--- title: "IMPC Mouse data - Variance in sex differences" author: "Susanne Zajitschek, Felix Zajitschek, Russell Bonduriansky,Robert Brooks, Will Cornwell, Daniel Falster, Malgortaza Lagisz, Jeremy Mason, Daniel Noble, Alistair Senior & Shinichi Nakagawa" date: "August 2019" output: html_document: code_download: true code_folding: hide depth: 4 number_sections: no theme: flatly toc: yes toc_depth: 4 toc_float: yes html_notebook: toc: yes pdf_document: toc: yes toc_depth: '4' subtitle: Electronic Supplementary Material --- # Set-up ## Loading packages & custom functions ```{r, include=FALSE} knitr::opts_chunk$set( echo = TRUE, warning = FALSE, message = FALSE, cache = TRUE, tidy = TRUE ) ``` ```{r} library(readr) library(dplyr) library(metafor) library(devtools) library(purrr) library(tidyverse) library(tidyr) library(tibble) library(kableExtra) library(robumeta) library(ggpubr) library(ggplot2) library(here) ``` Functions for preparing the data for meta analyses 1) Create function for sub-setting the data to choose only one data point per individual per trait: "data_subset_parameterid_individual_by_age" ```{r} data_subset_parameterid_individual_by_age <- function(mydata, parameter, age_min=0, age_center=100) { tmp <- mydata %>% filter( age_in_days >= age_min, id == parameter ) %>% # take results for single individual closest to age_center mutate(age_diff = abs(age_center - age_in_days)) %>% group_by(biological_sample_id) %>% filter(age_diff == min(age_diff)) %>% select(-age_diff)# %>% # filter(!duplicated(biological_sample_id)) # still some individuals with multiple records (because same individual appear under different procedures, so filter to one record) j <- match(unique(tmp$biological_sample_id), tmp$biological_sample_id) tmp[j, ] } ``` 2) "Population statistics": "calculate_population_stats" This function groups animals from the same strain and same insitiution together. This is done for each trait seoarately, and only for traits that have been measured in both sexes. Any group containing fewer than 5 individuals is excluded. ```{r} calculate_population_stats <- function(mydata, min_individuals = 5) { mydata %>% group_by(population, strain_name, production_center, sex) %>% summarise( trait = parameter_name[1], x_bar = mean(data_point), x_sd = sd(data_point), n_ind = n() ) %>% ungroup() %>% filter(n_ind > min_individuals) %>% # Check both sexes present & filter those missing group_by(population) %>% mutate( n_sex = n_distinct(sex) ) %>% ungroup() %>% filter(n_sex == 2) %>% select(-n_sex) %>% arrange(production_center, strain_name, population, sex) } ``` 3) Extraction of effect sizes and sample variances: "create_meta_analysis_effect_sizes" ```{r} create_meta_analysis_effect_sizes <- function(mydata) { i <- seq(1, nrow(mydata), by = 2) input <- data.frame( n1i = mydata$n_ind[i], n2i = mydata$n_ind[i + 1], x1i = mydata$x_bar[i], x2i = mydata$x_bar[i + 1], sd1i = mydata$x_sd[i], sd2i = mydata$x_sd[i + 1] ) mydata[i, ] %>% select(strain_name, production_center, trait) %>% mutate( effect_size_CVR = calculate_lnCVR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i), sample_variance_CVR = calculate_var_lnCVR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i), effect_size_VR = calculate_lnVR(CSD = input$sd1i, CN = input$n1i, ESD = input$sd2i, EN = input$n2i), sample_variance_VR = calculate_var_lnVR(CN = input$n1i, EN = input$n2i), effect_size_RR = calculate_lnRR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i), sample_variance_RR = calculate_var_lnRR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i), err = as.factor(seq_len(n())) ) } ``` 4) Calculate meta-analysis statistics Based on function created by A M Senior @ the University of Otago NZ 03/01/2014: * Calculates effect sizes for meta-analysis of variance. All functions take the mean, sd and n from the control and experimental groups. * The first function, calculate_lnCVR, calculates the the log response-ratio of the coefficient of variance (lnCVR) - see Nakagawa et al 2015. * The second function calculates the measurement error variance for lnCVR. As well as the aforementioned parameters, this function also takes Equal_E_C_Corr (default = T), which must be True or False. If true, the function assumes that the correlation between mean and sd (Taylor's Law) is equal for the mean and control groups, and, thus these data are pooled. If False the mean-SD correlation for the experimental and control groups are calculated separately from one another. * Similar functions are then implemented for lnVR (for comparison of standard deviations) and ln RR (for comparison of means) ```{r} calculate_lnCVR <- function(CMean, CSD, CN, EMean, ESD, EN) { log(ESD) - log(EMean) + 1 / (2 * (EN - 1)) - (log(CSD) - log(CMean) + 1 / (2 * (CN - 1))) } calculate_var_lnCVR <- function(CMean, CSD, CN, EMean, ESD, EN, Equal_E_C_Corr = T) { if (Equal_E_C_Corr == T) { mvcorr <- 0 # cor.test(log(c(CMean, EMean)), log(c(CSD, ESD)))$estimate old, slightly incorrect S2 <- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * mvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * mvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1)))) } else { Cmvcorr <- cor.test(log(CMean), log(CSD))$estimate Emvcorr <- cor.test(log(EMean), (ESD))$estimate S2 <- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * Cmvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * Emvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1)))) } S2 } calculate_lnVR <- function(CSD, CN, ESD, EN) { log(ESD) - log(CSD) + 1 / (2 * (EN - 1)) - 1 / (2 * (CN - 1)) } calculate_var_lnVR <- function(CN, EN) { 1 / (2 * (EN - 1)) + 1 / (2 * (CN - 1)) } calculate_lnRR <- function(CMean, CSD, CN, EMean, ESD, EN) { log(EMean) - log(CMean) } calculate_var_lnRR <- function(CMean, CSD, CN, EMean, ESD, EN) { CSD^2 / (CN * CMean^2) + ESD^2 / (EN * EMean^2) } ``` ## Load & clean data 1) Data loading and cleaning of the csv file This step we have already done and provide a cleaned up file which is less computing intensive and which we have saved in a folder called `export`. However, the cvs is provided in case this is preferred to be attempted, following the steps below: ```{r clean, eval=FALSE, include=TRUE} # loads the raw data, setting some default types for various columns load_raw <- function(filename) { read_csv(filename, col_types = cols( .default = col_character(), project_id = col_character(), id = col_character(), parameter_id = col_character(), age_in_days = col_integer(), date_of_experiment = col_datetime(format = ""), weight = col_double(), phenotyping_center_id = col_character(), production_center_id = col_character(), weight_date = col_datetime(format = ""), date_of_birth = col_datetime(format = ""), procedure_id = col_character(), pipeline_id = col_character(), biological_sample_id = col_character(), biological_model_id = col_character(), weight_days_old = col_integer(), datasource_id = col_character(), experiment_id = col_character(), data_point = col_double(), age_in_weeks = col_integer(), `_version_` = col_character() ) ) } # Apply some standard cleaning to the data clean_raw_data <- function(mydata) { group <- read_csv(here("data", "ParameterGrouping.csv")) tmp <- mydata %>% # Filter to IMPC source (recommend by Jeremey in email to Susi on 20 Aug 2018) filter(datasource_name == "IMPC") %>% # standardise trait names mutate(parameter_name = tolower(parameter_name)) %>% # remove extreme ages filter(age_in_days > 0 & age_in_days < 500) %>% # remove NAs filter(!is.na(data_point)) %>% # subset to reasonable set of variables, date_of_experiment used as an indicator of batch-level effects select(production_center, strain_name, strain_accession_id, biological_sample_id, pipeline_stable_id, procedure_group, procedure_name, sex, date_of_experiment, age_in_days, weight, parameter_name, data_point) %>% # sort arrange(production_center, biological_sample_id, age_in_days) # filter to groups with > 1 centre merge(tmp, tmp %>% group_by(parameter_name) %>% summarise(center_per_trait = length(unique(production_center, na.rm = TRUE))) )%>% filter(center_per_trait >= 2) %>% # Define population variable mutate(population = sprintf("%s-%s", production_center, strain_name)) %>% # add grouping variable: these were decided based on functional groups and procedures mutate(parameter_group = group$parameter[match(parameter_name, group$parameter_name)] ) %>% # Assign unique IDs (per trait) # each unique parameter_name (=trait,use trait variable) gets a unique number ('id') # We add a new variable, where redundant traits are combined #[note however, at this stage the dataset still contains nonsensical traits, i.e. traits that may not contain any information on variance] mutate(id = match(parameter_name, unique(parameter_name))) %>% as_tibble() } # Load raw data - save cleaned dataset as RDS for reuse data_raw <- load_raw(here("data","dr7.0_all_control_data.csv.gz")) dir.create("export", F, F) data <- data_raw %>% clean_raw_data() saveRDS(data, "export/data_clean.rds") ``` For analysis we load the RDS created above and other datasets: ```{r load} data <- readRDS(here("export", "data_clean.rds")) procedures <- read_csv(here("data", "procedures.csv")) ``` Checking length of different variables and sample sizes. # Table 1: "Strains and Center Sample Sizes" This table summarises the available numbers of male and female mice from each strain and originating institution. ```{r} length(unique(data$parameter_name)) # 232 traits length(unique(data$parameter_group)) # 161 parameter groups length(unique(data$procedure_name)) # 26 procedure groups length(unique(data$biological_sample_id)) # 27147 individial mice #number of males and females per strain per production center kable(cbind(data %>% group_by(production_center, strain_name) %>% count(biological_sample_id, sex) %>% count(sex) %>% print(n = Inf))) %>% kable_styling() %>% scroll_box(width = "60%", height = "200px") ``` # Meta-analyses ## 1. Population as analysis unit (Step C, Figure 3 in main document) ### Loop: Meta-analyses on all traits * The loop combines the functions mentioned above and fills the data matrix with results from our meta analysis. * Error messages indicate traits that either did not reach convergence, or that did not return meaningful results in the meta-analysis, due to absence of variance. Those traits will be removed in later steps, outlined below. ```{r} (n <- length(unique(data$id))) # Create dataframe to store results results_alltraits_grouping <- tibble(id = 