In MiguelRodo/VaccComp: Figures for vaccine comparison paper
if( !require("VaccComp") ) stop("Install package VaccComp using 'devtools::install_github( 'MiguelRodo\VaccComp' )'" )
if( !require("ggboot") ) stop("Install package VaccComp using 'devtools::install_github( 'MiguelRodo\ggboot' )'")
if( !require("VaccCompData") ) stop("Install package VaccComp using 'devtools::install_github( 'MiguelRodo\VaccCompData' )'")
library(VaccComp)
library(ggboot)
library(plyr)
library(tidyr)
library(stringr)
library(magrittr)
library(dplyr)
library(VaccCompData)
library(cowplot)
library(doParallel)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
echo = FALSE,
cache = TRUE,
message = FALSE,
warning = FALSE,
tidy = TRUE
)
save = TRUE
getSavePath = function(x) str_c( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/", x, ".pdf" )
Fig 1 - Pre-vaccination response magnitude by vaccine
# infected
## long table
lTbl1 = bl17ExcITbl %>%
filter( cd == 4 & infxn == 1 ) %>%
group_by( vaccine, infxn, ptid ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# uninfected
## long table
lTbl0 = bl17ExcITbl %>%
filter( cd == 4 & infxn == 0 ) %>%
group_by( vaccine, infxn, ptid ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# plot
set.seed(2)
scaleFactor = 0.56^-1
legendTextSize = 8 * scaleFactor
stripTextSize = 8 * scaleFactor
fontScaleFactor = 0.56 * scaleFactor
axisTitleTextSize = 8 * scaleFactor
p0 = ggbootUV( data = full_join( lTbl0, lTbl1 ),
resp = resp, xAxis = vaccine, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca",
B = 1e1, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 2e2 * 2.5, calcStat = "mean", trim = 0.2,
rotXText = TRUE, pointSize = 3, errBarSize = 1.75, yLab = "Pre-vaccination\nCD4 T cell response (%)",
nullValue = 0.005, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88,
colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec, bootSECorr = FALSE,
facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor,
lineScaleFactor = 2, plotTblName = "fig1aTbl" ) +
theme( legend.key.height = unit( 6, "mm" ),
legend.key.width = unit( 9, "mm" ),
legend.text = element_text( size = legendTextSize ),
strip.text = element_text( size = stripTextSize, colour = 'black' ),
legend.title = element_blank(),
legend.text.align = 0,
axis.title = element_text( size = axisTitleTextSize),
strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ) ) +
guides( linetype = guide_legend(override.aes = list(size=1.8)),
colour = guide_legend( order = 1 ) )
# infected
## long table
lTbl1 = bl17ExcITbl %>%
filter( cd == 8 & infxn == 1 ) %>%
group_by( vaccine, infxn, ptid ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# uninfected
## long table
lTbl0 = bl17ExcITbl %>%
filter( cd == 8 & infxn == 0 ) %>%
group_by( vaccine, infxn, ptid ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# plot
set.seed(2)
p1 = ggbootUV( data = full_join( lTbl0, lTbl1 ),
resp = resp, xAxis = vaccine, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca",
B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2,
rotXText = TRUE, pointSize = 3, errBarSize = 1.75, yLab = "Pre-vaccination\nCD8 T cell response (%)",
nullValue = 0.005, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88,
colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec,
facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor,
lineScaleFactor = 2, plotTblName = "fig1bTbl" ) +
theme( legend.key.height = unit( 6, "mm" ),
legend.key.width = unit( 9, "mm" ),
legend.text.align = 0,
legend.text = element_text( size = legendTextSize ),
strip.text = element_text( size = stripTextSize, colour = 'black' ),
legend.title = element_blank(),
axis.title = element_text( size = axisTitleTextSize),
strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ) ) +
guides( linetype = guide_legend(override.aes = list(size=1.8)),
colour = guide_legend( order = 1 ) )
plot_grid( p0, p1, ncol = 1, align = "v", labels = c( "A", "B" ), label_size = 12 * scaleFactor )
cowplot::ggsave( getSavePath( "Fig1" ), units = "cm", width = 13, height= 10, scale = 1.85, dpi = 300 )
S1 Fig - Pre-vaccination response magnitude by stimulation
# infected
## long table
lTbl1 = bl17ExcITbl %>%
filter( cd == 4 & infxn == 1 ) %>%
mutate( cytCombo = str_sub( cytCombo, 4 ),
vaccStim = str_c( vaccine, stim ) ) %>%
group_by( vaccine, infxn, ptid, stim, vaccStim ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# uninfected
## long table
lTbl0 = bl17ExcITbl %>%
filter( cd == 4 & infxn == 0 ) %>%
mutate( cytCombo = str_sub( cytCombo, 4 ),
vaccStim = str_c( vaccine, stim ) ) %>%
group_by( vaccine, infxn, ptid, stim, vaccStim ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# plot
set.seed(2)
scaleFactor = 0.56^-1
legendTextSize = 8 * scaleFactor
stripTextSize = 8 * scaleFactor
axisTitleTextSize = 8 * scaleFactor
fontScaleFactor = 0.56 * scaleFactor
xAxisTextSize = 8 * scaleFactor
p0 = ggbootUV( data = full_join( lTbl0, lTbl1 ),
resp = resp, xAxis = vaccStim, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca",
B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = FALSE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2,
rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Pre-vaccination\nCD4 T cell response (%)",
nullValue = 0.005, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88,
colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec,
facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor,
lineScaleFactor = 2, plotTblName = "figs1aTbl" ) +
theme( legend.key.height = unit( 6, "mm" ),
legend.key.width = unit( 9, "mm" ),
legend.text = element_text( size = legendTextSize ),
strip.text = element_text( size = stripTextSize, colour = 'black' ),
legend.title = element_blank(),
legend.text.align = 0,
axis.title = element_text( size = axisTitleTextSize),
strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ),
axis.text.x = element_text( hjust = 1, angle = 90, vjust = 0.45 ) ) +
guides( linetype = guide_legend(override.aes = list(size=1.8)),
colour = guide_legend( order = 1 ) )
# infected
## long table
lTbl1 = bl17ExcITbl %>%
filter( cd == 8 & infxn == 1 ) %>%
mutate( cytCombo = str_sub( cytCombo, 4 ),
vaccStim = str_c( vaccine, stim ) ) %>%
group_by( vaccine, infxn, ptid, stim, vaccStim ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# uninfected
## long table
lTbl0 = bl17ExcITbl %>%
filter( cd == 8 & infxn == 0 ) %>%
mutate( cytCombo = str_sub( cytCombo, 4 ),
vaccStim = str_c( vaccine, stim ) ) %>%
group_by( vaccine, infxn, ptid, stim, vaccStim ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# plot
set.seed(2)
p1 = ggbootUV( data = full_join( lTbl0, lTbl1 ),
resp = resp, xAxis = vaccStim, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca",
B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = FALSE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2,
rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Pre-vaccination\nCD8 T cell response (%)",
nullValue = 0.005, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88,
colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec,
facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor,
lineScaleFactor = 2, plotTblName = "figs1bTbl" ) +
theme( legend.key.height = unit( 6, "mm" ),
legend.key.width = unit( 9, "mm" ),
legend.text = element_text( size = legendTextSize ),
strip.text = element_text( size = stripTextSize, colour = 'black' ),
legend.title = element_blank(),
legend.text.align = 0,
axis.title = element_text( size = axisTitleTextSize),
strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ),
axis.text.x = element_text( hjust = 1, angle = 90, vjust = 0.45 ) ) +
guides( linetype = guide_legend(override.aes = list(size=1.8)),
colour = guide_legend( order = 1 ) )
plot_grid( p0, p1, ncol = 1, align = "v", labels = c( "A", "B" ), label_size = 12 * scaleFactor )
cowplot::ggsave( getSavePath( "S1_Fig" ), units = "cm", width = 13, height= 14, scale = 1.85, dpi = 300 )
S2 Fig - Effect of Mtb infection on baseline response size, by vaccine
Effect of baseline Mtb infection
# infected
## long table
lTbl1 = bl17ExcITbl %>%
filter( cd == 4 & infxn == 1 ) %>%
group_by( vaccine, infxn, ptid ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# uninfected
## long table
lTbl0 = bl17ExcITbl %>%
filter( cd == 4 & infxn == 0 ) %>%
group_by( vaccine, infxn, ptid ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
set.seed(2)
scaleFactor = 0.55^-1
ggbootUV( data = full_join( lTbl0, lTbl1 ) %>% filter( vaccine %in% 2:6 ),
resp = resp, xAxis = vaccine, col = vaccine, diff = infxn, pMethod = "percT", ciMethod = "bca",
B = 2e3 * 5, altSide = 'both', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2,
rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Change in pre-vaccination CD4\nT cell response (%) (infected vs uninfected)",
nullValue = 0, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88,
colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec[-c(1,6)],
facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = 2,
lineScaleFactor = 1.5, plotTblName = "s2Tbl" ) +
theme( legend.key.height = unit( 6, "mm" ),
legend.key.width = unit( 9, "mm" ),
legend.text = element_text( size = 8 * scaleFactor ),
axis.text = element_text( size = 8 * scaleFactor ),
legend.title = element_blank(),
legend.text.align = 0,
axis.title = element_text( size = 8 * scaleFactor ),
strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ) ) +
guides( linetype = guide_legend(override.aes = list(size=1.8)),
colour = guide_legend( order = 1 ) )
cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/S2_Fig.pdf", units = "cm", width = 10, height= 6, scale = 2 )
Fig 2 - Longitudinal response size
longPlotTbl = join17ExcTbl %>%
mutate( cd = str_sub( cytCombo, 3, 3 ),
cytCombo = str_sub( cytCombo, 4 ) ) %>%
filter( vaccine != 8 ) %>%
group_by( vaccine, infxn, timePoint, cd, ptid ) %>%
summarise( sumResp = sum( resp ) ) %>%
summarise( med = median( sumResp ),
lb = quantile( sumResp, 0.25 ),
ub = quantile( sumResp, 0.75 ) ) %>%
ungroup()
longPlotTbl = sub17ExcTbl %>%
mutate( cd = str_sub( cytCombo, 3, 3 ),
cytCombo = str_sub( cytCombo, 4 ) ) %>%
filter( vaccine != 8 ) %>%
group_by( vaccine, infxn, timePoint, cd, ptid ) %>%
summarise( sumResp = sum( resp ) ) %>%
summarise( med = median( sumResp ),
