immuneML.encodings.evenness_profile package

Submodules

immuneML.encodings.evenness_profile.EvennessProfileEncoder module

class immuneML.encodings.evenness_profile.EvennessProfileEncoder.EvennessProfileEncoder(min_alpha: float, max_alpha: float, dimension: int, name: str = None)[source]

Bases: DatasetEncoder

The EvennessProfileEncoder class encodes a repertoire based on the clonal frequency distribution. The evenness for a given repertoire is defined as follows:

\[^{\alpha} \mathrm{E}(\mathrm{f})=\frac{\left(\sum_{\mathrm{i}=1}^{\mathrm{n}} \mathrm{f}_{\mathrm{i}}^{\alpha}\right)^{\frac{1}{1-\alpha}}}{\mathrm{n}}\]

That is, it is the exponential of Renyi entropy at a given alpha divided by the species richness, or number of unique sequences.

Reference: Greiff et al. (2015). A bioinformatic framework for immune repertoire diversity profiling enables detection of immunological status. Genome Medicine, 7(1), 49. doi.org/10.1186/s13073-015-0169-8

Parameters:

  • min_alpha (float) – minimum alpha value to use

  • max_alpha (between min_alpha and) – maximum alpha value to use

  • dimension (int) – dimension of output evenness profile vector, or the number of alpha values to linearly space

  • max_alpha –

YAML specification:

my_evenness_profile:
    EvennessProfile:
        min_alpha: 0
        max_alpha: 10
        dimension: 51
STEP_ENCODED = 'encoded'
STEP_VECTORIZED = 'vectorized'
static build_object(dataset=None, **params)[source]
dataset_mapping = {'RepertoireDataset': 'EvennessProfileRepertoireEncoder'}
encode(dataset, params: EncoderParams)[source]

immuneML.encodings.evenness_profile.EvennessProfileRepertoireEncoder module

class immuneML.encodings.evenness_profile.EvennessProfileRepertoireEncoder.EvennessProfileRepertoireEncoder(min_alpha: float, max_alpha: float, dimension: int, name: str = None)[source]

Bases: EvennessProfileEncoder

encode_repertoire(repertoire, params: EncoderParams)[source]
get_encoded_repertoire(repertoire, params: EncoderParams)[source]

Module contents