1:n, lnCVR=0, lnCVR_lower=0, lnCVR_upper=0, lnCVR_se=0, lnVR=0, lnVR_lower=0, lnVR_upper=0, lnVR_se=0, lnRR=0, lnRR_lower=0, lnRR_upper=0, lnRR_se=0, sampleSize=0, trait=0) for (t in 1:n) { tryCatch( { results <- data %>% data_subset_parameterid_individual_by_age(t) %>% calculate_population_stats() %>% create_meta_analysis_effect_sizes() # lnCVR, log repsonse-ratio of the coefficient of variance cvr <- metafor::rma.mv(yi = effect_size_CVR, V = sample_variance_CVR, random = list(~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F, data = results) # lnVR, comparison of standard deviations cv <- metafor::rma.mv(yi = effect_size_VR, V = sample_variance_VR, random = list(~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F, data = results) # for means, lnRR means <- metafor::rma.mv(yi = effect_size_RR, V = sample_variance_RR, random = list(~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F, data = results) f <- function(x) unlist(x[c("b", "ci.lb", "ci.ub", "se")]) results_alltraits_grouping[t, 2:14] <- c(f(cvr), f(cv), f(means), means$k) results_alltraits_grouping[t, 15] <- unique(results$trait) }, error = function(e) { cat("ERROR :", t, conditionMessage(e), "\n") } ) } ``` In the above function, we use 'tryCatch' and 'conditionMessage' to prevent the loop from aborting when the first error at row 84 is produced. As convergence in the two listed non-converging cases can't be achieved by sensibly tweaking (other optim etc.), and we only learn about non-convergence in the loop, it is not possible to exclude the traits (N=2) beforehand. Similarly, there are 8 traits with very low variation, which can not be excluded prior running the loop. The produced "Warnings" indicate cases where variance components are set to zero during likelihood optimization. ### Merging datasets & removal of non-converged traits Procedure names, grouping variables and trait names ("parameter_names") are merged back together with the results from the metafor analysis above. ```{r} results_alltraits_grouping2 <- results_alltraits_grouping %>% left_join(by="id", data %>% select(id, parameter_group, procedure = procedure_name, procedure_name, parameter_name) %>% # We filter duplicated id's to get only one unique row per id (and there is one id per parameter_name) filter(!duplicated(id)) ) %>% # Below we add 'procedure' (from the previously loaded 'procedures.csv') as a variable left_join(by="procedure", procedures %>% distinct() ) (n <- length(unique(results_alltraits_grouping2$parameter_name))) # 232 ``` ### Removal of traits 14 traits from the originally 232 that had been included are removed because they either did not achieve convergence or are nonsensical for analysis of variance (such as traits that show no variation, see list below). Not converged: "dp t cells", "mzb (cd21/35 high)" Not enough variation: "number of caudal vertebrae", "number of cervical vertebrae", "number of digits", "number of lumbar vertebrae", "number of pelvic vertebrae", "number of ribs left","number of ribs right", "number of signals", "number of thoracic vertebrae", "total number of acquired events in panel a","total number of acquired events in panel b", "whole arena permanence". ```{r} # We exclude 14 parameter names for which metafor models didn't converge ("dp t cells", "mzb (cd21/35 high)"), and of parameters that don't harbour enough variation meta_clean <- results_alltraits_grouping2 %>% filter(!parameter_name %in% c("dp t cells", "mzb (cd21/35 high)", "number of caudal vertebrae", "number of cervical vertebrae", "number of digits", "number of lumbar vertebrae", "number of pelvic vertebrae", "number of ribs left", "number of ribs right", "number of signals", "number of thoracic vertebrae", "total number of acquired events in panel a", "total number of acquired events in panel b", "whole arena permanence")) ``` **Reveiw**: check against old script -- identical, remove once fixed # #Felix: not sure ```{r, eval=FALSE} meta_clean.test <- readRDS(here("export", "meta_clean.test.rds")) all.equal(meta_clean, meta_clean.test %>% mutate(id=as.integer(id), parameter_group = as.character(parameter_group), GroupingTerm = as.character(GroupingTerm))) ``` [1] "Rows in x but not y: 162, 161. Rows in y but not x: 162, 161. " Not sure?? ## 2. Meta-analysis: condensing non-independent traits (Step F in Figure 3 in main article) ### Dealing with Correlated Parameters, preparation This dataset contained a number of highly correlated traits, such as different kinds of cell counts (for example hierarchical parameterization within immunological assays). As those data-points are not independent of each other, we conducted meta analyses on these correlated parameters to collapse the number of levels. #### Collapsing and merging correlated parameters Here we double check numbers of trait parameters in the dataset ```{r} meta1 <- meta_clean length(unique(meta1$procedure)) #18 length(unique(meta1$GroupingTerm)) #9 length(unique(meta1$parameter_group)) # 148 levels. To be used as grouping factor for meta-meta analysis / collapsing down based on things that are classified identically in "parameter_group" but have different "parameter_name" length(unique(meta1$parameter_name)) #218 ``` #### Count of number of parameter names (correlated sub-traits) in each parameter group (par_group_size) ## Table: Numbers of correlated and uncorrelated traits This serves to identify and separate the traits that are correlated from the full dataset that can be processed as is. If the sample size (n) for a given "parameter group" equals 1, the trait is unique and uncorrelated. All instances, where there are 2 or more traits associated with the same parameter group (90 cases), are selected for a "mini-meta analysis", which removes the issue of correlation. ```{r} kable(cbind(meta1 %>% count(parameter_group))) %>% kable_styling() %>% scroll_box(width = "60%", height = "200px") ``` ```{r} meta1_sub <- meta1 %>% # add summary of number of parameter names in each parameter group group_by(parameter_group) %>% mutate(par_group_size = length(unique(parameter_name)), sampleSize = as.numeric(sampleSize)) %>% ungroup() %>% # Create subsets with > 1 count (par_group_size > 1) filter(par_group_size > 1) # 90 observations ``` #### Meta-analyses on correlated (sub-)traits, using robumeta` Here we pepare the subset of the data (using nest()), and in this first step the model of the meta analysis effect sizes are calculated ```{r} meta1b <- meta1 %>% group_by(parameter_group) %>% summarize(par_group_size = length(unique(parameter_name, na.rm = TRUE))) #this gives a summary of number of parameter names in each parameter group, now it neeeds to get merged it back together meta1$par_group_size <- meta1b$par_group_size[match(meta1$parameter_group, meta1b$parameter_group)] # Create subsets with > 1 count (par_group_size > 1) meta1_sub <- subset(meta1,par_group_size >1) # 90 observations meta1_sub$sampleSize <- as.numeric(meta1_sub$sampleSize) # nesting n_count <- meta1_sub %>% group_by(parameter_group) %>% mutate(raw_N = sum(sampleSize)) %>% nest() %>% ungroup() model_count <- n_count %>% mutate( model_lnRR = map(data, ~ robu(.x$lnRR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnRR_se)^2)), model_lnVR = map(data, ~ robu(.x$lnVR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnVR_se)^2)), model_lnCVR = map(data, ~ robu(.x$lnCVR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnCVR_se)^2)) ) ``` #### Extract and save parameter estimates: Function to collect the outcomes of the "mini" meta analysis ```{r} count_fun <- function(mod_sub) { return(c(mod_sub$reg_table$b.r, mod_sub$reg_table$CI.L, mod_sub$reg_table$CI.U, mod_sub$reg_table$SE)) } # estimate, lower ci, upper ci, SE ``` Extraction of values created during Meta analysis using robu meta: ```{r} robusub_RR <- model_count %>% transmute(parameter_group, estimatelnRR = map(model_lnRR, count_fun)) %>% mutate(r = map(estimatelnRR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnRR) %>% purrr::set_names(c("parameter_group", "lnRR", "lnRR_lower", "lnRR_upper", "lnRR_se")) robusub_CVR <- model_count %>% transmute(parameter_group, estimatelnCVR = map(model_lnCVR, count_fun)) %>% mutate(r = map(estimatelnCVR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnCVR) %>% purrr::set_names(c("parameter_group", "lnCVR", "lnCVR_lower", "lnCVR_upper", "lnCVR_se")) robusub_VR <- model_count %>% transmute(parameter_group, estimatelnVR = map(model_lnVR, count_fun)) %>% mutate(r = map(estimatelnVR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnVR) %>% purrr::set_names(c("parameter_group", "lnVR", "lnVR_lower", "lnVR_upper", "lnVR_se")) robu_all <- full_join(robusub_CVR, robusub_VR) %>% full_join(., robusub_RR) ``` #### Combine data Merge the two data sets (the new [robu_all] and the initial [uncorrelated sub-traits with count = 1]) ```{r} meta_all <- meta1 %>% filter(par_group_size == 1) %>% as_tibble() # str(meta_all) # str(robu_all) # which(is.na(match(names(meta_all),names(robu_all)))) # check #Step1: Columns are matched by name (in our case, 'parameter_group'), and any missing columns will be filled with NA combinedmeta <- bind_rows(robu_all, meta_all) # glimpse(combinedmeta) # Steps 2&3 (add information about number of traits in a parameter group, procedure, and grouping term) metacombo <- combinedmeta metacombo$counts <- meta1$par_group_size[match(metacombo$parameter_group, meta1$parameter_group)] metacombo$procedure2 <- meta1$procedure[match(metacombo$parameter_group, meta1$parameter_group)] metacombo$GroupingTerm2 <- meta1$GroupingTerm[match(metacombo$parameter_group, meta1$parameter_group)] ``` Clean-up, reorder, and rename ```{r} metacombo <- metacombo[c("parameter_group", "counts","procedure2","GroupingTerm2", "lnCVR","lnCVR_lower","lnCVR_upper","lnCVR_se","lnVR","lnVR_lower","lnVR_upper","lnVR_se","lnRR","lnRR_lower","lnRR_upper","lnRR_se")] names(metacombo)[names(metacombo)=="procedure2"] <- "procedure" names(metacombo)[names(metacombo)=="GroupingTerm2"] <- "GroupingTerm" # Quick pre-check before doing plots metacombo %>% group_by(GroupingTerm) %>% dplyr::summarize(MeanCVR = mean(lnCVR), MeanVR = mean(lnVR), MeanRR = mean(lnRR)) ``` # Table for SHINY APP We use this corrected (for correlated traits) "results" table, which contains each of the meta-analytic means for all effect sizes of interest, for further analyses. We further use this table as part of the Shiny App, which is able to provide the percentage differences between males and females for mean, variance and coefficient of variance. This is the full result dataset ```{r} kable(metacombo) %>% kable_styling() %>% scroll_box(width = "100%", height = "200px") # trait_meta_results <- write.csv(metacombo, file = "export/trait_meta_results.csv") ``` ## 3. Second-order meta analysis for functional groups (Section H in Figure 3 in main article) ### Performing meta-analyses (3 for each of the 9 grouping terms: lnCVR, lnVR, lnRR) #### Preparation of data Nesting, calculating the number of parameters within each grouping term, and running the meta-analysis ```{r} metacombo_final <- metacombo %>% group_by(GroupingTerm) %>% nest_legacy() # we're using 'nest_legacy' to keep old syntax/functionality # **calculate number of parameters per grouping term metacombo_final <- metacombo_final %>% mutate(para_per_GroupingTerm = map_dbl(data, nrow)) # For all grouping terms metacombo_final_all <- metacombo %>% nest_legacy() #'nest_legacy' to keep old syntax/functionality # **Final fixed effects meta-analyses within grouping terms, with SE of the estimate overall1 <- metacombo_final %>% mutate( model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F )), model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F )), model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F )) ) # **Final fixed effects meta-analyses ACROSS grouping terms, with SE of the estimate overall_all1 <- metacombo_final_all %>% mutate( model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F )), model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F )), model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F )) ) ``` ### Re-structuring the data for each grouping term We here delete unused variables, and select the respective effect sizes. Please note - the referencing of the cells does NOT depend on previous ordering of the data. This would only be affected if the output structure from metafor::rma.uni changes. ```{r} Behaviour <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Behaviour") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ))[, c(1, 7:18)] Immunology <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Immunology") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ))[, c(1, 7:18)] Hematology <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Hematology") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ))[, c(1, 7:18)] Hearing <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Hearing") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ))[, c(1, 7:18)] Physiology <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Physiology") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ))[, c(1, 7:18)] Metabolism <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Metabolism") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ))[, c(1, 7:18)] Morphology <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Morphology") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ))[, c(1, 7:18)] Heart <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Heart") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ))[, c(1, 7:18)] Eye <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Eye") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ))[, c(1, 7:18)] All <- as.data.frame(overall_all1 %>% mutate( lnCVR = .[[2]][[1]]$b, lnCVR_lower = .[[2]][[1]]$ci.lb, lnCVR_upper = .[[2]][[1]]$ci.ub, lnCVR_se = .[[2]][[1]]$se, lnVR = .[[3]][[1]]$b, lnVR_lower = .[[3]][[1]]$ci.lb, lnVR_upper = .[[3]][[1]]$ci.ub, lnVR_se = .[[3]][[1]]$se, lnRR = .[[4]][[1]]$b, lnRR_lower = .[[4]][[1]]$ci.lb, lnRR_upper = .[[4]][[1]]$ci.ub, lnRR_se = .[[4]][[1]]$se ))[, c(5:16)] All$lnCVR <- as.numeric(All$lnCVR) All$lnVR <- as.numeric(All$lnVR) All$lnRR <- as.numeric(All$lnRR) All <- All %>% mutate(GroupingTerm = "All") overall2 <- bind_rows(Behaviour, Morphology, Metabolism, Physiology, Immunology, Hematology, Heart, Hearing, Eye, All) #FZ: warnings are ok ``` # Visualisation ## Figure 4 #### Preparation for plots: Count data, based on First-order meta analysis results This includes all separate eligible traits. Re-ordering of grouping terms ```{r} meta_clean$GroupingTerm <- factor(meta_clean$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye")) meta_clean$GroupingTerm <- factor(meta_clean$GroupingTerm, rev(levels(meta_clean$GroupingTerm))) # *Preparing data for all traits meta.plot2.all <- meta_clean %>% select(lnCVR, lnVR, lnRR, GroupingTerm) %>% arrange(GroupingTerm) meta.plot2.all.b <- gather(meta.plot2.all, trait, value, c(lnCVR, lnRR)) # lnVR has been removed here and in the steps below, as this is only included in the supplemental figure meta.plot2.all.b$trait <- factor(meta.plot2.all.b$trait, levels = c("lnCVR", "lnRR")) meta.plot2.all.c <- meta.plot2.all.b %>% group_by_at(vars(trait, GroupingTerm)) %>% summarise( malebias = sum(value > 0), femalebias = sum(value <= 0), total = malebias + femalebias, malepercent = malebias * 100 / total, femalepercent = femalebias * 100 / total ) meta.plot2.all.c$label <- "All traits" # restructure to create stacked bar plots meta.plot2.all.d <- as.data.frame(meta.plot2.all.c) meta.plot2.all.e <- gather(meta.plot2.all.d, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE) # create new sample size variable meta.plot2.all.e$samplesize <- with(meta.plot2.all.e, ifelse(sex == "malepercent", malebias, femalebias)) # add summary row ('All') and re-arrange rows into correct order for plotting #FZ added meta.plot2.all.f <- meta.plot2.all.e %>% group_by(trait, sex) %>% summarise(GroupingTerm = "All", malebias = sum(malebias), femalebias = sum(femalebias), total = malebias + femalebias, label = "All traits", samplesize = sum(samplesize)) %>% mutate(percent = ifelse(sex == "femalepercent", femalebias*100/(malebias+femalebias), malebias*100/(malebias+femalebias))) %>% bind_rows(meta.plot2.all.e, .) %>% mutate(rownumber = row_number()) %>% .[c(37, 1:9, 39, 10:18, 38, 19:27, 40, 28:36), ] meta.plot2.all.f$GroupingTerm <- factor(meta.plot2.all.f$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) meta.plot2.all.f$GroupingTerm <- factor(meta.plot2.all.f$GroupingTerm, rev(levels(meta.plot2.all.f$GroupingTerm))) malebias_Fig2_alltraits <- ggplot(meta.plot2.all.f) + aes(x = GroupingTerm, y = percent, fill = sex) + geom_col() + geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") + geom_text( data = subset(meta.plot2.all.f, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5), color = "white", size = 3.5 ) + facet_grid( cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18), scales = "free", space = "free" ) + scale_fill_brewer(palette = "Set2") + theme_bw(base_size = 18) + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none", axis.title.x = element_blank(), axis.title.y = element_blank() ) + coord_flip() # malebias_Fig2_alltraits #(panel A in Figure 4 in ms) ``` ### Overall results of second order meta analysis (Figure 4, Panel B) #### Restructure data for plotting Data are restructured, and grouping terms are being re-ordered ```{r} overall3 <- gather(overall2, parameter, value, c(lnCVR, lnRR), factor_key = TRUE) # lnVR, lnCVR.ci <- overall3 %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3 %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3 %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4 <- bind_rows(lnCVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # lnVR.ci, # re-order Grouping Terms overall4$GroupingTerm <- factor(overall4$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall4$GroupingTerm <- factor(overall4$GroupingTerm, rev(levels(overall4$GroupingTerm))) overall4$label <- "All traits" kable(cbind(overall4, overall4)) %>% kable_styling() %>% scroll_box(width = "100%", height = "200px") ``` ```{r} Metameta_Fig3_alltraits <- overall4 %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "black", color = "black", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(-0.24, 0.25), breaks = c(-0.2, -0.1, 0, 0.1, 0.2), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_text(hjust = 0.5, size = 14), axis.title.y = element_blank() ) # Metameta_Fig3_alltraits ``` ### Fig 4 Join the different parts and #TO DO!! add M / F symbols in Metameta_Fig3_alltraits ```{r} #Test #male <- readPNG(system.file("img", "male")) #test <- Metameta_Fig3_alltraits #library(png) ``` ```{r} Fig4 <- ggarrange(malebias_Fig2_alltraits, Metameta_Fig3_alltraits, nrow = 2, align = "v", heights = c(1, 1), labels = c("A", "B")) Fig4 ``` ### Figure 4: Panel A shows the numbers of traits across functional groups that are either male-biased (blue-green) or female-biased (orange-red), as calculated in Step D (figure 3). Panel B shows effect sizes and 95% CI from separate meta-analysis for each functional group (step H in Figure 3). Both panels represent results evaluated across all traits (Phase 3, Figure 3). Traits that are male biased are Male data is shown in blue, whereas female bias data is represented in orange. ## Figure 5 #### Preparing data for traits with CI not overlapping 0 To further investigate sex bias in this dataset, and in particular if the extent of sex bias differs between traits, we investigate the magnitude of male- and female bias in significantly different traits on (both for means and variability) To do this, we select only traits that have CIs that do not overlap with zero. ### FELIX: "ALL" missing. This figure is panel A in Fig 5 ```{r} meta.plot2.sig <- meta_clean %>% mutate( lnCVRsig = ifelse(lnCVR_lower * lnCVR_upper > 0, 1, 0), lnVRsig = ifelse(lnVR_lower * lnVR_upper > 0, 1, 0), lnRRsig = ifelse(lnRR_lower * lnRR_upper > 0, 1, 0) ) meta.plot2.sig.b <- meta.plot2.sig[, c("lnCVR", "lnRR", "lnCVRsig", "lnVRsig", "lnRRsig", "GroupingTerm")] # "lnVR", meta.plot2.sig.c <- gather(meta.plot2.sig.b, trait, value, lnCVR:lnRR) meta.plot2.sig.c$sig <- "placeholder" meta.plot2.sig.c$trait <- factor(meta.plot2.sig.c$trait, levels = c("lnCVR", "lnRR")) # "lnVR", meta.plot2.sig.c$sig <- ifelse(meta.plot2.sig.c$trait == "lnCVR", meta.plot2.sig.c$lnCVRsig, ifelse(meta.plot2.sig.c$trait == "lnVR", meta.plot2.sig.c$lnVRsig, meta.plot2.sig.c$lnRRsig) ) # choosing sex biased ln-ratios significantly larger than 0 meta.plot2.sig.malebias <- meta.plot2.sig.c %>% group_by_at(vars(trait, GroupingTerm)) %>% filter(sig == 1) %>% summarise(male_sig = sum(value > 0), female_sig = sum(value < 0), total = male_sig + female_sig) meta.plot2.sig.malebias <- ungroup(meta.plot2.sig.malebias) %>% add_row(trait = "lnCVR", GroupingTerm = "Hearing", male_sig = 0, female_sig = 0, .before = 4) %>% # add "Hearing" for lnCVR (not filtered as only zeros) mutate(malepercent = male_sig * 100 / total, femalepercent = female_sig * 100 / total) meta.plot2.sig.malebias$label <- "CI not overlapping zero" # restructure to create stacked bar plots meta.plot2.sig.bothsexes <- as.data.frame(meta.plot2.sig.malebias) meta.plot2.sig.bothsexes.b <- gather(meta.plot2.sig.bothsexes, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE) # create new sample size variable meta.plot2.sig.bothsexes.b$samplesize <- with(meta.plot2.sig.bothsexes.b, ifelse(sex == "malepercent", male_sig, female_sig)) # Plot Fig2 all significant results (CI not overlapping zero): # Several grouing terms are added post-hoc (with no data to display): no significant lnCVR for 'Hearing' in either sex; no sig. male-biased lnCVR for 'Immunology' and 'Eye, and no significant male-biased lnVR for 'Eye'. malebias_Fig2_sigtraits <- ggplot(meta.plot2.sig.bothsexes.b) + aes(x = GroupingTerm, y = percent, fill = sex) + geom_col() + geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") + geom_text( data = subset(meta.plot2.sig.bothsexes.b, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5), color = "white", size = 3.5 ) + facet_grid( cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18), scales = "free", space = "free" ) + scale_fill_brewer(palette = "Set2") + theme_bw(base_size = 18) + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none", axis.title.x = element_blank(), axis.title.y = element_blank() ) + coord_flip() ``` ### Preparation for Plots on significant sex-bias (Second-order meta analysis results #### Figure 5 B - traits with CI not overlapping 0 Prepare data create column with 1= different from zero, 0= zero included in CI #### Male-biased (significant) traits ```{r} meta.male.plot3.sig <- metacombo %>% mutate( sigCVR = ifelse(lnCVR_lower > 0, 1, 0), sigVR = ifelse(lnVR_lower > 0, 1, 0), sigRR = ifelse(lnRR_lower > 0, 1, 0) ) # Significant subset for lnCVR metacombo_male.plot3.CVR <- meta.male.plot3.sig %>% filter(sigCVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.CVR.all <- meta.male.plot3.sig %>% filter(sigCVR == 1) %>% nest() # Significant subset for lnVR metacombo_male.plot3.VR <- meta.male.plot3.sig %>% filter(sigVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.VR.all <- meta.male.plot3.sig %>% filter(sigVR == 1) %>% nest() # Significant subset for lnRR metacombo_male.plot3.RR <- meta.male.plot3.sig %>% filter(sigRR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.RR.all <- meta.male.plot3.sig %>% filter(sigRR == 1) %>% nest() # **Final fixed effects meta-analyses within grouping terms, with SE of the estimate plot3.male.meta.CVR <- metacombo_male.plot3.CVR %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.VR <- metacombo_male.plot3.VR %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.RR <- metacombo_male.plot3.RR %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) # Across all grouping terms # plot3.male.meta.CVR.all <- metacombo_male.plot3.CVR.all %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.CVR.all <- plot3.male.meta.CVR.all %>% mutate(GroupingTerm = "All") plot3.male.meta.VR.all <- metacombo_male.plot3.VR.all %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.VR.all <- plot3.male.meta.VR.all %>% mutate(GroupingTerm = "All") plot3.male.meta.RR.all <- metacombo_male.plot3.RR.all %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.RR.all <- plot3.male.meta.RR.all %>% mutate(GroupingTerm = "All") # Combine with separate grouping term results plot3.male.meta.CVR <- bind_rows(plot3.male.meta.CVR, plot3.male.meta.CVR.all) plot3.male.meta.VR <- bind_rows(plot3.male.meta.VR, plot3.male.meta.VR.all) plot3.male.meta.RR <- bind_rows(plot3.male.meta.RR, plot3.male.meta.RR.all) # **Re-structure data for each grouping term; delete un-used variables plot3.male.meta.CVR.b <- as.data.frame(plot3.male.meta.CVR %>% group_by(GroupingTerm) %>% mutate( lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)), lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.CVR.b)) plot3.male.meta.CVR.b <- bind_rows(plot3.male.meta.CVR.b, add.row.hearing) plot3.male.meta.CVR.b <- plot3.male.meta.CVR.b[order(plot3.male.meta.CVR.b$GroupingTerm), ] plot3.male.meta.VR.b <- as.data.frame(plot3.male.meta.VR %>% group_by(GroupingTerm) %>% mutate( lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)), lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3)) ))[, c(1, 4:7)] plot3.male.meta.VR.b <- plot3.male.meta.VR.b[order(plot3.male.meta.VR.b$GroupingTerm), ] plot3.male.meta.RR.b <- as.data.frame(plot3.male.meta.RR %>% group_by(GroupingTerm) %>% mutate( lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)), lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3)) ))[, c(1, 4:7)] plot3.male.meta.RR.b <- plot3.male.meta.RR.b[order(plot3.male.meta.RR.b$GroupingTerm), ] overall.male.plot3 <- full_join(plot3.male.meta.CVR.b, plot3.male.meta.VR.b) overall.male.plot3 <- full_join(overall.male.plot3, plot3.male.meta.RR.b) overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, rev(levels(overall.male.plot3$GroupingTerm))) # add missing GroupingTerms for plot overall.male.plot3 <- add_row(overall.male.plot3, GroupingTerm = "Behaviour") overall.male.plot3 <- add_row(overall.male.plot3, GroupingTerm = "Immunology") overall.male.plot3 <- add_row(overall.male.plot3, GroupingTerm = "Eye") overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, rev(levels(overall.male.plot3$GroupingTerm))) # str(overall.male.plot3) ``` Restructure MALE data for plotting ```{r} overall3.male.sig <- gather(overall.male.plot3, parameter, value, c(lnCVR, lnRR), factor_key = TRUE) # lnVR, lnCVR.ci <- overall3.male.sig %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) # lnVR.ci <- overall3.male.sig %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.male.sig %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.male.sig <- bind_rows(lnCVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # lnVR.ci, overall4.male.sig$label <- "CI not overlapping zero" ``` Plot Fig5b all significant results (CI not overlapping zero) for males ```{r} Metameta_Fig3_male.sig <- overall4.male.sig %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "mediumaquamarine", color = "mediumaquamarine", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(0, 0.4), breaks = c(0, 0.3), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank() ) # Metameta_Fig3_male.sig ``` #### Female part, significant traits Female Fig5B sig Prepare data for traits with CI not overlapping 0 create column with 1= different from zero, 0= zero included in CI ```{r} # female-biased traits meta.female.plot3.sig <- metacombo %>% mutate( sigCVR = ifelse(lnCVR_upper < 0, 1, 0), sigVR = ifelse(lnVR_upper < 0, 1, 0), sigRR = ifelse(lnRR_upper < 0, 1, 0) ) # Significant subset for lnCVR metacombo_female.plot3.CVR <- meta.female.plot3.sig %>% filter(sigCVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_female.plot3.CVR.all <- meta.female.plot3.sig %>% filter(sigCVR == 1) %>% nest() # Significant subset for lnVR metacombo_female.plot3.VR <- meta.female.plot3.sig %>% filter(sigVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_female.plot3.VR.all <- meta.female.plot3.sig %>% filter(sigVR == 1) %>% nest() # Significant subset for lnRR metacombo_female.plot3.RR <- meta.female.plot3.sig %>% filter(sigRR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_female.plot3.RR.all <- meta.female.plot3.sig %>% filter(sigRR == 1) %>% nest() # **Final fixed effects meta-analyses within grouping terms, with SE of the estimate plot3.female.meta.CVR <- metacombo_female.plot3.CVR %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.female.meta.VR <- metacombo_female.plot3.VR %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.female.meta.RR <- metacombo_female.plot3.RR %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) # Across all grouping terms # plot3.female.meta.CVR.all <- metacombo_female.plot3.CVR.all %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.female.meta.CVR.all <- plot3.female.meta.CVR.all %>% mutate(GroupingTerm = "All") plot3.female.meta.VR.all <- metacombo_female.plot3.VR.all %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.female.meta.VR.all <- plot3.female.meta.VR.all %>% mutate(GroupingTerm = "All") plot3.female.meta.RR.all <- metacombo_female.plot3.RR.all %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.female.meta.RR.all <- plot3.female.meta.RR.all %>% mutate(GroupingTerm = "All") # Combine with separate grouping term results plot3.female.meta.CVR <- bind_rows(plot3.female.meta.CVR, plot3.female.meta.CVR.all) plot3.female.meta.VR <- bind_rows(plot3.female.meta.VR, plot3.female.meta.VR.all) plot3.female.meta.RR <- bind_rows(plot3.female.meta.RR, plot3.female.meta.RR.all) # **Re-structure data for each grouping term; delete un-used variables plot3.female.meta.CVR.b <- as.data.frame(plot3.female.meta.CVR %>% group_by(GroupingTerm) %>% mutate( lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)), lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.female.meta.CVR.b)) plot3.female.meta.CVR.b <- bind_rows(plot3.female.meta.CVR.b, add.row.hearing) plot3.female.meta.CVR.b <- plot3.female.meta.CVR.b[order(plot3.female.meta.CVR.b$GroupingTerm), ] plot3.female.meta.VR.b <- as.data.frame(plot3.female.meta.VR %>% group_by(GroupingTerm) %>% mutate( lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)), lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3)) ))[, c(1, 4:7)] plot3.female.meta.VR.b <- plot3.female.meta.VR.b[order(plot3.female.meta.VR.b$GroupingTerm), ] plot3.female.meta.RR.b <- as.data.frame(plot3.female.meta.RR %>% group_by(GroupingTerm) %>% mutate( lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)), lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3)) ))[, c(1, 4:7)] plot3.female.meta.RR.b <- plot3.female.meta.RR.b[order(plot3.female.meta.RR.b$GroupingTerm), ] overall.female.plot3 <- full_join(plot3.female.meta.CVR.b, plot3.female.meta.VR.b) overall.female.plot3 <- full_join(overall.female.plot3, plot3.female.meta.RR.b) overall.female.plot3$GroupingTerm <- factor(overall.female.plot3$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall.female.plot3$GroupingTerm <- factor(overall.female.plot3$GroupingTerm, rev(levels(overall.female.plot3$GroupingTerm))) ``` Restructure data for plotting ```{r} overall3.female.sig <- gather(overall.female.plot3, parameter, value, c(lnCVR, lnRR), factor_key = TRUE) # lnVR, lnCVR.ci <- overall3.female.sig %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) # lnVR.ci <- overall3.female.sig %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.female.sig %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.female.sig <- bind_rows(lnCVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # lnVR.ci, overall4.female.sig$label <- "CI not overlapping zero" ``` Plotting Fig5B all significant results (CI not overlapping zero, female ) ```{r} Metameta_Fig3_female.sig <- overall4.female.sig %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "salmon1", color = "salmon1", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(-0.4, 0), breaks = c(-0.3, 0), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), # rows = vars(label), # labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank() ) # Metameta_Fig3_female.sig #(Figure 5B left panel) ``` ## JOIN!!! CODE MISSING?? # Supplemental Plots ## Figure S1 ### Including lnVR ### Count data, including lnVR (Fig S1 panel A) ```{r} # *Prepare data for all traits meta.plot2.all <- meta_clean %>% select(lnCVR, lnVR, lnRR, GroupingTerm) %>% arrange(GroupingTerm) meta.plot2.all.bS1 <- gather(meta.plot2.all, trait, value, c(lnCVR, lnVR, lnRR)) meta.plot2.all.bS1$trait <- factor(meta.plot2.all.bS1$trait, levels = c("lnCVR", "lnVR", "lnRR")) meta.plot2.all.cS1 <- meta.plot2.all.bS1 %>% group_by_at(vars(trait, GroupingTerm)) %>% summarise( malebias = sum(value > 0), femalebias = sum(value <= 0), total = malebias + femalebias, malepercent = malebias * 100 / total, femalepercent = femalebias * 100 / total ) meta.plot2.all.cS1$label <- "All traits" # restructure to create stacked bar plots meta.plot2.all.dS1 <- as.data.frame(meta.plot2.all.cS1) meta.plot2.all.eS1 <- gather(meta.plot2.all.dS1, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE) # create new sample size variable meta.plot2.all.eS1$samplesize <- with(meta.plot2.all.eS1, ifelse(sex == "malepercent", malebias, femalebias)) malebias_FigS1_alltraits <- ggplot(meta.plot2.all.eS1) + aes(x = GroupingTerm, y = percent, fill = sex) + geom_col() + geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") + geom_text( data = subset(meta.plot2.all.eS1, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5), color = "white", size = 3.5 ) + facet_grid( cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18), scales = "free", space = "free" ) + scale_fill_brewer(palette = "Set2") + theme_bw(base_size = 18) + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none", axis.title.x = element_blank(), axis.title.y = element_blank() ) + coord_flip() # malebias_FigS1_alltraits #(panel A in Figure S1) ``` ### Overall results of second order meta analysis, INCLUDING VR #### Restructure data for plotting Restructure MALE data for plotting ```{r} overall3.male.sigS <- gather(overall.male.plot3, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) lnCVR.ci <- overall3.male.sigS %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3.male.sigS %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.male.sigS %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.male.sigS <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4.male.sigS$label <- "CI not overlapping zero" # Data are restructured, and grouping terms are being re-ordered overall3S <- gather(overall2, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) lnCVR.ci <- overall3S %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3S %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3S %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4S <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # re-order Grouping Terms overall4S$GroupingTerm <- factor(overall4S$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall4S$GroupingTerm <- factor(overall4S$GroupingTerm, rev(levels(overall4S$GroupingTerm))) overall4S$label <- "All traits" ``` #### Preparation for plot, including lnVR Preparation: Sub-Plot for Figure S1: all traits (S1 B) ```{r} Metameta_FigS1_alltraits <- overall4S %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "black", color = "black", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(-0.24, 0.25), breaks = c(-0.2, -0.1, 0, 0.1, 0.2), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_text(hjust = 0.5, size = 14), axis.title.y = element_blank() ) # Metameta_FigS1_alltraits ``` ### Heterogeneity The analysis for heterogeneity follows the workflow of the above steps for the different meta-analyses. However, in the initial meta-analysis we extract sigma^2 and errors for mouse strains and centers (Institutions). ```{r} results.allhetero.grouping <- as.data.frame(cbind(c(1:n), matrix(rep(0, n * 30), ncol = 30))) names(results.allhetero.grouping) <- c( "id", "sigma2_strain.CVR", "sigma2_center.CVR", "sigma2_error.CVR", "s.nlevels.strain.CVR", "s.nlevels.center.CVR", "s.nlevels.error.CVR", "sigma2_strain.VR", "sigma2_center.VR", "sigma2_error.VR", "s.nlevels.strain.VR", "s.nlevels.center.VR", "s.nlevels.error.VR", "sigma2_strain.RR", "sigma2_center.RR", "sigma2_error.RR", "s.nlevels.strain.RR", "s.nlevels.center.RR", "s.nlevels.error.RR", "lnCVR", "lnCVR_lower", "lnCVR_upper", "lnCVR_se", "lnVR", "lnVR_lower", "lnVR_upper", "lnVR_se", "lnRR", "lnRR_lower", "lnRR_upper", "lnRR_se" ) ``` LOOP Parameters to extract from metafor (sigma2's, s.nlevels) ```{r} for (t in 1:n) { tryCatch( { data_par_age <- data_subset_parameterid_individual_by_age(data, t, age_min = 0, age_center = 100) population_stats <- calculate_population_stats(data_par_age) results <- create_meta_analysis_effect_sizes(population_stats) # lnCVR, logaritm of the ratio of male and female coefficients of variance cvr. <- metafor::rma.mv(yi = effect_size_CVR, V = sample_variance_CVR, random = list( ~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err ), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), data = results) results.allhetero.grouping[t, 2] <- cvr.$sigma2[1] results.allhetero.grouping[t, 3] <- cvr.$sigma2[2] results.allhetero.grouping[t, 4] <- cvr.$sigma2[3] results.allhetero.grouping[t, 5] <- cvr.$s.nlevels[1] results.allhetero.grouping[t, 6] <- cvr.$s.nlevels[2] results.allhetero.grouping[t, 7] <- cvr.$s.nlevels[3] results.allhetero.grouping[t, 20] <- cvr.$b results.allhetero.grouping[t, 21] <- cvr.$ci.lb results.allhetero.grouping[t, 22] <- cvr.$ci.ub results.allhetero.grouping[t, 23] <- cvr.$se # lnVR, male to female variability ratio (logarithm of male and female standard deviations) vr. <- metafor::rma.mv(yi = effect_size_VR, V = sample_variance_VR, random = list( ~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err ), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), data = results) results.allhetero.grouping[t, 8] <- vr.$sigma2[1] results.allhetero.grouping[t, 9] <- vr.$sigma2[2] results.allhetero.grouping[t, 10] <- vr.$sigma2[3] results.allhetero.grouping[t, 11] <- vr.$s.nlevels[1] results.allhetero.grouping[t, 12] <- vr.$s.nlevels[2] results.allhetero.grouping[t, 13] <- vr.$s.nlevels[3] results.allhetero.grouping[t, 24] <- vr.$b results.allhetero.grouping[t, 25] <- vr.$ci.lb results.allhetero.grouping[t, 26] <- vr.$ci.ub results.allhetero.grouping[t, 27] <- vr.$se # lnRR, response ratio (logarithm of male and female means) rr. <- metafor::rma.mv(yi = effect_size_RR, V = sample_variance_RR, random = list( ~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err ), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), data = results) results.allhetero.grouping[t, 14] <- rr.$sigma2[1] results.allhetero.grouping[t, 15] <- rr.$sigma2[2] results.allhetero.grouping[t, 16] <- rr.$sigma2[3] results.allhetero.grouping[t, 17] <- rr.$s.nlevels[1] results.allhetero.grouping[t, 18] <- rr.$s.nlevels[2] results.allhetero.grouping[t, 19] <- rr.$s.nlevels[3] results.allhetero.grouping[t, 28] <- rr.$b results.allhetero.grouping[t, 29] <- rr.$ci.lb results.allhetero.grouping[t, 30] <- rr.$ci.ub results.allhetero.grouping[t, 31] <- rr.$se }, error = function(e) { cat("ERROR :", conditionMessage(e), "\n") } ) } ``` #### Exclude traits, merge datasets ```{r} results.allhetero.grouping2 <- results.allhetero.grouping[results.allhetero.grouping$s.nlevels.strain.VR != 0, ] # nrow(results.allhetero.grouping2) #218 SZ 223??? ``` Merge data sets containing metafor results with procedure etc. names ```{r} # procedures <- read.csv(here("export", "procedures.csv")) results.allhetero.grouping2$parameter_group <- data$parameter_group[match(results.allhetero.grouping2$id, data$id)] results.allhetero.grouping2$procedure <- data$procedure_name[match(results.allhetero.grouping2$id, data$id)] results.allhetero.grouping2$GroupingTerm <- procedures$GroupingTerm[match(results.allhetero.grouping2$procedure, procedures$procedure)] results.allhetero.grouping2$parameter_name <- data$parameter_name[match(results.allhetero.grouping2$id, data$id)] ``` #### Correlated parameters ##FELIX : check? numbers don't add up?? ```{r} metahetero1 <- results.allhetero.grouping2 # length(unique(metahetero1$procedure)) #18 SZ 19 # length(unique(metahetero1$GroupingTerm)) #9 Sz ok # length(unique(metahetero1$parameter_group)) # 149 SZ 152 # length(unique(metahetero1$parameter_name)) #218 SZ 223 # Count of number of parameter names (correlated sub-traits) in each parameter group (par_group_size) metahetero1b <- metahetero1 %>% group_by(parameter_group) %>% mutate(par_group_size = n_distinct(parameter_name)) metahetero1$par_group_size <- metahetero1b$par_group_size[match(metahetero1$parameter_group, metahetero1b$parameter_group)] # Create subsets with > 1 count (par_group_size > 1) metahetero1_sub <- subset(metahetero1, par_group_size > 1) # 90 observations # str(metahetero1_sub) # metahetero1_sub$sampleSize <- as.numeric(metahetero1_sub$sampleSize) #from previous analysis? don't think is used: : delete in final version # Nest data n_count. <- metahetero1_sub %>% group_by(parameter_group) %>% # mutate(raw_N = sum(sampleSize)) %>% #don't think is necessary: delete in final version nest() # meta-analysis preparation model_count. <- n_count. %>% mutate( model_lnRR = map(data, ~ robu(.x$lnRR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnRR_se)^2 )), model_lnVR = map(data, ~ robu(.x$lnVR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnVR_se)^2 )), model_lnCVR = map(data, ~ robu(.x$lnCVR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnCVR_se)^2 )) ) # Robumeta object details: # str(model_count.