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* upgrade to `neutcurve 0.10.1 * all serum titers adjusted to pass QC * mostly complete `aggregate_titers` * complete `aggregate_titers` * document aggregate titers * lint and format * save all results in test Github Actions artifact * update config to pass tests and plot QC failure in `serum_titers` in a clearer-to-look-at order
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@@ -21,4 +21,4 @@ dependencies: | |
| - ruff | ||
| - pip: | ||
| - dms_variants==1.4.3 | ||
| - neutcurve==0.10.0 | ||
| - neutcurve==0.10.1 | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,232 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "abe395dc-8a57-4ee8-b22e-8a1acacf1619", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Aggregate titers across all sera\n", | ||
| "Aggregate the titers across all sera, failing if there are QC failures for any individual sera." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "f404cc02-6d9a-4a3a-892e-fd73f1e00b52", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import pickle\n", | ||
| "\n", | ||
| "import altair as alt\n", | ||
| "\n", | ||
| "import neutcurve\n", | ||
| "\n", | ||
| "import pandas as pd\n", | ||
| "\n", | ||
| "_ = alt.data_transformers.disable_max_rows()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "be4112bc-7d79-4b38-8e76-ec81588f40e9", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Get variables from `snakemake`:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "83425f55-9905-4cc0-b42c-440926e9a81e", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "qc_failures_file = snakemake.input.qc_serum_titer_failures\n", | ||
| "input_pickles = snakemake.input.pickles\n", | ||
| "input_titers = snakemake.input.titers\n", | ||
| "output_pickle = snakemake.output.pickle\n", | ||
| "output_titers = snakemake.output.titers\n", | ||
| "titers_chart_html = snakemake.output.titers_chart\n", | ||
| "viral_strain_plot_order = snakemake.params.viral_strain_plot_order" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "d3668ae9-ad6d-436e-8e7b-9a0cd3b30548", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Check for quality control failures for any individual sera:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "1fbc6445-bbd0-4d7e-b50e-a861e03c06b6", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "with open(qc_failures_file) as f:\n", | ||
| " qc_failures = f.readlines()\n", | ||
| "if not all(line.strip().endswith(\"serum passed all QC\") for line in qc_failures):\n", | ||
| " raise ValueError(\n", | ||
| " f\"QC failures for some serum titers. See {qc_failures_file}:\\n\\n\"\n", | ||
| " + \"\".join(qc_failures)\n", | ||
| " )\n", | ||
| "else:\n", | ||
| " print(\"All serum titers pass the QC filters.\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "7a89d9f5-a789-454b-9ec6-3186a5d55c7b", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Get the merged titers and merged `CurveFits` object:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "dda8dec6-8981-4f10-9fe2-afc35066929e", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "assert len(input_titers) == len(input_pickles)\n", | ||
| "\n", | ||
| "titers = pd.concat([pd.read_csv(f) for f in input_titers], ignore_index=True)\n", | ||
| "assert len(titers) == len(titers.groupby([\"serum\", \"virus\"]))\n", | ||
| "print(f\"Writing aggregated titers to {output_titers}\")\n", | ||
| "titers.to_csv(output_titers, index=False, float_format=\"%.4g\")\n", | ||
| "\n", | ||
| "fits_list = []\n", | ||
| "for fname in input_pickles:\n", | ||
| " with open(fname, \"rb\") as f:\n", | ||
| " fits_list.append(pickle.load(f))\n", | ||
| "curvefits = neutcurve.CurveFits.combineCurveFits(fits_list)\n", | ||
| "print(f\"Pickling aggregated `CurveFits` to {output_pickle}\")\n", | ||
| "with open(output_pickle, \"wb\") as f:\n", | ||
| " pickle.dump(curvefits, f)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "e194786d-c227-403f-bbf9-d8ab3b19b502", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Plot all the titers:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "eb2c2a97-e54a-4875-bc75-d57975017565", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "viruses = [v for v in viral_strain_plot_order if v in curvefits.allviruses]\n", | ||
| "\n", | ||
| "sera = curvefits.sera\n", | ||
| "\n", | ||
| "virus_selection = alt.selection_point(fields=[\"virus\"], on=\"mouseover\", empty=False)\n", | ||
| "\n", | ||
| "serum_selection = alt.selection_point(\n", | ||
| " fields=[\"serum\"],\n", | ||
| " bind=\"legend\",\n", | ||
| " toggle=\"true\",\n", | ||
| ")\n", | ||
| "\n", | ||
| "ncols = 8\n", | ||
| "\n", | ||
| "titers_chart = (\n", | ||
| " alt.Chart(titers)\n", | ||
| " .add_params(virus_selection, serum_selection)\n", | ||
| " .transform_filter(serum_selection)\n", | ||
| " .encode(\n", | ||
| " alt.X(\n", | ||
| " \"nt50\",\n", | ||
| " title=\"neutralization titer\",\n", | ||
| " scale=alt.Scale(nice=False, padding=4, type=\"log\"),\n", | ||
| " axis=alt.Axis(labelOverlap=True),\n", | ||
| " ),\n", | ||
| " alt.Y(\"virus\", sort=viruses),\n", | ||
| " alt.Facet(\n", | ||
| " \"serum\",\n", | ||
| " header=alt.Header(\n", | ||
| " title=None, labelFontSize=11, labelFontStyle=\"bold\", labelPadding=0\n", | ||
| " ),\n", | ||
| " spacing=3,\n", | ||
| " columns=ncols,\n", | ||
| " ),\n", | ||
| " alt.StrokeWidth(\n", | ||
| " \"serum:N\",\n", | ||
| " scale=alt.Scale(domain=sera, range=[1] * len(sera)),\n", | ||
| " legend=alt.Legend(\n", | ||
| " orient=\"bottom\",\n", | ||
| " columns=ncols,\n", | ||
| " symbolFillColor=\"black\",\n", | ||
| " title=\"serum (click to select)\",\n", | ||
| " ),\n", | ||
| " ),\n", | ||
| " color=alt.condition(virus_selection, alt.value(\"red\"), alt.value(\"black\")),\n", | ||
| " tooltip=[\n", | ||
| " \"serum\",\n", | ||
| " \"virus\",\n", | ||
| " alt.Tooltip(\"nt50\", title=\"NT50\", format=\".3g\"),\n", | ||
| " \"n_replicates\",\n", | ||
| " ],\n", | ||
| " )\n", | ||
| " .mark_line(point=True)\n", | ||
| " .configure_axis(grid=False)\n", | ||
| " .configure_point(size=45)\n", | ||
| " .properties(\n", | ||
| " height=alt.Step(11),\n", | ||
| " width=100,\n", | ||
| " title=alt.TitleParams(\n", | ||
| " \"Interactive chart of serum neutralization titers\",\n", | ||
| " subtitle=\"Mouseover points for details, click serum legend at bottom to select sera to show\",\n", | ||
| " fontSize=15,\n", | ||
| " dx=100,\n", | ||
| " dy=-5,\n", | ||
| " ),\n", | ||
| " autosize=alt.AutoSizeParams(resize=True),\n", | ||
| " )\n", | ||
| ")\n", | ||
| "\n", | ||
| "print(f\"Saving chart to {titers_chart_html}\")\n", | ||
| "titers_chart.save(titers_chart_html)\n", | ||
| "\n", | ||
| "titers_chart" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "27ff41ab-c967-42a9-adb1-5856810c7f34", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3 (ipykernel)", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.11.6" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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