lapa.replication
Module Contents
Functions
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Concats the column from all the dfs into one dataframe |
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Returns replication rate for samples for score_column. |
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Calculates replication rate and filters samples based on |
Attributes
- lapa.replication._core_cols = ['Chromosome', 'Start', 'End', 'Strand']
- lapa.replication.agg_sample_cols(samples, column='Score')
Concats the column from all the dfs into one dataframe where column named with sample name in the dict key.
- Parameters
sample – Dictionary of sample name as key and dataframe with columns of ‘Chromosome’, ‘Start’, ‘End’, ‘Strand’ as value
columns – Column to aggreate cross samples
- lapa.replication.replication_rate(samples, score_column='Score', rolling_size=1000, num_samples=2, min_score=1)
Returns replication rate for samples for score_column.
- Parameters
sample – Dictionary of sample name as key and dataframe with columns of ‘Chromosome’, ‘Start’, ‘End’, ‘Strand’ as value
score_column – Column to calculate rank samples
rolling_size – Rolling size in replication rate calculation
min_sample – Number of samples which region need to be observed to be replicated.
min_score – Minimum score needed to recognize region as expressed.
- lapa.replication.replication_dataset(samples, score_column='Score', rolling_size=1000, min_replication_rate=0.95, num_sample=2, min_score=1)
Calculates replication rate and filters samples based on given replication rate.
- Parameters
sample – Dictionary of sample name as key and dataframe with columns of ‘Chromosome’, ‘Start’, ‘End’, ‘Strand’ as value
score_column – Column to calculate rank samples
rolling_size – Rolling size in replication rate calculation
min_replication_rate – minimum replication rate to filter samples