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Description

Real-Time PCR Data Sets by Rutledge et al. (2004).

Real-time quantitative polymerase chain reaction (qPCR) data by Rutledge et al. (2004) <doi:10.1093/nar/gnh177> in tidy format. The data comprises a six-point, ten-fold dilution series, repeated in five independent runs, for two different amplicons. In each run, each standard concentration is replicated four times. For the original raw data file see the Supplementary Data section: <https://academic.oup.com/nar/article/32/22/e178/2375678#supplementary-data>.

rutledge

CRANstatus R-CMD-check

{rutledge} is an R data package that provides real-time PCR raw fluorescence data by Rutledge et al. (2004) in tidy format.

The eponymous data set rutledge comprises a six-point, ten-fold dilution series repeated in 5 independent runs. In each run, for each concentration there are four replicates. Of a total of 240 amplification curves, 212 curves are 45 cycles long and 28 curves are 35 cycles long only. The data is for two targets (amplicons): K1/K2, 102 bp, and K3/K2, 218 bp.

Installation

Install {rutledge} from CRAN:

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

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

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

Data

rutledge is provided as a tidy data set, in long format, i.e. each row is for an amplication curve point (cycle/fluor).

library(rutledge)
rutledge
#> # A tibble: 10,800 × 10
#>    plate well  dye   target sample_type replicate   copies dilution cycle  fluor
#>    <fct> <fct> <chr> <fct>  <fct>           <int>    <int>    <int> <dbl>  <dbl>
#>  1 1     <NA>  SYBR  K1/K2  std                 1 41700000        1     1 0     
#>  2 1     <NA>  SYBR  K1/K2  std                 1 41700000        1     2 0     
#>  3 1     <NA>  SYBR  K1/K2  std                 1 41700000        1     3 0     
#>  4 1     <NA>  SYBR  K1/K2  std                 1 41700000        1     4 0     
#>  5 1     <NA>  SYBR  K1/K2  std                 1 41700000        1     5 0.0007
#>  6 1     <NA>  SYBR  K1/K2  std                 1 41700000        1     6 0.0022
#>  7 1     <NA>  SYBR  K1/K2  std                 1 41700000        1     7 0.0005
#>  8 1     <NA>  SYBR  K1/K2  std                 1 41700000        1     8 0.0047
#>  9 1     <NA>  SYBR  K1/K2  std                 1 41700000        1     9 0.0107
#> 10 1     <NA>  SYBR  K1/K2  std                 1 41700000        1    10 0.0203
#> # ℹ 10,790 more rows

The rutledge data set comprises 240 amplification curves: 2 amplicons $\times$ 5 runs (plates) $\times$ 6 dilution levels $\times$ 4 replicates.

rutledge |>
  dplyr::count(plate, target, copies, replicate)
#> # A tibble: 240 × 5
#>    plate target copies replicate     n
#>    <fct> <fct>   <int>     <int> <int>
#>  1 1     K1/K2     417         1    45
#>  2 1     K1/K2     417         2    45
#>  3 1     K1/K2     417         3    45
#>  4 1     K1/K2     417         4    45
#>  5 1     K1/K2    4170         1    45
#>  6 1     K1/K2    4170         2    45
#>  7 1     K1/K2    4170         3    45
#>  8 1     K1/K2    4170         4    45
#>  9 1     K1/K2   41700         1    45
#> 10 1     K1/K2   41700         2    45
#> # ℹ 230 more rows

rutledge |>
  ggplot(mapping = aes(
    x = cycle,
    y = fluor,
    group = interaction(plate, target, copies, replicate),
    col = as.character(copies)
  )) +
  geom_line(linewidth = 0.2) +
  geom_point(size = 0.2) +
  labs(y = "Raw fluorescence", colour = "No. of copies", title = "Six-point 10-fold dilution series") +
  guides(color = guide_legend(override.aes = list(linewidth = 0.5), reverse = TRUE)) +
  facet_grid(rows = vars(plate), cols = vars(target))
#> Warning: Removed 280 rows containing missing values or values outside the scale range
#> (`geom_line()`).
#> Warning: Removed 280 rows containing missing values or values outside the scale range
#> (`geom_point()`).

Code of Conduct

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

References

R. G. Rutledge. Sigmoidal curve-fitting redefines quantitative real-time PCR with the prospective of developing automated high-throughput applications. Nucleic Acids Research 32:e178 (2004). doi: 10.1093/nar/gnh177.

Metadata

Version

0.1.1

License

Unknown

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