pegasus.log_norm
- pegasus.log_norm(data, norm_count=100000.0, base_matrix=None, target_matrix=None, select=True)[source]
Normalize each cell by total counts, and then apply natural logarithm to the data.
- Parameters
data (
pegasusio.MultimodalData
) – Use current selected modality in data, which should contain one RNA expression matrix.norm_count (
int
, optional, default:1e5
.) – Total counts of one cell after normalization.base_matrix (
str
, optional, default:None
.) – The key name of the matrix to perform log_norm. If None, the current matrix.target_matrix (
str
, optional, default:None
.) – The key name of the matrix to store the log_normed results. If None, base_matrix + “.log_norm”.select (
bool
, optional, default:None
.) – Select the log_normed matrix as the current matrix (can be accessed via data.X).
- Return type
None
- Returns
None
Add the log-normalized matrix to
data.matrices
. Add the normalization scale vector todata.obs["scale"]
andnorm_count
parameter todata.uns["norm_count"]
.Note that if the detected base_matrix`==`X, we’ll change the name to counts instead.
Examples
>>> pg.log_norm(data)