{ "cells": [ { "cell_type": "markdown", "id": "f05643b2", "metadata": {}, "source": [ "# Tutorial 2: Analyzing G-Tensors\n", "\n", "This tutorial demonstrates how to use the python API to manipulate, analyze, and plot G-Tensor data.\n", "\n", "## Prerequisites\n", "\n", "Before starting this tutorial, ensure you have:\n", "- MuTopia package installed\n", "- Download the pre-compiled data to the `tutorial_data` directory\n", "\n", "## 1. The elements of a G-Tensor" ] }, { "cell_type": "code", "execution_count": 1, "id": "e4052080", "metadata": {}, "outputs": [], "source": [ "import mutopia.analysis as mu\n", "import mutopia.plot.track_plot as tr\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "id": "799ed777", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 352MB\n",
       "Dimensions:                      (configuration: 2, context: 96, locus: 388247,\n",
       "                                  sample: 185)\n",
       "Coordinates:\n",
       "  * configuration                (configuration) <U12 96B 'C/T-centered' 'A/G...\n",
       "  * context                      (context) <U7 3kB 'A[C>A]A' ... 'T[T>C]T'\n",
       "  * locus                        (locus) int64 3MB 0 1 2 ... 388245 388246\n",
       "  * sample                       (sample) <U36 27kB '0040b1b6-b07a-4b6e-90ef-...\n",
       "Data variables: (12/25)\n",
       "    Features/GeneExpression      (locus) float32 2MB nan nan nan ... nan nan nan\n",
       "    Features/GeneStrand          (locus) int8 388kB 0 0 0 0 0 0 ... 0 0 0 0 0 0\n",
       "    Features/GenePosition        (locus) float32 2MB nan nan nan ... nan nan nan\n",
       "    Features/ReplicationStrand   (locus) int8 388kB 0 0 0 1 1 1 ... 0 0 0 0 0 0\n",
       "    Features/RepliseqS4          (locus) float32 2MB 7.007 2.3 ... 5.495 5.105\n",
       "    Features/NucleotideRatio     (locus) float32 2MB 0.3104 0.2271 ... 0.239\n",
       "    ...                           ...\n",
       "    Regions/chrom                (locus) <U5 8MB 'chr1' 'chr1' ... 'chr9' 'chr9'\n",
       "    Regions/start                (locus) int64 3MB 810000 820000 ... 138190000\n",
       "    Regions/end                  (locus) int64 3MB 820000 830000 ... 138200000\n",
       "    Regions/length               (locus) float32 2MB 3.141e+03 ... 3.408e+03\n",
       "    Regions/context_frequencies  (configuration, context, locus) float32 298MB ...\n",
       "    Regions/exposures            (locus) float64 3MB 1.0 1.0 1.0 ... 1.0 1.0 1.0\n",
       "Attributes:\n",
       "    name:            liver_simple\n",
       "    dtype:           sbs\n",
       "    genome_file:     /n/data1/hms/dbmi/park/ctDNA_loci_project/locusregressio...\n",
       "    fasta_file:      /n/data1/hms/dbmi/park/SOFTWARE/REFERENCE/hg38/cgap_matc...\n",
       "    blacklist_file:  /n/data1/hms/dbmi/park/ctDNA_loci_project/locusregressio...\n",
       "    region_size:     10000\n",
       "    filename:        tutorial_data/Liver.nc\n",
       "    regions_file:    Liver.nc.regions.bed
" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data=mu.gt.load_dataset(\"tutorial_data/Liver.nc\", with_samples=False)\n", "data" ] }, { "cell_type": "markdown", "id": "64d1d406", "metadata": {}, "source": [ "You can see the G-Tensor comprises many hierarchical sections with data spanning multiple dimensions. The `Dimensions` listed at the top indicate the multiple dimensions that may be referenced by the data. In this instance, the G-Tensor contains dimensions for:\n", "* locus - regions across the genome\n", "* context - the trinucleotide context at which a mutation is found\n", "* configuration - configuration indicates whether a mutation was found at a C/T base or an A/G base. Typically, mutational signatures are oriented relative to the C/T base, but we track both orientations to account for stranded features.