######## 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\x10ACACGGGTTGGCTGTA\x94K!\x8c\x10ACAGTACGATCTACGC\x94K"\x8c\x10ACAGTCCACCATTGAG\x94K#\x8c\x10ACATTTTCCAATAGGT\x94K$\x8c\x10ACCAGCAATGAGTTGT\x94K%\x8c\x10ACCCCCGGAGCTTGGC\x94K&\x8c\x10ACCGAATGAATCATCC\x94K\'\x8c\x10ACCGATTCACGAATAA\x94K(\x8c\x10ACCGTTGTACACACCA\x94K)\x8c\x10ACGCAAATAGACCGAA\x94K*\x8c\x10ACGGGGATTGGCTGTT\x94K+\x8c\x10ACGTATGATTTTCGAG\x94K,\x8c\x10ACGTCCATTAAGATCA\x94K-\x8c\x10ACGTGTCTCCGAGCAA\x94K.\x8c\x10ACTACGAGGCTACGTA\x94K/\x8c\x10ACTCTGGCTCGCTAAT\x94K0\x8c\x10ACTGTCTAGAAATTTT\x94K1\x8c\x10AGAAAATCTCAGATAC\x94K2\x8c\x10AGACCATCGCACCCAA\x94K3\x8c\x10AGACCGCCAGTTTCGT\x94K4\x8c\x10AGAGCTAAAAAGAGGA\x94K5\x8c\x10AGATCCACCCTATAGT\x94K6\x8c\x10AGATCCCAGGTCCTTT\x94K7\x8c\x10AGCATAGGGATATGTG\x94K8\x8c\x10AGCATCTAACAGATAG\x94K9\x8c\x10AGCCCATGCTGGGGAT\x94K:\x8c\x10AGCGACATCGCCCTTT\x94K;\x8c\x10AGCTCCTGGGGTATCA\x94K<\x8c\x10AGCTGAATTAAGTATG\x94K=\x8c\x10AGGAAAGAAACTGGAG\x94K>\x8c\x10AGGACTATAGTTGGCA\x94K?\x8c\x10AGGAGTATGAAGAGCG\x94K@\x8c\x10AGGCCCGTAAGGACTA\x94KA\x8c\x10AGGTTCAGACTCTTGC\x94KB\x8c\x10AGTAAACATGCATTGG\x94KC\x8c\x10AGTATTTGCGCTTCAA\x94KD\x8c\x10AGTCCTATCCTCAAAT\x94KE\x8c\x10AGTCGTTTAGATAGTT\x94KF\x8c\x10AGTGTTGAATAGGCGA\x94KG\x8c\x10AGTGTTGGCTTGGTTA\x94KH\x8c\x10AGTTCCATAGGCATGG\x94KI\x8c\x10AGTTGGGGTCTCCCTT\x94KJ\x8c\x10AGTTTTTATAACTTGC\x94KK\x8c\x10ATAACGTTTGTGCAAA\x94KL\x8c\x10ATAACTGAGGGCATTG\x94KM\x8c\x10ATACACGAGGTTGTGA\x94KN\x8c\x10ATACACGCATGTGCCA\x94KO\x8c\x10ATAGAAAATTATCCGC\x94KP\x8c\x10ATAGAATCGCAAATTA\x94KQ\x8c\x10ATAGGATATATGGCTG\x94KR\x8c\x10ATATAAAAAACTTAGT\x94KS\x8c\x10ATCAGGATAATCGCGC\x94KT\x8c\x10ATCCGATTTAAAGGCA\x94KU\x8c\x10ATGGCCCACGGGCATA\x94KV\x8c\x10ATGGGATTGGAGAAAC\x94KW\x8c\x10ATGGTTTTACGTCCAT\x94KX\x8c\x10ATTAGATTATAACGTA\x94KY\x8c\x10ATTAGGGCTACGTGAG\x94KZ\x8c\x10ATTATCATATCTAATA\x94K[\x8c\x10ATTCCGAATGGGGTAG\x94K\\\x8c\x10ATTTAAATTCGAGGAC\x94K]\x8c\x10ATTTACTCATTATACG\x94K^\x8c\x10ATTTTTCTATGGCTAC\x94K_\x8c\x10CAAAAGCAGCACGATA\x94K`\x8c\x10CAAAATCTACGGCGAC\x94Ka\x8c\x10CAAATGCTGCATTAGG\x94Kb\x8c\x10CAATTCGCCGTTCCCC\x94Kc\x8c\x10CACAGACAATAAAAAA\x94Kd\x8c\x10CACCAATCTTCGAACT\x94Ke\x8c\x10CACCATCAGCACCTAG\x94Kf\x8c\x10CACCGCGCCGAGCACC\x94Kg\x8c\x10CACCTAGGATCGCACT\x94Kh\x8c\x10CACGGCCGGCGAACTC\x94Ki\x8c\x10CACGGGCTAATGTCTC\x94Kj\x8c\x10CACTAGATGTACAGTC\x94Kk\x8c\x10CAGAACCTCGTTGTCT\x94Kl\x8c\x10CAGATAGTGATGAACA\x94Km\x8c\x10CAGGCTCTAGAGCTCT\x94Kn\x8c\x10CATAAAAGACTGTATA\x94Ko\x8c\x10CATGGGAATTGCCACT\x94Kp\x8c\x10CATGTGGAGCCCAACA\x94Kq\x8c\x10CATTGAGACGCGCAAG\x94Kr\x8c\x10CATTTCTGATGAATTG\x94Ks\x8c\x10CCAACACAAAAAATTA\x94Kt\x8c\x10CCAAGCTTGGCGCATC\x94Ku\x8c\x10CCCCCGCTGTTTAAAA\x94Kv\x8c\x10CCCCTCCTCTAAAGTT\x94Kw\x8c\x10CCCTATGAAATAAGCT\x94Kx\x8c\x10CCCTGCGCGGCTCGGG\x94Ky\x8c\x10CCCTTTACGGATCTCT\x94Kz\x8c\x10CCGCATTAGCGGGAGG\x94K{\x8c\x10CCGCGCACGTTTAGAG\x94K|\x8c\x10CCGGATAAATCAGAAC\x94K}\x8c\x10CCTGGGTTAAGTTGTG\x94K~\x8c\x10CCTTGATGCATTCCCG\x94K\x7f\x8c\x10CCTTTCTCAAAACATA\x94K\x80\x8c\x10CGAAAACATTACAAAT\x94K\x81\x8c\x10CGAAACACGTCCCAGT\x94K\x82\x8c\x10CGAACCGCAGACACGT\x94K\x83\x8c\x10CGACTCCACGGACGCC\x94K\x84\x8c\x10CGATCTTTACGAAAAA\x94K\x85\x8c\x10CGCACTTTACGAGACA\x94K\x86\x8c\x10CGCAGCATTGGTCGCC\x94K\x87\x8c\x10CGCGACACCCTTCCGG\x94K\x88\x8c\x10CGGACCCTAGATGGTA\x94K\x89\x8c\x10CGGCCAGGGAATCAAA\x94K\x8a\x8c\x10CGGGAAATGTAAATGA\x94K\x8b\x8c\x10CGGGAACATACATAAC\x94K\x8c\x8c\x10CGGGAATCTCCCATAC\x94K\x8d\x8c\x10CGGGGACAAGATTGTA\x94K\x8e\x8c\x10CGGTCGGGACTCATCT\x94K\x8f\x8c\x10CGTACAGTGTAATCGA\x94K\x90\x8c\x10CGTACGTATGTCCCAG\x94K\x91\x8c\x10CGTGACCCCCTCCAAC\x94K\x92\x8c\x10CGTGTACCCTTCAGCA\x94K\x93\x8c\x10CGTTAACGGCCTATCC\x94K\x94\x8c\x10CGTTCAGCGATAACGG\x94K\x95\x8c\x10CGTTTTTGGTTCGAGG\x94K\x96\x8c\x10CTAATTTAAGTATCAA\x94K\x97\x8c\x10CTAGCACAGCGTAGGC\x94K\x98\x8c\x10CTATAAACCGTTTGTA\x94K\x99\x8c\x10CTATATTGCCCGGAAG\x94K\x9a\x8c\x10CTATCTTAATCTACAG\x94K\x9b\x8c\x10CTATTTAACAGACGTA\x94K\x9c\x8c\x10CTCAAATAATTGGCGC\x94K\x9d\x8c\x10CTCAATGTCGTAGGAT\x94K\x9e\x8c\x10CTCCTAGGGGACGATT\x94K\x9f\x8c\x10CTCTTACGCTCCTACG\x94K\xa0\x8c\x10CTGAACTTATCTGTGG\x94K\xa1\x8c\x10CTGAACTTGTCGATAT\x94K\xa2\x8c\x10CTGAGCTGCCAATAAG\x94K\xa3\x8c\x10CTGAGGGATTCAACTC\x94K\xa4\x8c\x10CTGGAGGCCTGGCCCC\x94K\xa5\x8c\x10CTGTACCTGCAGTTGA\x94K\xa6\x8c\x10CTTACAGAATACTAGA\x94K\xa7\x8c\x10CTTACTGCGCGAGAGT\x94K\xa8\x8c\x10CTTAGGTATTATATGC\x94K\xa9\x8c\x10CTTCGGCTCTTGATTA\