ruijter

CRAN status R-CMD-check

{ruijter} is an R data package that provides the real-time qPCR technical data sets used in Ruijter et al. (2013) in tidy format, namely:

Installation

Install {ruijter} from CRAN:

# Install from CRAN
install.packages("ruijter")

You can install the development version of {ruijter} like so:

# install.packages("remotes")
remotes::install_github("ramiromagno/ruijter")

Usage

Four-point 10-fold dilution series ds_94_4

library(ruijter)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(ggplot2)

head(ds_94_4)
#> # A tibble: 6 × 9
#>   well  replicate dye   target sample_type copies dilution cycle fluor
#>   <fct> <fct>     <fct> <fct>  <fct>        <int>    <dbl> <int> <dbl>
#> 1 A1    1         SYBR  MYCN   ntc              0      Inf     1 5202.
#> 2 A1    1         SYBR  MYCN   ntc              0      Inf     2 5229.
#> 3 A1    1         SYBR  MYCN   ntc              0      Inf     3 5252.
#> 4 A1    1         SYBR  MYCN   ntc              0      Inf     4 5256.
#> 5 A1    1         SYBR  MYCN   ntc              0      Inf     5 5270.
#> 6 A1    1         SYBR  MYCN   ntc              0      Inf     6 5282.

dplyr::count(ds_94_4, well, replicate, sample_type, copies)
#> # A tibble: 384 × 5
#>    well  replicate sample_type copies     n
#>    <fct> <fct>     <fct>        <int> <int>
#>  1 A1    1         ntc              0    45
#>  2 A2    2         ntc              0    45
#>  3 A3    1         std          15000    45
#>  4 A4    1         std            150    45
#>  5 A5    2         std          15000    45
#>  6 A6    2         std            150    45
#>  7 A7    3         std          15000    45
#>  8 A8    3         std            150    45
#>  9 A9    4         std          15000    45
#> 10 A10   4         std            150    45
#> # ℹ 374 more rows

ds_94_4 %>%
  ggplot(mapping = aes(x = cycle, y = fluor, group = well, col = as.character(copies))) +
  geom_line(size = 0.1) +
  labs(y = "Raw fluorescence", colour="Copy number", title = "Four-point 10-fold dilution series") +
  guides(color = guide_legend(override.aes = list(size = 0.5)))
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.

Replicates for assessment of precision ds_380

head(ds_380)
#> # A tibble: 6 × 9
#>   well  replicate dye   target sample_type copies dilution cycle fluor
#>   <fct> <fct>     <fct> <fct>  <fct>        <int>    <dbl> <int> <dbl>
#> 1 A1    1         SYBR  MYCN   std         150000        1     1 4340.
#> 2 A1    1         SYBR  MYCN   std         150000        1     2 4365.
#> 3 A1    1         SYBR  MYCN   std         150000        1     3 4381.
#> 4 A1    1         SYBR  MYCN   std         150000        1     4 4386.
#> 5 A1    1         SYBR  MYCN   std         150000        1     5 4398.
#> 6 A1    1         SYBR  MYCN   std         150000        1     6 4400.

dplyr::count(ds_380, well, replicate, sample_type, copies)
#> # A tibble: 384 × 5
#>    well  replicate sample_type copies     n
#>    <fct> <fct>     <fct>        <int> <int>
#>  1 A1    1         std         150000    45
#>  2 A2    2         std         150000    45
#>  3 A3    3         std         150000    45
#>  4 A4    4         std         150000    45
#>  5 A5    5         std         150000    45
#>  6 A6    6         std         150000    45
#>  7 A7    7         std         150000    45
#>  8 A8    8         std         150000    45
#>  9 A9    9         std         150000    45
#> 10 A10   10        std         150000    45
#> # ℹ 374 more rows

ds_380 %>%
  ggplot(mapping = aes(x = cycle, y = fluor, group = well, col = as.factor(copies))) +
  geom_line(size = 0.1) +
  labs(y = "Raw fluorescence", colour="Copy number", title = "380 replicates data set") +
  guides(color = guide_legend(override.aes = list(size = 0.5)))

Competimer primers for PCR efficiency modulation ds_competimer

head(ds_competimer)
#> # A tibble: 6 × 10
#>   well  replicate dye     pct  conc target sample_type dilution cycle fluor
#>   <fct> <fct>     <fct> <dbl> <dbl> <fct>  <fct>          <dbl> <int> <dbl>
#> 1 <NA>  1         SYBR      0    64 AluSx  std                1     1  2.02
#> 2 <NA>  1         SYBR      0    64 AluSx  std                1     2  2.30
#> 3 <NA>  1         SYBR      0    64 AluSx  std                1     3  2.32
#> 4 <NA>  1         SYBR      0    64 AluSx  std                1     4  2.36
#> 5 <NA>  1         SYBR      0    64 AluSx  std                1     5  2.42
#> 6 <NA>  1         SYBR      0    64 AluSx  std                1     6  2.55

dplyr::count(ds_competimer, well, pct, conc, replicate, sample_type)
#> # A tibble: 147 × 6
#>    well    pct   conc replicate sample_type     n
#>    <fct> <dbl>  <dbl> <fct>     <fct>       <int>
#>  1 <NA>      0 0      1         ntc            45
#>  2 <NA>      0 0      2         ntc            45
#>  3 <NA>      0 0      3         ntc            45
#>  4 <NA>      0 0.0625 1         std            45
#>  5 <NA>      0 0.0625 2         std            45
#>  6 <NA>      0 0.0625 3         std            45
#>  7 <NA>      0 0.25   1         std            45
#>  8 <NA>      0 0.25   2         std            45
#>  9 <NA>      0 0.25   3         std            45
#> 10 <NA>      0 1      1         std            45
#> # ℹ 137 more rows

ds_competimer %>%
  ggplot(mapping = aes(x = cycle, y = fluor, group = interaction(pct, conc, replicate), col = interaction(pct, conc))) +
  geom_line(size = 0.2) +
  guides(color = "none") +
  labs(y = "Raw fluorescence", title = "Competimer data set") +
  facet_grid(rows = vars(pct), cols = vars(conc))

Terms of use

If you use the data here provided please do not forget to cite the original work by Ruijter et al. (2013), and this package.

Code of Conduct

Please note that the {ruijter} project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

References

Jan M. Ruijter, Michael W. Pfaffl, Sheng Zhao, Andrej N. Spiess, Gregory Boggy, Jochen Blom,Robert G. Rutledge, Davide Sisti, Antoon Lievens, Katleen De Preter, Stefaan Derveaux, Jan Hellemans, Jo Vandesompele. Evaluation of qPCR curve analysis methods for reliable biomarker discovery: Bias, resolution, precision, and implications. Methods 59 32–46 (2013). doi: 10.1016/j.ymeth.2012.08.011.