#WHERE THE INDIVIDUAL SPECTRA ARE IN COLUMNS, STACK THE DATA
df2 <- melt(df1, id=c("Well_ID", "SampleName", "SampleType", "TechRep"))
names(df2)[names(df2) == "variable"] <- "Wavelength"
# Convert absorbance values to numeric, force NaN on the 'OVER' readings
df2$value <- as.numeric(as.character(df2$value))
#EXTRACT WATER CONTROL AND COMPUTE THE AVERAGE OF TECHREPS
df3_water <- df2 %>%
filter(SampleName == "Water Control")
df4_water <- df3_water %>%
group_by(SampleName, SampleType, Wavelength) %>%
summarize_at(vars(value), list(MeanAbs = mean, SdAbs = sd))
#CALCULATE THE WATER (BLANK) CORRECTED ABSORBANCE VALUES
df3 <- inner_join(df2, df4_water, by="Wavelength") %>%
mutate(BC_value = value - MeanAbs) %>%
dplyr::select(Well_ID, SampleName.x, SampleType.x, TechRep, Wavelength, value, BC_value)
names(df3)[names(df3) == "SampleName.x"] <- "SampleName"
names(df3)[names(df3) == "SampleType.x"] <- "SampleType"
#COMPUTE THE AVERAGE OF TECHREPS
df4 <- df3 %>%
group_by(SampleName, SampleType, Wavelength) %>%
summarize_at(vars(BC_value), list(MeanAbs = mean, SdAbs = sd))
df4$Wavelength <- as.numeric(as.character(df4$Wavelength))
name_df <- read_excel('Exp 20250702 - Fluorescence of Caramel Pt2.xlsx','Sample Names')
#RENAME FIRST COLUMN
names(df)[1]<-"Well_ID"
#MERGE NAMES_DF WITH MAIN DATAFRAME
df1 <- cbind(name_df, df[!names(df) %in% names(name_df)])
#WHERE THE INDIVIDUAL SPECTRA ARE IN COLUMNS, STACK THE DATA
df2 <- melt(df1, id=c("Well_ID", "SampleName", "SampleType", "TechRep"))
names(df2)[names(df2) == "variable"] <- "Wavelength"
# Convert absorbance values to numeric, force NaN on the 'OVER' readings
df2$value <- as.numeric(as.character(df2$value))
#EXTRACT WATER CONTROL AND COMPUTE THE AVERAGE OF TECHREPS
df3_water <- df2 %>%
filter(SampleName == "Water Control")
df4_water <- df3_water %>%
group_by(SampleName, SampleType, Wavelength) %>%
summarize_at(vars(value), list(MeanAbs = mean, SdAbs = sd))
#CALCULATE THE WATER (BLANK) CORRECTED ABSORBANCE VALUES
df3 <- inner_join(df2, df4_water, by="Wavelength") %>%
mutate(BC_value = value - MeanAbs) %>%
dplyr::select(Well_ID, SampleName.x, SampleType.x, TechRep, Wavelength, value, BC_value)
View(df2)
View(df4)
View(df4_water)
View(df3_water)
#OPEN FILES
df <- read_excel('Exp 20250702 - Fluorescence of Caramel Pt2.xlsx','Plate 1 Transposed')
name_df <- read_excel('Exp 20250702 - Fluorescence of Caramel Pt2.xlsx','Sample Names')
#RENAME FIRST COLUMN
names(df)[1]<-"Well_ID"
#MERGE NAMES_DF WITH MAIN DATAFRAME
df1 <- cbind(name_df, df[!names(df) %in% names(name_df)])
#WHERE THE INDIVIDUAL SPECTRA ARE IN COLUMNS, STACK THE DATA
df2 <- melt(df1, id=c("Well_ID", "SampleName", "SampleType", "TechRep"))
names(df2)[names(df2) == "variable"] <- "Wavelength"
# Convert absorbance values to numeric, force NaN on the 'OVER' readings
df2$value <- as.numeric(as.character(df2$value))
#EXTRACT WATER CONTROL AND COMPUTE THE AVERAGE OF TECHREPS
df3_water <- df2 %>%
filter(SampleName == "Water Control")
df4_water <- df3_water %>%
group_by(SampleName, SampleType, Wavelength) %>%
summarize_at(vars(value), list(MeanAbs = mean, SdAbs = sd))
