---
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
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,
warning = FALSE,
message = FALSE,
cache = TRUE,
tidy = TRUE)
```
```{r, include=FALSE}
getwd()
```
## Set up
#### Packages
```{r}
library(readr)
library(dplyr)
library(metafor)
library(devtools)
library(purrr)
library(tidyverse)
library(tibble)
library(kableExtra)
library(robumeta)
library(ggpubr)
library(ggplot2)
```
### Loading functions
#### Functions for preparing the data for meta analyses
1) "Population statistics"
```{r}
calculate_population_stats <- function(mydata, min_individuals = 5) {
mydata %>%
group_by(population, strain_name, production_center, sex) %>%
summarise(
trait = parameter_name[1],
x_bar = mean(data_point),
x_sd = sd(data_point),
n_ind = n()
) %>%
ungroup() %>%
filter(n_ind > min_individuals) %>%
# Check both sexes present & filter those missing
group_by(population) %>%
mutate(
n_sex = n_distinct(sex)
) %>%
ungroup() %>%
filter(n_sex ==2) %>%
select(-n_sex) %>%
arrange(production_center, strain_name, population, sex)
}
```
2) Extraction of effect sizes and sample variances
```{r}
create_meta_analysis_effect_sizes <- function(mydata) {
i <- seq(1, nrow(mydata), by = 2)
input <- data.frame(
n1i = mydata$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$x_sd[i + 1]
)
mydata[i,] %>%
select(strain_name, production_center, trait) %>%
mutate(
effect_size_CVR = Calc.lnCVR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i),
sample_variance_CVR = Calc.var.lnCVR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i),
effect_size_VR = Calc.lnVR(CSD = input$sd1i, CN = input$n1i, ESD = input$sd2i, EN = input$n2i),
sample_variance_VR = Calc.var.lnVR(CN = input$n1i, EN = input$n2i),
effect_size_RR = Calc.lnRR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i),
sample_variance_RR = Calc.var.lnRR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i),
err = as.factor(seq_len(n()))
)
}
```
3) Meta Analysis
Function to calculate meta-analysis statistics. Created by A M Senior @ the University of Otago NZ 03/01/2014
Below are functions for calculating effect sizes for meta-analysis of variance.
All functions take the mean, sd and n from the control and experimental groups.
The first function, Cal.lnCVR, calculates the the log response-ratio of the coefficient of variance (lnCVR) - see Nakagawa et al 2015.
The second function calculates the measurement error variance for lnCVR. As well as the aforementioned parameters, this function also takes
Equal.E.C.Corr (default = T), which must be True or False. If true, the function assumes that the correlation between mean and sd (Taylor's Law)
is equal for the mean and control groups, and, thus these data are pooled. If False the mean-SD correlation for the experimental and control groups
are calculated separately from one another.
Similar functions are then implemented for lnVR (for comparison of standard deviations) and ln RR (for comparison of means)
```{r}
Calc.lnCVR <- function(CMean, CSD, CN, EMean, ESD, EN){
log(ESD) - log(EMean) + 1 / (2*(EN - 1)) - (log(CSD) - log(CMean) + 1 / (2*(CN - 1)))
}
Calc.var.lnCVR <- function(CMean, CSD, CN, EMean, ESD, EN, Equal.E.C.Corr=T) {
if(Equal.E.C.Corr==T){
mvcorr <- 0 #cor.test(log(c(CMean, EMean)), log(c(CSD, ESD)))$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
}
Calc.lnVR <- function(CSD, CN, ESD, EN){
log(ESD) - log(CSD) + 1 / (2*(EN - 1)) - 1 / (2*(CN - 1))
}
Calc.var.lnVR <- function( CN, EN) {
1 / (2*(EN - 1)) + 1 / (2*(CN - 1))
}
Calc.lnRR <- function(CMean, CSD, CN, EMean, ESD, EN) {
log(EMean) - log(CMean)
}
Calc.var.lnRR <- function(CMean, CSD, CN, EMean, ESD, EN) {
CSD^2/(CN * CMean^2) + ESD^2/(EN * EMean^2)
}
```
## The dataset
Having loaded the necessary functions, we can get started on the dataset.
