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Dataset 12. Size corrected PCA.R
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Dataset 12. Size corrected PCA.R
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### Load packages
require( geomorph )
require( ape )
#Clear workspace
rm( list = ls() )
#Set working directory to where turtle landmark data (from supplemental folder) is stored
setwd( "INSERT DIRECTORY PATH" )
#Load landmark data and transform to correct format for GPA commands
temp.file <- list.files(pattern = ".csv")
landmark.data <- lapply (temp.file, read.csv, row.names=1)
names(landmark.data) <- gsub(".csv","",temp.file)
landmark.data.temp <- array(as.numeric(unlist(landmark.data)), dim = c(123, 3, 184)) #number of landmarks, number of dimensions (3D coordinates), number of specimens
dimnames(landmark.data.temp)[[3]] <- gsub(".csv","",temp.file)
dimnames(landmark.data.temp)[[1]] <- rownames(landmark.data[[1]])
dimnames(landmark.data.temp)[[2]] <- c("x","y","z")
#Load slider information and colour information for deformation plots
setwd( "INSERT DIRECTORY PATH" )
sliders <- read.csv("Dataset 4. sliders.turtles.csv", row.names=1)
colours <- as.character( read.csv("Dataset 5. landmark_colours.csv", row.names=1)[,1] )
#Load specimen information
setwd( "INSERT DIRECTORY PATH" )
specimen.info <- read.csv( "Dataset 2. Specimen info.csv", header = TRUE )
rownames( specimen.info ) <- specimen.info[ , "Specimen_name" ]
#Load tree
setwd( "INSERT DIRECTORY PATH" )
tree <- read.nexus( "Dataset 7. cal3tree.calibrated.txt" )
alternative.tree <- read.nexus("Dataset 8. mbltree.calibrated.txt")
#Do GPA of labyrinth shape for all taxa available
GPA.data <- landmark.data.temp
tree.names <- as.character( specimen.info[ dimnames( GPA.data )[[ 3 ]] , "Tree_names" ] )
skull.box.temp <- as.character( specimen.info[ dimnames( GPA.data )[[ 3 ]] , "logV_mm3" ] )
dimnames( GPA.data )[[ 3 ]][ !is.na( tree.names ) ] <- tree.names[ !is.na( tree.names ) ]
duplicate.specimens <- which( is.na(tree.names) == TRUE )
no.skull.box <- which (is.na(skull.box.temp) == TRUE )
delete.these <- unique(c(duplicate.specimens, no.skull.box))
GPA.data <- GPA.data[,, - delete.these]
GPA.labyrinth.all <- gpagen( GPA.data , curves = sliders , ProcD = F )
labyrinth.Csize.all <- GPA.labyrinth.all$Csize
labyrinth.Csize.all[ labyrinth.Csize.all > 5000 ] <- labyrinth.Csize.all[ labyrinth.Csize.all > 5000 ] / 1000
#DO PCA
PCA.labyrinth <- plotTangentSpace( GPA.labyrinth.all$coords , warpgrids = F )
#Prepare tree that has same tips as the shape data blocks
tree.temp <- drop.tip( tree , tree$tip.label[ ! tree$tip.label %in% names( GPA.labyrinth.all$Csize ) ] )
#For tests with alternative calibration
tree.temp.alternative <- drop.tip( alternative.tree , alternative.tree $tip.label[ ! alternative.tree $tip.label %in% names( GPA.labyrinth.all$Csize ) ] )
#Examine trees
plot(tree.temp, cex=0.4)
plot(tree.temp.alternative, cex=0.4)
##Make a version of the specimen data that matches the taxon sample
data.temp <- specimen.info[ specimen.info$Tree_names %in% tree.temp$tip.label , ]
rownames( data.temp ) <- data.temp$Tree_names
data.temp <- data.temp[ tree.temp$tip.label , ]
##Size proxies
skull_length.temp <- data.temp$Skull_length_mm
names(skull_length.temp) <- rownames (data.temp)
skull_length.temp <- skull_length.temp[ tree.temp$tip.label ]
skull_width.temp <- data.temp$Skull_width_mm
names(skull_width.temp) <- rownames (data.temp)
skull_width.temp <- skull_width.temp[ tree.temp$tip.label ]
skull_height.temp <- data.temp$Skull_height_mm
names(skull_height.temp) <- rownames (data.temp)
skull_height.temp <- skull_height.temp[ tree.temp$tip.label ]
##Skull geometry proxy
skull_geometry.temp <- skull_height.temp / skull_width.temp
#check frequency distribution
hist(skull_geometry.temp)
#check if these make sense
which(skull_geometry.temp[] == max(skull_geometry.temp))
which(skull_geometry.temp[] == min(skull_geometry.temp))
#Make a data frame for analyses
gdf <- geomorph.data.frame( shape = GPA.labyrinth.all$coords[ ,, tree.temp$tip.label ] ,
phy = tree.temp ,
skull_box = data.temp[ tree.temp$tip.label , "logV_mm3" ] ,
skull_geometry = skull_geometry.temp)
##In this script we're setting up all the combinations of explanatory variables for the right sizes of the models,
# and then running them all in a loop. This makes it easy to add regression models by extending the vector called "right.sides".
