######## snakemake preamble start (automatically inserted, do not edit) ########
import sys;sys.path.extend(['/fh/fast/bloom_j/software/miniforge3/envs/seqneut-pipeline/lib/python3.13/site-packages', '/fh/fast/bloom_j/computational_notebooks/jbloom/2025/flu-seqneut-2025/seqneut-pipeline', '/fh/fast/bloom_j/computational_notebooks/jbloom/2025/flu-seqneut-2025', '/fh/fast/bloom_j/software/miniforge3/envs/seqneut-pipeline/bin', '/fh/fast/bloom_j/software/miniforge3/envs/seqneut-pipeline/lib/python3.13', '/fh/fast/bloom_j/software/miniforge3/envs/seqneut-pipeline/lib/python3.13/lib-dynload', '/fh/fast/bloom_j/software/miniforge3/envs/seqneut-pipeline/lib/python3.13/site-packages', '/home/jbloom/.cache/snakemake/snakemake/source-cache/runtime-cache/tmpfro_n8p6/file/fh/fast/bloom_j/computational_notebooks/jbloom/2025/flu-seqneut-2025/seqneut-pipeline/notebooks', '/fh/fast/bloom_j/computational_notebooks/jbloom/2025/flu-seqneut-2025/seqneut-pipeline/notebooks']);import pickle;from snakemake import script;script.snakemake = 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\x8c\x10GCAACGAGGTGTAACC\x94K\xc1\x8c\x10GCAGCGTGCCGGTCAT\x94K\xc2\x8c\x10GCATCCTCAACTCCTA\x94K\xc3\x8c\x10GCATGGAACTAACTCC\x94K\xc4\x8c\x10GCCAGCTCGTATCCCT\x94K\xc5\x8c\x10GCCATTTACTGAAGGG\x94K\xc6\x8c\x10GCCGCTGCGGCGTGTG\x94K\xc7\x8c\x10GCCGGCGTTAGTGTCA\x94K\xc8\x8c\x10GCCTTTGCGCGCAGTC\x94K\xc9\x8c\x10GCGAAGTTTCATAGCG\x94K\xca\x8c\x10GGTTAACTTTGGAAGC\x94K\xcb\x8c\x10GTAAGCAAAGTTGACC\x94K\xcc\x8c\x10GTAAGCTTCATGGAGT\x94K\xcd\x8c\x10GTAATTCGCATGCGGA\x94K\xce\x8c\x10GTACCCAGTTCCTGCG\x94K\xcf\x8c\x10GTAGAACTGCGGCCCC\x94K\xd0\x8c\x10GTAGATACTAGGACCA\x94K\xd1\x8c\x10GTCAAGTTACGGATGG\x94K\xd2\x8c\x10GTCCGTCAGCATAAAC\x94K\xd3\x8c\x10GTCGCATCCTGGAATG\x94K\xd4\x8c\x10GTCGCCGCTAATCCGA\x94K\xd5\x8c\x10GTGAGCGAGAAAAGCA\x94K\xd6\x8c\x10GTGCATCCTAGTGACG\x94K\xd7\x8c\x10GTGCGATTGTCCGGAA\x94K\xd8\x8c\x10GTGGTATCAAGCCGGG\x94K\xd9\x8c\x10GTTATTATGACTTCAT\x94K\xda\x8c\x10GTTGCTCCGACACGCC\x94K\xdb\x8c\x10TAAAAAGCCTCCATGA\x94K\xdc\x8c\x10TAACGTGATTTCTCGA\x94K\xdd\x8c\x10TAATAAGCCAGCAAGA\x94K\xde\x8c\x10TACAAGAGAGGGGTCC\x94K\xdf\x8c\x10TACATACCGACGCAGT\x94K\xe0\x8c\x10TACCAATGTCATTTGA\x94K\xe1\x8c\x10TACCTGCTGCGGAACG\x94K\xe2\x8c\x10TACTAATGCCGTTGTC\x94K\xe3\x8c\x10TACTAGCAATAAAATC\x94K\xe4\x8c\x10TACTGATAACCCTGCG\x94K\xe5\x8c\x10TAGCATTGTCGGAAAG\x94K\xe6\x8c\x10TAGCTGATAGTAACTC\x94K\xe7\x8c\x10TATATTAGTAACATAA\x94K\xe8\x8c\x10TATCCAAGGGACGGAC\x94K\xe9\x8c\x10TATGTCGTATCCACAG\x94K\xea\x8c\x10TATTAAGAGAAGTGCG\x94K\xeb\x8c\x10TATTCCTAACTAGCGA\x94K\xec\x8c\x10TCAATCGGGGGCTAAA\x94K\xed\x8c\x10TCACGACTCGACTAAC\x94K\xee\x8c\x10TCATGGGTGTACGAGA\x94K\xef\x8c\x10TCCACACCCCTAGCTA\x94K\xf0\x8c\x10TCCAGCGCGGTAAGAG\x94K\xf1\x8c\x10TCCCCGTGGTTTGACA\x94K\xf2\x8c\x10TCGAACGAAGTAGGAG\x94K\xf3\x8c\x10TCGAGTTAATATGCGC\x94K\xf4\x8c\x10TCGATTACTAGCCGGA\x94K\xf5\x8c\x10TCGCTTCAACTAAAAA\x94K\xf6\x8c\x10TCGTCCGTTGGGAACT\x94K\xf7\x8c\x10TCGTCGCACTACTGCT\x94K\xf8\x8c\x10TCTAACTCTCGCGGCA\x94K\xf9\x8c\x10TCTCAGCTCTTAGCCG\x94K\xfa\x8c\x10TCTGGAAACGATCCCC\x94K\xfb\x8c\x10TCTTAGAGTGAACGAT\x94K\xfc\x8c\x10TCTTAGTCCTCGTATG\x94K\xfd\x8c\x10TCTTATTAGGCGGCAT\x94K\xfe\x8c\x10TCTTGACATAGCGATG\x94K\xff\x8c\x10TCTTTACCACTGCATC\x94M\x00\x01\x8c\x10TGACAACAATACAAAT\x94M\x01\x01\x8c\x10TGAGTTCATAGCTCCA\x94M\x02\x01\x8c\x10TGATCTGTGACATTGC\x94M\x03\x01\x8c\x10TGATCTTTTACATTTA\x94M\x04\x01\x8c\x10TGCAGTGGTATACATA\x94M\x05\x01\x8c\x10TGCGGTGGTCGATCCG\x94M\x06\x01\x8c\x10TGCTATTCCGGCGCGG\x94M\x07\x01\x8c\x10TGGAATCGTCACCGAT\x94M\x08\x01\x8c\x10TGGTCCGCTTCATGCT\x94M\t\x01\x8c\x10TGTAATAGGCGTCACA\x94M\n\x01\x8c\x10TGTCCGGATAAAGTAG\x94M\x0b\x01\x8c\x10TGTGGAGCGCCCTTAC\x94M\x0c\x01\x8c\x10TGTTGAGCCAGTCTGA\x94M\r\x01\x8c\x10TGTTGTAATCTGAATA\x94M\x0e\x01\x8c\x10TTAATGTAGCCGCTCC\x94M\x0f\x01\x8c\x10TTACGAATTTGATTCC\x94M\x10\x01\x8c\x10TTCATCAAGTTGGTGC\x94M\x11\x01\x8c\x10TTCTGTCCAGACTCGT\x94M\x12\x01\x8c\x10TTGAAAAAATCATAAA\x94M\x13\x01\x8c\x10TTGACTCACCGAATAA\x94M\x14\x01\x8c\x10TTGCAATTGAAACATA\x94M\x15\x01\x8c\x10TTGCTCCTGAGTAGTA\x94M\x16\x01\x8c\x10TTGGGCACTAAATTAA\x94M\x17\x01\x8c\x10TTGGGGAACGGGAAGC\x94M\x18\x01\x8c\x10TTGTATCAGTCGCGCC\x94M\x19\x01\x8c\x10TTTATATCCAACACCA\x94M\x1a\x01\x8c\x10TTTATATCGAGATTCA\x94M\x1b\x01\x8c\x10TTTCACAGAACCTATC\x94M\x1c\x01\x8c\x10TTTCAGCGTTGTTTTG\x94M\x1d\x01\x8c\x10TTTCGTGATACTCACA\x94u\x8c\x06strain\x94}\x94(K\x00\x8c\x19A/Minnesota/126/2024_H3N2\x94K\x01\x8c*A/Singapore/INFIMH-16-0019/2016X-307A_H3N2\x94K\x02\x8c\x18A/Wisconsin/67/2022_H1N1\x94K\x03\x8c\x16A/Lisboa/216/2023_H3N2\x94K\x04\x8c\x14A/Darwin/9/2021_H3N2\x94K\x05\x8c 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human serum samples collected in late 2024-2025 and combined pdmH1N1 and H3N2 influenza library\n\nThe numerical data and computer code are at 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script;from snakemake.logging import logger;from snakemake.script import snakemake;import os; os.chdir(r'/fh/fast/bloom_j/computational_notebooks/jbloom/2025/flu-seqneut-2025');
######## snakemake preamble end #########

Process plate counts to get fraction infectivities and fit curves

This notebook is designed to be run using snakemake, and analyzes a plate of sequencing-based neutralization assays.

The plots generated by this notebook are interactive, so you can mouseover points for details, use the mouse-scroll to zoom and pan, and use interactive dropdowns at the bottom of the plots.

Setup

Import Python modules:

import pickle
import sys
import warnings

import altair as alt

import matplotlib.pyplot as plt
import matplotlib.style

import neutcurve
from neutcurve.colorschemes import CBPALETTE, CBMARKERS

import numpy

import pandas as pd

import ruamel.yaml as yaml

_ = alt.data_transformers.disable_max_rows()

# avoid clutter w RuntimeWarning during curve fitting
warnings.filterwarnings("ignore", category=RuntimeWarning)

# faster plotting of neut curves
matplotlib.style.use("fast")

Get the variables passed by snakemake:

count_csvs = snakemake.input.count_csvs
fate_csvs = snakemake.input.fate_csvs
notebook_funcs = snakemake.input.notebook_funcs
qc_drops_yaml = snakemake.output.qc_drops
frac_infectivity_csv = snakemake.output.frac_infectivity_csv
fits_csv = snakemake.output.fits_csv
fits_pickle = snakemake.output.fits_pickle
viral_barcodes = snakemake.params.viral_barcodes
neut_standard_barcodes = snakemake.params.neut_standard_barcodes
samples = snakemake.params.samples
plate = snakemake.wildcards.plate
plate_params = snakemake.params.plate_params
curve_display_method = snakemake.params.curve_display_method

# get thresholds turning lists into tuples as needed
manual_drops = {
    filter_type: [tuple(w) if isinstance(w, list) else w for w in filter_drops]
    for (filter_type, filter_drops) in plate_params["manual_drops"].items()
}
group = plate_params["group"]
qc_thresholds = plate_params["qc_thresholds"]
curvefit_params = plate_params["curvefit_params"]
curvefit_qc = plate_params["curvefit_qc"]
curvefit_qc["barcode_serum_replicates_ignore_curvefit_qc"] = [
    tuple(w) for w in curvefit_qc["barcode_serum_replicates_ignore_curvefit_qc"]
]

print(f"Processing {plate=}")

samples_df = pd.DataFrame(plate_params["samples"])
print(f"\nPlate has {len(samples)} samples (wells)")
assert all(
    (len(samples_df) == samples_df[c].nunique())
    for c in ["well", "sample", "sample_noplate"]
)
assert len(samples_df) == len(
    samples_df.groupby(["serum_replicate", "dilution_factor"])
)
assert len(samples) == len(count_csvs) == len(fate_csvs) == len(samples_df)

for d, key, title in [
    (manual_drops, "manual_drops", "Data manually specified to drop:"),
    (qc_thresholds, "qc_thresholds", "QC thresholds applied to data:"),
    (curvefit_params, "curvefit_params", "Curve-fitting parameters:"),
    (curvefit_qc, "curvefit_qc", "Curve-fitting QC:"),
]:
    print(f"\n{title}")
    yaml.YAML(typ="rt").dump({key: d}, stream=sys.stdout)