\n", "* sample - the number of samples included in the dataset\n", "\n", "You can click on the various \"sections\" to view their contents. To access variables in a certain section, like \"length\" under the \"Regions\" section, you can use:" ] }, { "cell_type": "code", "execution_count": 3, "id": "f053f225", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'length' (locus: 388247)> Size: 2MB\n",
       "array([ 3141., 10725.,  4845., ...,  6438.,  3450.,  3408.], dtype=float32)\n",
       "Coordinates:\n",
       "  * locus    (locus) int64 3MB 0 1 2 3 4 ... 388242 388243 388244 388245 388246
" ], "text/plain": [ " Size: 2MB\n", "array([ 3141., 10725., 4845., ..., 6438., 3450., 3408.], dtype=float32)\n", "Coordinates:\n", " * locus (locus) int64 3MB 0 1 2 3 4 ... 388242 388243 388244 388245 388246" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.sections[\"Regions\"][\"length\"]" ] }, { "cell_type": "markdown", "id": "ce66becc", "metadata": {}, "source": [ "Or:" ] }, { "cell_type": "code", "execution_count": 4, "id": "d631ec79", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'Regions/length' (locus: 388247)> Size: 2MB\n",
       "array([ 3141., 10725.,  4845., ...,  6438.,  3450.,  3408.], dtype=float32)\n",
       "Coordinates:\n",
       "  * locus    (locus) int64 3MB 0 1 2 3 4 ... 388242 388243 388244 388245 388246
" ], "text/plain": [ " Size: 2MB\n", "array([ 3141., 10725., 4845., ..., 6438., 3450., 3408.], dtype=float32)\n", "Coordinates:\n", " * locus (locus) int64 3MB 0 1 2 3 4 ... 388242 388243 388244 388245 388246" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[\"Regions/length\"]" ] }, { "cell_type": "markdown", "id": "a14755d4", "metadata": {}, "source": [ "In fact the hierarchy is a trick of the UI here. A G-Tensor in fact is simply an X-array dataset where we've added a little functionality for variables saved with filepath-like names!\n", "I suggest reading a little bit about X-array to understand some of the semantics with dealing in X-array datasets.\n", "\n", "Besides the `Regions` section, which describes the genomic loci, we also have the `Features` section, which contains our genomic state features, sectioned by cell type (side note: whenever a variable does not belong to a section, it just goes under \"Data\" - you do not need to prefix with \"Data\" to access these.)" ] }, { "cell_type": "code", "execution_count": 5, "id": "2d20db01", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 35MB\n",
       "Dimensions:            (locus: 388247)\n",
       "Coordinates:\n",
       "  * locus              (locus) int64 3MB 0 1 2 3 ... 388243 388244 388245 388246\n",
       "Data variables: (12/19)\n",
       "    GeneExpression     (locus) float32 2MB nan nan nan nan ... nan nan nan nan\n",
       "    GeneStrand         (locus) int8 388kB 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0\n",
       "    GenePosition       (locus) float32 2MB nan nan nan nan ... nan nan nan nan\n",
       "    ReplicationStrand  (locus) int8 388kB 0 0 0 1 1 1 1 1 1 ... -1 0 0 0 0 0 0 0\n",
       "    RepliseqS4         (locus) float32 2MB 7.007 2.3 2.088 ... 5.1 5.495 5.105\n",
       "    NucleotideRatio    (locus) float32 2MB 0.3104 0.2271 0.1438 ... 0.239 0.239\n",
       "    ...                 ...\n",
       "    H3K4me3            (locus) float32 2MB 0.2934 5.248 0.3616 ... 0.1928 0.1672\n",
       "    RepliseqG1b        (locus) float32 2MB 45.48 52.38 52.3 ... 18.0 16.9 17.2\n",