x94K\xaa\x8c\x10CTTTTCTAGTACGCTT\x94K\xab\x8c\x10GAAAGCCCCGTGCAAT\x94K\xac\x8c\x10GAAAGTCCCTATGATG\x94K\xad\x8c\x10GAAATCCCCAAATAAC\x94K\xae\x8c\x10GAAGAAACTATAACCA\x94K\xaf\x8c\x10GAAGTACGCTGAATGA\x94K\xb0\x8c\x10GAAGTGCGTATTGAGT\x94K\xb1\x8c\x10GAAGTGCTGCTGAAGT\x94K\xb2\x8c\x10GAATAATAGAACAGAG\x94K\xb3\x8c\x10GACAAAAGGGACATAT\x94K\xb4\x8c\x10GACCCCTTGTAAGATG\x94K\xb5\x8c\x10GACGGGATGGGCACGT\x94K\xb6\x8c\x10GAGAGCTGCAGAAGCG\x94K\xb7\x8c\x10GAGCTTGCTATGGATC\x94K\xb8\x8c\x10GAGGGGATACGTCACC\x94K\xb9\x8c\x10GAGGGGTAGAGATACG\x94K\xba\x8c\x10GATCACGCAGAAAAAG\x94K\xbb\x8c\x10GATCGCCACTGATAAG\x94K\xbc\x8c\x10GATCGCCATCGACTTC\x94K\xbd\x8c\x10GATCTTGGGGAGAGTC\x94K\xbe\x8c\x10GATTCAGATGCCCACC\x94K\xbf\x8c\x10GCAAACAGTGTAGTTG\x94K\xc0\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='plate9'

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 1 wells for failing qc_thresholds['avg_barcode_counts_per_well']=500: ['F2']

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 0 barcodes for failing qc={'min_frac': 0.0001, 'max_fold_change': 4, 'max_wells': 2}: []

=========================================================================================
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 4 barcode-wells for failing qc_thresholds['min_no_serum_count_per_viral_barcode_well']=100: [('GATTCAGATGCCCACC', 'D12'), ('CACCAATCTTCGAACT', 'D12'), ('CTCTTACGCTCCTACG', 'F12'), ('TTAATGTAGCCGCTCC', '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 67 barcode-wells for failing qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=3: [('GCCGGCGTTAGTGTCA', 'D1'), ('GATCGCCACTGATAAG', 'F1'), ('CACCAATCTTCGAACT', 'F1'), ('CACCAATCTTCGAACT', 'D2'), ('AACCACCCCAGAGATG', 'E2'), ('CACCAATCTTCGAACT', 'E2'), ('AAGTATTGCTACACAT', 'G2'), ('CACTAGATGTACAGTC', 'G2'), ('GTGCATCCTAGTGACG', 'G2'), ('AGTCCTATCCTCAAAT', 'G2'), ('TCGAACGAAGTAGGAG', 'C3'), ('TTGGGCACTAAATTAA', 'C3'), ('ACGTCCATTAAGATCA', 'D3'), ('CGTGACCCCCTCCAAC', 'D3'), ('ACAAAGTCTCGAGAAG', 'E3'), ('TAATAAGCCAGCAAGA', 'E3'), ('TCGAACGAAGTAGGAG', 'E3'), ('GACCCCTTGTAAGATG', 'E3'), ('AGTCCTATCCTCAAAT', 'E3'), ('TAATAAGCCAGCAAGA', 'F3'), ('ACAGTCCACCATTGAG', 'F3'), ('GACCCCTTGTAAGATG', 'F3'), ('CCTTTCTCAAAACATA', 'G3'), ('AGCTCCTGGGGTATCA', 'H3'), ('CATAAAAGACTGTATA', 'H3'), ('CGGGAATCTCCCATAC', 'H3'), ('ACCGATTCACGAATAA', 'H3'), ('TCGAACGAAGTAGGAG', 'H3'), ('CACTAGATGTACAGTC', 'H3'), ('CGTGACCCCCTCCAAC', 'H3'), ('CACGGGCTAATGTCTC', 'H3'), ('GAGCTTGCTATGGATC', 'H3'), ('GTGCATCCTAGTGACG', 'H3'), ('CACCAATCTTCGAACT', 'H3'), ('TTTATATCCAACACCA', 'F4'), ('GAAGTGCGTATTGAGT', 'F4'), ('AGGAAAGAAACTGGAG', 'F4'), ('GCCATTTACTGAAGGG', 'F4'), ('GTAGAACTGCGGCCCC', 'F4'), ('CGGGGACAAGATTGTA', 'F4'), ('GCCTTTGCGCGCAGTC', 'F4'), ('TTGACTCACCGAATAA', 'F4'), ('AAAGCTCTTTTCGTTC', 'F4'), ('TAACGTGATTTCTCGA', 'F4'), ('TATTAAGAGAAGTGCG', 'F4'), ('CGTGACCCCCTCCAAC', 'F4'), ('ATAACTGAGGGCATTG', 'F4'), ('CACTAGATGTACAGTC', 'F4'), ('GCCGCTGCGGCGTGTG', 'F4'), ('CTCAAATAATTGGCGC', 'F4'), ('CCGCATTAGCGGGAGG', 'F4'), ('TCGAGTTAATATGCGC', 'F4'), ('GCCGGCGTTAGTGTCA', 'F4'), ('ACGCAAATAGACCGAA', 'H5'), ('CAAAAGCAGCACGATA', 'C7'), ('GAAATCCCCAAATAAC', 'E8'), ('GATCACGCAGAAAAAG', 'F8'), ('TATCCAAGGGACGGAC', 'G8'), ('AACCACCCCAGAGATG', 'F9'), ('ATTAGATTATAACGTA', 'E10'), ('CCTTTCTCAAAACATA', 'F10'), ('CGTACGTATGTCCCAG', 'G10'), ('GCCGGCGTTAGTGTCA', 'G10'), ('AAAGTAGCAGAGGATT', 'D11'), ('CGTTAACGGCCTATCC', 'E11'), ('AACACGTAGAACCGCC', 'F11'), ('AGTCCTATCCTCAAAT', 'G11')]

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 2 barcode/serum-replicates for failing qc_thresholds['min_dilutions_per_barcode_serum_replicate']=6: [('CACCAATCTTCGAACT', 'SCH_17'), ('TCGAACGAAGTAGGAG', 'SCH_18')]

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	SCH_16	AAACCCATAAGACCCC	0.624492	A/Minnesota/126/2024_H3N2	4.629890e-01	0.171664	False	True	True	False
1	SCH_16	AAATTCACAATATCCA	0.523484	A/Cambodia/e0826360/2020egg_H3N2	4.534223e-01	0.153128	False	True	True	False
2	SCH_16	AACAGAAGTCCATGTA	0.674145	A/New_York/GKISBBBE61555/2025_H3N2	4.639704e-01	0.163748	False	True	True	False
3	SCH_16	ACTGTCTAGAAATTTT	0.654493	A/France/PAC-RELAB-HCL024172122101/2024_H3N2	4.950709e-01	0.155799	False	True	True	False
4	SCH_16	CCCTATGAAATAAGCT	0.659316	A/Colombia/7681/2024_H3N2	4.628465e-01	0.160768	False	True	True	False
...	...	...	...	...	...	...	...	...	...	...