lb = quantile( sumResp, 0 ),
ub = quantile( sumResp, 1 ) ) %>%
ungroup()
hlineCalcTbl = longPlotTbl %>%
group_by( vaccine ) %>%
summarise( max = max( ub ) )
x = hlineCalcTbl$vaccine[1]
hlinePlotTbl = llply( as.list( hlineCalcTbl$vaccine ), function( x ){
currMax = hlineCalcTbl$max[ which( hlineCalcTbl$vaccine == x ) ]
tibble( vaccine = x, int = seq( 0, currMax, by = 0.5 )[-1] )
} ) %>%
bind_rows()
vaccTimePointTbl = tibble( cd = "4", infxn = "1",
vaccine = c( "1", rep( "2", 2 ), rep( "3", 2), "4", rep( "5", 2 ), rep( "6", 3 ), "7" ),
timePoint = c( 0, c( 0, 56 ), c( 0, 30 ), 0, c( 0, 56 ), c( 0, 28, 112 ), 0 ),
label = "V",
y = -0.13 )
infLineTypeVec = c("0" = "solid", "1" = "11")
hlineTbl = tibble( vaccine = 1, int = 0.5 )
adjVal = 1.3
cdInfAdjVec = c( "4.0" = -1.5 * adjVal, "4.1" = -0.5 * adjVal, "8.0"= 0.5 * adjVal, "8.1" = 1.5 * adjVal )
longPlotTbl %<>% mutate( timePoint = timePoint + cdInfAdjVec[str_c( cd, ".", infxn)] )
testFunc = function(){
for( i in 1:nrow( longPlotTbl ) ){
currVacc = longPlotTbl$vaccine[i]
if( currVacc != 3 ) longPlotTbl$vaccine[i] = fullVaccLabelVecInt[currVacc]
if( currVacc == 3 )longPlotTbl$vaccine[i] = "M72:AS01 [E]"
}
for( i in 1:nrow( hlineCalcTbl ) ){
currVacc = hlineCalcTbl$vaccine[i]
if( currVacc != 3 ) hlineCalcTbl$vaccine[i] = fullVaccLabelVecInt[currVacc]
if( currVacc == 3 )hlineCalcTbl$vaccine[i] = "M72:AS01 [E]"
}
for( i in 1:nrow( vaccTimePointTbl ) ){
currVacc = vaccTimePointTbl$vaccine[i]
if( currVacc != 3 ) vaccTimePointTbl$vaccine[i] = fullVaccLabelVecInt[currVacc]
if( currVacc == 3 )vaccTimePointTbl$vaccine[i] = "M72:AS01 [E]"
}
}
scaleFactor = 0.56^-1
ggplot( longPlotTbl, aes( x = timePoint, col = cd, linetype = infxn ) ) +
geom_line( aes( y = med ), size = 0.7 ) +
geom_errorbar( aes( ymin = lb, ymax = ub ), size = 0.5 ) +
geom_text( data = vaccTimePointTbl,
aes( x = timePoint, y = y ), colour = 'black', size = 4.75, label = "V", fontface = "bold" ) +
facet_wrap( ~vaccine, label = labeller( vaccine = fullVaccLabelVec),
ncol = 4, scales = 'free' ) +
scale_linetype_manual( name = "",
values = infLineTypeVec,
labels = infLabelVec ) +
scale_colour_manual( name = "",
values = c( "4" = "orange", "8" = "royalblue1" ),
labels = c( "4" = "CD4 ", "8" = "CD8") ) +
labs( x = "Days since first vaccination", y = "T cell response (%)" ) +
theme_cowplot( line_size = 1 ) +
theme( legend.key.height = unit( 6, "mm" ),
legend.key.width = unit( 10, "mm" ),
legend.title = element_blank(),
legend.text.align = 0,
legend.text = element_text( size = 8 * scaleFactor),
axis.title = element_text( size = 8 * scaleFactor ),
axis.text.x = element_text( size = 8 * scaleFactor ),
strip.background = element_rect( 'white' ),
legend.position = c( 0.782, 0.3 ),
legend.background = element_rect( colour = 'gray80', size = 1, linetype = 'solid' ),
strip.text = element_text( size = 8 * scaleFactor ) ) +
guides( linetype = guide_legend(override.aes = list(size=1.8)),
colour = guide_legend( override.aes = list( size = 1.8 ) ) ) +
coord_cartesian( x = c( 0, 366 ), y = c( -0.1, 2.5 ) ) +
background_grid( major = 'y', minor = 'none', size.major = 0.25 )
cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/Fig2.pdf", units = "cm", width = 13, height= 10, scale = 1.85 )
Fig 3 - Vaccine-induced memory response size, by vaccine
Summed
CD4
# infected
## long table
lTbl1 = tmaxSub17ExcTbl %>%
filter( cd == 4 & infxn == 1 ) %>%
group_by( vaccine, infxn, ptid ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# uninfected
## long table
lTbl0 = tmaxSub17ExcTbl %>%
filter( cd == 4 & infxn == 0 ) %>%
group_by( vaccine, infxn, ptid ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# plot
set.seed(2)
scaleFactor = 0.56^-1
legendTextSize = 8 * scaleFactor
stripTextSize = 8 * scaleFactor
axisTitleTextSize = 8 * scaleFactor
fontScaleFactor = 0.65 * scaleFactor
p0 = ggbootUV( data = full_join( lTbl0, lTbl1 ) %>% filter( vaccine != 8 ),
resp = resp, xAxis = vaccine, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca",
B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2,
rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Vaccine-induced memory\nCD4 T cell response (%)",
nullValue = 0, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88,
colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec,
facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor,
lineScaleFactor = 2, plotTblName = "fig3aTbl" ) +
theme( legend.key.height = unit( 6, "mm" ),
legend.key.width = unit( 9, "mm" ),
legend.text = element_text( size = legendTextSize ),
strip.text = element_text( size = stripTextSize, colour = 'black' ),
legend.title = element_blank(),
legend.text.align = 0,
axis.title = element_text( size = axisTitleTextSize),
strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ) ) +
guides( linetype = guide_legend(override.aes = list(size=1.8)),
colour = guide_legend( order = 1 ) )
CD8
# infected
## long table
lTbl1 = tmaxSub17ExcTbl %>%
filter( cd == 8 & infxn == 1 ) %>%
group_by( vaccine, infxn, ptid ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# uninfected
## long table
lTbl0 = tmaxSub17ExcTbl %>%
filter( cd == 8 & infxn == 0 ) %>%
group_by( vaccine, infxn, ptid ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
set.seed(2)
p1 = ggbootUV( data = full_join( lTbl0, lTbl1 ) %>% filter( vaccine != 8 ),
resp = resp, xAxis = vaccine, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca",
B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2,
rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Vaccine-induced memory\nCD8 T cell response (%)",
nullValue = 0, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88,
colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec,
facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor,
lineScaleFactor = 2, plotTblName = "fig3bTbl" ) +
theme( legend.key.height = unit( 6, "mm" ),
legend.key.width = unit( 9, "mm" ),
legend.text = element_text( size = legendTextSize ),
strip.text = element_text( size = stripTextSize, colour = 'black' ),
legend.title = element_blank(),
legend.text.align = 0,
axis.title = element_text( size = axisTitleTextSize ),
strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ) ) +
guides( linetype = guide_legend(override.aes = list(size=1.8)),
colour = guide_legend( order = 1 ) )
Combined CD4 and CD8
plot_grid( p0, p1, ncol = 1, align = "v", labels = c( "A", "B" ), label_size = 12 * scaleFactor )
cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/Fig3.pdf", units = "cm", width = 13, height= 10, scale = 1.85, dpi = 300 )
S3 Fig - Vaccine-induced memory response size, by vaccine, pair-wise comparisons
Pair-wise comparison
# infected
## long table
lTbl1 = tmaxSub17ExcTbl %>%
filter( cd == 4 & infxn == 1 ) %>%
group_by( vaccine, infxn, ptid ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# uninfected
## long table
lTbl0 = tmaxSub17ExcTbl %>%
filter( cd == 4 & infxn == 0 ) %>%
group_by( vaccine, infxn, ptid ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
scaleFactor = 1
ggbootPW( full_join( lTbl0, lTbl1 ) %>% filter( vaccine != 8 ), resp = resp, facet = infxn,
group = vaccine,
facetLabVec = infLabelVec,
groupLabVec = fullVaccLabelVec,
facetScale = 'fixed',
axisLab = "Vaccine",
facetOrderVec = c( "1", "3", "4", "5", "6" ),
plotTblName = "plotTbl", fdr = 0.05, B = 2e3 * 5, seB = 200 * 2.5,
rotX = TRUE ) +
theme( strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ),
strip.text = element_text( size = 8 * scaleFactor ),
legend.text = element_text( size = 8 * scaleFactor ),
legend.position = 'bottom',
legend.text.align = 0,
axis.text.y = element_text( size = 8 * scaleFactor ),
axis.text.x = element_text( size = 8 * scaleFactor, hjust = 1 ),
axis.title = element_text( size = 8 * scaleFactor ),
legend.title = element_blank(),
legend.key.width = unit( 5, "mm" ) )
cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/S3_Fig.pdf", units = "cm", width = 13, height= 9.4, scale = 1 )
S4 Fig - Effect of Mtb infection on vaccine-induced memory response size, by vaccine
Effect of baseline Mtb infection
# infected
## long table
lTbl1 = tmaxSub17ExcTbl %>%
filter( cd == 4 & infxn == 1 ) %>%
group_by( vaccine, infxn, ptid ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# uninfected
## long table
lTbl0 = tmaxSub17ExcTbl %>%
filter( cd == 4 & infxn == 0 ) %>%
group_by( vaccine, infxn, ptid ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
set.seed(2)
scaleFactor = 0.55^-1
ggbootUV( data = full_join( lTbl0, lTbl1 ) %>% filter( vaccine %in% 2:6 ),
resp = resp, xAxis = vaccine, col = vaccine, diff = infxn, pMethod = "percT", ciMethod = "bca",
B = 2e3 * 5, altSide = 'both', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 2e2 * 2.5, calcStat = "mean", trim = 0.2,
rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Change in vaccine-induced memory\nCD4 T cell response (%) (infected vs uninfected)",
nullValue = 0, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88,
colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec,
facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = 2,
lineScaleFactor = 1.5, plotTblName = "figs5Tbl" ) +
theme( legend.key.height = unit( 6, "mm" ),
legend.key.width = unit( 9, "mm" ),
legend.text = element_text( size = 8 * scaleFactor ),
axis.text = element_text( size = 8 * scaleFactor ),
legend.title = element_blank(),
legend.text.align = 0,
axis.title = element_text( size = 8 * scaleFactor ),
strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ) ) +
guides( linetype = guide_legend(override.aes = list(size=1.8)),
colour = guide_legend( order = 1 ) )
cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/S5_Fig.pdf", units = "cm", width = 10, height= 7, scale = 2 )
S5 Fig - Vaccine-induced memory response size, by stim
Stim
CD4
# infected
## long table
lTbl1 = tmaxSub17ExcITbl %>%
filter( cd == 4 & infxn == 1 ) %>%
mutate( cytCombo = str_sub( cytCombo, 4 ),
vaccStim = str_c( vaccine, stim ) ) %>%
group_by( vaccine, infxn, ptid, stim, vaccStim ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# uninfected
## long table
lTbl0 = tmaxSub17ExcITbl %>%
filter( cd == 4 & infxn == 0 ) %>%
mutate( cytCombo = str_sub( cytCombo, 4 ),
vaccStim = str_c( vaccine, stim ) ) %>%
group_by( vaccine, infxn, ptid, stim, vaccStim ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# plot