Processing plate='plate20'

Plate has 96 samples (wells)

Data manually specified to drop:
manual_drops: {}

QC thresholds applied to data:
qc_thresholds:
  avg_barcode_counts_per_well: 500
  min_neut_standard_frac_per_well: 0.005
  no_serum_per_viral_barcode_filters:
    min_frac: 0.0001
    max_fold_change: 4
    max_wells: 2
  per_neut_standard_barcode_filters:
    min_frac: 0.005
    max_fold_change: 4
    max_wells: 2
  min_neut_standard_count_per_well: 1000
  min_no_serum_count_per_viral_barcode_well: 100
  max_frac_infectivity_per_viral_barcode_well: 3
  min_dilutions_per_barcode_serum_replicate: 6

Curve-fitting parameters:
curvefit_params:
  frac_infectivity_ceiling: 1
  fixtop:
  - 0.6
  - 1
  fixbottom: 0
  fixslope:
  - 0.8
  - 10

Curve-fitting QC:
curvefit_qc:
  max_frac_infectivity_at_least: 0.0
  goodness_of_fit:
    min_R2: 0.5
    max_RMSD: 0.15
  serum_replicates_ignore_curvefit_qc: []
  barcode_serum_replicates_ignore_curvefit_qc: []

Load the notebook functions:

print(f"Loading {notebook_funcs=}")
%run {notebook_funcs}

Loading notebook_funcs='/home/jbloom/.cache/snakemake/snakemake/source-cache/runtime-cache/tmpfro_n8p6/file/fh/fast/bloom_j/computational_notebooks/jbloom/2025/flu-seqneut-2025/seqneut-pipeline/notebook_funcs.py'

Set up dictionary to keep track of wells, barcodes, well-barcodes, and serum-replicates that are dropped:

qc_drops = {
    "wells": {},
    "barcodes": {},
    "barcode_wells": {},
    "barcode_serum_replicates": {},
    "serum_replicates": {},
}

assert set(manual_drops).issubset(
    qc_drops
), f"{manual_drops.keys()=}, {qc_drops.keys()}"

Statistics on barcode-parsing for each sample

Make interactive chart of the "fates" of the sequencing reads parsed for each sample on the plate.

If most sequencing reads are not "valid barcodes", this could potentially indicate some problem in the sequencing or barcode set you are parsing.

Potential fates are:

• valid barcode: barcode that matches a known virus or neutralization standard, we hope most reads are this.
• invalid barcode: a barcode with proper flanking sequences, but does not match a known virus or neutralization standard. If you have a lot of reads of this type, it is probably a good idea to look at the invalid barcode CSVs (in the ./results/barcode_invalid/ subdirectory created by the pipeline) to see what these invalid barcodes are.
• unparseable barcode: could not parse a barcode from this read as there was not a sequence of the correct length with the appropriate flanking sequence.
• invalid outer flank: if using an outer upstream or downstream region (upstream2 or downstream2 for the illuminabarcodeparser), reads that are otherwise valid except for this outer flank. Typically you would be using upstream2 if you have a plate index embedded in your primer, and reads with this classification correspond to a different index than the one for this plate.
• low quality barcode: low-quality or N nucleotides in barcode, could indicate problem with sequencing.
• failed chastity filter: reads that failed the Illumina chastity filter, if these are reported in the FASTQ (they may not be).

Also, if the number of reads per sample is very uneven, that could indicate that you did not do a good job of balancing the different samples in the Illumina sequencing.

fates = (
    pd.concat([pd.read_csv(f).assign(sample=s) for f, s in zip(fate_csvs, samples)])
    .merge(samples_df, validate="many_to_one", on="sample")
    .assign(
        fate_counts=lambda x: x.groupby("fate")["count"].transform("sum"),
        sample_well=lambda x: x["sample_noplate"] + " (" + x["well"] + ")",
    )
    .query("fate_counts > 0")[  # only keep fates with at least one count
        ["fate", "count", "well", "serum_replicate", "sample_well", "dilution_factor"]
    ]
)

assert len(fates) == len(fates.drop_duplicates())

serum_replicates = sorted(fates["serum_replicate"].unique())
sample_wells = list(
    fates.sort_values(["serum_replicate", "dilution_factor"])["sample_well"]
)


serum_selection = alt.selection_point(
    fields=["serum_replicate"],
    bind=alt.binding_select(
        options=[None] + serum_replicates,
        labels=["all"] + serum_replicates,
        name="serum",
    ),
)

fates_chart = (
    alt.Chart(fates)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X("count", scale=alt.Scale(nice=False, padding=3)),
        alt.Y(
            "sample_well",
            title=None,
            sort=sample_wells,
        ),
        alt.Color("fate", sort=sorted(fates["fate"].unique(), reverse=True)),
        alt.Order("fate", sort="descending"),
        tooltip=fates.columns.tolist(),
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=200,
        title=f"Barcode parsing for {plate}",
    )
    .configure_axis(grid=False)
)

fates_chart

Read barcode counts and apply manually specified drops

Read the counts per barcode:

# get barcode counts
counts = (
    pd.concat([pd.read_csv(c).assign(sample=s) for c, s in zip(count_csvs, samples)])
    .merge(samples_df, validate="many_to_one", on="sample")
    .drop(columns=["replicate", "plate", "fastq"])
    .assign(sample_well=lambda x: x["sample_noplate"] + " (" + x["well"] + ")")
)

# classify barcodes as viral or neut standard
barcode_class = pd.concat(
    [
        pd.DataFrame(viral_barcodes).assign(neut_standard=False),
        pd.DataFrame(neut_standard_barcodes).assign(neut_standard=True, strain=pd.NA),
    ],
    ignore_index=True,
)

# merge counts and classification of barcodes
assert set(counts["barcode"]) == set(barcode_class["barcode"])
counts = counts.merge(barcode_class, on="barcode", validate="many_to_one")
assert set(sample_wells) == set(counts["sample_well"])
assert set(serum_replicates) == set(counts["serum_replicate"])

Apply any manually specified data drops:

for filter_type, filter_drops in manual_drops.items():
    print(f"\nDropping {len(filter_drops)} {filter_type} specified in manual_drops")
    assert filter_type in qc_drops
    qc_drops[filter_type].update(
        {w: "manual_drop" for w in filter_drops if not isinstance(w, list)}
    )
    if filter_type == "barcode_wells":
        counts = counts[
            ~counts.assign(
                barcode_well=lambda x: x.apply(
                    lambda r: (r["barcode"], r["well"]), axis=1
                )
            )["barcode_well"].isin(qc_drops[filter_type])
        ]
    elif filter_type == "barcode_serum_replicates":
        counts = counts[
            ~counts.assign(
                barcode_serum_replicate=lambda x: x.apply(
                    lambda r: (r["barcode"], r["serum_replicate"]), axis=1
                )
            )["barcode_serum_replicate"].isin(qc_drops[filter_type])
        ]
    elif filter_type == "wells":
        counts = counts[~counts["well"].isin(qc_drops[filter_type])]
    elif filter_type == "barcodes":
        counts = counts[~counts["barcode"].isin(qc_drops[filter_type])]
    elif filter_type == "serum_replicates":
        counts = counts[~counts["serum_replicate"].isin(qc_drops[filter_type])]
    elif filter_type == "barcode_serum_replicates":
        counts = counts[~counts["barcode_serum_replicate"].isin(qc_drops[filter_type])]
    else:
        assert filter_type in set(counts.columns)
        counts = counts[~counts[filter_type].isin(qc_drops[filter_type])]

Average counts per barcode in each well

Plot average counts per barcode. If a sample has inadequate barcode counts, it may not have good enough statistics for accurate analysis, and a QC-threshold is applied:

avg_barcode_counts = (
    counts.groupby(
        ["well", "serum_replicate", "sample_well"],
        dropna=False,
        as_index=False,
    )
    .aggregate(avg_count=pd.NamedAgg("count", "mean"))
    .assign(
        fails_qc=lambda x: (
            x["avg_count"] < qc_thresholds["avg_barcode_counts_per_well"]
        ),
    )
)

avg_barcode_counts_chart = (
    alt.Chart(avg_barcode_counts)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X(
            "avg_count",
            title="average barcode counts per well",
            scale=alt.Scale(nice=False, padding=3),
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Color(
            "fails_qc",
            title=f"fails {qc_thresholds['avg_barcode_counts_per_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        tooltip=[
            alt.Tooltip(c, format=".3g") if avg_barcode_counts[c].dtype == float else c
            for c in avg_barcode_counts.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=250,
        title=f"Average barcode counts per well for {plate}",
    )
    .configure_axis(grid=False)
)

display(avg_barcode_counts_chart)

# drop wells failing QC
avg_barcode_counts_per_well_drops = list(avg_barcode_counts.query("fails_qc")["well"])
print(
    f"\nDropping {len(avg_barcode_counts_per_well_drops)} wells for failing "
    f"{qc_thresholds['avg_barcode_counts_per_well']=}: "
    + str(avg_barcode_counts_per_well_drops)
)
qc_drops["wells"].update(
    {w: "avg_barcode_counts_per_well" for w in avg_barcode_counts_per_well_drops}
)
counts = counts[~counts["well"].isin(qc_drops["wells"])]

Dropping 0 wells for failing qc_thresholds['avg_barcode_counts_per_well']=500: []

Fraction of counts from neutralization standard

Determine the fraction of counts from the neutralization standard in each sample, and make sure this fraction passess the QC threshold.

neut_standard_fracs = (
    counts.assign(
        neut_standard_count=lambda x: x["count"] * x["neut_standard"].astype(int)
    )
    .groupby(
        ["well", "serum_replicate", "sample_well"],
        dropna=False,
        as_index=False,
    )
    .aggregate(
        total_count=pd.NamedAgg("count", "sum"),
        neut_standard_count=pd.NamedAgg("neut_standard_count", "sum"),
    )
    .assign(
        neut_standard_frac=lambda x: x["neut_standard_count"] / x["total_count"],
        fails_qc=lambda x: (
            x["neut_standard_frac"] < qc_thresholds["min_neut_standard_frac_per_well"]
        ),
    )
)

neut_standard_fracs_chart = (
    alt.Chart(neut_standard_fracs)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X(
            "neut_standard_frac",
            title="frac counts from neutralization standard per well",
            scale=alt.Scale(nice=False, padding=3),
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Color(
            "fails_qc",
            title=f"fails {qc_thresholds['min_neut_standard_frac_per_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        tooltip=[
            alt.Tooltip(c, format=".3g") if neut_standard_fracs[c].dtype == float else c
            for c in neut_standard_fracs.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=250,
        title=f"Neutralization-standard fracs per well for {plate}",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
)

display(neut_standard_fracs_chart)

# drop wells failing QC
min_neut_standard_frac_per_well_drops = list(
    neut_standard_fracs.query("fails_qc")["well"]
)
print(
    f"\nDropping {len(min_neut_standard_frac_per_well_drops)} wells for failing "
    f"{qc_thresholds['min_neut_standard_frac_per_well']=}: "
    + str(min_neut_standard_frac_per_well_drops)
)
qc_drops["wells"].update(
    {
        w: "min_neut_standard_frac_per_well"
        for w in min_neut_standard_frac_per_well_drops
    }
)
counts = counts[~counts["well"].isin(qc_drops["wells"])]

Dropping 0 wells for failing qc_thresholds['min_neut_standard_frac_per_well']=0.005: []

Consistency and minimum fractions for barcodes

We examine the fraction of counts attributable to each barcode. We do this splitting the data two ways:

1. Looking at all viral (but not neut-standard) barcodes only for the no-serum samples (wells).

2. Looking at just the neut-standard barcodes for all samples (wells).

The reasons is that if the experiment is set up perfectly, these fractions should be the same across all samples for each barcode. (We do not expect viral barcodes to have consistent fractions across no-serum samples as they will be neutralized differently depending on strain).

