{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Heatmap and contour plots\n", "=========================\n", "\n", "Heatmaps and contour plots are a good way to view inter-nucleotide data when\n", "the data are very dense, or when you wish to view inter-nucleotide data\n", "density. The x and y axes are nucleotide positions. XY-positions are colored by\n", "inter-nucleotide data values or density (heatmap) and overlayed with a contour\n", "map. This contour map can show either 3D distance or secondary structure graph\n", "path distance (contact distance). These countours will outline areas where\n", "nucleotide *X* is close, in 3D space or secondary structure, to nucleotide *Y*." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import rnavigate as rnav\n", "from rnavigate.examples import rnasep_1\n", "\n", "plot = rnav.plot_heatmap(\n", " samples=[rnasep_1],\n", " sequence=\"pdb\",\n", " structure=\"pdb\",\n", " interactions=\"shapejump\",\n", " plot_type=\"kde\",\n", ")\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function plot_heatmap in module rnavigate.plotting_functions:\n", "\n", "plot_heatmap(samples, sequence, structure=None, interactions=None, regions=None, labels=None, levels=None, interpolation='nearest', atom=\"O2'\", plot_type='heatmap', weights=None, rows=None, cols=None, plot_kwargs=None)\n", " Generates a multipanel plot displaying a heatmap of inter-nucleotide\n", " data (nucleotide resolution of 2D KDE) and/or contour map of pdb\n", " distances. Each plot may display a unique sample and/or filtering scheme.\n", " \n", " Parameters\n", " ----------\n", " samples : list of rnavigate Samples\n", " samples used to retrieve data\n", " sequence : data keyword string, data object, or sequence string\n", " All data are mapped to this sequence before plotting\n", " structure : data keyword string or data object, defaults to None\n", " secondary structure or 3D structure used to plot contour lines\n", " contour lines are drawn according to levels argument\n", " interactions : one of the formats below, defaults to None\n", " format 1 (data or data keyword)\n", " Interactions to plot as a heatmap, no filtering performed\n", " format 2 (dictionary)\n", " e.g. {\"interactions\": format 1}\n", " additional filtering options can be added to the dictionary\n", " format 3 (list of format 2 dictionaries)\n", " This format allows multiple filtering schemes to be applied,\n", " each will be plotted on a seperate axis\n", " regions : list of lists of 4 integers, defaults to None (no boxes)\n", " each inner list defines two regions of the RNA that are interacting\n", " a box will be drawn around this interaction on the heatmap\n", " e.g. [[10, 20, 50, 60], [35, 45, 70, 80]] draws 2 boxes\n", " the first box will connect nucleotides 10-20 and 50-60\n", " the second box will connect nucleotides 35-45 and 70-80\n", " labels : list of strings, defaults to sample.sample for each sample\n", " Labels to be used as titles, must be same length as samples list\n", " levels : list of floats, defaults to [5] contact distance or [20] 3D distance\n", " contours are drawn separating nucleotides above and below these\n", " distances\n", " if structure argument is a secondary structure\n", " distance refers to contact distance\n", " if structure argument is a 3D structure\n", " distance refers to spatial distance in angstroms\n", " interpolation : string, defaults to \"nearest\"\n", " one of matplotlib's interpolations for heatmap (used with imshow)\n", " \"nearest\" works well for shorter RNAs (under 300 nt)\n", " \"none\" works well for longer RNAs (over 1200 nt)\n", " atom : string or dictionary, defaults to \"O2'\"\n", " from which atoms to calculate distances\n", " for DMS reactive atoms (N1 for A and G, N3 for U and C) use \"DMS\"\n", " use a dictionary to specify a different atom for each nucleotide\n", " e.g. \"DMS\" == {\"A\": \"N1\", \"G\": \"N1\", \"U\": \"N3\", \"C\": \"N3\"}\n", " plot_type : \"heatmap\" or \"kde\", defaults to \"heatmap\"\n", " how to plot interactions data\n", " \"heatmap\" will plot raw data, each interaction is a pixel in a grid\n", " \"kde\" will calculate a kernel density estimate and plot 5 levels\n", " weights : string, defaults to None (no weights)\n", " weights to be used in kernel density estimation\n", " must be a column of interactions data\n", " rows : integer, defaults to None (determined automatically)\n", " number of rows of plots\n", " cols : integer, defaults to None (determined automatically)\n", " number of columns of plots\n", " plot_kwargs : dictionary, defaults to {}\n", " Keyword-arguments passed to matplotlib.pyplot.subplots\n", " \n", " Returns\n", " -------\n", " rnavigate.plots.Heatmap\n", " object containing matplotlib figure and axes with additional plotting and\n", " file saving methods\n", "\n" ] } ], "source": [ "help(rnav.plot_heatmap)\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 }