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k__plot-pli_occ_temp_violinplot.R
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#!/home1/wim/R-3.2.3/bin/Rscript --no-save --no-restore
library( 'reshape2' )
library( 'ggplot2' )
library( 'dplyr' )
library( 'BayesFactor' )
#################################
# FUNCTIONS
#################################
###
# Label bayes factors.
# https://www.r-bloggers.com/what-does-a-bayes-factor-feel-like/
##
label.bf <- function( df )
{
df$classification <- NA
df[ df$bf.with.asl > 100, 'classification' ] <- "01. Extreme evidence for H1"
df[ df$bf.with.asl > 30 & df$bf.with.asl <= 100, 'classification' ] <- "02. Very strong evidence for H1"
df[ df$bf.with.asl > 10 & df$bf.with.asl <= 30, 'classification' ] <- "03. Strong evidence for H1"
df[ df$bf.with.asl > 3 & df$bf.with.asl <= 10, 'classification' ] <- "04. Moderate evidence for H1"
df[ df$bf.with.asl > 1 & df$bf.with.asl <= 3, 'classification' ] <- "05. Anecdotal evidence for H1"
df[ df$bf.with.asl == 1, 'classification' ] <- "No evidence"
return( df )
}
###
# Return bayes factor for model with interaction term included.
##
get.bf <- function( data, freq )
{
# get subset
data <- data[ data$bands == freq, ]
# remove missings
data <- data[ !is.na( data$value ), ]
# bayes
withGroup <- lmBF( value ~ group + Eyes, data = data )
noGroup <- lmBF( value ~ Eyes, data = data )
# Bayes Factor in favor of model WITH asl_duration
bf <- withGroup / noGroup
# get MCMC proportional error < 2%
# bf <- recompute( bf, iterations = 1500000 )
# bf is stored as log10 value
bf.with.group <- exp( bf@bayesFactor$bf )
# plot bfs
#plot( allBFs <- c( withAsl, noAsl ) )
out <- data.frame( freq = freq, metric = 'pli', bf.with.asl = bf.with.group )
return( out )
}
# ggplot function
number_ticks <- function( n ) { function( limits ) pretty( limits, n ) }
###
# Plot traces.
##
get.plot.eeg <- function( df )
{
df$time <- 1:nrow( df ) / sf
plot.eeg <- ggplot( data = melt( df, id = 'time' ), aes( x = time, color = variable, group = variable, y = value ) ) +
geom_line() +
facet_wrap( ~variable, scale = 'free_y', ncol = 2 ) +
theme_minimal( base_size = 12 ) + theme( legend.position = 'none' ) + xlab( "Time (s)" ) +
scale_x_continuous( breaks = number_ticks( 25 ) ) +
scale_y_continuous( breaks = number_ticks( 6 ) )
return( plot.eeg )
}
###
# 95% confidence interval for the mean.
# https://www.r-bloggers.com/standard-deviation-vs-standard-error/
##
ci.dev <- function( x )
{
#computation of the standard error of the mean
sem <- sd( x, na.rm = TRUE ) / sqrt( length( x ) )
#95% confidence intervals of the mean
return( 2 * sem )
}
###
# get functional connectivity data
##
get.functional.connectivity.data <- function( indir, metadir )
{
# get meta data
head( meta <- read.csv( paste0( metadir, '/', 'demographics.csv' ), row.names = 1 ) )
# keep relevant columns only
head( meta <- meta[, c( "subject.id", "group", "deafness", "asl_duration", "sex", "age" ) ] ) # "time_of_deafness", "hears", "years_education"
head( df <- read.csv( paste0( indir, '/', 'all.pli.csv' ), row.names = 1 ) )
# merge O1 and O2 and P1 and P2
df$meta.channel <- NA
df$meta.variable <- NA
df[ df$channel %in% c( "O1", "O2" ), 'meta.channel' ] <- 'Occipital'
df[ df$channel %in% c( "T7", "T8" ), 'meta.channel' ] <- 'Temporal'
df[ df$variable %in% c( "O1", "O2" ), 'meta.variable' ] <- 'Occipital'
df[ df$variable %in% c( "T7", "T8" ), 'meta.variable' ] <- 'Temporal'
df$meta.channel <- as.factor( df$meta.channel )
df$meta.variable <- as.factor( df$meta.variable )
# only keep Occipital and Temporal channel data
head( df.sub <- df[ df$meta.channel == 'Occipital' | df$meta.channel == 'Temporal', ] )
head( df.sub <- df.sub[ ! is.na( df.sub$meta.channel ), ] )
head( df.sub <- df.sub[ ! is.na( df.sub$meta.variable ), ] )
# remove self-connectivity
s <- df.sub[ df.sub$meta.channel != df.sub$meta.variable, ]
# remove duplicates by sorting channel names and combine this with fc-value
s$name <- NA
for( i in 1:nrow( s ) )
s[ i, 'name' ] <- paste0(
s[ i, 'bands' ],
s[ i, 'subject.id' ],
s[ i, 'session' ],
sort( c( as.character( s[ i, 'channel' ] ), as.character( s[ i, 'variable' ] ) ) ),
s[ i, 'value' ], collapse = '-' )
s <- s[ !duplicated( s$name ), ]
# merge with meta-data
mdata <- merge( s, meta )
# rename bands
levels( mdata$bands ) <- c( 'delta', 'theta', 'alpha', 'beta', 'gamma' )
# only keep deaf and control
mdata <- mdata[ mdata$group %in% c( 'control', 'deaf' ), ]
mdata$group <- as.factor( as.character( mdata$group ) )
# return data
return( mdata )
