{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing data\n",
    "================\n",
    "\n",
    "With your data loaded using an RNAvigate `Sample` object, you can start\n",
    "creating visualizations and analyses. This document will show you what types of\n",
    "plots can be created and where to find more information.\n",
    "\n",
    "Contents:\n",
    "\n",
    "- [Alignment plots](#alignment-plots)\n",
    "- [Skyline plots](#skyline-plots)\n",
    "- [Profile plots](#profile-plots)\n",
    "- [Arc plots](#arc-plots)\n",
    "- [Arc comparison plots](#arc-compare-plots)\n",
    "- [Circle plots](#circle-plots)\n",
    "- [Secondary structure diagrams](#secondary-structure-diagrams)\n",
    "- [Interactive 3d molecule renderings](#interactive-3d-molecule-renderings)\n",
    "- [Distance distribution histograms](#distance-distribution-histograms)\n",
    "- [Heatmaps and contour maps](#heatmaps-and-contour-maps)\n",
    "- [Linear regression plots](#linear-regression-plots)\n",
    "- [Receiver operator characteristic curves](#receiver-operator-characteristic-curves)\n",
    "- [Shapemapper2 quality control plots](#shapemapper2-quality-control-plots)\n",
    "- [Shapemapper2 profiles](#shapemapper2-profiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rnavigate as rnav\n",
    "from rnavigate.examples import tpp, rnasep_1, rnasep_2, rnasep_3\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alignment plots\n",
    "--------------------------------------------------\n",
    "\n",
    "[full guide](../plot_options/alignment.ipynb)\n",
    "\n",
    "Alignment plots visualize how two sets data will be positionally aligned in\n",
    "RNAvigate plots. It is a good idea to check the automatic alignment if two\n",
    "sequences differ significantly. For the most part, this is not necessary for\n",
    "deletion or point mutants or subsequences.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = rnav.plot_alignment(\n",
    "    data1=(tpp, \"dmsmap\"),\n",
    "    data2=(tpp, \"pdb\"),\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Skyline plots\n",
    "----------------------------------------------\n",
    "\n",
    "[full guide](../plot_options/skyline.ipynb)\n",
    "\n",
    "Skyline plots flexibly display and compare per-nucleotide data sets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = rnav.plot_skyline(\n",
    "    samples=[tpp],\n",
    "    profile=\"dmsmap\",\n",
    "    columns=[\"Modified_rate\", \"Untreated_rate\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Profile plots\n",
    "----------------------------------------------\n",
    "\n",
    "[full guide](../plot_options/profile.ipynb)\n",
    "\n",
    "Profile plots display per-nucleotide data as colored bar graphs similar to ShapeMapper style bar graphs, but very flexible.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = rnav.plot_profile(\n",
    "    samples=[tpp],\n",
    "    profile=\"dmsmap\",\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Arc plots\n",
    "--------------------------------------\n",
    "\n",
    "[full guide](../plot_options/arc.ipynb)\n",
    "\n",
    "Arc plots flexibly display inter-nucleotide relationships and secondary\n",
    "structures as arcs. Per-nucleotide measurements and sequence annotations can\n",
    "also be displayed without over-crowding.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = rnav.plot_arcs(\n",
    "    samples=[tpp],\n",
    "    sequence=\"pdb\",\n",
    "    structure=\"ss\",\n",
    "    profile=\"dmsmap\",\n",
    "    profile_scale_factor=5,\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Arc compare plots\n",
    "------------------------------------------------------\n",
    "\n",
    "[full guide](../plot_options/arc_compare.ipynb)\n",
    "\n",
    "Arc compare plots are the same as arc plots above, but compare two samples\n",
    "on the same axes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = rnav.plot_arcs_compare(\n",
    "    samples=[rnasep_1, rnasep_2],\n",
    "    sequence=\"pdb\",\n",
    "    interactions=\"ringmap\",\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Circle plots\n",
    "--------------------------------------------\n",
    "\n",
    "[full guide](../plot_options/circle.ipynb)\n",
    "\n",
    "Circle plots are similar to arc plots, but display nucleotides in a circle so\n",
    "that any size RNA fits in a square area.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = rnav.plot_circle(\n",
    "    samples=[tpp],\n",
    "    sequence=\"pdb\",\n",
    "    structure=\"ss\",\n",
    "    profile=\"dmsmap\",\n",