# Here, we only use one model, which accounts for allometry and spatial constraints of braincase aspect ratio
right.sides <- c(
#following models test relations of size variables as correlates of shape, exploring allometric effects, plus the braincase aspect ratio
"skull_box + skull_geometry"
)
models <- paste( "shape ~" , right.sides )
models <- lapply( models , as.formula )
##Run Procrustes distance pGLS analyses (Adams 2014)
procD.pgls.fits <- list()
for( i in 1:length( models ) ) {
procD.pgls.fits[[ i ]] <- procD.pgls( models[[ i ]] , phy = phy , SS.type = "II" , data = gdf )
}
##See summaries of procD.pgls results
model.summaries <- lapply( procD.pgls.fits , summary )
#Print all coefficents to file
capture.output(model.summaries, file = "Allometry_and_braincase_spatial_constraint_model.txt")
#After running the 'shape ~ skull size + braincase ratio' model:
procD.model <- procD.pgls.fits[[ 1 ]]
#Numbers for data point identification
numbers <- specimen.info[ match( tree.temp $tip.label , specimen.info$Tree_names ) , "Tree_node_number" ]
#Colour sheme for ecological categories
ecology.bg <- c( "dodgerblue3" , "lightblue2" , "tan" )
names( ecology.bg ) <- c( "marine" , "freshwater" , "terrestrial" )
ecology.bg <- ecology.bg[ as.character( specimen.info[ , "Plotting_habitat" ][ match ( dimnames(procD.model$pgls.residuals)[[1]] , specimen.info$Species_ID ) ] ) ]
#Panel A - Skull size scores
dev.new(width = 8 , height = 4)
close.screen( all.screens = T )
split.screen( c( 1 , 2 ) )
screen( 1 ) ; par( mar = c( 4, 4, 1, 1 ) )
size.scores <- plot ( procD.model, type="regression", reg.type="RegScore", predictor = procD.model$data$skull_box)$RegScore
plot(size.scores ~ procD.model$data$skull_box, xlab="Skull box volume (mm^3)", ylab="Shape regression scores", pch=21, bg=ecology.bg, col = "darkgrey", lwd = 1.2, cex = 1.9)
#text(size.scores ~ procD.model$data$skull_box, cex = 0.5 , labels = rownames(size.scores) )
text(size.scores ~ procD.model$data$skull_box, cex = 0.3 , labels = numbers , col = "black" )
legend("topleft", legend = c( "marine" , "freshwater" , "terrestrial" ) , pch = 21 , pt.bg = c( "dodgerblue3" , "lightblue2" , "tan" ) , cex = 1 , pt.cex = 1.5 , bty = "n" )
#Panel B - Braincase ratio scores
screen( 2 ) ; par( mar = c( 4, 4, 1, 1 ) )
bc.scores <- plot ( procD.model, type="regression", reg.type="RegScore", predictor=procD.model$data$skull_geometry)$RegScore
plot(bc.scores ~ procD.model$data$skull_geometry, xlab="Braincase aspect ratio", ylab="Shape regression scores", pch=21, bg= ecology.bg, col = "darkgrey", lwd = 1.2, cex = 1.9)
#text(bc.scores ~ procD.model$data$skull_geometry, cex = 0.5 , labels = rownames(size.scores) )
text(bc.scores ~ procD.model$data$skull_geometry, cex = 0.3 , labels = numbers , col = "black" )
#Panel C - PCA with procD residuals
dev.new( width = 8 , height = 4 )
close.screen( all.screens = T )
split.screen( c( 1 , 2 ) )