365	SCH_26	TTGCAATTGAAACATA	1.000000	A/Cambodia/e0826360/2020_H3N2	-1.016800e-10	0.212099	False	True	True	False
366	SCH_26	TTGCTCCTGAGTAGTA	1.000000	A/Indiana/46/2024_H3N2	3.182924e-01	0.192754	False	True	True	False
367	SCH_26	TTTATATCCAACACCA	1.000000	A/Mato_Grosso_do_Sul/518/2025_H3N2	4.212612e-01	0.208681	False	True	True	False
368	SCH_26	TTTCACAGAACCTATC	1.000000	A/Badajoz/18680568/2025_H3N2	1.989249e-01	0.200760	False	True	True	False
369	SCH_26	TTTCGTGATACTCACA	1.000000	A/DE/DE-DHSS-901/2025_(H3N2)_H3N2	0.000000e+00	0.207134	False	True	True	False

370 rows × 10 columns

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 370 barcode/serum-replicates for failing goodness_of_fit={'min_R2': 0.5, 'max_RMSD': 0.15}: [('AAACCCATAAGACCCC', 'SCH_16'), ('AAATTCACAATATCCA', 'SCH_16'), ('AACAGAAGTCCATGTA', 'SCH_16'), ('ACTGTCTAGAAATTTT', 'SCH_16'), ('CCCTATGAAATAAGCT', 'SCH_16'), ('CCGCATTAGCGGGAGG', 'SCH_16'), ('CGTTTTTGGTTCGAGG', 'SCH_16'), ('CTATAAACCGTTTGTA', 'SCH_16'), ('TACCTGCTGCGGAACG', 'SCH_16'), ('TCAATCGGGGGCTAAA', 'SCH_16'), ('TTACGAATTTGATTCC', 'SCH_16'), ('TTTCACAGAACCTATC', 'SCH_16'), ('GACGGGATGGGCACGT', 'SCH_17'), ('CGGGGACAAGATTGTA', 'SCH_18'), ('AAAGGCGCGCCTTCAA', 'SCH_19'), ('AACAGAAGTCCATGTA', 'SCH_19'), ('AACCTACGAGACGTAA', 'SCH_19'), ('AACTTCCCTGACTGCT', 'SCH_19'), ('AACTTCCGTCGCCTGA', 'SCH_19'), ('AAGAAGACTTTGTGAT', 'SCH_19'), ('AATCGCTGGCACCCGT', 'SCH_19'), ('AATGAAACAATCGAAC', 'SCH_19'), ('ACAAGATTCGGGGGAC', 'SCH_19'), ('ACCAGCAATGAGTTGT', 'SCH_19'), ('ACGTATGATTTTCGAG', 'SCH_19'), ('ACTCTGGCTCGCTAAT', 'SCH_19'), ('AGAAAATCTCAGATAC', 'SCH_19'), ('AGACCATCGCACCCAA', 'SCH_19'), ('AGCATAGGGATATGTG', 'SCH_19'), ('AGTCGTTTAGATAGTT', 'SCH_19'), ('AGTGTTGAATAGGCGA', 'SCH_19'), ('AGTGTTGGCTTGGTTA', 'SCH_19'), ('ATACACGCATGTGCCA', 'SCH_19'), ('ATTCCGAATGGGGTAG', 'SCH_19'), ('CAAAATCTACGGCGAC', 'SCH_19'), ('CACAGACAATAAAAAA', 'SCH_19'), ('CATGGGAATTGCCACT', 'SCH_19'), ('CATTTCTGATGAATTG', 'SCH_19'), ('CCCCCGCTGTTTAAAA', 'SCH_19'), ('CCCCTCCTCTAAAGTT', 'SCH_19'), ('CCCTTTACGGATCTCT', 'SCH_19'), ('CCGCGCACGTTTAGAG', 'SCH_19'), ('CCTTGATGCATTCCCG', 'SCH_19'), ('CGCACTTTACGAGACA', 'SCH_19'), ('CGGACCCTAGATGGTA', 'SCH_19'), ('CGGCCAGGGAATCAAA', 'SCH_19'), ('CGGTCGGGACTCATCT', 'SCH_19'), ('CGTTTTTGGTTCGAGG', 'SCH_19'), ('CTAATTTAAGTATCAA', 'SCH_19'), ('CTATAAACCGTTTGTA', 'SCH_19'), ('CTATATTGCCCGGAAG', 'SCH_19'), ('CTATTTAACAGACGTA', 'SCH_19'), ('CTCAATGTCGTAGGAT', 'SCH_19'), ('CTGAACTTATCTGTGG', 'SCH_19'), ('CTGAGGGATTCAACTC', 'SCH_19'), ('CTTTTCTAGTACGCTT', 'SCH_19'), ('GAAGTACGCTGAATGA', 'SCH_19'), ('GAAGTGCGTATTGAGT', 'SCH_19'), ('GAAGTGCTGCTGAAGT', 'SCH_19'), ('GACGGGATGGGCACGT', 'SCH_19'), ('GAGAGCTGCAGAAGCG', 'SCH_19'), ('GCAAACAGTGTAGTTG', 'SCH_19'), ('GCAGCGTGCCGGTCAT', 'SCH_19'), ('GCCGCTGCGGCGTGTG', 'SCH_19'), ('GTAAGCAAAGTTGACC', 'SCH_19'), ('GTAAGCTTCATGGAGT', 'SCH_19'), ('GTCAAGTTACGGATGG', 'SCH_19'), ('GTTGCTCCGACACGCC', 'SCH_19'), ('TACATACCGACGCAGT', 'SCH_19'), ('TACCTGCTGCGGAACG', 'SCH_19'), ('TAGCATTGTCGGAAAG', 'SCH_19'), ('TATTCCTAACTAGCGA', 'SCH_19'), ('TCGTCCGTTGGGAACT', 'SCH_19'), ('TCTCAGCTCTTAGCCG', 'SCH_19'), ('TGTAATAGGCGTCACA', 'SCH_19'), ('TGTGGAGCGCCCTTAC', 'SCH_19'), ('TTACGAATTTGATTCC', 'SCH_19'), ('TTGCTCCTGAGTAGTA', 'SCH_19'), ('TTTCACAGAACCTATC', 'SCH_19'), ('TTTCGTGATACTCACA', 'SCH_19'), ('AAAGGCGCGCCTTCAA', 'SCH_20'), ('AACTTCCCTGACTGCT', 'SCH_20'), ('AAGAAGACTTTGTGAT', 'SCH_20'), ('AAGAAGCTATAGAAGT', 'SCH_20'), ('AAGATTGATTGAAGTT', 'SCH_20'), ('AAGCGGTGATGTGATT', 'SCH_20'), ('AATCGCTGGCACCCGT', 'SCH_20'), ('ACAAGATTCGGGGGAC', 'SCH_20'), ('AGACCGCCAGTTTCGT', 'SCH_20'), ('AGCATAGGGATATGTG', 'SCH_20'), ('AGGAGTATGAAGAGCG', 'SCH_20'), ('AGTGTTGGCTTGGTTA', 'SCH_20'), ('AGTTCCATAGGCATGG', 'SCH_20'), ('ATATAAAAAACTTAGT', 'SCH_20'), ('ATGGCCCACGGGCATA', 'SCH_20'), ('CAAAATCTACGGCGAC', 'SCH_20'), ('CAATTCGCCGTTCCCC', 'SCH_20'), ('CCAACACAAAAAATTA', 'SCH_20'), ('CCCCTCCTCTAAAGTT', 'SCH_20'), ('CCGCATTAGCGGGAGG', 'SCH_20'), ('CCGCGCACGTTTAGAG', 'SCH_20'), ('CCTTGATGCATTCCCG', 'SCH_20'), ('CGCACTTTACGAGACA', 'SCH_20'), ('CGGGGACAAGATTGTA', 'SCH_20'), ('CGTTTTTGGTTCGAGG', 'SCH_20'), ('CTAGCACAGCGTAGGC', 'SCH_20'), ('CTATTTAACAGACGTA', 'SCH_20'), ('CTCAATGTCGTAGGAT', 'SCH_20'), ('CTTTTCTAGTACGCTT', 'SCH_20'), ('GAAGTGCGTATTGAGT', 'SCH_20'), ('GAATAATAGAACAGAG', 'SCH_20'), ('GACGGGATGGGCACGT', 'SCH_20'), ('GAGAGCTGCAGAAGCG', 'SCH_20'), ('GATCGCCACTGATAAG', 'SCH_20'), ('GCAACGAGGTGTAACC', 'SCH_20'), ('GCCGCTGCGGCGTGTG', 'SCH_20'), ('GTAAGCAAAGTTGACC', 'SCH_20'), ('TCGTCCGTTGGGAACT', 'SCH_20'), ('TCTTATTAGGCGGCAT', 'SCH_20'), ('TGATCTGTGACATTGC', 'SCH_20'), ('TGTAATAGGCGTCACA', 'SCH_20'), ('TTTCACAGAACCTATC', 'SCH_20'), ('TTTCAGCGTTGTTTTG', 'SCH_20'), ('AAGCGGTGATGTGATT', 'SCH_21'), ('AGCGACATCGCCCTTT', 'SCH_21'), ('CAATTCGCCGTTCCCC', 'SCH_21'), ('CCCCCGCTGTTTAAAA', 'SCH_21'), ('CCGCGCACGTTTAGAG', 'SCH_21'), ('CGTTCAGCGATAACGG', 'SCH_21'), ('CGTTTTTGGTTCGAGG', 'SCH_21'), ('GAAGTACGCTGAATGA', 'SCH_21'), ('GCCATTTACTGAAGGG', 'SCH_21'), ('GTAGATACTAGGACCA', 'SCH_21'), ('GTGAGCGAGAAAAGCA', 'SCH_21'), ('TATTCCTAACTAGCGA', 'SCH_21'), ('TTGACTCACCGAATAA', 'SCH_21'), ('TTTCACAGAACCTATC', 'SCH_21'), ('AAATTCACAATATCCA', 'SCH_22'), ('AACCACCCCAGAGATG', 'SCH_22'), ('AACCGTACCGCGTTTA', 'SCH_22'), ('AACGGTTCCGACTAAG', 'SCH_22'), ('AACTGCGTTCATCGAT', 'SCH_22'), ('AACTTCCCTGACTGCT', 'SCH_22'), ('AACTTCCGTCGCCTGA', 'SCH_22'), ('AAGAAGACTTTGTGAT', 'SCH_22'), ('AAGAAGCTATAGAAGT', 'SCH_22'), ('AAGATTGATTGAAGTT', 'SCH_22'), ('AAGCCCAGCGGGTGAT', 'SCH_22'), ('AAGCGGTGATGTGATT', 'SCH_22'), ('AATGAAACAATCGAAC', 'SCH_22'), ('ACCGTTGTACACACCA', 'SCH_22'), ('ACGTATGATTTTCGAG', 'SCH_22'), ('ACGTGTCTCCGAGCAA', 'SCH_22'), ('ACTACGAGGCTACGTA', 