# Analyzing non-slendr tree sequences

In previous vignettes we have demonstrated the slendr R interface for defining and executing msprime and SLiM population genetic models. We have also provided an overview for its interface to the tree-sequence processing library tskit.

However, although its model-definition interface is quite convenient, slendr cannot (and never will) support every possible msprime or SLiM. model The array of features provided by these simulation frameworks is simply to big and implementing an R interface to every single one of them would not make much sense.

That said, the tree-sequence outputs produced by “pure” (i.e. non-slendr) msprime and SLiM scripts are no different from those generated by slendr models themselves. For users who would rather use their own simulation scripts but find slendr’s tskit R interface (not its simulation interface) appealing, the R package provides a possibility to load, process, visualize and analyze standard tree sequences which were not generated by slendr itself.

In this vignette we give a brief overview of how this works, using example tree sequences produced by two very simple simulation scripts written in pure SLiM and msprime (i.e. scripts which don’t utilize slendr’s spatial features, symbolic names of simulated individuals, and automated translation of time units). We won’t be going into detail explaining how those scripts work, because we assume this functionality will be used by those already familiar with msprime or SLiM. Similarly, we won’t cover the R-tskit functionality of slendr in detail either, simply because the contents of this vignette is already covered by other tutorials provided by slendr.

    library(slendr)

    library(dplyr)
    library(magrittr)
    library(ggplot2)

    init_env()

    SEED <- 42
    set.seed(SEED)

#### Non-spatial SLiM tree sequences

Consider the following SLiM script, which creates a couple of populations (with different \(N_e\)) splitting from an ancestral population `p1` (lets save it to `/tmp/nonspatial.slim`):

    initialize() {
        setSeed(42);
        initializeTreeSeq();
        initializeMutationRate(0);
        initializeMutationType("m1", 0.5, "f", 0.0);
        initializeGenomicElementType("g1", m1, 1.0);
        initializeGenomicElement(g1, 0, 1e6);
        initializeRecombinationRate(1e-8);
    }

    1 early() {
        sim.addSubpop("p1", 10);
    }

    1000 early() {
        sim.addSubpopSplit("p2", 500, p1);
    }

    3000 early() {
        sim.addSubpopSplit("p3", 2500, p1);
    }

    5000 early() {
        sim.addSubpopSplit("p4", 10000, p1);
    }

    6000 late() {
        sim.treeSeqOutput("/var/folders/lq/bl36db_s6w908hnjkntdp4140000gn/T//RtmpyDHthW/filebb4e7700da51");
    }

After we run this script in SLiM, we can use slendr to load the output tree sequence (saved to `/tmp/nonspatial-slim.trees`), simplify it, and overlay mutations on it using the standard functionality originally developed for slendr tree sequences. Note that this is the same command we would use for loading slendr tree sequences, except we direct the `ts_read()` function straight to the tree-sequence output file rather than using the `ts_read(<model-object>)` format used when working with standard slendr simulations. This way, slendr

    ts <- ts_read(nonspatial_trees_file) %>%
      ts_simplify() %>%
      ts_mutate(mutation_rate = 1e-7, random_seed = SEED)

We can extract information about individual’s names, nodes, population assignments, etc. just as with any slendr tree sequence with the function `ts_nodes()`. As with standard slendr models, this function loads the “raw” node and individual tree-sequences tables, performs a couple of join operations, and presents the whole thing in a nice unified form for interactive data analysis (which can also include spatial information—see below):

    data <- ts_nodes(ts) %>% dplyr::filter(sampled)
    data
    #> # A tibble: 26,020 × 11
    #>    pop   node_id  time time_tskit sampled remembered retained alive pedigree_id
    #>    <chr>   <int> <dbl>      <dbl> <lgl>   <lgl>      <lgl>    <lgl>       <dbl>
    #>  1 p1          0     0          0 TRUE    FALSE      FALSE    TRUE     20073000
    #>  2 p1          1     0          0 TRUE    FALSE      FALSE    TRUE     20073000
    #>  3 p1          2     0          0 TRUE    FALSE      FALSE    TRUE     20073001
    #>  4 p1          3     0          0 TRUE    FALSE      FALSE    TRUE     20073001
    #>  5 p1          4     0          0 TRUE    FALSE      FALSE    TRUE     20073002
    #>  6 p1          5     0          0 TRUE    FALSE      FALSE    TRUE     20073002
    #>  7 p1          6     0          0 TRUE    FALSE      FALSE    TRUE     20073003
    #>  8 p1          7     0          0 TRUE    FALSE      FALSE    TRUE     20073003
    #>  9 p1          8     0          0 TRUE    FALSE      FALSE    TRUE     20073004
    #> 10 p1          9     0          0 TRUE    FALSE      FALSE    TRUE     20073004
    #> # ℹ 26,010 more rows
    #> # ℹ 2 more variables: ind_id <dbl>, pop_id <int>