$model_lnCVR[[1]]) ## *Perform meta-analyses on correlated sub-traits, using robumeta # Susi / FELIX: what's this below? # Shinichi: We think we want to use these for further analyses: # residual variance: as.numeric(robu_fit$mod_info$term1) (same as 'mod_info$tau.sq') # sample size: robu_fit$N ## **Extract and save parameter estimates # Felix: doesn't work , error message: #!!!!!!!!!!! ERROR!!!!!!!!!!!!!!!!!!!! #Error: Column `parameter_group` can't be modified because it's a grouping variable count_fun. <- function(mod_sub) { return(c(as.numeric(mod_sub$mod_info$term1), mod_sub$N)) } robusub_RR. <- model_count. %>% transmute(parameter_group, estimatelnRR = map(model_lnRR, count_fun.)) %>% mutate(r = map(estimatelnRR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnRR) %>% purrr::set_names(c("parameter_group", "var.RR", "N.RR")) robusub_CVR. <- model_count. %>% transmute(parameter_group, estimatelnCVR = map(model_lnCVR, count_fun.)) %>% mutate(r = map(estimatelnCVR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnCVR) %>% purrr::set_names(c("parameter_group", "var.CVR", "N.CVR")) robusub_VR. <- model_count. %>% transmute(parameter_group, estimatelnVR = map(model_lnVR, count_fun.)) %>% mutate(r = map(estimatelnVR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnVR) %>% purrr::set_names(c("parameter_group", "var.VR", "N.VR")) robu_all. <- full_join(robusub_CVR., robusub_VR.) %>% full_join(., robusub_RR.) ``` Merge the two data sets (the new [robu_all.] and the initial [uncorrelated sub-traits with count = 1]) In this step, we 1) merge the N from robumeta and the N from metafor (s.nlevels.error) together into the same columns (N.RR, N.VR, N.CVR) 2) calculate the total variance for metafor models as the sum of random effect variances and the residual error, then add in the same columns together with the residual variances from robumeta ```{r} metahetero_all <- metahetero1 %>% filter(par_group_size == 1) %>% as_tibble() metahetero_all$N.RR <- metahetero_all$s.nlevels.error.RR metahetero_all$N.CVR <- metahetero_all$s.nlevels.error.CVR metahetero_all$N.VR <- metahetero_all$s.nlevels.error.VR metahetero_all$var.RR <- log(sqrt(metahetero_all$sigma2_strain.RR + metahetero_all$sigma2_center.RR + metahetero_all$sigma2_error.RR)) metahetero_all$var.VR <- log(sqrt(metahetero_all$sigma2_strain.VR + metahetero_all$sigma2_center.VR + metahetero_all$sigma2_error.VR)) metahetero_all$var.CVR <- log(sqrt(metahetero_all$sigma2_strain.CVR + metahetero_all$sigma2_center.CVR + metahetero_all$sigma2_error.CVR)) # str(metahetero_all) # str(robu_all.) metahetero_all <- metahetero_all %>% mutate( var.RR = if_else(var.RR == -Inf, -7, var.RR), var.VR = if_else(var.VR == -Inf, -5, var.VR), var.CVR = if_else(var.CVR == -Inf, -6, var.CVR) ) # **Combine data ## Step1 combinedmetahetero <- bind_rows(robu_all., metahetero_all) # glimpse(combinedmetahetero) # Steps 2&3 metacombohetero <- combinedmetahetero metacombohetero$counts <- metahetero1$par_group_size[match(metacombohetero$parameter_group, metahetero1$parameter_group)] metacombohetero$procedure2 <- metahetero1$procedure[match(metacombohetero$parameter_group, metahetero1$parameter_group)] metacombohetero$GroupingTerm2 <- metahetero1$GroupingTerm[match(metacombohetero$parameter_group, metahetero1$parameter_group)] # **Clean-up and rename metacombohetero <- metacombohetero[, c(1:7, 43:45)] names(metacombohetero)[9] <- "procedure" names(metacombohetero)[10] <- "GroupingTerm" ``` #### Meta-analysis of heterogeneity ```{r} ## Perform meta-meta-analysis (3 for each of the 9 grouping terms: var.CVR, var.VR, var.RR) metacombohetero_final <- metacombohetero %>% group_by(GroupingTerm) %>% nest() # Final fixed effects meta-analyses within grouping terms, with SE of the estimate # metacombohetero$var.CVR heterog1 <- metacombohetero_final %>% mutate( model_heteroCVR = map(data, ~ metafor::rma.uni( yi = .x$var.CVR, sei = sqrt(1 / 2 * (.x$N.CVR - 1)), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 10000, stepadj = 0.5), verbose = F )), model_heteroVR = map(data, ~ metafor::rma.uni( yi = .x$var.VR, sei = sqrt(1 / 2 * (.x$N.VR - 1)), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 10000, stepadj = 0.5), verbose = F )), model_heteroRR = map(data, ~ metafor::rma.uni( yi = .x$var.RR, sei = sqrt(1 / 2 * (.x$N.RR - 1)), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 10000, stepadj = 0.5), verbose = F )) ) # Re-structure data for each grouping term; extract heterogenenity/variance terms; delete un-used variables ## FELIX: ADD "ALL" Behaviour. <- heterog1 %>% filter(., GroupingTerm == "Behaviour") %>% select(., -data) %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Immunology. <- heterog1 %>% filter(., GroupingTerm == "Immunology") %>% select(., -data) %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Hematology. <- heterog1 %>% filter(., GroupingTerm == "Hematology") %>% select(., -data) %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Hearing. <- heterog1 %>% filter(., GroupingTerm == "Hearing") %>% select(., -data) %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Physiology. <- heterog1 %>% filter(., GroupingTerm == "Physiology") %>% select(., -data) %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Metabolism. <- heterog1 %>% filter(., GroupingTerm == "Metabolism") %>% select(., -data) %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Morphology. <- heterog1 %>% filter(., GroupingTerm == "Morphology") %>% select(., -data) %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Heart. <- heterog1 %>% filter(., GroupingTerm == "Heart") %>% select(., -data) %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) Eye. <- heterog1 %>% filter(., GroupingTerm == "Eye") %>% select(., -data) %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ) %>% select(., GroupingTerm, heteroCVR:heteroRR_se) #FELIX: check.I have added / modifie this can't run to check All. <- as.data.frame(heterog1 %>% mutate( heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heeroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se, heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se ))[, c(5:16)] All.$heteroCVR <- as.numeric(All.$heteroCVR) All.$heteroVR <- as.numeric(All.$heteroVR) All.$heteroRR <- as.numeric(All.$lheteroRR) All. <- All. %>% mutate(GroupingTerm = "All") heterog2 <- bind_rows(Behaviour., Morphology., Metabolism., Physiology., Immunology., Hematology., Heart., Hearing., Eye., All.) # str(heterog2) ``` #### Heterogeneity PLOT Restructure data for plotting ```{r} heterog3 <- gather(heterog2, parameter, value, c(heteroCVR, heteroVR, heteroRR), factor_key = TRUE) heteroCVR.ci <- heterog3 %>% filter(parameter == "heteroCVR") %>% mutate(ci.low = heteroCVR_lower, ci.high = heteroCVR_upper) heteroVR.ci <- heterog3 %>% filter(parameter == "heteroVR") %>% mutate(ci.low = heteroVR_lower, ci.high = heteroVR_upper) heteroRR.ci <- heterog3 %>% filter(parameter == "heteroRR") %>% mutate(ci.low = heteroRR_lower, ci.high = heteroRR_upper) heterog4 <- bind_rows(heteroCVR.ci, heteroVR.ci, heteroRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # **Re-order grouping terms heterog4$GroupingTerm <- factor(heterog4$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye")) heterog4$GroupingTerm <- factor(heterog4$GroupingTerm, rev(levels(heterog4$GroupingTerm))) heterog4$label <- "All traits" # write.csv(heterog4, "heterog4.csv") ``` #### Plot S1 C (Second-order meta analysis on heterogeneity) ```{r} heterog5 <- heterog4 heterog5$mean <- as.numeric(exp(heterog5$value)) heterog5$ci.l <- as.numeric(exp(heterog5$ci.low)) heterog5$ci.h <- as.numeric(exp(heterog5$ci.high)) heterog6 <- heterog5 HeteroS1 <- heterog6 %>% filter( parameter == "heteroCVR" | parameter == "heteroRR" ) %>% ## FELIX : do we need this "filter" - we want VR as well?? ggplot(aes(y = GroupingTerm, x = mean)) + geom_errorbarh(aes( xmin = ci.l, xmax = ci.h ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "black", color = "black", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(-0.1, 1.4), # breaks = c(0, 0.1, 0.2), name = "sigma^2" ) + # geom_vline(xintercept=0, # color='black', # linetype='dashed')+ facet_grid( cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_text(hjust = 0.5, size = 14), axis.title.y = element_blank() ) # HeteroS1 ``` #### Combined Figure S1: overall Count data, Meta anlysis results, Heterogeneity) ```{r} FigS1 <- ggarrange(malebias_FigS1_alltraits + xlab("percentage sex bias"), Metameta_FigS1_alltraits, HeteroS1, nrow = 3, align = "v", heights = c(1, 1, 1), labels = c("A", "B", "C")) FigS1 # ggsave("FigS1_OverallResults.pdf", plot = Fig4, width = 6, height = 5) ``` ## Figure S2 Plot FigS2 all significant results (CI not overlapping zero) for males ### FELIX: "ALL" missing. ```{r} meta.plot2.sig.bS <- meta.plot2.sig[, c("lnCVR", "lnVR", "lnRR", "lnCVRsig", "lnVRsig", "lnRRsig", "GroupingTerm")] meta.plot2.sig.cS <- gather(meta.plot2.sig.bS, trait, value, lnCVR:lnRR) meta.plot2.sig.cS$sig <- "placeholder" meta.plot2.sig.cS$trait <- factor(meta.plot2.sig.cS$trait, levels = c("lnCVR", "lnVR", "lnRR")) meta.plot2.sig.cS$sig <- ifelse(meta.plot2.sig.cS$trait == "lnCVR", meta.plot2.sig.cS$lnCVRsig, ifelse(meta.plot2.sig.cS$trait == "lnVR", meta.plot2.sig.cS$lnVRsig, meta.plot2.sig.cS$lnRRsig) ) # choosing sex biased ln-ratios significantly larger than 0 meta.plotS2.sig.malebias <- meta.plot2.sig.cS %>% group_by_at(vars(trait, GroupingTerm)) %>% filter(sig == 1) %>% summarise(male_sig = sum(value > 0), female_sig = sum(value < 0), total = male_sig + female_sig) meta.plotS2.sig.malebias <- ungroup(meta.plotS2.sig.malebias) %>% add_row(trait = "lnCVR", GroupingTerm = "Hearing", male_sig = 0, female_sig = 0, .before = 4) %>% # add "Hearing" for lnCVR (not filtered as only zeros) mutate(malepercent = male_sig * 100 / total, femalepercent = female_sig * 100 / total) meta.plotS2.sig.malebias$label <- "CI not overlapping zero" # restructure to create stacked bar plots meta.plotS2.sig.bothsexes <- as.data.frame(meta.plotS2.sig.malebias) meta.plotS2.sig.bothsexes.b <- gather(meta.plotS2.sig.bothsexes, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE) # create new sample size variable meta.plotS2.sig.bothsexes.b$samplesize <- with(meta.plotS2.sig.bothsexes.b, ifelse(sex == "malepercent", male_sig, female_sig)) # *Plot Fig2 all significant results (CI not overlapping zero): # no sig. lnCVR for 'Hearing' in either sex; no sig. male-biased lnCVR for 'Immunology' and 'Eye, and no sig. male-biased lnVR for 'Eye' malebias_FigS2_sigtraits <- ggplot(meta.plotS2.sig.bothsexes.b) + aes(x = GroupingTerm, y = percent, fill = sex) + geom_col() + geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") + geom_text( data = subset(meta.plotS2.sig.bothsexes.b, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5), color = "white", size = 3.5 ) + facet_grid( cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18), scales = "free", space = "free" ) + scale_fill_brewer(palette = "Set2") + theme_bw(base_size = 18) + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none", axis.title.x = element_blank(), axis.title.y = element_blank() ) + coord_flip() # malebias_FigS2_sigtraits # this is Figure S2 A ``` ### Prepare data for traits with effect size ratios > 10% larger in males, supplemental Figure S2 ### FELIX: "ALL" missing. This Figure extends Figure 4, as it includes results not only for lnCVR and lnRR but also lnCVR. In addition, we compare two different assessments of sex-bias, significance (CI not overlapping zero) and sex differences in male / female ratios > 10% ### Over 10% male bias, count data (first- order metanalysis) ```{r} meta.plot2.over10 <- meta_clean %>% select(lnCVR, lnVR, lnRR, GroupingTerm) %>% arrange(GroupingTerm) meta.plot2.over10.b <- gather(meta.plot2.over10, trait, value, c(lnCVR, lnVR, lnRR)) meta.plot2.over10.b$trait <- factor(meta.plot2.over10.b$trait, levels = c("lnCVR", "lnVR", "lnRR")) meta.plot2.over10.c <- meta.plot2.over10.b %>% group_by_at(vars(trait, GroupingTerm)) %>% summarise( malebias = sum(value > log(11 / 10)), femalebias = sum(value < log(9 / 10)), total = malebias + femalebias, malepercent = malebias * 100 / total, femalepercent = femalebias * 100 / total ) meta.plot2.over10.c$label <- "Sex difference in m/f ratios > 10%" # restructure to create stacked bar plots meta.plot2.over10.c <- as.data.frame(meta.plot2.over10.c) meta.plot2.over10.d <- gather(meta.plot2.over10.c, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE) # create new sample size variable meta.plot2.over10.d$samplesize <- with(meta.plot2.over10.d, ifelse(sex == "malepercent", malebias, femalebias)) # *Plot Fig2 Sex difference in m/f ratio > 10% malebias_Fig2_over10 <- ggplot(meta.plot2.over10.d) + aes(x = GroupingTerm, y = percent, fill = sex) + geom_col() + geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") + geom_text( data = subset(meta.plot2.over10.d, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5), color = "white", size = 3.5 ) + facet_grid( cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18), scales = "free", space = "free" ) + scale_fill_brewer(palette = "Set2") + theme_bw(base_size = 18) + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_blank(), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none", axis.title.x = element_blank(), axis.title.y = element_blank() ) + coord_flip() # malebias_Fig2_over10 (supplemental Figure S2) ``` #### Fig S2, second-order meta-analysis, male traits #### Female Figure, significant traits Female FigS2 B sig Prepare data for traits with CI not overlapping 0 create column with 1= different from zero, 0= zero included in CI Restructure data for plotting ```{r} overall3.female.sigS <- gather(overall.female.plot3, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) lnCVR.ci <- overall3.female.sigS %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3.female.sigS %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.female.sigS %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.female.sigS <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4.female.sigS$label <- "CI not overlapping zero" ## Metameta_FigS2_female.sig <- overall4.female.sigS %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "salmon1", color = "salmon1", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(-0.4, 0), breaks = c(-0.3, 0), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), # rows = vars(label), # labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank() ) # Metameta_FigS2_female.sig ``` Prepare data for traits with m/f difference > 10% Create column with 1= larger, 0= difference not larger than 10% between male/female ratios ```{r} meta.male.plot3.perc <- metacombo %>% mutate( percCVR = ifelse(lnCVR > log(11 / 10), 1, 0), percVR = ifelse(lnVR > log(11 / 10), 1, 0), percRR = ifelse(lnRR > log(11 / 10), 1, 0) ) # Significant subset for lnCVR metacombo_male.plot3.CVR.perc <- meta.male.plot3.perc %>% filter(percCVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.CVR.perc.all <- meta.male.plot3.perc %>% filter(percCVR == 1) %>% nest() # Significant subset for lnVR metacombo_male.plot3.VR.perc <- meta.male.plot3.perc %>% filter(percVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.VR.perc.all <- meta.male.plot3.perc %>% filter(percVR == 1) %>% nest() # Significant subset for lnRR metacombo_male.plot3.RR.perc <- meta.male.plot3.perc %>% filter(percRR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.RR.perc.all <- meta.male.plot3.perc %>% filter(percRR == 1) %>% nest() # **Final fixed effects meta-analyses within grouping terms and across grouping terms, with SE of the estimate plot3.male.meta.CVR.perc <- metacombo_male.plot3.CVR.perc %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.VR.perc <- metacombo_male.plot3.VR.perc %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.RR.perc <- metacombo_male.plot3.RR.perc %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) # Across all grouping terms # plot3.male.meta.CVR.perc.all <- metacombo_male.plot3.CVR.perc.all %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.CVR.perc.all <- plot3.male.meta.CVR.perc.all %>% mutate(GroupingTerm = "All") plot3.male.meta.VR.perc.all <- metacombo_male.plot3.VR.perc.all %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.VR.perc.all <- plot3.male.meta.VR.perc.all %>% mutate(GroupingTerm = "All") plot3.male.meta.RR.perc.all <- metacombo_male.plot3.RR.perc.all %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.RR.perc.all <- plot3.male.meta.RR.perc.all %>% mutate(GroupingTerm = "All") # Combine with separate grouping term results plot3.male.meta.CVR.perc <- bind_rows(plot3.male.meta.CVR.perc, plot3.male.meta.CVR.perc.all) plot3.male.meta.VR.perc <- bind_rows(plot3.male.meta.VR.perc, plot3.male.meta.VR.perc.all) plot3.male.meta.RR.perc <- bind_rows(plot3.male.meta.RR.perc, plot3.male.meta.RR.perc.all) # **Re-structure data for each grouping term; delete un-used variables: "Hearing missing for all 3 parameters" plot3.male.meta.CVR.perc.b <- as.data.frame(plot3.male.meta.CVR.perc %>% group_by(GroupingTerm) %>% mutate( lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)), lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.CVR.perc.b)) plot3.male.meta.CVR.perc.b <- rbind(plot3.male.meta.CVR.perc.b, add.row.hearing) plot3.male.meta.CVR.perc.b <- plot3.male.meta.CVR.perc.b[order(plot3.male.meta.CVR.perc.b$GroupingTerm), ] plot3.male.meta.VR.perc.b <- as.data.frame(plot3.male.meta.VR.perc %>% group_by(GroupingTerm) %>% mutate( lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)), lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.VR.perc.b)) plot3.male.meta.VR.perc.b <- rbind(plot3.male.meta.VR.perc.b, add.row.hearing) plot3.male.meta.VR.perc.b <- plot3.male.meta.VR.perc.b[order(plot3.male.meta.VR.perc.b$GroupingTerm), ] plot3.male.meta.RR.perc.b <- as.data.frame(plot3.male.meta.RR.perc %>% group_by(GroupingTerm) %>% mutate( lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)), lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.RR.perc.b)) plot3.male.meta.RR.perc.b <- rbind(plot3.male.meta.RR.perc.b, add.row.hearing) add.row.eye <- as.data.frame(t(c("Eye", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.RR.perc.b)) plot3.male.meta.RR.perc.b <- rbind(plot3.male.meta.RR.perc.b, add.row.eye) plot3.male.meta.RR.perc.b <- plot3.male.meta.RR.perc.b[order(plot3.male.meta.RR.perc.b$GroupingTerm), ] plot3.male.meta.CVR.Vr.perc <- full_join(plot3.male.meta.CVR.perc.b, plot3.male.meta.VR.perc.b) overall.male.plot3.perc <- full_join(plot3.male.meta.CVR.Vr.perc, plot3.male.meta.RR.perc.b) overall.male.plot3.perc$GroupingTerm <- factor(overall.male.plot3.perc$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall.male.plot3.perc$GroupingTerm <- factor(overall.male.plot3.perc$GroupingTerm, rev(levels(overall.male.plot3.perc$GroupingTerm))) ``` Restructure data for plotting : Male biased, 10% difference ```{r} overall3.perc <- gather(overall.male.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) lnCVR.ci <- overall3.perc %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3.perc %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.perc %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.male.perc <- bind_rows(lnCVR.ci,lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4.male.perc$label <- "Sex difference in m/f ratios > 10%" overall4.male.perc$value <- as.numeric(overall4.male.perc$value) overall4.male.perc$ci.low <- as.numeric(overall4.male.perc$ci.low) overall4.male.perc$ci.high <- as.numeric(overall4.male.perc$ci.high) ``` Plot Fig S2 all >10% difference (male bias) ```{r} Metameta_Fig3_male.perc <- overall4.male.perc %>% # filter(., GroupingTerm != "Hearing") %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes( shape = parameter, fill = parameter ), color = "mediumaquamarine", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(-0.2, 0.62), breaks = c(0, 0.3), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_blank(), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_text(hjust = 0.5, size = 14), axis.title.y = element_blank() ) # Metameta_Fig3_male.perc (Figure S2 right panel) ``` #### Female Fig S2 >10% ```{r} meta.plot3.perc <- metacombo %>% mutate( percCVR = ifelse(lnCVR < log(9 / 10), 1, 0), percVR = ifelse(lnVR < log(9 / 10), 1, 0), percRR = ifelse(lnRR < log(9 / 10), 1, 0) ) # Significant subset for lnCVR metacombo_plot3.CVR.perc <- meta.plot3.perc %>% filter(percCVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_plot3.CVR.perc.all <- meta.plot3.perc %>% filter(percCVR == 1) %>% nest() # Significant subset for lnVR metacombo_plot3.VR.perc <- meta.plot3.perc %>% filter(percVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_plot3.VR.perc.all <- meta.plot3.perc %>% filter(percVR == 1) %>% nest() # Significant subset for lnRR metacombo_plot3.RR.perc <- meta.plot3.perc %>% filter(percRR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_plot3.RR.perc.all <- meta.plot3.perc %>% filter(percRR == 1) %>% nest() # **Final fixed effects meta-analyses within grouping terms, with SE of the estimate plot3.meta.CVR.perc <- metacombo_plot3.CVR.perc %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.meta.VR.perc <- metacombo_plot3.VR.perc %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.meta.RR.perc <- metacombo_plot3.RR.perc %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) # Across all grouping terms # plot3.meta.CVR.perc.all <- metacombo_plot3.CVR.perc.all %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.meta.CVR.perc.all <- plot3.meta.CVR.perc.all %>% mutate(GroupingTerm = "All") plot3.meta.VR.perc.all <- metacombo_plot3.VR.perc.all %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.meta.VR.perc.all <- plot3.meta.VR.perc.all %>% mutate(GroupingTerm = "All") plot3.meta.RR.perc.all <- metacombo_plot3.RR.perc.all %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.meta.RR.perc.all <- plot3.meta.RR.perc.all %>% mutate(GroupingTerm = "All") # Combine with separate grouping term results plot3.meta.CVR.perc <- bind_rows(plot3.meta.CVR.perc, plot3.meta.CVR.perc.all) plot3.meta.VR.perc <- bind_rows(plot3.meta.VR.perc, plot3.meta.VR.perc.all) plot3.meta.RR.perc <- bind_rows(plot3.meta.RR.perc, plot3.meta.RR.perc.all) # **Re-structure data for each grouping term; delete un-used variables: "Hearing missing for all 3 parameters" plot3.meta.CVR.perc.b <- as.data.frame(plot3.meta.CVR.perc %>% group_by(GroupingTerm) %>% mutate( lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)), lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.meta.CVR.perc.b)) plot3.meta.CVR.perc.b <- rbind(plot3.meta.CVR.perc.b, add.row.hearing) plot3.meta.CVR.perc.b <- plot3.meta.CVR.perc.b[order(plot3.meta.CVR.perc.b$GroupingTerm), ] plot3.meta.VR.perc.b <- as.data.frame(plot3.meta.VR.perc %>% group_by(GroupingTerm) %>% mutate( lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)), lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.meta.VR.perc.b)) plot3.meta.VR.perc.b <- rbind(plot3.meta.VR.perc.b, add.row.hearing) plot3.meta.VR.perc.b <- plot3.meta.VR.perc.b[order(plot3.meta.VR.perc.b$GroupingTerm), ] plot3.meta.RR.perc.b <- as.data.frame(plot3.meta.RR.perc %>% group_by(GroupingTerm) %>% mutate( lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)), lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.meta.RR.perc.b)) plot3.meta.RR.perc.b <- rbind(plot3.meta.RR.perc.b, add.row.hearing) add.row.hematology <- as.data.frame(t(c("Hematology", NA, NA, NA, NA))) %>% setNames(names(plot3.meta.RR.perc.b)) plot3.meta.RR.perc.b <- rbind(plot3.meta.RR.perc.b, add.row.hematology) plot3.meta.RR.perc.b <- plot3.meta.RR.perc.b[order(plot3.meta.RR.perc.b$GroupingTerm), ] plot3.meta.CVR.perc.c <- full_join(plot3.meta.CVR.perc.b, plot3.meta.VR.perc.b) overall.plot3.perc <- full_join(plot3.meta.CVR.perc.c, plot3.meta.RR.perc.b) overall.plot3.perc$GroupingTerm <- factor(overall.plot3.perc$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall.plot3.perc$GroupingTerm <- factor(overall.plot3.perc$GroupingTerm, rev(levels(overall.plot3.perc$GroupingTerm))) ``` Restructure data for plotting Female bias, 10 percent difference ```{r} overall3.perc <- gather(overall.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) lnCVR.ci <- overall3.perc %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3.perc %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.perc %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.perc <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4.perc$label <- "Sex difference in m/f ratios > 10%" overall4.perc$value <- as.numeric(overall4.perc$value) overall4.perc$ci.low <- as.numeric(overall4.perc$ci.low) overall4.perc$ci.high <- as.numeric(overall4.perc$ci.high) ``` Plot FigS2 all >10% difference (female) ```{r} Metameta_Fig3_female.perc <- overall4.perc %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "salmon1", color = "salmon1", size = 2.2, show.legend = FALSE ) + # scale_shape_manual(values = scale_x_continuous( limits = c(-0.53, 0.2), breaks = c(-0.3, 0), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), # rows = vars(label), # labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_blank(), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_text(hjust = 0.5, size = 14), axis.title.y = element_blank() ) # Metameta_Fig3_female.perc (Figure 5D left panel) ``` #### Plot Fig S2: plots combined ```{r} library(ggpubr) FigS2b <- ggarrange(Metameta_Fig3_female.sig, Metameta_Fig3_male.sig, ncol = 2, nrow = 1, widths = c(1, 1.20), heights = c(1, 1) ) FigS2d <- ggarrange(Metameta_Fig3_female.perc, Metameta_Fig3_male.perc, ncol = 2, nrow = 1, widths = c(1, 1.20), heights = c(1, 1) ) # end combination Figure 5 FigS2 <- ggarrange(malebias_FigS2_sigtraits, malebias_Fig2_over10, FigS2b, FigS2d, ncol = 1, nrow = 4, heights = c(2.3, 2, 2.1, 2), labels = c("A", " ", "B", " ")) FigS2 ``` ## NOT SURE WHAT THIS BELOW IS?? ## Figure S2: sex-bias, including VR Prepare data for traits with effect size ratios > 10% larger in males ```{r} meta.plotS2.over10 <- meta_clean %>% select(lnCVR, lnVR, lnRR, GroupingTerm) %>% arrange(GroupingTerm) meta.plotS2.over10.b <- gather(meta.plotS2.over10, trait, value, c(lnCVR, lnVR, lnRR)) meta.plotS2.over10.b$trait <- factor(meta.plotS2.over10.b$trait, levels = c("lnCVR", "lnVR", "lnRR")) meta.plotS2.over10.c <- meta.plotS2.over10.b %>% group_by_at(vars(trait, GroupingTerm)) %>% summarise( malebias = sum(value > log(11 / 10)), femalebias = sum(value < log(9 / 10)), total = malebias + femalebias, malepercent = malebias * 100 / total, femalepercent = femalebias * 100 / total ) meta.plotS2.over10.c$label <- "Sex difference in m/f ratios > 10%" # restructure to create stacked bar plots meta.plotS2.over10.c <- as.data.frame(meta.plotS2.over10.c) meta.plotS2.over10.d <- gather(meta.plotS2.over10.c, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE) # create new sample size variable meta.plotS2.over10.d$samplesize <- with(meta.plotS2.over10.d, ifelse(sex == "malepercent", malebias, femalebias)) # *Plot FigS2 Sex difference in m/f ratio > 10% malebias_FigS2_over10 <- ggplot(meta.plotS2.over10.d) + aes(x = GroupingTerm, y = percent, fill = sex) + geom_col() + geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") + geom_text( data = subset(meta.plot2.over10.d, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5), color = "white", size = 3.5 ) + facet_grid( cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18), scales = "free", space = "free" ) + scale_fill_brewer(palette = "Set2") + theme_bw(base_size = 18) + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_blank(), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none", axis.title.x = element_blank(), axis.title.y = element_blank() ) + coord_flip() # malebias_FigS2_over10 #(Panel B in Fig S2 in ms) ``` #Metameta_FigS2_male.sig (Figure 5B right panel) Restructure MALE data for plotting ```{r} overall3.male.sigS <- gather(overall.male.plot3, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) lnCVR.ci <- overall3.male.sigS %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3.male.sigS %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.male.sigS %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.male.sigS <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4.male.sigS$label <- "CI not overlapping zero" ``` Plot FigS2 all significant results (CI not overlapping zero, male ) ```{r} Metameta_FigS2_male.sig <- overall4.male.sigS %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "mediumaquamarine", color = "mediumaquamarine", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(0, 0.4), breaks = c(0, 0.3), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank() ) # Metameta_FigS2_male.sig ``` ### 10 % Perc sex difference, male bias Restructure data for plotting : Male biased, 10% difference ```{r} overall3S.perc <- gather(overall.male.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) # lnVR, lnCVR.ci <- overall3S.perc %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3S.perc %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3S.perc %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4S.male.perc <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # lnVR.ci, overall4S.male.perc$label <- "Sex difference in m/f ratios > 10%" overall4S.male.perc$value <- as.numeric(overall4S.male.perc$value) overall4S.male.perc$ci.low <- as.numeric(overall4S.male.perc$ci.low) overall4S.male.perc$ci.high <- as.numeric(overall4S.male.perc$ci.high) ``` Plot FigS2 all >10% difference (male bias) ```{r} Metameta_FigS2_male.perc <- overall4S.male.perc %>% # filter(., GroupingTerm != "Hearing") %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes( shape = parameter, fill = parameter ), color = "mediumaquamarine", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(-0.2, 0.62), breaks = c(0, 0.3), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_blank(), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_text(hjust = 0.5, size = 14), axis.title.y = element_blank() ) # Metameta_FigS2_male.perc (Figure 5D right panel) ``` Restructure data for plotting: Female bias, 10 percent difference, including VR ```{r} overall3S.perc <- gather(overall.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) # lnVR, lnCVR.ci <- overall3S.perc %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3S.perc %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3S.perc %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4S.perc <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4S.perc$label <- "Sex difference in m/f ratios > 10%" overall4S.perc$value <- as.numeric(overall4S.perc$value) overall4S.perc$ci.low <- as.numeric(overall4S.perc$ci.low) overall4S.perc$ci.high <- as.numeric(overall4S.perc$ci.high) ``` Plot Fig5D all >10% difference (female) ```{r} Metameta_Fig3S_female.perc <- overall4S.perc %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "salmon1", color = "salmon1", size = 2.2, show.legend = FALSE ) + # scale_shape_manual(values = scale_x_continuous( limits = c(-0.53, 0.2), breaks = c(-0.3, 0), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), # rows = vars(label), # labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_blank(), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_text(hjust = 0.5, size = 14), axis.title.y = element_blank() ) # Metameta_Fig3S_female.perc (Figure 5D left panel) ``` Figure S2 ```{r} FigS2c <- ggarrange(Metameta_FigS2_female.sig, Metameta_FigS2_male.sig, ncol = 2, nrow = 1, widths = c(1, 1.20), heights = c(1, 1) ) FigS2d <- ggarrange(Metameta_Fig3S_female.perc, Metameta_FigS2_male.perc, ncol = 2, nrow = 1, widths = c(1, 1.20), heights = c(1, 1) ) # end combination Figure 5 FigS2 <- ggarrange(malebias_FigS2_sigtraits, malebias_FigS2_over10, FigS2c, FigS2d, ncol = 1, nrow = 4, heights = c(2.2, 2, 2.2, 2), labels = c("A", " ", "B", " ")) FigS2 ``` ## Acknowledgements tbd ## R Session Information ```{r} sessionInfo() ```