{ "cells": [ { "cell_type": "markdown", "id": "e0a8466b", "metadata": {}, "source": [ "# 0. Introduction\n", "\n", "This tutorial will:\n", "\n", "- Use a small example dataset to show how to use wotplot to create and visualize dot plot matrices (**section 1**).\n", "\n", "- Demonstrate a few techniques for creating fancy visualizations of dot plot matrices (**section 2**).\n", "\n", "- Use wotplot to create and visualize a large dot plot matrix comparing two _E. coli_ genomes (**section 3**).\n", "\n", "If you are unfamiliar with dot plot matrices, you may want to check out [the Wikipedia article on them](https://en.wikipedia.org/wiki/Dot_plot_(bioinformatics)). Chapter 6 of [_Bioinformatics Algorithms_](https://www.bioinformaticsalgorithms.org) also explains them well." ] }, { "cell_type": "markdown", "id": "1a629bc8", "metadata": {}, "source": [ "## 0.1. Import and set up a few things for later" ] }, { "cell_type": "code", "execution_count": 1, "id": "3803d7d1", "metadata": {}, "outputs": [], "source": [ "import os\n", "import wotplot as wp\n", "from matplotlib import pyplot\n", "# the facecolor and transparent kargs force the saved figures to have a white background;\n", "# from https://stackoverflow.com/a/64585557\n", "savefig_kwargs = {\"bbox_inches\": \"tight\", \"facecolor\": \"white\", \"transparent\": False}" ] }, { "cell_type": "markdown", "id": "62009818", "metadata": {}, "source": [ "# 1. A small example" ] }, { "cell_type": "markdown", "id": "e6f43764", "metadata": {}, "source": [ "## 1.1. Define a small dataset\n", "\n", "Adapted from Figure 6.20 in Chapter 6 of _Bioinformatics Algorithms_ (Compeau & Pevzner), edition 2." ] }, { "cell_type": "code", "execution_count": 2, "id": "7ae7a74c", "metadata": {}, "outputs": [], "source": [ "s1 = \"AGCAGGTTATCTACCTGT\"\n", "s2 = \"AGCAGGAGATAAACCTGT\"\n", "k = 3" ] }, { "cell_type": "markdown", "id": "8f9834a8", "metadata": {}, "source": [ "## 1.2. Create a dot plot matrix" ] }, { "cell_type": "code", "execution_count": 3, "id": "f5c74061", "metadata": { "scrolled": true }, "outputs": [], "source": [ "m = wp.DotPlotMatrix(s1, s2, k)" ] }, { "cell_type": "markdown", "id": "995b9451", "metadata": {}, "source": [ "## 1.3. Inspect the dot plot matrix" ] }, { "cell_type": "code", "execution_count": 4, "id": "1d2bc430", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "wotplot._matrix.DotPlotMatrix" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(m)" ] }, { "cell_type": "code", "execution_count": 5, "id": "b0c8dbf3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DotPlotMatrix(mat=<16x16 sparse matrix of type ''\n", "\twith 18 stored elements in COOrdinate format>, k=3, yorder=\"BT\")" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m" ] }, { "cell_type": "code", "execution_count": 6, "id": "a0d1750b", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DotPlotMatrix(k = 3, bottom β†’ top): 16 x 16\n" ] } ], "source": [ "print(str(m))" ] }, { "cell_type": "markdown", "id": "29fdad46", "metadata": {}, "source": [ "The `DotPlotMatrix` object we just created, `m`, contains some extra information about the way it was created (e.g. the value of `k` we used). But the most interesting part of it is the actual matrix describing the dot plot! This matrix is stored in the `mat` attribute." ] }, { "cell_type": "code", "execution_count": 7, "id": "9379267f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<16x16 sparse matrix of type ''\n", "\twith 18 stored elements in COOrdinate format>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.mat" ] }, { "cell_type": "markdown", "id": "a91d9ecd", "metadata": {}, "source": [ "### 1.3.1. Why is this matrix 16x16? Shouldn't it be 18x18?\n", "\n", "You might have noticed that `s1` and `s2` are 18 nucleotides long. Why, then, does `m.mat` only have 16 rows and 16 columns?\n", "\n", "The reason for this is that the number of $k$-mers in an arbitrary string $s$ is slightly smaller than $|s|$: it's $|s| - k + 1$. For `s1` and `s2`, $|s| - k + 1 = 18 - 3 + 1 = 16$." ] }, { "cell_type": "markdown", "id": "ff4026da", "metadata": {}, "source": [ "### 1.3.2. What's the deal with sparse matrices?\n", "\n", "Most dot plot matrices are _sparse_: that is, most of their entries are zeroes. We exploit this by storing `m.mat` in a [sparse matrix format](https://en.wikipedia.org/wiki/Sparse_matrix), which allows us to only bother storing the non-zero entriesβ€”this drastically reduces the memory requirements when our input sequences are long (more than a few thousand nucleotides)." ] }, { "cell_type": "markdown", "id": "9af18e2b", "metadata": {}, "source": [ "#### Sidenote: what's the exact type of `m.mat`?\n", "Depending on the version of SciPy you have installed, `m.mat` will be of type [`scipy.sparse.coo_matrix`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.coo_matrix.html) (SciPy < 1.8) or type [`scipy.sparse.coo_array`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.coo_array.html) (SciPy β‰₯ 1.8). There shouldn't be much of a difference, at least for our use of these objects." ] }, { "cell_type": "code", "execution_count": 8, "id": "6c75fa5f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This notebook is using SciPy version 1.5.2.\n" ] } ], "source": [ "import scipy\n", "print(f\"This notebook is using SciPy version {scipy.__version__}.