'SCH_22'), ('AGACCATCGCACCCAA', 'SCH_22'), ('AGCATAGGGATATGTG', 'SCH_22'), ('AGCCCATGCTGGGGAT', 'SCH_22'), ('AGTCGTTTAGATAGTT', 'SCH_22'), ('AGTGTTGGCTTGGTTA', 'SCH_22'), ('ATAACGTTTGTGCAAA', 'SCH_22'), ('ATACACGCATGTGCCA', 'SCH_22'), ('ATATAAAAAACTTAGT', 'SCH_22'), ('ATGGCCCACGGGCATA', 'SCH_22'), ('ATTAGATTATAACGTA', 'SCH_22'), ('ATTTACTCATTATACG', 'SCH_22'), ('CAAAATCTACGGCGAC', 'SCH_22'), ('CAATTCGCCGTTCCCC', 'SCH_22'), ('CACCGCGCCGAGCACC', 'SCH_22'), ('CAGAACCTCGTTGTCT', 'SCH_22'), ('CCAACACAAAAAATTA', 'SCH_22'), ('CCCTATGAAATAAGCT', 'SCH_22'), ('CCGCGCACGTTTAGAG', 'SCH_22'), ('CCTGGGTTAAGTTGTG', 'SCH_22'), ('CCTTGATGCATTCCCG', 'SCH_22'), ('CGAAAACATTACAAAT', 'SCH_22'), ('CGCACTTTACGAGACA', 'SCH_22'), ('CGGGAATCTCCCATAC', 'SCH_22'), ('CGGGGACAAGATTGTA', 'SCH_22'), ('CGTACGTATGTCCCAG', 'SCH_22'), ('CGTTTTTGGTTCGAGG', 'SCH_22'), ('CTATAAACCGTTTGTA', 'SCH_22'), ('CTATTTAACAGACGTA', 'SCH_22'), ('CTGAACTTATCTGTGG', 'SCH_22'), ('GAATAATAGAACAGAG', 'SCH_22'), ('GAGAGCTGCAGAAGCG', 'SCH_22'), ('GATCGCCACTGATAAG', 'SCH_22'), ('GCAACGAGGTGTAACC', 'SCH_22'), ('GCCGCTGCGGCGTGTG', 'SCH_22'), ('GGTTAACTTTGGAAGC', 'SCH_22'), ('GTAAGCAAAGTTGACC', 'SCH_22'), ('GTAAGCTTCATGGAGT', 'SCH_22'), ('GTAATTCGCATGCGGA', 'SCH_22'), ('GTCCGTCAGCATAAAC', 'SCH_22'), ('GTGAGCGAGAAAAGCA', 'SCH_22'), ('GTTATTATGACTTCAT', 'SCH_22'), ('GTTGCTCCGACACGCC', 'SCH_22'), ('TAAAAAGCCTCCATGA', 'SCH_22'), ('TACATACCGACGCAGT', 'SCH_22'), ('TACCTGCTGCGGAACG', 'SCH_22'), ('TCACGACTCGACTAAC', 'SCH_22'), ('TCATGGGTGTACGAGA', 'SCH_22'), ('TCGTCGCACTACTGCT', 'SCH_22'), ('TCTTATTAGGCGGCAT', 'SCH_22'), ('TGAGTTCATAGCTCCA', 'SCH_22'), ('TGGTCCGCTTCATGCT', 'SCH_22'), ('TTACGAATTTGATTCC', 'SCH_22'), ('TTCTGTCCAGACTCGT', 'SCH_22'), ('TTGCAATTGAAACATA', 'SCH_22'), ('TTTATATCCAACACCA', 'SCH_22'), ('TTTCACAGAACCTATC', 'SCH_22'), ('TTTCGTGATACTCACA', 'SCH_22'), ('CCGCGCACGTTTAGAG', 'SCH_23'), ('GAAGTACGCTGAATGA', 'SCH_23'), ('AGCTGAATTAAGTATG', 'SCH_24'), ('ATTTACTCATTATACG', 'SCH_24'), ('GTAATTCGCATGCGGA', 'SCH_24'), ('TCTTAGAGTGAACGAT', 'SCH_24'), ('TTGACTCACCGAATAA', 'SCH_24'), ('AACAGAAGTCCATGTA', 'SCH_25'), ('AACTGCGTTCATCGAT', 'SCH_25'), ('AACTTCCCTGACTGCT', 'SCH_25'), ('AACTTCCGTCGCCTGA', 'SCH_25'), ('AAGAAGACTTTGTGAT', 'SCH_25'), ('AAGGGGCCTCATAATG', 'SCH_25'), ('AATCGCTGGCACCCGT', 'SCH_25'), ('AATGAAACAATCGAAC', 'SCH_25'), ('ACAAGATTCGGGGGAC', 'SCH_25'), ('ACCGTTGTACACACCA', 'SCH_25'), ('ACTACGAGGCTACGTA', 'SCH_25'), ('ACTCTGGCTCGCTAAT', 'SCH_25'), ('AGCATAGGGATATGTG', 'SCH_25'), ('AGGAGTATGAAGAGCG', 'SCH_25'), ('AGTGTTGAATAGGCGA', 'SCH_25'), ('ATACACGCATGTGCCA', 'SCH_25'), ('ATATAAAAAACTTAGT', 'SCH_25'), ('ATGGCCCACGGGCATA', 'SCH_25'), ('ATTATCATATCTAATA', 'SCH_25'), ('CAAAATCTACGGCGAC', 'SCH_25'), ('CACCGCGCCGAGCACC', 'SCH_25'), ('CAGATAGTGATGAACA', 'SCH_25'), ('CATGTGGAGCCCAACA', 'SCH_25'), ('CCCTATGAAATAAGCT', 'SCH_25'), ('CCCTTTACGGATCTCT', 'SCH_25'), ('CCGCGCACGTTTAGAG', 'SCH_25'), ('CCTTGATGCATTCCCG', 'SCH_25'), ('CGCACTTTACGAGACA', 'SCH_25'), ('CGGGGACAAGATTGTA', 'SCH_25'), ('CGTTAACGGCCTATCC', 'SCH_25'), ('CGTTTTTGGTTCGAGG', 'SCH_25'), ('CTAGCACAGCGTAGGC', 'SCH_25'), ('CTATAAACCGTTTGTA', 'SCH_25'), ('CTATATTGCCCGGAAG', 'SCH_25'), ('CTATTTAACAGACGTA', 'SCH_25'), ('CTCAATGTCGTAGGAT', 'SCH_25'), ('CTTTTCTAGTACGCTT', 'SCH_25'), ('GAAGTACGCTGAATGA', 'SCH_25'), ('GAGAGCTGCAGAAGCG', 'SCH_25'), ('GCAAACAGTGTAGTTG', 'SCH_25'), ('GTAATTCGCATGCGGA', 'SCH_25'), ('GTTGCTCCGACACGCC', 'SCH_25'), ('TAAAAAGCCTCCATGA', 'SCH_25'), ('TACCAATGTCATTTGA', 'SCH_25'), ('TACTGATAACCCTGCG', 'SCH_25'), ('TATCCAAGGGACGGAC', 'SCH_25'), ('TCGATTACTAGCCGGA', 'SCH_25'), ('TCTCAGCTCTTAGCCG', 'SCH_25'), ('TCTTATTAGGCGGCAT', 'SCH_25'), ('TGTAATAGGCGTCACA', 'SCH_25'), ('TGTGGAGCGCCCTTAC', 'SCH_25'), ('TTACGAATTTGATTCC', 'SCH_25'), ('AAAGGCGCGCCTTCAA', 'SCH_26'), ('AAATTCACAATATCCA', 'SCH_26'), ('AACTGCGTTCATCGAT', 'SCH_26'), ('AACTTCCCTGACTGCT', 'SCH_26'), ('AACTTCCGTCGCCTGA', 'SCH_26'), ('AAGAAGACTTTGTGAT', 'SCH_26'), ('AAGCGGTGATGTGATT', 'SCH_26'), ('AAGTATTGCTACACAT', 'SCH_26'), ('AATGAAACAATCGAAC', 'SCH_26'), ('ACAAGATTCGGGGGAC', 'SCH_26'), ('ACCAGCAATGAGTTGT', 'SCH_26'), ('ACCGAATGAATCATCC', 'SCH_26'), ('ACGTATGATTTTCGAG', 'SCH_26'), ('ACTACGAGGCTACGTA', 'SCH_26'), ('ACTGTCTAGAAATTTT', 'SCH_26'), ('AGAAAATCTCAGATAC', 'SCH_26'), ('AGACCATCGCACCCAA', 'SCH_26'), ('AGACCGCCAGTTTCGT', 'SCH_26'), ('AGCATAGGGATATGTG', 'SCH_26'), ('AGCCCATGCTGGGGAT', 'SCH_26'), ('AGCGACATCGCCCTTT', 'SCH_26'), ('AGGAGTATGAAGAGCG', 'SCH_26'), ('AGTGTTGAATAGGCGA', 'SCH_26'), ('AGTGTTGGCTTGGTTA', 'SCH_26'), ('AGTTCCATAGGCATGG', 'SCH_26'), ('ATAACGTTTGTGCAAA', 'SCH_26'), ('ATACACGCATGTGCCA', 'SCH_26'), ('ATATAAAAAACTTAGT', 'SCH_26'), ('ATGGCCCACGGGCATA', 'SCH_26'), ('ATTAGATTATAACGTA', 'SCH_26'), ('ATTATCATATCTAATA', 'SCH_26'), ('ATTCCGAATGGGGTAG', 'SCH_26'), ('CAAAATCTACGGCGAC', 'SCH_26'), ('CACCATCAGCACCTAG', 'SCH_26'), ('CAGATAGTGATGAACA', 'SCH_26'), ('CAGGCTCTAGAGCTCT', 'SCH_26'), ('CCAACACAAAAAATTA', 'SCH_26'), ('CCCCCGCTGTTTAAAA', 'SCH_26'), ('CCCCTCCTCTAAAGTT', 'SCH_26'), ('CCCTATGAAATAAGCT', 'SCH_26'), ('CCCTGCGCGGCTCGGG', 'SCH_26'), ('CCCTTTACGGATCTCT', 'SCH_26'), ('CCGCGCACGTTTAGAG', 'SCH_26'), ('CCTTGATGCATTCCCG', 'SCH_26'), ('CGCACTTTACGAGACA', 'SCH_26'), ('CGCAGCATTGGTCGCC', 'SCH_26'), ('CGGACCCTAGATGGTA', 'SCH_26'), ('CGGGGACAAGATTGTA', 'SCH_26'), ('CGGTCGGGACTCATCT', 'SCH_26'), ('CGTACAGTGTAATCGA', 'SCH_26'), ('CGTTTTTGGTTCGAGG', 'SCH_26'), ('CTAATTTAAGTATCAA', 'SCH_26'), ('CTAGCACAGCGTAGGC', 'SCH_26'), ('CTATAAACCGTTTGTA', 'SCH_26'), ('CTATTTAACAGACGTA', 'SCH_26'), ('CTCCTAGGGGACGATT', 'SCH_26'), ('CTGAACTTATCTGTGG', 'SCH_26'), ('CTGAGGGATTCAACTC', 'SCH_26'), ('GAAGTACGCTGAATGA', 'SCH_26'), ('GAATAATAGAACAGAG', 'SCH_26'), ('GACGGGATGGGCACGT', 