######## 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='plate17'

Plate has 96 samples (wells)

Data manually specified to drop:
manual_drops: {}

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

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

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

Load the notebook functions:

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

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

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

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

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

Statistics on barcode-parsing for each sample

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

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

Potential fates are:

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

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

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

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

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


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

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

fates_chart

Read barcode counts and apply manually specified drops

Read the counts per barcode:

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

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

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

Apply any manually specified data drops:

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

Average counts per barcode in each well

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

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

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

display(avg_barcode_counts_chart)

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

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

Fraction of counts from neutralization standard

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

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

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

display(neut_standard_fracs_chart)

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

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

Consistency and minimum fractions for barcodes

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

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

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

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

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

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

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

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

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

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

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

    display(evenness_chart)

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

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

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


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

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

Compute fraction infectivity

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

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

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

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

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

display(neut_standard_counts_chart)

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

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

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

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

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

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

display(no_serum_counts_chart)

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

Dropping 6 barcode-wells for failing qc_thresholds['min_no_serum_count_per_viral_barcode_well']=100: [('TACAAGAGAGGGGTCC', 'A12'), ('CCTTTCTCAAAACATA', 'B12'), ('TTAATGTAGCCGCTCC', 'B12'), ('CTTACTGCGCGAGAGT', 'B12'), ('CACCAATCTTCGAACT', 'C12'), ('GCCGGCGTTAGTGTCA', 'D12')]