PCA.resid <- prcomp(procD.model$pgls.residuals)
X=1
Y=2
screen( 1 ) ; par( mar = c( 4, 4, 1, 1 ) )
plot ( PCA.resid$x [,c(X,Y)] , bty="l", pch=21, bg = ecology.bg , col="darkgrey" , cex = 2 ,
xlab = paste0("PC",X," (",round((PCA.resid$sdev^2 / sum(PCA.resid$sdev^2))*100,2)[X], "%",")") ,
ylab = paste0("PC",Y," (",round((PCA.resid$sdev^2 / sum(PCA.resid$sdev^2))*100,2)[Y], "%",")") )
#text( PCA.resid$x [,c(X,Y)] , cex = 0.5 , labels = rownames(PCA.resid$x) )
text(PCA.resid$x [,c(X,Y)] , cex = 0.3 , labels = numbers , col = "black" )
Z=3
screen( 2 ) ; par( mar = c( 4, 4, 1, 1 ) )
plot ( PCA.resid$x [,c(X,Z)] , bty="l", pch=21, bg = ecology.bg , col="darkgrey" , cex = 2 ,
xlab = paste0("PC",X," (",round((PCA.resid$sdev^2 / sum(PCA.resid$sdev^2))*100,2)[X], "%",")") ,
ylab = paste0("PC",Z," (",round((PCA.resid$sdev^2 / sum(PCA.resid$sdev^2))*100,2)[Z], "%",")") )
#text( PCA.resid$x [,c(X,Z)] , cex = 0.5 , labels = rownames(PCA.resid$x) )
text(PCA.resid$x [,c(X,Z)] , cex = 0.3 , labels = numbers , col = "black" )
### Write source data file
source.data <- data.temp[, c("Specimen_name", "Species_ID", "Plotting_habitat", "Tree_node_number")]
PC.values <- PCA.resid$x [,c(X,Y,Z)]
source.data[,c("PC1.values", "PC2.values", "PC3.values")] <- PC.values
shape.regression.skull.box.volume.scores <- size.scores
skull.box.volume <- procD.model$data$skull_box
names(skull.box.volume) <- rownames(procD.model$data)
source.data[,"shape.regression.skull.box.volume.scores"] <- shape.regression.skull.box.volume.scores
source.data[,"skull.box.volume"] <- skull.box.volume
shape.regression.braincase.aspect.ratio.scores <- bc.scores
braincase.aspect.ratio <- procD.model$data$skull_geometry
names(braincase.aspect.ratio) <- rownames(procD.model$data)
source.data[,"shape.regression.braincase.aspect.ratio.scores"] <- shape.regression.braincase.aspect.ratio.scores
source.data[,"braincase.aspect.ratio"] <- braincase.aspect.ratio
write.table(source.data, file = "Source_Data_file_2.txt")
###
##Deformation plots
#Plot shape deformation for "skull size" and "braincase ratio" variables
#Get coefficients from 'procD.pgls' object
coefficients.temp <- procD.model$pgls.coefficients
#Optional: Get 1st and 3rd quartiles of continuous variables to avoid potential outliers (minimum and maximum values) -> exchange c(0,1) with c(0.25,0.75)
#Currently it takes the minimum and maximum values without averaging over a quartile
skull.size.quart <- quantile(procD.model$data$skull_box,prob=c(0,1))
bc.ratio.quart <- quantile(procD.model$data$skull_geometry,prob=c(0,1))
##Run these next two blocks separately for deformation plots for skull size and braincase aspect ratio
#Allometry variation (skull size)
variables1 <- list( Intercept = 1 , skull_box = min(skull.size.quart) , skull_geometry = mean(procD.model$data$skull_geometry) )
variables2 <- list( Intercept = 1 , skull_box = max(skull.size.quart) , skull_geometry = mean(procD.model$data$skull_geometry) )