       "    RepliseqS2         (locus) float32 2MB 6.4 6.0 6.1 6.1 ... 29.4 29.7 31.5\n",
       "    RepliseqS1         (locus) float32 2MB 24.92 27.42 27.89 ... 18.4 19.49 21.0\n",
       "    H3K27ac            (locus) float32 2MB 0.9603 7.082 ... 0.07612 0.05248\n",
       "    H3K27me3           (locus) float32 2MB 0.0852 0.0844 ... 0.2999 0.1846\n",
       "Attributes:\n",
       "    name:            liver_simple\n",
       "    dtype:           sbs\n",
       "    genome_file:     /n/data1/hms/dbmi/park/ctDNA_loci_project/locusregressio...\n",
       "    fasta_file:      /n/data1/hms/dbmi/park/SOFTWARE/REFERENCE/hg38/cgap_matc...\n",
       "    blacklist_file:  /n/data1/hms/dbmi/park/ctDNA_loci_project/locusregressio...\n",
       "    region_size:     10000\n",
       "    filename:        tutorial_data/Liver.nc\n",
       "    regions_file:    Liver.nc.regions.bed
" ], "text/plain": [ " Size: 35MB\n", "Dimensions: (locus: 388247)\n", "Coordinates:\n", " * locus (locus) int64 3MB 0 1 2 3 ... 388243 388244 388245 388246\n", "Data variables: (12/19)\n", " GeneExpression (locus) float32 2MB nan nan nan nan ... nan nan nan nan\n", " GeneStrand (locus) int8 388kB 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0\n", " GenePosition (locus) float32 2MB nan nan nan nan ... nan nan nan nan\n", " ReplicationStrand (locus) int8 388kB 0 0 0 1 1 1 1 1 1 ... -1 0 0 0 0 0 0 0\n", " RepliseqS4 (locus) float32 2MB 7.007 2.3 2.088 ... 5.1 5.495 5.105\n", " NucleotideRatio (locus) float32 2MB 0.3104 0.2271 0.1438 ... 0.239 0.239\n", " ... ...\n", " H3K4me3 (locus) float32 2MB 0.2934 5.248 0.3616 ... 0.1928 0.1672\n", " RepliseqG1b (locus) float32 2MB 45.48 52.38 52.3 ... 18.0 16.9 17.2\n", " RepliseqS2 (locus) float32 2MB 6.4 6.0 6.1 6.1 ... 29.4 29.7 31.5\n", " RepliseqS1 (locus) float32 2MB 24.92 27.42 27.89 ... 18.4 19.49 21.0\n", " H3K27ac (locus) float32 2MB 0.9603 7.082 ... 0.07612 0.05248\n", " H3K27me3 (locus) float32 2MB 0.0852 0.0844 ... 0.2999 0.1846\n", "Attributes:\n", " name: liver_simple\n", " dtype: sbs\n", " genome_file: /n/data1/hms/dbmi/park/ctDNA_loci_project/locusregressio...\n", " fasta_file: /n/data1/hms/dbmi/park/SOFTWARE/REFERENCE/hg38/cgap_matc...\n", " blacklist_file: /n/data1/hms/dbmi/park/ctDNA_loci_project/locusregressio...\n", " region_size: 10000\n", " filename: tutorial_data/Liver.nc\n", " regions_file: Liver.nc.regions.bed" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.sections[\"Features\"]" ] }, { "cell_type": "markdown", "id": "b092e580", "metadata": {}, "source": [ "The methods under `mu.gt` are operations which operate on G-Tensors. `annot_empirical_marginal` calculates the average counts for each locus and context across your dataset:" ] }, { "cell_type": "code", "execution_count": 6, "id": "0873ce2b", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Reducing samples: 100%|██████████| 184/184 [00:27<00:00, 6.70it/s]\n", "INFO: Mutopia:Added key: \"empirical_marginal\"\n", "INFO: Mutopia:Added key: \"empirical_marginal_locus\"\n" ] }, { "data": { "text/html": [ "
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<xarray.Dataset> Size: 652MB\n",
       "Dimensions:                      (configuration: 2, context: 96, locus: 388247,\n",
       "                                  sample: 185)\n",
       "Coordinates:\n",
       "  * configuration                (configuration) <U12 96B 'C/T-centered' 'A/G...\n",
       "  * context                      (context) <U7 3kB 'A[C>A]A' ... 'T[T>C]T'\n",
       "  * locus                        (locus) int64 3MB 0 1 2 ... 388245 388246\n",
       "  * sample                       (sample) <U36 27kB '0040b1b6-b07a-4b6e-90ef-...\n",