'SCH_26'), ('GAGGGGTAGAGATACG', 'SCH_26'), ('GATCGCCACTGATAAG', 'SCH_26'), ('GCAACGAGGTGTAACC', 'SCH_26'), ('GCCATTTACTGAAGGG', 'SCH_26'), ('GCCTTTGCGCGCAGTC', 'SCH_26'), ('GGTTAACTTTGGAAGC', 'SCH_26'), ('GTAAGCAAAGTTGACC', 'SCH_26'), ('GTAAGCTTCATGGAGT', 'SCH_26'), ('GTCAAGTTACGGATGG', 'SCH_26'), ('GTCCGTCAGCATAAAC', 'SCH_26'), ('GTCGCCGCTAATCCGA', 'SCH_26'), ('GTTGCTCCGACACGCC', 'SCH_26'), ('TAAAAAGCCTCCATGA', 'SCH_26'), ('TAATAAGCCAGCAAGA', 'SCH_26'), ('TACCAATGTCATTTGA', 'SCH_26'), ('TACCTGCTGCGGAACG', 'SCH_26'), ('TACTAATGCCGTTGTC', 'SCH_26'), ('TACTAGCAATAAAATC', 'SCH_26'), ('TAGCATTGTCGGAAAG', 'SCH_26'), ('TATCCAAGGGACGGAC', 'SCH_26'), ('TATTCCTAACTAGCGA', 'SCH_26'), ('TCAATCGGGGGCTAAA', 'SCH_26'), ('TCATGGGTGTACGAGA', 'SCH_26'), ('TCGTCCGTTGGGAACT', 'SCH_26'), ('TCTTAGAGTGAACGAT', 'SCH_26'), ('TCTTATTAGGCGGCAT', 'SCH_26'), ('TGAGTTCATAGCTCCA', 'SCH_26'), ('TGGTCCGCTTCATGCT', 'SCH_26'), ('TGTAATAGGCGTCACA', 'SCH_26'), ('TGTGGAGCGCCCTTAC', 'SCH_26'), ('TGTTGTAATCTGAATA', 'SCH_26'), ('TTACGAATTTGATTCC', 'SCH_26'), ('TTCTGTCCAGACTCGT', 'SCH_26'), ('TTGACTCACCGAATAA', 'SCH_26'), ('TTGCAATTGAAACATA', 'SCH_26'), ('TTGCTCCTGAGTAGTA', 'SCH_26'), ('TTTATATCCAACACCA', 'SCH_26'), ('TTTCACAGAACCTATC', 'SCH_26'), ('TTTCGTGATACTCACA', 'SCH_26')]

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 SCH

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/plate9/frac_infectivity.csv

Writing fit parameters to results/plates/plate9/curvefits.csv

Pickling neutcurve.CurveFits object for these data to results/plates/plate9/curvefits.pickle

Writing QC drops to results/plates/plate9/qc_drops.yml

Here are the QC drops:
***************************
wells:
  F2: avg_barcode_counts_per_well
barcodes: {}
barcode_wells:
  GATTCAGATGCCCACC D12: min_no_serum_count_per_viral_barcode_well
  CACCAATCTTCGAACT D12: min_no_serum_count_per_viral_barcode_well
  CTCTTACGCTCCTACG F12: min_no_serum_count_per_viral_barcode_well
  TTAATGTAGCCGCTCC H12: min_no_serum_count_per_viral_barcode_well
  GCCGGCGTTAGTGTCA D1: max_frac_infectivity_per_viral_barcode_well
  GATCGCCACTGATAAG F1: max_frac_infectivity_per_viral_barcode_well
  CACCAATCTTCGAACT F1: max_frac_infectivity_per_viral_barcode_well
  CACCAATCTTCGAACT D2: max_frac_infectivity_per_viral_barcode_well
  AACCACCCCAGAGATG E2: max_frac_infectivity_per_viral_barcode_well
  CACCAATCTTCGAACT E2: max_frac_infectivity_per_viral_barcode_well
  AAGTATTGCTACACAT G2: max_frac_infectivity_per_viral_barcode_well
  CACTAGATGTACAGTC G2: max_frac_infectivity_per_viral_barcode_well
  GTGCATCCTAGTGACG G2: max_frac_infectivity_per_viral_barcode_well
  AGTCCTATCCTCAAAT G2: max_frac_infectivity_per_viral_barcode_well
  TCGAACGAAGTAGGAG C3: max_frac_infectivity_per_viral_barcode_well
  TTGGGCACTAAATTAA C3: max_frac_infectivity_per_viral_barcode_well
  ACGTCCATTAAGATCA D3: max_frac_infectivity_per_viral_barcode_well
  CGTGACCCCCTCCAAC D3: max_frac_infectivity_per_viral_barcode_well
  ACAAAGTCTCGAGAAG E3: max_frac_infectivity_per_viral_barcode_well
  TAATAAGCCAGCAAGA E3: max_frac_infectivity_per_viral_barcode_well
  TCGAACGAAGTAGGAG E3: max_frac_infectivity_per_viral_barcode_well
  GACCCCTTGTAAGATG E3: max_frac_infectivity_per_viral_barcode_well
  AGTCCTATCCTCAAAT E3: max_frac_infectivity_per_viral_barcode_well
  TAATAAGCCAGCAAGA F3: max_frac_infectivity_per_viral_barcode_well
  ACAGTCCACCATTGAG F3: max_frac_infectivity_per_viral_barcode_well
  GACCCCTTGTAAGATG F3: max_frac_infectivity_per_viral_barcode_well
  CCTTTCTCAAAACATA G3: max_frac_infectivity_per_viral_barcode_well
  AGCTCCTGGGGTATCA H3: max_frac_infectivity_per_viral_barcode_well
  CATAAAAGACTGTATA H3: max_frac_infectivity_per_viral_barcode_well
  CGGGAATCTCCCATAC H3: max_frac_infectivity_per_viral_barcode_well
  ACCGATTCACGAATAA H3: max_frac_infectivity_per_viral_barcode_well
  TCGAACGAAGTAGGAG H3: max_frac_infectivity_per_viral_barcode_well
  CACTAGATGTACAGTC H3: max_frac_infectivity_per_viral_barcode_well
  CGTGACCCCCTCCAAC H3: max_frac_infectivity_per_viral_barcode_well
  CACGGGCTAATGTCTC H3: max_frac_infectivity_per_viral_barcode_well
  GAGCTTGCTATGGATC H3: max_frac_infectivity_per_viral_barcode_well
  GTGCATCCTAGTGACG H3: max_frac_infectivity_per_viral_barcode_well
  CACCAATCTTCGAACT H3: max_frac_infectivity_per_viral_barcode_well
  TTTATATCCAACACCA F4: max_frac_infectivity_per_viral_barcode_well
  GAAGTGCGTATTGAGT F4: max_frac_infectivity_per_viral_barcode_well
  AGGAAAGAAACTGGAG F4: max_frac_infectivity_per_viral_barcode_well
  GCCATTTACTGAAGGG F4: max_frac_infectivity_per_viral_barcode_well
  GTAGAACTGCGGCCCC F4: max_frac_infectivity_per_viral_barcode_well
  CGGGGACAAGATTGTA F4: max_frac_infectivity_per_viral_barcode_well
  GCCTTTGCGCGCAGTC F4: max_frac_infectivity_per_viral_barcode_well
  TTGACTCACCGAATAA F4: max_frac_infectivity_per_viral_barcode_well
  AAAGCTCTTTTCGTTC F4: max_frac_infectivity_per_viral_barcode_well
  TAACGTGATTTCTCGA F4: max_frac_infectivity_per_viral_barcode_well
  TATTAAGAGAAGTGCG F4: max_frac_infectivity_per_viral_barcode_well
  CGTGACCCCCTCCAAC F4: max_frac_infectivity_per_viral_barcode_well
  ATAACTGAGGGCATTG F4: max_frac_infectivity_per_viral_barcode_well
  CACTAGATGTACAGTC F4: max_frac_infectivity_per_viral_barcode_well
  GCCGCTGCGGCGTGTG F4: max_frac_infectivity_per_viral_barcode_well
  CTCAAATAATTGGCGC F4: max_frac_infectivity_per_viral_barcode_well
  CCGCATTAGCGGGAGG F4: max_frac_infectivity_per_viral_barcode_well
  TCGAGTTAATATGCGC F4: max_frac_infectivity_per_viral_barcode_well
  GCCGGCGTTAGTGTCA F4: max_frac_infectivity_per_viral_barcode_well
  ACGCAAATAGACCGAA H5: max_frac_infectivity_per_viral_barcode_well