\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "f29e862a", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "scipy.sparse.coo.coo_matrix" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(m.mat)" ] }, { "cell_type": "markdown", "id": "6053417a", "metadata": {}, "source": [ "It's possible to convert `m.mat` from this sparse format to an equivalent \"dense\" format; this can make the matrix easier to work with, although it might require a large amount of memory if your input sequences were long. (For relatively small sequences like `s1` and `s2`, though, we should be fine.)\n", "\n", "Here's an example of creating a dense version of `m.mat`:" ] }, { "cell_type": "code", "execution_count": 10, "id": "b9e3031e", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n", " [ 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n", " [ 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n", " [ 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0],\n", " [ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0],\n", " [ 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.mat.toarray()" ] }, { "cell_type": "markdown", "id": "7d808534", "metadata": {}, "source": [ "In the above matrix:\n", "\n", "- `1` values represent cells where there there is a forward $k$-mer match,\n", "- `-1` values represent cells where there is a reverse-complementary $k$-mer match, and\n", "- `2` values represent cells where there is a palindromic $k$-mer match (i.e. both a forward and a reverse-complementary $k$-mer match), and\n", "- `0` values represent cells where there are not any forward and/or reverse-complementary $k$-mer matches.\n", "\n", "Note that there are no palindromic matches (i.e. `2` cells) in this example matrix." ] }, { "cell_type": "markdown", "id": "d3a8aa09", "metadata": {}, "source": [ "## 1.4. Visualize the dot plot matrix\n", "\n", "### 1.4.1. Available visualization functions\n", "Currently, we provide two functions for visualizing these matrices: `viz_imshow()` and `viz_spy()`. Both of these are essentially wrappers for matplotlib's [`imshow()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.imshow.html) and [`spy()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.spy.html) functions; you can even provide additional keyword arguments to `viz_imshow()` and `viz_spy()` which will be passed directly to `imshow()` / `spy()`. \n", "\n", "A brief summary of (in my opinion) the most important differences between these functions:\n", "\n", "- `imshow()`\n", " - Draws zero and nonzero matrix cells as the same size, giving a \"perfect\" representation of the exact matrix.\n", " - For small matrices (e.g. both sequences < 200 nt), this looks nice.\n", " - For large matrices, the nonzero cells may be hard to see without enlarging the figure.\n", " - Doesn't support sparse matrices.\n", " - This means that `viz_imshow()` has to convert the sparse matrix to a dense format before calling `imshow()`. This will require a lot of memory if your matrix is large.\n", "\n", "\n", "- `spy()`\n", " - Only draws nonzero matrix cells, meaning that the points representing each cell may cover other close-by cells in the matrix.\n", " - You can increase / decrease nonzero cells' sizes as desired via the `markersize` parameter.\n", " - For large matrices, this way of drawing things is actually nicer than the \"perfect\" representation offered by `imshow()` -- it makes nonzero cells much easier to see.\n", " - Works with sparse matrices.\n", " - This makes `viz_spy()` much more memory-efficient than `viz_imshow()`.\n", "\n", "In general, I recommend using `viz_imshow()` for small matrices (e.g. both sequences < 200 nt) and `viz_spy()` for large matrices.\n", "\n", "### 1.4.2. `viz_imshow()`\n", "\n", "First, let's use `viz_imshow()` -- our sequences here are very short, so the total cost of storing a dense 16 x 16 = 256-cell matrix in memory is small." ] }, { "cell_type": "code", "execution_count": 11, "id": "75193267", "metadata": {}, "outputs": [ { "data": { "image/png": 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RXGfwcETcVda+N8VFSJc0sbLyLMfchg/CDq+I8KvLL4oN6lrgEYrz4b8EbgLm72L5BcCGNtdxBEVv4qRx5t1azhvvdes4y88BLivrfZGid/FD4L0Va1lUtv1nu6hlw5hpCyl6HlvLz106Zl4Ah/T7/3EqvnxPwAEkaQGwJKqfBRj93A3AXhFxTDfq6gdJP6IIjPf2u5apyMcABoikaZJmAHsUP2pGeQVhVWcCcyUd350Ke6sMwkOAs/tcypTlHsAAkbSIl66NH7Wx3Z6AWVUOALPEvAtglpgDwCwxB4BZYg4As8QcAGaJOQDMEnMAmCX2/38ZXAZCE4YLAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = wp.viz_imshow(m)\n", "\n", "# Since we include it in the README, we'll save this drawing to a file.\n", "# We can do this using the fig object returned by viz_imshow() (or by viz_spy()).\n", "# (If you don't care about saving / modifying your drawing, you can just run\n", "# \"wp.viz_imshow(m)\" without saving the returned matplotlib Figure and Axes objects.)