#CALCULATE THE WATER (BLANK) CORRECTED ABSORBANCE VALUES
df3 <- inner_join(df2, df4_water, by="Wavelength") %>%
mutate(BC_value = value - MeanAbs) %>%
dplyr::select(Well_ID, SampleName.x, SampleType.x, TechRep, Wavelength, value, BC_value)
names(df3)[names(df3) == "SampleName.x"] <- "SampleName"
names(df3)[names(df3) == "SampleType.x"] <- "SampleType"
#COMPUTE THE AVERAGE OF TECHREPS
df4 <- df3 %>%
group_by(SampleName, SampleType, Wavelength) %>%
summarize_at(vars(BC_value), list(MeanAbs = mean, SdAbs = sd))
df4$Wavelength <- as.numeric(as.character(df4$Wavelength))
#SEPARATE THE SAMPLES
df5.a <- df4 %>%
filter(SampleName %in% c("E150A", "Neg Ctrl"))
df5.d <- df4 %>%
filter(SampleName %in% c("E150D", "Neg Ctrl"))
#PLOT DATA - CARAMEL SAMPLES
ggplot(df5.a, aes(x=Wavelength, y=MeanAbs, group=as.factor(SampleType))) +
geom_line(aes(color=as.factor(SampleType))) +
geom_ribbon(aes(ymin=MeanAbs-SdAbs, ymax=MeanAbs+SdAbs, fill=as.factor(SampleType)), alpha=0.3, show.legend=FALSE) +
ggtitle("0.2% (v/v) E150A") +
xlab("Wavelength (nm)") +
ylab(expression("Absorbance (a.u.)")) +
theme_classic() +
theme(aspect.ratio=1,
title =element_text(size=14),
axis.title=element_text(size=14),
axis.text=element_text(colour = 1, size = 14),
legend.title=element_blank(),
legend.justification=c(0,1),
legend.position.inside=c(1.05,1),
legend.text=element_text(size = 14),
panel.border = element_rect(colour = "black", fill=NA)) +
guides(color=guide_legend(byrow=TRUE, reverse=TRUE))
#PRINT/SAVE IMAGES, CHANGE FILE NAMES AS APPROPRIATE
ggsave("250702 - e150a.jpg", height=10, width=20, units="cm")
ggplot(df5.d, aes(x=Wavelength, y=MeanAbs, group=as.factor(SampleType))) +
geom_line(aes(color=as.factor(SampleType))) +
geom_ribbon(aes(ymin=MeanAbs-SdAbs, ymax=MeanAbs+SdAbs, fill=as.factor(SampleType)), alpha=0.3, show.legend=FALSE) +
ggtitle("0.2% (v/v) E150D") +
xlab("Wavelength (nm)") +
ylab(expression("Absorbance (a.u.)")) +
theme_classic() +
theme(aspect.ratio=1,
title =element_text(size=14),
axis.title=element_text(size=14),
axis.text=element_text(colour = 1, size = 14),
legend.title=element_blank(),
legend.justification=c(0,1),
legend.position.inside=c(1.05,1),
legend.text=element_text(size = 14),
panel.border = element_rect(colour = "black", fill=NA)) +
guides(color=guide_legend(byrow=TRUE, reverse=TRUE))
#PRINT/SAVE IMAGES, CHANGE FILE NAMES AS APPROPRIATE
ggsave("250702 - e150d.jpg", height=10, width=20, units="cm")
View(name_df)
#EXTRACT THE NEGATIVE CONTROL (0 uM Im) AVERAGE
df6_neg <- df5 %>%
filter(SampleName == "Neg Ctrl")
#EXTRACT THE NEGATIVE CONTROL (0 uM Im) AVERAGE
df6_neg <- df5.a %>%
filter(SampleName == "Neg Ctrl")
df6_neg$Wavelength <- as.factor(df6_neg$Wavelength)
#CALCULATE THE NEGATIVE CONTROL (0 uM Im) SUBTRACTED FROM CARAMEL
df3.1 <- df3 %>%
filter(SampleName == "E150A" & SampleType %in% c("AMTA SDR", "AMTA SAR"))
df3.1.2 <- inner_join(df3.1, df6_neg, by=c("Wavelength")) %>%
mutate(NC_value = BC_value - MeanAbs) %>%
dplyr::select(Well_ID, SampleName.x, Conc.x, TechRep, Wavelength, NC_value)
df3.1.2 <- inner_join(df3.1, df6_neg, by=c("Wavelength")) %>%