Moving on to tskit statistics, we can use the data table above to extract a list of nodes belonging to each population (this is what various tskit tree-sequence statistics operate on, and slendr follows that design). Here we are computing the nucleotide diversity in each of the four populations using the `ts_diversity()` function, by first creating a list of lists with node IDs (i.e. chromosomes) of individuals in each population:

    sample_sets <- split(data$node_id, data$pop)

    # compute nucleotide diversity in each population
    # (any other ts_*() tskit R interface function should work)
    ts_diversity(ts, sample_sets)
    #> # A tibble: 4 × 2
    #>   set    diversity
    #>   <chr>      <dbl>
    #> 1 p1    0.00000755
    #> 2 p2    0.000241
    #> 3 p3    0.000463
    #> 4 p4    0.000201

Just as with slendr tree sequences (as demonstrated in our paper) we can get a individual trees too, extracted in the in the phylogenetic format provided by the ape R package. Here we first simplify the tree sequence even further to just 10 nodes to make things manageable:

    samples <- sample(data$node_id, 10)
    ts_small <- ts_simplify(ts, simplify_to = samples)

    # extract the 42nd tree in the genealogy to an R 'phylo' format
    tree <- ts_phylo(ts_small, 42 - 1)
    #> Starting checking the validity of tree...
    #> Found number of tips: n = 10
    #> Found number of nodes: m = 9
    #> Done.
    tree
    #>
    #> Phylogenetic tree with 10 tips and 9 internal nodes.
    #>
    #> Tip labels:
    #>   1, 0, 9, 4, 8, 7, ...
    #> Node labels:
    #>   45, 28, 22, 31, 35, 36, ...
    #>
    #> Rooted; includes branch length(s).

Once we have that R tree object, we can use packages like ggtree to visualize the tree (any other phylogenetic package would work too). Note that because nodes of ‘ape phylo’ trees must conform to a strict format (they must be labelled `1...N`), we will extract the information about the node IDs in the tskit tree-sequence data to be able to plot them in the tree.

    library(ggtree)

    labels <- ts_nodes(tree) %>% select(node = phylo_id, tskit_id = node_id)

    ggtree(tree, branch.length = "none") %<+% labels +
      geom_label(aes(label = tskit_id))

    library(ape)
    plot(tree, show.tip.label = FALSE)
    nodelabels()
    tiplabels()

#### msprime (non-slendr) tree sequences

The same as above applies also to msprime tree sequences (which is really not that surprising, given that it’s all tskit under the hood).

We can start with Python:

    import msprime
    ts = msprime.sim_ancestry(100)
    ts.dump(<trees file>)

And then we can proceed with loading the msprime tree sequence into R and analyze it with the slendr functionality:

    ts <- ts_read(msprime_trees_file)

    ts_nodes(ts)
    #> # A tibble: 199 × 7
    #>    pop   ind_id node_id  time time_tskit sampled pop_id
    #>    <chr>  <dbl>   <int> <dbl>      <dbl> <lgl>    <int>
    #>  1 0         NA       0     0          0 TRUE         0
    #>  2 0         NA       1     0          0 TRUE         0
    #>  3 0         NA       2     0          0 TRUE         0
    #>  4 0         NA       3     0          0 TRUE         0
    #>  5 0         NA       4     0          0 TRUE         0
    #>  6 0         NA       5     0          0 TRUE         0
    #>  7 0         NA       6     0          0 TRUE         0
    #>  8 0         NA       7     0          0 TRUE         0
    #>  9 0         NA       8     0          0 TRUE         0
    #> 10 0         NA       9     0          0 TRUE         0
    #> # ℹ 189 more rows

#### Spatial SLiM (non-slendr) tree sequences

Furthermore, the generalized interface also supports slendr’s spatial tree-sequence features, with all the bells and whistles.