\n", "fig.savefig(os.path.join(\"img\", \"small_example_dotplot.png\"), **savefig_kwargs)" ] }, { "cell_type": "markdown", "id": "d5009e71", "metadata": {}, "source": [ "By default, the visualization will use:\n", "\n", "- red cells (πŸŸ₯) to represent forward matches (`1`),\n", "- blue cells (🟦) to represent reverse-complementary matches (`-1`),\n", "- purple cells (πŸŸͺ) to represent palindromic matches (`2`), and\n", "- white cells (⬜) to represent no matches (`0`)." ] }, { "cell_type": "markdown", "id": "191fbb06", "metadata": {}, "source": [ "### 1.4.3. For comparison's sake: `viz_spy()`\n", "\n", "The default `markersize` used by `viz_spy()` is `0.5`. I set this as the default because it's useful for plots of very long sequences; however, it's less useful for plots of tiny sequences like this one." ] }, { "cell_type": "code", "execution_count": 12, "id": "70489125", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", " )" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wp.viz_spy(m)" ] }, { "cell_type": "markdown", "id": "3ea0237d", "metadata": {}, "source": [ "Those are some tiny dots! We can adjust the `markersize` when we call `viz_spy()`, which makes this visualization look essentially the same as what we'd get from `viz_imshow()`:" ] }, { "cell_type": "code", "execution_count": 13, "id": "20c79d41", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", " )" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wp.viz_spy(m, markersize=13)" ] }, { "cell_type": "markdown", "id": "d3071ccf", "metadata": {}, "source": [ "# 2. Fancy visualizations\n", "\n", "## 2.1. Another small example dataset that includes palindromes\n", "\n", "Just for demonstration, let's see how palindromic $k$-mers look in these visualizations. Note that palindromic $k$-mers can only occur for even values of $k$, since only even-length strings `s` can satisfy the condition `s == ReverseComplement(s)`; see [this discussion](https://bioinformatics.stackexchange.com/q/156) for details.\n", "\n", "We'll create a \"self dot plot,\" in which both the horizontal and vertical axes of the dot plot matrix correspond to the same sequence (named `s3` below). \"Self dot plots\" like this one can be helpful for visualizing self-similarity; for another example of a self dot plot, see [this visualization](https://commons.wikimedia.org/wiki/File:Zinc-finger-dot-plot.png) from [this Wikipedia page](https://en.wikipedia.org/wiki/Dot_plot_(bioinformatics)).\n", "\n", "#### Sidenote: Aren't \"self dot plots\" like this symmetric?\n", "\n", "Yep! Exactly _how_ your self dot plot is symmetric will depend on the `yorder` of your `wotplot.DotPlotMatrix` (see below), but all self dot plots will be symmetric in some way. If you wanted, you could only visualize one \"triangle\" of a self dot plot matrix; wotplot's built-in visualization functions don't support this (yet), but see [ModDotPlot](https://github.com/marbl/ModDotPlot) for an example of doing this." ] }, { "cell_type": "code", "execution_count": 14, "id": "1b419201", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, '$s_3$ (21 nt) β†’')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "s3 = \"AGCAGAAAGAGATAAACCTGT\"\n", "p = wp.DotPlotMatrix(s3, s3, 2)\n", "\n", "fig, ax = wp.viz_imshow(p)\n", "\n", "# Adjust labels to make clear that both axes correspond to the same sequence\n", "s3_lbl = f\"$s_3$ ({len(s3)} nt) \\u2192\"\n", "ax.set_xlabel(s3_lbl, fontsize=18)\n", "ax.set_ylabel(s3_lbl, fontsize=18)" ] }, { "cell_type": "markdown", "id": "347523cf", "metadata": {}, "source": [ "There are three palindromic $k = 2$-mers in `s3` (listed in the table below). These 2-mers are shown as purple cells in the visualization above.\n", "\n", "| 2-mer | Starting position in `s3` (0-indexed) |\n", "| --- | --- |\n", "| `GC` | 1 |\n", "| `AT` | 11 |\n", "| `TA` | 12 |" ] }, { "cell_type": "markdown", "id": "fb1d1a8d", "metadata": {}, "source": [ "## 2.2. Adjusting the visualization color scheme\n", "\n", "If you'd prefer something other than the default red / blue / purple / white colors, you can adjust this with the `nbcmap` parameters of `viz_imshow()` and `viz_spy()`.\n", "\n", "### 2.2.1. For `viz_imshow()`\n", "\n", "For `viz_imshow()`, `nbcmap` should be a `dict` mapping the four possible cell values (`0`, `1`, `-1`, `2`) to decimal RGB triplet colors (see [Wikipedia](https://en.wikipedia.org/wiki/Web_colors#Extended_colors) for context).\n", "\n", "As an example, here we'll use `nbcmap` to create a \"dark mode\" version of the above dot plot:" ] }, { "cell_type": "code", "execution_count": 15, "id": "c6bd2bbe", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, '$s_3$ (21 nt) β†’')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# it's gamer time\n", "# no match = black; forward match = white; RC match = green; palindromic match = pink\n", "dark_mode_cmap_255 = {0: [0, 0, 0], 1: [255, 255, 255], -1: [0, 255, 0], 2: [255, 0, 255]}\n", "\n", "fig, ax = wp.viz_imshow(p, nbcmap=dark_mode_cmap_255)\n", "\n", "ax.set_xlabel(s3_lbl, fontsize=18)\n", "ax.set_ylabel(s3_lbl, fontsize=18)" ] }, { "cell_type": "markdown", "id": "78791a0c", "metadata": {}, "source": [ "### 2.2.2. For `viz_spy()`\n", "\n", "For `viz_spy()`, `nbcmap` should again be a `dict` mapping the four possible cell values (`0`, `1`, `-1`, `2`) -- but the values in this dictionary (representing the color each type of cell is represented by) **cannot be decimal RGB triplets**. See [matplotlib's documentation](https://matplotlib.org/stable/gallery/color/color_demo.html) for a list of accepted color formats; I think hex colors are probably easiest.