class NSP(jitter: float = 0.5, random_state: Optional[int] = None, sample_size: int = inf)#

Noise corrected and Scaled Protein counts (NSP)

This is a novel normalization method for Tapestri protein data. It is based on the assumption that the background and signal are linearly dependent on the total read counts. The method fits a linear model to the background and signal and then corrects the read counts for the linear dependence.


transform(reads[, scale])

Normalize read counts.


Identifies the appropriate scaling factor for oversequenced runs.


Plot the signal and background


The standard deviation of the jitter to be added to the read counts before applying the normalization. The jitter is sampled from a normal distribution cenetered at 0. This is only applicable for NSP and asinh

random_state: int

The random state to use for the NSP and asinh methods.


The number of cells to use to estimate the signal and background linear models. All cells are used when set to np.inf.