set.seed(2)
scaleFactor = 0.56^-1
legendTextSize = 8 * scaleFactor
stripTextSize = 8 * scaleFactor
axisTitleTextSize = 8 * scaleFactor
fontScaleFactor = 0.56 * scaleFactor
xAxisTextSize = 8 * scaleFactor
p0 = ggbootUV( data = full_join( lTbl0, lTbl1 ) %>% filter( vaccine != 8 ),
resp = resp, xAxis = vaccStim, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca",
B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = FALSE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2,
rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Vaccine-induced memory\nCD4 T cell response (%)",
nullValue = 0, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88,
colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec,
facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor,
lineScaleFactor = 2, plotTblName = "figs4aTbl" ) +
theme( legend.key.height = unit( 6, "mm" ),
legend.key.width = unit( 9, "mm" ),
legend.text = element_text( size = legendTextSize ),
strip.text = element_text( size = stripTextSize, colour = 'black' ),
legend.title = element_blank(),
axis.title = element_text( size = axisTitleTextSize),
strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ),
legend.text.align = 0,
axis.text.x = element_text( hjust = 1, angle = 90, vjust = 0.45 ) ) +
guides( linetype = guide_legend(override.aes = list(size=1.8)),
colour = guide_legend( order = 1 ) )
CD8
# infected
## long table
lTbl1 = tmaxSub17ExcITbl %>%
filter( cd == 8 & infxn == 1 ) %>%
mutate( cytCombo = str_sub( cytCombo, 4 ),
vaccStim = str_c( vaccine, stim ) ) %>%
group_by( vaccine, infxn, ptid, stim, vaccStim ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# uninfected
## long table
lTbl0 = tmaxSub17ExcITbl %>%
filter( cd == 8 & infxn == 0 ) %>%
mutate( cytCombo = str_sub( cytCombo, 4 ),
vaccStim = str_c( vaccine, stim ) ) %>%
group_by( vaccine, infxn, ptid, stim, vaccStim ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# plot
set.seed(2)
p1 = ggbootUV( data = full_join( lTbl0, lTbl1 ) %>% filter( vaccine != 8 ),
resp = resp, xAxis = vaccStim, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca",
B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = FALSE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2,
rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Vaccine-induced memory\nCD8 T cell response (%)",
nullValue = 0, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88,
colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec,
facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor,
lineScaleFactor = 2, plotTblName = "figs4bTbl" ) +
theme( legend.key.height = unit( 6, "mm" ),
legend.key.width = unit( 9, "mm" ),
legend.text = element_text( size = legendTextSize ),
strip.text = element_text( size = stripTextSize, colour = 'black' ),
legend.title = element_blank(),
axis.title = element_text( size = axisTitleTextSize),
strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ),
legend.text.align = 0,
axis.text.x = element_text( hjust = 1, angle = 90, vjust = 0.45 ) ) +
guides( linetype = guide_legend(override.aes = list(size=1.8)),
colour = guide_legend( order = 1 ) )
Combined CD4 and CD8
plot_grid( p0, p1, ncol = 1, align = "v", labels = c( "A", "B" ), label_size = 12 * scaleFactor )
cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/S5_Fig.pdf", units = "cm", width = 13, height= 14, scale = 1.85, dpi = 300 )
S6 Fig - Vaccine-induced memory IL17 response, by vaccine
Summed
CD4
# infected
## long table
lTbl1 = tmaxSubSingle17Tbl %>%
filter( cd == 4 & infxn == 1 ) %>%
group_by( vaccine, infxn, ptid ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# uninfected
## long table
lTbl0 = tmaxSubSingle17Tbl %>%
filter( cd == 4 & infxn == 0 ) %>%
group_by( vaccine, infxn, ptid ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# plot
set.seed(2)
scaleFactor = 0.56^-1
legendTextSize = 8 * scaleFactor
stripTextSize = 8 * scaleFactor
axisTitleTextSize = 8 * scaleFactor
fontScaleFactor = 0.65 * scaleFactor
p0 = ggbootUV( data = full_join( lTbl0, lTbl1 ) %>% filter( vaccine != 8 ),
resp = resp, xAxis = vaccine, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca",
B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2,
rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Vaccine-induced memory\nIL17+ CD4 T cell response (%)",
nullValue = 0, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88,
colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec,
facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor,
lineScaleFactor = 2, plotTblName = "figs6aTbl" ) +
theme( legend.key.height = unit( 6, "mm" ),
legend.key.width = unit( 9, "mm" ),
legend.text = element_text( size = legendTextSize ),
strip.text = element_text( size = stripTextSize, colour = 'black' ),
legend.title = element_blank(),
legend.text.align = 0,
axis.title = element_text( size = axisTitleTextSize),
strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ) ) +
guides( linetype = guide_legend(override.aes = list(size=1.8)),
colour = guide_legend( order = 1 ) )
CD8
# infected
## long table
lTbl1 = tmaxSubSingle17Tbl %>%
filter( cd == 8 & infxn == 1 ) %>%
group_by( vaccine, infxn, ptid ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
# uninfected©
## long table
lTbl0 = tmaxSubSingle17Tbl %>%
filter( cd == 8 & infxn == 0 ) %>%
group_by( vaccine, infxn, ptid ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
set.seed(2)
p1 = ggbootUV( data = full_join( lTbl0, lTbl1 ) %>% filter( vaccine != 8 ),
resp = resp, xAxis = vaccine, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca",
B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2,
rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Vaccine-induced memory\nIL17+ CD8 T cell response (%)",
nullValue = 0, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88,
colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec,
facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor,
lineScaleFactor = 2, plotTblName = "figs6bTbl" ) +
theme( legend.key.height = unit( 6, "mm" ),
legend.key.width = unit( 9, "mm" ),
legend.text = element_text( size = legendTextSize ),
strip.text = element_text( size = stripTextSize, colour = 'black' ),
legend.title = element_blank(),
legend.text.align = 0,
axis.title = element_text( size = axisTitleTextSize ),
strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ) ) +
guides( linetype = guide_legend(override.aes = list(size=1.8)),
colour = guide_legend( order = 1 ) )
Combined CD4 and CD8
plot_grid( p0, p1, ncol = 1, align = "v", labels = c( "A", "B" ), label_size = 12 * scaleFactor )
cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/S6_Fig.pdf", units = "cm", width = 13, height= 10, scale = 1.85, dpi = 300 )
Fig 4 - Vaccine-induced memory response profile, by vaccine, biplot
lTbl0 = tmaxSub17ExcTbl %>%
filter( cd == 4 & infxn == 0 ) %>%
mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>%
group_by( ptid ) %>%
filter( sum( abs( resp ) ) > 0.02 ) %>%
ungroup()
lPropTbl0 = lTbl0 %>%
group_by( ptid ) %>%
mutate( resp = resp / sum( abs(resp) ) ) %>%
ungroup() %>%
mutate( cytCombo = shortCytComboLabVec[cytCombo]) %>%
select( -timePoint )
wTbl0 = lPropTbl0 %>% filter( cd == 4 ) %>% spread( key = cytCombo, value = resp )
wInfoTbl0 = wTbl0 %>% select_if( is.character )
wNumTbl0 = wTbl0 %>% select_if( is.numeric )
### infected
# long table
lTbl1 = tmaxSub17ExcTbl %>%
filter( cd == 4 & infxn == 1) %>%
mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>%
group_by( ptid ) %>%
filter( sum( abs( resp ) ) > 0.02 ) %>%
ungroup()
lPropTbl1 = lTbl1 %>%
group_by( ptid ) %>%
mutate( resp = resp / sum( abs( resp ) ) ) %>%
ungroup() %>%
mutate( cytCombo = shortCytComboLabVec[cytCombo]) %>%
select( -timePoint )
wTbl1 = lPropTbl1 %>% spread( key = cytCombo, value = resp )
wInfoTbl1 = wTbl1 %>% select_if( is.character )
wNumTbl1 = wTbl1 %>% select_if( is.numeric )
p0 = ggbootMV( data = wNumTbl0 %>% select( `G-2+T+`,everything() ),
group = wInfoTbl0$vaccine, B = 2e3 * 5,
seed = 2, dispPlot = TRUE, labelVec = fullVaccLabelVec,
pcaAxesLabSize = 3, colVec = vaccColVec,
legendTitle = "Vaccine", textAdjVal = 0.067,
selAxisLab = c( "G-2+T+", "G-2+T-", "G+2+T+" ), arrow = TRUE, arrowSize = 1.25 ) +
theme_cowplot( font_size = 18, line_size = 0.75 ) +
theme( legend.title = element_blank(),
legend.text = element_text( size = 10 ),
axis.text = element_blank(),
legend.text.align = 0,
axis.ticks = element_blank(),
axis.title = element_text( size = 10 ),
legend.key.height = unit( 0.42, "cm" ) ) +
coord_equal(y = c( -0.2, 0.4 ),
x = c( -0.53, 0.65 ))
p0 = p0 + coord_equal( x = c( -0.55, 0.65 ),
y = c( -0.52, 0.55 ) )
p1 = ggbootMV( data = wNumTbl1 %>% select( `G+2+T-`, everything() ),
group = wInfoTbl1$vaccine, B = 2e3 * 5,
seed = 2, dispPlot = TRUE, labelVec = fullVaccLabelVec,
pcaAxesLabSize = 3, colVec = vaccColVec,
legendTitle = "Vaccine", textAdjVal = 0.05,
selAxisLab = c( "G+2-T-", "G+2+T+" ), arrow = TRUE, arrowSize = 1.25 ) +
theme_cowplot( font_size = 18, line_size = 0.75 ) +
theme( legend.title = element_blank(),
legend.text = element_text( size = 10 ),
axis.text = element_blank(),
legend.text.align = 0,
axis.ticks = element_blank(),
axis.title = element_text( size = 10 ),
legend.key.height = unit( 0.42, "cm" ) ) +
coord_equal( x = c( -0.75, 0.42 ),
y = c( -0.3, 0.55 ) )
plot_grid( p0, p1, ncol = 1, align = 'h', labels = c( "A", "B" ), label_x = 0 )
cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/Fig4.pdf", units = "cm", width = 13, height= 18, scale = 1 )
Fig 5 - Vaccine-induced memory response profile, CD4
lTbl0 = tmaxSub17ExcTbl %>%
filter( cd == 4 & infxn == 0 ) %>%
mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>%
group_by( ptid ) %>%
filter( sum( abs( resp ) ) > 0.02 ) %>%
ungroup()
lPropTbl0 = lTbl0 %>%
group_by( ptid ) %>%
mutate( resp = resp / sum( abs( resp ) ) ) %>%
ungroup()
wTbl0 = lPropTbl0 %>% filter( cd == 4 ) %>% spread( key = cytCombo, value = resp )
wInfoTbl0 = wTbl0 %>% select_if( is.character )
wNumTbl0 = wTbl0 %>% select_if( is.numeric )
set.seed(3) # was 2 initially
scaleFactor = 2
p0 = ggbootUV( data = lPropTbl0,
resp = resp, xAxis = vaccine, col = vaccine, facet = cytCombo, pMethod = "percT", ciMethod = "bca",