\n", "\n", "We'll recreate the above figure, but using `viz_spy()` and hex colors." ] }, { "cell_type": "code", "execution_count": 16, "id": "7392683f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, '$s_3$ (21 nt) β†’')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# no match = black; forward match = white; RC match = green; palindromic match = pink\n", "dark_mode_cmap_hex = {0: \"#000000\", 1: \"#ffffff\", -1: \"#00ff00\", 2: \"#ff00ff\"}\n", "\n", "# Using markersize=13 is a bit too big for this dataset, so let's use a smaller value\n", "fig, ax = wp.viz_spy(p, markersize=9, nbcmap=dark_mode_cmap_hex)\n", "\n", "ax.set_xlabel(s3_lbl, fontsize=18)\n", "ax.set_ylabel(s3_lbl, fontsize=18)" ] }, { "cell_type": "markdown", "id": "e112bd91", "metadata": {}, "source": [ "## 2.3. Binary visualizations\n", "\n", "A lot of dot plot visualizations in the literature use black cells (⬛) to represent all match cells (forward, reverse-complementary, and palindromic), and white cells (⬜) to represent no-match cells.\n", "\n", "If you would like to visualize a dot plot matrix in this \"binary\" way, you can pass the parameter `binary=True` to `viz_spy()` or `viz_imshow()`.\n", "\n", "(If you're working with large datasets, this also has the nice side effect of making `viz_spy()` run slightly faster.)" ] }, { "cell_type": "code", "execution_count": 17, "id": "174ad0fb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, '$s_3$ (21 nt) β†’')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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/QxYMlxd4izuBp8i6s53e/xKyg/9a4AMlX3/o1/gFuMl6LwuB7SVdr5hB9olJ8gc/+BrAoPwV8EZJ64AtZMF7KnAa8LmI+MVgm4j4NPBpSa8FzgR+MlXjEbFL0lpgiaQZEfH8+DpJ5wAfBx4CNgDvbcui7RFxU+sCSb/HC+fps4F9JF2Uf70tIq4t/q1P6Q6yns5ySa8AngUeiIj/yGvZH3gTcHUZbxYRT0haVLMQHJ6I8KPiB/B+4F+Ah8k+D/8J8F1g8RSvWwp8r+B7nEDWm3hX2/LV+fKJHrd0aOuWbrbv8Ppl+bZvnaDtB9uWnUU2LmJn/rrVbesCOGbY/4/T8eGbgWpM0nvJegiHFdx+PbBfRLyp2soGR9L3yQLDNwNVwNcAakLSgfkQ2oOUOZbsiv0NXTTzMWCRpFOqqXKwJC0hG6p8wZBLmbbcA6gJSQeQDY2dD+xDdhFwLbAiOtyya1YGB4BZwnwKYJYwB4BZwhwAZglzAJglzAFgljAHgFnCHABmCft/98AyUgNrSMIAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = wp.viz_imshow(p, binary=True)\n", "ax.set_xlabel(s3_lbl, fontsize=18)\n", "ax.set_ylabel(s3_lbl, fontsize=18)" ] }, { "cell_type": "markdown", "id": "39f7cb47", "metadata": {}, "source": [ "### 2.3.1. Adjusting the colors used for binary visualizations" ] }, { "cell_type": "markdown", "id": "25d4393c", "metadata": {}, "source": [ "#### 2.3.1.1. For `viz_imshow()`\n", "\n", "You can adjust this using the `cmap` parameter, which can be set to any recognized [matplotlib colormap](https://matplotlib.org/stable/users/explain/colors/colormaps.html); the default is `\"gray_r\"`." ] }, { "cell_type": "code", "execution_count": 18, "id": "c9c0d24c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, '$s_3$ (21 nt) β†’')" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = wp.viz_imshow(p, binary=True, cmap=\"viridis\")\n", "ax.set_xlabel(s3_lbl, fontsize=18)\n", "ax.set_ylabel(s3_lbl, fontsize=18)" ] }, { "cell_type": "markdown", "id": "4faf58a1", "metadata": {}, "source": [ "#### 2.3.1.2. For `viz_spy()`\n", "\n", "You can adjust this using the `color` parameter, which should be in the color formats accepted by the `nbcmap` parameter of `viz_spy()` (e.g. hex colors)." ] }, { "cell_type": "code", "execution_count": 19, "id": "b61e5e74", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, '$s_3$ (21 nt) β†’')" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = wp.viz_spy(p, markersize=9, binary=True, color=\"#80653e\")\n", "ax.set_xlabel(s3_lbl, fontsize=18)\n", "ax.set_ylabel(s3_lbl, fontsize=18)\n", "\n", "# (If you want to change the background color, also, you can call ax.set_facecolor().)" ] }, { "cell_type": "markdown", "id": "b4138e7f", "metadata": {}, "source": [ "## 2.4. Flipping the y-axis\n", "\n", "By default, wotplot visualizes dot plots in such a way that the sequence on the x-axis goes from left to right, and the sequence on the y-axis goes from bottom to top.\n", "\n", "However, some dot plot visualizations (for example, [Gepard](https://academic.oup.com/bioinformatics/article/23/8/1026/198110)'s) \"flip\" the y-axis, so that the sequence on the y-axis goes from top to bottom.\n", "\n", "If you would like to flip your y-axis in this way, you can set `yorder=\"TB\"` when creating the dot plot matrix.\n", "\n", "Note that, when you visualize a dot plot matrix, the direction of the arrow on the y-axis will be adjusted (to either ↑ or ↓) to reflect what direction your y-axis sequence is going in." ] }, { "cell_type": "code", "execution_count": 20, "id": "40a1ea34", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create another version of the original dot plot, but using yorder=\"TB\"\n", "m_flipped = wp.DotPlotMatrix(s1, s2, k, yorder=\"TB\")\n", "\n", "fig, (axLeft, axRight) = pyplot.subplots(1, 2)\n", "\n", "# Notice how we provide an argument to the ax parameters of these functions.\n", "# This way, we can create these Axes objects in advance (when we call pyplot.subplots()).\n", "wp.viz_imshow(m, title=\"Default (bottom-to-top)\", ax=axLeft)\n", "wp.viz_imshow(m_flipped, title='yorder=\"TB\" (top-to-bottom)', ax=axRight)\n", "\n", "fig.set_size_inches(12, 5)" ] }, { "cell_type": "markdown", "id": "b0875cdb", "metadata": {}, "source": [ "## 2.5. Tiling multiple dot plots\n", "\n", "One of the main reasons I wrote this library was so that I could create figures containing grids of many dot plots using matplotlib. wotplot makes this process fairly painless!