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
`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).
`othr`([plusdiscards])	Return SHORT names for samples defined as OTHR.
`positive`([allpos])	Return SHORT names for positive control samples (CAT_S). Samples
`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.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:: minimal (bool (default: False)) – Flag to include all negative control samples where process is found to be turned off (False) or when proteins expected to bind sequenced RNA or are known to be essential to sustain the biological process under study have been inactivated/deleted; this is the minimal set of negative controls (True)`), marked by CAT_U in uppercase in EXPFILE.

coalispr.bedgraph_analyze.experiment.othr(plusdiscards=False)¶

Return SHORT names for samples defined as OTHR.

Parameters:: plusdiscards (bool) – If ‘True’, any CAT_D samples will be included.

coalispr.bedgraph_analyze.experiment.positive(allpos=True)¶: Return SHORT names for positive control samples (CAT_S). Samples marked by CAT_S in uppercase in EXPFILE are collected only.

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.