mutate(NC_value = BC_value - MeanAbs) %>%
dplyr::select(Well_ID, SampleName.x, SampleType.x, TechRep, Wavelength, NC_value)
names(df3.1.2)[names(df3.1.2) == "SampleName.x"] <- "SampleName"
names(df3.1.2)[names(df3.1.2) == "SampleType.x"] <- "SampleType"
df3.2 <- df3 %>%
filter(SampleName == "E150D" & SampleType %in% c("AMTA SDR", "AMTA SAR"))
df3.2.2 <- inner_join(df3.2, df6_neg, by=c("Wavelength")) %>%
mutate(NC_value = BC_value - MeanAbs) %>%
dplyr::select(Well_ID, SampleName.x, SampleType.x, TechRep, Wavelength, NC_value)
names(df3.2.2)[names(df3.2.2) == "SampleName.x"] <- "SampleName"
names(df3.2.2)[names(df3.2.2) == "SampleType.x"] <- "SampleType"
#COMPUTE THE AVERAGE OF TECHREPS
df4.1.2 <- df3.1.2 %>%
group_by(SampleName, SampleType, Wavelength) %>%
summarize_at(vars(NC_value), list(MeanAbs = mean, SdAbs = sd))
df4.2.2 <- df3.2.2 %>%
group_by(SampleName, SampleType, Wavelength) %>%
summarize_at(vars(NC_value), list(MeanAbs = mean, SdAbs = sd))
df4.2.2$Wavelength <- as.numeric(as.character(df4.2.2$Wavelength))
#PLOT DATA - CARAMEL IN AMTA
ggplot(df4.1.2, aes(x=Wavelength, y=MeanAbs, group=as.factor(Conc))) +
geom_line(aes(color=as.factor(Conc))) +
geom_ribbon(aes(ymin=MeanAbs-SdAbs, ymax=MeanAbs+SdAbs, fill=as.factor(Conc)), alpha=0.3, show.legend=FALSE) +
ggtitle("10 mM AMTA with Imidazole") +
xlab("Wavelength (nm)") +
ylab(expression("Reagent Background-Corr. Abs. (a.u.)")) +
theme_classic() +
#scale_x_continuous(limits = c(300,600)) +
#scale_y_continuous(limits = c(-0.15, 0.5)) +
theme(aspect.ratio=1,
title =element_text(size=14),
axis.title=element_text(size=14),
axis.text=element_text(colour = 1, size = 14),
legend.title=element_blank(),
legend.justification=c(0,1),
legend.position=c(1.05,1),
legend.text=element_text(size = 14),
panel.border = element_rect(colour = "black", fill=NA)) +
scale_color_discrete(labels=c("5 \U003BCM","10 \U003BCM","25 \U003BCM", "50 \U003BCM", "75 \U003BCM", "100 \U003BCM")) +
guides(color=guide_legend(byrow=TRUE, reverse=TRUE)) +
geom_vline(xintercept=492, linetype="dashed")
#PLOT DATA - CARAMEL IN AMTA
ggplot(df4.1.2, aes(x=Wavelength, y=MeanAbs, group=as.factor(SampleType))) +
geom_line(aes(color=as.factor(SampleType))) +
geom_ribbon(aes(ymin=MeanAbs-SdAbs, ymax=MeanAbs+SdAbs, fill=as.factor(SampleType)), alpha=0.3, show.legend=FALSE) +
ggtitle("10 mM AMTA with Imidazole") +
xlab("Wavelength (nm)") +
ylab(expression("Reagent Background-Corr. Abs. (a.u.)")) +
theme_classic() +
#scale_x_continuous(limits = c(300,600)) +
#scale_y_continuous(limits = c(-0.15, 0.5)) +
theme(aspect.ratio=1,
title =element_text(size=14),
axis.title=element_text(size=14),
axis.text=element_text(colour = 1, size = 14),
legend.title=element_blank(),
legend.justification=c(0,1),
legend.position=c(1.05,1),
legend.text=element_text(size = 14),
panel.border = element_rect(colour = "black", fill=NA)) +
scale_color_discrete(labels=c("5 \U003BCM","10 \U003BCM","25 \U003BCM", "50 \U003BCM", "75 \U003BCM", "100 \U003BCM")) +
guides(color=guide_legend(byrow=TRUE, reverse=TRUE)) +
geom_vline(xintercept=492, linetype="dashed")
