# Run deepSTRAPP to test for a relationship between diversification rates and trait data at a given focal time Wrapper function to run deepSTRAPP workflow for a given point in the past (i.e. the `focal_time`). It starts from traits mapped on a phylogeny (trait data) and BAMM output (diversification data) and carries out the appropriate statistical method to test for a relationship between diversification rates and trait data. Tests are based on block-permutations: rates data are randomized across tips following blocks defined by the diversification regimes identified on each tip (typically from a BAMM). Such tests are called STructured RAte Permutations on Phylogenies (STRAPP) as described in Rabosky, D. L., & Huang, H. (2016). A robust semi-parametric test for detecting trait-dependent diversification. Systematic biology, 65(2), 181-193. tools:::Rd_expr_doi("10.1093/sysbio/syv066") . See the original `BAMMtools::traitDependentBAMM()` function used to carry out STRAPP test on extant time-calibrated phylogenies. Tests can be carried out on speciation, extinction and net diversification rates. ```r run_deepSTRAPP_for_focal_time( contMap = NULL, densityMaps = NULL, ace = NULL, tip_data = NULL, trait_data_type, BAMM_object, focal_time, keep_tip_labels = TRUE, rate_type = "net_diversification", seed = NULL, nb_permutations = NULL, replace_samples = FALSE, alpha = 0.05, two_tailed = TRUE, one_tailed_hypothesis = NULL, posthoc_pairwise_tests = FALSE, p.adjust_method = "none", return_perm_data = FALSE, nthreads = 1, print_hypothesis = TRUE, extract_diversification_data_melted_df = FALSE, return_updated_trait_data_with_Map = FALSE, return_updated_BAMM_object = FALSE, verbose = TRUE ) ``` ## Arguments - `contMap`: For continuous trait data. Object of class `"contMap"`, typically generated with `prepare_trait_data()` or `phytools::contMap()`, that contains a phylogenetic tree and associated continuous trait mapping. The phylogenetic tree must be rooted and fully resolved/dichotomous, but it does not need to be ultrametric (it can includes fossils). - `densityMaps`: For categorical trait or biogeographic data. List of objects of class `"densityMap"`, typically generated with `prepare_trait_data()`, that contains a phylogenetic tree and associated posterior probability of being in a given state/range along branches. Each object (i.e., `densityMap`) corresponds to a state/range. The phylogenetic tree must be rooted and fully resolved/dichotomous, but it does not need to be ultrametric (it can includes fossils). - `ace`: (Optional) Ancestral Character Estimates (ACE) at the internal nodes. Obtained with `prepare_trait_data()` as output in the `$ace` slot. * For continuous trait data: Named numerical vector typically generated with `phytools::fastAnc()`, `phytools::anc.ML()`, or `ape::ace()`. Names are nodes_ID of the internal nodes. Values are ACE of the trait. * For categorical trait or biogeographic data: Matrix that record the posterior probabilities of ancestral states/ranges. Rows are internal nodes_ID. Columns are states/ranges. Values are posterior probabilities of each state per node. Needed in all cases to provide accurate estimates of trait values. - `tip_data`: (Optional) Named vector of tip values of the trait. * For continuous trait data: Named numerical vector of trait values. * For categorical trait or biogeographic data: Character string vector of states/ranges Names are nodes_ID of the internal nodes. Needed to provide accurate tip values. * For biogeographic data, ranges should follow the coding scheme of BioGeoBEARS with a unique CAPITAL letter per unique areas (ex: A, B), combined to form multi-area ranges (Ex: AB). Alternatively, you can provide tip_data as a matrix or data.frame of binary presence/absence in each area (coded as unique CAPITAL letter). In this case, columns are unique areas, rows are taxa, and values are integer (0/1) signaling absence or presence of the taxa in the area. - `trait_data_type`: Character string. Specify