B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 2e2 * 2.5, calcStat = "mean", trim = 0.2,
pointSize = 2, errBarSize = 1, yLab = "Scaled vaccine-induced memory\nCD4 T cell response",
nullValue = 0, xLab = "Vaccine", errBarAlpha = 0.88,
colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec, hLineFactor = 0.6,
facetLabVec = shortCytComboLabVec, nCol = 4, facetScale = 'fixed', fontScaleFactor = 1,
lineScaleFactor = 1, plotTblName = "fig5bTbl" ) +
theme( legend.key.height = unit( 6 * scaleFactor, "mm" ),
legend.key.width = unit( 6, "mm" ),
strip.text = element_text( size = 8 * scaleFactor, colour = 'black' ),
legend.title = element_blank(),
axis.title = element_text( size = 8* scaleFactor ),
legend.text.align = 0,
axis.text = element_text( size = 8* scaleFactor ),
strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ),
legend.background = element_rect( colour = 'gray80', size = 1, linetype = 'solid' ),
legend.text = element_text( size = 8 * scaleFactor),
legend.position = 'bottom' ) +
guides( linetype = guide_legend(override.aes = list(size=1.8)),
colour = guide_legend( order = 1 ) )
lTbl1 = tmaxSub17ExcTbl %>%
filter( cd == 4 & infxn == 1 ) %>%
mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>%
group_by( ptid ) %>%
filter( sum( abs( resp ) ) > 0.02 ) %>%
ungroup()
lPropTbl1 = lTbl1 %>%
group_by( ptid ) %>%
mutate( resp = resp / sum( abs( resp ) ) ) %>%
ungroup()
wTbl1 = lPropTbl1 %>% filter( cd == 4 ) %>% spread( key = cytCombo, value = resp )
wInfoTbl1 = wTbl1 %>% select_if( is.character )
wNumTbl1 = wTbl1 %>% select_if( is.numeric )
set.seed(2)
p1 = ggbootUV( data = lPropTbl1,
resp = resp, xAxis = vaccine, col = vaccine, facet = cytCombo, pMethod = "percT", ciMethod = "bca",
B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 2e2 * 2.5, calcStat = "mean", trim = 0.2,
pointSize = 2, errBarSize = 1, yLab = "Scaled vaccine-induced memory\nCD4 T cell response",
nullValue = 0, xLab = "Vaccine", errBarAlpha = 0.88,
colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec, hLineFactor = 0.6,
facetLabVec = shortCytComboLabVec, nCol = 4, facetScale = 'fixed', fontScaleFactor = 1,
lineScaleFactor = 1, plotTblName = "fig5bTbl" ) +
theme( legend.key.height = unit( 6 * scaleFactor, "mm" ),
legend.key.width = unit( 6, "mm" ),
strip.text = element_text( size = 8 * scaleFactor, colour = 'black' ),
legend.title = element_blank(),
axis.title = element_text( size = 8* scaleFactor ),
legend.text.align = 0,
axis.text = element_text( size = 8* scaleFactor ),
strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ),
legend.background = element_rect( colour = 'gray80', size = 1, linetype = 'solid' ),
legend.text = element_text( size = 8 * scaleFactor),
legend.position = 'bottom' ) +
guides( linetype = guide_legend(override.aes = list(size=1.8)),
colour = guide_legend( order = 1 ) )
plot_grid( p0, p1, labels = c("A","B"), align = "v", ncol = 1, label_size = 12 * scaleFactor )
cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/Fig5.pdf", units = "cm", width = 13, height= 18, scale = 1 * scaleFactor)
S7 Fig - Axis predictivity
Summed
CD4
Uninfected
lTbl0 = tmaxSub17ExcTbl %>%
filter( cd == 4 & infxn == 0 ) %>%
mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>%
group_by( ptid ) %>%
filter( sum( abs( resp ) ) > 0.02 ) %>%
ungroup()
lPropTbl0 = lTbl0 %>%
group_by( ptid ) %>%
mutate( resp = resp / sum( abs(resp) ) ) %>%
ungroup() %>%
mutate( cytCombo = shortCytComboLabVec[cytCombo]) %>%
select( -timePoint )
wTbl0 = lPropTbl0 %>% filter( cd == 4 ) %>% spread( key = cytCombo, value = resp )
wInfoTbl0 = wTbl0 %>% select_if( is.character )
wNumTbl0 = wTbl0 %>% select_if( is.numeric )
p0 = plotCumQual( wNumTbl0, centre = TRUE, scale = FALSE, legendTitle = "Cytokine\ncombination",
legendLab = c( shortCytComboLabVec, mean = "Mean" ) ) +
labs( y = "Cumulative quality", x = "Ordered principal component number" ) +
theme( axis.title = element_text( size = 8 ),
legend.text = element_text( size = 8 ),
legend.text.align = 0,
axis.text = element_text( size = 8),
legend.title = element_text( size = 8 ) )
Infected
lTbl1 = tmaxSub17ExcTbl %>%
filter( cd == 4 & infxn == 1) %>%
mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>%
group_by( ptid ) %>%
filter( sum( abs( resp ) ) > 0.02 ) %>%
ungroup()
lPropTbl1 = lTbl1 %>%
group_by( ptid ) %>%
mutate( resp = resp / sum( abs( resp ) ) ) %>%
ungroup() %>%
mutate( cytCombo = shortCytComboLabVec[cytCombo]) %>%
select( -timePoint )
wTbl1 = lPropTbl1 %>% spread( key = cytCombo, value = resp )
wInfoTbl1 = wTbl1 %>% select_if( is.character )
wNumTbl1 = wTbl1 %>% select_if( is.numeric )
p1 = plotCumQual( wNumTbl1, centre = TRUE, scale = FALSE, legendTitle = "Cytokine\ncombination",
legendLab = c( shortCytComboLabVec, mean = "Mean" ) ) +
labs( y = "Cumulative quality", x = "Ordered principal component number" ) +
theme( axis.title = element_text( size = 8 ),
legend.text = element_text( size = 8 ),
axis.text = element_text( size = 8),
legend.text.align = 0,
legend.title = element_text( size = 8 ) )
plot_grid( p0, p1, labels = c("A","B"), align = "v", ncol = 1)
cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/S7_Fig.pdf", units = "cm", width = 13, height= 14, scale = 1 )
S8 Fig - Vaccine-induced memory response profile, pair-wise comparisons
lTbl0 = tmaxSub17ExcTbl %>%
filter( cd == 4 & infxn == 0 ) %>%
mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>%
group_by( ptid ) %>%
filter( sum( abs( resp ) ) > 0.02 ) %>%
ungroup()
lPropTbl0 = lTbl0 %>%
group_by( ptid ) %>%
mutate( resp = resp / sum( abs( resp ) ) ) %>%
ungroup()
set.seed(2)
scaleFactor = 1
p0 = ggbootPW( lPropTbl0 %>% filter( cytCombo %in% c( "Gp2pTp", "Gn2pTp" ) ),
resp = resp, group = vaccine, facet = cytCombo,
groupLabVec = fullVaccLabelVec, facetLabVec = shortCytComboLabVec,
facetScale = 'fixed', facetOrderVec = c( "1", "3", "4", "5", "6" ),
plotTblName = "plotTbl", fdr = 0.05, B = 2e3 * 5, seB = 200 * 2.5,
rotX = TRUE, axisLab = "Vaccine" ) +
theme( strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ),
strip.text = element_text( size = 12 * scaleFactor ),
legend.text = element_text( size = 8 * scaleFactor ),
legend.position = 'bottom',
legend.text.align = 0,
axis.text.y = element_text( size = 8 * scaleFactor ),
axis.text.x = element_text( size = 8 * scaleFactor, hjust = 1 ),
axis.title = element_text( size = 9 * scaleFactor ),
legend.title = element_blank(),
legend.key.width = unit( 7, "mm" ) )
lTbl1= tmaxSub17ExcTbl %>%
filter( cd == 4 & infxn == 1 ) %>%
mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>%
group_by( ptid ) %>%
filter( sum( abs( resp ) ) > 0.02 ) %>%
ungroup()
lPropTbl1 = lTbl1 %>%
group_by( ptid ) %>%
mutate( resp = resp / sum( abs( resp ) ) ) %>%
ungroup()
set.seed(2)
scaleFactor = 1
p1 = ggbootPW( lPropTbl1 %>% filter( cytCombo %in% c( "Gp2pTp", "Gp2nTn" ) ),
resp = resp, group = vaccine, facet = cytCombo,
groupLabVec = fullVaccLabelVec, facetLabVec = shortCytComboLabVec,
facetScale = 'fixed', facetOrderVec = c( "1", "3", "4", "5", "6" ),
plotTblName = "plotTbl", fdr = 0.05, B = 2e3 * 5, seB = 200 * 2.5,
rotX = TRUE, axisLab = "Vaccine" ) +
theme( strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ),
strip.text = element_text( size = 12 * scaleFactor ),
legend.text = element_text( size = 8 * scaleFactor ),
legend.position = 'bottom',
legend.text.align = 0,
axis.text.y = element_text( size = 8 * scaleFactor ),
axis.text.x = element_text( size = 8 * scaleFactor, hjust = 1 ),
axis.title = element_text( size = 9 * scaleFactor ),
legend.title = element_blank(),
legend.key.width = unit( 7, "mm" ) )
plot_grid( p0, p1, ncol = 1, labels = c( "A", "B" ) )
cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/S8_Fig.pdf", units = "cm", width = 13, height= 18.8, scale = 1 )
S9 Fig - Effect of Mtb infection on vaccine-induced CD4 memory response profile, by vaccine
lTbl0 = tmaxSub17ExcTbl %>%
filter( cd == 4 & infxn == 0 ) %>%
mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>%
group_by( ptid ) %>%
filter( sum( abs( resp ) ) > 0.02 ) %>%
ungroup()
lPropTbl0 = lTbl0 %>%
group_by( ptid ) %>%
mutate( resp = resp / sum( abs( resp ) ) ) %>%
ungroup()
wTbl0 = lPropTbl0 %>% filter( cd == 4 ) %>% spread( key = cytCombo, value = resp )
wInfoTbl0 = wTbl0 %>% select_if( is.character )
wNumTbl0 = wTbl0 %>% select_if( is.numeric )
### infected
# long table
lTbl1 = tmaxSub17ExcTbl %>%
filter( cd == 4 & infxn == 1) %>%
mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>%
group_by( ptid ) %>%
filter( sum( abs( resp ) ) > 0.02 ) %>%
ungroup()
lPropTbl1 = lTbl1 %>%
group_by( ptid ) %>%
mutate( resp = resp / sum( abs( resp ) ) ) %>%
ungroup()
wTbl1 = lPropTbl1 %>% spread( key = cytCombo, value = resp )
wInfoTbl1 = wTbl1 %>% select_if( is.character )
wNumTbl1 = wTbl1 %>% select_if( is.numeric )
set.seed(2)
scaleFactor = 2
ggbootUV( data = full_join( lPropTbl0, lPropTbl1 ) %>% filter( vaccine %in% 2:6 & cytCombo %in% c( "Gp2nTn", "Gn2pTp", "Gp2pTp", "Gn2pTn" ) ),
resp = resp, xAxis = vaccine, col = vaccine, facet = cytCombo, diff = infxn, pMethod = "percT", ciMethod = "bca",
B = 2e3 * 5, altSide = 'both', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2,
rotXText = TRUE, pointSize = 3, errBarSize = 1.75, yLab = "Change in scaled vaccine-induced CD4 T cell\nresponse (%) (infected vs uninfected)",
nullValue = 0, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88,
colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec,
facetLabVec = shortCytComboLabVec, nCol = 4, facetScale = 'free_x', fontScaleFactor = 2,
lineScaleFactor = 1.5, plotTblName = "figs9Tbl" ) +
theme( legend.key.height = unit( 6, "mm" ),
legend.key.width = unit( 9, "mm" ),
strip.text = element_text( size = 8 * scaleFactor, colour = 'black' ),
legend.text = element_text( size = 8 * scaleFactor ),
axis.text = element_text( size = 8 * scaleFactor ),
legend.title = element_blank(),
legend.text.align = 0,
axis.title = element_text( size = 8 * scaleFactor ),
strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ) ) +
guides( linetype = guide_legend(override.aes = list(size=1.8)),
colour = guide_legend( order = 1 ) )
cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/S9_Fig.pdf", units = "cm", width = 13, height= 10, scale = scaleFactor )
MiguelRodo/VaccComp documentation built on Nov. 9, 2023, 10:16 a.m.
