In MiguelRodo/VaccComp: Figures for vaccine comparison paper
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 = FALSE,
comment = "#>",
cache = TRUE,
echo = TRUE,
message = FALSE,
warning = FALSE,
tidy = FALSE
)
save = TRUE
getSavePath = function(x) str_c( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/", x, ".pdf" )
Minor comments
2.2 - Age effect
Size
ageTbl <- tmax17ExcTbl %>%
filter( vaccine %in% c( 2, 3, 5 ) ) %>%
group_by( vaccine, prid, infxn, ptid ) %>%
summarise( resp = sum( resp ) ) %>%
ungroup()
ageTbl %>%
group_by( vaccine, prid, age ) %>%
slice(1) %>%
ungroup()
ageTbl %<>%
mutate( age = -1 ) %>%
mutate( age = Vectorize(ifelse)( vaccine == 2,
"1", age ),
age = Vectorize(ifelse)( vaccine == 5,
"0", age ),
age = Vectorize(ifelse)( prid == 3,
"1", age ),
age = Vectorize(ifelse)( prid == 4,
"0", age ) ) # add age group
ageTbl %<>%
mutate( comp = -1 ) %>%
mutate( comp = Vectorize(ifelse)( vaccine == 3, "3", comp ),
comp = Vectorize(ifelse)( vaccine == 2, "h", comp ),
comp = Vectorize(ifelse)( vaccine == 5, "h", comp ), )
age22LabVec <- c( "1" = "Adults", "0" = "Adolescents")
age22ColVec <- c( "1" = "orange", "0" = "dodgerblue" )
comp22ColVec <- c( "3" = "orange", "h" = "dodgerblue" )
comp22LabVec <- c( "3" = "M72/AS01E", "h" = "H(56/1):IC31")
# plot
set.seed(2)
scaleFactor = 0.56^-1
legendTextSize = 8 * scaleFactor
stripTextSize = 8 * scaleFactor
axisTitleTextSize = 8 * scaleFactor
fontScaleFactor = 0.65 * scaleFactor
startTime <- proc.time()[3]
ggbootUV( data = ageTbl,
resp = resp, xAxis = comp, col = comp, facet = infxn, diff = age, pMethod = "percT", ciMethod = "bca",
B = 5e3, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 5e2, calcStat = "mean", trim = 0.2,
rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Increase in vaccine-induced memory\nCD4 T cell response (%) for adults over adolescents",
nullValue = 0, xLab = "Vaccine", xAxisLabVec = comp22LabVec, errBarAlpha = 0.88,
colLabName = "", colourVec = comp22ColVec, colLabVec = comp22LabVec,
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 ) )
proc.time()[3] - startTime
cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Revisions/R/size-uv-age-diff-2.pdf", units = "cm", width = 16, height= 13, scale = 1 )
3.4
Stats
Pre-vaccination
Prepare the data to calculate the statistics for the pre-vaccination responses.
# 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()
Use ggboot::ggbootUV to calculate the point estimates, confidence intervals and statistical significance, and save as table fig34PVTbl.
# 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 = 5e3, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = FALSE, seB = 5e2, 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 = "fig34PVTbl" ) +
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 ) )
Vaccine-induced memory
Prepare the data to calculate the statistics for the vaccine-induced memory responses.
# 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()
Use ggboot::ggbootUV to calculate the point estimates, confidence intervals and statistical significance, and save as table fig34VIMTbl.
# 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 = 5e3, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = FALSE, seB = 5e2, 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 = "fig34VIMTbl" ) +
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 ) )
Plots
Data
Prepare fig34PVTbl and fig34VIMTbl for plotting. Result saved as fig34Tbl.
fig34Tbl <- bind_rows( fig34PVTbl %>%
mutate( timePoint = 0 ),
fig34VIMTbl %>%
mutate( timePoint = 1 ) ) %>% # set timePoint variable to distinguish pre-vaccination from vaccine-induced memory response
mutate( vaccine = str_sub( split, start = -3, end = -3 ),
infxn = str_sub( split, start = -1, end = -1 ) ) # get vaccine and infxn status from split variable
Ag85
Plot the point estimates, confidence intervals and statistical significance of the pre-vaccination and vaccine-induced memory responses for the vaccines containing Ag85A/B, using fig34PVTbl and fig34VIMTbl obtained above.