df4.1.2$Wavelength <- as.numeric(as.character(df4.1.2$Wavelength))
#PLOT DATA - CARAMEL IN AMTA
ggplot(df4.1.2, aes(x=Wavelength, y=MeanAbs, group=as.factor(SampleType))) +
geom_line(aes(color=as.factor(SampleType))) +
geom_ribbon(aes(ymin=MeanAbs-SdAbs, ymax=MeanAbs+SdAbs, fill=as.factor(SampleType)), alpha=0.3, show.legend=FALSE) +
ggtitle("10 mM AMTA with Imidazole") +
xlab("Wavelength (nm)") +
ylab(expression("Reagent Background-Corr. Abs. (a.u.)")) +
theme_classic() +
#scale_x_continuous(limits = c(300,600)) +
#scale_y_continuous(limits = c(-0.15, 0.5)) +
theme(aspect.ratio=1,
title =element_text(size=14),
axis.title=element_text(size=14),
axis.text=element_text(colour = 1, size = 14),
legend.title=element_blank(),
legend.justification=c(0,1),
legend.position=c(1.05,1),
legend.text=element_text(size = 14),
panel.border = element_rect(colour = "black", fill=NA)) +
scale_color_discrete(labels=c("5 \U003BCM","10 \U003BCM","25 \U003BCM", "50 \U003BCM", "75 \U003BCM", "100 \U003BCM")) +
guides(color=guide_legend(byrow=TRUE, reverse=TRUE)) +
geom_vline(xintercept=492, linetype="dashed")
#PLOT DATA - CARAMEL IN AMTA
ggplot(df4.1.2, aes(x=Wavelength, y=MeanAbs, group=as.factor(SampleType))) +
geom_line(aes(color=as.factor(SampleType))) +
geom_ribbon(aes(ymin=MeanAbs-SdAbs, ymax=MeanAbs+SdAbs, fill=as.factor(SampleType)), alpha=0.3, show.legend=FALSE) +
ggtitle("10 mM AMTA with Imidazole") +
xlab("Wavelength (nm)") +
ylab(expression("Reagent Background-Corr. Abs. (a.u.)")) +
theme_classic() +
#scale_x_continuous(limits = c(300,600)) +
#scale_y_continuous(limits = c(-0.15, 0.5)) +
theme(aspect.ratio=1,
title =element_text(size=14),
axis.title=element_text(size=14),
axis.text=element_text(colour = 1, size = 14),
legend.title=element_blank(),
legend.justification=c(0,1),
legend.position=c(1.05,1),
legend.text=element_text(size = 14),
panel.border = element_rect(colour = "black", fill=NA)) +
guides(color=guide_legend(byrow=TRUE, reverse=TRUE)) +
geom_vline(xintercept=492, linetype="dashed")
#PLOT DATA - CARAMEL IN AMTA, REAGENT BACKGROUND CORRECTED
ggplot(df4.1.2, aes(x=Wavelength, y=MeanAbs, group=as.factor(SampleType))) +
geom_line(aes(color=as.factor(SampleType))) +
geom_ribbon(aes(ymin=MeanAbs-SdAbs, ymax=MeanAbs+SdAbs, fill=as.factor(SampleType)), alpha=0.3, show.legend=FALSE) +
ggtitle("10 mM AMTA with 0.2% (v/V) E150A") +
xlab("Wavelength (nm)") +
ylab(expression("Reagent Background-Corr. Abs. (a.u.)")) +
theme_classic() +
#scale_x_continuous(limits = c(300,600)) +
#scale_y_continuous(limits = c(-0.15, 0.5)) +
theme(aspect.ratio=1,
title =element_text(size=14),
axis.title=element_text(size=14),
axis.text=element_text(colour = 1, size = 14),
legend.title=element_blank(),
legend.justification=c(0,1),
legend.position=c(1.05,1),
legend.text=element_text(size = 14),
panel.border = element_rect(colour = "black", fill=NA)) +
guides(color=guide_legend(byrow=TRUE, reverse=TRUE)) +
geom_vline(xintercept=492, linetype="dashed")
#PRINT/SAVE IMAGES, CHANGE FILE NAMES AS APPROPRIATE
ggsave("250702 - 10mM-AMTA_E150a-bgsubtract.jpg", height=10, width=20, units="cm")
