scdynomics.utils.pp.normalize

Normalization modules for scDynOmics

author: jy

Functions

`cpm_promoter`([adata, layer, target_sum, ...])	Normalize PCAM by number of gene's associated transcripts.
`l1_normalize`(tensor)	L1 normalize a tensor Note: Not really for adata but explanation results.
`normalize_adata`([adata, layer, target_sum, ...])	Normalize the adata using the specified method and save if required.
`tpm`([adata, layer, target_sum])	Calculate TPM from a csr sparse matrix of gene expression data.

scdynomics.utils.pp.normalize.cpm_promoter(adata=None, layer: str = None, target_sum: float = None, exclude_highly_expressed: bool = False, max_fraction: float = 0.05) → anndata.AnnData: Normalize PCAM by number of gene’s associated transcripts.

scdynomics.utils.pp.normalize.l1_normalize(tensor: torch.Tensor): L1 normalize a tensor Note: Not really for adata but explanation results.

scdynomics.utils.pp.normalize.normalize_adata(adata: anndata.AnnData = None, layer: str = None, target_sum: float = 1000000.0, exclude_highly_expressed: bool = False, max_fraction: float = 0.05, method: str = 'cpm', log1p: bool = True, savepath: str = None) → anndata.AnnData: Normalize the adata using the specified method and save if required.

scdynomics.utils.pp.normalize.tpm(adata=None, layer: str = None, target_sum: float = 1000000.0) → anndata.AnnData: Calculate TPM from a csr sparse matrix of gene expression data.