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if( !require("VaccComp") ) stop("Install package VaccComp using 'devtools::install_github( 'MiguelRodo\VaccComp' )'" ) if( !require("ggboot") ) stop("Install package VaccComp using 'devtools::install_github( 'MiguelRodo\ggboot' )'") if( !require("VaccCompData") ) stop("Install package VaccComp using 'devtools::install_github( 'MiguelRodo\VaccCompData' )'") library(VaccComp) library(ggboot) library(plyr) library(tidyr) library(stringr) library(magrittr) library(dplyr) library(VaccCompData) library(cowplot) library(doParallel) knitr::opts_chunk$set( collapse = TRUE, comment = "#>", echo = FALSE, cache = TRUE, message = FALSE, warning = FALSE, tidy = TRUE )
save = TRUE getSavePath = function(x) str_c( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/", x, ".pdf" )
Fig 1 - Pre-vaccination response magnitude by vaccine
# infected ## long table lTbl1 = bl17ExcITbl %>% filter( cd == 4 & infxn == 1 ) %>% group_by( vaccine, infxn, ptid ) %>% summarise( resp = sum( resp ) ) %>% ungroup() # uninfected ## long table lTbl0 = bl17ExcITbl %>% filter( cd == 4 & infxn == 0 ) %>% group_by( vaccine, infxn, ptid ) %>% summarise( resp = sum( resp ) ) %>% ungroup()
# plot set.seed(2) scaleFactor = 0.56^-1 legendTextSize = 8 * scaleFactor stripTextSize = 8 * scaleFactor fontScaleFactor = 0.56 * scaleFactor axisTitleTextSize = 8 * scaleFactor p0 = ggbootUV( data = full_join( lTbl0, lTbl1 ), resp = resp, xAxis = vaccine, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca", B = 1e1, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 2e2 * 2.5, calcStat = "mean", trim = 0.2, rotXText = TRUE, pointSize = 3, errBarSize = 1.75, yLab = "Pre-vaccination\nCD4 T cell response (%)", nullValue = 0.005, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88, colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec, bootSECorr = FALSE, facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor, lineScaleFactor = 2, plotTblName = "fig1aTbl" ) + theme( legend.key.height = unit( 6, "mm" ), legend.key.width = unit( 9, "mm" ), legend.text = element_text( size = legendTextSize ), strip.text = element_text( size = stripTextSize, colour = 'black' ), legend.title = element_blank(), legend.text.align = 0, axis.title = element_text( size = axisTitleTextSize), strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white', colour = 'white' ) ) + guides( linetype = guide_legend(override.aes = list(size=1.8)), colour = guide_legend( order = 1 ) )
# infected ## long table lTbl1 = bl17ExcITbl %>% filter( cd == 8 & infxn == 1 ) %>% group_by( vaccine, infxn, ptid ) %>% summarise( resp = sum( resp ) ) %>% ungroup() # uninfected ## long table lTbl0 = bl17ExcITbl %>% filter( cd == 8 & infxn == 0 ) %>% group_by( vaccine, infxn, ptid ) %>% summarise( resp = sum( resp ) ) %>% ungroup()
# plot set.seed(2) p1 = ggbootUV( data = full_join( lTbl0, lTbl1 ), resp = resp, xAxis = vaccine, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca", B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2, rotXText = TRUE, pointSize = 3, errBarSize = 1.75, yLab = "Pre-vaccination\nCD8 T cell response (%)", nullValue = 0.005, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88, colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec, facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor, lineScaleFactor = 2, plotTblName = "fig1bTbl" ) + theme( legend.key.height = unit( 6, "mm" ), legend.key.width = unit( 9, "mm" ), legend.text.align = 0, legend.text = element_text( size = legendTextSize ), strip.text = element_text( size = stripTextSize, colour = 'black' ), legend.title = element_blank(), axis.title = element_text( size = axisTitleTextSize), strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white', colour = 'white' ) ) + guides( linetype = guide_legend(override.aes = list(size=1.8)), colour = guide_legend( order = 1 ) )
plot_grid( p0, p1, ncol = 1, align = "v", labels = c( "A", "B" ), label_size = 12 * scaleFactor )
cowplot::ggsave( getSavePath( "Fig1" ), units = "cm", width = 13, height= 10, scale = 1.85, dpi = 300 )
S1 Fig - Pre-vaccination response magnitude by stimulation
# infected ## long table lTbl1 = bl17ExcITbl %>% filter( cd == 4 & infxn == 1 ) %>% mutate( cytCombo = str_sub( cytCombo, 4 ), vaccStim = str_c( vaccine, stim ) ) %>% group_by( vaccine, infxn, ptid, stim, vaccStim ) %>% summarise( resp = sum( resp ) ) %>% ungroup() # uninfected ## long table lTbl0 = bl17ExcITbl %>% filter( cd == 4 & infxn == 0 ) %>% mutate( cytCombo = str_sub( cytCombo, 4 ), vaccStim = str_c( vaccine, stim ) ) %>% group_by( vaccine, infxn, ptid, stim, vaccStim ) %>% summarise( resp = sum( resp ) ) %>% ungroup()
# plot set.seed(2) scaleFactor = 0.56^-1 legendTextSize = 8 * scaleFactor stripTextSize = 8 * scaleFactor axisTitleTextSize = 8 * scaleFactor fontScaleFactor = 0.56 * scaleFactor xAxisTextSize = 8 * scaleFactor p0 = ggbootUV( data = full_join( lTbl0, lTbl1 ), resp = resp, xAxis = vaccStim, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca", B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = FALSE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2, rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Pre-vaccination\nCD4 T cell response (%)", nullValue = 0.005, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88, colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec, facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor, lineScaleFactor = 2, plotTblName = "figs1aTbl" ) + theme( legend.key.height = unit( 6, "mm" ), legend.key.width = unit( 9, "mm" ), legend.text = element_text( size = legendTextSize ), strip.text = element_text( size = stripTextSize, colour = 'black' ), legend.title = element_blank(), legend.text.align = 0, axis.title = element_text( size = axisTitleTextSize), strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white', colour = 'white' ), axis.text.x = element_text( hjust = 1, angle = 90, vjust = 0.45 ) ) + guides( linetype = guide_legend(override.aes = list(size=1.8)), colour = guide_legend( order = 1 ) )
# infected ## long table lTbl1 = bl17ExcITbl %>% filter( cd == 8 & infxn == 1 ) %>% mutate( cytCombo = str_sub( cytCombo, 4 ), vaccStim = str_c( vaccine, stim ) ) %>% group_by( vaccine, infxn, ptid, stim, vaccStim ) %>% summarise( resp = sum( resp ) ) %>% ungroup() # uninfected ## long table lTbl0 = bl17ExcITbl %>% filter( cd == 8 & infxn == 0 ) %>% mutate( cytCombo = str_sub( cytCombo, 4 ), vaccStim = str_c( vaccine, stim ) ) %>% group_by( vaccine, infxn, ptid, stim, vaccStim ) %>% summarise( resp = sum( resp ) ) %>% ungroup()
# plot set.seed(2) p1 = ggbootUV( data = full_join( lTbl0, lTbl1 ), resp = resp, xAxis = vaccStim, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca", B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = FALSE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2, rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Pre-vaccination\nCD8 T cell response (%)", nullValue = 0.005, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88, colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec, facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor, lineScaleFactor = 2, plotTblName = "figs1bTbl" ) + theme( legend.key.height = unit( 6, "mm" ), legend.key.width = unit( 9, "mm" ), legend.text = element_text( size = legendTextSize ), strip.text = element_text( size = stripTextSize, colour = 'black' ), legend.title = element_blank(), legend.text.align = 0, axis.title = element_text( size = axisTitleTextSize), strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white', colour = 'white' ), axis.text.x = element_text( hjust = 1, angle = 90, vjust = 0.45 ) ) + guides( linetype = guide_legend(override.aes = list(size=1.8)), colour = guide_legend( order = 1 ) )
plot_grid( p0, p1, ncol = 1, align = "v", labels = c( "A", "B" ), label_size = 12 * scaleFactor )
cowplot::ggsave( getSavePath( "S1_Fig" ), units = "cm", width = 13, height= 14, scale = 1.85, dpi = 300 )
S2 Fig - Effect of Mtb infection on baseline response size, by vaccine
Effect of baseline Mtb infection
# infected ## long table lTbl1 = bl17ExcITbl %>% filter( cd == 4 & infxn == 1 ) %>% group_by( vaccine, infxn, ptid ) %>% summarise( resp = sum( resp ) ) %>% ungroup() # uninfected ## long table lTbl0 = bl17ExcITbl %>% filter( cd == 4 & infxn == 0 ) %>% group_by( vaccine, infxn, ptid ) %>% summarise( resp = sum( resp ) ) %>% ungroup()
set.seed(2) scaleFactor = 0.55^-1 ggbootUV( data = full_join( lTbl0, lTbl1 ) %>% filter( vaccine %in% 2:6 ), resp = resp, xAxis = vaccine, col = vaccine, diff = infxn, pMethod = "percT", ciMethod = "bca", B = 2e3 * 5, altSide = 'both', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2, rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Change in pre-vaccination CD4\nT cell response (%) (infected vs uninfected)", nullValue = 0, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88, colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec[-c(1,6)], facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = 2, lineScaleFactor = 1.5, plotTblName = "s2Tbl" ) + theme( legend.key.height = unit( 6, "mm" ), legend.key.width = unit( 9, "mm" ), legend.text = element_text( size = 8 * scaleFactor ), axis.text = element_text( size = 8 * scaleFactor ), legend.title = element_blank(), legend.text.align = 0, axis.title = element_text( size = 8 * scaleFactor ), strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white', colour = 'white' ) ) + guides( linetype = guide_legend(override.aes = list(size=1.8)), colour = guide_legend( order = 1 ) ) cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/S2_Fig.pdf", units = "cm", width = 10, height= 6, scale = 2 )
Fig 2 - Longitudinal response size