We plot these fractions in interactive plots (you can mouseover points and zoom) so you can identify barcodes that fail the expected consistency QC thresholds.

We also make sure the barcodes meet specified QC minimum thresholds for all samples, and flag any that do not.

barcode_selection = alt.selection_point(fields=["barcode"], on="mouseover", empty=False)

# look at all samples for neut standard barcodes, or no-serum samples for all barcodes
for is_neut_standard, df in counts.groupby("neut_standard"):
    if is_neut_standard:
        print(
            f"\n\n{'=' * 89}\nAnalyzing neut-standard barcodes from all samples (wells)"
        )
        qc_name = "per_neut_standard_barcode_filters"
    else:
        print(f"\n\n{'=' * 89}\nAnalyzing all barcodes from no-serum samples (wells)")
        qc_name = "no_serum_per_viral_barcode_filters"
        df = df.query("serum == 'none'")

    df = df.assign(
        sample_counts=lambda x: x.groupby("sample")["count"].transform("sum"),
        count_frac=lambda x: x["count"] / x["sample_counts"],
        median_count_frac=lambda x: x.groupby("barcode")["count_frac"].transform(
            "median"
        ),
        fold_change_from_median=lambda x: numpy.where(
            x["count_frac"] > x["median_count_frac"],
            x["count_frac"] / x["median_count_frac"],
            x["median_count_frac"] / x["count_frac"],
        ),
    )[
        [
            "barcode",
            "count",
            "sample_well",
            "count_frac",
            "fold_change_from_median",
        ]
        + ([] if is_neut_standard else ["strain"])
    ]

    # barcode fails QC if fails in sufficient wells
    qc = qc_thresholds[qc_name]
    print(f"Apply QC {qc_name}: {qc}\n")
    fails_qc = (
        df.assign(
            fails_qc=lambda x: ~(
                (x["count_frac"] >= qc["min_frac"])
                & (x["fold_change_from_median"] <= qc["max_fold_change"])
            ),
        )
        .groupby("barcode", as_index=False)
        .aggregate(n_wells_fail_qc=pd.NamedAgg("fails_qc", "sum"))
        .assign(fails_qc=lambda x: x["n_wells_fail_qc"] >= qc["max_wells"])[
            ["barcode", "fails_qc"]
        ]
    )
    df = df.merge(fails_qc, on="barcode", validate="many_to_one")

    # make chart
    evenness_chart = (
        alt.Chart(df)
        .add_params(barcode_selection)
        .encode(
            alt.X(
                "count_frac",
                title=(
                    "barcode's fraction of neut standard counts"
                    if is_neut_standard
                    else "barcode's fraction of non-neut standard counts"
                ),
                scale=alt.Scale(nice=False, padding=5),
            ),
            alt.Y("sample_well", sort=sample_wells),
            alt.Fill(
                "fails_qc",
                title=f"fails {qc_name}",
                legend=alt.Legend(titleLimit=500),
            ),
            strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
            size=alt.condition(barcode_selection, alt.value(60), alt.value(35)),
            tooltip=[
                alt.Tooltip(c, format=".2g") if df[c].dtype == float else c
                for c in df.columns
            ],
        )
        .mark_circle(fillOpacity=0.45, stroke="black", strokeOpacity=1)
        .properties(
            height=alt.Step(10),
            width=300,
            title=alt.TitleParams(
                (
                    f"{plate} all samples, neut-standard barcodes"
                    if is_neut_standard
                    else f"{plate} no-serum samples, all barcodes"
                ),
                subtitle="x-axis is zoomable (use mouse scroll/pan)",
            ),
        )
        .configure_axis(grid=False)
        .configure_legend(titleLimit=1000)
        .interactive()
    )

    display(evenness_chart)

    # drop barcodes failing QC
    barcode_drops = list(fails_qc.query("fails_qc")["barcode"])
    print(
        f"\nDropping {len(barcode_drops)} barcodes for failing {qc=}: {barcode_drops}"
    )
    qc_drops["barcodes"].update(
        {bc: "min_neut_standard_frac_per_well" for bc in barcode_drops}
    )
    counts = counts[~counts["barcode"].isin(qc_drops["barcodes"])]

=========================================================================================
Analyzing all barcodes from no-serum samples (wells)
Apply QC no_serum_per_viral_barcode_filters: {'min_frac': 0.0001, 'max_fold_change': 4, 'max_wells': 2}

Dropping 3 barcodes for failing qc={'min_frac': 0.0001, 'max_fold_change': 4, 'max_wells': 2}: ['CCTTTCTCAAAACATA', 'CTCTTACGCTCCTACG', 'GATTCAGATGCCCACC']


=========================================================================================
Analyzing neut-standard barcodes from all samples (wells)
Apply QC per_neut_standard_barcode_filters: {'min_frac': 0.005, 'max_fold_change': 4, 'max_wells': 2}

Dropping 0 barcodes for failing qc={'min_frac': 0.005, 'max_fold_change': 4, 'max_wells': 2}: []

Compute fraction infectivity

The fraction infectivity for viral barcode $v_b$ in sample $s$ is computed as: $$ F_{v_b,s} = \frac{c_{v_b,s} / \left(\sum_{n_b} c_{n_b,s}\right)}{{\rm median}_{s_0}\left[ c_{v_b,s_0} / \left(\sum_{n_b} c_{n_b,s_0}\right)\right]} $$ where

• $c_{v_b,s}$ is the counts of viral barcode $v_b$ in sample $s$.
• $\sum_{n_b} c_{n_b,s}$ is the sum of the counts for all neutralization standard barcodes $n_b$ for sample $s$.
• $c_{v_b,s_0}$ is the counts of viral barcode $v_b$ in no-serum sample $s_0$.
• $\sum_{n_b} c_{n_b,s_0}$ is the sum of the counts for all neutralization standard barcodes $n_b$ for no-serum sample $s_0$.
• ${\rm median}_{s_0}\left[ c_{v_b,s_0} / \left(\sum_{n_b} c_{n_b,s_0}\right)\right]$ is the median taken across all no-serum samples of the counts of viral barcode $v_b$ versus the total counts for all neutralization standard barcodes.

First, compute the total neutralization-standard counts for each sample (well). Plot these, and drop any wells that do not meet the QC threshold.

neut_standard_counts = (
    counts.query("neut_standard")
    .groupby(
        ["well", "serum_replicate", "sample_well", "dilution_factor"],
        dropna=False,
        as_index=False,
    )
    .aggregate(neut_standard_count=pd.NamedAgg("count", "sum"))
    .assign(
        fails_qc=lambda x: (
            x["neut_standard_count"] < qc_thresholds["min_neut_standard_count_per_well"]
        ),
    )
)

neut_standard_counts_chart = (
    alt.Chart(neut_standard_counts)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X(
            "neut_standard_count",
            title="counts from neutralization standard",
            scale=alt.Scale(nice=False, padding=3),
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Color(
            "fails_qc",
            title=f"fails {qc_thresholds['min_neut_standard_count_per_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        tooltip=[
            (
                alt.Tooltip(c, format=".3g")
                if neut_standard_counts[c].dtype == float
                else c
            )
            for c in neut_standard_counts.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=250,
        title=f"Neutralization-standard counts for {plate}",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
)

display(neut_standard_counts_chart)

# drop wells failing QC
min_neut_standard_count_per_well_drops = list(
    neut_standard_counts.query("fails_qc")["well"]
)
print(
    f"\nDropping {len(min_neut_standard_count_per_well_drops)} wells for failing "
    f"{qc_thresholds['min_neut_standard_count_per_well']=}: "
    + str(min_neut_standard_count_per_well_drops)
)
qc_drops["wells"].update(
    {
        w: "min_neut_standard_count_per_well"
        for w in min_neut_standard_count_per_well_drops
    }
)
neut_standard_counts = neut_standard_counts[
    ~neut_standard_counts["well"].isin(qc_drops["wells"])
]
counts = counts[~counts["well"].isin(qc_drops["wells"])]

Dropping 0 wells for failing qc_thresholds['min_neut_standard_count_per_well']=1000: []

Compute and plot the no-serum sample viral barcode counts and check if they pass the QC filters.

no_serum_counts = (
    counts.query("serum == 'none'")
    .query("not neut_standard")
    .merge(neut_standard_counts, validate="many_to_one")[
        ["barcode", "strain", "well", "sample_well", "count", "neut_standard_count"]
    ]
    .assign(
        fails_qc=lambda x: (
            x["count"] <= qc_thresholds["min_no_serum_count_per_viral_barcode_well"]
        ),
    )
)

strains = sorted(no_serum_counts["strain"].unique())
strain_selection_dropdown = alt.selection_point(
    fields=["strain"],
    bind=alt.binding_select(
        options=[None] + strains,
        labels=["all"] + strains,
        name="virus strain",
    ),
)

# make chart
no_serum_counts_plot_df = no_serum_counts.drop(columns=["well", "neut_standard_count"])
no_serum_counts_chart = (
    alt.Chart(no_serum_counts_plot_df)
    .add_params(barcode_selection, strain_selection_dropdown)
    .transform_filter(strain_selection_dropdown)
    .encode(
        alt.X(
            "count", title="viral barcode count", scale=alt.Scale(nice=False, padding=5)
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Fill(
            "fails_qc",
            title=f"fails {qc_thresholds['min_no_serum_count_per_viral_barcode_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
        size=alt.condition(barcode_selection, alt.value(60), alt.value(35)),
        tooltip=no_serum_counts_plot_df.columns.tolist(),
    )
    .mark_circle(fillOpacity=0.6, stroke="black", strokeOpacity=1)
    .properties(
        height=alt.Step(10),
        width=400,
        title=f"{plate} viral barcode counts in no-serum samples",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
    .interactive()
)

display(no_serum_counts_chart)

# drop barcode / wells failing QC
min_no_serum_count_per_viral_barcode_well_drops = list(
    no_serum_counts.query("fails_qc")[["barcode", "well"]].itertuples(
        index=False, name=None
    )
)
print(
    f"\nDropping {len(min_no_serum_count_per_viral_barcode_well_drops)} barcode-wells for failing "
    f"{qc_thresholds['min_no_serum_count_per_viral_barcode_well']=}: "
    + str(min_no_serum_count_per_viral_barcode_well_drops)
)
qc_drops["barcode_wells"].update(
    {
        w: "min_no_serum_count_per_viral_barcode_well"
        for w in min_no_serum_count_per_viral_barcode_well_drops
    }
)
no_serum_counts = no_serum_counts[
    ~no_serum_counts.assign(
        barcode_well=lambda x: x.apply(lambda r: (r["barcode"], r["well"]), axis=1)
    )["barcode_well"].isin(qc_drops["barcode_wells"])
]
counts = counts[
    ~counts.assign(
        barcode_well=lambda x: x.apply(lambda r: (r["barcode"], r["well"]), axis=1)
    )["barcode_well"].isin(qc_drops["barcode_wells"])
]

Dropping 5 barcode-wells for failing qc_thresholds['min_no_serum_count_per_viral_barcode_well']=100: [('TACAAGAGAGGGGTCC', 'A12'), ('AGTCCTATCCTCAAAT', 'B12'), ('TTAATGTAGCCGCTCC', 'C12'), ('TACAAGAGAGGGGTCC', 'G12'), ('CTTACTGCGCGAGAGT', 'H12')]