       "Data variables: (12/27)\n",
       "    Features/GeneExpression      (locus) float32 2MB nan nan nan ... nan nan nan\n",
       "    Features/GeneStrand          (locus) int8 388kB 0 0 0 0 0 0 ... 0 0 0 0 0 0\n",
       "    Features/GenePosition        (locus) float32 2MB nan nan nan ... nan nan nan\n",
       "    Features/ReplicationStrand   (locus) int8 388kB 0 0 0 1 1 1 ... 0 0 0 0 0 0\n",
       "    Features/RepliseqS4          (locus) float32 2MB 7.007 2.3 ... 5.495 5.105\n",
       "    Features/NucleotideRatio     (locus) float32 2MB 0.3104 0.2271 ... 0.239\n",
       "    ...                           ...\n",
       "    Regions/end                  (locus) int64 3MB 820000 830000 ... 138200000\n",
       "    Regions/length               (locus) float32 2MB 3.141e+03 ... 3.408e+03\n",
       "    Regions/context_frequencies  (configuration, context, locus) float32 298MB ...\n",
       "    Regions/exposures            (locus) float64 3MB 1.0 1.0 1.0 ... 1.0 1.0 1.0\n",
       "    empirical_marginal           (configuration, context, locus) float32 298MB ...\n",
       "    empirical_marginal_locus     (locus) float32 2MB 0.0001265 3.065e-05 ... 0.0\n",
       "Attributes:\n",
       "    name:            liver_simple\n",
       "    dtype:           sbs\n",
       "    genome_file:     /n/data1/hms/dbmi/park/ctDNA_loci_project/locusregressio...\n",
       "    fasta_file:      /n/data1/hms/dbmi/park/SOFTWARE/REFERENCE/hg38/cgap_matc...\n",
       "    blacklist_file:  /n/data1/hms/dbmi/park/ctDNA_loci_project/locusregressio...\n",
       "    region_size:     10000\n",
       "    filename:        tutorial_data/Liver.nc\n",
       "    regions_file:    Liver.nc.regions.bed
" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = mu.gt.annot_empirical_marginal(data)\n", "data" ] }, { "cell_type": "markdown", "id": "05977e67", "metadata": {}, "source": [ "You can see it added keys for `empirical_marginal`, which has dimensions (context, locus), but also `empirical_marginal_locus`, which simply sums over the \"context\" dimension. This is a common pattern in MuTopia methods, giving you access to multiple levels of summarized data.\n", "\n", "Another important accessor is `mu.gt.fetch_features`, which returns a matrix of features which match some query (wildcards with `*` may be provided):" ] }, { "cell_type": "code", "execution_count": 7, "id": "dfbf7e5e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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featureGeneExpressionGeneStrandGenePosition
locus
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\n", "
" ], "text/plain": [ "feature GeneExpression GeneStrand GenePosition\n", "locus \n", "0 NaN 0.0 NaN\n", "1 NaN 0.0 NaN\n", "2 NaN 0.0 NaN" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mu.gt.fetch_features(data, \"Gene*\").to_pandas().T.head(3)" ] }, { "cell_type": "markdown", "id": "8e40fcbb", "metadata": {}, "source": [ "## 2. Manipulating G-Tensors\n", "\n", "Since G-Tensors are really just souped-up X-arrays, we can do all sorts of fancy manipulations with the data. Mutopia provides a few additional operations like `mu.gt.slice_regions` and `mu.gt.slice_samples`. \n", "\n", "Of particular note is the ability to sum across dimensions. To view the overall length distribution of the data, for example, we can do:" ] }, { "cell_type": "code", "execution_count": 8, "id": "20a06f2b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data.modality().plot(\n", " data[\"empirical_marginal\"].sum(dim=(\"locus\", \"configuration\"))\n", ")" ] }, { "cell_type": "markdown", "id": "7374effb", "metadata": {}, "source": [ "One