Just stumbled upon this. Let's say that my response data is not normalized to a range 0-1. Example:
| sample |
drug |
dose |
replicate |
response |
| CL1 |
D1 |
10 |
1 |
1276 |
| CL1 |
D1 |
1.1 |
1 |
71650 |
| CL1 |
D1 |
0.37 |
1 |
125234 |
| CL1 |
D1 |
0.12 |
1 |
177396 |
| CL1 |
D1 |
0.04 |
1 |
203986 |
| CL1 |
D1 |
0.01 |
1 |
231502 |
| CL1 |
D1 |
0.00 |
1 |
312330 |
where 0.0 := DMSO.
Why are we making the user normalize their data first, can't CurveCurator do this?
All responses must be normalized against the control already without the response for the control.
|
if "replicate" in curve_df.columns: |
|
control_df = pd.DataFrame({(0.0, col_id): 1.0 for col_id in range(n_replicates)}, index=df.index) |
|
else: |
|
control_df = pd.DataFrame({0.0: 1.0}, index=df.index) |
I already have DMSO, why am I always adding cells with dose=0.0, response=1.0 if I have the normalize=True/False option? Why not just check whether dose=0.00 occurs in the data for every drug/cl/replicate combination and then not add this?
Just stumbled upon this. Let's say that my response data is not normalized to a range 0-1. Example:
where 0.0 := DMSO.
Why are we making the user normalize their data first, can't CurveCurator do this?
drevalpy/drevalpy/datasets/curvecurator.py
Lines 47 to 50 in a37e1f5
I already have DMSO, why am I always adding cells with dose=0.0, response=1.0 if I have the normalize=True/False option? Why not just check whether dose=0.00 occurs in the data for every drug/cl/replicate combination and then not add this?