}
#######################
# END FUNCTIONS
#######################
# to be sure to replicate stuff (like mst calculation).
set.seed( 123 )
# create output directory
metadir <- '/home1/wim/projects/willem_buitenhuis/3_health_res/demographics'
indir <- '/home1/wim/projects/willem_buitenhuis/3_health_res/merged_fc_epochs_nigeria'
outdir <- 'violinplot_pli_nigeria__occ_temp'
dir.create( outdir, showWarnings = FALSE )
# get functional connectivity
head( fc <- get.functional.connectivity.data( indir, metadir ) )
# save FC data for sharing online
write.csv( fc, file = paste0( outdir, '/epoch_data.csv' ) )
# average across epochs to get single value for condition A and single value for condition B, per subject
#grouped <- dplyr::group_by( fc, group, Eyes, bands, meta.channel, meta.variable )
#( s <- as.data.frame( dplyr::summarise( grouped, mean = mean( value ), ci.dev = ci.dev( value ) ) ) )
# average per individual
grouped <- dplyr::group_by( fc, subject.id, group, Eyes, bands, meta.channel, meta.variable )
( s <- as.data.frame( dplyr::summarise( grouped, mean = mean( value ), ci.dev = ci.dev( value ) ) ) )
# FC between parietal - temporal cortex
p <- ggplot( data = s, aes( x = Eyes, y = mean, color = group, fill = group ) ) +
facet_wrap( ~bands, nrow = 1, scale = 'free_y' ) +
geom_violin(position = position_dodge(1) ) +
geom_boxplot(width=0.1, position = position_dodge(1), color = "black" ) +
#geom_bar( stat = 'identity', position = position_dodge(), alpha = 0.8, color = 'gray30' ) +
#geom_errorbar( stat = 'identity', position = position_dodge( 0.9), color = 'gray70', width = 0.4 ) +
scale_y_continuous( breaks = number_ticks( 4 ) ) +
scale_fill_manual( values = c("#1f78b4", "#E69F00", "gray50" ) ) +
scale_color_manual( values = c("#1f78b4", "#E69F00", "gray50" ) ) +
ylab( "Functional connectivity (PLI)" ) +
xlab( "Eyes condition" ) +
theme_classic( base_size = 14 ) +
theme( legend.position = 'top', axis.title = element_text( face = "bold" ) )
# save
ggsave( file = paste0( outdir, '/pli-temporal-occipital-violin.png' ), plot = p, dpi = 200, height = 8, width = 12 )
# write raw data for stats
write.csv( fc, file = paste0( outdir, '/pli-temporal-occipital-violin.csv' ) )
# BFs are already calculated in previous script for bar graph plots
###################
# CREATE Delta's
###################
# average across epochs to get single value for condition A and single value for condition B, per subject
grouped <- dplyr::group_by( fc, subject.id, group, Eyes, bands, meta.channel, meta.variable )
s <- as.data.frame( dplyr::summarise( grouped, mean = mean( value ) ) )
dim( s.open <- s[ s$Eyes == 'open', ] )
s.open$value.open <- s.open$mean
s.open$mean <- s.open$Eyes <- NULL
dim( s.closed <- s[ s$Eyes == 'closed', ] )
s.closed$value.closed <- s.closed$mean
s.closed$mean <- s.closed$Eyes <- NULL
# get %delta relative to open condition -> (open - closed) / open
dim( s.m <- merge( s.open, s.closed ) )
s.m$delta <- 100 * ( ( s.m$value.open - s.m$value.closed ) / s.m$value.open )
# average per subject
grouped <- dplyr::group_by( s.m, subject.id, group, bands, meta.channel, meta.variable )
s.m.mean <- as.data.frame( dplyr::summarise( grouped, mean = mean( delta ) ) )
s.m.ci <- as.data.frame( dplyr::summarise( grouped, ci.dev = ci.dev( delta ) ) )
s.m.avg <- merge( s.m.mean, s.m.ci )
s.m.avg$Eyes <- as.factor( 'Delta' )
# write to file
write.csv( s.m.avg, file = paste0( outdir, '/delta_FC_values_used_for_delta_plot_violinplot.csv' ) )
# FC between parietal - temporal cortex
p.delta <- ggplot( data = s.m.avg, aes( x = Eyes, y = mean, color = group, fill = group ) ) +
facet_wrap( ~bands, nrow = 1 ) + #, scale = 'free_y' ) +
geom_violin(position = position_dodge(1) ) +
geom_boxplot(width=0.1, position = position_dodge(1), color = "black" ) +
#geom_bar( stat = 'identity', position = position_dodge(), alpha = 0.8, color = 'gray30' ) +
#geom_errorbar( stat = 'identity', position = position_dodge( 0.9), color = 'gray70', width = 0.4 ) +
scale_y_continuous( breaks = number_ticks( 8 ) ) +
scale_fill_manual( values = c("#1f78b4", "#E69F00", "gray50" ) ) +
scale_color_manual( values = c("#1f78b4", "#E69F00", "gray50" ) ) +
ylab( expression( bold( Delta~"Functional connectivity (%)" ) ) ) +
xlab( "Eyes condition" ) +
theme_classic( base_size = 14 ) +
theme( legend.position = 'top', axis.title = element_text( face = "bold" ) )
# save
ggsave( file = paste0( outdir, '/pli-occipital-temporal__delta_violin.png' ), plot = p.delta, dpi = 200, height = 8, width = 5)