longPlotTbl = join17ExcTbl %>% mutate( cd = str_sub( cytCombo, 3, 3 ), cytCombo = str_sub( cytCombo, 4 ) ) %>% filter( vaccine != 8 ) %>% group_by( vaccine, infxn, timePoint, cd, ptid ) %>% summarise( sumResp = sum( resp ) ) %>% summarise( med = median( sumResp ), lb = quantile( sumResp, 0.25 ), ub = quantile( sumResp, 0.75 ) ) %>% ungroup() longPlotTbl = sub17ExcTbl %>% mutate( cd = str_sub( cytCombo, 3, 3 ), cytCombo = str_sub( cytCombo, 4 ) ) %>% filter( vaccine != 8 ) %>% group_by( vaccine, infxn, timePoint, cd, ptid ) %>% summarise( sumResp = sum( resp ) ) %>% summarise( med = median( sumResp ), lb = quantile( sumResp, 0 ), ub = quantile( sumResp, 1 ) ) %>% ungroup() hlineCalcTbl = longPlotTbl %>% group_by( vaccine ) %>% summarise( max = max( ub ) ) x = hlineCalcTbl$vaccine[1] hlinePlotTbl = llply( as.list( hlineCalcTbl$vaccine ), function( x ){ currMax = hlineCalcTbl$max[ which( hlineCalcTbl$vaccine == x ) ] tibble( vaccine = x, int = seq( 0, currMax, by = 0.5 )[-1] ) } ) %>% bind_rows() vaccTimePointTbl = tibble( cd = "4", infxn = "1", vaccine = c( "1", rep( "2", 2 ), rep( "3", 2), "4", rep( "5", 2 ), rep( "6", 3 ), "7" ), timePoint = c( 0, c( 0, 56 ), c( 0, 30 ), 0, c( 0, 56 ), c( 0, 28, 112 ), 0 ), label = "V", y = -0.13 ) infLineTypeVec = c("0" = "solid", "1" = "11") hlineTbl = tibble( vaccine = 1, int = 0.5 ) adjVal = 1.3 cdInfAdjVec = c( "4.0" = -1.5 * adjVal, "4.1" = -0.5 * adjVal, "8.0"= 0.5 * adjVal, "8.1" = 1.5 * adjVal ) longPlotTbl %<>% mutate( timePoint = timePoint + cdInfAdjVec[str_c( cd, ".", infxn)] ) testFunc = function(){ for( i in 1:nrow( longPlotTbl ) ){ currVacc = longPlotTbl$vaccine[i] if( currVacc != 3 ) longPlotTbl$vaccine[i] = fullVaccLabelVecInt[currVacc] if( currVacc == 3 )longPlotTbl$vaccine[i] = "M72:AS01 [E]" } for( i in 1:nrow( hlineCalcTbl ) ){ currVacc = hlineCalcTbl$vaccine[i] if( currVacc != 3 ) hlineCalcTbl$vaccine[i] = fullVaccLabelVecInt[currVacc] if( currVacc == 3 )hlineCalcTbl$vaccine[i] = "M72:AS01 [E]" } for( i in 1:nrow( vaccTimePointTbl ) ){ currVacc = vaccTimePointTbl$vaccine[i] if( currVacc != 3 ) vaccTimePointTbl$vaccine[i] = fullVaccLabelVecInt[currVacc] if( currVacc == 3 )vaccTimePointTbl$vaccine[i] = "M72:AS01 [E]" } }
scaleFactor = 0.56^-1 ggplot( longPlotTbl, aes( x = timePoint, col = cd, linetype = infxn ) ) + geom_line( aes( y = med ), size = 0.7 ) + geom_errorbar( aes( ymin = lb, ymax = ub ), size = 0.5 ) + geom_text( data = vaccTimePointTbl, aes( x = timePoint, y = y ), colour = 'black', size = 4.75, label = "V", fontface = "bold" ) + facet_wrap( ~vaccine, label = labeller( vaccine = fullVaccLabelVec), ncol = 4, scales = 'free' ) + scale_linetype_manual( name = "", values = infLineTypeVec, labels = infLabelVec ) + scale_colour_manual( name = "", values = c( "4" = "orange", "8" = "royalblue1" ), labels = c( "4" = "CD4 ", "8" = "CD8") ) + labs( x = "Days since first vaccination", y = "T cell response (%)" ) + theme_cowplot( line_size = 1 ) + theme( legend.key.height = unit( 6, "mm" ), legend.key.width = unit( 10, "mm" ), legend.title = element_blank(), legend.text.align = 0, legend.text = element_text( size = 8 * scaleFactor), axis.title = element_text( size = 8 * scaleFactor ), axis.text.x = element_text( size = 8 * scaleFactor ), strip.background = element_rect( 'white' ), legend.position = c( 0.782, 0.3 ), legend.background = element_rect( colour = 'gray80', size = 1, linetype = 'solid' ), strip.text = element_text( size = 8 * scaleFactor ) ) + guides( linetype = guide_legend(override.aes = list(size=1.8)), colour = guide_legend( override.aes = list( size = 1.8 ) ) ) + coord_cartesian( x = c( 0, 366 ), y = c( -0.1, 2.5 ) ) + background_grid( major = 'y', minor = 'none', size.major = 0.25 ) cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/Fig2.pdf", units = "cm", width = 13, height= 10, scale = 1.85 )
Fig 3 - Vaccine-induced memory response size, by vaccine
Summed
CD4
# infected ## long table lTbl1 = tmaxSub17ExcTbl %>% filter( cd == 4 & infxn == 1 ) %>% group_by( vaccine, infxn, ptid ) %>% summarise( resp = sum( resp ) ) %>% ungroup() # uninfected ## long table lTbl0 = tmaxSub17ExcTbl %>% filter( cd == 4 & infxn == 0 ) %>% group_by( vaccine, infxn, ptid ) %>% summarise( resp = sum( resp ) ) %>% ungroup()
# plot set.seed(2) scaleFactor = 0.56^-1 legendTextSize = 8 * scaleFactor stripTextSize = 8 * scaleFactor axisTitleTextSize = 8 * scaleFactor fontScaleFactor = 0.65 * scaleFactor p0 = ggbootUV( data = full_join( lTbl0, lTbl1 ) %>% filter( vaccine != 8 ), resp = resp, xAxis = vaccine, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca", B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2, rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Vaccine-induced memory\nCD4 T cell response (%)", nullValue = 0, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88, colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec, facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor, lineScaleFactor = 2, plotTblName = "fig3aTbl" ) + theme( legend.key.height = unit( 6, "mm" ), legend.key.width = unit( 9, "mm" ), legend.text = element_text( size = legendTextSize ), strip.text = element_text( size = stripTextSize, colour = 'black' ), legend.title = element_blank(), legend.text.align = 0, axis.title = element_text( size = axisTitleTextSize), strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white', colour = 'white' ) ) + guides( linetype = guide_legend(override.aes = list(size=1.8)), colour = guide_legend( order = 1 ) )
CD8
# infected ## long table lTbl1 = tmaxSub17ExcTbl %>% filter( cd == 8 & infxn == 1 ) %>% group_by( vaccine, infxn, ptid ) %>% summarise( resp = sum( resp ) ) %>% ungroup() # uninfected ## long table lTbl0 = tmaxSub17ExcTbl %>% filter( cd == 8 & infxn == 0 ) %>% group_by( vaccine, infxn, ptid ) %>% summarise( resp = sum( resp ) ) %>% ungroup()
set.seed(2) p1 = ggbootUV( data = full_join( lTbl0, lTbl1 ) %>% filter( vaccine != 8 ), resp = resp, xAxis = vaccine, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca", B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2, rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Vaccine-induced memory\nCD8 T cell response (%)", nullValue = 0, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88, colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec, facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor, lineScaleFactor = 2, plotTblName = "fig3bTbl" ) + theme( legend.key.height = unit( 6, "mm" ), legend.key.width = unit( 9, "mm" ), legend.text = element_text( size = legendTextSize ), strip.text = element_text( size = stripTextSize, colour = 'black' ), legend.title = element_blank(), legend.text.align = 0, axis.title = element_text( size = axisTitleTextSize ), strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white', colour = 'white' ) ) + guides( linetype = guide_legend(override.aes = list(size=1.8)), colour = guide_legend( order = 1 ) )
Combined CD4 and CD8
plot_grid( p0, p1, ncol = 1, align = "v", labels = c( "A", "B" ), label_size = 12 * scaleFactor ) cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/Fig3.pdf", units = "cm", width = 13, height= 10, scale = 1.85, dpi = 300 )
S3 Fig - Vaccine-induced memory response size, by vaccine, pair-wise comparisons
Pair-wise comparison
# infected ## long table lTbl1 = tmaxSub17ExcTbl %>% filter( cd == 4 & infxn == 1 ) %>% group_by( vaccine, infxn, ptid ) %>% summarise( resp = sum( resp ) ) %>% ungroup() # uninfected ## long table lTbl0 = tmaxSub17ExcTbl %>% filter( cd == 4 & infxn == 0 ) %>% group_by( vaccine, infxn, ptid ) %>% summarise( resp = sum( resp ) ) %>% ungroup()
scaleFactor = 1 ggbootPW( full_join( lTbl0, lTbl1 ) %>% filter( vaccine != 8 ), resp = resp, facet = infxn, group = vaccine, facetLabVec = infLabelVec, groupLabVec = fullVaccLabelVec, facetScale = 'fixed', axisLab = "Vaccine", facetOrderVec = c( "1", "3", "4", "5", "6" ), plotTblName = "plotTbl", fdr = 0.05, B = 2e3 * 5, seB = 200 * 2.5, rotX = TRUE ) + theme( strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white', colour = 'white' ), strip.text = element_text( size = 8 * scaleFactor ), legend.text = element_text( size = 8 * scaleFactor ), legend.position = 'bottom', legend.text.align = 0, axis.text.y = element_text( size = 8 * scaleFactor ), axis.text.x = element_text( size = 8 * scaleFactor, hjust = 1 ), axis.title = element_text( size = 8 * scaleFactor ), legend.title = element_blank(), legend.key.width = unit( 5, "mm" ) ) cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/S3_Fig.pdf", units = "cm", width = 13, height= 9.4, scale = 1 )
S4 Fig - Effect of Mtb infection on vaccine-induced memory response size, by vaccine
Effect of baseline Mtb infection
# infected ## long table lTbl1 = tmaxSub17ExcTbl %>% filter( cd == 4 & infxn == 1 ) %>% group_by( vaccine, infxn, ptid ) %>% summarise( resp = sum( resp ) ) %>% ungroup() # uninfected ## long table lTbl0 = tmaxSub17ExcTbl %>% filter( cd == 4 & infxn == 0 ) %>% group_by( vaccine, infxn, ptid ) %>% summarise( resp = sum( resp ) ) %>% ungroup()
set.seed(2) scaleFactor = 0.55^-1 ggbootUV( data = full_join( lTbl0, lTbl1 ) %>% filter( vaccine %in% 2:6 ), resp = resp, xAxis = vaccine, col = vaccine, diff = infxn, pMethod = "percT", ciMethod = "bca", B = 2e3 * 5, altSide = 'both', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 2e2 * 2.5, calcStat = "mean", trim = 0.2, rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Change in vaccine-induced memory\nCD4 T cell response (%) (infected vs uninfected)", nullValue = 0, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88, colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec, facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = 2, lineScaleFactor = 1.5, plotTblName = "figs5Tbl" ) + theme( legend.key.height = unit( 6, "mm" ), legend.key.width = unit( 9, "mm" ), legend.text = element_text( size = 8 * scaleFactor ), axis.text = element_text( size = 8 * scaleFactor ), legend.title = element_blank(), legend.text.align = 0, axis.title = element_text( size = 8 * scaleFactor ), strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white', colour = 'white' ) ) + guides( linetype = guide_legend(override.aes = list(size=1.8)), colour = guide_legend( order = 1 ) ) cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/S5_Fig.pdf", units = "cm", width = 10, height= 7, scale = 2 )
S5 Fig - Vaccine-induced memory response size, by stim
Stim
CD4
