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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# ruijter
<!-- badges: start -->
[](https://CRAN.R-project.org/package=ruijter)
[](/~https://github.com/ramiromagno/ruijter/actions/workflows/R-CMD-check.yaml)
<!-- badges: end -->
`{ruijter}` is an R data package that provides the real-time qPCR technical data
sets used in [Ruijter et al.
(2013)](https://doi.org/10.1016/j.ymeth.2012.08.011) in tidy format, namely:
- The 94-replicates-4-dilutions data set: `ds_94_4`
- The 380 replicates data set: `ds_380`
- The competimer data set: `ds_competimer`
## Installation
Install `{ruijter}` from CRAN:
``` r
# Install from CRAN
install.packages("ruijter")
```
You can install the development version of `{ruijter}` like so:
``` r
# install.packages("remotes")
remotes::install_github("ramiromagno/ruijter")
```
## Usage
### Four-point 10-fold dilution series `ds_94_4`
```{r dpi=300, fig.width=8, fig.height=4}
library(ruijter)
library(dplyr)
library(ggplot2)
head(ds_94_4)
dplyr::count(ds_94_4, well, replicate, sample_type, copies)
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)))
```
### Replicates for assessment of precision `ds_380`
```{r dpi=300, fig.width=8, fig.height=4}
head(ds_380)
dplyr::count(ds_380, well, replicate, sample_type, copies)
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`
```{r dpi=300, fig.width=8, fig.height=8}
head(ds_competimer)
dplyr::count(ds_competimer, well, pct, conc, replicate, sample_type)
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](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). 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](https://doi.org/10.1016/j.ymeth.2012.08.011).