Compute and plot the median ratio of viral barcode count to neut standard counts across no-serum samples. If library composition is equal, all of these values should be similar:

median_no_serum_ratio = (
    no_serum_counts.assign(ratio=lambda x: x["count"] / x["neut_standard_count"])
    .groupby(["barcode", "strain"], as_index=False)
    .aggregate(median_no_serum_ratio=pd.NamedAgg("ratio", "median"))
)

strain_selection = alt.selection_point(fields=["strain"], on="mouseover", empty=False)

median_no_serum_ratio_chart = (
    alt.Chart(median_no_serum_ratio)
    .add_params(strain_selection)
    .encode(
        alt.X(
            "median_no_serum_ratio",
            title="median ratio of counts",
            scale=alt.Scale(nice=False, padding=5),
        ),
        alt.Y(
            "barcode",
            sort=alt.SortField("median_no_serum_ratio", order="descending"),
            axis=alt.Axis(labelFontSize=5),
        ),
        color=alt.condition(strain_selection, alt.value("orange"), alt.value("gray")),
        tooltip=[
            (
                alt.Tooltip(c, format=".3g")
                if median_no_serum_ratio[c].dtype == float
                else c
            )
            for c in median_no_serum_ratio.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(5),
        width=250,
        title=f"{plate} no-serum median ratio viral barcode to neut-standard barcode",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
)

display(median_no_serum_ratio_chart)

Compute the actual fraction infectivities. We compute both the raw fraction infectivities and the ones with the ceiling applied:

frac_infectivity = (
    counts.query("not neut_standard")
    .query("serum != 'none'")
    .merge(median_no_serum_ratio, validate="many_to_one")
    .merge(neut_standard_counts, validate="many_to_one")
    .assign(
        frac_infectivity_raw=lambda x: (
            (x["count"] / x["neut_standard_count"]) / x["median_no_serum_ratio"]
        ),
        frac_infectivity_ceiling=lambda x: x["frac_infectivity_raw"].clip(
            upper=curvefit_params["frac_infectivity_ceiling"]
        ),
        concentration=lambda x: 1 / x["dilution_factor"],
        plate_barcode=lambda x: x["plate_replicate"] + "-" + x["barcode"],
    )[
        [
            "barcode",
            "plate_barcode",
            "well",
            "strain",
            "serum",
            "serum_replicate",
            "dilution_factor",
            "concentration",
            "frac_infectivity_raw",
            "frac_infectivity_ceiling",
        ]
    ]
)

assert len(
    frac_infectivity.groupby(["serum", "plate_barcode", "dilution_factor"])
) == len(frac_infectivity)
assert frac_infectivity["dilution_factor"].notnull().all()
assert frac_infectivity["frac_infectivity_raw"].notnull().all()
assert frac_infectivity["frac_infectivity_ceiling"].notnull().all()

Plot the fraction infectivities, both the raw values and with the ceiling applied:

frac_infectivity_cols = {
    "frac_infectivity_raw": "raw fraction infectivity",
    "frac_infectivity_ceiling": f"fraction infectivity with ceiling at {curvefit_params['frac_infectivity_ceiling']}",
}

frac_infectivity_chart_df = frac_infectivity.assign(
    fails_qc=lambda x: (
        x["frac_infectivity_raw"]
        > qc_thresholds["max_frac_infectivity_per_viral_barcode_well"]
    ),
)[
    [
        "barcode",
        "strain",
        "well",
        "serum_replicate",
        "dilution_factor",
        "fails_qc",
        *list(frac_infectivity_cols),
    ]
].rename(
    columns=frac_infectivity_cols
)

# some manipulations to shrink data frame plotted by altair below by putting
# them in smaller data frames that are used via transform_lookup
barcode_lookup_df = frac_infectivity[["barcode", "strain"]].drop_duplicates()
assert len(barcode_lookup_df) == barcode_lookup_df["barcode"].nunique()
well_lookup_df = frac_infectivity[
    ["well", "serum_replicate", "dilution_factor"]
].drop_duplicates()
assert len(well_lookup_df) == well_lookup_df["well"].nunique()

frac_infectivity_chart_df = frac_infectivity_chart_df.drop(
    columns=["strain", "serum_replicate", "dilution_factor"]
)

frac_infectivity_chart = (
    alt.Chart(frac_infectivity_chart_df)
    .transform_lookup(
        lookup="barcode",
        from_=alt.LookupData(barcode_lookup_df, key="barcode", fields=["strain"]),
    )
    .transform_lookup(
        lookup="well",
        from_=alt.LookupData(
            well_lookup_df, key="well", fields=["serum_replicate", "dilution_factor"]
        ),
    )
    .transform_fold(
        frac_infectivity_cols.values(), ["ceiling_applied", "frac_infectivity"]
    )
    .add_params(strain_selection_dropdown, barcode_selection)
    .transform_filter(strain_selection_dropdown)
    .encode(
        alt.X(
            "dilution_factor:Q",
            title="dilution factor",
            scale=alt.Scale(nice=False, padding=5, type="log"),
        ),
        alt.Y(
            "frac_infectivity:Q",
            title="fraction infectivity",
            scale=alt.Scale(nice=False, padding=5),
        ),
        alt.Column(
            "ceiling_applied:N",
            sort="descending",
            title=None,
            header=alt.Header(labelFontSize=13, labelFontStyle="bold", labelPadding=2),
        ),
        alt.Row(
            "serum_replicate:N",
            title=None,
            spacing=3,
            header=alt.Header(labelFontSize=13, labelFontStyle="bold"),
        ),
        alt.Detail("barcode"),
        alt.Shape(
            "fails_qc",
            title=f"fails {qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=}",
            legend=alt.Legend(titleLimit=500, orient="bottom"),
        ),
        color=alt.condition(
            barcode_selection, alt.value("black"), alt.value("MediumBlue")
        ),
        strokeWidth=alt.condition(barcode_selection, alt.value(3), alt.value(1)),
        opacity=alt.condition(barcode_selection, alt.value(1), alt.value(0.25)),
        tooltip=[
            (
                alt.Tooltip(c, format=".3g")
                if frac_infectivity_chart_df[c].dtype == float
                else c
            )
            for c in frac_infectivity_chart_df.columns
        ]
        + [
            alt.Tooltip("strain:N"),
            alt.Tooltip("serum_replicate:N"),
            alt.Tooltip("dilution_factor:Q"),
        ],
    )
    .mark_line(point=True)
    .properties(
        height=150,
        width=250,
        title=f"Fraction infectivities for {plate}",
    )
    .interactive(bind_x=False)
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
    .configure_point(size=50)
    .resolve_scale(x="independent", y="independent")
)

display(frac_infectivity_chart)

# drop barcode / wells failing QC
max_frac_infectivity_per_viral_barcode_well_drops = list(
    frac_infectivity_chart_df.query("fails_qc")[["barcode", "well"]]
    .drop_duplicates()
    .itertuples(index=False, name=None)
)
print(
    f"\nDropping {len(max_frac_infectivity_per_viral_barcode_well_drops)} barcode-wells for failing "
    f"{qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=}: "
    + str(max_frac_infectivity_per_viral_barcode_well_drops)
)
qc_drops["barcode_wells"].update(
    {
        w: "max_frac_infectivity_per_viral_barcode_well"
        for w in max_frac_infectivity_per_viral_barcode_well_drops
    }
)
frac_infectivity = frac_infectivity[
    ~frac_infectivity.assign(
        barcode_well=lambda x: x.apply(lambda r: (r["barcode"], r["well"]), axis=1)
    )["barcode_well"].isin(qc_drops["barcode_wells"])
]