the type of trait data. Must be one of "continuous", "categorical", "biogeographic". - `BAMM_object`: Object of class `"bammdata"`, typically generated with `prepare_diversification_data()`, that contains a phylogenetic tree and associated diversification rate mapping across selected posterior samples. The phylogenetic tree must the same as the one associated with the `contMap`/`densityMaps`, `ace` and `tip_data`. - `focal_time`: Numerical. The time, in terms of time distance from the present, at which data must be extracted and the phylogeny and mappings must be cut. It must be smaller than the root age of the phylogeny. - `keep_tip_labels`: Logical. Specify whether terminal branches with a single descendant tip must retained their initial `tip.label` on the updated phylogeny. Default is `TRUE`. - `rate_type`: A character string specifying the type of diversification rates to use. Must be one of 'speciation', 'extinction' or 'net_diversification' (default). - `seed`: Integer. Set the seed to ensure reproducibility. Default is `NULL` (a random seed is used). - `nb_permutations`: Integer. To select the number of random permutations to perform during the tests. If NULL (default), all posterior samples will be used once. - `replace_samples`: Logical. To specify whether to allow 'replacement' (i.e., multiple use) of a posterior sample when drawing samples used to carry out the STRAPP test. Default is `FALSE`. - `alpha`: Numerical. Significance level to use to compute the `estimate` corresponding to the values of the test statistic used to assess significance of the test. This does NOT affect p-values. Default is `0.05`. - `two_tailed`: Logical. To define the type of tests. If `TRUE` (default), tests for correlations/differences in rates will be carried out with a null hypothesis that rates are not correlated with trait values (continuous data) or equals between trait states (categorical and biogeographic data). If `FALSE`, one-tailed tests are carried out. * For continuous data, it involves defining a `one_tailed_hypothesis` testing for either a "positive" or "negative" correlation under the alternative hypothesis. * For binary data (two states), it involves defining a `one_tailed_hypothesis` indicating which states have higher rates under the alternative hypothesis. * For multinominal data (more than two states), it defines the type of post hoc pairwise tests to carry out between pairs of states. If `posthoc_pairwise_tests = TRUE`, all two-tailed (if `two_tailed = TRUE`) or one-tailed (if `two_tailed = FALSE`) tests are automatically carried out. - `one_tailed_hypothesis`: A character string specifying the alternative hypothesis in the one-tailed test. For continuous data, it is either "negative" or "positive" correlation. For binary data, it lists the trait states with states ordered in increasing rates under the alternative hypothesis, separated by a greater-than such as c('A > B'). - `posthoc_pairwise_tests`: Logical. Only for multinominal data (with more than two states). If `TRUE`, all possible post hoc pairwise (Dunn) tests will be computed across all pairs of states. This is a way to detect which pairs of states have significant differences in rates if the overall test (Kruskal-Wallis) is significant. Default is `FALSE`. - `p.adjust_method`: A character string. Only for multinominal data (with more than two states). It specifies the type of correction to apply to the p-values in the post hoc pairwise tests to account for multiple comparisons. See `stats::p.adjust()` for the available methods. Default is `none`. - `return_perm_data`: Logical. Whether to return the stats data computed from the posterior samples for observed and permuted data in the output. This is needed to plot the histogram of the null distribution used to assess significance of the test with `plot_histogram_STRAPP_test_for_focal_time()`. Default is `FALSE`. - `nthreads`: Integer. Number of threads to use for paralleled computing of the STRAPP tests across the permutations. The