pegasus.log_norm¶
- pegasus.log_norm(data, norm_count=100000.0, backup_matrix='raw.X')[source]¶
Normalization, 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.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 asbackup_matrix
.In case of rerunning normalization while
backup_matrix
already exists, usebackup_matrix
instead ofdata.X
for normalization.
Examples
>>> pg.log_norm(data)