Validation neutralization assays versus `polyclonal` fits¶

Compare actual measured neutralization values for specific mutants to the polyclonal fits.

Import Python modules:

[1]:

import os
import pickle

import altair as alt

import pandas as pd

import yaml

Read configuration and validation assay measurements:

[2]:

with open("config.yaml") as f:
    config = yaml.safe_load(f)

validation_ic50s = pd.read_csv(config["validation_ic50s"], na_filter=None)

validation_ic50s

[2]:

	antibody	aa_substitutions	measured IC50	lower_bound
0	S2M11		0.002572	False
1	S2M11	Y449N	3.000000	True
2	S2M11	E484Y	3.000000	True
3	S2M11	Q493K	3.000000	True

Now get the predictions by the averaged polyclonal model fits:

[3]:

validation_vs_prediction = []
for antibody, antibody_df in validation_ic50s.groupby("antibody"):
    with open(os.path.join(config["escape_dir"], f"{antibody}.pickle"), "rb") as f:
        model = pickle.load(f)
    validation_vs_prediction.append(model.icXX(antibody_df))

validation_vs_prediction = pd.concat(validation_vs_prediction, ignore_index=True)

validation_vs_prediction

[3]:

	antibody	aa_substitutions	measured IC50	lower_bound	mean_IC50	median_IC50	std_IC50	n_models	frac_models
0	S2M11		0.002572	False	0.014581	0.013821	0.010911	4	1.0
1	S2M11	E484Y	3.000000	True	12.680002	8.836638	13.500400	4	1.0
2	S2M11	Q493K	3.000000	True	6.845216	5.779082	6.342219	4	1.0
3	S2M11	Y449N	3.000000	True	1.645030	1.408748	1.233082	4	1.0

Now plot the results. We will plot the median across the replicate polyclonal fits to different deep mutational scanning replicates. This is an interactive plot that you can mouse over for details:

[4]:

corr_chart = (
    alt.Chart(validation_vs_prediction)
    .encode(
        x=alt.X("measured IC50", scale=alt.Scale(type="log")),
        y=alt.Y(
            "median_IC50",
            title="predicted IC50 from DMS",
            scale=alt.Scale(type="log"),
        ),
        facet=alt.Facet("antibody", columns=4, title=None),
        color=alt.Color("lower_bound", title="lower_bound"),
        tooltip=[
            alt.Tooltip(c, format=".3g") if validation_vs_prediction[c].dtype == float
            else c
            for c in validation_vs_prediction.columns.tolist()
        ],
    )
    .mark_circle(filled=True, size=60, opacity=0.6)
    .configure_axis(grid=False)
    .resolve_scale(y="independent", x="independent")
    .properties(width=150, height=150)
)

corr_chart

/fh/fast/bloom_j/software/miniconda3/envs/dms-vep-pipeline/lib/python3.9/site-packages/altair/utils/core.py:317: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.
  for col_name, dtype in df.dtypes.iteritems():

[4]:

[ ]:

Validation neutralization assays versus polyclonal fits¶

Validation neutralization assays versus `polyclonal` fits¶