We here provide the cleaned dataset, which we have saved in a folder called "export", as easy starting point. However, the full dataset can be loaded and cleaned using the data cleaning function (Function 1 above), if "#" signs in the code below are removed (created as that is much smaller than the .csv - which we can still provide for those who absolutely want to start from scratch?)
#### Preparation of raw 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. However, the cvs is provided in case this is pereferred to be attempted, following the steps below:
```{r Raw Data, 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) {
mydata %>%
# Fileter to IMPC source (recommened 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: Jeremy suggested using 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) %>%
arrange(production_center, biological_sample_id, age_in_days)
}
```
2) Subsetting the data to choose only one data point per individual per trait
```{r}
#2) Subsetting the data to choose only one data point per individual per trait
# this is a necessary step for the loop across all traits
data_subset_parameterid_individual_by_age <- function(mydata, parameter, age_min, age_center) {
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)
# still some individuals with multiple records (because same individual appears under different procedures, so filter to one record)
j <- match(unique(tmp$biological_sample_id), tmp$biological_sample_id)
tmp[j, ]
}
```
### Load data (raw / pre-clean)
```{r}
## Load raw data - save cleaned dataset as RDS for reuse
#data_raw <- load_raw("data/dr7.0_all_control_data.csv") %>%
# clean_raw_data()
#dir.create("export", F, F)
#saveRDS(data_raw, "export/data_clean.rds")
getwd()
data1 <- readRDS("export/data_clean.rds")
#data1
```
### Clean data
This requires the selection of traits that have been measured in at least 2 centers. Consequently, rare or unusual methods and procedures are being filtered out in this step.
```{r }
dat1 <-
data1 %>%
group_by(parameter_name) %>%
summarize(center_per_trait = length(unique(production_center, na.rm = TRUE)))
dat2 <- merge(data1, dat1)
dat_moreThan1center <-
dat2 %>%
filter(center_per_trait >= 2)
data2 <- dat_moreThan1center
#min(data2$center_per_trait) # as a check if there indeed are no single occurences
```
## Meta analysis, Phase 1: across all traits
### Preparation
####Define population variable
& add grouping variables
In this step, a grouping variable is added (found in "Parameter.Grouping.csv")
The grouping variables were decided based on functional groups and procedures
```{r }
data3 <- data2 %>%
mutate(population = sprintf("%s-%s", production_center, strain_name))
group <- read.csv("export/ParameterGrouping.csv")
data <- data3
data$parameterGroup <- group$parameter[match(data$parameter_name, group$parameter_name)]
```
#### Assign unique IDs (per trait)
each unique parameter_name (=trait,use trait variable) gets a unique number ('id')
We add a new variable, where redundant traits are combined
[note however, at this stage the dataset still contains nonsensical traits, i.e. traits that may not contain any information on variance]
```{r}
#head(data)
names(data)[16] <- "parameter_group"
data <- transform(data, id = match(parameter_name, unique(parameter_name)))
n1 <- length(unique(data$parameter_name)) #232
n2 <- length(unique(data$parameter_group)) #161
n3 <- length(unique(data$procedure_name)) # 26
n <- length(unique(data$id))
#n # just to check that the number of traits is 232
```
#### Create matrix
As the current version of this script utilizes a loop instead of tidyR code, it is here necessary to create an empty matrix, in which the returning values will be stored.
```{r}
results.alltraits.grouping <- as.data.frame(cbind(c(1:n), matrix(rep(0, n*14), ncol = 14))) #number of individual results per trait = 10
names(results.alltraits.grouping) <- c("id", "lnCVR", "lnCVR_lower", "lnCVR_upper", "lnCVR_se", "lnVR", "lnVR_lower", "lnVR_upper", "lnVR_se", "lnRR", "lnRR_lower", "lnRR_upper" ,"lnRR_se" , "sampleSize", "trait")
```
#### Loop running meta-analysis on all traits
The loop combines the functions mentioned above and fills the data matrix with results from our meta analysis.
