lib5c.algorithms.trimming module

Module for trimming low or “dead” 5C fragments away from 5C datasets.

lib5c.algorithms.trimming.trim_counts(counts, indices)[source]

Removes specified rows and columns from the counts matrix.

Parameters

  • counts (np.ndarray) – The square symmetric counts matrix to trim.

  • indices (Iterable[int]) – The indices to wipe

Returns

The trimmed counts matrix.

Return type

np.ndarray

lib5c.algorithms.trimming.trim_counts_superdict(counts_superdict, indices)[source]

Applies trim_counts() to each replicate in a counts_superdict.

Parameters

  • counts_superdict (Dict[str, np.ndarray]) – The keys are replicate names, the values are the counts for that rep.

  • indices (Iterable[int]) – The indices to trim.

Returns

The keys are replicate names, the values are the trimmed counts for that rep.

Return type

Dict[str, np.ndarray]

lib5c.algorithms.trimming.trim_primers(primermap, counts_superdict, min_sum=100.0, min_frac=0.5)[source]

Trim a primermap using counts information from many replicates.

Parameters

  • primermap (List[Dict[str, Any]]) – The primermap to trim. See lib5c.parsers.primers.get_primermap().

  • counts_superdict (Dict[str, np.ndarray]) – The keys are replicate names, the values are the counts for that rep.

  • min_sum (Optional[float]) – Primers with a total cis sum lower than this value will be trimmed.

  • min_frac (Optional[float]) – Primers with fewer than this fraction of nonzero interactions out of all their finite interactions will be trimmed.

Returns

The first element is the trimmed primermap, the second is the set of indices of the original primermap which were removed.

Return type

Tuple[List[Dict[str, Any]], Set[int]]

lib5c.algorithms.trimming.wipe_counts(counts, indices, wipe_value=nan)[source]

Wipes specified rows and columns of the counts matrix with a specified value.

Parameters

  • counts (np.ndarray) – The square symmetric counts matrix to wipe.

  • indices (Iterable[int]) – The indices of the rows and columns to wipe.

  • wipe_value (Optional[float]) – The value to wipe the selected indices with.

Returns

The wiped counts matrix.

Return type

np.ndarray

lib5c.algorithms.trimming.wipe_counts_superdict(counts_superdict, indices, wipe_value=nan)[source]

Applies wipe_counts() to each replicate in a counts_superdict.

Parameters

  • counts_superdict (Dict[str, np.ndarray]) – The keys are replicate names, the values are the counts for that rep.

  • indices (Iterable[int]) – The indices to wipe

  • wipe_value (Optional[float]) – The value to wipe the selected indices with.

Returns

The keys are replicate names, the values are the wiped counts for that rep.

Return type

Dict[str, np.ndarray]