R package `parallel` must be loaded for `nthreads > 1`. Default is `1`. - `print_hypothesis`: Logical. Whether to print information on what test is carried out, detailing the null and alternative hypotheses, and what significant level is used to rejected or not the null hypothesis. Default is `TRUE`. - `extract_diversification_data_melted_df`: Logical. Specify whether diversification data (regimes ID and tip rates) must be extracted from the `updated_BAMM_object` and returned in a melted data.frame. Default is `FALSE`. - `return_updated_trait_data_with_Map`: Logical. Specify whether the `trait_data` extracted for the given `focal_time` and the updated version of mapped phylogeny (`contMap`/`densityMaps`) provided as input should be returned among the outputs. The updated `contMap`/`densityMaps` consists in cutting off branches and mapping that are younger than the `focal_time`. Default is `FALSE`. - `return_updated_BAMM_object`: Logical. Specify whether the `updated_BAMM_object` with phylogeny and mapped diversification rates cut-off at the `focal_time` should be returned among the outputs. - `verbose`: Logical. Should progression be displayed? A message will be printed at each stepof the deepSTRAPP workflow, and for every batch of 100 BAMM posterior samples whose rates are regimes are updated, and optionally extracted in a melted data.frame (if `extract_diversification_data_melted_df = TRUE`). Default is `TRUE`. ## Returns The function returns a list with at least two elements. * `$STRAPP_results` List with at least eight elements summarizing the results of the STRAPP tests. See `compute_STRAPP_test_for_focal_time()` for a detailed description of the output. * `$focal_time` Integer. The time, in terms of time distance from the present, at which the data were extracted and the STRAPP test carried out. Optional formatted output: * `$diversification_data_df` A data.frame with six columns summarizing the diversification data as found on the phylogeny for the `focal_time`. See `extract_diversification_data_melted_df_for_focal_time()` for a detailed description of the output. Optional data updated for the `focal_time`: * `$updated_trait_data_with_Map` A list with four elements that contains trait data found at the `focal_time` and an updated `contMap` or `densityMaps` that can be used as input of `plot_contMap()` or `plot_densityMaps_overlay()` to display a phylogeny mapped with trait values/states/ranges with branches cut at the `focal_time`. See `extract_most_likely_trait_values_for_focal_time()` for a detailed description of the output. * `$updated_BAMM_object` An updated `BAMM_object` of class `"bammdata"` that contains rates and regimes ID found at the `focal_time`. Can be used as input of `plot_BAMM_rates()` to display a phylogeny mapped with diversification rates with branches cut at the `focal_time`. See `update_rates_and_regimes_for_focal_time()` for a detailed description of the output. ## Details The function encapsulates several functions carrying out each step of the deepSTRAPP workflow: ### Extract trait data `extract_most_likely_trait_values_for_focal_time()` extracts the most likely trait values found along branches at the `focal_time`. Optionally, the function can update the mapped phylogeny (`contMap`/`densityMaps`) such as branches overlapping the `focal_time` are shorten to the `focal_time`, and the trait mapping for the cut off branches are removed by updating the `$tree$maps` and `$tree$mapped.edge` elements. ### Extract diversification data `update_rates_and_regimes_for_focal_time()` updates the `BAMM_object` to obtain the diversification rates/regimes found along branches the `focal_time`. Optionally, the function can update the `BAMM_object` to display a mapped phylogeny such as branches overlapping the `focal_time` are shorten to the `focal_time` ### Extract diversification data in a melted df If requested (`extract_diversification_data_melted_df = TRUE`), `extract_diversification_data_melted_df_for_focal_time()` will