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 146 barcode-wells for failing qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=3: [('AGATCCACCCTATAGT', 'D1'), ('TCGAGTTAATATGCGC', 'D1'), ('ACCGATTCACGAATAA', 'F1'), ('GTAATTCGCATGCGGA', 'G1'), ('GATCACGCAGAAAAAG', 'G1'), ('CGGGAATCTCCCATAC', 'H1'), ('GATCACGCAGAAAAAG', 'C2'), ('ACACGGGTTGGCTGTA', 'D2'), ('AACCACCCCAGAGATG', 'D2'), ('AATGCGAGCATGTCAA', 'D2'), ('CGTGACCCCCTCCAAC', 'D2'), ('GATCACGCAGAAAAAG', 'E2'), ('TTTATATCGAGATTCA', 'E2'), ('GAGCTTGCTATGGATC', 'E2'), ('TTAATGTAGCCGCTCC', 'E2'), ('AGCTCCTGGGGTATCA', 'F2'), ('AACACGTAGAACCGCC', 'G2'), ('TCTCAGCTCTTAGCCG', 'H2'), ('CATAAAAGACTGTATA', 'H2'), ('AGATCCACCCTATAGT', 'H2'), ('ACCGATTCACGAATAA', 'D3'), ('ACCCCCGGAGCTTGGC', 'D3'), ('AATGCGAGCATGTCAA', 'D3'), ('CTTACTGCGCGAGAGT', 'D3'), ('TCTTTACCACTGCATC', 'E3'), ('TCGAACGAAGTAGGAG', 'E3'), ('TCTAACTCTCGCGGCA', 'E3'), ('AACCACCCCAGAGATG', 'E3'), ('CGTGACCCCCTCCAAC', 'E3'), ('CAATTCGCCGTTCCCC', 'E3'), ('AATTCGTGAGTACTAG', 'F3'), ('AACCACCCCAGAGATG', 'F3'), ('GTAATTCGCATGCGGA', 'G3'), ('ATAGAATCGCAAATTA', 'G3'), ('TAGCTGATAGTAACTC', 'G3'), ('GAAATCCCCAAATAAC', 'G3'), ('AACACGTAGAACCGCC', 'H3'), ('ATAGAAAATTATCCGC', 'H3'), ('GACCCCTTGTAAGATG', 'H3'), ('TTAATGTAGCCGCTCC', 'H3'), ('CTTACTGCGCGAGAGT', 'C4'), ('ATAGAATCGCAAATTA', 'D4'), ('TTTATATCGAGATTCA', 'D4'), ('CACCAATCTTCGAACT', 'D4'), ('CCAAGCTTGGCGCATC', 'E4'), ('TATCCAAGGGACGGAC', 'E4'), ('CGCGACACCCTTCCGG', 'F4'), ('AGATCCACCCTATAGT', 'F4'), ('CTTACTGCGCGAGAGT', 'F4'), ('CCAAGCTTGGCGCATC', 'G4'), ('ATCAGGATAATCGCGC', 'G4'), ('TTTATATCGAGATTCA', 'G4'), ('ACAGTACGATCTACGC', 'G4'), ('CGACTCCACGGACGCC', 'G4'), ('GTAATTCGCATGCGGA', 'H4'), ('GTGCATCCTAGTGACG', 'H4'), ('CGTTAACGGCCTATCC', 'H4'), ('CACCAATCTTCGAACT', 'C5'), ('CACCAATCTTCGAACT', 'E5'), ('AATGCGAGCATGTCAA', 'F5'), ('CACCAATCTTCGAACT', 'G5'), ('AATGCGAGCATGTCAA', 'G5'), ('TCGAGTTAATATGCGC', 'G5'), ('GCATGGAACTAACTCC', 'H5'), ('CTTACAGAATACTAGA', 'H5'), ('CCAAGCTTGGCGCATC', 'H5'), ('TCTCAGCTCTTAGCCG', 'H5'), ('TAGCTGATAGTAACTC', 'H5'), ('CGAAACACGTCCCAGT', 'H5'), ('CAATTCGCCGTTCCCC', 'H5'), ('TCGAGTTAATATGCGC', 'H5'), ('CACGGGCTAATGTCTC', 'E6'), ('TTTATATCGAGATTCA', 'G6'), ('AGATCCACCCTATAGT', 'G6'), ('ATCAGGATAATCGCGC', 'G6'), ('CAATTCGCCGTTCCCC', 'H6'), ('TCGAGTTAATATGCGC', 'H6'), ('TAACGTGATTTCTCGA', 'D7'), ('TCGAGTTAATATGCGC', 'E7'), ('CACCAATCTTCGAACT', 'E7'), ('TATCCAAGGGACGGAC', 'F7'), ('ATAGAATCGCAAATTA', 'F7'), ('CGAAACACGTCCCAGT', 'F7'), ('GAAATCCCCAAATAAC', 'F7'), ('CGCGACACCCTTCCGG', 'G7'), ('CTTACTGCGCGAGAGT', 'G7'), ('TTAATGTAGCCGCTCC', 'G7'), ('TAATAAGCCAGCAAGA', 'H7'), ('GTGGTATCAAGCCGGG', 'E8'), ('GATCACGCAGAAAAAG', 'E8'), ('CGTGACCCCCTCCAAC', 'E8'), ('CTTACTGCGCGAGAGT', 'E8'), ('CTTACAGAATACTAGA', 'F8'), ('GATCACGCAGAAAAAG', 'F8'), ('CATTGAGACGCGCAAG', 'F8'), ('CAATTCGCCGTTCCCC', 'F8'), ('TCGAGTTAATATGCGC', 'F8'), ('ACAGTCCACCATTGAG', 'G8'), ('AATGCGAGCATGTCAA', 'G8'), ('ACAGTACGATCTACGC', 'G8'), ('CAGATAGTGATGAACA', 'H8'), ('ACCGATTCACGAATAA', 'C9'), ('ATGGTTTTACGTCCAT', 'D9'), ('CACTAGATGTACAGTC', 'E9'), ('CTTACTGCGCGAGAGT', 'E9'), ('AACCACCCCAGAGATG', 'F9'), ('CTTACTGCGCGAGAGT', 'F9'), ('AGCTCCTGGGGTATCA', 'G9'), ('CAATTCGCCGTTCCCC', 'G9'), ('AACACGTAGAACCGCC', 'H9'), ('TGTTGAGCCAGTCTGA', 'H9'), ('GAGGGGATACGTCACC', 'H9'), ('CACTAGATGTACAGTC', 'H9'), ('ATTTTTCTATGGCTAC', 'F10'), ('TCTTAGTCCTCGTATG', 'F10'), ('CGGGAATCTCCCATAC', 'F10'), ('AGGTTCAGACTCTTGC', 'F10'), ('TCTAACTCTCGCGGCA', 'F10'), ('GAGCTTGCTATGGATC', 'F10'), ('CTTACTGCGCGAGAGT', 'F10'), ('TCGAGTTAATATGCGC', 'F10'), ('TTAATGTAGCCGCTCC', 'F10'), ('TCTCAGCTCTTAGCCG', 'G10'), ('AACACGTAGAACCGCC', 'G10'), ('GAAATCCCCAAATAAC', 'G10'), ('TATCCAAGGGACGGAC', 'H10'), ('GCCGGCGTTAGTGTCA', 'H10'), ('GAAATCCCCAAATAAC', 'B11'), ('CAGATAGTGATGAACA', 'D11'), ('TATCCAAGGGACGGAC', 'D11'), ('GACCCCTTGTAAGATG', 'D11'), ('CAATTCGCCGTTCCCC', 'D11'), ('CTTACAGAATACTAGA', 'F11'), ('ACAGTACGATCTACGC', 'F11'), ('AACCACCCCAGAGATG', 'F11'), ('TTTATATCGAGATTCA', 'F11'), ('TCGAGTTAATATGCGC', 'F11'), ('GTAGATACTAGGACCA', 'G11'), ('CGGGAATCTCCCATAC', 'G11'), ('CACCGCGCCGAGCACC', 'G11'), ('TCTAACTCTCGCGGCA', 'G11'), ('ACCGATTCACGAATAA', 'G11'), ('TCGAGTTAATATGCGC', 'G11'), ('ATAGAATCGCAAATTA', 'H11'), ('GACCCCTTGTAAGATG', 'H11'), ('CGTACGTATGTCCCAG', 'H11')]