# infected ## long table lTbl1 = tmaxSub17ExcITbl %>% filter( cd == 4 & infxn == 1 ) %>% mutate( cytCombo = str_sub( cytCombo, 4 ), vaccStim = str_c( vaccine, stim ) ) %>% group_by( vaccine, infxn, ptid, stim, vaccStim ) %>% summarise( resp = sum( resp ) ) %>% ungroup() # uninfected ## long table lTbl0 = tmaxSub17ExcITbl %>% filter( cd == 4 & infxn == 0 ) %>% mutate( cytCombo = str_sub( cytCombo, 4 ), vaccStim = str_c( vaccine, stim ) ) %>% group_by( vaccine, infxn, ptid, stim, vaccStim ) %>% summarise( resp = sum( resp ) ) %>% ungroup()
# plot set.seed(2) scaleFactor = 0.56^-1 legendTextSize = 8 * scaleFactor stripTextSize = 8 * scaleFactor axisTitleTextSize = 8 * scaleFactor fontScaleFactor = 0.56 * scaleFactor xAxisTextSize = 8 * scaleFactor p0 = ggbootUV( data = full_join( lTbl0, lTbl1 ) %>% filter( vaccine != 8 ), resp = resp, xAxis = vaccStim, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca", B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = FALSE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2, rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Vaccine-induced memory\nCD4 T cell response (%)", nullValue = 0, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88, colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec, facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor, lineScaleFactor = 2, plotTblName = "figs4aTbl" ) + theme( legend.key.height = unit( 6, "mm" ), legend.key.width = unit( 9, "mm" ), legend.text = element_text( size = legendTextSize ), strip.text = element_text( size = stripTextSize, colour = 'black' ), legend.title = element_blank(), axis.title = element_text( size = axisTitleTextSize), strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white', colour = 'white' ), legend.text.align = 0, axis.text.x = element_text( hjust = 1, angle = 90, vjust = 0.45 ) ) + guides( linetype = guide_legend(override.aes = list(size=1.8)), colour = guide_legend( order = 1 ) )
CD8
# infected ## long table lTbl1 = tmaxSub17ExcITbl %>% filter( cd == 8 & infxn == 1 ) %>% mutate( cytCombo = str_sub( cytCombo, 4 ), vaccStim = str_c( vaccine, stim ) ) %>% group_by( vaccine, infxn, ptid, stim, vaccStim ) %>% summarise( resp = sum( resp ) ) %>% ungroup() # uninfected ## long table lTbl0 = tmaxSub17ExcITbl %>% filter( cd == 8 & infxn == 0 ) %>% mutate( cytCombo = str_sub( cytCombo, 4 ), vaccStim = str_c( vaccine, stim ) ) %>% group_by( vaccine, infxn, ptid, stim, vaccStim ) %>% summarise( resp = sum( resp ) ) %>% ungroup()
# plot set.seed(2) p1 = ggbootUV( data = full_join( lTbl0, lTbl1 ) %>% filter( vaccine != 8 ), resp = resp, xAxis = vaccStim, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca", B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = FALSE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2, rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Vaccine-induced memory\nCD8 T cell response (%)", nullValue = 0, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88, colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec, facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor, lineScaleFactor = 2, plotTblName = "figs4bTbl" ) + theme( legend.key.height = unit( 6, "mm" ), legend.key.width = unit( 9, "mm" ), legend.text = element_text( size = legendTextSize ), strip.text = element_text( size = stripTextSize, colour = 'black' ), legend.title = element_blank(), axis.title = element_text( size = axisTitleTextSize), strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white', colour = 'white' ), legend.text.align = 0, axis.text.x = element_text( hjust = 1, angle = 90, vjust = 0.45 ) ) + guides( linetype = guide_legend(override.aes = list(size=1.8)), colour = guide_legend( order = 1 ) )
Combined CD4 and CD8
plot_grid( p0, p1, ncol = 1, align = "v", labels = c( "A", "B" ), label_size = 12 * scaleFactor ) cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/S5_Fig.pdf", units = "cm", width = 13, height= 14, scale = 1.85, dpi = 300 )
S6 Fig - Vaccine-induced memory IL17 response, by vaccine
Summed
CD4
# infected ## long table lTbl1 = tmaxSubSingle17Tbl %>% filter( cd == 4 & infxn == 1 ) %>% group_by( vaccine, infxn, ptid ) %>% summarise( resp = sum( resp ) ) %>% ungroup() # uninfected ## long table lTbl0 = tmaxSubSingle17Tbl %>% filter( cd == 4 & infxn == 0 ) %>% group_by( vaccine, infxn, ptid ) %>% summarise( resp = sum( resp ) ) %>% ungroup()
# plot set.seed(2) scaleFactor = 0.56^-1 legendTextSize = 8 * scaleFactor stripTextSize = 8 * scaleFactor axisTitleTextSize = 8 * scaleFactor fontScaleFactor = 0.65 * scaleFactor p0 = ggbootUV( data = full_join( lTbl0, lTbl1 ) %>% filter( vaccine != 8 ), resp = resp, xAxis = vaccine, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca", B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2, rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Vaccine-induced memory\nIL17+ CD4 T cell response (%)", nullValue = 0, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88, colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec, facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor, lineScaleFactor = 2, plotTblName = "figs6aTbl" ) + theme( legend.key.height = unit( 6, "mm" ), legend.key.width = unit( 9, "mm" ), legend.text = element_text( size = legendTextSize ), strip.text = element_text( size = stripTextSize, colour = 'black' ), legend.title = element_blank(), legend.text.align = 0, axis.title = element_text( size = axisTitleTextSize), strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white', colour = 'white' ) ) + guides( linetype = guide_legend(override.aes = list(size=1.8)), colour = guide_legend( order = 1 ) )
CD8
# infected ## long table lTbl1 = tmaxSubSingle17Tbl %>% filter( cd == 8 & infxn == 1 ) %>% group_by( vaccine, infxn, ptid ) %>% summarise( resp = sum( resp ) ) %>% ungroup() # uninfected© ## long table lTbl0 = tmaxSubSingle17Tbl %>% filter( cd == 8 & infxn == 0 ) %>% group_by( vaccine, infxn, ptid ) %>% summarise( resp = sum( resp ) ) %>% ungroup()
set.seed(2) p1 = ggbootUV( data = full_join( lTbl0, lTbl1 ) %>% filter( vaccine != 8 ), resp = resp, xAxis = vaccine, col = vaccine, facet = infxn, pMethod = "percT", ciMethod = "bca", B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2, rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Vaccine-induced memory\nIL17+ CD8 T cell response (%)", nullValue = 0, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88, colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec, facetLabVec = infLabelVec, nCol = 3, facetScale = 'free_x', fontScaleFactor = fontScaleFactor, lineScaleFactor = 2, plotTblName = "figs6bTbl" ) + theme( legend.key.height = unit( 6, "mm" ), legend.key.width = unit( 9, "mm" ), legend.text = element_text( size = legendTextSize ), strip.text = element_text( size = stripTextSize, colour = 'black' ), legend.title = element_blank(), legend.text.align = 0, axis.title = element_text( size = axisTitleTextSize ), strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white', colour = 'white' ) ) + guides( linetype = guide_legend(override.aes = list(size=1.8)), colour = guide_legend( order = 1 ) )
Combined CD4 and CD8
plot_grid( p0, p1, ncol = 1, align = "v", labels = c( "A", "B" ), label_size = 12 * scaleFactor ) cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/S6_Fig.pdf", units = "cm", width = 13, height= 10, scale = 1.85, dpi = 300 )
Fig 4 - Vaccine-induced memory response profile, by vaccine, biplot
lTbl0 = tmaxSub17ExcTbl %>% filter( cd == 4 & infxn == 0 ) %>% mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>% group_by( ptid ) %>% filter( sum( abs( resp ) ) > 0.02 ) %>% ungroup() lPropTbl0 = lTbl0 %>% group_by( ptid ) %>% mutate( resp = resp / sum( abs(resp) ) ) %>% ungroup() %>% mutate( cytCombo = shortCytComboLabVec[cytCombo]) %>% select( -timePoint ) wTbl0 = lPropTbl0 %>% filter( cd == 4 ) %>% spread( key = cytCombo, value = resp ) wInfoTbl0 = wTbl0 %>% select_if( is.character ) wNumTbl0 = wTbl0 %>% select_if( is.numeric ) ### infected # long table lTbl1 = tmaxSub17ExcTbl %>% filter( cd == 4 & infxn == 1) %>% mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>% group_by( ptid ) %>% filter( sum( abs( resp ) ) > 0.02 ) %>% ungroup() lPropTbl1 = lTbl1 %>% group_by( ptid ) %>% mutate( resp = resp / sum( abs( resp ) ) ) %>% ungroup() %>% mutate( cytCombo = shortCytComboLabVec[cytCombo]) %>% select( -timePoint ) wTbl1 = lPropTbl1 %>% spread( key = cytCombo, value = resp ) wInfoTbl1 = wTbl1 %>% select_if( is.character ) wNumTbl1 = wTbl1 %>% select_if( is.numeric )
p0 = ggbootMV( data = wNumTbl0 %>% select( `G-2+T+`,everything() ), group = wInfoTbl0$vaccine, B = 2e3 * 5, seed = 2, dispPlot = TRUE, labelVec = fullVaccLabelVec, pcaAxesLabSize = 3, colVec = vaccColVec, legendTitle = "Vaccine", textAdjVal = 0.067, selAxisLab = c( "G-2+T+", "G-2+T-", "G+2+T+" ), arrow = TRUE, arrowSize = 1.25 ) + theme_cowplot( font_size = 18, line_size = 0.75 ) + theme( legend.title = element_blank(), legend.text = element_text( size = 10 ), axis.text = element_blank(), legend.text.align = 0, axis.ticks = element_blank(), axis.title = element_text( size = 10 ), legend.key.height = unit( 0.42, "cm" ) ) + coord_equal(y = c( -0.2, 0.4 ), x = c( -0.53, 0.65 )) p0 = p0 + coord_equal( x = c( -0.55, 0.65 ), y = c( -0.52, 0.55 ) ) p1 = ggbootMV( data = wNumTbl1 %>% select( `G+2+T-`, everything() ), group = wInfoTbl1$vaccine, B = 2e3 * 5, seed = 2, dispPlot = TRUE, labelVec = fullVaccLabelVec, pcaAxesLabSize = 3, colVec = vaccColVec, legendTitle = "Vaccine", textAdjVal = 0.05, selAxisLab = c( "G+2-T-", "G+2+T+" ), arrow = TRUE, arrowSize = 1.25 ) + theme_cowplot( font_size = 18, line_size = 0.75 ) + theme( legend.title = element_blank(), legend.text = element_text( size = 10 ), axis.text = element_blank(), legend.text.align = 0, axis.ticks = element_blank(), axis.title = element_text( size = 10 ), legend.key.height = unit( 0.42, "cm" ) ) + coord_equal( x = c( -0.75, 0.42 ), y = c( -0.3, 0.55 ) ) plot_grid( p0, p1, ncol = 1, align = 'h', labels = c( "A", "B" ), label_x = 0 ) cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/Fig4.pdf", units = "cm", width = 13, height= 18, scale = 1 )
Fig 5 - Vaccine-induced memory response profile, CD4
lTbl0 = tmaxSub17ExcTbl %>% filter( cd == 4 & infxn == 0 ) %>% mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>% group_by( ptid ) %>% filter( sum( abs( resp ) ) > 0.02 ) %>% ungroup() lPropTbl0 = lTbl0 %>% group_by( ptid ) %>% mutate( resp = resp / sum( abs( resp ) ) ) %>% ungroup() wTbl0 = lPropTbl0 %>% filter( cd == 4 ) %>% spread( key = cytCombo, value = resp ) wInfoTbl0 = wTbl0 %>% select_if( is.character ) wNumTbl0 = wTbl0 %>% select_if( is.numeric )