of the unique features of G-Tensors is the ability to load sample data from disk. You can check out the samples your dataset contains with:" ] }, { "cell_type": "code", "execution_count": 9, "id": "8eb4a1f2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['0040b1b6-b07a-4b6e-90ef-133523eaf412',\n", " '00c27940-c623-11e3-bf01-24c6515278c0',\n", " '03c88506-d72e-4a44-a34e-a7f0564f1799',\n", " '0831e45e-c623-11e3-bf01-24c6515278c0',\n", " '0a9c9db0-c623-11e3-bf01-24c6515278c0'], dtype='\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 2MB\n",
       "Dimensions:  (configuration: 2, context: 96, locus: 388247)\n",
       "Coordinates:\n",
       "    sample   <U36 144B '0040b1b6-b07a-4b6e-90ef-133523eaf412'\n",
       "Dimensions without coordinates: configuration, context, locus\n",
       "Data variables:\n",
       "    X        (configuration, context, locus) float32 2MB <GCXS: nnz=13687, fill_value=0.0>\n",
       "    ploidy   (locus) float64 0B <COO: nnz=0, fill_value=0.0>
" ], "text/plain": [ " Size: 2MB\n", "Dimensions: (configuration: 2, context: 96, locus: 388247)\n", "Coordinates:\n", " sample \n", " ploidy (locus) float64 0B " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.fetch_sample(\"0040b1b6-b07a-4b6e-90ef-133523eaf412\")" ] }, { "cell_type": "markdown", "id": "f0e1ebad", "metadata": {}, "source": [ "A sample contains two data elements: the `X` matrix - the counts of each observed fragment for each fragment length bin at each locus, and `ploidy` - the copy number at each locus, but transformed such that: `p = CN/2 - 1`, which allows us to save the data in a sparse manner (since a diploid region corresponds with p=0). \n", "\n", "The ability to load data persists through slicing the data:" ] }, { "cell_type": "code", "execution_count": 11, "id": "fd32e637", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO: Mutopia:Found 2343/388247 regions matching query.\n" ] }, { "data": { "text/plain": [ "FrozenMappingWarningOnValuesAccess({'configuration': 2, 'context': 96, 'locus': 2343})" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(\n", " mu.gt.slice_regions(data, \"chr1:41007569-54616441\")\n", " .fetch_sample(\"0040b1b6-b07a-4b6e-90ef-133523eaf412\")\n", " .dims\n", ")" ] }, { "cell_type": "markdown", "id": "fbb4e816", "metadata": {}, "source": [ "## 3. Plotting G-Tensor data\n", "\n", "Plotting G-Tensors works by composing `views` - sections of the genome, and `configs` - the configuration of the tracks you want to plot. Configurations themselves are functions of `views`, and are data-independent descriptions of the plot you want to make.\n", "\n", "The general form of a `view` is a list, tuple, or other iterable of plot elements, like `tr.scale_bar`, `tr.scatterplot`, `tr.heatmap_plot`, etc. Each of these plots are ultimately wrappers around the corresponding `matplotlib` plot, so you can pass the familiar keyword argument to configure plot aesthetics.\n", "\n", "Unlike `matplotlib` or `seaborn`, however, you do not directly provide any data to a `tr.{plot}` element. Instead, the first argument describes *how* to access that data from a G-Tensor object. For this, you can use the methods:\n", "1. `tr.select` - select this variable (or variables) from the G-Tensor.\n", "2. `tr.feature_matrix` - select the genomic features which match the query string.