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

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

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

display(n_dilutions_chart)

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

Dropping 1 barcode/serum-replicates for failing qc_thresholds['min_dilutions_per_barcode_serum_replicate']=6: [('CACCAATCTTCGAACT', 'NIID_16')]

Fit neutralization curves without applying QC to curves

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

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

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

goodness_of_fit = curvefit_qc["goodness_of_fit"]

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

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

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

alt.vconcat(*fit_params_noqc_chart)

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

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

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

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

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

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

	serum_replicate	barcode	max_frac_infectivity	virus	r2	rmsd	fails_max_frac_infectivity_at_least	fails_goodness_of_fit	fails_qc	ignore_qc
0	NIID_12	AACGGTTCCGACTAAG	1.0	A/Washington/284/2024_H3N2	0.309501	0.170405	False	True	True	False
1	NIID_12	AGTGTTGGCTTGGTTA	1.0	A/Pennsylvania/288/2024_H3N2	0.114533	0.188968	False	True	True	False
2	NIID_12	ATTAGATTATAACGTA	1.0	A/Cambodia/e0826360/2020_H3N2	0.000000	0.211709	False	True	True	False
3	NIID_12	ATTAGGGCTACGTGAG	1.0	A/Manitoba/RV04865/2024_H3N2	0.362644	0.207044	False	True	True	False
4	NIID_12	CAAAATCTACGGCGAC	1.0	A/France/BRE-IPP01880/2025_H3N2	0.000000	0.189236	False	True	True	False
...	...	...	...	...	...	...	...	...	...	...
66	NIID_21	CTCCTAGGGGACGATT	1.0	A/Washington/15245/2025_H3N2	0.263669	0.264123	False	True	True	False
67	NIID_21	TAAAAAGCCTCCATGA	1.0	A/HongKong/45/2019_H3N2	0.121797	0.271112	False	True	True	False
68	NIID_21	TTGACTCACCGAATAA	1.0	A/Cambodia/e0826360/2020_H3N2	0.195111	0.211648	False	True	True	False
69	NIID_22	CATAAAAGACTGTATA	1.0	A/Thailand/8/2022_H3N2	0.000000	0.187344	False	True	True	False
70	NIID_22	TACTAGCAATAAAATC	1.0	A/Singapore/INFIMH-16-0019/2016_H3N2	0.498372	0.155971	False	True	True	False