be used to extract regimes ID and tip rates from the `updated_BAMM_object` and provide a melted data.frame summarizing the diversification data as found on the phylogeny for the `focal_time`. ### Compute STRAPP test `compute_STRAPP_test_for_focal_time()` carries out the appropriate statistical method to test for a relationship between diversification rates and trait data for a given point in the past (i.e. the `focal_time`). It can handle three types of statistical tests depending on the type of trait data provided: * Continuous trait data: Test for correlations with the Spearman's rank correlation test (See stats::cor.test ). * Binary trait data (two states): Test for differences in rates between states with the Mann-Whitney-Wilcoxon rank-sum test (See stats::wilcox.test ). * Multinominal trait data (More than two states): Test for differences in rates across all states with the Kruskal-Wallis H test (See stats::kruskal.test ). If `posthoc_pairwise_tests = TRUE`, Dunn's post hoc pairwise rank-sum tests between pairs of states will be carried out too (See dunn.test::dunn.test ). ## Examples ```r if (deepSTRAPP::is_dev_version()) { # ----- Example 1: Continuous trait ----- # ## Load data # Load trait df data(Ponerinae_trait_tip_data, package = "deepSTRAPP") # Load phylogeny with old calibration data(Ponerinae_tree_old_calib, package = "deepSTRAPP") # Load the BAMM_object summarizing 1000 posterior samples of BAMM data(Ponerinae_BAMM_object_old_calib, package = "deepSTRAPP") ## This dataset is only available in development versions installed from GitHub. # It is not available in CRAN versions. # Use remotes::install_github(repo = "MaelDore/deepSTRAPP") to get the latest development version. ## Prepare trait data # Extract continuous trait data as a named vector Ponerinae_cont_tip_data <- setNames(object = Ponerinae_trait_tip_data$fake_cont_tip_data, nm = Ponerinae_trait_tip_data$Taxa) # Select a color scheme from lowest to highest values color_scale = c("darkgreen", "limegreen", "orange", "red") # (May take several minutes to run) # Get Ancestral Character Estimates based on a Brownian Motion model # To obtain values at internal nodes Ponerinae_ACE <- phytools::fastAnc(tree = Ponerinae_tree_old_calib, x = Ponerinae_cont_tip_data) # Run a Stochastic Mapping based on a Brownian Motion model # to interpolate values along branches and obtain a "contMap" object Ponerinae_contMap <- phytools::contMap(Ponerinae_tree_old_calib, x = Ponerinae_cont_tip_data, res = 100, # Number of time steps plot = FALSE) # Plot contMap = stochastic mapping of continuous trait plot_contMap(contMap = Ponerinae_contMap, color_scale = color_scale) ## Set focal time to 10 Mya focal_time <- 10 ## Run deepSTRAPP on net diversification rates for focal time = 10 Mya. deepSTRAPP_output <- run_deepSTRAPP_for_focal_time( contMap = Ponerinae_contMap, ace = Ponerinae_ACE, tip_data = Ponerinae_cont_tip_data, trait_data_type = "continuous", BAMM_object = Ponerinae_BAMM_object_old_calib, focal_time = focal_time, rate_type = "net_diversification", return_perm_data = TRUE, extract_diversification_data_melted_df = TRUE, return_updated_trait_data_with_Map = TRUE, return_updated_BAMM_object = TRUE) ## Explore output str(deepSTRAPP_output, max.level = 1) # Access deepSTRAPP results str(deepSTRAPP_output$STRAPP_results) # Access trait data head(deepSTRAPP_output$updated_trait_data_with_Map$trait_data) # Access the diversification data in a melted data.frame head(deepSTRAPP_output$diversification_data_df) # Plot rates vs. trait values across branches plot_rates_vs_trait_data_for_focal_time(deepSTRAPP_output) # Plot updated contMap plot_contMap(deepSTRAPP_output$updated_trait_data_with_Map$contMap) ape::nodelabels(text = deepSTRAPP_output$updated_trait_data_with_Map$contMap$tree$initial_nodes_ID) # Plot diversification rates on updated phylogeny plot_BAMM_rates(deepSTRAPP_output$updated_BAMM_object, labels = TRUE) # Plot histogram of test stats plot_histogram_STRAPP_test_for_focal_time( deepSTRAPP_outputs = deepSTRAPP_output) # ----- Example 2: Categorical trait ----- # ## Load data # Load phylogeny data(Ponerinae_tree, package = "deepSTRAPP") # Load trait df data(Ponerinae_trait_tip_data, package = "deepSTRAPP") # Load the BAMM_object summarizing 1000 posterior samples of BAMM data(Ponerinae_BAMM_object_old_calib, package = "deepSTRAPP") ## This dataset is only available in development versions installed from GitHub. # It is not available in CRAN versions. # Use remotes::install_github(repo = "MaelDore/deepSTRAPP") to get the latest development version. ## Prepare trait data # Extract categorical data with 3-levels Ponerinae_cat_3lvl_tip_data <- setNames(object = Ponerinae_trait_tip_data$fake_cat_3lvl_tip_data, nm = Ponerinae_trait_tip_data$Taxa) table(Ponerinae_cat_3lvl_tip_data) # Select color scheme for states colors_per_states <- c("forestgreen", "sienna", "goldenrod") names(colors_per_states) <- c("arboreal", "subterranean", "terricolous") # (May take several minutes to run) ## Produce densityMaps using stochastic character mapping based on an equal-rates (ER) Mk model Ponerinae_cat_3lvl_data_old_calib <- prepare_trait_data( tip_data = Ponerinae_cat_3lvl_tip_data, phylo = Ponerinae_tree_old_calib, trait_data_type = "categorical", colors_per_levels = colors_per_states, evolutionary_models = "ARD", # Use default ARD model nb_simulations = 100, # Reduce number of simulations to save time seed = 1234, # Seet seed for reproducibility return_best_model_fit = TRUE, return_model_selection_df = TRUE, plot_map = FALSE) # Load directly output data(Ponerinae_cat_3lvl_data_old_calib, package = "deepSTRAPP") ## Set focal time to 10 Mya focal_time <- 10 # (May take several minutes to run) ## Run deepSTRAPP on net diversification rates for focal time = 10 Mya. deepSTRAPP_output <- run_deepSTRAPP_for_focal_time( densityMaps = Ponerinae_cat_data_old_calib$densityMaps, ace = Ponerinae_cat_data_old_calib$ace, tip_data = Ponerinae_cat_3lvl_tip_data, trait_data_type = "categorical", BAMM_object = Ponerinae_BAMM_object_old_calib, focal_time = focal_time, rate_type = "net_diversification", posthoc_pairwise_tests = TRUE, return_perm_data = TRUE, extract_diversification_data_melted_df = TRUE, return_updated_trait_data_with_Map = TRUE, return_updated_BAMM_object = TRUE) ## Explore output str(deepSTRAPP_output, max.level = 1) # Access deepSTRAPP results str(deepSTRAPP_output$STRAPP_results, max.level = 2) # Result for overall Kruskal-Wallis test deepSTRAPP_output$STRAPP_results[1:3] # Results for posthoc pairwise Dunn's tests deepSTRAPP_output$STRAPP_results$posthoc_pairwise_tests$summary_df # Access trait data head(deepSTRAPP_output$updated_trait_data_with_Map$trait_data) # Access the diversification data in a melted data.frame head(deepSTRAPP_output$diversification_data_df) # Plot rates vs. states across branches plot_rates_vs_trait_data_for_focal_time( deepSTRAPP_outputs = deepSTRAPP_output, colors_per_levels = colors_per_states) # Plot updated densityMaps cut at focal time plot_densityMaps_overlay(deepSTRAPP_output$updated_trait_data_with_Map$densityMaps) # Plot diversification rates on updated phylogeny plot_BAMM_rates(BAMM_object = deepSTRAPP_output$updated_BAMM_object, legend = TRUE, labels = FALSE, colorbreaks = deepSTRAPP_output$updated_BAMM_object$initial_colorbreaks$net_diversification) # Plot histogram of Kruskal-Wallis overall test stats plot_histogram_STRAPP_test_for_focal_time( deepSTRAPP_outputs = deepSTRAPP_output) # Plot histograms of posthoc pairwise Dunn's test stats plot_histogram_STRAPP_test_for_focal_time( deepSTRAPP_outputs = deepSTRAPP_output, plot_posthoc_tests = TRUE) # ----- Example 3: Biogeographic ranges ----- # ## Load data # Load phylogeny data(Ponerinae_tree_old_calib, package = "deepSTRAPP") # Load trait df data(Ponerinae_binary_range_table, package = "deepSTRAPP") # Load the BAMM_object summarizing 1000 posterior samples of BAMM data(Ponerinae_BAMM_object_old_calib, package = "deepSTRAPP") ## This dataset is only available