Dropping 174 barcode-wells for failing qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=3: [('TGCTATTCCGGCGCGG', 'D1'), ('AGGTTCAGACTCTTGC', 'D1'), ('TGGAATCGTCACCGAT', 'G1'), ('CGATCTTTACGAAAAA', 'G2'), ('AACCACCCCAGAGATG', 'H2'), ('CTTACTGCGCGAGAGT', 'E3'), ('AACACGTAGAACCGCC', 'F3'), ('ATAGAATCGCAAATTA', 'F3'), ('TCGAGTTAATATGCGC', 'F3'), ('CTTACAGAATACTAGA', 'G3'), ('GCAGCGTGCCGGTCAT', 'H3'), ('TATCCAAGGGACGGAC', 'H3'), ('GTGCGATTGTCCGGAA', 'H3'), ('ACGTCCATTAAGATCA', 'H3'), ('CAATTCGCCGTTCCCC', 'H3'), ('AATGCGAGCATGTCAA', 'H3'), ('AACCACCCCAGAGATG', 'H3'), ('CGATCTTTACGAAAAA', 'E4'), ('TCGAGTTAATATGCGC', 'F4'), ('GAAATCCCCAAATAAC', 'F4'), ('ACACGGGTTGGCTGTA', 'G4'), ('TGGAATCGTCACCGAT', 'E5'), ('TTGAAAAAATCATAAA', 'F5'), ('TCTTTACCACTGCATC', 'F5'), ('TCGAACGAAGTAGGAG', 'F5'), ('ATAGAATCGCAAATTA', 'F5'), ('GTGCGATTGTCCGGAA', 'F5'), ('GAAAGTCCCTATGATG', 'F5'), ('CTTACAGAATACTAGA', 'F5'), ('AGGACTATAGTTGGCA', 'F5'), ('AACACGTAGAACCGCC', 'F5'), ('TGTCCGGATAAAGTAG', 'F5'), ('TGCGGTGGTCGATCCG', 'F5'), ('AAGGTCCCTATGTAAT', 'F5'), ('GCGAAGTTTCATAGCG', 'F5'), ('TCGAGTTAATATGCGC', 'F5'), ('CTTACTGCGCGAGAGT', 'F5'), ('AATTCGTGAGTACTAG', 'G5'), ('GTAATTCGCATGCGGA', 'H5'), ('ACAGTACGATCTACGC', 'E6'), ('TTGAAAAAATCATAAA', 'F6'), ('ATAGAATCGCAAATTA', 'F6'), ('CGAAACACGTCCCAGT', 'F6'), ('AGGTTCAGACTCTTGC', 'G6'), ('CGAAACACGTCCCAGT', 'G6'), ('CTCAATGTCGTAGGAT', 'H6'), ('AAAGGCGCGCCTTCAA', 'H6'), ('AACACGTAGAACCGCC', 'H6'), ('ATAGAAAATTATCCGC', 'H6'), ('GACCCCTTGTAAGATG', 'H6'), ('TGCGGTGGTCGATCCG', 'E7'), ('AGTCCTATCCTCAAAT', 'E7'), ('GCAAACAGTGTAGTTG', 'F7'), ('CACCTAGGATCGCACT', 'F7'), ('TGTTGAGCCAGTCTGA', 'F7'), ('CAAAAGCAGCACGATA', 'F7'), ('GAAATCCCCAAATAAC', 'F7'), ('TCGAGTTAATATGCGC', 'G7'), ('ACTGTCTAGAAATTTT', 'H7'), ('CCCTGCGCGGCTCGGG', 'H7'), ('AGTGTTGAATAGGCGA', 'H7'), ('ATATAAAAAACTTAGT', 'H7'), ('TCGTCCGTTGGGAACT', 'H7'), ('TTCTGTCCAGACTCGT', 'H7'), ('CAGGCTCTAGAGCTCT', 'H7'), ('CTGAGCTGCCAATAAG', 'H7'), ('CGGGAAATGTAAATGA', 'H7'), ('TTCATCAAGTTGGTGC', 'H7'), ('AAGAAGACTTTGTGAT', 'H7'), ('AAGTTAGTAGACCCAC', 'H7'), ('TAAAAAGCCTCCATGA', 'H7'), ('AAAGCTCTTTTCGTTC', 'H7'), ('AAGTTAAGAGAAAGTT', 'H7'), ('CTATAAACCGTTTGTA', 'H7'), ('TCAATCGGGGGCTAAA', 'H7'), ('ATGGCCCACGGGCATA', 'H7'), ('AGCCCATGCTGGGGAT', 'H7'), ('TACCTGCTGCGGAACG', 'H7'), ('CGAACCGCAGACACGT', 'H7'), ('TCTTAGAGTGAACGAT', 'H7'), ('CTCAATGTCGTAGGAT', 'H7'), ('GCATGGAACTAACTCC', 'H7'), ('AAGGGGCCTCATAATG', 'H7'), ('GCAGCGTGCCGGTCAT', 'H7'), ('AACTTCCGTCGCCTGA', 'H7'), ('CGGGAATCTCCCATAC', 'H7'), ('TATCCAAGGGACGGAC', 'H7'), ('CGCAGCATTGGTCGCC', 'H7'), ('TGGAATCGTCACCGAT', 'H7'), ('GTACCCAGTTCCTGCG', 'H7'), ('ACGCAAATAGACCGAA', 'H7'), ('AGATCCCAGGTCCTTT', 'H7'), ('CAAAAGCAGCACGATA', 'H7'), ('TGCTATTCCGGCGCGG', 'H7'), ('TAATAAGCCAGCAAGA', 'H7'), ('TGCAGTGGTATACATA', 'H7'), ('CACAGACAATAAAAAA', 'H7'), ('CGTTTTTGGTTCGAGG', 'H7'), ('CCGGATAAATCAGAAC', 'H7'), ('ATTTAAATTCGAGGAC', 'H7'), ('TCCCCGTGGTTTGACA', 'H7'), ('ATAGAATCGCAAATTA', 'H7'), ('TCTTAGTCCTCGTATG', 'H7'), ('TCGAACGAAGTAGGAG', 'H7'), ('CCCCCGCTGTTTAAAA', 'H7'), ('TATTCCTAACTAGCGA', 'H7'), ('TCTTTACCACTGCATC', 'H7'), ('CACCGCGCCGAGCACC', 'H7'), ('TATATTAGTAACATAA', 'H7'), ('ACACGGGTTGGCTGTA', 'H7'), ('GATCACGCAGAAAAAG', 'H7'), ('ACAGTCCACCATTGAG', 'H7'), ('AGGTTCAGACTCTTGC', 'H7'), ('AGTAAACATGCATTGG', 'H7'), ('CTTACAGAATACTAGA', 'H7'), ('TTGAAAAAATCATAAA', 'H7'), ('AGACCGCCAGTTTCGT', 'H7'), ('ACCGATTCACGAATAA', 'H7'), ('ATCAGGATAATCGCGC', 'H7'), ('CGATCTTTACGAAAAA', 'H7'), ('CTGGAGGCCTGGCCCC', 'H7'), ('AAGCCCAGCGGGTGAT', 'H7'), ('CACTAGATGTACAGTC', 'H7'), ('GTCCGTCAGCATAAAC', 'H7'), ('CGTACGTATGTCCCAG', 'H7'), ('AATTCGTGAGTACTAG', 'H7'), ('TTTATATCGAGATTCA', 'H7'), ('GATCGCCACTGATAAG', 'H7'), ('CATTGAGACGCGCAAG', 'H7'), ('AACCACCCCAGAGATG', 'H7'), ('CACGGGCTAATGTCTC', 'H7'), ('TCGCTTCAACTAAAAA', 'H7'), ('GCGAAGTTTCATAGCG', 'H7'), ('TATTAAGAGAAGTGCG', 'H7'), ('AGTCCTATCCTCAAAT', 'H7'), ('TCGAGTTAATATGCGC', 'H7'), ('AAGGTCCCTATGTAAT', 'H7'), ('CACGGCCGGCGAACTC', 'H7'), ('CTCAAATAATTGGCGC', 'H7'), ('CGACTCCACGGACGCC', 'H7'), ('AATGCGAGCATGTCAA', 'H7'), ('ATAACTGAGGGCATTG', 'H7'), ('CGAAACACGTCCCAGT', 'H7'), ('GACCCCTTGTAAGATG', 'H7'), ('CTTACTGCGCGAGAGT', 'H7'), ('GAAATCCCCAAATAAC', 'H7'), ('AGTTTTTATAACTTGC', 'H7'), ('GCCGGCGTTAGTGTCA', 'H7'), ('CACCAATCTTCGAACT', 'H7'), ('TACAAGAGAGGGGTCC', 'H7'), ('TGCGGTGGTCGATCCG', 'B8'), ('GATCACGCAGAAAAAG', 'D8'), ('GAAATCCCCAAATAAC', 'E8'), ('GAAATCCCCAAATAAC', 'F8'), ('TCTTTACCACTGCATC', 'G8'), ('CGTACGTATGTCCCAG', 'G8'), ('AGTCCTATCCTCAAAT', 'G8'), ('AAGGTCCCTATGTAAT', 'H8'), ('GACCCCTTGTAAGATG', 'H8'), ('CAATTCGCCGTTCCCC', 'D9'), ('TGGAATCGTCACCGAT', 'E9'), ('ACCGATTCACGAATAA', 'G9'), ('GATCGCCACTGATAAG', 'H9'), ('AGGTTCAGACTCTTGC', 'E10'), ('TCTCAGCTCTTAGCCG', 'E10'), ('GCGAAGTTTCATAGCG', 'E10'), ('AACCACCCCAGAGATG', 'F10'), ('GAAATCCCCAAATAAC', 'F10'), ('AAAGGCGCGCCTTCAA', 'G10'), ('GAGGGGATACGTCACC', 'H10'), ('AACACGTAGAACCGCC', 'D11'), ('AACACGTAGAACCGCC', 'G11'), ('CAATTCGCCGTTCCCC', 'G11'), ('ATAGAATCGCAAATTA', 'H11')]

Check how many dilutions we have per barcode / serum-replicate:

n_dilutions = (
    frac_infectivity.groupby(["serum_replicate", "strain", "barcode"], as_index=False)
    .aggregate(**{"number of dilutions": pd.NamedAgg("dilution_factor", "nunique")})
    .assign(
        fails_qc=lambda x: (
            x["number of dilutions"]
            < qc_thresholds["min_dilutions_per_barcode_serum_replicate"]
        ),
    )
)

n_dilutions_chart = (
    alt.Chart(n_dilutions)
    .add_params(barcode_selection)
    .encode(
        alt.X("number of dilutions", scale=alt.Scale(nice=False, padding=4)),
        alt.Y("strain", title=None),
        alt.Column(
            "serum_replicate",
            title=None,
            header=alt.Header(labelFontSize=12, labelFontStyle="bold", labelPadding=0),
        ),
        alt.Fill(
            "fails_qc",
            title=f"fails {qc_thresholds['min_dilutions_per_barcode_serum_replicate']=}",
            legend=alt.Legend(titleLimit=500, orient="bottom"),
        ),
        strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
        size=alt.condition(barcode_selection, alt.value(55), alt.value(35)),
        tooltip=[
            alt.Tooltip(c, format=".3g") if n_dilutions[c].dtype == float else c
            for c in n_dilutions.columns
        ],
    )
    .mark_circle(stroke="black", strokeOpacity=1, fillOpacity=0.45)
    .properties(
        height=alt.Step(10),
        width=120,
        title=alt.TitleParams(
            "number of dilutions for each barcode for each serum-replicate", dy=-2
        ),
    )
)

display(n_dilutions_chart)

# drop barcode / serum-replicates failing QC
min_dilutions_per_barcode_serum_replicate_drops = list(
    n_dilutions.query("fails_qc")[["barcode", "serum_replicate"]].itertuples(
        index=False, name=None
    )
)
print(
    f"\nDropping {len(min_dilutions_per_barcode_serum_replicate_drops)} barcode/serum-replicates for failing "
    f"{qc_thresholds['min_dilutions_per_barcode_serum_replicate']=}: "
    + str(min_dilutions_per_barcode_serum_replicate_drops)
)
qc_drops["barcode_serum_replicates"].update(
    {
        w: "min_dilutions_per_barcode_serum_replicate"
        for w in min_dilutions_per_barcode_serum_replicate_drops
    }
)
frac_infectivity = frac_infectivity[
    ~frac_infectivity.assign(
        barcode_serum_replicate=lambda x: x.apply(
            lambda r: (r["barcode"], r["serum_replicate"]), axis=1
        )
    )["barcode_serum_replicate"].isin(qc_drops["barcode_serum_replicates"])
]

Dropping 0 barcode/serum-replicates for failing qc_thresholds['min_dilutions_per_barcode_serum_replicate']=6: []

Fit neutralization curves without applying QC to curves

First fit curves to all serum replicates, then we will apply QC on the curve fits. Note that the fitting is done to the fraction infectivities with the ceiling:

fits_noqc = neutcurve.CurveFits(
    frac_infectivity.rename(
        columns={
            "frac_infectivity_ceiling": "fraction infectivity",
            "concentration": "serum concentration",
        }
    ),
    conc_col="serum concentration",
    fracinf_col="fraction infectivity",
    virus_col="strain",
    serum_col="serum_replicate",
    replicate_col="barcode",
    fixtop=curvefit_params["fixtop"],
    fixbottom=curvefit_params["fixbottom"],
    fixslope=curvefit_params["fixslope"],
)

Determine which fits fail the curve fitting QC, and plot them. Note the plot indicates as failing QC any barcode / serum-replicate that fails, even if we are also specified to ignore the QC for that one (so it will not be removed later):

goodness_of_fit = curvefit_qc["goodness_of_fit"]

fit_params_noqc = (
    frac_infectivity.groupby(["serum_replicate", "barcode"], as_index=False)
    .aggregate(max_frac_infectivity=pd.NamedAgg("frac_infectivity_ceiling", "max"))
    .merge(
        fits_noqc.fitParams(average_only=False, no_average=True)[
            ["serum", "virus", "replicate", "r2", "rmsd"]
        ].rename(columns={"serum": "serum_replicate", "replicate": "barcode"}),
        validate="one_to_one",
    )
    .assign(
        fails_max_frac_infectivity_at_least=lambda x: (
            x["max_frac_infectivity"] < curvefit_qc["max_frac_infectivity_at_least"]
        ),
        fails_goodness_of_fit=lambda x: (
            (x["r2"] < goodness_of_fit["min_R2"])
            & (x["rmsd"] > goodness_of_fit["max_RMSD"])
        ),
        fails_qc=lambda x: (
            x["fails_max_frac_infectivity_at_least"] | x["fails_goodness_of_fit"]
        ),
        ignore_qc=lambda x: x.apply(
            lambda r: (
                (
                    r["serum_replicate"]
                    in curvefit_qc["serum_replicates_ignore_curvefit_qc"]
                )
                or (
                    (r["barcode"], r["serum_replicate"])
                    in curvefit_qc["barcode_serum_replicates_ignore_curvefit_qc"]
                )
            ),
            axis=1,
        ),
    )
)

print(f"Plotting barcode / serum-replicates that fail {curvefit_qc=}\n")

fit_params_noqc_base_chart = alt.Chart(fit_params_noqc).add_params(barcode_selection)
fit_params_noqc_chart = []
for prop, col in [
    ("max frac infectivity", "max_frac_infectivity"),
    ("curve fit R2", "r2"),
    ("curve fit RMSD", "rmsd"),
]:
    fit_params_noqc_chart.append(
        fit_params_noqc_base_chart.encode(
            alt.X(col, title=prop, scale=alt.Scale(nice=False, padding=4)),
            alt.Y("virus", title=None),
            alt.Fill("fails_qc"),
            alt.Column(
                "serum_replicate",
                title=None,
                header=alt.Header(
                    labelFontSize=12, labelFontStyle="bold", labelPadding=0
                ),
            ),
            strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
            size=alt.condition(barcode_selection, alt.value(55), alt.value(35)),
            tooltip=[
                alt.Tooltip(c, format=".3g") if fit_params_noqc[c].dtype == float else c
                for c in fit_params_noqc.columns
            ],
        )
        .mark_circle(stroke="black", strokeOpacity=1, fillOpacity=0.55)
        .properties(
            height=alt.Step(10),
            width=90,
            title=alt.TitleParams(f"{prop} for each barcode serum-replicate", dy=-2),
        )
    )

alt.vconcat(*fit_params_noqc_chart)

