1-Introduction-and-motivation.ipynb

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   "source": [
    "### Presenter details\n",
    "\n",
    "Georgia Tsambos (`gtsambos` at `uw.edu`) is a postdoctoral scholar in Kelley Harris' lab at the University of Washington, USA.\n",
    "\n",
    "\n",
    "### Sharing these notebooks\n",
    "\n",
    "\n",
    "These materials were designed for the [2025 WORKSHOP ON POPULATION AND SPECIATION GENOMICS](diff_in_anc) in Český Krumlov. They may be shared and re-used, but please say where you got them from! [Here](https://github.com/gtsambos/2025-ts-workshops) is a link to the public GitHub repository that holds these notebooks.\n",
    "\n",
    "### A rough plan for today\n",
    "\n",
    "9am – 12pm session:\n",
    " \n",
    " 1. Introduction to tree sequences\n",
    " 2. Simulation with `msprime` 1\n",
    " 3. Simulation with `msprime` 2\n",
    "\n",
    "2pm – 5pm session:\n",
    "\n",
    " 4. Inference with `tsinfer` and `tsdate`, analysis with `tskit`\n",
    " 5. Other simulation software\n",
    " \n",
    " <img src=\"pics/tskit-logo.png\" width=\"200\" height=\"200\">\n"
   ]
  },
  {
   "attachments": {
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"
    },
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Introduction to tree sequences\n",
    "\n",
    " - [1.1 Why tree sequences?](#WhyTS)\n",
    " - [1.2 Trees](#1.2Trees)\n",
    " - [1.3 Tree sequences](#1.3TreeSequences)\n",
    " - [1.4 Table collections](#1.4TableCollections)\n",
    " - [1.5 Mutations and variation](#1.5MutationsAndVariation)\n",
    " - [1.6 Uses of tree sequences](#1.6UsesOfTreeSequences)\n",
    " - [1.7 Relationship to ARGs](#1.7RelationshipToARGs)\n",
    "\n",
    "<a id='WhyTS'></a>\n",
    "## 1.1 Why tree sequences?\n",
    "\n",
    "Genetic sequences are BIG and VERY REPETITIVE:\n",
    "\n",
    "```\n",
    "   ...GTAACGCGATAAGAGATTAGCCCAAAAACACAGACATGGAAATAGCGTA...\n",
    "   ...GTAACGCGATAAGAGATTAGCCCAAAAACACAGACATGGAAATAGCGTA...\n",
    "   ...GTAACGCGATAAGATATTAGCCCAAAAACACAGACATGGAAATAGCGTA...\n",
    "   ...GTAACGCGATAAGATATTAGCCCAAAAACACAGACATGGAAATAGCGTA...\n",
    "   ...GTAACGCGATAAGATATTAGCCCAAAAACACAGACATGGAAATAGCGTA...\n",
    "   ...GTAACGCGATAAGATATTAGCCCAAAAACACAGACATGGTAATAGCGTA...\n",
    "   ...GTAACGCGATAAGATATTAGCCCAAAAACACAGACATGGTAATAGCGTA...\n",
    "```\n",
    "   \n",
    "Because of this, you are probably used to storing your data in a compressed format, and decompressing it only when you need to perform analyses or query the data. Doing this can be time-consuming and computationally expensive, however.\n",
    "\n",
    "### The key idea\n",
    "\n",
    "Common haplotypes in a sample are often simply a consequence of some common history. So if we know this history (as we always do in simulations!), storing it directly is often more convenient and efficient than storing the raw haplotypes.\n",
    "A tree sequence is an encoding of a complete genetic genealogy for a sample of chromosomes at each location along their chromosomes.\n",
    "\n",
    "As they hold rich detail about the history of the sample, many important processes can be observed directly from the tree structure. So a tree sequence is often more informative than raw genotype/haplotype data, even though it is also more compact.\n",
    "\n",
    "<img src=\"pics/worksheet1-ts-pics.png\" width=\"700\" height=\"400\">\n",
    "\n",
    "### The key benefits\n",
    "\n",
    "**File size**\n",
    "\n",
    "They can store large simulated datasets extremely compactly. (Often >100 of times smaller than VCFs for real-sized datasets!)\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "**Computation**\n",
    "\n",
    "\n",
    "They can be queried and modified extremely quickly. In later sessions we will see that this enables quick calculation of many important population statistics.\n",
    "\n",
    "![image-2.png](attachment:image-2.png)\n",
    "\n",
    "**Accessibility**\n",
    "\n",
    "Tree sequences also make it possible to analyse 'normal-sized' genomic datasets quickly, using modest computational resources. \n",
    "\n",
    "\n",
    "### Who is the tskit-dev community?\n",
    "\n",
    "<img src=\"pics/tskit-people.png\" width=\"1200\" height=\"500\">\n",
    "\n",
    "and many more...\n",
    "\n",
    "We develop and maintain software to *simulate*, *analyse* and *infer* genetic information with tree sequence structures.\n",
    "Although we'll look at many of these packages today, we can't cover everything -- see the [tskit website](https://tskit.dev/) for a current list of packages in this ecosystem,\n",
    "as well as links to useful tutorials and documentation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tskit\n",
    "import io\n",
    "from IPython.display import SVG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1.2Trees'></a>\n",
    "## 1.2 Trees\n",
    "\n",
    "At a single nucleotide base, the genealogy of a sample can be represented by a single tree.\n",
    "\n",
    "The tree consists of nodes, which represent the alleles held by different chromosomes in the history of the sample, and edges, which represent genealogical relationships between the alleles.\n",
    "\n",
    "Suppose we have a sample of 4 alleles, each from a different DNA sequence, and we wish to understand their history. We could represent this history with a tree like this one:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = tskit.load(\"data/example.trees\")\n",