ggplot(df4.2.2, aes(x=Wavelength, y=MeanAbs, group=as.factor(SampleType))) +
geom_line(aes(color=as.factor(SampleType))) +
geom_ribbon(aes(ymin=MeanAbs-SdAbs, ymax=MeanAbs+SdAbs, fill=as.factor(SampleType)), alpha=0.3, show.legend=FALSE) +
ggtitle("10 mM AMTA with 0.2% (v/V) E150D") +
xlab("Wavelength (nm)") +
ylab(expression("Reagent Background-Corr. Abs. (a.u.)")) +
theme_classic() +
theme(aspect.ratio=1,
title =element_text(size=14),
axis.title=element_text(size=14),
axis.text=element_text(colour = 1, size = 14),
legend.title=element_blank(),
legend.justification=c(0,1),
legend.position=c(1.05,1),
legend.text=element_text(size = 14),
panel.border = element_rect(colour = "black", fill=NA)) +
guides(color=guide_legend(byrow=TRUE, reverse=TRUE)) +
geom_vline(xintercept=492, linetype="dashed")
#PLOT DATA - CARAMEL IN AMTA, REAGENT BACKGROUND CORRECTED
ggplot(df4.1.2, aes(x=Wavelength, y=MeanAbs, group=as.factor(SampleType))) +
geom_line(aes(color=as.factor(SampleType))) +
geom_ribbon(aes(ymin=MeanAbs-SdAbs, ymax=MeanAbs+SdAbs, fill=as.factor(SampleType)), alpha=0.3, show.legend=FALSE) +
ggtitle("10 mM AMTA with 0.2% (v/v) E150A") +
xlab("Wavelength (nm)") +
ylab(expression("Reagent Background-Corr. Abs. (a.u.)")) +
theme_classic() +
theme(aspect.ratio=1,
title =element_text(size=14),
axis.title=element_text(size=14),
axis.text=element_text(colour = 1, size = 14),
legend.title=element_blank(),
legend.justification=c(0,1),
legend.position=c(1.05,1),
legend.text=element_text(size = 14),
panel.border = element_rect(colour = "black", fill=NA)) +
guides(color=guide_legend(byrow=TRUE, reverse=TRUE)) +
geom_vline(xintercept=492, linetype="dashed")
#PRINT/SAVE IMAGES, CHANGE FILE NAMES AS APPROPRIATE
ggsave("250702 - 10mM-AMTA_E150a-bgsubtract.jpg", height=10, width=20, units="cm")
ggplot(df4.2.2, aes(x=Wavelength, y=MeanAbs, group=as.factor(SampleType))) +
geom_line(aes(color=as.factor(SampleType))) +
geom_ribbon(aes(ymin=MeanAbs-SdAbs, ymax=MeanAbs+SdAbs, fill=as.factor(SampleType)), alpha=0.3, show.legend=FALSE) +
ggtitle("10 mM AMTA with 0.2% (v/v) E150D") +
xlab("Wavelength (nm)") +
ylab(expression("Reagent Background-Corr. Abs. (a.u.)")) +
theme_classic() +
theme(aspect.ratio=1,
title =element_text(size=14),
axis.title=element_text(size=14),
axis.text=element_text(colour = 1, size = 14),
legend.title=element_blank(),
legend.justification=c(0,1),
legend.position=c(1.05,1),
legend.text=element_text(size = 14),
panel.border = element_rect(colour = "black", fill=NA)) +
guides(color=guide_legend(byrow=TRUE, reverse=TRUE)) +
geom_vline(xintercept=492, linetype="dashed")
#PRINT/SAVE IMAGES, CHANGE FILE NAMES AS APPROPRIATE
ggsave("250702 - 10mM-AMTA_E150d-bgsubtract.jpg", height=10, width=20, units="cm")
#EXTRACT THE SAMPLE BACKGROUND (WATER CONTROL) AVERAGE
df6_BGa <- df5.a %>%
filter(SampleName == "E150A" & SampleType == "Water Ctrl")
df6_BGa$Wavelength <- as.factor(df6_BGa$Wavelength)
df6_BGd <- df5.d %>%
filter(SampleName == "E150D" & SampleType == "Water Ctrl")
df6_BGd$Wavelength <- as.factor(df6_BGd$Wavelength)
#CALCULATE THE BACKGROUND CONTROL SUBTRACTED FROM CARAMEL
