lapa.result
Module Contents
Classes
Attributes
- lapa.result._core_cols = ['Chromosome', 'Start', 'End', 'Strand']
- class lapa.result._LapaResult(path, replicated=True, prefix='')
- abstract _read_cluster(self, path)
- property samples(self)
- property datasets(self)
- property count_dir(self)
- property sample_dir(self)
- property dataset_dir(self)
- property cluster_path(self)
- property dataset_path(self)
- read_clusters(self, filter_intergenic=True)
- read_sample(self, sample, filter_intergenic=True)
- read_dataset(self, dataset)
- _set_index(self, df)
- read_counts(self, sample=None, strand=None)
- attribute(self, field, samples=None)
Read attribute of samples as dataframe
- counts(self, samples=None)
- total_counts(self, samples=None)
- gene_id(self)
- static _agg_per_groups(df, groups, agg_func)
- _k_n(self, groups, min_gene_count)
- replication_rate(self, samples=None, num_samples=2, min_score=1)
Calculate replication rate of samples
- plot_replication_rate(self, samples=None, num_samples=2, min_score=1, line_kws=None)
- fisher_exact_test(self, groups, min_gene_count=10, correction_method='fdr_bh')
Fisher-exact test for sites.
- Parameters
groups (Dict[str, List[str]]) – dict of two elements as assinging groups. Two keys are group names and values are list of keys annotating samples belong to each group.
min_gene_count (int) – Number of reads in the gene to be consider in analysis.
correction_method (str) – multiple testing correction method. methods in statsmodels.stats.multitest.multipletests are valid.
- beta_binomial_test(self, min_gene_count=10, theta=0.001, max_iter=1000)
P-values based on betabinomial test.
- class lapa.result.LapaResult(path, replicated=True)
Bases:
_LapaResult- _read_cluster(self, path)
- read_clusters(self, filter_intergenic=True, filter_internal_priming=True)
- class lapa.result.LapaTssResult(path, replicated=True)
Bases:
_LapaResult- _read_cluster(self, path)