scaleFactor <- 0.56^-1
p0 <- ggplot( fig34Tbl %>%
filter( timePoint == "0" &
str_detect( split, "ag85" ) ), aes( x = vaccine, col = vaccine ) ) +
geom_hline( yintercept = 0, linetype = 'dashed', 0.6 ) +
geom_point( aes( y = origStat ), size = 2 ) +
geom_errorbar( aes( ymin = lb, ymax = ub, linetype = sig ), size = 1, alpha = 0.88 ) +
facet_wrap( ~infxn, ncol = 2, labeller = labeller( infxn = infLabelVec ) ) +
theme( axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
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 ),
strip.background = element_rect( linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ),
legend.text = element_text( size = 8 * scaleFactor) ) +
labs( x = "Vaccine", y = "Pre-vaccination\nCD4 T cell response (%)" ) +
scale_colour_manual( labels = fullVaccLabelVec,
values = vaccColVec ) +
scale_linetype_manual( labels = c( "0" = "Not significant", "1" = "Significant" ),
values = c( "0" = "dotted", '1' = 'solid' ),
drop = FALSE,
limits = c( "0", "1" ) )
p1 <- ggplot( fig34Tbl %>%
filter( timePoint == "1" &
str_detect( split, "ag85" ) ), aes( x = vaccine, col = vaccine ) ) +
geom_hline( yintercept = 0, linetype = 'dashed', 0.6 ) +
geom_point( aes( y = origStat ), size = 2 ) +
geom_errorbar( aes( ymin = lb, ymax = ub, linetype = sig ), size = 1, alpha = 0.88 ) +
facet_wrap( ~infxn, ncol = 2, labeller = labeller( infxn = infLabelVec ) ) +
theme( axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
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 ),
strip.background = element_rect( linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ),
legend.text = element_text( size = 8 * scaleFactor) ) +
labs( x = "Vaccine", y = "Vaccine-induced memory\nCD4 T cell response (%)" ) +
scale_colour_manual( labels = fullVaccLabelVec,
values = vaccColVec ) +
scale_linetype_manual( labels = c( "0" = "Not significant", "1" = "Significant" ),
values = c( "0" = "dotted", '1' = 'solid' ),
drop = FALSE,
limits = c( "0", "1" ) )
p2 <- plot_grid( p0, p1, ncol = 1, labels = c( "A", "B" ) )
Save the above plot as S10_Fig.pdf into Dropbox/Rodo et al Vaccine Immune Response Paper/Figures/Final/R.
cowplot::ggsave( getSavePath( "S10_Fig" ), units = "cm", width = 13, height= 10, scale = 1.85, dpi = 300 )
ESAT-6
Plot the point estimates, confidence intervals and statistical significance of the pre-vaccination and vaccine-induced memory responses for the vaccines containing Ag85A/B, using fig34PVTbl and fig34VIMTbl obtained above.