    "tree = ts.first()\n",
    "SVG(tree.draw())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The four *sample nodes* are those labelled 0-3 at the leaves of the tree. All other nodes are *ancestral nodes*: these are the alleles held by individuals that are ancestral to the sample.\n",
    "(Ignore the red marks and numbers for the moment.)\n",
    "\n",
    "The height of the nodes in the tree indicates the age of the node, and an edge joining a pair of nodes is used to indicate that the allele of the lower ('child') node is descended from the allele of the upper ('parent') node.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1.3TreeSequences'></a>\n",
    "## 1.3 Tree sequences\n",
    "\n",
    "The sample history encoded by a tree at a single base will typically also apply to some interval of neighbouring bases. However, due to recombinations in the history of the sample, the genealogy will typically be different at more distant locations on the chromosome, and so must be represented by a different tree. Thus, the history of a sample of sequences can be encoded in a sequence of trees - a tree sequence!\n",
    "\n",
    "<img src=\"pics/worksheet1-treesequence.png\" width=\"700\" height=\"300\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SVG(ts.draw_svg())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The endpoints of the intervals are the locations where recombination has occurred in the history of the sample.\n",
    "\n",
    "Notice that the adjacent trees look very similar to each other. This makes sense: each recombination event should correspond to a single \"tree edit\" (or \"subtree-prune-and-regraft\" operation). Because these recombinations are specific to particular lineages, many genealogical relationships are unaffected by a given recombination. This means that topological features are often shared over many neighbouring trees."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Exercise:* `tskit` has a number of inbuilt methods and attributes for `Tree` objects. The following code shows three of these: `total_branch_length`, `span` and `tmrca`. Have a look at the outputted trees and see if you can figure out what they each calculate. \n",
    "\n",
    "If you have time to spare, have a look at some of the other methods and attributes that `tskit` provides for trees in the [API documentation](https://tskit.dev/tskit/docs/stable/python-api.html#tree-api).\n",
    "Modify the code below to output any of these that seem interesting to you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for tree in ts.trees():\n",
    "    display(SVG(tree.draw(width=300)))\n",
    "    print(\"total branch length:\", tree.total_branch_length)\n",
    "    print(\"span\", tree.span)\n",
    "    print(\"TMRCA of 0 and 1:\", tree.tmrca(0, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1.4TableCollections'></a>\n",
    "## 1.4 Table collections\n",
    "\n",
    "It turns out that each tree sequence object can be entirely specified by a set of tables. Instead of storing each tree individually, each individual topological feature is stored as a row in a relevant table.\n",
    "\n",
    "Our example tree sequence can be represented with the following NodeTable and EdgeTable:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ts.tables.nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts.tables.edges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are other tables like Mutations, Sites, Populations, etc. that we will see later in this workshop.\n",
    "\n",
    "Any topological feature that is common to several trees must only be recorded once in the corresponding collection of tables. For instance, all of the trees in our example tree sequence have an edge joining nodes 1 and 5, and this edge is recorded just once in the table.\n",
    "\n",
    "This *succinctness* is one of main reasons why the tree sequence format is so compact!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Exercise*: Can you modify the following Table Collection until the corresponding tree sequence looks like the one in this plot?\n",
    "\n",
    "<img src=\"pics/worksheet1-pic-for-example.png\" width=\"400\" height=\"250\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modify this code.\n",
    "nodes_ex = io.StringIO(\"\"\"\\\n",
    "id      is_sample   population      time\n",
    "0       1       0               0.0\n",
    "1       1       0               0.0\n",
    "2       1       0               0.0\n",
    "3       1       0               0.0\n",
    "4       1       0               0.0\n",
    "5       0       0               1.0\n",
    "6       0       0               1.5\n",
    "7       0       0               2.0\n",
    "8       0       0               3.0\n",
    "9       0       0               3.5\n",
    "10      0       0               4.0\n",
    "\"\"\")\n",
    "edges_ex = io.StringIO(\"\"\"\\\n",
    "id      left            right           parent  child\n",
    "0       0.00000000      1.00000000      5       0\n",
    "1       0.00000000      1.00000000      5       1\n",
    "2       0.00000000      1.00000000      6       2\n",
    "3       0.00000000      1.00000000      6       3\n",
    "4       0.00000000      0.50000000      7       5\n",
    "5       0.50000000      1.00000000      8       5\n",
    "6       0.50000000      1.00000000      8       6\n",
    "7       0.00000000      0.50000000      9       6\n",
    "8       0.00000000      0.50000000      9       7\n",