Error messages indicate traits that either did not reach convergence, or that did not return meaningful results in the meta-analysis, due to absence of variance. Those traits will be removed in later steps, outlined below.
```{r}
for(t in 1:n) {
tryCatch({
data_par_age <- data_subset_parameterid_individual_by_age(data, t, age_min = 0, age_center = 100)
population_stats <- calculate_population_stats(data_par_age)
results <- create_meta_analysis_effect_sizes(population_stats)
#lnCVR, log repsonse-ratio of the coefficient of variance
cvr <- metafor::rma.mv(yi = effect_size_CVR, V = sample_variance_CVR, random = list(~1| strain_name, ~1|production_center, ~1|err), control=list(optimizer="optim", optmethod="Nelder-Mead", maxit= 1000), verbose=F, data = results)
results.alltraits.grouping[t, 2] <- cvr$b
results.alltraits.grouping[t, 3] <- cvr$ci.lb
results.alltraits.grouping[t, 4] <- cvr$ci.ub
results.alltraits.grouping[t, 5] <- cvr$se
cvr
#lnVR, comparison of standard deviations
cv <- metafor::rma.mv(yi = effect_size_VR, V = sample_variance_VR, random = list(~1| strain_name, ~1|production_center,~1|err), control=list(optimizer="optim", optmethod="Nelder-Mead", maxit= 1000), verbose=F, data = results)
results.alltraits.grouping[t, 6] <- cv$b
results.alltraits.grouping[t, 7] <- cv$ci.lb
results.alltraits.grouping[t, 8] <- cv$ci.ub
results.alltraits.grouping[t, 9] <- cv$se
# for means, lnRR
means <- metafor::rma.mv(yi = effect_size_RR, V = sample_variance_RR, random = list(~1| strain_name, ~1|production_center, ~1|err), control=list(optimizer="optim", optmethod="Nelder-Mead", maxit= 1000), data = results)
results.alltraits.grouping[t, 10] <- means$b
results.alltraits.grouping[t, 11] <- means$ci.lb
results.alltraits.grouping[t, 12] <- means$ci.ub
results.alltraits.grouping[t, 13] <- means$se
results.alltraits.grouping[t, 14] <- means$k
results.alltraits.grouping[t, 15] <- unique(results$trait)
}, error=function(e){cat("ERROR :",conditionMessage(e), "\n")})
}
# Now that we have a "results" table with each of the meta-analytic means for all effect sizes of interest, we can use this table as part of the Shiny App, which will then be able to back calculate the percentage differences between males and females for mean, variance and coefficient of variance. We'll export and use this in the Shiny App. **Note that I have not dealt with convergence issues in some of these models, and so, this will need to be done down the road**
## Note Susi 31/7/2019: This dataset contains dublicated values, plus no info on what the "traits" mean. I will change Dan N's to one further belwo, that have been cleaned up already
#FILE TO USE: METACOMBO (around line 500)
#trait_meta_results <- write.csv(results.alltraits.grouping, file = "export/trait_meta_results.csv")
```
#### Merging datasets & removal of non-converged traits
Procedure names, grouping variables etc. are merged back together with the results from the metafor analysis above.
This requires loading of another excel sheet, "procedures.csv"
```{r}
procedures <- read.csv("export/procedures.csv")
results.alltraits.grouping$parameter_group <- data$parameter_group[match(results.alltraits.grouping$id, data$id)]
results.alltraits.grouping$procedure <- data$procedure_name[match(results.alltraits.grouping$id, data$id)]
results.alltraits.grouping$GroupingTerm <- procedures$GroupingTerm[match(results.alltraits.grouping$procedure, procedures$procedure)]
results.alltraits.grouping$parameter_name <- data$parameter_name[match(results.alltraits.grouping$id, data$id)]
meta1 <- results.alltraits.grouping
n <- length(unique(meta1$parameter_name)) # 232
```
Removal of traits that did not achieve convergence, are nonsensical for analysis of variance (such as traits that show variation, such as number of ribs, digits, etc). 14 traits from the originally 232 that had been included are removed.
