pegasus.arcsinh_transform

pegasus.arcsinh_transform(data, cofactor=5.0, jitter=False, random_state=0, backup_matrix='raw.X')[source]

Conduct arcsinh transform on the current matrix.

If jitter == True, jittering by adding a randomized value in U([-0.5, 0.5)). This will also make the matrix dense. Mimic Cytobank.

Parameters

  • cofactor (float, optional, default: 5.0) – Cofactor used in cytobank, arcsinh(x / cofactor).

  • jitter (bool, optional, default: False) – Add a ‘arcsinh.jitter’ matrix in dense format, jittering by adding a randomized value in U([-0.5, 0.5)).

  • random_state (int, optional, default: 0) – Random seed for generating jitters.

  • backup_matrix (str, optional, default: raw.X.) – The key name of the backup count matrix, usually the raw counts.

Return type

None

Returns

  • None

  • Update data.X with count matrix after log-normalization. In addition, back up the original count matrix as backup_matrix.

  • In case of rerunning normalization while backup_matrix already exists, use backup_matrix instead of data.X for normalization.

Examples

>>> pg.arcsinh_transform(data)