\n", "\n", "The example above (showing flipping the y-axis) already demonstrates this, actually. The key idea is **using matplotlib's [`subplots`](https://matplotlib.org/stable/gallery/subplots_axes_and_figures/subplots_demo.html) functionality to generate multiple `Axes` objects, then passing these objects to the `viz_spy()` or `viz_imshow()` functions using these functions' `ax` parameter**.\n", "\n", "Here is a more intricate example than the one above, demonstrating how we can create a grid showing an all-versus-all comparison of five (pseudo)random sequences. In practice, this kind of figure could be useful for a situation where you want to compare a bunch of different types of sequences -- maybe different strains of a virus, or contigs within the same metagenome assembly bin, etc." ] }, { "cell_type": "code", "execution_count": 21, "id": "dd3026c6", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import random\n", "# Set a seed so that we get the same results when we rerun this cell\n", "random.seed(333)\n", "\n", "def gen_random_seq(n):\n", " out = \"\"\n", " for i in range(n):\n", " out += random.choice(\"ACGT\")\n", " return out\n", "\n", "# create 5 random sequences of random lengths\n", "lowlen = 500\n", "highlen = 2000\n", "seq_lens = [\n", " random.randrange(lowlen, highlen), random.randrange(lowlen, highlen), random.randrange(lowlen, highlen),\n", " random.randrange(lowlen, highlen), random.randrange(lowlen, highlen)\n", "]\n", "seqs = [gen_random_seq(slen) for slen in seq_lens]\n", "seq_names = [\"A\", \"B\", \"C\", \"D\", \"E\"]\n", "\n", "# Create a grid of dot plots\n", "K = 7\n", "fig, axes = pyplot.subplots(5, 5)\n", "for row in range(0, 5):\n", " for col in range(0, 5):\n", " if col >= row:\n", " # Each sequence is assigned its own row and column (\"A\" in the top row and leftmost column,\n", " # \"B\" in the second-from-the-top row and second-from-the-left column, etc.)\n", " wp.viz_spy(\n", " wp.DotPlotMatrix(seqs[col], seqs[row], K),\n", " ax=axes[row, col],\n", " s1_name=seq_names[col],\n", " s2_name=seq_names[row],\n", " )\n", " # By default, each plot will have its own x- and y-axis label. To reduce clutter,\n", " # let's (i) only show x-axis labels on the top of the top row's plots ...\n", " if row == 0:\n", " axes[row, col].set_title(axes[row, col].get_xlabel(), fontsize=18)\n", " axes[row, col].set_xlabel(\"\")\n", " # and (ii) only show y-axis labels on the left of the leftmost plots.\n", " if col != row:\n", " axes[row, col].set_ylabel(\"\")\n", " else:\n", " # Since the \"lower triangle\" of this grid contains the same information as the upper triangle,\n", " # hide these plots using .axis(\"off\") -- https://stackoverflow.com/a/25864515\n", " axes[row, col].axis(\"off\")\n", "\n", "fig.suptitle(f\"Dot plots ($k$ = {K}) of five random sequences\", fontsize=24, y=0.94)\n", "fig.set_size_inches(15, 15)\n", "fig.savefig(os.path.join(\"img\", \"grid.png\"), **savefig_kwargs)" ] }, { "cell_type": "markdown", "id": "123d9ec4", "metadata": {}, "source": [ "## 2.6. Passing arbitrary keyword arguments to `imshow()` / `spy()`\n", "\n", "These functions have a lot of options available (see [the matplotlib docs for `imshow()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.imshow.html) and [the matplotlib docs for `spy()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.spy.html)), and we can make use of these options without too much effort:" ] }, { "cell_type": "code", "execution_count": 22, "id": "9a6c3f79", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", " )" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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uYK+qjUn2B/4ZOAT4c+BW4FvAsVV1xkj1zl3vmiRHA6/uv4d7AKfSzZ8HeCbdPPqTR7ymob6D8pmnWjZJjgBOnGBWzNz7zgJ2r6qDl6Ku5ZDkG8DVVfVny12LVj7H2LXNJdk5ya50vdYk2bVfsTrUscBBSZ66NBVuW/0vuEfQ9ealmdlj1zaXZB2/vHfKnI2T9twlLc5gl6TGOBQjSY0x2CWpMQa7JDXGYJekxhjsktQYg12SGmOwS1Jj/h8tDOgYoS7oYQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# - Set aspect to 0.5 (stretches out the x-axis; this can be useful if you're creating a dot plot where\n", "# the sequence used for the x-axis is much smaller than the sequence used for the y-axis).\n", "wp.viz_imshow(m, aspect=0.5)" ] }, { "cell_type": "code", "execution_count": 23, "id": "30be1fa7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", " )" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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sWQOnnx6u4tix4/Dn162DE06I59OVkUpxeCgA+i3bGhuDTZvSKfeSkRpkeDQxpPINgEHLtpYv1wYvs+pneDRZQZhnAKRYtiWt1PRQzO8gYKplW9I6MQzF/AIg1bItaZ0YhmJeAZBy2Za0SixDMa8ASLlsS1ollqGYVwCkXLYlrRLLUMwrAFIu25JWiWUo5hUAKZdtSavEMhTzCoCUy7akVWIZinkFAKRbtiWtE8NQzC8AUi3bktaJYSi26+hW2WqKFMu2pJWaHortCIAq1RSq6pNINDkU0w+AQaspVNUnkWhiKKZ9DCCGagqRhKUdADFUU4gkLN0AiKWaQiRh6QZALNUUIglLNwBiqaYQSVi6ARBLNYVIwtINgFiqKUQSlm4AxFJNIZKwdAMA4qimEElY2gEQQzWFSMLSPyLWdDWFSMLiDABN1yUyEnEFgKbrEhmpeAKg6TmSRDIUx0FAVfWJNCKOAFBVn0gjmg+AqSlV9Yk0pPkAOHAgPKqqT2Tkmg8A92qvU1WfyMCaDwCzaq9TVZ/IwJoPgMWLw6Oq+kRGrvkAWLBAVX0iDWk+AEBVfSINiSMAVNUn0oh4jqSpqk9k5MyrnoarqwNmu4CJ3mULYcFiWGRgDn4ADh6CkkcJRWQGa9x91fSFjQeAiDQnjmMAItIIBYBIxhQAIhlTAIhkTAHQImZ2nJkdNLMXNN2XOpjZZjO738we13Rf2koB0C4fA65z96t6F5rZP5jZVjO73czczO6cayVmtszM/tHMbjGzPWbWMbPvmtkWs6rVW7O+11PM7H1mtnb6c+5+GXAL8JE631MepABoCTPbCLyAEALTfRB4LrADmLPowswWAFcAHwBuBN4J/DOwEPgc8OH6eg3AU4D3Amtnef7jwMvM7Ek1v6+gAGiTNwEdYNsMz61z95Xu/gLgrnnWczLwDOAT7n6mu3/G3c8FngncAby+xj6XcSlwH/CGEb9vFhQAI2RmDzezc8zsh2b2GzP7rZndambnDbjeRcBpwNXufmD68+4+w33WZ/Xw4vEhQeHu9xMC5t4S/dlSfNV4rpn9vZntMLPfmdlPzeyMnnbvI+xVAHyreI2b2YU977sXuBZ4ZR//h/n6d5qZ6f7xxFQL0HJmtoQwkNcQBv2twJHAk4FBD3JtAJYBNwy4Hop1/Bo4uzhW8D1CP88o3qefv8QfBI4Azgd+B7wRuNDMbnP36wh/3R8FvK5o+6PiddOLQa4HTjWzJ7j7jyv8nx5gZo8GLgbGzeyF7r5nkPWlTgEwOi8F/hg41d2/XvO6n1g8zlBF1R93nzSzlwKfBb7U89Qe4BXu/uU+VrcEeGqx94CZXQLcDpxFOFj5QzO7nhAAV7n7NbOsp/v/ehIwUAC4+y/N7FXAVuCKIgSyvcOsAmB0urc9PsnMrnb3OoubukUe99S0vr3AduBy4LvA0cCbgf8ws83TzzLM4VPdjR8e2Ph+Sv97PLuLx3lvA1V8rSjjRuDphBDY5O77+uxTKygARucSwoG6DwBnmdlXgMuAbd0wKP4yvYVwZLzj7mtLrrtb0TXwKTozezJho3+7u3+6Z/kXCaHwb2a2zt0PlVjdTMcedhO+BvXVreKxTOXae/tc9ymEYJmYr2Eb6SDgiLj7PYTv0C8E/pNwyu4rwHfM7GFFs0ngk8A5fa5+V/F4dA1dfTuwlLCL/AB3vw/4KmHjXVtyXbOFRL9B1f1/7ZqzFeDuNt8PcBTwTeAg8Gp3z3LjBwXASLn7IXf/mru/FVgHfAHYCPxJ8fxV7n4x/f812l481nHF3KOLx4UzPLdo2mMdyvxVX188bp+zVQlmdiQheJ9F2PgvGXSdKVMAjICZrZp+BV2xC32IsAH8csC3+AHwW8Lu7KBuLR639C40s0cAmwl7KbfV8D5d3QNwc+29nAL8yt1/UsP7LSGcMcl+4wcdAxiVjwLPMLPLCBvPAuBU4MXAv7j7fBfnzMndD5nZpcBpZrbE3X/X+7yZ/RUPfu9eBTzMzN5T/D7h7p/vaX4u8Brgw8XxgOsIG+drCafs3lzy+39ZNxLu9nSOma0gXGdwh7t/r+j7MsJFSBfU8WbFWY6NNR+ETZe762fIP4QN6qvALwjnw3cC3wA2z9L+NODOPt/jJMLexCtmeO6a4rmZfq6Zof064KKivwcIexffBl5esi9binU/e5a+3Dlt2RmEPY/7i9ddOO05B45r+nNs449uCRYhMzsNONfLnwXovu5K4Ch3f+Yw+tUEM7uJEBgvb7ovbaRjABExs4VmthRYHH61pcUVhGW9E9hoZpuG08PRKoLwOODdDXeltbQHEBEz28KD18Z3TfS7JyBSlgJAJGP6CiCSMQWASMYUACIZUwCIZEwBIJIxBYBIxhQAIhlTAIhk7P8BOf1lSp6AdqgAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# - Set marker to \"o\" (draw circles instead of squares for matching cells).\n", "# - Set alpha to 0.3 (add some transparency to the matching cells).\n", "wp.viz_spy(m, markersize=13, marker=\"o\", alpha=0.3)" ] }, { "cell_type": "markdown", "id": "4d426aa3", "metadata": {}, "source": [ "## 2.7. Adding ticks, tick labels, and a grid (to make very fancy plots)\n", "We'll show this by reusing the above example.\n", "\n", "Adding ticks can be a little complicated, especially if we want the labels to be correct. So this code is a bit long, sorry -- I've tried to document it thoroughly to make it easier to read.\n", "\n", "(Honestly, if you are reading this tutorial for the first time, you should probably just skip this section. I spent an embarrassing amount of time getting this to work right and now I'm too attached to this code to delete it.)" ] }, { "cell_type": "code", "execution_count": 24, "id": "ad05eb6f", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# While we're adding this other explanatory stuff, let's add a title for good measure.\n", "fig, ax = wp.viz_spy(m, markersize=13, marker=\"o\", alpha=0.3, title=f\"Dot plot ($k = {m.k}$)\")\n", "\n", "###### PART 1: TICKS ######\n", "# First, let's add ticks. We need to add ticks for ax.grid() to work.\n", "# We'll add both major and minor ticks -- the major ticks will be used to position the grid lines,\n", "# and the minor ticks will be used to position the tick labels. This allows us to center our labels\n", "# in between ticks, which is nice when visualizing a matrix. This approach is inspired by\n", "# https://matplotlib.org/stable/gallery/ticks/centered_ticklabels.html. (For context on major vs.\n", "# minor ticks, see https://matplotlib.org/stable/gallery/ticks/major_minor_demo.html.)\n", "\n", "# Our major ticks' positions will look like [-0.5, 0.5, 1.5, ..., 13.5, 14.5, 15.5]. We\n", "# position them like this because the cells of the matrix occur at integer coordinates.\n", "majticks = [t - 0.5 for t in range(0, 17)]\n", "\n", "# We'll add minor ticks in between each of the major ticks. Our minor ticks use a smaller\n", "# range than our major ticks because there are (n - 1) spaces between n consecutive points (e.g.\n", "# your hands probably have 5 fingers each, but 4 spaces-between-fingers).\n", "minticks = range(0, 16)\n", "\n", "# The x-axis is simple -- we'll just use these major and minor ticks.\n", "ax.set_xticks(majticks)\n", "ax.set_xticks(minticks, minor=True)\n", "\n", "# The y-axis is a bit more complicated. Keep in mind that, when creating the DotPlotMatrix \"m\"\n", "# above, we left the DotPlotMatrix \"yorder\" parameter at its default value of \"BT\" -- so the\n", "# y-axis goes from bottom to top.\n", "#\n", "# However, matplotlib doesn't know about this -- by default, it'll consider \"row 0\" to be the\n", "# topmost row in the matrix, then row 1 the second-from-the-top row, ...)\n", "# We can address this by adjusting the ticks on the y-axis (so that \"row 0\" is now the\n", "# bottommost row, etc.) -- we'll do this using the t2y() function defined below.\n", "def t2y(tl):\n", " return [m.mat.shape[0] - t - 1 for t in tl]\n", "# Now, we can apply these corrected ticks to the y-axis.\n", "ax.set_yticks(t2y(majticks))\n", "ax.set_yticks(t2y(minticks), minor=True)\n", "\n", "###### PART 2: TICK LABELS ######\n", "# Next, let's add tick labels for the minor axis ticks. Adding one label for every minor tick\n", "# is a bit excessive, so let's just add labels for every other minor tick. (For dot plots of\n", "# long sequences, you'd probably want to add ticks and tick labels a lot farther apart -- e.g.\n", "# separated by 1 Mbp.)\n", "tick_labels = []\n", "# Even though Python uses 0-indexing, we'll use 1-indexing here to make this plot a bit easier\n", "# to interpret -- so we'll use range(1, 17) instead of range(0, 16).