  CAAAAGCAGCACGATA C7: max_frac_infectivity_per_viral_barcode_well
  GAAATCCCCAAATAAC E8: max_frac_infectivity_per_viral_barcode_well
  GATCACGCAGAAAAAG F8: max_frac_infectivity_per_viral_barcode_well
  TATCCAAGGGACGGAC G8: max_frac_infectivity_per_viral_barcode_well
  AACCACCCCAGAGATG F9: max_frac_infectivity_per_viral_barcode_well
  ATTAGATTATAACGTA E10: max_frac_infectivity_per_viral_barcode_well
  CCTTTCTCAAAACATA F10: max_frac_infectivity_per_viral_barcode_well
  CGTACGTATGTCCCAG G10: max_frac_infectivity_per_viral_barcode_well
  GCCGGCGTTAGTGTCA G10: max_frac_infectivity_per_viral_barcode_well
  AAAGTAGCAGAGGATT D11: max_frac_infectivity_per_viral_barcode_well
  CGTTAACGGCCTATCC E11: max_frac_infectivity_per_viral_barcode_well
  AACACGTAGAACCGCC F11: max_frac_infectivity_per_viral_barcode_well
  AGTCCTATCCTCAAAT G11: max_frac_infectivity_per_viral_barcode_well
barcode_serum_replicates:
  CACCAATCTTCGAACT SCH_17: min_dilutions_per_barcode_serum_replicate
  TCGAACGAAGTAGGAG SCH_18: min_dilutions_per_barcode_serum_replicate
  AAACCCATAAGACCCC SCH_16: goodness_of_fit
  AAATTCACAATATCCA SCH_16: goodness_of_fit
  AACAGAAGTCCATGTA SCH_16: goodness_of_fit
  ACTGTCTAGAAATTTT SCH_16: goodness_of_fit
  CCCTATGAAATAAGCT SCH_16: goodness_of_fit
  CCGCATTAGCGGGAGG SCH_16: goodness_of_fit
  CGTTTTTGGTTCGAGG SCH_16: goodness_of_fit
  CTATAAACCGTTTGTA SCH_16: goodness_of_fit
  TACCTGCTGCGGAACG SCH_16: goodness_of_fit
  TCAATCGGGGGCTAAA SCH_16: goodness_of_fit
  TTACGAATTTGATTCC SCH_16: goodness_of_fit
  TTTCACAGAACCTATC SCH_16: goodness_of_fit
  GACGGGATGGGCACGT SCH_17: goodness_of_fit
  CGGGGACAAGATTGTA SCH_18: goodness_of_fit
  AAAGGCGCGCCTTCAA SCH_19: goodness_of_fit
  AACAGAAGTCCATGTA SCH_19: goodness_of_fit
  AACCTACGAGACGTAA SCH_19: goodness_of_fit
  AACTTCCCTGACTGCT SCH_19: goodness_of_fit
  AACTTCCGTCGCCTGA SCH_19: goodness_of_fit
  AAGAAGACTTTGTGAT SCH_19: goodness_of_fit
  AATCGCTGGCACCCGT SCH_19: goodness_of_fit
  AATGAAACAATCGAAC SCH_19: goodness_of_fit
  ACAAGATTCGGGGGAC SCH_19: goodness_of_fit
  ACCAGCAATGAGTTGT SCH_19: goodness_of_fit
  ACGTATGATTTTCGAG SCH_19: goodness_of_fit
  ACTCTGGCTCGCTAAT SCH_19: goodness_of_fit
  AGAAAATCTCAGATAC SCH_19: goodness_of_fit
  AGACCATCGCACCCAA SCH_19: goodness_of_fit
  AGCATAGGGATATGTG SCH_19: goodness_of_fit
  AGTCGTTTAGATAGTT SCH_19: goodness_of_fit
  AGTGTTGAATAGGCGA SCH_19: goodness_of_fit
  AGTGTTGGCTTGGTTA SCH_19: goodness_of_fit
  ATACACGCATGTGCCA SCH_19: goodness_of_fit
  ATTCCGAATGGGGTAG SCH_19: goodness_of_fit
  CAAAATCTACGGCGAC SCH_19: goodness_of_fit
  CACAGACAATAAAAAA SCH_19: goodness_of_fit
  CATGGGAATTGCCACT SCH_19: goodness_of_fit
  CATTTCTGATGAATTG SCH_19: goodness_of_fit
  CCCCCGCTGTTTAAAA SCH_19: goodness_of_fit
  CCCCTCCTCTAAAGTT SCH_19: goodness_of_fit
  CCCTTTACGGATCTCT SCH_19: goodness_of_fit
  CCGCGCACGTTTAGAG SCH_19: goodness_of_fit
  CCTTGATGCATTCCCG SCH_19: goodness_of_fit
  CGCACTTTACGAGACA SCH_19: goodness_of_fit
  CGGACCCTAGATGGTA SCH_19: goodness_of_fit
  CGGCCAGGGAATCAAA SCH_19: goodness_of_fit
  CGGTCGGGACTCATCT SCH_19: goodness_of_fit
  CGTTTTTGGTTCGAGG SCH_19: goodness_of_fit
  CTAATTTAAGTATCAA SCH_19: goodness_of_fit
  CTATAAACCGTTTGTA SCH_19: goodness_of_fit
  CTATATTGCCCGGAAG SCH_19: goodness_of_fit
  CTATTTAACAGACGTA SCH_19: goodness_of_fit
  CTCAATGTCGTAGGAT SCH_19: goodness_of_fit
  CTGAACTTATCTGTGG SCH_19: goodness_of_fit
  CTGAGGGATTCAACTC SCH_19: goodness_of_fit
  CTTTTCTAGTACGCTT SCH_19: goodness_of_fit
  GAAGTACGCTGAATGA SCH_19: goodness_of_fit
  GAAGTGCGTATTGAGT SCH_19: goodness_of_fit
  GAAGTGCTGCTGAAGT SCH_19: goodness_of_fit
  GACGGGATGGGCACGT SCH_19: goodness_of_fit
  GAGAGCTGCAGAAGCG SCH_19: goodness_of_fit
  GCAAACAGTGTAGTTG SCH_19: goodness_of_fit
  GCAGCGTGCCGGTCAT SCH_19: goodness_of_fit
  GCCGCTGCGGCGTGTG SCH_19: goodness_of_fit
  GTAAGCAAAGTTGACC SCH_19: goodness_of_fit
  GTAAGCTTCATGGAGT SCH_19: goodness_of_fit
  GTCAAGTTACGGATGG SCH_19: goodness_of_fit
  GTTGCTCCGACACGCC SCH_19: goodness_of_fit
  TACATACCGACGCAGT SCH_19: goodness_of_fit
  TACCTGCTGCGGAACG SCH_19: goodness_of_fit
  TAGCATTGTCGGAAAG SCH_19: goodness_of_fit
  TATTCCTAACTAGCGA SCH_19: goodness_of_fit
  TCGTCCGTTGGGAACT SCH_19: goodness_of_fit
  TCTCAGCTCTTAGCCG SCH_19: goodness_of_fit
  TGTAATAGGCGTCACA SCH_19: goodness_of_fit
  TGTGGAGCGCCCTTAC SCH_19: goodness_of_fit
  TTACGAATTTGATTCC SCH_19: goodness_of_fit
  TTGCTCCTGAGTAGTA SCH_19: goodness_of_fit
  TTTCACAGAACCTATC SCH_19: goodness_of_fit
  TTTCGTGATACTCACA SCH_19: goodness_of_fit
  AAAGGCGCGCCTTCAA SCH_20: goodness_of_fit
  AACTTCCCTGACTGCT SCH_20: goodness_of_fit
  AAGAAGACTTTGTGAT SCH_20: goodness_of_fit
  AAGAAGCTATAGAAGT SCH_20: goodness_of_fit
  AAGATTGATTGAAGTT SCH_20: goodness_of_fit
  AAGCGGTGATGTGATT SCH_20: goodness_of_fit
  AATCGCTGGCACCCGT SCH_20: goodness_of_fit
  ACAAGATTCGGGGGAC SCH_20: goodness_of_fit
  AGACCGCCAGTTTCGT SCH_20: goodness_of_fit
  AGCATAGGGATATGTG SCH_20: goodness_of_fit
  AGGAGTATGAAGAGCG SCH_20: goodness_of_fit
  AGTGTTGGCTTGGTTA SCH_20: goodness_of_fit
  AGTTCCATAGGCATGG SCH_20: goodness_of_fit
  ATATAAAAAACTTAGT SCH_20: goodness_of_fit
  ATGGCCCACGGGCATA SCH_20: goodness_of_fit
  CAAAATCTACGGCGAC SCH_20: goodness_of_fit
  CAATTCGCCGTTCCCC SCH_20: goodness_of_fit
  CCAACACAAAAAATTA SCH_20: goodness_of_fit
  CCCCTCCTCTAAAGTT SCH_20: goodness_of_fit
  CCGCATTAGCGGGAGG SCH_20: goodness_of_fit
  CCGCGCACGTTTAGAG SCH_20: goodness_of_fit
  CCTTGATGCATTCCCG SCH_20: goodness_of_fit
  CGCACTTTACGAGACA SCH_20: goodness_of_fit
  CGGGGACAAGATTGTA SCH_20: goodness_of_fit
  CGTTTTTGGTTCGAGG SCH_20: goodness_of_fit
  CTAGCACAGCGTAGGC SCH_20: goodness_of_fit
  CTATTTAACAGACGTA SCH_20: goodness_of_fit
  CTCAATGTCGTAGGAT SCH_20: goodness_of_fit
