vlpi

Variational Latent Phenotype Model

https://github.com/daverblair/vlpi

Science Score: 54.0%

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Variational Latent Phenotype Model

Basic Info
  • Host: GitHub
  • Owner: daverblair
  • License: mit
  • Language: Python
  • Default Branch: master
  • Size: 30.4 MB
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  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created over 6 years ago · Last pushed about 2 years ago
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Readme License Citation

README.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variational Latent Phenotype Inference (vLPI)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The vlpi software package implements the latent phenotype model described in Blair et al, which infers the cryptic, quantitative traits that underlie some set of observed disease symptoms. Details concerning the implementation of the model and inference algorithm can be found in the Supplementary Materials of Blair et al. Please contact david.blair@ucsf.edu with any questions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The software package can be installed using pip by running the following command:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "pip install vlpi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The software package is essentially broken into two sections. The first implements a data structure (ClinicalDataset class) that efficiently stores and manipulates large clinical datasets. It is essentially a sparse binary array with added functionality that automates many tasks, such as constructing training and validation splits and converting among different symptom encodings. In addition, another class (ClinicalDatasetSampler) is used to efficiently generate random subsets of the ClinicalDataset, which is important for training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second part of the software package, the vLPI class, implements the model fitting itself using a stochastic, amortized variational inference algorithm (see Blair et al. for details). It requires a ClinicalDataset class passed in the form of a ClinicalDatasetSampler. Below, we provide an example of how to use the software package by simulating a relatively simple symptom dataset. Further details regarding the package and it's functionality can be found by reading the source code documentation associated with the individual functions and classes.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will import the functions required for dataset simulation and inference from the vlpi package. Note, torch and string are imported simply to assist with the simulation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from vlpi.data.ClinicalDataset import ClinicalDataset,ClinicalDatasetSampler\n",
    "from vlpi.data.ClinicalDataSimulator import ClinicalDataSimulator\n",
    "from vlpi.vLPI import vLPI\n",
    "import torch\n",
    "import string\n",
    "\n",
    "seed=torch.manual_seed(1023)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the ClinicalDataSimulator class, simulating a clinical dataset of binary symptoms is straightforward. Additional details regarding the functionality of ClinicalDataSimulator are provided in the source code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "numberOfSamples=50000\n",
    "numberOfSymptoms=20\n",
    "\n",
    "rareDiseaseFrequency=0.001\n",
    "numLatentPhenotypes=2\n",
    "simulator = ClinicalDataSimulator(numberOfSymptoms,numLatentPhenotypes,rareDiseaseFrequency)\n",
    "simulatedData=simulator.GenerateClinicalData(numberOfSamples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simulatedData returned by ClinicalDataSimulator is a nested dictionary of results, but to use the vLPI model, we need to get this information into a ClinicalDataset. First, we initialize an empty dataset, which by default is constructed using the full ICD10-CM codebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "clinicalData = ClinicalDataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most applications, however, do not require the complete codebook. In fact, we recommend against trying to fit this model to more than 10-100 hundred symptoms, as it unlikely to be able to reliably tease apart such a complex latent structure. Therefore, a ClinicalDataset can be a priori manipulated such that it aligns to a different encoding, which is what we do below. Note, the ClinicalDataset class can also read a dataset directly from a text file (see ClinicalDataset.ReadDatasetFromFile), and this should work for arbitrary encodings as long as the ClinicalDataset class is set up appropriately. However, we have only tested this function on raw, ICD10-CM encoded datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4, 'F': 5, 'G': 6, 'H': 7, 'I': 8, 'J': 9, 'K': 10, 'L': 11, 'M': 12, 'N': 13, 'O': 14, 'P': 15, 'Q': 16, 'R': 17, 'S': 18, 'T': 19}\n"
     ]
    }
   ],
   "source": [
    "allICDCodes = list(clinicalData.dxCodeToDataIndexMap.keys())\n",
    "symptomConversionMap=dict(zip(allICDCodes[0:numberOfSymptoms],string.ascii_uppercase[0:numberOfSymptoms]))\n",
    "clinicalData.ConstructNewDataArray(symptomConversionMap)\n",
    "print(clinicalData.dxCodeToDataIndexMap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can load our simulated symptom data into the ClinicalDataset. Note, the empty list argument would normally contain a list of covariate names, but since we didn't simulate any covariates, there are no names to provide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "clinicalData.LoadFromArrays(simulatedData['incidence_data'],simulatedData['covariate_data'],[],catCovDicts=None, arrayType = 'Torch')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is contruct a ClinicalDatasetSampler class from the ClinicalDataset, which enables the stochastic sampling that is required for inference. To do so, you must specify a fraction of the dataset to withhold for testing/validation. Note, there is also a way to write ClinicalDataset and ClinicalDatasetSamplers to disk, that way the same dataset and sampler class can be reloaded to ensure replicability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data_fraction=0.75\n",