Plotting barcode / serum-replicates that fail curvefit_qc={'max_frac_infectivity_at_least': 0.0, 'goodness_of_fit': {'min_R2': 0.5, 'max_RMSD': 0.15}, 'serum_replicates_ignore_curvefit_qc': [], 'barcode_serum_replicates_ignore_curvefit_qc': []}

Now plot curves for all virus vs serum-replicates that have a barcode that fails any of the QC. In these plots, the suffix on the barcode name in the color key indicates if it passed or failed QC:

barcode_serum_replicates_fail_qc = fit_params_noqc.query("fails_qc").reset_index(
    drop=True
)
print(f"Here are barcode / serum-replicates that fail {curvefit_qc=}")
display(barcode_serum_replicates_fail_qc)

if len(barcode_serum_replicates_fail_qc):
    print(
        "\nCurves for virus vs serum-replicates with at least one failed barcode."
        "\nColor key labels indicate if barcodes failed or passed QC."
    )
    plots = {}
    ncol = 6
    for iplot, (serum, virus, failed_barcodes) in enumerate(
        barcode_serum_replicates_fail_qc.groupby(
            ["serum_replicate", "virus"], as_index=False
        )
        .aggregate(barcodes=pd.NamedAgg("barcode", list))
        .itertuples(index=False)
    ):
        passed_barcodes = [
            bc
            for bc in fits_noqc.replicates[serum, virus]
            if (bc not in failed_barcodes) and (bc != "average")
        ]
        curvelist = []
        assert len(CBMARKERS) >= len(failed_barcodes + passed_barcodes)
        assert len(CBPALETTE) >= len(failed_barcodes + passed_barcodes)
        for replicate, marker, color in zip(
            failed_barcodes + passed_barcodes, CBMARKERS, CBPALETTE
        ):
            curvelist.append(
                {
                    "serum": serum,
                    "virus": virus,
                    "replicate": replicate,
                    "label": replicate
                    + ("-fail" if replicate in failed_barcodes else "-pass"),
                    "color": color,
                    "marker": marker,
                }
            )
        plots[iplot // ncol, iplot % ncol] = (f"{serum} vs {virus}", curvelist)

    fig, _ = fits_noqc.plotGrid(
        plots,
        attempt_shared_legend=False,
        legendfontsize=8,
        titlesize=9,
        ticksize=10,
        draw_in_bounds=True,
    )
    display_curve_fig(fig, curve_display_method)
    plt.close(fig)

Here are barcode / serum-replicates that fail curvefit_qc={'max_frac_infectivity_at_least': 0.0, 'goodness_of_fit': {'min_R2': 0.5, 'max_RMSD': 0.15}, 'serum_replicates_ignore_curvefit_qc': [], 'barcode_serum_replicates_ignore_curvefit_qc': []}

	serum_replicate	barcode	max_frac_infectivity	virus	r2	rmsd	fails_max_frac_infectivity_at_least	fails_goodness_of_fit	fails_qc	ignore_qc
0	NIID_2_post	CTTTTCTAGTACGCTT	1.000000	A/Nevada/216/2024_H3N2	2.868290e-01	0.241540	False	True	True	False
1	NIID_2_post	GTTGCTCCGACACGCC	1.000000	A/Kentucky/57/2024_H3N2	4.699339e-01	0.174514	False	True	True	False
2	NIID_3_post	TCACGACTCGACTAAC	1.000000	A/Massachusetts/93/2024_H3N2	2.771614e-01	0.256073	False	True	True	False
3	NIID_45	CACCAATCTTCGAACT	1.000000	A/Santiago/101713/2024_H1N1	4.680879e-01	0.175085	False	True	True	False
4	NIID_45	CTGAACTTGTCGATAT	1.000000	A/Wisconsin/67/2022_H1N1	2.220446e-16	0.181548	False	True	True	False
5	NIID_45	GCCGGCGTTAGTGTCA	1.000000	A/Singapore/MOH0547/2024_H1N1	-2.220446e-16	0.175472	False	True	True	False
6	NIID_46	AACCACCCCAGAGATG	0.955233	A/Kansas/14/2017_H3N2	3.293518e-01	0.162064	False	True	True	False
7	NIID_46	CCGCGCACGTTTAGAG	1.000000	A/Maldives/2147/2024_H3N2	1.911612e-01	0.159298	False	True	True	False
8	NIID_46	CGTTAACGGCCTATCC	0.970881	A/Darwin/9/2021_H3N2	3.332393e-01	0.154846	False	True	True	False
9	NIID_46	GACGGGATGGGCACGT	1.000000	A/Massachusetts/ISC-1684/2025_H3N2	1.066387e-01	0.218054	False	True	True	False
10	NIID_46	GCCGCTGCGGCGTGTG	1.000000	A/Norway/12374/2023_H3N2	1.093557e-01	0.179899	False	True	True	False
11	NIID_46	GCCTTTGCGCGCAGTC	1.000000	A/Badajoz/18680568/2025_H3N2	4.886156e-01	0.233437	False	True	True	False
12	NIID_47	AGACCATCGCACCCAA	1.000000	A/Thailand/8/2022_H3N2	1.573616e-01	0.177498	False	True	True	False
13	NIID_47	AGTCCTATCCTCAAAT	1.000000	A/Wisconsin/588/2019_H1N1	2.275226e-01	0.313895	False	True	True	False
14	NIID_47	CAAAATCTACGGCGAC	1.000000	A/France/BRE-IPP01880/2025_H3N2	3.939200e-01	0.155103	False	True	True	False
15	NIID_47	CACCGCGCCGAGCACC	1.000000	A/Victoria/3482/2024_H3N2	4.382369e-01	0.157075	False	True	True	False
16	NIID_47	CGCACTTTACGAGACA	1.000000	A/France/BRE-IPP01880/2025_H3N2	4.523252e-01	0.152510	False	True	True	False
17	NIID_47	CGTACGTATGTCCCAG	1.000000	A/Thailand/8/2022_H3N2	0.000000e+00	0.184593	False	True	True	False
18	NIID_47	GAATAATAGAACAGAG	1.000000	A/DistrictOfColumbia/27/2023_H3N2	1.960311e-01	0.152066	False	True	True	False
19	NIID_47	GATCGCCACTGATAAG	1.000000	A/Colombia/7681/2024_H3N2	3.638264e-01	0.183463	False	True	True	False
20	NIID_47	GTTGCTCCGACACGCC	1.000000	A/Kentucky/57/2024_H3N2	4.737053e-01	0.205969	False	True	True	False
21	NIID_47	TATCCAAGGGACGGAC	1.000000	A/HongKong/45/2019_H3N2	2.718645e-01	0.262996	False	True	True	False
22	NIID_47	TGCGGTGGTCGATCCG	1.000000	A/TOKYO/EIS11-277/2024_H1N1	1.338454e-01	0.190356	False	True	True	False
23	NIID_48	CATAAAAGACTGTATA	1.000000	A/Thailand/8/2022_H3N2	4.742920e-01	0.151732	False	True	True	False
24	NIID_48	GCCTTTGCGCGCAGTC	1.000000	A/Badajoz/18680568/2025_H3N2	2.753478e-01	0.262144	False	True	True	False
25	NIID_48	TACATACCGACGCAGT	0.761906	A/Minnesota/126/2024_H3N2	3.881813e-01	0.179485	False	True	True	False
26	NIID_4_post	AAGGTCCCTATGTAAT	1.000000	A/Utah/39/2025_H1N1	3.041942e-01	0.184016	False	True	True	False
27	NIID_4_post	GCCATTTACTGAAGGG	0.678930	A/Mato_Grosso_do_Sul/518/2025_H3N2	4.893220e-01	0.162072	False	True	True	False
28	NIID_5_post	CGGGAACATACATAAC	1.000000	A/Massachusetts/BI_MGH-23147/2025_H3N2	4.440928e-01	0.186542	False	True	True	False
29	NIID_6_post	ATTAGATTATAACGTA	1.000000	A/Cambodia/e0826360/2020_H3N2	4.414393e-01	0.248842	False	True	True	False
30	NIID_6_post	ATTTACTCATTATACG	1.000000	A/HongKong/4801/2014egg_H3N2	4.246794e-01	0.241689	False	True	True	False
31	NIID_7_post	CGGGGACAAGATTGTA	1.000000	A/HongKong/45/2019_H3N2	0.000000e+00	0.264297	False	True	True	False
32	NIID_7_post	GCAACGAGGTGTAACC	1.000000	A/Kansas/14/2017_H3N2	3.712933e-01	0.180924	False	True	True	False

Curves for virus vs serum-replicates with at least one failed barcode.
Color key labels indicate if barcodes failed or passed QC.

# drop barcode / serum-replicates failing QC
for qc_filter in ["max_frac_infectivity_at_least", "goodness_of_fit"]:
    fits_qc_drops = list(
        fit_params_noqc.query(f"fails_{qc_filter} and (not ignore_qc)")[
            ["barcode", "serum_replicate"]
        ].itertuples(index=False, name=None)
    )
    print(
        f"\nDropping {len(fits_qc_drops)} barcode/serum-replicates for failing "
        f"{qc_filter}={curvefit_qc[qc_filter]}: " + str(fits_qc_drops)
    )
    qc_drops["barcode_serum_replicates"].update({w: qc_filter for w in fits_qc_drops})
    frac_infectivity = frac_infectivity[
        ~frac_infectivity.assign(
            barcode_serum_replicate=lambda x: x.apply(
                lambda r: (r["barcode"], r["serum_replicate"]), axis=1
            )
        )["barcode_serum_replicate"].isin(qc_drops["barcode_serum_replicates"])
    ]
    fit_params_noqc = fit_params_noqc[
        ~fit_params_noqc.assign(
            barcode_serum_replicate=lambda x: x.apply(
                lambda r: (r["barcode"], r["serum_replicate"]), axis=1
            )
        )["barcode_serum_replicate"].isin(qc_drops["barcode_serum_replicates"])
    ]

Dropping 0 barcode/serum-replicates for failing max_frac_infectivity_at_least=0.0: []

Dropping 33 barcode/serum-replicates for failing goodness_of_fit={'min_R2': 0.5, 'max_RMSD': 0.15}: [('CTTTTCTAGTACGCTT', 'NIID_2_post'), ('GTTGCTCCGACACGCC', 'NIID_2_post'), ('TCACGACTCGACTAAC', 'NIID_3_post'), ('CACCAATCTTCGAACT', 'NIID_45'), ('CTGAACTTGTCGATAT', 'NIID_45'), ('GCCGGCGTTAGTGTCA', 'NIID_45'), ('AACCACCCCAGAGATG', 'NIID_46'), ('CCGCGCACGTTTAGAG', 'NIID_46'), ('CGTTAACGGCCTATCC', 'NIID_46'), ('GACGGGATGGGCACGT', 'NIID_46'), ('GCCGCTGCGGCGTGTG', 'NIID_46'), ('GCCTTTGCGCGCAGTC', 'NIID_46'), ('AGACCATCGCACCCAA', 'NIID_47'), ('AGTCCTATCCTCAAAT', 'NIID_47'), ('CAAAATCTACGGCGAC', 'NIID_47'), ('CACCGCGCCGAGCACC', 'NIID_47'), ('CGCACTTTACGAGACA', 'NIID_47'), ('CGTACGTATGTCCCAG', 'NIID_47'), ('GAATAATAGAACAGAG', 'NIID_47'), ('GATCGCCACTGATAAG', 'NIID_47'), ('GTTGCTCCGACACGCC', 'NIID_47'), ('TATCCAAGGGACGGAC', 'NIID_47'), ('TGCGGTGGTCGATCCG', 'NIID_47'), ('CATAAAAGACTGTATA', 'NIID_48'), ('GCCTTTGCGCGCAGTC', 'NIID_48'), ('TACATACCGACGCAGT', 'NIID_48'), ('AAGGTCCCTATGTAAT', 'NIID_4_post'), ('GCCATTTACTGAAGGG', 'NIID_4_post'), ('CGGGAACATACATAAC', 'NIID_5_post'), ('ATTAGATTATAACGTA', 'NIID_6_post'), ('ATTTACTCATTATACG', 'NIID_6_post'), ('CGGGGACAAGATTGTA', 'NIID_7_post'), ('GCAACGAGGTGTAACC', 'NIID_7_post')]