71 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 71 barcode/serum-replicates for failing goodness_of_fit={'min_R2': 0.5, 'max_RMSD': 0.15}: [('AACGGTTCCGACTAAG', 'NIID_12'), ('AGTGTTGGCTTGGTTA', 'NIID_12'), ('ATTAGATTATAACGTA', 'NIID_12'), ('ATTAGGGCTACGTGAG', 'NIID_12'), ('CAAAATCTACGGCGAC', 'NIID_12'), ('CCCTTTACGGATCTCT', 'NIID_12'), ('CCTTGATGCATTCCCG', 'NIID_12'), ('CGCACTTTACGAGACA', 'NIID_12'), ('CGTACGTATGTCCCAG', 'NIID_12'), ('CGTTAACGGCCTATCC', 'NIID_12'), ('CTATAAACCGTTTGTA', 'NIID_12'), ('CTTTTCTAGTACGCTT', 'NIID_12'), ('GCAGCGTGCCGGTCAT', 'NIID_12'), ('GTCGCCGCTAATCCGA', 'NIID_12'), ('TTTCACAGAACCTATC', 'NIID_12'), ('AAAGCTCTTTTCGTTC', 'NIID_13'), ('AAGAAGACTTTGTGAT', 'NIID_13'), ('ACCGTTGTACACACCA', 'NIID_13'), ('ACGGGGATTGGCTGTT', 'NIID_13'), ('AGACCGCCAGTTTCGT', 'NIID_13'), ('AGGAAAGAAACTGGAG', 'NIID_13'), ('AGTGTTGAATAGGCGA', 'NIID_13'), ('AGTTTTTATAACTTGC', 'NIID_13'), ('ATAACGTTTGTGCAAA', 'NIID_13'), ('ATACACGCATGTGCCA', 'NIID_13'), ('ATTATCATATCTAATA', 'NIID_13'), ('CAAAAGCAGCACGATA', 'NIID_13'), ('CAAAATCTACGGCGAC', 'NIID_13'), ('CCGCATTAGCGGGAGG', 'NIID_13'), ('CGCACTTTACGAGACA', 'NIID_13'), ('CGGACCCTAGATGGTA', 'NIID_13'), ('CTAGCACAGCGTAGGC', 'NIID_13'), ('CTATATTGCCCGGAAG', 'NIID_13'), ('CTTACTGCGCGAGAGT', 'NIID_13'), ('GAAAGTCCCTATGATG', 'NIID_13'), ('GATCGCCACTGATAAG', 'NIID_13'), ('GCAAACAGTGTAGTTG', 'NIID_13'), ('GTCGCATCCTGGAATG', 'NIID_13'), ('GTGAGCGAGAAAAGCA', 'NIID_13'), ('TATTCCTAACTAGCGA', 'NIID_13'), ('TCACGACTCGACTAAC', 'NIID_13'), ('TCGCTTCAACTAAAAA', 'NIID_13'), ('TGTTGAGCCAGTCTGA', 'NIID_13'), ('TTGACTCACCGAATAA', 'NIID_13'), ('TTTATATCCAACACCA', 'NIID_13'), ('TTTCACAGAACCTATC', 'NIID_13'), ('AAAGCTCTTTTCGTTC', 'NIID_14'), ('GAAAGCCCCGTGCAAT', 'NIID_14'), ('TATTCCTAACTAGCGA', 'NIID_14'), ('CACCAATCTTCGAACT', 'NIID_15'), ('GATCACGCAGAAAAAG', 'NIID_15'), ('TACAAGAGAGGGGTCC', 'NIID_15'), ('TTAATGTAGCCGCTCC', 'NIID_15'), ('GCCATTTACTGAAGGG', 'NIID_16'), ('TGTGGAGCGCCCTTAC', 'NIID_16'), ('GTAATTCGCATGCGGA', 'NIID_17'), ('ACACGGGTTGGCTGTA', 'NIID_18'), ('TTAATGTAGCCGCTCC', 'NIID_18'), ('ATTAGGGCTACGTGAG', 'NIID_19'), ('CCAAGCTTGGCGCATC', 'NIID_19'), ('CCGCGCACGTTTAGAG', 'NIID_19'), ('CGAAACACGTCCCAGT', 'NIID_19'), ('TTTCACAGAACCTATC', 'NIID_19'), ('CGAACCGCAGACACGT', 'NIID_20'), ('ACCGTTGTACACACCA', 'NIID_21'), ('CGGGGACAAGATTGTA', 'NIID_21'), ('CTCCTAGGGGACGATT', 'NIID_21'), ('TAAAAAGCCTCCATGA', 'NIID_21'), ('TTGACTCACCGAATAA', 'NIID_21'), ('CATAAAAGACTGTATA', 'NIID_22'), ('TACTAGCAATAAAATC', 'NIID_22')]

Fit neutralization curves after applying QC

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

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

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

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

Assigning fits for this plate to NIID

Plot all the curves that passed QC:

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

Save results to files

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

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

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

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


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


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

Writing fraction infectivities to results/plates/plate17/frac_infectivity.csv

Writing fit parameters to results/plates/plate17/curvefits.csv

Pickling neutcurve.CurveFits object for these data to results/plates/plate17/curvefits.pickle