```{r}
meta_clean <- meta1[ !(meta1$id %in% c(84,144,158,160,161,162,163,165,166,167,168,221,222,231)), ]
removed <-length(unique(meta_clean$parameter_name)) #218
meta_problem <- results.alltraits.grouping[ (results.alltraits.grouping$id %in% c(84,144,158,160,161,162,163,165,166,167,168,221,222,231)), ]
```
## Meta-analysis, Phase 2: non-independent traits
### Dealing with Correlated Parameters, preparation
This dataset contained a number of highly correlated traits, such as different kinds of cell counts (for example, hierarchical parameterization within immunological assays). As those data-points are not independent of each other, we conducted a meta analyses on these correlated parameters to collapse the number of levels.
#### Collapsing and merging correlated parameters
Here we double check numbers of trait parameters in the dataset
```{r}
meta1 <- meta_clean
# length(unique(meta1$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$parameter_name)) #218
```
#### Count of number of parameter names (correlated sub-traits) in each parameter group (par_group_size)
This serves to identify and separate the traits that are correlated from the full dataset that can be processed as is. If the sample size (n) for a given "parameter group" equals 1, the trait is unique and uncorrelated. All instances, where there are 2 or more traits associated with the same parameter group (90 cases), are selected for a "mini-meta analysis", which removes the issue of correlation.
```{r}
kable(cbind (meta1 %>% count(parameter_group) )) %>%
kable_styling() %>%
scroll_box(width = "100%", height = "200px")
meta1b <-
meta1 %>%
group_by(parameter_group) %>%
summarize(par_group_size = length(unique(parameter_name, na.rm = TRUE)))
#this gives a summary of number of parameter names in each parameter group, now it neeeds to get merged it back together
meta1$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$sampleSize)
```
#### Meta-analyses on correlated (sub-)traits, using robumeta`
The subset of the data is prepared (nested), and in this first step the model of the meta analysis effect sizes are calculated
```{r}
# nesting
n_count <- meta1_sub %>%
group_by(parameter_group) %>%
mutate(raw_N = sum(sampleSize)) %>%
nest()
model_count <- n_count %>%
mutate(model_lnRR = map(data, ~ robu(.x$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)
kable(cbind(robu_all, robu_all)) %>%
kable_styling() %>%
scroll_box(width = "100%", height = "200px")
```
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
```
#### Combine data
```{r}
# Step1
combinedmeta <- bind_rows(robu_all, meta_all)
#glimpse(combinedmeta)
# Steps 2&3
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)]
kable(cbind (metacombo, metacombo)) %>%
kable_styling() %>%
scroll_box(width = "100%", height = "200px")
```
Clean-up and rename
```{r}
metacombo <-metacombo[, c(1, 21:23, 2:13)]
names(metacombo)[3] <- "procedure"
names(metacombo)[4] <- "GroupingTerm"
# Quick pre-check before doing plots
compare <- metacombo %>%
group_by(GroupingTerm) %>%
dplyr::summarize(MeanCVR = mean(lnCVR),MeanVR = mean(lnVR), MeanRR = mean(lnRR) )
compare
```
```{r}
# SHINY APP # Now that we have a corrected "results" table with each of the meta-analytic means for all effect sizes of interest, we can use this table as part of the Shiny App, which will then be able to back calculate the percentage differences between males and females for mean, variance and coefficient of variance. We'll export and use this in the Shiny App. **Note that I have not dealt with convergence issues in some of these models, and so, this will need to be done down the road**
## Note Susi 31/7/2019: This has been cleaned up already
#FILE TO USE: METACOMBO
### note: to use
#trait_meta_results <- write.csv(metacombo, file = "export/trait_meta_results.csv")
```