scaleFactor <- 0.56^-1
p0 <- ggplot( fig34Tbl %>%
filter( timePoint == "0" &
str_detect( split, "esat6" ) ), aes( x = vaccine, col = vaccine ) ) +
geom_hline( yintercept = 0, linetype = 'dashed', 0.6 ) +
geom_point( aes( y = origStat ), size = 2 ) +
geom_errorbar( aes( ymin = lb, ymax = ub, linetype = sig ), size = 1, alpha = 0.88 ) +
facet_wrap( ~infxn, ncol = 2, labeller = labeller( infxn = infLabelVec ) ) +
theme( axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
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 ),
strip.background = element_rect( linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ),
legend.text = element_text( size = 8 * scaleFactor) ) +
labs( x = "Vaccine", y = "Pre-vaccination\nCD4 T cell response (%)" ) +
scale_colour_manual( labels = fullVaccLabelVec,
values = vaccColVec ) +
scale_linetype_manual( labels = c( "0" = "Not significant", "1" = "Significant" ),
values = c( "0" = "dotted", '1' = 'solid' ),
drop = FALSE,
limits = c( "0", "1" ) )
p1 <- ggplot( fig34Tbl %>%
filter( timePoint == "1" &
str_detect( split, "esat6" ) ), aes( x = vaccine, col = vaccine ) ) +
geom_hline( yintercept = 0, linetype = 'dashed', 0.6 ) +
geom_point( aes( y = origStat ), size = 2 ) +
geom_errorbar( aes( ymin = lb, ymax = ub, linetype = sig ), size = 1, alpha = 0.88 ) +
facet_wrap( ~infxn, ncol = 2, labeller = labeller( infxn = infLabelVec ) ) +
theme( axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
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 ),
strip.background = element_rect( linetype = 'solid', size = 1, fill = 'white',
colour = 'white' ),
legend.text = element_text( size = 8 * scaleFactor) ) +
labs( x = "Vaccine", y = "Vaccine-induced memory\nCD4 T cell response (%)" ) +
scale_colour_manual( labels = fullVaccLabelVec,
values = vaccColVec ) +
scale_linetype_manual( labels = c( "0" = "Not significant", "1" = "Significant" ),
values = c( "0" = "dotted", '1' = 'solid' ),
drop = FALSE,
limits = c( "0", "1" ) )
p2 <- plot_grid( p0, p1, ncol = 1, labels = c( "A", "B" ) )
Save the above plot as S11_Fig.pdf into Dropbox/Rodo et al Vaccine Immune Response Paper/Figures/Final/R.
cowplot::ggsave( getSavePath( "S11_Fig" ), units = "cm", width = 13, height= 10, scale = 1.85, dpi = 300 )
MiguelRodo/VaccComp documentation built on Nov. 9, 2023, 10:16 a.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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 = FALSE, comment = "#>", cache = TRUE, echo = TRUE, message = FALSE, warning = FALSE, tidy = FALSE )
save = TRUE getSavePath = function(x) str_c( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Final/R/", x, ".pdf" )
Minor comments
2.2 - Age effect
Size
ageTbl <- tmax17ExcTbl %>% filter( vaccine %in% c( 2, 3, 5 ) ) %>% group_by( vaccine, prid, infxn, ptid ) %>% summarise( resp = sum( resp ) ) %>% ungroup() ageTbl %>% group_by( vaccine, prid, age ) %>% slice(1) %>% ungroup() ageTbl %<>% mutate( age = -1 ) %>% mutate( age = Vectorize(ifelse)( vaccine == 2, "1", age ), age = Vectorize(ifelse)( vaccine == 5, "0", age ), age = Vectorize(ifelse)( prid == 3, "1", age ), age = Vectorize(ifelse)( prid == 4, "0", age ) ) # add age group ageTbl %<>% mutate( comp = -1 ) %>% mutate( comp = Vectorize(ifelse)( vaccine == 3, "3", comp ), comp = Vectorize(ifelse)( vaccine == 2, "h", comp ), comp = Vectorize(ifelse)( vaccine == 5, "h", comp ), ) age22LabVec <- c( "1" = "Adults", "0" = "Adolescents") age22ColVec <- c( "1" = "orange", "0" = "dodgerblue" ) comp22ColVec <- c( "3" = "orange", "h" = "dodgerblue" ) comp22LabVec <- c( "3" = "M72/AS01E", "h" = "H(56/1):IC31")
# plot set.seed(2) scaleFactor = 0.56^-1 legendTextSize = 8 * scaleFactor stripTextSize = 8 * scaleFactor axisTitleTextSize = 8 * scaleFactor fontScaleFactor = 0.65 * scaleFactor startTime <- proc.time()[3] ggbootUV( data = ageTbl, resp = resp, xAxis = comp, col = comp, facet = infxn, diff = age, pMethod = "percT", ciMethod = "bca", B = 5e3, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = TRUE, seB = 5e2, calcStat = "mean", trim = 0.2, rotXText = TRUE, pointSize = 3, errBarSize = 1.5, yLab = "Increase in vaccine-induced memory\nCD4 T cell response (%) for adults over adolescents", nullValue = 0, xLab = "Vaccine", xAxisLabVec = comp22LabVec, errBarAlpha = 0.88, colLabName = "", colourVec = comp22ColVec, colLabVec = comp22LabVec, 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 ) ) proc.time()[3] - startTime cowplot::ggsave( "C:/Users/migue/Dropbox/Rodo et al Vaccine Immune Response paper/Figures/Revisions/R/size-uv-age-diff-2.pdf", units = "cm", width = 16, height= 13, scale = 1 )
3.4
Stats
Pre-vaccination
Prepare the data to calculate the statistics for the pre-vaccination responses.