    "9       0.50000000      1.00000000      10      8\n",
    "\"\"\")\n",
    "\n",
    "# Load the tree sequence.\n",
    "ts_ex = tskit.load_text(nodes=nodes_ex, edges=edges_ex, strict=False)\n",
    "\n",
    "# Test by plotting it.\n",
    "SVG(ts_ex.draw_svg())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1.5MutationsAndVariation'></a>\n",
    "## 1.5 Mutations and variation\n",
    "\n",
    "Variation is a consequence of mutations in the history of the sample. Thus, by adding some information about mutations to our tree sequences, we can use them to encode full haplotype data for each of our samples.\n",
    "\n",
    "We need to store the genomic location of each variant site, as well as the lineage of the tree affected by the mutation. (This corresponds to an edge in the tree sequence). The subsample of haplotypes with the mutated type is simply the subset of nodes in the part of the tree that descends from the mutation.\n",
    "\n",
    "<img src=\"pics/worksheet1-ts-with-muts.png\" width=\"550\" height=\"550\">\n",
    "\n",
    "\n",
    "This information is encoded with two extra tables: a site table showing the locations of the variant sites, and a mutations table showing the lineage of the tree affected by the mutation.\n",
    "\n",
    "<img src=\"pics/worksheet1-tables-mut-highlight.png\" width=\"700\" height=\"250\">\n",
    "\n",
    "The mutation and alleles highlighted in pink correspond to the highlighted rows of the site and mutation tables.\n",
    "\n",
    "Note that even if we had 400 or 400 000 samples, we would still only need two rows to store information about the alleles held by the samples at this position!\n",
    "\n",
    "Here's a tree sequence file corresponding to our coded example above. By default, the `draw` method will plot each mutation on the relevant edge of the relevant tree in red."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SVG(ts.draw_svg(y_axis=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mutations table records, at minimum, an integer ID, a node that it lies above, and a site at which the mutation occurs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts.tables.mutations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts.num_mutations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for m in ts.mutations():\n",
    "    print(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sites table records, at minimum, an integer ID, a position along the genome where the site is, and the type of the ancestral allele at that site."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts.tables.sites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts.num_sites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for s in ts.sites():\n",
    "    print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1.6UsesOfTreeSequences'></a>\n",
    "## 1.6 Uses of tree sequences\n",
    "\n",
    "When I began my PhD candidature in 2017, details of the tree sequence structure had only recently been published, and there were very few methods making use of them. It has been exciting to follow the rapid uptake of tree sequences in the population genetics community since then!\n",
    "\n",
    "### Simulation\n",
    "\n",
    " - [msprime](https://github.com/tskit-dev/msprime)\n",
    " - [SLiM](https://messerlab.org/slim/)\n",
    " - [pyslim](https://github.com/tskit-dev/pyslim)\n",
    " - [stdpopsim](https://popsim-consortium.github.io/stdpopsim-docs/stable/index.html)\n",
    " - [slendr](https://www.slendr.net/)\n",
    "\n",
    "### Inference\n",
    "\n",
    " - [tsinfer](https://github.com/tskit-dev/tsinfer)\n",
    " - [Relate](https://myersgroup.github.io/relate/)\n",
    " - [ARGinfer](https://github.com/alimahmoudi29/arginfer)\n",
    " \n",
    "\n",
    "### Population genetic analyses\n",
    "\n",
    " - [tskit](https://github.com/tskit-dev/tskit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1.7RelationshipToARGs'></a>\n",
    "## 1.7 Relationship to ARGs\n",
    "\n",
    "Most of the current literature on gene genealogies names the objects of interest as *ancestral recombination graphs (ARGs)*. However, this term has been used in many different ways since it was initially defined, inviting considerable confusion! See [this paper](https://doi.org/10.1093/genetics/iyae100) for further discussion of this problem. In this workshop, I will use the term 'ARG' to refer to a realisation of the coalescent process with recombination, and to 'tree sequences' as a data structure that encodes a relevant subset of the information in these ARGs.\n",
    "\n",
    "Most of the tree sequences that we will be working with today will be generated by `msprime` and `tsinfer`,\n",
    "and will look like diagram (c) below.\n",
    "Every node in these tree sequences represents either a present-day sample, or a coalescent ancestor that contributed genetic material to two or more lineages moving forward in time.\n",
    "This is the minimum amount of information needed to fully represent genetic variation in the dataset.\n",
    "However, other tree sequences may contain more or less information than this.\n",
    "For instance, diagram (b) adds information about recombinant ancestors in the history of the sample."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"pics/arg-vs-ts.png\" width=\"600\" height=\"250\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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