## Meta-analysis, Phase 3: Second-order meta analysis for functional groups
#### Perform meta-analyses (3 for each of the 9 grouping terms: lnCVR, lnVR, lnRR)
This is the full result dataset
```{r}
kable(cbind (metacombo,metacombo)) %>%
kable_styling() %>%
scroll_box(width = "100%", height = "200px")
```
#### Prepare data
Nesting, calculating the number of parameters within each grouping term, and running the meta-analysis
```{r}
metacombo_final <- metacombo %>%
group_by(GroupingTerm) %>%
nest()
# **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()
# **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-structure data for each grouping term; delete unused variables
```{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)
```
## Visualisation
#### Plot: FIGURE 2 [Figure 4 in ms] (First-order meta analysis results)
Re-order 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$GroupingTerm)))
# *Prepare data for all traits
meta.plot2.all <- meta_clean %>% select(lnCVR, lnVR, lnRR, GroupingTerm) %>% arrange(GroupingTerm)
meta.plot2.all.b <- gather(meta.plot2.all, trait, value, c(lnCVR, lnRR)) #lnVR,
meta.plot2.all.b$trait <- factor(meta.plot2.all.b$trait, levels =c("lnCVR","lnRR") )#"lnVR",
meta.plot2.all.c <- meta.plot2.all.b %>%
group_by_at(vars(trait, GroupingTerm)) %>%
summarise(malebias = sum(value > 0), femalebias = sum(value<= 0), total= malebias + femalebias,
malepercent = malebias*100/total, femalepercent = femalebias*100/total)
meta.plot2.all.c$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$samplesize <- with(meta.plot2.all.e, ifelse(sex == "malepercent", malebias, femalebias) )
malebias_Fig2_alltraits <-
ggplot(meta.plot2.all.e) +
aes(x = GroupingTerm, y = percent, fill = sex) +
geom_col() +
geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") +
geom_text(data = subset(meta.plot2.all.e, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5),
color = "white", size = 3.5) +
facet_grid(cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18),
scales= 'free', space='free') +
scale_fill_brewer(palette = "Set2") +
theme_bw(base_size = 18) +
theme(strip.text.y = element_text(angle = 270, size = 10, margin = margin(t=15, r=15, b=15, l=15)),
strip.text.x = element_text(size = 12),
strip.background = element_rect(colour = NULL,linetype = "blank", fill = "gray90"),
text = element_text(size=14),
panel.spacing = unit(0.5, "lines"),
panel.border= element_blank(),
axis.line=element_line(),
panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.position = "none",
axis.title.x = element_blank(),
axis.title.y = element_blank() ) +
coord_flip()
#malebias_Fig2_alltraits
```
#### Prepare data for traits with CI not overlapping 0
create column with 1= different from zero, 0= zero included in CI
```{r}
meta.plot2.sig <- meta_clean %>%
mutate(lnCVRsig = ifelse(lnCVR_lower*lnCVR_upper >0, 1, 0), lnVRsig = ifelse(lnVR_lower*lnVR_upper >0, 1, 0),
lnRRsig = ifelse(lnRR_lower*lnRR_upper > 0, 1,0))
meta.plot2.sig.b <- meta.plot2.sig[, c("lnCVR", "lnRR", "lnCVRsig", "lnVRsig", "lnRRsig", "GroupingTerm")] #"lnVR",
meta.plot2.sig.c <- gather(meta.plot2.sig.b, trait, value, lnCVR:lnRR)
meta.plot2.sig.c$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):
# 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_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_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()
```
Prepare data for traits with effect size ratios > 10% larger in males
```{r}
meta.plot2.over10 <- meta_clean %>% select(lnCVR, lnRR, GroupingTerm) %>% arrange(GroupingTerm) #lnVR,
meta.plot2.over10.b <- gather(meta.plot2.over10, trait, value, c(lnCVR, lnRR)) #lnVR,
meta.plot2.over10.b$trait <- factor(meta.plot2.over10.b$trait, levels =c("lnCVR","lnRR") ) #"lnVR",
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
```
#### Create final combined Figure (Figure 2)
```{r}