For instance, lets take the following spatial SLiM script (modified from the SLiM manual) and execute it with SLiM the usual way:

    initialize() {
        setSeed(42);
          initializeSLiMOptions(keepPedigrees=T, dimensionality="xy");
          initializeTreeSeq();
          initializeMutationRate(1e-7);
          initializeMutationType("m1", 0.5, "f", 0.0);
          initializeGenomicElementType("g1", m1, 1.0);
          initializeGenomicElement(g1, 0, 1e6);
          initializeRecombinationRate(1e-8);
    }
    1 early() {
        sim.addSubpop("p1", 500);

        // initial positions are random in ([0,1], [0,1])
        p1.individuals.x = runif(p1.individualCount);
          p1.individuals.y = runif(p1.individualCount);
    }
    modifyChild() {
          // draw a child position near the first parent, within bounds
          do child.x = parent1.x + rnorm(1, 0, 0.02);
          while ((child.x < 0.0) | (child.x > 1.0));

          do child.y = parent1.y + rnorm(1, 0, 0.02);
          while ((child.y < 0.0) | (child.y > 1.0));

          return T;
    }
    1: late() {
        sim.treeSeqRememberIndividuals(sim.subpopulations.individuals, permanent = F);
    }

    10000 late() {
        sim.treeSeqOutput("/var/folders/lq/bl36db_s6w908hnjkntdp4140000gn/T//RtmpyDHthW/filebb4e58a127c4");
    }

We can then load and simplify the output tree sequence in just as we did above in this vignette (or anywhere in the slendr documentation):

    ts <- ts_read(spatial_trees_file) %>% ts_simplify()

Finally, we can access the spatio-temporal data embedded in the output tree sequence in the standard slendr way (note the spatial sf column `location` with the `POINT` data type):

    data <- ts_nodes(ts)
    data
    #> Simple feature collection with 1955 features and 11 fields
    #> Geometry type: POINT
    #> Dimension:     XY
    #> Bounding box:  xmin: 0.07649281 ymin: 0.0006142841 xmax: 0.9984433 ymax: 0.9892587
    #> CRS:           NA
    #> # A tibble: 1,955 × 12
    #>    pop   node_id  time time_tskit               location sampled remembered
    #>    <chr>   <int> <dbl>      <dbl>                <POINT> <lgl>   <lgl>
    #>  1 p1          0     0          0   (0.904485 0.0777489) TRUE    FALSE
    #>  2 p1          1     0          0   (0.904485 0.0777489) TRUE    FALSE
    #>  3 p1          2     0          0   (0.668828 0.5246381) TRUE    FALSE
    #>  4 p1          3     0          0   (0.668828 0.5246381) TRUE    FALSE
    #>  5 p1          4     0          0  (0.7707701 0.1518462) TRUE    FALSE
    #>  6 p1          5     0          0  (0.7707701 0.1518462) TRUE    FALSE
    #>  7 p1          6     0          0  (0.7617792 0.2616453) TRUE    FALSE
    #>  8 p1          7     0          0  (0.7617792 0.2616453) TRUE    FALSE
    #>  9 p1          8     0          0 (0.9807475 0.04223008) TRUE    FALSE
    #> 10 p1          9     0          0 (0.9807475 0.04223008) TRUE    FALSE
    #> # ℹ 1,945 more rows
    #> # ℹ 5 more variables: retained <lgl>, alive <lgl>, pedigree_id <dbl>,
    #> #   ind_id <dbl>, pop_id <int>

Because we get the tree sequence converted to the spatial sf data format, we can use standard geospatial packages to use any spatial data analysis methods that those packages provide.

To briefly demonstrate what this means, we can trivially plot the location of each recorded node:

    ggplot() + geom_sf(data = data, aes(color = time), alpha = 0.5)

We can also collect spatio-temporal ancestry information of a particular node (i.e. the times and locations of all of its ancestors all the way to the root, with each “link” in the plot signifying parent-child edge somewhere along the tree sequence) and plot it on a 2D surface (x and y dimensions [0, 1]). The plot is a little chaotic, but hopefully conveys the idea (the “focal node” 0 is highlighted in red). This is essentially the same plot we have in the last figure of our paper.

    ancestral_links <- ts_ancestors(ts, 0)

    ggplot() +
        geom_sf(data = ancestral_links, size = 0.5, aes(alpha = parent_time)) +
        geom_sf(data = sf::st_set_geometry(ancestral_links, "parent_location"), aes(color = parent_time)) +
        geom_sf(data = data[data$node_id == 0, ], size = 3, color = "red")

## Conclusion

In this vignette we gave a brief overview of using slendr’s R-tskit interface for loading, processing, and analyzing “pure” non-slendr tree sequences produced by msprime and SLiM scripts.

Although we have only touched upon the most basic features of its R-tskit interface for standard tree sequences, it is important to note that as far as slendr is concerned, it does not matter how a tree sequence was produced, as long as it conforms to the tskit specification. This means that regardless of the source of your tree sequence data, you should be able to use slendr’s tskit functionality to run your analyses.