  CTTTTCTAGTACGCTT SCH_20: goodness_of_fit
  GAAGTGCGTATTGAGT SCH_20: goodness_of_fit
  GAATAATAGAACAGAG SCH_20: goodness_of_fit
  GACGGGATGGGCACGT SCH_20: goodness_of_fit
  GAGAGCTGCAGAAGCG SCH_20: goodness_of_fit
  GATCGCCACTGATAAG SCH_20: goodness_of_fit
  GCAACGAGGTGTAACC SCH_20: goodness_of_fit
  GCCGCTGCGGCGTGTG SCH_20: goodness_of_fit
  GTAAGCAAAGTTGACC SCH_20: goodness_of_fit
  TCGTCCGTTGGGAACT SCH_20: goodness_of_fit
  TCTTATTAGGCGGCAT SCH_20: goodness_of_fit
  TGATCTGTGACATTGC SCH_20: goodness_of_fit
  TGTAATAGGCGTCACA SCH_20: goodness_of_fit
  TTTCACAGAACCTATC SCH_20: goodness_of_fit
  TTTCAGCGTTGTTTTG SCH_20: goodness_of_fit
  AAGCGGTGATGTGATT SCH_21: goodness_of_fit
  AGCGACATCGCCCTTT SCH_21: goodness_of_fit
  CAATTCGCCGTTCCCC SCH_21: goodness_of_fit
  CCCCCGCTGTTTAAAA SCH_21: goodness_of_fit
  CCGCGCACGTTTAGAG SCH_21: goodness_of_fit
  CGTTCAGCGATAACGG SCH_21: goodness_of_fit
  CGTTTTTGGTTCGAGG SCH_21: goodness_of_fit
  GAAGTACGCTGAATGA SCH_21: goodness_of_fit
  GCCATTTACTGAAGGG SCH_21: goodness_of_fit
  GTAGATACTAGGACCA SCH_21: goodness_of_fit
  GTGAGCGAGAAAAGCA SCH_21: goodness_of_fit
  TATTCCTAACTAGCGA SCH_21: goodness_of_fit
  TTGACTCACCGAATAA SCH_21: goodness_of_fit
  TTTCACAGAACCTATC SCH_21: goodness_of_fit
  AAATTCACAATATCCA SCH_22: goodness_of_fit
  AACCACCCCAGAGATG SCH_22: goodness_of_fit
  AACCGTACCGCGTTTA SCH_22: goodness_of_fit
  AACGGTTCCGACTAAG SCH_22: goodness_of_fit
  AACTGCGTTCATCGAT SCH_22: goodness_of_fit
  AACTTCCCTGACTGCT SCH_22: goodness_of_fit
  AACTTCCGTCGCCTGA SCH_22: goodness_of_fit
  AAGAAGACTTTGTGAT SCH_22: goodness_of_fit
  AAGAAGCTATAGAAGT SCH_22: goodness_of_fit
  AAGATTGATTGAAGTT SCH_22: goodness_of_fit
  AAGCCCAGCGGGTGAT SCH_22: goodness_of_fit
  AAGCGGTGATGTGATT SCH_22: goodness_of_fit
  AATGAAACAATCGAAC SCH_22: goodness_of_fit
  ACCGTTGTACACACCA SCH_22: goodness_of_fit
  ACGTATGATTTTCGAG SCH_22: goodness_of_fit
  ACGTGTCTCCGAGCAA SCH_22: goodness_of_fit
  ACTACGAGGCTACGTA SCH_22: goodness_of_fit
  AGACCATCGCACCCAA SCH_22: goodness_of_fit
  AGCATAGGGATATGTG SCH_22: goodness_of_fit
  AGCCCATGCTGGGGAT SCH_22: goodness_of_fit
  AGTCGTTTAGATAGTT SCH_22: goodness_of_fit
  AGTGTTGGCTTGGTTA SCH_22: goodness_of_fit
  ATAACGTTTGTGCAAA SCH_22: goodness_of_fit
  ATACACGCATGTGCCA SCH_22: goodness_of_fit
  ATATAAAAAACTTAGT SCH_22: goodness_of_fit
  ATGGCCCACGGGCATA SCH_22: goodness_of_fit
  ATTAGATTATAACGTA SCH_22: goodness_of_fit
  ATTTACTCATTATACG SCH_22

: goodness_of_fit
  CAAAATCTACGGCGAC SCH_22: goodness_of_fit
  CAATTCGCCGTTCCCC SCH_22: goodness_of_fit
  CACCGCGCCGAGCACC SCH_22: goodness_of_fit
  CAGAACCTCGTTGTCT SCH_22: goodness_of_fit
  CCAACACAAAAAATTA SCH_22: goodness_of_fit
  CCCTATGAAATAAGCT SCH_22: goodness_of_fit
  CCGCGCACGTTTAGAG SCH_22: goodness_of_fit
  CCTGGGTTAAGTTGTG SCH_22: goodness_of_fit
  CCTTGATGCATTCCCG SCH_22: goodness_of_fit
  CGAAAACATTACAAAT SCH_22: goodness_of_fit
  CGCACTTTACGAGACA SCH_22: goodness_of_fit
  CGGGAATCTCCCATAC SCH_22: goodness_of_fit
  CGGGGACAAGATTGTA SCH_22: goodness_of_fit
  CGTACGTATGTCCCAG SCH_22: goodness_of_fit
  CGTTTTTGGTTCGAGG SCH_22: goodness_of_fit
  CTATAAACCGTTTGTA SCH_22: goodness_of_fit
  CTATTTAACAGACGTA SCH_22: goodness_of_fit
  CTGAACTTATCTGTGG SCH_22: goodness_of_fit
  GAATAATAGAACAGAG SCH_22: goodness_of_fit
  GAGAGCTGCAGAAGCG SCH_22: goodness_of_fit
  GATCGCCACTGATAAG SCH_22: goodness_of_fit
  GCAACGAGGTGTAACC SCH_22: goodness_of_fit
  GCCGCTGCGGCGTGTG SCH_22: goodness_of_fit
  GGTTAACTTTGGAAGC SCH_22: goodness_of_fit
  GTAAGCAAAGTTGACC SCH_22: goodness_of_fit
  GTAAGCTTCATGGAGT SCH_22: goodness_of_fit
  GTAATTCGCATGCGGA SCH_22: goodness_of_fit
  GTCCGTCAGCATAAAC SCH_22: goodness_of_fit
  GTGAGCGAGAAAAGCA SCH_22: goodness_of_fit
  GTTATTATGACTTCAT SCH_22: goodness_of_fit
  GTTGCTCCGACACGCC SCH_22: goodness_of_fit
  TAAAAAGCCTCCATGA SCH_22: goodness_of_fit
  TACATACCGACGCAGT SCH_22: goodness_of_fit
  TACCTGCTGCGGAACG SCH_22: goodness_of_fit
  TCACGACTCGACTAAC SCH_22: goodness_of_fit
  TCATGGGTGTACGAGA SCH_22: goodness_of_fit
  TCGTCGCACTACTGCT SCH_22: goodness_of_fit
  TCTTATTAGGCGGCAT SCH_22: goodness_of_fit
  TGAGTTCATAGCTCCA SCH_22: goodness_of_fit
  TGGTCCGCTTCATGCT SCH_22: goodness_of_fit
  TTACGAATTTGATTCC SCH_22: goodness_of_fit
  TTCTGTCCAGACTCGT SCH_22: goodness_of_fit
  TTGCAATTGAAACATA SCH_22: goodness_of_fit
  TTTATATCCAACACCA SCH_22: goodness_of_fit
  TTTCACAGAACCTATC SCH_22: goodness_of_fit
  TTTCGTGATACTCACA SCH_22: goodness_of_fit
  CCGCGCACGTTTAGAG SCH_23: goodness_of_fit
  GAAGTACGCTGAATGA SCH_23: goodness_of_fit
  AGCTGAATTAAGTATG SCH_24: goodness_of_fit
  ATTTACTCATTATACG SCH_24: goodness_of_fit
  GTAATTCGCATGCGGA SCH_24: goodness_of_fit
  TCTTAGAGTGAACGAT SCH_24: goodness_of_fit
  TTGACTCACCGAATAA SCH_24: goodness_of_fit
  AACAGAAGTCCATGTA SCH_25: goodness_of_fit
  AACTGCGTTCATCGAT SCH_25: goodness_of_fit
  AACTTCCCTGACTGCT SCH_25: goodness_of_fit
  AACTTCCGTCGCCTGA SCH_25: goodness_of_fit
  AAGAAGACTTTGTGAT SCH_25: goodness_of_fit
  AAGGGGCCTCATAATG SCH_25: goodness_of_fit
  AATCGCTGGCACCCGT SCH_25: goodness_of_fit
  AATGAAACAATCGAAC SCH_25: goodness_of_fit
  ACAAGATTCGGGGGAC SCH_25: goodness_of_fit
  ACCGTTGTACACACCA SCH_25: goodness_of_fit
  ACTACGAGGCTACGTA SCH_25: goodness_of_fit
  ACTCTGGCTCGCTAAT SCH_25: goodness_of_fit
  AGCATAGGGATATGTG SCH_25: goodness_of_fit
  AGGAGTATGAAGAGCG SCH_25: goodness_of_fit
  AGTGTTGAATAGGCGA SCH_25: goodness_of_fit
  ATACACGCATGTGCCA SCH_25: goodness_of_fit
  ATATAAAAAACTTAGT SCH_25: goodness_of_fit
  ATGGCCCACGGGCATA SCH_25: goodness_of_fit
  ATTATCATATCTAATA SCH_25: goodness_of_fit
  CAAAATCTACGGCGAC SCH_25: goodness_of_fit
  CACCGCGCCGAGCACC SCH_25: goodness_of_fit
