Standard curve and estimation fo concentration
June 2026, R version 4.5.3
Example data: data from all plots in Validation of Protocol
Hashtag (#) in the code chucks contain notes or additional code that can be run if the # is removed.
Install and Load packages
library(readxl)
library(dplyr)
library(tidyr)
library(ggplot2)
library(readr)Set working directory
setwd("...")
data <- read.csv("Example_uptake_data.csv")
blank <- data %>%
filter(Concentration == "0") %>% #filter for blank sample. #0 in standard curve.
summarize(mean = mean(Signal))%>% pull(mean) #calculate the mean blank value
data <- data%>%
mutate(
Signal_corr = case_when(
Sample_ID == "Oocyte" ~ Signal - blank, # subtract for oocyte samples
Sample_type == "Standard" ~ Signal - blank, # subtract for standards (Sample_ID is NA)
Sample_ID == "Buffer" ~ Signal, # keep raw for buffer samples
TRUE ~ Signal), # fallback (if any other type)
Signal_corr = pmax(Signal_corr, 0) # Set negatives to 0
)
Standard <- filter(data, Sample_type == "Standard") %>% #make dataframe with only standard samples
dplyr::select(-c(4:9))%>% # remove unnecessary columns
mutate(Concentration = Concentration / 1000) #Convert to µM
Samples <- filter(data, !Sample_type %in% c("Standard", "Blank")) %>% #make dataframe with only assay samples
dplyr::select(-c(4:5))Check that signal do not increase above standard curve
std_min <- min(Standard$Signal_corr, na.rm = TRUE) #lowest signal, should be 0 since negatives are 0
std_max <- max(Standard$Signal_corr, na.rm = TRUE) #highest signal
Samples_check <- Samples %>%
mutate(Out_of_range = case_when(
Signal_corr < std_min ~ "Below standard range",
Signal_corr > std_max ~ "Above standard range",
TRUE ~ "OK"
)
)
Samples_check %>% filter(Out_of_range != "OK") #if any samples about of range they will be shown when run## [1] Signal Concentration Sample_type Sample_ID RNA
## [6] Assay_comments Assay Signal_corr Out_of_range
## <0 rækker> (eller 0-længde row.names)Generate standard curve
curve <- lm(Signal_corr ~ Concentration, data = Standard)
slope <- coef(curve)[2]
intercept <- coef(curve)[1]
summary(curve)##
## Call:
## lm(formula = Signal_corr ~ Concentration, data = Standard)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18221 -251 313 388 39868
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -410.9 1557.6 -0.264 0.794
## Concentration 22686.2 472.7 47.994 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8210 on 34 degrees of freedom
## Multiple R-squared: 0.9855, Adjusted R-squared: 0.985
## F-statistic: 2303 on 1 and 34 DF, p-value: < 2.2e-16r2 <- summary(curve)$r.squared
eq_label <- paste0(
"y = ", round(slope, 3), "x + ", round(intercept, 3),
"\nR² = ", round(r2, 3)
)ggplot(Standard, aes(x = Concentration, y = Signal_corr)) +
geom_point(size = 3, alpha = 0.7, color = "steelblue") +
geom_abline(intercept = intercept, slope = slope, color = "red", size = 1) +
theme_classic() +
labs(x = "Concentration (µM)", y = "RFU")+
annotate("text", x = 0.1, y = max(Standard$Signal_corr)*0.95,
label = eq_label, hjust = 0, size = 4)+
scale_y_continuous(labels = scales::comma, breaks = c(seq(0,250000,50000)))
Estimated concentrations
Calculate the estimated concentration in the samples based on the standard curve
Calculated concentration is saved in “Concentration” column
Samples <- Samples %>%
mutate(
Concentration = ((Signal_corr - intercept) / slope),
Concentration = pmax(Concentration, 0), # negative → 0
Concentration = Concentration * 110 # apply dilution factor
)#write_csv(Samples, "Assays_calculated.csv")
#write_xlsx(Samples, "Assays_calculated.xlsx")# R studio Version
rstudioapi::versionInfo()$version## [1] '2026.7.1.147'# Session report
sessioninfo::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
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## date 2026-08-25
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