Writing QC drops to results/plates/plate17/qc_drops.yml

Here are the QC drops:
***************************
wells: {}
barcodes:
  AGTCCTATCCTCAAAT: min_neut_standard_frac_per_well
  CTCTTACGCTCCTACG: min_neut_standard_frac_per_well
  GATTCAGATGCCCACC: min_neut_standard_frac_per_well
barcode_wells:
  TACAAGAGAGGGGTCC A12: min_no_serum_count_per_viral_barcode_well
  CCTTTCTCAAAACATA B12: min_no_serum_count_per_viral_barcode_well
  TTAATGTAGCCGCTCC B12: min_no_serum_count_per_viral_barcode_well
  CTTACTGCGCGAGAGT B12: min_no_serum_count_per_viral_barcode_well
  CACCAATCTTCGAACT C12: min_no_serum_count_per_viral_barcode_well
  GCCGGCGTTAGTGTCA D12: min_no_serum_count_per_viral_barcode_well
  AGATCCACCCTATAGT D1: max_frac_infectivity_per_viral_barcode_well
  TCGAGTTAATATGCGC D1: max_frac_infectivity_per_viral_barcode_well
  ACCGATTCACGAATAA F1: max_frac_infectivity_per_viral_barcode_well
  GTAATTCGCATGCGGA G1: max_frac_infectivity_per_viral_barcode_well
  GATCACGCAGAAAAAG G1: max_frac_infectivity_per_viral_barcode_well
  CGGGAATCTCCCATAC H1: max_frac_infectivity_per_viral_barcode_well
  GATCACGCAGAAAAAG C2: max_frac_infectivity_per_viral_barcode_well
  ACACGGGTTGGCTGTA D2: max_frac_infectivity_per_viral_barcode_well
  AACCACCCCAGAGATG D2: max_frac_infectivity_per_viral_barcode_well
  AATGCGAGCATGTCAA D2: max_frac_infectivity_per_viral_barcode_well
  CGTGACCCCCTCCAAC D2: max_frac_infectivity_per_viral_barcode_well
  GATCACGCAGAAAAAG E2: max_frac_infectivity_per_viral_barcode_well
  TTTATATCGAGATTCA E2: max_frac_infectivity_per_viral_barcode_well
  GAGCTTGCTATGGATC E2: max_frac_infectivity_per_viral_barcode_well
  TTAATGTAGCCGCTCC E2: max_frac_infectivity_per_viral_barcode_well
  AGCTCCTGGGGTATCA F2: max_frac_infectivity_per_viral_barcode_well
  AACACGTAGAACCGCC G2: max_frac_infectivity_per_viral_barcode_well
  TCTCAGCTCTTAGCCG H2: max_frac_infectivity_per_viral_barcode_well
  CATAAAAGACTGTATA H2: max_frac_infectivity_per_viral_barcode_well
  AGATCCACCCTATAGT H2: max_frac_infectivity_per_viral_barcode_well
  ACCGATTCACGAATAA D3: max_frac_infectivity_per_viral_barcode_well
  ACCCCCGGAGCTTGGC D3: max_frac_infectivity_per_viral_barcode_well
  AATGCGAGCATGTCAA D3: max_frac_infectivity_per_viral_barcode_well
  CTTACTGCGCGAGAGT D3: max_frac_infectivity_per_viral_barcode_well
  TCTTTACCACTGCATC E3: max_frac_infectivity_per_viral_barcode_well
  TCGAACGAAGTAGGAG E3: max_frac_infectivity_per_viral_barcode_well
  TCTAACTCTCGCGGCA E3: max_frac_infectivity_per_viral_barcode_well
  AACCACCCCAGAGATG E3: max_frac_infectivity_per_viral_barcode_well
  CGTGACCCCCTCCAAC E3: max_frac_infectivity_per_viral_barcode_well
  CAATTCGCCGTTCCCC E3: max_frac_infectivity_per_viral_barcode_well
  AATTCGTGAGTACTAG F3: max_frac_infectivity_per_viral_barcode_well
  AACCACCCCAGAGATG F3: max_frac_infectivity_per_viral_barcode_well
  GTAATTCGCATGCGGA G3: max_frac_infectivity_per_viral_barcode_well
  ATAGAATCGCAAATTA G3: max_frac_infectivity_per_viral_barcode_well
  TAGCTGATAGTAACTC G3: max_frac_infectivity_per_viral_barcode_well
  GAAATCCCCAAATAAC G3: max_frac_infectivity_per_viral_barcode_well
  AACACGTAGAACCGCC H3: max_frac_infectivity_per_viral_barcode_well
  ATAGAAAATTATCCGC H3: max_frac_infectivity_per_viral_barcode_well
  GACCCCTTGTAAGATG H3: max_frac_infectivity_per_viral_barcode_well
  TTAATGTAGCCGCTCC H3: max_frac_infectivity_per_viral_barcode_well
  CTTACTGCGCGAGAGT C4: max_frac_infectivity_per_viral_barcode_well