    "    colors={\"sequence\": \"contrast\",\n",
    "            \"nucleotides\": \"profile\"},\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Secondary structure diagrams\n",
    "--------------------------------------------------------\n",
    "\n",
    "[full guide](../plot_options/ss.ipynb)\n",
    "\n",
    "Secondary structure diagrams display per-nucleotide and inter-nucleotide\n",
    "measurements and sequence annotations on a secondary structure diagram.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plots = rnav.plot_ss(\n",
    "    samples=[tpp],\n",
    "    structure=\"ss\",\n",
    "    profile=\"dmsmap\",\n",
    "    colors={\"nucleotides\": \"profile\"},\n",
    "    nt_ticks=20\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interactive 3D molecule renderings\n",
    "---------------------------------------------------------------\n",
    "\n",
    "[full guide](../plot_options/mol.ipynb)\n",
    "\n",
    "These renderings display per-nucleotide and inter-nucleotide data on\n",
    "3D RNA molecular structures.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = rnav.plot_mol(\n",
    "    samples=[tpp],\n",
    "    structure=\"pdb\",\n",
    "    colors=\"sequence\",\n",
    "    rotation={'x': -110, 'z': -30, 'y':-10},\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distance distribution histograms\n",
    "------------------------------------------------------------------\n",
    "\n",
    "[full guide](../plot_options/disthist.ipynb)\n",
    "\n",
    "Distance distribution histograms display the 3D distance distribution of sets\n",
    "of inter-nucleotide measurements.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = rnav.plot_disthist(\n",
    "    samples=[tpp],\n",
    "    structure=\"pdb\",\n",
    "    interactions=\"ringmap\"\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heatmaps and contour maps\n",
    "----------------------------------------------------------\n",
    "\n",
    "[full guide](../plot_options/heatmap.ipynb)\n",
    "\n",
    "Heatmaps and contour maps are useful for displaying dense inter-nucleotide\n",
    "measurements or inter-nucleotide data density while highlighting defined\n",
    "regions such as helices or 3D interactions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = rnav.plot_heatmap(\n",
    "    samples=[tpp],\n",
    "    sequence=\"ss\",\n",
    "    structure=\"ss\",\n",
    "    interactions=\"ringmap\",\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear regression plots\n",
    "-------------------------------------------------------\n",
    "\n",
    "[full guide](../plot_options/linreg.ipynb)\n",
    "\n",
    "Linear regression plots quickly compare multiple sets of per-nucleotide data\n",
    "and display slope, R^2, and density.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = rnav.plot_linreg(\n",
    "    samples=[rnasep_1, rnasep_2, rnasep_3],\n",
    "    profile=\"shapemap\",\n",
    "    scale=\"log\",\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Receiver operator characteristic curves\n",
    "--------------------------------------------------------------------\n",
    "\n",
    "[full guide](../plot_options/roc.ipynb)\n",
    "\n",
    "ROC curves are a way to determine how well per-nucleotide data predict a\n",
    "classifier, such as base-paired vs. unpaired status.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = rnav.plot_roc(\n",
    "    samples=[rnasep_1, rnasep_2, rnasep_3],\n",
    "    profile=\"shapemap\",\n",
    "    structure=\"ss_ct\",\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ShapeMapper2 quality control plots\n",
    "--------------------------------------------------------------\n",
    "\n",
    "[full guide](../plot_options/qc.ipynb)\n",
    "\n",
    "ShapeMapper2 QC plots display useful quality control metrics from ShapeMapper2\n",
    "analyses.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = rnav.plot_qc(\n",
    "    samples=[rnasep_1],\n",
    "    profile=\"shapemap\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ShapeMapper2 profiles\n",
    "-------------------------------------------------\n",
    "\n",
    "[full guide](../plot_options/sm.ipynb)\n",
    "\n",
    "ShapeMapper2 profiles display normalized SHAPE profiles, mutation rates, and/or\n",
    "read depths in ShapeMapper2's default layout.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = rnav.plot_shapemapper(\n",
    "    sample=rnasep_1,\n",
    "    profile=\"shapemap\",\n",
    "    )\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "RNAvigate",
   "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}