Process plate counts to get fraction infectivities and fit curves

This report analyzes a plate of sequencing-based neutralization assays.

The plots 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.

Processing plate8

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: []

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.

Read barcode counts and apply manually specified drops

Read the counts per barcode, then apply any manually specified drops.

No drops specified in manual_drops

Collapse the barcodes for each strain

If collapse_strain_barcodes is set in the configuration, sum the counts of all of a strain's barcodes, so that everything below is per strain rather than per barcode.

Collapsed the 301 viral barcodes into 148 strains, with 1 to 3 barcodes per strain.

Everything below is therefore per strain even where it is labeled as being per barcode, since each strain now has a single barcode named for the strain. In particular, any QC threshold on the counts or the fraction of counts for a barcode now applies to the summed counts of all of a strain's barcodes.

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:

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.

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 reason 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.

=========================================================================================

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.

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.

Dropping 0 barcode-wells for failing qc_thresholds['min_no_serum_count_per_viral_barcode_well']=100: []

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:

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

Dropping 2 barcode-wells for failing qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=3: [('A/Galicia/GA-CHUAC-612/2025_H1N1', 'H11'), ('A/Galicia/GA-CHUAC-612/2025_H1N1', 'D11')]

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

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

Fit neutralization curves without applying QC to curves

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

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):

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:

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': []}

Curves for virus vs serum-replicates with at least one failed barcode.

Color key labels indicate if barcodes failed or passed QC.

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

Dropping 11 barcode/serum-replicates for failing goodness_of_fit={'min_R2': 0.5, 'max_RMSD': 0.15}: [('A/Hawaii/ISC-1140/2025_H1N1', 'flu-seqneut-2026-UWMC-016'), ('A/NewYork/NIRC-IS-1086/2026_H3N2', 'flu-seqneut-2026-UWMC-016'), ('A/South_Australia/2527213276/2025_H3N2', 'flu-seqneut-2026-UWMC-016'), ('A/StPetersburg/RII-25-2600S/2026_H3N2', 'flu-seqneut-2026-UWMC-016'), ('A/Thuringen/39/2026_H3N2', 'flu-seqneut-2026-UWMC-016'), ('A/Badajoz/18811358/2026_H3N2', 'flu-seqneut-2026-UWMC-018'), ('A/Colorado/218/2024_H1N1', 'flu-seqneut-2026-UWMC-020'), ('A/Uppsala/SE26-01293/2026_H1N1', 'flu-seqneut-2026-UWMC-021'), ('A/South_Australia/2527213276/2025_H3N2', 'flu-seqneut-2026-UWMC-024'), ('A/Missouri/11/2025_cell_H1N1', 'flu-seqneut-2026-UWMC-026'), ('A/South_Australia/2527213276/2025_H3N2', 'flu-seqneut-2026-UWMC-026')]

Fit neutralization curves after applying QC

No we re-fit and plot curves after applying all the QC.

Assigning fits for this plate to human