  CAGATAGTGATGAACA SCH_25: goodness_of_fit
  CATGTGGAGCCCAACA SCH_25: goodness_of_fit
  CCCTATGAAATAAGCT SCH_25: goodness_of_fit
  CCCTTTACGGATCTCT SCH_25: goodness_of_fit
  CCGCGCACGTTTAGAG SCH_25: goodness_of_fit
  CCTTGATGCATTCCCG SCH_25: goodness_of_fit
  CGCACTTTACGAGACA SCH_25: goodness_of_fit
  CGGGGACAAGATTGTA SCH_25: goodness_of_fit
  CGTTAACGGCCTATCC SCH_25: goodness_of_fit
  CGTTTTTGGTTCGAGG SCH_25: goodness_of_fit
  CTAGCACAGCGTAGGC SCH_25: goodness_of_fit
  CTATAAACCGTTTGTA SCH_25: goodness_of_fit
  CTATATTGCCCGGAAG SCH_25: goodness_of_fit
  CTATTTAACAGACGTA SCH_25: goodness_of_fit
  CTCAATGTCGTAGGAT SCH_25: goodness_of_fit
  CTTTTCTAGTACGCTT SCH_25: goodness_of_fit
  GAAGTACGCTGAATGA SCH_25: goodness_of_fit
  GAGAGCTGCAGAAGCG SCH_25: goodness_of_fit
  GCAAACAGTGTAGTTG SCH_25: goodness_of_fit
  GTAATTCGCATGCGGA SCH_25: goodness_of_fit
  GTTGCTCCGACACGCC SCH_25: goodness_of_fit
  TAAAAAGCCTCCATGA SCH_25: goodness_of_fit
  TACCAATGTCATTTGA SCH_25: goodness_of_fit
  TACTGATAACCCTGCG SCH_25: goodness_of_fit
  TATCCAAGGGACGGAC SCH_25: goodness_of_fit
  TCGATTACTAGCCGGA SCH_25: goodness_of_fit
  TCTCAGCTCTTAGCCG SCH_25: goodness_of_fit
  TCTTATTAGGCGGCAT SCH_25: goodness_of_fit
  TGTAATAGGCGTCACA SCH_25: goodness_of_fit
  TGTGGAGCGCCCTTAC SCH_25: goodness_of_fit
  TTACGAATTTGATTCC SCH_25: goodness_of_fit
  AAAGGCGCGCCTTCAA SCH_26: goodness_of_fit
  AAATTCACAATATCCA SCH_26: goodness_of_fit
  AACTGCGTTCATCGAT SCH_26: goodness_of_fit
  AACTTCCCTGACTGCT SCH_26: goodness_of_fit
  AACTTCCGTCGCCTGA SCH_26: goodness_of_fit
  AAGAAGACTTTGTGAT SCH_26: goodness_of_fit
  AAGCGGTGATGTGATT SCH_26: goodness_of_fit
  AAGTATTGCTACACAT SCH_26: goodness_of_fit
  AATGAAACAATCGAAC SCH_26: goodness_of_fit
  ACAAGATTCGGGGGAC SCH_26: goodness_of_fit
  ACCAGCAATGAGTTGT SCH_26: goodness_of_fit
  ACCGAATGAATCATCC SCH_26: goodness_of_fit
  ACGTATGATTTTCGAG SCH_26: goodness_of_fit
  ACTACGAGGCTACGTA SCH_26: goodness_of_fit
  ACTGTCTAGAAATTTT SCH_26: goodness_of_fit
  AGAAAATCTCAGATAC SCH_26: goodness_of_fit
  AGACCATCGCACCCAA SCH_26: goodness_of_fit
  AGACCGCCAGTTTCGT SCH_26: goodness_of_fit
  AGCATAGGGATATGTG SCH_26: goodness_of_fit
  AGCCCATGCTGGGGAT SCH_26: goodness_of_fit
  AGCGACATCGCCCTTT SCH_26: goodness_of_fit
  AGGAGTATGAAGAGCG SCH_26: goodness_of_fit
  AGTGTTGAATAGGCGA SCH_26: goodness_of_fit
  AGTGTTGGCTTGGTTA SCH_26: goodness_of_fit
  AGTTCCATAGGCATGG SCH_26: goodness_of_fit
  ATAACGTTTGTGCAAA SCH_26: goodness_of_fit
  ATACACGCATGTGCCA SCH_26: goodness_of_fit
  ATATAAAAAACTTAGT SCH_26: goodness_of_fit
  ATGGCCCACGGGCATA SCH_26: goodness_of_fit
  ATTAGATTATAACGTA SCH_26: goodness_of_fit
  ATTATCATATCTAATA SCH_26: goodness_of_fit
  ATTCCGAATGGGGTAG SCH_26: goodness_of_fit
  CAAAATCTACGGCGAC SCH_26: goodness_of_fit
  CACCATCAGCACCTAG SCH_26: goodness_of_fit
  CAGATAGTGATGAACA SCH_26: goodness_of_fit
  CAGGCTCTAGAGCTCT SCH_26: goodness_of_fit
  CCAACACAAAAAATTA SCH_26: goodness_of_fit
  CCCCCGCTGTTTAAAA SCH_26: goodness_of_fit
  CCCCTCCTCTAAAGTT SCH_26: goodness_of_fit
  CCCTATGAAATAAGCT SCH_26: goodness_of_fit
  CCCTGCGCGGCTCGGG SCH_26: goodness_of_fit
  CCCTTTACGGATCTCT SCH_26: goodness_of_fit
  CCGCGCACGTTTAGAG SCH_26: goodness_of_fit
  CCTTGATGCATTCCCG SCH_26: goodness_of_fit
  CGCACTTTACGAGACA SCH_26: goodness_of_fit
  CGCAGCATTGGTCGCC SCH_26: goodness_of_fit
  CGGACCCTAGATGGTA SCH_26: goodness_of_fit
  CGGGGACAAGATTGTA SCH_26: goodness_of_fit
  CGGTCGGGACTCATCT SCH_26: goodness_of_fit
  CGTACAGTGTAATCGA SCH_26: goodness_of_fit
  CGTTTTTGGTTCGAGG SCH_26: goodness_of_fit
  CTAATTTAAGTATCAA SCH_26: goodness_of_fit
  CTAGCACAGCGTAGGC SCH_26: goodness_of_fit
  CTATAAACCGTTTGTA SCH_26: goodness_of_fit
  CTATTTAACAGACGTA SCH_26: goodness_of_fit
  CTCCTAGGGGACGATT SCH_26: goodness_of_fit
  CTGAACTTATCTGTGG SCH_26: goodness_of_fit
  CTGAGGGATTCAACTC SCH_26: goodness_of_fit
  GAAGTACGCTGAATGA SCH_26: goodness_of_fit
  GAATAATAGAACAGAG SCH_26: goodness_of_fit
  GACGGGATGGGCACGT SCH_26: goodness_of_fit
  GAGGGGTAGAGATACG SCH_26: goodness_of_fit
  GATCGCCACTGATAAG SCH_26: goodness_of_fit
  GCAACGAGGTGTAACC SCH_26: goodness_of_fit
  GCCATTTACTGAAGGG SCH_26: goodness_of_fit
  GCCTTTGCGCGCAGTC SCH_26: goodness_of_fit
  GGTTAACTTTGGAAGC SCH_26: goodness_of_fit
  GTAAGCAAAGTTGACC SCH_26: goodness_of_fit
  GTAAGCTTCATGGAGT SCH_26: goodness_of_fit
  GTCAAGTTACGGATGG SCH_26: goodness_of_fit
  GTCCGTCAGCATAAAC SCH_26: goodness_of_fit
  GTCGCCGCTAATCCGA SCH_26: goodness_of_fit
  GTTGCTCCGACACGCC SCH_26: goodness_of_fit
  TAAAAAGCCTCCATGA SCH_26: goodness_of_fit
  TAATAAGCCAGCAAGA SCH_26: goodness_of_fit
  TACCAATGTCATTTGA SCH_26: goodness_of_fit
  TACCTGCTGCGGAACG SCH_26: goodness_of_fit
  TACTAATGCCGTTGTC SCH_26: goodness_of_fit
  TACTAGCAATAAAATC SCH_26: goodness_of_fit
  TAGCATTGTCGGAAAG SCH_26: goodness_of_fit
  TATCCAAGGGACGGAC SCH_26: goodness_of_fit
  TATTCCTAACTAGCGA SCH_26: goodness_of_fit
  TCAATCGGGGGCTAAA SCH_26: goodness_of_fit
  TCATGGGTGTACGAGA SCH_26: goodness_of_fit
  TCGTCCGTTGGGAACT SCH_26: goodness_of_fit
  TCTTAGAGTGAACGAT SCH_26: goodness_of_fit
  TCTTATTAGGCGGCAT SCH_26: goodness_of_fit
  TGAGTTCATAGCTCCA SCH_26: goodness_of_fit
  TGGTCCGCTTCATGCT SCH_26: goodness_of_fit
  TGTAATAGGCGTCACA SCH_26: goodness_of_fit
  TGTGGAGCGCCCTTAC SCH_26: goodness_of_fit
  TGTTGTAATCTGAATA SCH_26: goodness_of_fit
  TTACGAATTTGATTCC SCH_26: goodness_of_fit
  TTCTGTCCAGACTCGT SCH_26: goodness_of_fit
  TTGACTCACCGAATAA SCH_26: goodness_of_fit
  TTGCAATTGAAACATA SCH_26: goodness_of_fit
  TTGCTCCTGAGTAGTA SCH_26: goodness_of_fit
  TTTATATCCAACACCA SCH_26: goodness_of_fit
  TTTCACAGAACCTATC SCH_26: goodness_of_fit
  TTTCGTGATACTCACA SCH_26: goodness_of_fit
serum_replicates: {}