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Feature: Ignore channel in normalization (e.g. carrier) #3

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Feature: Ignore channel in normalization (e.g. carrier) #3

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c130970
feat: ignore channel in normalization
HenriettaHolze Oct 13, 2022
3e5f752
Make previous behaviour reproducible
HenriettaHolze Oct 13, 2022
40fe4da
Merge branch 'bfurtwa:master' into master
HenriettaHolze Oct 13, 2022
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27 changes: 24 additions & 3 deletions sceptre/sceptre.py
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Original file line number Diff line number Diff line change
Expand Up @@ -534,6 +534,7 @@ def normalize(
iter_thresh: float = 1.1,
na_thresh: Optional[float] = 1.1,
drop_na: bool = True,
ignore_channel: Optional[str] = None
):
"""Normalize expression values in AnnData object.
The median expression of each protein is equalized across files/channels
Expand All @@ -558,6 +559,9 @@ def normalize(
If not None, replace expression values below this value with 0.
drop_na
Drop proteins with no expression values (i.e. 0)
ignore_channel
Channel to ignore for channel normalization, calculation of file medians and
stopping criterion (iter_thresh). Only for 'file_channel'.
Returns
-------
:obj:`None`
Expand All @@ -577,7 +581,11 @@ def normalize(
quant_0 = quant.copy()
# file bias normalization
# calculate median for each protein in each sample
med = quant.T.reset_index().groupby("File ID").median().T
if ignore_channel == None:
med = quant.T.reset_index().groupby("File ID").median().T
else:
med = quant.loc[: , quant.columns.get_level_values(1) != ignore_channel].T.reset_index().groupby("File ID").median().T

# calculate the factors needed for a median shift
med_tot = med.median(axis=1)
factors = med.divide(med_tot, axis=0)
Expand All @@ -588,17 +596,30 @@ def normalize(

# channel bias normalization
# calculate median for each protein in each channel
med = quant.T.reset_index().groupby("Channel").median().T
if ignore_channel == None:
med = quant.T.reset_index().groupby("Channel").median().T
else:
med = quant.loc[: , quant.columns.get_level_values(1) != ignore_channel].T.reset_index().groupby("Channel").median().T

# calculate the factors needed for a median shift
med_tot = med.median(axis=1)
factors = med.divide(med_tot, axis=0)
if ignore_channel != None:
factors[ignore_channel] = 1

quant = quant.groupby(axis=1, level=1).apply(
lambda x: x.divide(factors.loc[:, x.name], axis=0)
)

# stop iterating when the change in quant to the previous iteration is below iter_thresh
if (abs(quant - quant_0).max().max()) <= iter_thresh:
if (
abs(
quant.loc[: , quant.columns.get_level_values(1) != ignore_channel].T
- quant_0.loc[: , quant.columns.get_level_values(1) != ignore_channel].T
)
.max()
.max()
) <= iter_thresh:
break
print("performed {} iterations".format(i + 1))

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