Fit neutralization curves after applying QC

No we re-fit curves after applying all the QC:

fits_qc = neutcurve.CurveFits(
    frac_infectivity.rename(
        columns={
            "frac_infectivity_ceiling": "fraction infectivity",
            "concentration": "serum concentration",
        }
    ),
    conc_col="serum concentration",
    fracinf_col="fraction infectivity",
    virus_col="strain",
    serum_col="serum",
    replicate_col="plate_barcode",
    fixtop=curvefit_params["fixtop"],
    fixbottom=curvefit_params["fixbottom"],
    fixslope=curvefit_params["fixslope"],
)

fit_params_qc = fits_qc.fitParams(average_only=False, no_average=True)
assert len(fit_params_qc) <= len(
    fits_noqc.fitParams(average_only=False, no_average=True)
)

print(f"Assigning fits for this plate to {group}")
fit_params_qc.insert(0, "group", group)

Assigning fits for this plate to NIID

Plot all the curves that passed QC:

if fits_qc.sera:
    fig, _ = fits_qc.plotReplicates(
        attempt_shared_legend=False,
        legendfontsize=8,
        titlesize=9,
        ticksize=10,
        ncol=6,
        draw_in_bounds=True,
    )
    display_curve_fig(fig, curve_display_method)
    plt.close(fig)
else:
    print("No sera passed QC.")

Save results to files

print(f"Writing fraction infectivities to {frac_infectivity_csv}")
(
    frac_infectivity[
        [
            "serum",
            "strain",
            "plate_barcode",
            "dilution_factor",
            "frac_infectivity_raw",
            "frac_infectivity_ceiling",
        ]
    ]
    .sort_values(["serum", "plate_barcode", "dilution_factor"])
    .to_csv(frac_infectivity_csv, index=False, float_format="%.4g")
)

print(f"\nWriting fit parameters to {fits_csv}")
(
    fit_params_qc.drop(columns=["nreplicates", "ic50_str"]).to_csv(
        fits_csv, index=False, float_format="%.4g"
    )
)

print(f"\nPickling neutcurve.CurveFits object for these data to {fits_pickle}")
with open(fits_pickle, "wb") as f:
    pickle.dump(fits_qc, f)

print(f"\nWriting QC drops to {qc_drops_yaml}")


def tup_to_str(x):
    return " ".join(x) if isinstance(x, tuple) else x


qc_drops_for_yaml = {
    key: {tup_to_str(key2): val2 for key2, val2 in val.items()}
    for key, val in qc_drops.items()
}
with open(qc_drops_yaml, "w") as f:
    yaml.YAML(typ="rt").dump(qc_drops_for_yaml, f)
print("\nHere are the QC drops:\n***************************")
yaml.YAML(typ="rt").dump(qc_drops_for_yaml, sys.stdout)

Writing fraction infectivities to results/plates/plate20/frac_infectivity.csv

Writing fit parameters to results/plates/plate20/curvefits.csv

Pickling neutcurve.CurveFits object for these data to results/plates/plate20/curvefits.pickle