df5.1 <- df3.1.2 %>%
filter(SampleName == "E150A" & SampleType %in% c("AMTA SDR", "AMTA SAR"))
df5.1.2 <- inner_join(df5.1, df6_BGa, by=c("Wavelength")) %>%
mutate(BGC_value = NC_value - MeanAbs) %>%
dplyr::select(Well_ID, SampleName.x, SampleType.x, TechRep, Wavelength, NC_value)
names(df5.1.2)[names(df5.1.2) == "SampleName.x"] <- "SampleName"
names(df5.1.2)[names(df5.1.2) == "SampleType.x"] <- "SampleType"
df5.2 <- df3.2.2 %>%
filter(SampleName == "E150D" & SampleType %in% c("AMTA SDR", "AMTA SAR"))
df5.2.2 <- inner_join(df5.2, df6_BGd, by=c("Wavelength")) %>%
mutate(NC_value = BC_value - MeanAbs) %>%
dplyr::select(Well_ID, SampleName.x, SampleType.x, TechRep, Wavelength, NC_value)
df5.2.2 <- inner_join(df5.2, df6_BGd, by=c("Wavelength")) %>%
mutate(BGC_value = NC_value - MeanAbs) %>%
dplyr::select(Well_ID, SampleName.x, SampleType.x, TechRep, Wavelength, NC_value)
names(df5.2.2)[names(df5.2.2) == "SampleName.x"] <- "SampleName"
names(df5.2.2)[names(df5.2.2) == "SampleType.x"] <- "SampleType"
#COMPUTE THE AVERAGE OF TECHREPS
df6.1.2 <- df5.1.2 %>%
group_by(SampleName, SampleType, Wavelength) %>%
summarize_at(vars(BGC_value), list(MeanAbs = mean, SdAbs = sd))
#CALCULATE THE BACKGROUND CONTROL SUBTRACTED FROM CARAMEL
df5.1 <- df3.1.2 %>%
filter(SampleName == "E150A" & SampleType %in% c("AMTA SDR", "AMTA SAR"))
df5.1.2 <- inner_join(df5.1, df6_BGa, by=c("Wavelength")) %>%
mutate(BGC_value = NC_value - MeanAbs) %>%
dplyr::select(Well_ID, SampleName.x, SampleType.x, TechRep, Wavelength, NC_value)
names(df5.1.2)[names(df5.1.2) == "SampleName.x"] <- "SampleName"
names(df5.1.2)[names(df5.1.2) == "SampleType.x"] <- "SampleType"
df5.2 <- df3.2.2 %>%
filter(SampleName == "E150D" & SampleType %in% c("AMTA SDR", "AMTA SAR"))
df5.2.2 <- inner_join(df5.2, df6_BGd, by=c("Wavelength")) %>%
mutate(BGC_value = NC_value - MeanAbs) %>%
dplyr::select(Well_ID, SampleName.x, SampleType.x, TechRep, Wavelength, NC_value)
names(df5.2.2)[names(df5.2.2) == "SampleName.x"] <- "SampleName"
names(df5.2.2)[names(df5.2.2) == "SampleType.x"] <- "SampleType"
#COMPUTE THE AVERAGE OF TECHREPS
df6.1.2 <- df5.1.2 %>%
group_by(SampleName, SampleType, Wavelength) %>%
summarize_at(vars(BGC_value), list(MeanAbs = mean, SdAbs = sd))
df5.1.2 <- inner_join(df5.1, df6_BGa, by=c("Wavelength")) %>%
mutate(BGC_value = NC_value - MeanAbs) %>%
dplyr::select(Well_ID, SampleName.x, SampleType.x, TechRep, Wavelength, BGC_value)
names(df5.1.2)[names(df5.1.2) == "SampleName.x"] <- "SampleName"
names(df5.1.2)[names(df5.1.2) == "SampleType.x"] <- "SampleType"
df5.2 <- df3.2.2 %>%
filter(SampleName == "E150D" & SampleType %in% c("AMTA SDR", "AMTA SAR"))
df5.2.2 <- inner_join(df5.2, df6_BGd, by=c("Wavelength")) %>%
mutate(BGC_value = NC_value - MeanAbs) %>%
dplyr::select(Well_ID, SampleName.x, SampleType.x, TechRep, Wavelength, BGC_value)
names(df5.2.2)[names(df5.2.2) == "SampleName.x"] <- "SampleName"
names(df5.2.2)[names(df5.2.2) == "SampleType.x"] <- "SampleType"
#COMPUTE THE AVERAGE OF TECHREPS
df6.1.2 <- df5.1.2 %>%
group_by(SampleName, SampleType, Wavelength) %>%