  ATAGAATCGCAAATTA D4: max_frac_infectivity_per_viral_barcode_well
  TTTATATCGAGATTCA D4: max_frac_infectivity_per_viral_barcode_well
  CACCAATCTTCGAACT D4: max_frac_infectivity_per_viral_barcode_well
  CCAAGCTTGGCGCATC E4: max_frac_infectivity_per_viral_barcode_well
  TATCCAAGGGACGGAC E4: max_frac_infectivity_per_viral_barcode_well
  CGCGACACCCTTCCGG F4: max_frac_infectivity_per_viral_barcode_well
  AGATCCACCCTATAGT F4: max_frac_infectivity_per_viral_barcode_well
  CTTACTGCGCGAGAGT F4: max_frac_infectivity_per_viral_barcode_well
  CCAAGCTTGGCGCATC G4: max_frac_infectivity_per_viral_barcode_well
  ATCAGGATAATCGCGC G4: max_frac_infectivity_per_viral_barcode_well
  TTTATATCGAGATTCA G4: max_frac_infectivity_per_viral_barcode_well
  ACAGTACGATCTACGC G4: max_frac_infectivity_per_viral_barcode_well
  CGACTCCACGGACGCC G4: max_frac_infectivity_per_viral_barcode_well
  GTAATTCGCATGCGGA H4: max_frac_infectivity_per_viral_barcode_well
  GTGCATCCTAGTGACG H4: max_frac_infectivity_per_viral_barcode_well
  CGTTAACGGCCTATCC H4: max_frac_infectivity_per_viral_barcode_well
  CACCAATCTTCGAACT C5: max_frac_infectivity_per_viral_barcode_well
  CACCAATCTTCGAACT E5: max_frac_infectivity_per_viral_barcode_well
  AATGCGAGCATGTCAA F5: max_frac_infectivity_per_viral_barcode_well
  CACCAATCTTCGAACT G5: max_frac_infectivity_per_viral_barcode_well
  AATGCGAGCATGTCAA G5: max_frac_infectivity_per_viral_barcode_well
  TCGAGTTAATATGCGC G5: max_frac_infectivity_per_viral_barcode_well
  GCATGGAACTAACTCC H5: max_frac_infectivity_per_viral_barcode_well
  CTTACAGAATACTAGA H5: max_frac_infectivity_per_viral_barcode_well
  CCAAGCTTGGCGCATC H5: max_frac_infectivity_per_viral_barcode_well
  TCTCAGCTCTTAGCCG H5: max_frac_infectivity_per_viral_barcode_well
  TAGCTGATAGTAACTC H5: max_frac_infectivity_per_viral_barcode_well
  CGAAACACGTCCCAGT H5: max_frac_infectivity_per_viral_barcode_well
  CAATTCGCCGTTCCCC H5: max_frac_infectivity_per_viral_barcode_well
  TCGAGTTAATATGCGC H5: max_frac_infectivity_per_viral_barcode_well
  CACGGGCTAATGTCTC E6: max_frac_infectivity_per_viral_barcode_well
  TTTATATCGAGATTCA G6: max_frac_infectivity_per_viral_barcode_well
  AGATCCACCCTATAGT G6: max_frac_infectivity_per_viral_barcode_well
  ATCAGGATAATCGCGC G6: max_frac_infectivity_per_viral_barcode_well
  CAATTCGCCGTTCCCC H6: max_frac_infectivity_per_viral_barcode_well
  TCGAGTTAATATGCGC H6: max_frac_infectivity_per_viral_barcode_well
  TAACGTGATTTCTCGA D7: max_frac_infectivity_per_viral_barcode_well
  TCGAGTTAATATGCGC E7: max_frac_infectivity_per_viral_barcode_well
  CACCAATCTTCGAACT E7: max_frac_infectivity_per_viral_barcode_well
  TATCCAAGGGACGGAC F7: max_frac_infectivity_per_viral_barcode_well
  ATAGAATCGCAAATTA F7: max_frac_infectivity_per_viral_barcode_well
  CGAAACACGTCCCAGT F7: max_frac_infectivity_per_viral_barcode_well
  GAAATCCCCAAATAAC F7: max_frac_infectivity_per_viral_barcode_well
  CGCGACACCCTTCCGG G7: max_frac_infectivity_per_viral_barcode_well
  CTTACTGCGCGAGAGT G7: max_frac_infectivity_per_viral_barcode_well
  TTAATGTAGCCGCTCC G7: max_frac_infectivity_per_viral_barcode_well
  TAATAAGCCAGCAAGA H7: max_frac_infectivity_per_viral_barcode_well
  GTGGTATCAAGCCGGG E8: max_frac_infectivity_per_viral_barcode_well
  GATCACGCAGAAAAAG E8: max_frac_infectivity_per_viral_barcode_well
  CGTGACCCCCTCCAAC E8: max_frac_infectivity_per_viral_barcode_well
  CTTACTGCGCGAGAGT E8: max_frac_infectivity_per_viral_barcode_well