set.seed(3) # was 2 initially scaleFactor = 2 p0 = ggbootUV( data = lPropTbl0, resp = resp, xAxis = vaccine, col = vaccine, facet = cytCombo, pMethod = "percT", ciMethod = "bca", B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 2e2 * 2.5, calcStat = "mean", trim = 0.2, pointSize = 2, errBarSize = 1, yLab = "Scaled vaccine-induced memory\nCD4 T cell response", nullValue = 0, xLab = "Vaccine", errBarAlpha = 0.88, colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec, hLineFactor = 0.6, facetLabVec = shortCytComboLabVec, nCol = 4, facetScale = 'fixed', fontScaleFactor = 1, lineScaleFactor = 1, plotTblName = "fig5bTbl" ) + theme( legend.key.height = unit( 6 * scaleFactor, "mm" ), legend.key.width = unit( 6, "mm" ), strip.text = element_text( size = 8 * scaleFactor, colour = 'black' ), legend.title = element_blank(), axis.title = element_text( size = 8* scaleFactor ), legend.text.align = 0, axis.text = element_text( size = 8* scaleFactor ), strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white', colour = 'white' ), legend.background = element_rect( colour = 'gray80', size = 1, linetype = 'solid' ), legend.text = element_text( size = 8 * scaleFactor), legend.position = 'bottom' ) + guides( linetype = guide_legend(override.aes = list(size=1.8)), colour = guide_legend( order = 1 ) )
lTbl1 = tmaxSub17ExcTbl %>% filter( cd == 4 & infxn == 1 ) %>% mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>% group_by( ptid ) %>% filter( sum( abs( resp ) ) > 0.02 ) %>% ungroup() lPropTbl1 = lTbl1 %>% group_by( ptid ) %>% mutate( resp = resp / sum( abs( resp ) ) ) %>% ungroup() wTbl1 = lPropTbl1 %>% filter( cd == 4 ) %>% spread( key = cytCombo, value = resp ) wInfoTbl1 = wTbl1 %>% select_if( is.character ) wNumTbl1 = wTbl1 %>% select_if( is.numeric )
set.seed(2) p1 = ggbootUV( data = lPropTbl1, resp = resp, xAxis = vaccine, col = vaccine, facet = cytCombo, pMethod = "percT", ciMethod = "bca", B = 2e3 * 5, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 2e2 * 2.5, calcStat = "mean", trim = 0.2, pointSize = 2, errBarSize = 1, yLab = "Scaled vaccine-induced memory\nCD4 T cell response", nullValue = 0, xLab = "Vaccine", errBarAlpha = 0.88, colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec, hLineFactor = 0.6, facetLabVec = shortCytComboLabVec, nCol = 4, facetScale = 'fixed', fontScaleFactor = 1, lineScaleFactor = 1, plotTblName = "fig5bTbl" ) + theme( legend.key.height = unit( 6 * scaleFactor, "mm" ), legend.key.width = unit( 6, "mm" ), strip.text = element_text( size = 8 * scaleFactor, colour = 'black' ), legend.title = element_blank(), axis.title = element_text( size = 8* scaleFactor ), legend.text.align = 0, axis.text = element_text( size = 8* scaleFactor ), strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white', colour = 'white' ), legend.background = element_rect( colour = 'gray80', size = 1, linetype = 'solid' ), legend.text = element_text( size = 8 * scaleFactor), legend.position = 'bottom' ) + guides( linetype = guide_legend(override.aes = list(size=1.8)), colour = guide_legend( order = 1 ) )
plot_grid( p0, p1, labels = c("A","B"), align = "v", ncol = 1, label_size = 12 * scaleFactor ) cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/Fig5.pdf", units = "cm", width = 13, height= 18, scale = 1 * scaleFactor)
S7 Fig - Axis predictivity
Summed
CD4
Uninfected
lTbl0 = tmaxSub17ExcTbl %>% filter( cd == 4 & infxn == 0 ) %>% mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>% group_by( ptid ) %>% filter( sum( abs( resp ) ) > 0.02 ) %>% ungroup() lPropTbl0 = lTbl0 %>% group_by( ptid ) %>% mutate( resp = resp / sum( abs(resp) ) ) %>% ungroup() %>% mutate( cytCombo = shortCytComboLabVec[cytCombo]) %>% select( -timePoint ) wTbl0 = lPropTbl0 %>% filter( cd == 4 ) %>% spread( key = cytCombo, value = resp ) wInfoTbl0 = wTbl0 %>% select_if( is.character ) wNumTbl0 = wTbl0 %>% select_if( is.numeric )
p0 = plotCumQual( wNumTbl0, centre = TRUE, scale = FALSE, legendTitle = "Cytokine\ncombination", legendLab = c( shortCytComboLabVec, mean = "Mean" ) ) + labs( y = "Cumulative quality", x = "Ordered principal component number" ) + theme( axis.title = element_text( size = 8 ), legend.text = element_text( size = 8 ), legend.text.align = 0, axis.text = element_text( size = 8), legend.title = element_text( size = 8 ) )
Infected
lTbl1 = tmaxSub17ExcTbl %>% filter( cd == 4 & infxn == 1) %>% mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>% group_by( ptid ) %>% filter( sum( abs( resp ) ) > 0.02 ) %>% ungroup() lPropTbl1 = lTbl1 %>% group_by( ptid ) %>% mutate( resp = resp / sum( abs( resp ) ) ) %>% ungroup() %>% mutate( cytCombo = shortCytComboLabVec[cytCombo]) %>% select( -timePoint ) wTbl1 = lPropTbl1 %>% spread( key = cytCombo, value = resp ) wInfoTbl1 = wTbl1 %>% select_if( is.character ) wNumTbl1 = wTbl1 %>% select_if( is.numeric )
p1 = plotCumQual( wNumTbl1, centre = TRUE, scale = FALSE, legendTitle = "Cytokine\ncombination", legendLab = c( shortCytComboLabVec, mean = "Mean" ) ) + labs( y = "Cumulative quality", x = "Ordered principal component number" ) + theme( axis.title = element_text( size = 8 ), legend.text = element_text( size = 8 ), axis.text = element_text( size = 8), legend.text.align = 0, legend.title = element_text( size = 8 ) )
plot_grid( p0, p1, labels = c("A","B"), align = "v", ncol = 1) cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/S7_Fig.pdf", units = "cm", width = 13, height= 14, scale = 1 )
S8 Fig - Vaccine-induced memory response profile, pair-wise comparisons
lTbl0 = tmaxSub17ExcTbl %>% filter( cd == 4 & infxn == 0 ) %>% mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>% group_by( ptid ) %>% filter( sum( abs( resp ) ) > 0.02 ) %>% ungroup() lPropTbl0 = lTbl0 %>% group_by( ptid ) %>% mutate( resp = resp / sum( abs( resp ) ) ) %>% ungroup()
set.seed(2) scaleFactor = 1 p0 = ggbootPW( lPropTbl0 %>% filter( cytCombo %in% c( "Gp2pTp", "Gn2pTp" ) ), resp = resp, group = vaccine, facet = cytCombo, groupLabVec = fullVaccLabelVec, facetLabVec = shortCytComboLabVec, facetScale = 'fixed', facetOrderVec = c( "1", "3", "4", "5", "6" ), plotTblName = "plotTbl", fdr = 0.05, B = 2e3 * 5, seB = 200 * 2.5, rotX = TRUE, axisLab = "Vaccine" ) + theme( strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white', colour = 'white' ), strip.text = element_text( size = 12 * scaleFactor ), legend.text = element_text( size = 8 * scaleFactor ), legend.position = 'bottom', legend.text.align = 0, axis.text.y = element_text( size = 8 * scaleFactor ), axis.text.x = element_text( size = 8 * scaleFactor, hjust = 1 ), axis.title = element_text( size = 9 * scaleFactor ), legend.title = element_blank(), legend.key.width = unit( 7, "mm" ) )
lTbl1= tmaxSub17ExcTbl %>% filter( cd == 4 & infxn == 1 ) %>% mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>% group_by( ptid ) %>% filter( sum( abs( resp ) ) > 0.02 ) %>% ungroup() lPropTbl1 = lTbl1 %>% group_by( ptid ) %>% mutate( resp = resp / sum( abs( resp ) ) ) %>% ungroup()
set.seed(2) scaleFactor = 1 p1 = ggbootPW( lPropTbl1 %>% filter( cytCombo %in% c( "Gp2pTp", "Gp2nTn" ) ), resp = resp, group = vaccine, facet = cytCombo, groupLabVec = fullVaccLabelVec, facetLabVec = shortCytComboLabVec, facetScale = 'fixed', facetOrderVec = c( "1", "3", "4", "5", "6" ), plotTblName = "plotTbl", fdr = 0.05, B = 2e3 * 5, seB = 200 * 2.5, rotX = TRUE, axisLab = "Vaccine" ) + theme( strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white', colour = 'white' ), strip.text = element_text( size = 12 * scaleFactor ), legend.text = element_text( size = 8 * scaleFactor ), legend.position = 'bottom', legend.text.align = 0, axis.text.y = element_text( size = 8 * scaleFactor ), axis.text.x = element_text( size = 8 * scaleFactor, hjust = 1 ), axis.title = element_text( size = 9 * scaleFactor ), legend.title = element_blank(), legend.key.width = unit( 7, "mm" ) )
plot_grid( p0, p1, ncol = 1, labels = c( "A", "B" ) ) cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/S8_Fig.pdf", units = "cm", width = 13, height= 18.8, scale = 1 )
S9 Fig - Effect of Mtb infection on vaccine-induced CD4 memory response profile, by vaccine
lTbl0 = tmaxSub17ExcTbl %>% filter( cd == 4 & infxn == 0 ) %>% mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>% group_by( ptid ) %>% filter( sum( abs( resp ) ) > 0.02 ) %>% ungroup() lPropTbl0 = lTbl0 %>% group_by( ptid ) %>% mutate( resp = resp / sum( abs( resp ) ) ) %>% ungroup() wTbl0 = lPropTbl0 %>% filter( cd == 4 ) %>% spread( key = cytCombo, value = resp ) wInfoTbl0 = wTbl0 %>% select_if( is.character ) wNumTbl0 = wTbl0 %>% select_if( is.numeric ) ### infected # long table lTbl1 = tmaxSub17ExcTbl %>% filter( cd == 4 & infxn == 1) %>% mutate( cytCombo = str_sub( cytCombo, 4 ) ) %>% group_by( ptid ) %>% filter( sum( abs( resp ) ) > 0.02 ) %>% ungroup() lPropTbl1 = lTbl1 %>% group_by( ptid ) %>% mutate( resp = resp / sum( abs( resp ) ) ) %>% ungroup() wTbl1 = lPropTbl1 %>% spread( key = cytCombo, value = resp ) wInfoTbl1 = wTbl1 %>% select_if( is.character ) wNumTbl1 = wTbl1 %>% select_if( is.numeric )
set.seed(2) scaleFactor = 2 ggbootUV( data = full_join( lPropTbl0, lPropTbl1 ) %>% filter( vaccine %in% 2:6 & cytCombo %in% c( "Gp2nTn", "Gn2pTp", "Gp2pTp", "Gn2pTn" ) ), resp = resp, xAxis = vaccine, col = vaccine, facet = cytCombo, diff = infxn, pMethod = "percT", ciMethod = "bca", B = 2e3 * 5, altSide = 'both', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 200 * 2.5, calcStat = "mean", trim = 0.2, rotXText = TRUE, pointSize = 3, errBarSize = 1.75, yLab = "Change in scaled vaccine-induced CD4 T cell\nresponse (%) (infected vs uninfected)", nullValue = 0, xLab = "Vaccine", xAxisLabVec = vaccStim2StimLabVec, errBarAlpha = 0.88, colLabName = "", colourVec = vaccColVec, colLabVec = fullVaccLabelVec, facetLabVec = shortCytComboLabVec, nCol = 4, facetScale = 'free_x', fontScaleFactor = 2, lineScaleFactor = 1.5, plotTblName = "figs9Tbl" ) + theme( legend.key.height = unit( 6, "mm" ), legend.key.width = unit( 9, "mm" ), strip.text = element_text( size = 8 * scaleFactor, colour = 'black' ), legend.text = element_text( size = 8 * scaleFactor ), axis.text = element_text( size = 8 * scaleFactor ), legend.title = element_blank(), legend.text.align = 0, axis.title = element_text( size = 8 * scaleFactor ), strip.background = element_rect(linetype = 'solid', size = 1, fill = 'white', colour = 'white' ) ) + guides( linetype = guide_legend(override.aes = list(size=1.8)), colour = guide_legend( order = 1 ) ) cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/S9_Fig.pdf", units = "cm", width = 13, height= 10, scale = scaleFactor )
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