#Braincase ratio variation
variables1 <- list( Intercept = 1 , skull_box = mean(procD.model$data$skull_box) , skull_geometry = min(bc.ratio.quart) )
variables2 <- list( Intercept = 1 , skull_box = mean(procD.model$data$skull_box) , skull_geometry = max(bc.ratio.quart) )
##All following lines must be run for each block separatly:
# Change the names of these lists to match the names in your 'coefficients.temp' object
names( variables1 ) <- rownames ( coefficients.temp )
names( variables2 ) <- rownames ( coefficients.temp )
# Create lists to which you will save the values calculated for each landmark in each shape deformation
coefficient.list.temp1 <- list()
coefficient.list.temp2 <- list()
# Calculate predicted shapes (min and max)
# (step 1)
for( row.temp in 1:nrow( coefficients.temp ) ) {
coefficient.list.temp1[[ row.temp ]] <- coefficients.temp[ row.temp , ] * variables1[[ rownames( coefficients.temp )[ row.temp ] ]]
coefficient.list.temp2[[ row.temp ]] <- coefficients.temp[ row.temp , ] * variables2[[ rownames( coefficients.temp )[ row.temp ] ]]
}
# (step 2)
shape1 <- matrix( apply( do.call( rbind , coefficient.list.temp1 ) , 2 , sum ) , ncol = 3 , byrow = T )
shape2 <- matrix( apply( do.call( rbind , coefficient.list.temp2 ) , 2 , sum ) , ncol = 3 , byrow = T )
# define the colours you want
point.distance.scale <- colorRamp(c("lightgrey" ,'red'))
point.distances <- c()
Edist <- function ( x , Y ) { ( sum( ( x - Y ) ^ 2 ) ) ^ 0.5 } #Euclidean distance function
for ( i in 1:nrow(shape2) ){
# here you calculate the Euclidean distance between the points in each of your shape matrices ('shape1' and 'shape2')
point.distances[i] <- Edist(shape1[i,] , shape2[i,])
}
#point.distances
# normalise the distances so they range from 0 to 1
point.distances.norm <- (point.distances - min(point.distances)) / max ( point.distances - min(point.distances))
# and then you're able to create a colorRamp that goes from grey (0) to red (1)
point.colours <- point.distance.scale(point.distances.norm)
# 3D plot of shape deformation
sliders.temp <- as.matrix(sliders)
open3d()
plot3d(shape1, size = 10, col='lightgrey' , box = "n" , aspect="iso" )
lines.plot.temp <- rbind( sliders.temp[ , 1:2 ] , sliders.temp[ , 2:3 ] )
for( j in 1:nrow( lines.plot.temp ) ) { lines3d( shape1[ lines.plot.temp[j,] , ] , lwd = 2 , col = 'lightgrey' ) }
points3d(shape2, size = 15, col=rgb(point.colours,maxColorValue = 255) )
lines.plot.temp <- rbind( sliders.temp[ , 1:2 ] , sliders.temp[ , 2:3 ] )
for( j in 1:nrow( lines.plot.temp ) ) { lines3d( shape2[ lines.plot.temp[j,] , ] , lwd = 3 , col = rgb(point.colours,maxColorValue = 255) ) }
rgl.snapshot( "Skull size deformation lateral.png" )
rgl.snapshot( "Skull size deformation dorsal.png" )
rgl.snapshot( "Braincase aspect ratio deformation lateral.png" )
rgl.snapshot( "Braincase aspect ratio deformation dorsal.png" )