  CTTACAGAATACTAGA F8: max_frac_infectivity_per_viral_barcode_well
  GATCACGCAGAAAAAG F8: max_frac_infectivity_per_viral_barcode_well
  CATTGAGACGCGCAAG F8: max_frac_infectivity_per_viral_barcode_well
  CAATTCGCCGTTCCCC F8: max_frac_infectivity_per_viral_barcode_well
  TCGAGTTAATATGCGC F8: max_frac_infectivity_per_viral_barcode_well
  ACAGTCCACCATTGAG G8: max_frac_infectivity_per_viral_barcode_well
  AATGCGAGCATGTCAA G8: max_frac_infectivity_per_viral_barcode_well
  ACAGTACGATCTACGC G8: max_frac_infectivity_per_viral_barcode_well
  CAGATAGTGATGAACA H8: max_frac_infectivity_per_viral_barcode_well
  ACCGATTCACGAATAA C9: max_frac_infectivity_per_viral_barcode_well
  ATGGTTTTACGTCCAT D9: max_frac_infectivity_per_viral_barcode_well
  CACTAGATGTACAGTC E9: max_frac_infectivity_per_viral_barcode_well
  CTTACTGCGCGAGAGT E9: max_frac_infectivity_per_viral_barcode_well
  AACCACCCCAGAGATG F9: max_frac_infectivity_per_viral_barcode_well
  CTTACTGCGCGAGAGT F9: max_frac_infectivity_per_viral_barcode_well
  AGCTCCTGGGGTATCA G9: max_frac_infectivity_per_viral_barcode_well
  CAATTCGCCGTTCCCC G9: max_frac_infectivity_per_viral_barcode_well
  AACACGTAGAACCGCC H9: max_frac_infectivity_per_viral_barcode_well
  TGTTGAGCCAGTCTGA H9: max_frac_infectivity_per_viral_barcode_well
  GAGGGGATACGTCACC H9: max_frac_infectivity_per_viral_barcode_well
  CACTAGATGTACAGTC H9: max_frac_infectivity_per_viral_barcode_well
  ATTTTTCTATGGCTAC F10: max_frac_infectivity_per_viral_barcode_well
  TCTTAGTCCTCGTATG F10: max_frac_infectivity_per_viral_barcode_well
  CGGGAATCTCCCATAC F10: max_frac_infectivity_per_viral_barcode_well
  AGGTTCAGACTCTTGC F10: max_frac_infectivity_per_viral_barcode_well
  TCTAACTCTCGCGGCA F10: max_frac_infectivity_per_viral_barcode_well
  GAGCTTGCTATGGATC F10: max_frac_infectivity_per_viral_barcode_well
  CTTACTGCGCGAGAGT F10: max_frac_infectivity_per_viral_barcode_well
  TCGAGTTAATATGCGC F10: max_frac_infectivity_per_viral_barcode_well
  TTAATGTAGCCGCTCC F10: max_frac_infectivity_per_viral_barcode_well
  TCTCAGCTCTTAGCCG G10: max_frac_infectivity_per_viral_barcode_well
  AACACGTAGAACCGCC G10: max_frac_infectivity_per_viral_barcode_well
  GAAATCCCCAAATAAC G10: max_frac_infectivity_per_viral_barcode_well
  TATCCAAGGGACGGAC H10: max_frac_infectivity_per_viral_barcode_well
  GCCGGCGTTAGTGTCA H10: max_frac_infectivity_per_viral_barcode_well
  GAAATCCCCAAATAAC B11: max_frac_infectivity_per_viral_barcode_well
  CAGATAGTGATGAACA D11: max_frac_infectivity_per_viral_barcode_well
  TATCCAAGGGACGGAC D11: max_frac_infectivity_per_viral_barcode_well
  GACCCCTTGTAAGATG D11: max_frac_infectivity_per_viral_barcode_well
  CAATTCGCCGTTCCCC D11: max_frac_infectivity_per_viral_barcode_well
  CTTACAGAATACTAGA F11: max_frac_infectivity_per_viral_barcode_well
  ACAGTACGATCTACGC F11: max_frac_infectivity_per_viral_barcode_well
  AACCACCCCAGAGATG F11: max_frac_infectivity_per_viral_barcode_well
  TTTATATCGAGATTCA F11: max_frac_infectivity_per_viral_barcode_well
  TCGAGTTAATATGCGC F11: max_frac_infectivity_per_viral_barcode_well
  GTAGATACTAGGACCA G11: max_frac_infectivity_per_viral_barcode_well
  CGGGAATCTCCCATAC G11: max_frac_infectivity_per_viral_barcode_well
  CACCGCGCCGAGCACC G11: max_frac_infectivity_per_viral_barcode_well
  TCTAACTCTCGCGGCA G11: max_frac_infectivity_per_viral_barcode_well
  ACCGATTCACGAATAA G11: max_frac_infectivity_per_viral_barcode_well
  TCGAGTTAATATGCGC G11: max_frac_infectivity_per_viral_barcode_well