\n", "\n", "Sometimes, you want to plot a transformation of some stored variable. This is simple with `tr.pipeline`. You simply provide the series of functions you wish to run on the underlying data. For example, to first select some data, then apply the sqrt transformation, one would provide:\n", "```\n", "tr.pipeline(\n", " tr.select(variable),\n", " lambda x : np.sqrt(x)\n", ")\n", "```\n", "\n", "We provide a few ready-to-go transformations, like and `tr.renorm`, `tr.clip`. The `tr.apply_rows` function applies any provided transformation to each row of a matrix independently. This style of composing configurations enables the construction complex multimodal genome plots. Let's build one!\n", "\n", "(**Note:** you can specify additional arguments for a config that must be filled at run-time).\n", "\n", "The following example has a little bit of everything:" ] }, { "cell_type": "code", "execution_count": 12, "id": "22f73414", "metadata": {}, "outputs": [], "source": [ "plot_config = lambda view, scalebar_bp : ( #supply scalebar_bp at runtime\n", " tr.scale_bar(scalebar_bp, scale=\"mb\" if scalebar_bp >= 1_000_000 else \"kb\"),\n", " tr.ideogram(\"tutorial_data/cytoBand.txt\"),\n", " tr.stack_plots( # this context overlays whichever plots are provided\n", " tr.scatterplot(\n", " tr.pipeline(\n", " tr.select(\"empirical_marginal_locus\"),\n", " view.smooth(5),\n", " ),\n", " s=0.5,\n", " alpha=0.5,\n", " color=\"grey\",\n", " label=\"Observed\"\n", " ),\n", " tr.line_plot(\n", " tr.pipeline(\n", " tr.select(\"empirical_marginal_locus\"),\n", " view.smooth(20),\n", " ),\n", " label=\"Smoothed\",\n", " linewidth=0.5,\n", " color=mu.pl.categorical_palette[0], # nice colors\n", " ),\n", " height=1.5,\n", " label=\"(log) Inverse\\nfragmentation rate\"\n", " ),\n", " tr.spacer(0.2),\n", " tr.fill_plot(\n", " tr.select(\"Features/H3K27me3\"),\n", " color=mu.pl.categorical_palette[1],\n", " alpha=0.8,\n", " height=0.75,\n", " ),\n", " tr.fill_plot(\n", " tr.select(\"Features/H3K27ac\"),\n", " color=mu.pl.categorical_palette[1],\n", " alpha=0.8,\n", " height=0.75,\n", " )\n", ")" ] }, { "cell_type": "markdown", "id": "9a11fe6f", "metadata": {}, "source": [ "Now, to use the configuration, make a \"view\" of the data using `make_view`, supplying the data a region of interest:" ] }, { "cell_type": "code", "execution_count": 13, "id": "928f27ef", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO: Mutopia:Found 2343/388247 regions matching query.\n" ] } ], "source": [ "view = tr.make_view(data, \"chr1:41007569-54616441\")" ] }, { "cell_type": "markdown", "id": "49f2d4e7", "metadata": {}, "source": [ "Finally, use `tr.plot_view` to render the configuration - make sure to supply any required runtime arguments!" ] }, { "cell_type": "code", "execution_count": 14, "id": "21fe4043", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tr.plot_view(plot_config, view, scalebar_bp=1_000_000)\n", "None" ] }, { "cell_type": "markdown", "id": "087325f3", "metadata": {}, "source": [ "To demonstrate the extensibility of a plotting configuration, let's say you found an interesting locus and you want to \"zoom in\". You can simply re-use the same configuration, modify the run-time paramters, and provide a view of a different region of the genome:" ] }, { "cell_type": "code", "execution_count": 15, "id": "dcbc843f", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO: Mutopia:Found 403/388247 regions matching query.\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "zoomed_view = tr.make_view(data, \"chr1:43007569-45116441\")\n", "tr.plot_view(plot_config, zoomed_view, scalebar_bp=100_000)\n", "None" ] }, { "cell_type": "markdown", "id": "6ea4ffd6", "metadata": {}, "source": [ "Plotting only becomes more important once you've trained models on the data - so more on this later!" ] } ], "metadata": { "kernelspec": { "display_name": "my-mutopia", "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": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 5 }