# 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()
Use ggboot::ggbootUV to calculate the point estimates, confidence intervals and statistical significance, and save as table fig34PVTbl.
# 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 = 5e3, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = FALSE, seB = 5e2, 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 = "fig34PVTbl" ) + 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 ) )
Vaccine-induced memory
Prepare the data to calculate the statistics for the vaccine-induced memory responses.
# 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()
Use ggboot::ggbootUV to calculate the point estimates, confidence intervals and statistical significance, and save as table fig34VIMTbl.
# 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 = 5e3, altSide = 'high', eqErrBarWidth = TRUE, errBarLineType = "11", remXAxisMarks = FALSE, seB = 5e2, 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 = "fig34VIMTbl" ) + 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 ) )
Plots
Data
Prepare fig34PVTbl and fig34VIMTbl for plotting. Result saved as fig34Tbl.
fig34Tbl <- bind_rows( fig34PVTbl %>% mutate( timePoint = 0 ), fig34VIMTbl %>% mutate( timePoint = 1 ) ) %>% # set timePoint variable to distinguish pre-vaccination from vaccine-induced memory response mutate( vaccine = str_sub( split, start = -3, end = -3 ), infxn = str_sub( split, start = -1, end = -1 ) ) # get vaccine and infxn status from split variable
Ag85
Plot the point estimates, confidence intervals and statistical significance of the pre-vaccination and vaccine-induced memory responses for the vaccines containing Ag85A/B, using fig34PVTbl and fig34VIMTbl obtained above.
scaleFactor <- 0.56^-1 p0 <- ggplot( fig34Tbl %>% filter( timePoint == "0" & str_detect( split, "ag85" ) ), aes( x = vaccine, col = vaccine ) ) + geom_hline( yintercept = 0, linetype = 'dashed', 0.6 ) + geom_point( aes( y = origStat ), size = 2 ) + geom_errorbar( aes( ymin = lb, ymax = ub, linetype = sig ), size = 1, alpha = 0.88 ) + facet_wrap( ~infxn, ncol = 2, labeller = labeller( infxn = infLabelVec ) ) + theme( axis.text.x = element_blank(), axis.ticks.x = element_blank(), 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 ), strip.background = element_rect( linetype = 'solid', size = 1, fill = 'white', colour = 'white' ), legend.text = element_text( size = 8 * scaleFactor) ) + labs( x = "Vaccine", y = "Pre-vaccination\nCD4 T cell response (%)" ) + scale_colour_manual( labels = fullVaccLabelVec, values = vaccColVec ) + scale_linetype_manual( labels = c( "0" = "Not significant", "1" = "Significant" ), values = c( "0" = "dotted", '1' = 'solid' ), drop = FALSE, limits = c( "0", "1" ) ) p1 <- ggplot( fig34Tbl %>% filter( timePoint == "1" & str_detect( split, "ag85" ) ), aes( x = vaccine, col = vaccine ) ) + geom_hline( yintercept = 0, linetype = 'dashed', 0.6 ) + geom_point( aes( y = origStat ), size = 2 ) + geom_errorbar( aes( ymin = lb, ymax = ub, linetype = sig ), size = 1, alpha = 0.88 ) + facet_wrap( ~infxn, ncol = 2, labeller = labeller( infxn = infLabelVec ) ) + theme( axis.text.x = element_blank(), axis.ticks.x = element_blank(), 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 ), strip.background = element_rect( linetype = 'solid', size = 1, fill = 'white', colour = 'white' ), legend.text = element_text( size = 8 * scaleFactor) ) + labs( x = "Vaccine", y = "Vaccine-induced memory\nCD4 T cell response (%)" ) + scale_colour_manual( labels = fullVaccLabelVec, values = vaccColVec ) + scale_linetype_manual( labels = c( "0" = "Not significant", "1" = "Significant" ), values = c( "0" = "dotted", '1' = 'solid' ), drop = FALSE, limits = c( "0", "1" ) ) p2 <- plot_grid( p0, p1, ncol = 1, labels = c( "A", "B" ) )
Save the above plot as S10_Fig.pdf into Dropbox/Rodo et al Vaccine Immune Response Paper/Figures/Final/R.