\n", "for i in range(1, 17):\n", " if i % 2 == 1:\n", " # Only add labels for odd-numbered positions.\n", " tick_labels.append(f\"{i:,}\")\n", " else:\n", " # We still need to add something for the even-numbered positions (to match the\n", " # number of ticks we added above), so we'll add an empty string for each of these ticks.\n", " tick_labels.append(\"\")\n", "\n", "# Since we already reversed the ticks for the y-axis to account for how yorder=\"BT\", we can now\n", "# provide the tick labels for both the x- and y-axes in ascending order.\n", "ax.set_xticklabels(tick_labels, minor=True)\n", "ax.set_yticklabels(tick_labels, minor=True)\n", "\n", "# Show ticks and labels on the left and bottom axes only\n", "ax.tick_params(\n", " labeltop=False, labelbottom=True, labelleft=True, labelright=False,\n", " top=False, bottom=True, left=True, right=False, which=\"both\"\n", ")\n", "# Also, don't show minor ticks' lines -- we'll still show their labels,\n", "# but showing their tick lines (when we're already showing the major ticks' lines)\n", "# is unnecessary (in my opinion)\n", "ax.tick_params(bottom=False, left=False, which=\"minor\")\n", "\n", "###### PART 3: GRID LINES ######\n", "# This part is easy :)\n", "#\n", "# We'll only draw grid lines for the major ticks -- this creates the nice appearance of minor\n", "# ticks being lined up with their matrix rows / columns.\n", "#\n", "# Note that ax.grid() has a lot of other options you may want to play around with\n", "# (linestyle, color, ...), should you want to make your dot plots even fancier.\n", "ax.grid(which=\"major\")" ] }, { "cell_type": "markdown", "id": "04bd6970", "metadata": {}, "source": [ "# 3. Creating dot plots of longer sequences\n", "\n", "As a final example, let's create a dot plot of two _E. coli_ strains' genomes. We'll use _E. coli_ K-12 [(from this assembly)](https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000005845.2/) and _E. coli_ O157:H7 [(from this assembly)](https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000008865.2/).\n", "\n", "I haven't included these FASTA files in the repository because they're both fairly large (both around ~5 MB), but -- for reference -- I downloaded them from the NCBI's website, moved them to a folder in this repository named `docs/data/`, and removed the two plasmid sequences from the O157:H7 assembly. (The K-12 assembly doesn't include any plasmid sequences; I guess this makes sense, given [the history of the K-12 strain](https://en.wikipedia.org/wiki/Escherichia_coli_in_molecular_biology#K-12)?)" ] }, { "cell_type": "code", "execution_count": 25, "id": "8b38c3ef", "metadata": {}, "outputs": [], "source": [ "# Note that pyfastx (https://github.com/lmdu/pyfastx), the library I use here to\n", "# load these FASTA files' sequences into memory, isn't included as a dependency of\n", "# wotplot; you can load your sequences however you'd like.\n", "import pyfastx\n", "e1 = pyfastx.Fasta(os.path.join(\"data\", \"ecoli_k12.fna\"))\n", "e2 = pyfastx.Fasta(os.path.join(\"data\", \"ecoli_o157h7.fna\"))" ] }, { "cell_type": "code", "execution_count": 26, "id": "3c353c78", "metadata": {}, "outputs": [], "source": [ "# Extract the sequences from these pyfastx.Fasta objects\n", "e1s = str(e1[0])\n", "e2s = str(e2[0])" ] }, { "cell_type": "markdown", "id": "f93d0f94", "metadata": {}, "source": [ "#### Sidenote: how do I select the $k$-mer size when dealing with big sequences?\n", "I don't have a single perfect answer. In general: smaller values of $k$ will show more details, but they'll increase the number of random matches between the two sequences, which will in turn increase the density of the dot plot matrix, which will in turn increase the memory footprint of the matrix. So you should consider the length of your sequences, their expected degree of similarity, what sort of \"signals\" you're looking for, and how much memory your system has when selecting the $k$-mer size.\n", "\n", "If you're really curious, here are some vague, informal suggestions that might be a good starting point. For tiny sequences (e.g. < 100 bp), any $k$ should be ok. For small-ish sequences (e.g. 100 bp – 1 kbp), any $k > 2$ should be ok. For sequences in the range 1–100 kbp, I tend to use $k = 10$. For large sequences (e.g. > 100 kbp), I recommend using $k > 10$. (But none of this is written in stone, so please don't quote me on it...)\n", "\n", "#### Sidenote: I didn't read that sidenote and you're bad at writing documentation\n", "Wow, that's kind of harsh??? But don't worry, you didn't miss much.\n", "\n", "## 3.1. Creating a dot plot matrix for the two _E. coli_ strains\n", "\n", "We'll use a $k$-mer size of $k$ = 20. We'll also set `verbose=True` in order to get detailed logging output as the matrix is constructed -- this helps reassure us that wotplot isn't frozen." ] }, { "cell_type": "code", "execution_count": 27, "id": "2c43f8b3", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.00s: Validating inputs...\n", "0.72s: Will find matches using common_substrings().\n", "0.72s: Finding forward matches between s1 and s2...\n", "34.95s: Found 3,357,713 forward match cell(s).\n", "34.95s: Computing ReverseComplement(s2)...\n", "34.99s: Finding reverse-complementary matches between s1 and s2...\n", "47.63s: Found 3,536,693 total (fwd and/or RC) match cell(s).\n", "47.63s: Dot plot matrix density = 0.00%.