Fig2 <- ggarrange(malebias_Fig2_alltraits, malebias_Fig2_sigtraits,malebias_Fig2_over10, nrow = 3, align = "v", heights = c(1.22,1,1), labels = c("A", "B", "C"))
Fig2
ggsave("CountDataCVR_RR_only.pdf", plot = Fig2, width = 6, height = 5)
```
### Overall results of second order meta analysis (Figure 4a)
#### 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")
```
#### Plot FIGURE 4 (Second-order meta analysis results)
Preparation: Sub-Plot for Figure 3: all traits
```{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_blank(),
axis.title.y = element_blank())
#Metameta_Fig3_alltraits
```
#### Figure 4B- 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$GroupingTerm),]
#overall.male.plot3 <- full_join(plot3.male.meta.CVR.b, plot3.male.meta.VR.b)
overall.male.plot3 <- full_join(plot3.male.meta.CVR.b, plot3.male.meta.RR.b)
overall.male.plot3$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$GroupingTerm)))
```
Restructure MALE data for plotting
```{r}
overall3.male.sig <- gather(overall.male.plot3, parameter, value, c(lnCVR, lnRR), factor_key= TRUE) #lnVR,
lnCVR.ci <- overall3.male.sig %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
#lnVR.ci <- overall3.male.sig %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3.male.sig %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)
overall4.male.sig <- bind_rows(lnCVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) #lnVR.ci,
overall4.male.sig$label <- "CI not overlapping zero"
```
Plot Fig3 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 Figure, significant traits
Female Fig3 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)))
#add missing GroupingTerms for plot POTENTIALLY DELETE
#overall.female.plot3 <- add_row(overall.female.plot3, GroupingTerm = "Morphology")
#overall.female.plot3 <- add_row(overall.female.plot3, GroupingTerm = "Metabolism")
#overall.female.plot3 <- add_row(overall.female.plot3, GroupingTerm = "Hematology")
#overall.female.plot3 <- add_row(overall.female.plot3, GroupingTerm = "Hearing")
#overall.female.plot3 <- add_row(overall.female.plot3, GroupingTerm = "Eye")
#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"
```
Plot Fig3 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
```
#### Fig4 C >10%
Prepare data for traits with m/f difference > 10%
create column with 1= larger, 0= diff not larger than 10%
#### Male Fig 4 > 10% (male biased traits)
```{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$GroupingTerm),]
#overall.male.plot3.perc <- full_join(plot3.male.meta.CVR.perc.b, plot3.male.meta.VR.perc.b)
overall.male.plot3.perc <- full_join(plot3.male.meta.CVR.perc.b, plot3.male.meta.RR.perc.b)
overall.male.plot3.perc$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, lnRR), factor_key= TRUE) #lnVR,
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, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) #lnVR.ci,
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 Fig3 (4 in ms) 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
```
####Female Fig 3 >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),]
#overall.plot3.perc <- full_join(plot3.meta.CVR.perc.b, plot3.meta.VR.perc.b)
overall.plot3.perc <- full_join(plot3.meta.CVR.perc.b, 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$GroupingTerm)))
```
Restructure data for plotting
Female bias, 10 percent difference
```{r}
overall3.perc <- gather(overall.plot3.perc, parameter, value, c(lnCVR, lnRR), factor_key= TRUE) #lnVR,
lnCVR.ci <- overall3.perc %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
#lnVR.ci <- overall3.perc %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3.perc %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)
overall4.perc <- bind_rows(lnCVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) #lnVR.ci,
overall4.perc$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 Fig3 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