    "sampler = ClinicalDatasetSampler(clinicalData,training_data_fraction,returnArrays='Torch')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we're ready to perform model inference. Technically, only the initial number of latent phenotypes needs to be specified, although there are additional optional arguments as well (see source code). We've really only tested the model on symptoms sets that likely contain <10 latent phenotypes. The model may be effective at inferring more complex structures, but we have not thoroughly tested this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "infNumberOfLatentPhenotypes=10\n",
    "vlpiModel= vLPI(sampler,infNumberOfLatentPhenotypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting the model is very straightforward, although there are multiple hyper-parameters (learning rate, batch size, max epochs, etc) that can be changed from their baseline values. The default hyperparameters and different options used in practice are described in Blair et al. We always recommend saving any model that was successfully fit using the vLPI.PackageModel function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference complete. Final Loss:   80850.05\n"
     ]
    }
   ],
   "source": [
    "inference_output = vlpiModel.FitModel(batch_size=1000,errorTol=(1.0/numberOfSamples),verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Now, we can check model fit with little bit of visualization. We'll use the matplotlib and seaborn plotting libraries for this, which are not included with the vlpi package. First, we can track the loss function on the held-out testing data, where the loss function is the negative evidence lower bound. We dropped the first 20 epochs because the they obscure the rest of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "from matplotlib import cm\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "sns.set(context='talk',color_codes=True,style='ticks',font='Arial',font_scale=2.5,rc={'axes.linewidth':5,\"font.weight\":\"bold\",'axes.labelweight':\"bold\",'xtick.major.width':4,'xtick.minor.width': 2})\n", "cmap = cm.get_cmap('viridis', 12)\n", "color_list=[cmap(x) for x in [0.0,0.1,0.25,0.5,0.75,0.9,1.0]]\n", "\n", "sns.lineplot(x=range(1,len(inference_output[2])+1)[20:],y=inference_output[2][20:],color=color_list[0],lw=3.0)\n", "o=plt.xlabel('Epoch')\n", "o=plt.ylabel('-ELBO')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Convergence appears to have been attained, but we can also compare the simulated and inferred symptom risk functions. If the inference algorithm is converging to the correct mode, then the two functions should be nearly identical. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f,axes = plt.subplots(1, 2,figsize=(16,8))\n", "for ax in axes:\n", " ax.tick_params(axis='x',which='both',bottom=False,top=False,left=False,right=False,labelbottom=False)\n", " ax.tick_params(axis='y',which='both',left=False,right=False,bottom=False,top=False,labelleft=False)\n", "\n", "infRiskFunc=vlpiModel.ReturnComponents()\n", "simRiskFunc=simulatedData['model_params']['latentPhenotypeEffects']\n", "im1=axes[0].imshow(simRiskFunc,cmap=cmap)\n", "im2=axes[1].imshow(infRiskFunc,cmap=cmap)\n", "o=axes[0].set_title('Simulated Symptom\\nRisk Function',fontsize=36)\n", "o=axes[1].set_title('Inferred Symptom\\nRisk Function',fontsize=36)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note, the inference algorithm automatically selects the appropriate number of latent phenotypes by zeroing out the parts of the risk function that correspond to the unneeded components. As a final step, we can compare the inferred latent phenotypes themselves. In this case, we simply visually match the simulated and inferred latent phenotypes based on the risk functions depicted above, but there are formal ways to align matrices of parameters (Orthogonal Procrustes Analysis, see Blair et al)." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "inferredCrypticPhenotypes=vlpiModel.ComputeEmbeddings((simulatedData['incidence_data'],simulatedData['covariate_data']))\n", "f,axes = plt.subplots(1, 2,figsize=(16,8))\n", "f.tight_layout(pad=3.0)\n", "\n", "sns.scatterplot(simulatedData['latent_phenotypes'][:,0],inferredCrypticPhenotypes[:,-1],color=color_list[0],ax=axes[0])\n", "sns.scatterplot(simulatedData['latent_phenotypes'][:,1],inferredCrypticPhenotypes[:,-4],color=color_list[2],ax=axes[1])\n", "axes[0].plot([-3,3],[-3,3],'--',lw=5.0,color='r')\n", "axes[1].plot([-3,3],[-3,3],'--',lw=5.0,color='r')\n", "\n", "o=axes[0].set_xlabel('Simulated Latent\\nPhenotype 1',fontsize=20)\n", "o=axes[1].set_xlabel('Simulated Latent\\nPhenotype 2',fontsize=20)\n", "\n", "o=axes[0].set_ylabel('Inferred Latent\\nPhenotype 10',fontsize=20)\n", "o=axes[1].set_ylabel('Inferred Latent\\nPhenotype 7',fontsize=20)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clearly, the inferred and simulated latent phenotypes are highly correlated. However, there is a fair amount of noise associated with the inferred latent phenotypes, and in addition, there are floor/ceiling effects. These reflect a loss of information that occurs when continuous traits are transformed into noisy, binary symptoms. This noise level is greatly reduced by simulating datasets with hundreds of symptoms, although this is not a realistic clinical scenario. " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 4 }

Citation (CITATION.CFF)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Blair"
  given-names: "David Randall"
  orcid: "https://orcid.org/0000-0002-7455-6488"
title: "variational Latent Phenotype Inference (vLPI)"
version: 0.1.8
doi: 10.5281/zenodo.6459597
date-released: 2022-04-13
url: "https://github.com/daverblair/vlpi"

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Dependencies

setup.py pypi
  • numpy >=1.19.0
  • pandas >=1.0.5
  • pyro-ppl >=1.3.1
  • scikit-learn >=0.22.1
  • scipy >=1.5.2
  • torch ==1.5.1
  • typing *
  • unidecode *
vlpi.egg-info/requires.txt pypi
  • numpy >=1.19.0
  • pandas >=1.0.5
  • pyro-ppl >=1.3.1
  • scikit-learn >=0.22.1
  • scipy >=1.5.2
  • torch ==1.5.1
  • typing *
  • unidecode *