coalispr.bedgraph_analyze.experiment¶

Module to manage experiments and samples.

Functions¶

`all_exps`([plusdiscards, allpos, allneg, allmut])	Return SHORT names for all (non-redundant) experimental/data samples.
`controls`([allpos, allneg])	Return SHORT names for positive and negative control samples.
`discarded`()	Return SHORT names for discarded samples (CAT_D).
`for_group`(group[, plusdiscards, samples])	Return dicts of group member names vs. lenghts and samples.
`get_grplabels`(keygrp)	Return labels to denote various samples in a group. This could be for
`get_picks`()	Return list of keys and items for comparing groups.
`goption`()	Assemble values for -g command-line options according to availability.
`has_discards`()	Return True if samples have been declared as unused (CAT_D)
`has_redundant_muts`()	Return True if samples have been declared as CAT_M.lower()
`has_grouparg`()	Boolean to indicate if -g command-line option is feasible
`mixed_groups`([plusdiscards, allmut, samples])	Return nested dict with sample names for groups with multiple subgroups
`mutant`([allmut])	Return SHORT names for mutant samples (CAT_M).
`negative`([allneg])	Return list of SHORT names for negative control samples (CAT_U).
`positive`([allpos])	Return SHORT names for positive control samples (CAT_S).
`reference`()	Return SHORT names for reference samples (CAT_R).
`mutant_groups`(df)	Build and return a list of tuples for generating sidepanels with
`drop_discarded`(df)	Return dataframe without discarded samples.
`get_discard`([df])	Provide discard selection present in dataframe df.
`get_mutant`([df, allmut])	Provide mutant selection present in dataframe df.
`get_negative`([df, allneg])	Provide negative control selection present in dataframe df.
`get_positive`([df, allpos])	Provide positive control selection present in dataframe df.
`get_reference`([df])	Provide reference selection present in dataframe df.

Module Contents¶

coalispr.bedgraph_analyze.experiment.all_exps(plusdiscards=True, allpos=True, allneg=True, allmut=True)¶

Return SHORT names for all (non-redundant) experimental/data samples.

Parameters:

plusdiscards (bool) – If ‘True’, any CAT_D samples will be included.
minimal (bool (default: False)) – Flag to include redundant samples, i.e. to return all mutant samples.

coalispr.bedgraph_analyze.experiment.controls(allpos=True, allneg=True)¶: Return SHORT names for positive and negative control samples.

coalispr.bedgraph_analyze.experiment.discarded()¶: Return SHORT names for discarded samples (CAT_D).

coalispr.bedgraph_analyze.experiment.for_group(group, plusdiscards=False, samples=None)¶

Return dicts of group member names vs. lenghts and samples.

Parameters:

group (str) – Name of group to use.
plusdiscards (bool) – If ‘True’, any CAT_D samples will be included.
samples (list) – Samples to group

coalispr.bedgraph_analyze.experiment.get_grplabels(keygrp)¶

Return labels to denote various samples in a group. This could be for REST or ALL.

Parameters:: keygrp (str) – Key for selecting proper dictionary with group labels.

coalispr.bedgraph_analyze.experiment.get_picks()¶: Return list of keys and items for comparing groups.

coalispr.bedgraph_analyze.experiment.goption()¶

Assemble values for -g command-line options according to availability.

Returns:: Dictionary to assemble command parser item ‘-g’ and options vs. choices. {‘usegroups’:{1:CATEGORY, 2:grouping2,}, ‘-g’:’ -g{1,2,}’, ‘glist’:[1, 2,], ‘-h’:’Group libraries according to 1: CATEGORY; 2: grouping2;.’ }
Return type:: dict

coalispr.bedgraph_analyze.experiment.has_discards()¶: Return True if samples have been declared as unused (CAT_D)

coalispr.bedgraph_analyze.experiment.has_redundant_muts()¶: Return True if samples have been declared as CAT_M.lower()

coalispr.bedgraph_analyze.experiment.has_grouparg()¶: Boolean to indicate if -g command-line option is feasible

coalispr.bedgraph_analyze.experiment.mixed_groups(plusdiscards=False, allmut=False, samples=None)¶

Return nested dict with sample names for groups with multiple subgroups (for group-comparison).

Parameters:

plusdiscards (bool) – If ‘True’, any CAT_D samples will be included.
allmut (bool (default: False)) – Flag to include redundant samples, i.e. to return all mutant samples.
samples (list) – List of samples.

coalispr.bedgraph_analyze.experiment.mutant(allmut=False)¶

Return SHORT names for mutant samples (CAT_M).

Get mutants to be analyzed. Set ‘allmut’ to True to include redundant samples. This could change/induce bias when deviance (from positive controls) of mapped reads gets more weight due to a larger number of similar (technical) repeats.

Parameters:: allmut (bool (default: False)) – Flag to include redundant samples, i.e. to return all mutant samples.
Returns:: A list of SHORT names for non-redundant or (all) mutant samples.
Return type:: list

coalispr.bedgraph_analyze.experiment.negative(allneg=True)¶

Return list of SHORT names for negative control samples (CAT_U).

Parameters:: allneg (bool) – Flag to include all negative control samples where process is found to be turned off (False); this is the minimal set of negative controls, marked by CAT_U in uppercase in EXPFILE. Samples can also be included if their inactivated/deleted protein is expected to bind sequenced RNA or is known to be essential to sustain the biological process under study (True);

coalispr.bedgraph_analyze.experiment.positive(allpos=True)¶

Return SHORT names for positive control samples (CAT_S).

Parameters:: allpos (bool) – If False, only samples marked by CAT_S in uppercase in EXPFILE are collected. By default all are returned.

coalispr.bedgraph_analyze.experiment.reference()¶: Return SHORT names for reference samples (CAT_R).

coalispr.bedgraph_analyze.experiment.mutant_groups(df)¶

Build and return a list of tuples for generating sidepanels with labels described in group dicts like MUTGROUPS in EXPFILE; Samples are selected from dataframe incl. discards if needed; being marked irrelevant, discards do not get a legend in the side panel.

Determines order of appearance: [
    (NEGCTL, _getshortsdict(GROUP, UNSPECIFICS)),
    (METHOD, _getshortsdict(METHOD, METHODS)),
    (FRACTION, _getshortsdict(FRACTION, FRACTIONS)),
    (CONDITION, _getshortsdict(CONDITION, CONDITIONS)),
    (MUTANT, _getshortsdict(GROUP, MUTANTS)),
    ]

Parameters:: df (Pandas.DataFrame) – Dataframe like that for merged bedgraphs: sample names to be retrieved - to define groupings via the EXPFILE - are in the columns.

coalispr.bedgraph_analyze.experiment.drop_discarded(df)¶

Return dataframe without discarded samples.

Calling function should check for empty discarded() (and then skip this)

coalispr.bedgraph_analyze.experiment.get_discard(df=None)¶: Provide discard selection present in dataframe df.

coalispr.bedgraph_analyze.experiment.get_mutant(df=None, allmut=False)¶: Provide mutant selection present in dataframe df.

coalispr.bedgraph_analyze.experiment.get_negative(df=None, allneg=True)¶: Provide negative control selection present in dataframe df.

coalispr.bedgraph_analyze.experiment.get_positive(df=None, allpos=True)¶: Provide positive control selection present in dataframe df.

coalispr.bedgraph_analyze.experiment.get_reference(df=None)¶: Provide reference selection present in dataframe df.