```
#### Plot Fig4: all plots combined
```{r}
library(ggpubr)
Fig3.bottom <- ggarrange(Metameta_Fig3_female.sig, Metameta_Fig3_male.sig, Metameta_Fig3_female.perc, Metameta_Fig3_male.perc,
ncol = 2, nrow = 2, widths = c(1, 1.20), heights = c(1, 1))
Fig3 <- ggarrange(Metameta_Fig3_alltraits, Fig3.bottom, ncol = 1, nrow = 2, heights = c(1.4, 2.5))
Fig3
#ggsave("MetaResultsCVR_RR.pdf", plot = Fig3, width = 9, height = 6)
```
## Heterogeneity
#### FIGURE 4 (Second-order meta analysis on heterogeneity)
Create matrix to store results for all traits
```{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$s.nlevels.strain.VR != 0, ]
nrow(results.allhetero.grouping2) #218
```
Merge data sets containing metafor results with procedure etc. names
```{r}
#procedures <- read.csv("../procedures.csv")
results.allhetero.grouping2$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$id)]
```
#### Correlated parameters
```{r}
metahetero1 <- results.allhetero.grouping2
#length(unique(metahetero1$procedure)) #18
#length(unique(metahetero1$GroupingTerm)) #9
#length(unique(metahetero1$parameter_group)) # 149
#length(unique(metahetero1$parameter_name)) #218
# Count of number of parameter names (correlated sub-traits) in each parameter group (par_group_size)
metahetero1b <-
metahetero1 %>%
group_by(parameter_group) %>%
mutate(par_group_size = n_distinct(parameter_name))
metahetero1$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.$model_lnCVR[[1]])
## *Perform meta-analyses on correlated sub-traits, using robumeta
# Shinichi: We think we want to use these for further analyses:
# residual variance: as.numeric(robu_fit$mod_info$term1) (same as 'mod_info$tau.sq')
# sample size: robu_fit$N
## **Extract and save parameter estimates
count_fun. <- function(mod_sub)
return(c(as.numeric(mod_sub$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$N.RR - 1)),
control=list(optimizer="optim", optmethod="Nelder-Mead", maxit= 10000, stepadj=0.5), verbose=F)))
heterog1
# **Re-structure data for each grouping term; extract heterogenenity/variance terms; delete un-used variables
Behaviour. <- heterog1 %>% filter(., GroupingTerm == "Behaviour") %>% select(., -data) %>% mutate(heteroCVR=.[[2]][[1]]$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]]$se) %>%
select(., GroupingTerm, heteroCVR:heteroRR_se)
heterog2 <- bind_rows(Behaviour., Morphology., Metabolism., Physiology., Immunology., Hematology., Heart., Hearing., Eye.)
#str(heterog2)
```
#### Heterogeneity PLOT
Restructure data for plotting
```{r}
heterog3 <- gather(heterog2, parameter, value, c(heteroCVR, heteroVR, heteroRR), factor_key= TRUE)
heteroCVR.ci <- heterog3 %>% filter(parameter == "heteroCVR") %>% mutate(ci.low = heteroCVR_lower, ci.high = heteroCVR_upper)
heteroVR.ci <- heterog3 %>% filter(parameter == "heteroVR") %>% mutate(ci.low = heteroVR_lower, ci.high = heteroVR_upper)
heteroRR.ci <- heterog3 %>% filter(parameter == "heteroRR") %>% mutate(ci.low = heteroRR_lower, ci.high = heteroRR_upper)
heterog4 <- bind_rows(heteroCVR.ci, heteroVR.ci, heteroRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high)
# **Re-order grouping terms
heterog4$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 FIGURE 4 (5 in ms) (Second-order meta analysis on heterogeneity)
Plot Fig4 all traits
```{r}
Metameta_Fig4_alltraits <- heterog4 %>%
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 = parameter), size = 2.2,
show.legend = FALSE) +
scale_x_continuous(limits=c(-7.0, 1),
#breaks = c(-2.0, -1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5, 2.0),
name='Effect size') +
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_Fig4_alltraits
#ggsave("Fig4.pdf", plot = Metameta_Fig4_alltraits, width = 7, height = 6)
```
## Acknowledgements
tbd
## R Session Information
```{r}
sessionInfo()
```