cowplot::ggsave( getSavePath( "S10_Fig" ), units = "cm", width = 13, height= 10, scale = 1.85, dpi = 300 )
ESAT-6
Plot the point estimates, confidence intervals and statistical significance of the pre-vaccination and vaccine-induced memory responses for the vaccines containing Ag85A/B, using fig34PVTbl and fig34VIMTbl obtained above.
scaleFactor <- 0.56^-1 p0 <- ggplot( fig34Tbl %>% filter( timePoint == "0" & str_detect( split, "esat6" ) ), aes( x = vaccine, col = vaccine ) ) + geom_hline( yintercept = 0, linetype = 'dashed', 0.6 ) + geom_point( aes( y = origStat ), size = 2 ) + geom_errorbar( aes( ymin = lb, ymax = ub, linetype = sig ), size = 1, alpha = 0.88 ) + facet_wrap( ~infxn, ncol = 2, labeller = labeller( infxn = infLabelVec ) ) + theme( axis.text.x = element_blank(), axis.ticks.x = element_blank(), 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 ), strip.background = element_rect( linetype = 'solid', size = 1, fill = 'white', colour = 'white' ), legend.text = element_text( size = 8 * scaleFactor) ) + labs( x = "Vaccine", y = "Pre-vaccination\nCD4 T cell response (%)" ) + scale_colour_manual( labels = fullVaccLabelVec, values = vaccColVec ) + scale_linetype_manual( labels = c( "0" = "Not significant", "1" = "Significant" ), values = c( "0" = "dotted", '1' = 'solid' ), drop = FALSE, limits = c( "0", "1" ) ) p1 <- ggplot( fig34Tbl %>% filter( timePoint == "1" & str_detect( split, "esat6" ) ), aes( x = vaccine, col = vaccine ) ) + geom_hline( yintercept = 0, linetype = 'dashed', 0.6 ) + geom_point( aes( y = origStat ), size = 2 ) + geom_errorbar( aes( ymin = lb, ymax = ub, linetype = sig ), size = 1, alpha = 0.88 ) + facet_wrap( ~infxn, ncol = 2, labeller = labeller( infxn = infLabelVec ) ) + theme( axis.text.x = element_blank(), axis.ticks.x = element_blank(), 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 ), strip.background = element_rect( linetype = 'solid', size = 1, fill = 'white', colour = 'white' ), legend.text = element_text( size = 8 * scaleFactor) ) + labs( x = "Vaccine", y = "Vaccine-induced memory\nCD4 T cell response (%)" ) + scale_colour_manual( labels = fullVaccLabelVec, values = vaccColVec ) + scale_linetype_manual( labels = c( "0" = "Not significant", "1" = "Significant" ), values = c( "0" = "dotted", '1' = 'solid' ), drop = FALSE, limits = c( "0", "1" ) ) p2 <- plot_grid( p0, p1, ncol = 1, labels = c( "A", "B" ) )
Save the above plot as S11_Fig.pdf into Dropbox/Rodo et al Vaccine Immune Response Paper/Figures/Final/R.
cowplot::ggsave( getSavePath( "S11_Fig" ), units = "cm", width = 13, height= 10, scale = 1.85, dpi = 300 )
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