summarize_at(vars(BGC_value), list(MeanAbs = mean, SdAbs = sd))
df5.1.2$Wavelength <- as.numeric(as.character(df5.1.2$Wavelength))
df5.2.2 <- df5.2.2 %>%
group_by(SampleName, SampleType, Wavelength) %>%
summarize_at(vars(BGC_value), list(MeanAbs = mean, SdAbs = sd))
df5.2.2$Wavelength <- as.numeric(as.character(df5.2.2$Wavelength))
#PLOT DATA - CARAMEL IN AMTA, REAGENT AND SAMPLE BACKGROUND CORRECTED
ggplot(df5.1.2, aes(x=Wavelength, y=MeanAbs, group=as.factor(SampleType))) +
geom_line(aes(color=as.factor(SampleType))) +
geom_ribbon(aes(ymin=MeanAbs-SdAbs, ymax=MeanAbs+SdAbs, fill=as.factor(SampleType)), alpha=0.3, show.legend=FALSE) +
ggtitle("10 mM AMTA with 0.2% (v/v) E150A") +
xlab("Wavelength (nm)") +
ylab(expression("Reagent & Sample\nBackground-Corr. Abs. (a.u.)")) +
theme_classic() +
theme(aspect.ratio=1,
title =element_text(size=14),
axis.title=element_text(size=14),
axis.text=element_text(colour = 1, size = 14),
legend.title=element_blank(),
legend.justification=c(0,1),
legend.position=c(1.05,1),
legend.text=element_text(size = 14),
panel.border = element_rect(colour = "black", fill=NA)) +
guides(color=guide_legend(byrow=TRUE, reverse=TRUE)) +
geom_vline(xintercept=492, linetype="dashed")
#PLOT DATA - CARAMEL IN AMTA, REAGENT AND SAMPLE BACKGROUND CORRECTED
ggplot(df6.1.2, aes(x=Wavelength, y=MeanAbs, group=as.factor(SampleType))) +
geom_line(aes(color=as.factor(SampleType))) +
geom_ribbon(aes(ymin=MeanAbs-SdAbs, ymax=MeanAbs+SdAbs, fill=as.factor(SampleType)), alpha=0.3, show.legend=FALSE) +
ggtitle("10 mM AMTA with 0.2% (v/v) E150A") +
xlab("Wavelength (nm)") +
ylab(expression("Reagent & Sample\nBackground-Corr. Abs. (a.u.)")) +
theme_classic() +
theme(aspect.ratio=1,
title =element_text(size=14),
axis.title=element_text(size=14),
axis.text=element_text(colour = 1, size = 14),
legend.title=element_blank(),
legend.justification=c(0,1),
legend.position=c(1.05,1),
legend.text=element_text(size = 14),
panel.border = element_rect(colour = "black", fill=NA)) +
guides(color=guide_legend(byrow=TRUE, reverse=TRUE)) +
geom_vline(xintercept=492, linetype="dashed")
df6.1.2$Wavelength <- as.numeric(as.character(df6.1.2$Wavelength))
df5.2.2 <- df5.2.2 %>%
group_by(SampleName, SampleType, Wavelength) %>%
summarize_at(vars(BGC_value), list(MeanAbs = mean, SdAbs = sd))
df6.2.2$Wavelength <- as.numeric(as.character(df6.2.2$Wavelength))
df5.2 <- df3.2.2 %>%
filter(SampleName == "E150D" & SampleType %in% c("AMTA SDR", "AMTA SAR"))
df5.2.2 <- inner_join(df5.2, df6_BGd, by=c("Wavelength")) %>%
mutate(BGC_value = NC_value - MeanAbs) %>%
dplyr::select(Well_ID, SampleName.x, SampleType.x, TechRep, Wavelength, BGC_value)
names(df5.2.2)[names(df5.2.2) == "SampleName.x"] <- "SampleName"
names(df5.2.2)[names(df5.2.2) == "SampleType.x"] <- "SampleType"
#COMPUTE THE AVERAGE OF TECHREPS
df6.1.2 <- df5.1.2 %>%
group_by(SampleName, SampleType, Wavelength) %>%
summarize_at(vars(BGC_value), list(MeanAbs = mean, SdAbs = sd))
df6.1.2$Wavelength <- as.numeric(as.character(df6.1.2$Wavelength))
df6.2.2 <- df5.2.2 %>%
group_by(SampleName, SampleType, Wavelength) %>%
