tests/noepistasisbottlenecklikelihood_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `NoEpistasisBottleneckLikelihood` model\n",
    "Simulates some variants and analyzes them under the `globalepistasis.NoEpistasisBottleneckLikelihood` model.\n",
    "\n",
    "This notebook is designed as a test, not for user documentation--and it uses some private methods that should not be used in real analyses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up\n",
    "Import Python modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "import math\n",
    "import random\n",
    "import time\n",
    "import warnings\n",
    "\n",
    "import binarymap\n",
    "\n",
    "import numpy\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from plotnine import *\n",
    "\n",
    "import scipy\n",
    "\n",
    "import dms_variants.globalepistasis\n",
    "import dms_variants.codonvarianttable\n",
    "import dms_variants.simulate\n",
    "from dms_variants.constants import CBPALETTE, CODONS_NOSTOP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parameters for simulations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 1  # random number seed\n",
    "genelength = 25  # gene length in codons\n",
    "bclen = 16  # length of barcodes\n",
    "libs = [\"lib_1\", \"lib_2\"]  # distinct libraries of gene\n",
    "variants_per_lib = 500 * genelength  # variants per library\n",
    "avgmuts = 2.5  # average codon mutations per variant\n",
    "variant_error_rate = 0  # rate variants are mis-sequenced\n",
    "depth = 1000 * variants_per_lib  # per-sample sequencing depth\n",
    "lib_uniformity = 5  # uniformity of library pre-selection\n",
    "noise = 0.0  # non-statistical noise in selections\n",
    "bottleneck = variants_per_lib * 5\n",
    "bottlenecks = {  # bottlenecks from pre- to post-selection\n",
    "    \"loose_bottle\": bottleneck,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seed random number generators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(seed)\n",
    "numpy.random.seed(seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppress warnings that clutter output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulate variants\n",
    "Simulate wildtype gene sequence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wildtype gene of 25 codons:\n",
      "AGATCCGTGATTCTGCGTGCTTACACCAACTCACGGGTGAAACGTGTAATCTTATGCAACAACGACTTACCTATC\n"
     ]
    }
   ],
   "source": [
    "geneseq = \"\".join(random.choices(CODONS_NOSTOP, k=genelength))\n",
    "print(f\"Wildtype gene of {genelength} codons:\\n{geneseq}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simulate a `CodonVariantTable`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "variants = dms_variants.simulate.simulate_CodonVariantTable(\n",
    "    geneseq=geneseq,\n",
    "    bclen=bclen,\n",
    "    library_specs={\n",
    "        lib: {\"avgmuts\": avgmuts, \"nvariants\": variants_per_lib} for lib in libs\n",
    "    },\n",
    "    seed=seed,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of amino-acid mutations per variant in library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 550x210 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "p = variants.plotNumMutsHistogram(\"aa\", samples=None, max_muts=8)\n",
    "p = p + theme(panel_grid_major_x=element_blank())  # no vertical grid lines\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulate variant counts\n",
    "We will simulate using a `SigmoidPhenotypeSimulator`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "phenosimulator = dms_variants.simulate.SigmoidPhenotypeSimulator(\n",
    "    geneseq,\n",
    "    seed=seed,\n",
    "    norm_weights=((0.4, -0.5, 0.7), (0.6, -5, 1.5)),\n",
    "    stop_effect=-9,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But since we are using a non-epistatic (linear model), we will simulate variants according to the *latent phenotype* rather than their *observed phenotype* (since in a non-epistatic model these two are the same).\n",
    "First, look at the distribution of latent phenotypes for all single mutants (the dashed orange line is the latent phenotype of wildtype):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "p = phenosimulator.plotMutsHistogram(\"latentPhenotype\")\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our simulation function is then the exponential of the latent phenotype, as mutants as the functional scores represent the log enrichment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def phenotype_func(x):\n",
    "    return math.exp(phenosimulator.latentPhenotype(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we simulate the counts, passing to the simulate function the latent rather than observed phenotype:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = dms_variants.simulate.simulateSampleCounts(\n",
    "    variants=variants,\n",
    "    phenotype_func=phenotype_func,\n",
    "    variant_error_rate=variant_error_rate,\n",
    "    pre_sample={\"total_count\": depth, \"uniformity\": lib_uniformity},\n",
    "    pre_sample_name=\"pre-selection\",\n",
    "    post_samples={\n",
    "        name: {\"noise\": noise, \"total_count\": depth, \"bottleneck\": bottle}\n",
    "        for name, bottle in bottlenecks.items()\n",
    "    },\n",
    "    seed=seed,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the counts to the variant table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "variants.add_sample_counts_df(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confirm expected selection on mutations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 520x360 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "p = variants.plotNumCodonMutsByType(\"all\")\n",
    "p = p + theme(panel_grid_major_x=element_blank())  # no vertical grid lines\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get functional scores\n",
    "Get the functional scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_scores = variants.func_scores(\"pre-selection\", libraries=variants.libraries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Classify the functional scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_scores = variants.classifyVariants(func_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `plotnine` to plot distribution of functional scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 550x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "p = (\n",