Writing QC drops to results/plates/plate20/qc_drops.yml

Here are the QC drops:
***************************
wells: {}
barcodes:
  CCTTTCTCAAAACATA: min_neut_standard_frac_per_well
  CTCTTACGCTCCTACG: min_neut_standard_frac_per_well
  GATTCAGATGCCCACC: min_neut_standard_frac_per_well
barcode_wells:
  TACAAGAGAGGGGTCC A12: min_no_serum_count_per_viral_barcode_well
  AGTCCTATCCTCAAAT B12: min_no_serum_count_per_viral_barcode_well
  TTAATGTAGCCGCTCC C12: min_no_serum_count_per_viral_barcode_well
  TACAAGAGAGGGGTCC G12: min_no_serum_count_per_viral_barcode_well
  CTTACTGCGCGAGAGT H12: min_no_serum_count_per_viral_barcode_well
  TGCTATTCCGGCGCGG D1: max_frac_infectivity_per_viral_barcode_well
  AGGTTCAGACTCTTGC D1: max_frac_infectivity_per_viral_barcode_well
  TGGAATCGTCACCGAT G1: max_frac_infectivity_per_viral_barcode_well
  CGATCTTTACGAAAAA G2: max_frac_infectivity_per_viral_barcode_well
  AACCACCCCAGAGATG H2: max_frac_infectivity_per_viral_barcode_well
  CTTACTGCGCGAGAGT E3: max_frac_infectivity_per_viral_barcode_well
  AACACGTAGAACCGCC F3: max_frac_infectivity_per_viral_barcode_well
  ATAGAATCGCAAATTA F3: max_frac_infectivity_per_viral_barcode_well
  TCGAGTTAATATGCGC F3: max_frac_infectivity_per_viral_barcode_well
  CTTACAGAATACTAGA G3: max_frac_infectivity_per_viral_barcode_well
  GCAGCGTGCCGGTCAT H3: max_frac_infectivity_per_viral_barcode_well
  TATCCAAGGGACGGAC H3: max_frac_infectivity_per_viral_barcode_well
  GTGCGATTGTCCGGAA H3: max_frac_infectivity_per_viral_barcode_well
  ACGTCCATTAAGATCA H3: max_frac_infectivity_per_viral_barcode_well
  CAATTCGCCGTTCCCC H3: max_frac_infectivity_per_viral_barcode_well
  AATGCGAGCATGTCAA H3: max_frac_infectivity_per_viral_barcode_well
  AACCACCCCAGAGATG H3: max_frac_infectivity_per_viral_barcode_well
  CGATCTTTACGAAAAA E4: max_frac_infectivity_per_viral_barcode_well
  TCGAGTTAATATGCGC F4: max_frac_infectivity_per_viral_barcode_well
  GAAATCCCCAAATAAC F4: max_frac_infectivity_per_viral_barcode_well
  ACACGGGTTGGCTGTA G4: max_frac_infectivity_per_viral_barcode_well
  TGGAATCGTCACCGAT E5: max_frac_infectivity_per_viral_barcode_well
  TTGAAAAAATCATAAA F5: max_frac_infectivity_per_viral_barcode_well
  TCTTTACCACTGCATC F5: max_frac_infectivity_per_viral_barcode_well
  TCGAACGAAGTAGGAG F5: max_frac_infectivity_per_viral_barcode_well
  ATAGAATCGCAAATTA F5: max_frac_infectivity_per_viral_barcode_well
  GTGCGATTGTCCGGAA F5: max_frac_infectivity_per_viral_barcode_well
  GAAAGTCCCTATGATG F5: max_frac_infectivity_per_viral_barcode_well
  CTTACAGAATACTAGA F5: max_frac_infectivity_per_viral_barcode_well
  AGGACTATAGTTGGCA F5: max_frac_infectivity_per_viral_barcode_well
  AACACGTAGAACCGCC F5: max_frac_infectivity_per_viral_barcode_well
  TGTCCGGATAAAGTAG F5: max_frac_infectivity_per_viral_barcode_well
  TGCGGTGGTCGATCCG F5: max_frac_infectivity_per_viral_barcode_well
  AAGGTCCCTATGTAAT F5: max_frac_infectivity_per_viral_barcode_well
  GCGAAGTTTCATAGCG F5: max_frac_infectivity_per_viral_barcode_well
  TCGAGTTAATATGCGC F5: max_frac_infectivity_per_viral_barcode_well
  CTTACTGCGCGAGAGT F5: max_frac_infectivity_per_viral_barcode_well
  AATTCGTGAGTACTAG G5: max_frac_infectivity_per_viral_barcode_well
  GTAATTCGCATGCGGA H5: max_frac_infectivity_per_viral_barcode_well
  ACAGTACGATCTACGC E6: max_frac_infectivity_per_viral_barcode_well
  TTGAAAAAATCATAAA F6: max_frac_infectivity_per_viral_barcode_well
  ATAGAATCGCAAATTA F6: max_frac_infectivity_per_viral_barcode_well
  CGAAACACGTCCCAGT F6: max_frac_infectivity_per_viral_barcode_well
  AGGTTCAGACTCTTGC G6: max_frac_infectivity_per_viral_barcode_well
  CGAAACACGTCCCAGT G6: max_frac_infectivity_per_viral_barcode_well
  CTCAATGTCGTAGGAT H6: max_frac_infectivity_per_viral_barcode_well
  AAAGGCGCGCCTTCAA H6: max_frac_infectivity_per_viral_barcode_well
  AACACGTAGAACCGCC H6: max_frac_infectivity_per_viral_barcode_well
  ATAGAAAATTATCCGC H6: max_frac_infectivity_per_viral_barcode_well
  GACCCCTTGTAAGATG H6: max_frac_infectivity_per_viral_barcode_well
  TGCGGTGGTCGATCCG E7: max_frac_infectivity_per_viral_barcode_well
  AGTCCTATCCTCAAAT E7: max_frac_infectivity_per_viral_barcode_well
  GCAAACAGTGTAGTTG F7: max_frac_infectivity_per_viral_barcode_well
  CACCTAGGATCGCACT F7: max_frac_infectivity_per_viral_barcode_well
  TGTTGAGCCAGTCTGA F7: max_frac_infectivity_per_viral_barcode_well
  CAAAAGCAGCACGATA F7: max_frac_infectivity_per_viral_barcode_well
  GAAATCCCCAAATAAC F7: max_frac_infectivity_per_viral_barcode_well
  TCGAGTTAATATGCGC G7: max_frac_infectivity_per_viral_barcode_well
  ACTGTCTAGAAATTTT H7: max_frac_infectivity_per_viral_barcode_well
  CCCTGCGCGGCTCGGG H7: max_frac_infectivity_per_viral_barcode_well
  AGTGTTGAATAGGCGA H7: max_frac_infectivity_per_viral_barcode_well
  ATATAAAAAACTTAGT H7: max_frac_infectivity_per_viral_barcode_well
  TCGTCCGTTGGGAACT H7: max_frac_infectivity_per_viral_barcode_well
  TTCTGTCCAGACTCGT H7: max_frac_infectivity_per_viral_barcode_well
  CAGGCTCTAGAGCTCT H7: max_frac_infectivity_per_viral_barcode_well
  CTGAGCTGCCAATAAG H7: max_frac_infectivity_per_viral_barcode_well
  CGGGAAATGTAAATGA H7: max_frac_infectivity_per_viral_barcode_well
  TTCATCAAGTTGGTGC H7: max_frac_infectivity_per_viral_barcode_well
  AAGAAGACTTTGTGAT H7: max_frac_infectivity_per_viral_barcode_well
  AAGTTAGTAGACCCAC H7: max_frac_infectivity_per_viral_barcode_well
  TAAAAAGCCTCCATGA H7: max_frac_infectivity_per_viral_barcode_well
  AAAGCTCTTTTCGTTC H7: max_frac_infectivity_per_viral_barcode_well
  AAGTTAAGAGAAAGTT H7: max_frac_infectivity_per_viral_barcode_well
  CTATAAACCGTTTGTA H7: max_frac_infectivity_per_viral_barcode_well
  TCAATCGGGGGCTAAA H7: max_frac_infectivity_per_viral_barcode_well
  ATGGCCCACGGGCATA H7: max_frac_infectivity_per_viral_barcode_well
  AGCCCATGCTGGGGAT H7: max_frac_infectivity_per_viral_barcode_well
  TACCTGCTGCGGAACG H7: max_frac_infectivity_per_viral_barcode_well
  CGAACCGCAGACACGT H7: max_frac_infectivity_per_viral_barcode_well
  TCTTAGAGTGAACGAT H7: max_frac_infectivity_per_viral_barcode_well
  CTCAATGTCGTAGGAT H7: max_frac_infectivity_per_viral_barcode_well
  GCATGGAACTAACTCC H7: max_frac_infectivity_per_viral_barcode_well
  AAGGGGCCTCATAATG H7: max_frac_infectivity_per_viral_barcode_well
  GCAGCGTGCCGGTCAT H7: max_frac_infectivity_per_viral_barcode_well
  AACTTCCGTCGCCTGA H7: max_frac_infectivity_per_viral_barcode_well
  CGGGAATCTCCCATAC H7: max_frac_infectivity_per_viral_barcode_well
  TATCCAAGGGACGGAC H7: max_frac_infectivity_per_viral_barcode_well
  CGCAGCATTGGTCGCC H7: max_frac_infectivity_per_viral_barcode_well
  TGGAATCGTCACCGAT H7: max_frac_infectivity_per_viral_barcode_well
  GTACCCAGTTCCTGCG H7: max_frac_infectivity_per_viral_barcode_well
  ACGCAAATAGACCGAA H7: max_frac_infectivity_per_viral_barcode_well
  AGATCCCAGGTCCTTT H7: max_frac_infectivity_per_viral_barcode_well
  CAAAAGCAGCACGATA H7: max_frac_infectivity_per_viral_barcode_well
  TGCTATTCCGGCGCGG H7: max_frac_infectivity_per_viral_barcode_well
  TAATAAGCCAGCAAGA H7: max_frac_infectivity_per_viral_barcode_well
  TGCAGTGGTATACATA H7: max_frac_infectivity_per_viral_barcode_well
  CACAGACAATAAAAAA H7: max_frac_infectivity_per_viral_barcode_well
  CGTTTTTGGTTCGAGG H7: max_frac_infectivity_per_viral_barcode_well
  CCGGATAAATCAGAAC H7: max_frac_infectivity_per_viral_barcode_well
  ATTTAAATTCGAGGAC H7: max_frac_infectivity_per_viral_barcode_well
  TCCCCGTGGTTTGACA H7: max_frac_infectivity_per_viral_barcode_well
  ATAGAATCGCAAATTA H7: max_frac_infectivity_per_viral_barcode_well
  TCTTAGTCCTCGTATG H7: max_frac_infectivity_per_viral_barcode_well
  TCGAACGAAGTAGGAG H7: max_frac_infectivity_per_viral_barcode_well
  CCCCCGCTGTTTAAAA H7: max_frac_infectivity_per_viral_barcode_well
  TATTCCTAACTAGCGA H7: max_frac_infectivity_per_viral_barcode_well
  TCTTTACCACTGCATC H7: max_frac_infectivity_per_viral_barcode_well
  CACCGCGCCGAGCACC H7: max_frac_infectivity_per_viral_barcode_well
  TATATTAGTAACATAA H7: max_frac_infectivity_per_viral_barcode_well
  ACACGGGTTGGCTGTA H7: max_frac_infectivity_per_viral_barcode_well
  GATCACGCAGAAAAAG H7: max_frac_infectivity_per_viral_barcode_well
  ACAGTCCACCATTGAG H7: max_frac_infectivity_per_viral_barcode_well
  AGGTTCAGACTCTTGC H7: max_frac_infectivity_per_viral_barcode_well
  AGTAAACATGCATTGG H7: max_frac_infectivity_per_viral_barcode_well
  CTTACAGAATACTAGA H7: max_frac_infectivity_per_viral_barcode_well
  TTGAAAAAATCATAAA H7: max_frac_infectivity_per_viral_barcode_well
  AGACCGCCAGTTTCGT H7: max_frac_infectivity_per_viral_barcode_well
  ACCGATTCACGAATAA H7: max_frac_infectivity_per_viral_barcode_well
  ATCAGGATAATCGCGC H7: max_frac_infectivity_per_viral_barcode_well
  CGATCTTTACGAAAAA H7: max_frac_infectivity_per_viral_barcode_well
  CTGGAGGCCTGGCCCC H7: max_frac_infectivity_per_viral_barcode_well
  AAGCCCAGCGGGTGAT H7: max_frac_infectivity_per_viral_barcode_well
  CACTAGATGTACAGTC H7: max_frac_infectivity_per_viral_barcode_well
  GTCCGTCAGCATAAAC H7: max_frac_infectivity_per_viral_barcode_well
  CGTACGTATGTCCCAG H7: max_frac_infectivity_per_viral_barcode_well
  AATTCGTGAGTACTAG H7: max_frac_infectivity_per_viral_barcode_well
  TTTATATCGAGATTCA H7: max_frac_infectivity_per_viral_barcode_well
  GATCGCCACTGATAAG H7: max_frac_infectivity_per_viral_barcode_well
  CATTGAGACGCGCAAG H7: max_frac_infectivity_per_viral_barcode_well
  AACCACCCCAGAGATG H7: max_frac_infectivity_per_viral_barcode_well
  CACGGGCTAATGTCTC H7: max_frac_infectivity_per_viral_barcode_well
  TCGCTTCAACTAAAAA H7: max_frac_infectivity_per_viral_barcode_well
  GCGAAGTTTCATAGCG H7: max_frac_infectivity_per_viral_barcode_well
  TATTAAGAGAAGTGCG H7: max_frac_infectivity_per_viral_barcode_well
  AGTCCTATCCTCAAAT H7: max_frac_infectivity_per_viral_barcode_well
  TCGAGTTAATATGCGC H7: max_frac_infectivity_per_viral_barcode_well
  AAGGTCCCTATGTAAT H7: max_frac_infectivity_per_viral_barcode_well
  CACGGCCGGCGAACTC H7: max_frac_infectivity_per_viral_barcode_well
  CTCAAATAATTGGCGC H7: max_frac_infectivity_per_viral_barcode_well
  CGACTCCACGGACGCC H7: max_frac_infectivity_per_viral_barcode_well
  AATGCGAGCATGTCAA H7: max_frac_infectivity_per_viral_barcode_well
  ATAACTGAGGGCATTG H7: max_frac_infectivity_per_viral_barcode_well
  CGAAACACGTCCCAGT H7: max_frac_infectivity_per_viral_barcode_well
  GACCCCTTGTAAGATG H7: max_frac_infectivity_per_viral_barcode_well
  CTTACTGCGCGAGAGT H7: max_frac_infectivity_per_viral_barcode_well
  GAAATCCCCAAATAAC H7: max_frac_infectivity_per_viral_barcode_well
  AGTTTTTATAACTTGC H7: max_frac_infectivity_per_viral_barcode_well
  GCCGGCGTTAGTGTCA H7: max_frac_infectivity_per_viral_barcode_well
  CACCAATCTTCGAACT H7: max_frac_infectivity_per_viral_barcode_well
  TACAAGAGAGGGGTCC H7: max_frac_infectivity_per_viral_barcode_well
  TGCGGTGGTCGATCCG B8: max_frac_infectivity_per_viral_barcode_well
  GATCACGCAGAAAAAG D8: max_frac_infectivity_per_viral_barcode_well
  GAAATCCCCAAATAAC E8: max_frac_infectivity_per_viral_barcode_well
  GAAATCCCCAAATAAC F8: max_frac_infectivity_per_viral_barcode_well
  TCTTTACCACTGCATC G8: max_frac_infectivity_per_viral_barcode_well
  CGTACGTATGTCCCAG G8: max_frac_infectivity_per_viral_barcode_well
  AGTCCTATCCTCAAAT G8: max_frac_infectivity_per_viral_barcode_well
  AAGGTCCCTATGTAAT H8: max_frac_infectivity_per_viral_barcode_well
  GACCCCTTGTAAGATG H8: max_frac_infectivity_per_viral_barcode_well
  CAATTCGCCGTTCCCC D9: max_frac_infectivity_per_viral_barcode_well
  TGGAATCGTCACCGAT E9: max_frac_infectivity_per_viral_barcode_well
  ACCGATTCACGAATAA G9: max_frac_infectivity_per_viral_barcode_well
  GATCGCCACTGATAAG H9: max_frac_infectivity_per_viral_barcode_well
  AGGTTCAGACTCTTGC E10: max_frac_infectivity_per_viral_barcode_well
  TCTCAGCTCTTAGCCG E10: max_frac_infectivity_per_viral_barcode_well
  GCGAAGTTTCATAGCG E10: max_frac_infectivity_per_viral_barcode_well
  AACCACCCCAGAGATG F10: max_frac_infectivity_per_viral_barcode_well
  GAAATCCCCAAATAAC F10: max_frac_infectivity_per_viral_barcode_well
  AAAGGCGCGCCTTCAA G10: max_frac_infectivity_per_viral_barcode_well
  GAGGGGATACGTCACC H10: max_frac_infectivity_per_viral_barcode_well
  AACACGTAGAACCGCC D11: max_frac_infectivity_per_viral_barcode_well
  AACACGTAGAACCGCC G11: max_frac_infectivity_per_viral_barcode_well
  CAATTCGCCGTTCCCC G11: max_frac_infectivity_per_viral_barcode_well
  ATAGAATCGCAAATTA H11: max_frac_infectivity_per_viral_barcode_well
barcode_serum_replicates:
  CTTTTCTAGTACGCTT NIID_2_post: goodness_of_fit
  GTTGCTCCGACACGCC NIID_2_post: goodness_of_fit
  TCACGACTCGACTAAC NIID_3_post: goodness_of_fit
  CACCAATCTTCGAACT NIID_45: goodness_of_fit
  CTGAACTTGTCGATAT NIID_45: goodness_of_fit
  GCCGGCGTTAGTGTCA NIID_45: goodness_of_fit
  AACCACCCCAGAGATG NIID_46: goodness_of_fit
  CCGCGCACGTTTAGAG NIID_46: goodness_of_fit
  CGTTAACGGCCTATCC NIID_46: goodness_of_fit
  GACGGGATGGGCACGT NIID_46: goodness_of_fit
  GCCGCTGCGGCGTGTG NIID_46: goodness_of_fit
  GCCTTTGCGCGCAGTC NIID_46: goodness_of_fit
  AGACCATCGCACCCAA NIID_47: goodness_of_fit
  AGTCCTATCCTCAAAT NIID_47: goodness_of_fit
  CAAAATCTACGGCGAC NIID_47: goodness_of_fit
  CACCGCGCCGAGCACC NIID_47: goodness_of_fit
  CGCACTTTACGAGACA NIID_47: goodness_of_fit
  CGTACGTATGTCCCAG NIID_47: goodness_of_fit
  GAATAATAGAACAGAG NIID_47: goodness_of_fit
  GATCGCCACTGATAAG NIID_47: goodness_of_fit
  GTTGCTCCGACACGCC NIID_47: goodness_of_fit
  TATCCAAGGGACGGAC NIID_47: goodness_of_fit
  TGCGGTGGTCGATCCG NIID_47: goodness_of_fit
  CATAAAAGACTGTATA NIID_48: goodness_of_fit
  GCCTTTGCGCGCAGTC NIID_48: goodness_of_fit
  TACATACCGACGCAGT NIID_48: goodness_of_fit
  AAGGTCCCTATGTAAT NIID_4_post: goodness_of_fit
  GCCATTTACTGAAGGG NIID_4_post: goodness_of_fit
  CGGGAACATACATAAC NIID_5_post: goodness_of_fit
  ATTAGATTATAACGTA NIID_6_post: goodness_of_fit
  ATTTACTCATTATACG NIID_6_post: goodness_of_fit
  CGGGGACAAGATTGTA NIID_7_post: goodness_of_fit
  GCAACGAGGTGTAACC NIID_7_post: goodness_of_fit
serum_replicates: {}