quantile normalization scaling for the specified iteration
- set_score_means(self, iteration_result, matrix)
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class WeederRunner |
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Wrapper around Weeder so we can use the multiprocessing module.
The function basically runs Weeder ont the specified set of sequences,
converts its output to a MEME output file and runs MAST on the MEME output
to generate a MEME run result.
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Methods defined here:
- __call__(self, params)
- call the runner like a function
- __init__(self, meme_suite, config_params, remove_tempfiles=True)
- create a runner object
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class WeederScoringFunction(MotifScoringFunctionBase) |
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Motif scoring function that runs Weeder instead of MEME
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- Method resolution order:
- WeederScoringFunction
- MotifScoringFunctionBase
- cmonkey.scoring.ScoringFunctionBase
Methods defined here:
- __init__(self, organism, membership, ratios, config_params=None)
- creates a scoring function
- check_requirements(self)
- meme_runner(self)
- returns the MEME runner object
Methods inherited from MotifScoringFunctionBase:
- compute(self, iteration_result, ref_matrix=None)
- override base class compute() method, behavior is more complicated,
since it nests Motif and MEME runs
- compute_force(self, iteration_result, ref_matrix=None)
- override base class compute() method, behavior is more complicated,
since it nests Motif and MEME runs
- compute_pvalues(self, iteration_result, num_motifs, force)
- Compute motif scores.
The result is a dictionary from cluster -> (feature_id, pvalue)
containing a sparse gene-to-pvalue mapping for each cluster
In order to influence the sequences
that go into meme, the user can specify a list of sequence filter
functions that have the signature
(seqs, feature_ids, distance) -> seqs
These filters are applied in the order they appear in the list.
- last_cached(self)
- motif_in_iteration(self, i)
- TODO: change to an id that is not called 'MEME'
- run_logs(self)
Methods inherited from cmonkey.scoring.ScoringFunctionBase:
- do_compute(self, iteration_result, ref_matrix=None)
- gene_names(self)
- returns the gene names
- num_clusters(self)
- returns the number of clusters
- pickle_path(self)
- returns the function-specific pickle-path
- rows_for_cluster(self, cluster)
- returns the rows for the specified cluster
- run_in_iteration(self, i)
- scaling(self, iteration)
- returns the quantile normalization scaling for the specified iteration
- set_score_means(self, iteration_result, matrix)
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