    "    ggplot(func_scores, aes(\"variant_class\", \"func_score\"))\n",
    "    + geom_violin(aes(fill=\"variant_class\"))\n",
    "    + ylab(\"functional score\")\n",
    "    + xlab(\"\")\n",
    "    + facet_grid(\"post_sample ~ library\")\n",
    "    + theme(\n",
    "        figure_size=(2.75 * len(libs), 2 * len(bottlenecks)),\n",
    "        axis_text_x=element_text(angle=90),\n",
    "        panel_grid_major_x=element_blank(),  # no vertical grid lines\n",
    "    )\n",
    "    + scale_fill_manual(values=CBPALETTE[1:])\n",
    ")\n",
    "_ = p.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a binary map for the variants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of binary map is 500\n"
     ]
    }
   ],
   "source": [
    "binarymap = binarymap.BinaryMap(func_scores)\n",
    "print(f\"Length of binary map is {binarymap.binarylength}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now initialize the `NoEpistasisBottleneckLikelihood` model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = dms_variants.globalepistasis.NoEpistasisBottleneckLikelihood(\n",
    "    binarymap, bottleneck=bottleneck\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at some model attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "501"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.nparams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "for param, val in model.likelihood_calc_params_dict.items():\n",
    "    print(f\"{param}: {val:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert numpy.allclose(model.loglik, -116908, rtol=0.01, atol=3), model.loglik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.latent_phenotype_wt()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the analytical gradient versus numerical approximation, and make sure the difference is small compared to total gradient:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "err = scipy.optimize.check_grad(\n",
    "    model._loglik_by_allparams, model._dloglik_by_allparams, model._allparams\n",
    ")\n",
    "tot = math.sqrt(sum(model._dloglik_by_allparams(model._allparams) ** 2))\n",
    "if err / tot > 1e-5:\n",
    "    raise ValueError(\n",
    "        f\"Numerical and analytical gradients differ: {err}, {tot}, {err / tot}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial least squares fitting\n",
    "Perform initial least squares optimization (normally this is actually performed by the `fit` method):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = model._fit_latent_leastsquares()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Re-check the gradients:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "err = scipy.optimize.check_grad(\n",
    "    model._loglik_by_allparams, model._dloglik_by_allparams, model._allparams\n",
    ")\n",
    "tot = math.sqrt(sum(model._dloglik_by_allparams(model._allparams) ** 2))\n",
    "if err / tot > 1e-3:\n",
    "    raise ValueError(\n",
    "        f\"Numerical and analytical gradients differ: {err}, {tot}, {err / tot}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert numpy.allclose(model.loglik, -1072778, rtol=0.01, atol=3), model.loglik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R = 0.8, P = 0\n"
     ]
    }
   ],
   "source": [
    "r, p = scipy.stats.pearsonr(\n",
    "    binarymap.func_scores,\n",
    "    model.phenotypes_frombinary(binarymap.binary_variants, \"latent\"),\n",
    ")\n",
    "print(f\"R = {r:.1f}, P = {p:.2g}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"actual\": binarymap.func_scores,\n",
    "        \"predicted\": model.phenotypes_frombinary(binarymap.binary_variants, \"latent\"),\n",
    "    }\n",
    ")\n",
    "\n",
    "_ = (ggplot(df, aes(\"actual\", \"predicted\")) + geom_bin2d()).draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "for param, val in model.likelihood_calc_params_dict.items():\n",
    "    print(f\"{param}: {val:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.6"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(model.latent_phenotype_wt(), 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit the full model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now fit the model by maximum likelihood.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting took 0.570 seconds\n"
     ]
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "start = time.time()\n",
    "optres = model.fit()\n",
    "print(f\"Fitting took {time.time() - start:.3f} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert numpy.allclose(model.loglik, -54492, rtol=0.01, atol=3), model.loglik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "for param, val in model.likelihood_calc_params_dict.items():\n",
    "    print(f\"{param}: {val:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(model.latent_phenotype_wt(), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R = 0.7, P = 0\n"
     ]
    }
   ],
   "source": [
    "r, p = scipy.stats.pearsonr(\n",
    "    binarymap.func_scores,\n",
    "    model.phenotypes_frombinary(binarymap.binary_variants, \"latent\"),\n",
    ")\n",
    "print(f\"R = {r:.1f}, P = {p:.2g}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"actual\": binarymap.func_scores,\n",
    "        \"predicted\": model.phenotypes_frombinary(binarymap.binary_variants, \"latent\"),\n",
    "    }\n",
    ")\n",
    "\n",
    "_ = (ggplot(df, aes(\"actual\", \"predicted\")) + geom_bin2d()).draw()"
   ]
  }
 ],
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   "display_name": "Python 3 (ipykernel)",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
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  "toc": {
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   "toc_position": {
    "height": "1033.71px",
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   "toc_section_display": "block",
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