\n", "47.63s: Converting match information to COO format inputs...\n", "50.43s: Creating sparse matrix from COO format inputs...\n", "52.29s: Done creating the matrix.\n" ] } ], "source": [ "em = wp.DotPlotMatrix(e1s, e2s, 20, verbose=True)" ] }, { "cell_type": "markdown", "id": "8902f8f0", "metadata": {}, "source": [ "Creating the matrix usually takes about 30 seconds (on my six-year old laptop with 8 GB of RAM). (... Or closer to 40 seconds when I have a lot of stuff open.) All things considered, not too shabby!" ] }, { "cell_type": "markdown", "id": "6f994010", "metadata": {}, "source": [ "#### Sidenote: setting `suff_only=True` when creating the matrix to use a lower-memory, slower method\n", "\n", "If you would like to create a dot plot matrix of very long sequences, and your system has a limited amount of memory, then the default matrix construction methods might require too much memory for your system. (For my laptop with 8 GB of RAM, things start getting dicey when both sequences are above ~10 Mbp.)\n", "\n", "If you don't mind taking a bit longer, you can use the optional `suff_only=True` parameter when running `wp.DotPlotMatrix()` to use a different method. This different method requires less memory, at the cost of taking longer to run. Please see the \"Two methods for finding shared _k_-mers\" section of the README for more details." ] }, { "cell_type": "markdown", "id": "75949fae", "metadata": {}, "source": [ "## 3.2. Visualizing the _E. coli_ dot plot matrix\n", "\n", "Now let's visualize the matrix. We pretty much have to use `viz_spy()` here -- using `viz_imshow()` is not feasible, because creating a dense-format copy of this matrix would require us to have... wait, how many cells were in that matrix?" ] }, { "cell_type": "code", "execution_count": 28, "id": "59645c8a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(5498559, 4641633)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "em.mat.shape" ] }, { "cell_type": "code", "execution_count": 29, "id": "afd6818d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'The matrix has 25,522,292,906,847 cells.'" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f\"The matrix has {em.mat.shape[0] * em.mat.shape[1]:,} cells.\"" ] }, { "cell_type": "markdown", "id": "9d928ba5", "metadata": {}, "source": [ "Okay, so if we make the unrealistic assumption that each cell in the matrix can somehow be stored in a single bit, then we'd still need ~3.19 terabytes (!!!) of memory to store the matrix in dense format. That's not happening on my laptop, so we'll have to use `viz_spy()`." ] }, { "cell_type": "markdown", "id": "ea043999", "metadata": {}, "source": [ "Again, we can use `verbose=True` for `viz_spy()` in order to get information about how long visualization is taking. (This should go by quickly, though -- visualization is fast, compared to the process of creating the matrix.)" ] }, { "cell_type": "code", "execution_count": 30, "id": "f8bc2e3a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.03s: binary is not True, so we'll draw matches in different colors.\n", "0.03s: Visualizing \"1\" cells with spy()...\n", "0.65s: Done visualizing \"1\" cells.\n", "0.65s: Visualizing \"-1\" cells with spy()...\n", "1.00s: Done visualizing \"-1\" cells.\n", "1.00s: Visualizing \"2\" cells with spy()...\n", "1.45s: Done visualizing \"2\" cells.\n", "1.45s: Slightly restyling the visualization...\n", "1.46s: Done.\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = wp.viz_spy(\n", " em, markersize=0.01, title=f\"Comparison of two $E. coli$ genomes ($k$ = {em.k})\", verbose=True\n", ")\n", "ax.set_xlabel(f\"$E. coli$ K-12 substr. MG1655 ({len(e1s)/1e6:.2f} Mbp) \\u2192\")\n", "ax.set_ylabel(f\"$E. coli$ O157:H7 str. Sakai ({len(e2s)/1e6:.2f} Mbp) \\u2192\")\n", "fig.set_size_inches(8, 8)\n", "fig.savefig(os.path.join(\"img\", \"ecoli_example_dotplot.png\"), **savefig_kwargs)" ] }, { "cell_type": "markdown", "id": "a66b1c8a", "metadata": {}, "source": [ "There we have it! You may want to play around with the `markersize` parameter a bit in order to see what looks best." ] }, { "cell_type": "markdown", "id": "fa442b30", "metadata": {}, "source": [ "## 3.3. Visualizing the _E. coli_ dot plot matrix with `binary=True`\n", "\n", "When we're working with long sequences like these, the process of dot plot matrix visualization is (very slightly) faster if we draw all match cells as the same color." ] }, { "cell_type": "code", "execution_count": 31, "id": "7bf44264", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.05s: binary is True; visualizing all match cells with spy()...\n", "0.34s: Done visualizing all match cells.\n", "0.34s: Slightly restyling the visualization...\n", "0.34s: Done.\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# This code is the exact same as the above visualization of the binary E. coli matrix, except for binary=True\n", "fig, ax = wp.viz_spy(\n", " em, markersize=0.01, title=f\"Comparison of two $E. coli$ genomes ($k$ = {em.k})\", binary=True, verbose=True\n", ")\n", "ax.set_xlabel(f\"$E. coli$ K-12 substr. MG1655 ({len(e1s)/1e6:.2f} Mbp) \\u2192\")\n", "ax.set_ylabel(f\"$E. coli$ O157:H7 str. Sakai ({len(e2s)/1e6:.2f} Mbp) \\u2192\")\n", "fig.set_size_inches(8, 8)" ] } ], "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": "3.6.13" } }, "nbformat": 4, "nbformat_minor": 5 }