in development versions installed from GitHub. # It is not available in CRAN versions. # Use remotes::install_github(repo = "MaelDore/deepSTRAPP") to get the latest development version. ## Prepare range data for Old World vs. New World # No overlap in ranges table(Ponerinae_binary_range_table$Old_World, Ponerinae_binary_range_table$New_World) Ponerinae_NO_data <- stats::setNames(object = Ponerinae_binary_range_table$Old_World, nm = Ponerinae_binary_range_table$Taxa) Ponerinae_NO_data <- as.character(Ponerinae_NO_data) Ponerinae_NO_data[Ponerinae_NO_data == "TRUE"] <- "O" # O = Old World Ponerinae_NO_data[Ponerinae_NO_data == "FALSE"] <- "N" # N = New World names(Ponerinae_NO_data) <- Ponerinae_binary_range_table$Taxa table(Ponerinae_NO_data) colors_per_ranges <- c("mediumpurple2", "peachpuff2") names(colors_per_ranges) <- c("N", "O") # (May take several minutes to run) ## Run evolutionary models Ponerinae_biogeo_data <- prepare_trait_data( tip_data = Ponerinae_NO_data, trait_data_type = "biogeographic", phylo = Ponerinae_tree_old_calib, evolutionary_models = "DEC+J", # Default = "DEC" for biogeographic BioGeoBEARS_directory_path = tempdir(), # Ex: "./BioGeoBEARS_directory/" keep_BioGeoBEARS_files = FALSE, prefix_for_files = "Ponerinae_old_calib", max_range_size = 2, split_multi_area_ranges = TRUE, # Set to TRUE to display the two outputs nb_simulations = 100, # Reduce to save time (Default = '1000') colors_per_levels = colors_per_ranges, return_model_selection_df = TRUE, verbose = TRUE) # Load directly output data(Ponerinae_biogeo_data_old_calib, package = "deepSTRAPP") ## Explore output str(Ponerinae_biogeo_data_old_calib, 1) ## Set focal time to 10 Mya focal_time <- 10 # (May take several minutes to run) ## Run deepSTRAPP on net diversification rates for focal time = 10 Mya. deepSTRAPP_output <- run_deepSTRAPP_for_focal_time( densityMaps = Ponerinae_biogeo_data_old_calib$densityMaps, ace = Ponerinae_biogeo_data_old_calib$ace, tip_data = Ponerinae_NO_data, trait_data_type = "biogeographic", BAMM_object = Ponerinae_BAMM_object_old_calib, focal_time = focal_time, rate_type = "net_diversification", return_perm_data = TRUE, extract_diversification_data_melted_df = TRUE, return_updated_trait_data_with_Map = TRUE, return_updated_BAMM_object = TRUE) ## Explore output str(deepSTRAPP_output, max.level = 1) # Access deepSTRAPP results str(deepSTRAPP_output$STRAPP_results, max.level = 2) # Result for Mann-Whitney-Wilcoxon test deepSTRAPP_output$STRAPP_results[1:3] # Access trait data head(deepSTRAPP_output$updated_trait_data_with_Map$trait_data) # Access the diversification data in a melted data.frame head(deepSTRAPP_output$diversification_data_df) # Plot rates vs. ranges across branches plot_rates_vs_trait_data_for_focal_time( deepSTRAPP_outputs = deepSTRAPP_output, colors_per_levels = colors_per_ranges) # Plot updated densityMaps cut at focal time plot_densityMaps_overlay(deepSTRAPP_output$updated_trait_data_with_Map$densityMaps) # Plot diversification rates on updated phylogeny plot_BAMM_rates(BAMM_object = deepSTRAPP_output$updated_BAMM_object, legend = TRUE, labels = FALSE, colorbreaks = deepSTRAPP_output$updated_BAMM_object$initial_colorbreaks$net_diversification) # Plot histogram of Mann-Whitney-Wilcoxon test plot_histogram_STRAPP_test_for_focal_time( STRAPP_results = deepSTRAPP_output$STRAPP_results) } ``` ## See Also `extract_most_likely_trait_values_for_focal_time()` `update_rates_and_regimes_for_focal_time()` `extract_diversification_data_melted_df_for_focal_time()` `compute_STRAPP_test_for_focal_time()` For a guided tutorial on complete deepSTRAPP workflow, see the associated vignettes: * For continuous trait data: `vignette("deepSTRAPP_continuous_data", package = "deepSTRAPP")` * For categorical trait data: `vignette("deepSTRAPP_categorical_3lvl_data", package = "deepSTRAPP")` * For biogeographic range data: `vignette("deepSTRAPP_biogeographic_data", package = "deepSTRAPP")` ## Author(s) Maël Doré

run_deepSTRAPP_for_focal_time function