  ATAGAATCGCAAATTA H11: max_frac_infectivity_per_viral_barcode_well
  GACCCCTTGTAAGATG H11: max_frac_infectivity_per_viral_barcode_well
  CGTACGTATGTCCCAG H11: max_frac_infectivity_per_viral_barcode_well
barcode_serum_replicates:
  CACCAATCTTCGAACT NIID_16: min_dilutions_per_barcode_serum_replicate
  AACGGTTCCGACTAAG NIID_12: goodness_of_fit
  AGTGTTGGCTTGGTTA NIID_12: goodness_of_fit
  ATTAGATTATAACGTA NIID_12: goodness_of_fit
  ATTAGGGCTACGTGAG NIID_12: goodness_of_fit
  CAAAATCTACGGCGAC NIID_12: goodness_of_fit
  CCCTTTACGGATCTCT NIID_12: goodness_of_fit
  CCTTGATGCATTCCCG NIID_12: goodness_of_fit
  CGCACTTTACGAGACA NIID_12: goodness_of_fit
  CGTACGTATGTCCCAG NIID_12: goodness_of_fit
  CGTTAACGGCCTATCC NIID_12: goodness_of_fit
  CTATAAACCGTTTGTA NIID_12: goodness_of_fit
  CTTTTCTAGTACGCTT NIID_12: goodness_of_fit
  GCAGCGTGCCGGTCAT NIID_12: goodness_of_fit
  GTCGCCGCTAATCCGA NIID_12: goodness_of_fit
  TTTCACAGAACCTATC NIID_12: goodness_of_fit
  AAAGCTCTTTTCGTTC NIID_13: goodness_of_fit
  AAGAAGACTTTGTGAT NIID_13: goodness_of_fit
  ACCGTTGTACACACCA NIID_13: goodness_of_fit
  ACGGGGATTGGCTGTT NIID_13: goodness_of_fit
  AGACCGCCAGTTTCGT NIID_13: goodness_of_fit
  AGGAAAGAAACTGGAG NIID_13: goodness_of_fit
  AGTGTTGAATAGGCGA NIID_13: goodness_of_fit
  AGTTTTTATAACTTGC NIID_13: goodness_of_fit
  ATAACGTTTGTGCAAA NIID_13: goodness_of_fit
  ATACACGCATGTGCCA NIID_13: goodness_of_fit
  ATTATCATATCTAATA NIID_13: goodness_of_fit
  CAAAAGCAGCACGATA NIID_13: goodness_of_fit
  CAAAATCTACGGCGAC NIID_13: goodness_of_fit
  CCGCATTAGCGGGAGG NIID_13: goodness_of_fit
  CGCACTTTACGAGACA NIID_13: goodness_of_fit
  CGGACCCTAGATGGTA NIID_13: goodness_of_fit
  CTAGCACAGCGTAGGC NIID_13: goodness_of_fit
  CTATATTGCCCGGAAG NIID_13: goodness_of_fit
  CTTACTGCGCGAGAGT NIID_13: goodness_of_fit
  GAAAGTCCCTATGATG NIID_13: goodness_of_fit
  GATCGCCACTGATAAG NIID_13: goodness_of_fit
  GCAAACAGTGTAGTTG NIID_13: goodness_of_fit
  GTCGCATCCTGGAATG NIID_13: goodness_of_fit
  GTGAGCGAGAAAAGCA NIID_13: goodness_of_fit
  TATTCCTAACTAGCGA NIID_13: goodness_of_fit
  TCACGACTCGACTAAC NIID_13: goodness_of_fit
  TCGCTTCAACTAAAAA NIID_13: goodness_of_fit
  TGTTGAGCCAGTCTGA NIID_13: goodness_of_fit
  TTGACTCACCGAATAA NIID_13: goodness_of_fit
  TTTATATCCAACACCA NIID_13: goodness_of_fit
  TTTCACAGAACCTATC NIID_13: goodness_of_fit
  AAAGCTCTTTTCGTTC NIID_14: goodness_of_fit
  GAAAGCCCCGTGCAAT NIID_14: goodness_of_fit
  TATTCCTAACTAGCGA NIID_14: goodness_of_fit
  CACCAATCTTCGAACT NIID_15: goodness_of_fit
  GATCACGCAGAAAAAG NIID_15: goodness_of_fit
  TACAAGAGAGGGGTCC NIID_15: goodness_of_fit
  TTAATGTAGCCGCTCC NIID_15: goodness_of_fit
  GCCATTTACTGAAGGG NIID_16: goodness_of_fit
  TGTGGAGCGCCCTTAC NIID_16: goodness_of_fit
  GTAATTCGCATGCGGA NIID_17: goodness_of_fit
  ACACGGGTTGGCTGTA NIID_18: goodness_of_fit
  TTAATGTAGCCGCTCC NIID_18: goodness_of_fit
  ATTAGGGCTACGTGAG NIID_19: goodness_of_fit
  CCAAGCTTGGCGCATC NIID_19: goodness_of_fit
  CCGCGCACGTTTAGAG NIID_19: goodness_of_fit
  CGAAACACGTCCCAGT NIID_19: goodness_of_fit
  TTTCACAGAACCTATC NIID_19: goodness_of_fit
  CGAACCGCAGACACGT NIID_20: goodness_of_fit
  ACCGTTGTACACACCA NIID_21: goodness_of_fit
  CGGGGACAAGATTGTA NIID_21: goodness_of_fit
  CTCCTAGGGGACGATT NIID_21: goodness_of_fit
  TAAAAAGCCTCCATGA NIID_21: goodness_of_fit
  TTGACTCACCGAATAA NIID_21: goodness_of_fit
  CATAAAAGACTGTATA NIID_22: goodness_of_fit
  TACTAGCAATAAAATC NIID_22: goodness_of_fit
serum_replicates: {}