summarize_at(vars(BGC_value), list(MeanAbs = mean, SdAbs = sd))
df6.2.2$Wavelength <- as.numeric(as.character(df6.2.2$Wavelength))
#PLOT DATA - CARAMEL IN AMTA, REAGENT AND SAMPLE BACKGROUND CORRECTED
ggplot(df6.1.2, aes(x=Wavelength, y=MeanAbs, group=as.factor(SampleType))) +
geom_line(aes(color=as.factor(SampleType))) +
geom_ribbon(aes(ymin=MeanAbs-SdAbs, ymax=MeanAbs+SdAbs, fill=as.factor(SampleType)), alpha=0.3, show.legend=FALSE) +
ggtitle("10 mM AMTA with 0.2% (v/v) E150A") +
xlab("Wavelength (nm)") +
ylab(expression("Reagent & Sample\nBackground-Corr. Abs. (a.u.)")) +
theme_classic() +
theme(aspect.ratio=1,
title =element_text(size=14),
axis.title=element_text(size=14),
axis.text=element_text(colour = 1, size = 14),
legend.title=element_blank(),
legend.justification=c(0,1),
legend.position=c(1.05,1),
legend.text=element_text(size = 14),
panel.border = element_rect(colour = "black", fill=NA)) +
guides(color=guide_legend(byrow=TRUE, reverse=TRUE)) +
geom_vline(xintercept=492, linetype="dashed")
ggplot(df6.2.2, aes(x=Wavelength, y=MeanAbs, group=as.factor(SampleType))) +
geom_line(aes(color=as.factor(SampleType))) +
geom_ribbon(aes(ymin=MeanAbs-SdAbs, ymax=MeanAbs+SdAbs, fill=as.factor(SampleType)), alpha=0.3, show.legend=FALSE) +
ggtitle("10 mM AMTA with 0.2% (v/v) E150D") +
xlab("Wavelength (nm)") +
ylab(expression("Reagent & Sample\nBackground-Corr. Abs. (a.u.)")) +
theme_classic() +
theme(aspect.ratio=1,
title =element_text(size=14),
axis.title=element_text(size=14),
axis.text=element_text(colour = 1, size = 14),
legend.title=element_blank(),
legend.justification=c(0,1),
legend.position=c(1.05,1),
legend.text=element_text(size = 14),
panel.border = element_rect(colour = "black", fill=NA)) +
guides(color=guide_legend(byrow=TRUE, reverse=TRUE)) +
geom_vline(xintercept=492, linetype="dashed")
#PRINT/SAVE IMAGES, CHANGE FILE NAMES AS APPROPRIATE
ggsave("250702 - 10mM-AMTA_E150d-rbgsubtract.jpg", height=10, width=20, units="cm")
#EXTRACT THE SPIKE AFTER REACTION CONTROL AVERAGE
df6_SARa <- df5.a %>%
filter(SampleName == "E150A" & SampleType == "AMTA SAR")
df6_SARa$Wavelength <- as.factor(df6_SARa$Wavelength)
df6_SARd <- df5.d %>%
filter(SampleName == "E150D" & SampleType == "AMTA SAR")
df6_SARd$Wavelength <- as.factor(df6_SARd$Wavelength)
#CALCULATE THE SAR CONTROL SUBTRACTED FROM CARAMEL
df7.1 <- df3.1.2 %>%
filter(SampleName == "E150A" & SampleType == "AMTA SDR")
df7.1.2 <- inner_join(df7.1, df6_SARa, by=c("Wavelength")) %>%
mutate(BGC_value = NC_value - MeanAbs) %>%
dplyr::select(Well_ID, SampleName.x, SampleType.x, TechRep, Wavelength, BGC_value)
names(df7.1.2)[names(df7.1.2) == "SampleName.x"] <- "SampleName"
names(df7.1.2)[names(df7.1.2) == "SampleType.x"] <- "SampleType"
df7.2 <- df3.2.2 %>%
filter(SampleName == "E150D" & SampleType == "AMTA SDR")
df7.2.2 <- inner_join(df7.2, df6_SARd, by=c("Wavelength")) %>%
mutate(BGC_value = NC_value - MeanAbs) %>%
dplyr::select(Well_ID, SampleName.x, SampleType.x, TechRep, Wavelength, BGC_value)
names(df7.2.2)[names(df7.2.2) == "SampleName.x"] <- "SampleName"
names(df7.2.2)[names(df7.2.2) == "SampleType.x"] <- "SampleType"
