Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/mahshaaban/pcr
Quality assessing, analyzing and testing the statistical significance of real-time quantitative PCR data
https://github.com/mahshaaban/pcr
data-analyses molecular-biology qpcr
Last synced: 4 days ago
JSON representation
Quality assessing, analyzing and testing the statistical significance of real-time quantitative PCR data
- Host: GitHub
- URL: https://github.com/mahshaaban/pcr
- Owner: MahShaaban
- License: gpl-3.0
- Created: 2017-10-21T14:24:58.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2024-07-15T14:08:42.000Z (4 months ago)
- Last Synced: 2024-09-16T14:09:06.291Z (about 2 months ago)
- Topics: data-analyses, molecular-biology, qpcr
- Language: R
- Homepage: https://CRAN.R-project.org/package=pcr
- Size: 281 KB
- Stars: 25
- Watchers: 5
- Forks: 8
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
Awesome Lists containing this project
README
[](https://travis-ci.org/MahShaaban/pcr)
[](https://ci.appveyor.com/project/MahShaaban/pcr)
[](https://ci.appveyor.com/project/MahShaaban/pcr/branch/master)
[](https://codecov.io/github/MahShaaban/pcr?branch=master)
[](https://CRAN.R-project.org/package=pcr)
# pcr
# Overview
Quantitative real-time PCR is an important technique in medical and biomedical applications. The `pcr` package provides a unified interface for quality assessing, analyzing and testing qPCR data for statistical significance. The aim of this document is to describe the different methods and modes used to relatively quantify gene expression of qPCR and their implementation in the `pcr` package.
# Getting started
The `pcr` is available on CRAN. To install it, use:
```r
# install package CRAN
install.packages('pcr')
```
The development version of the package can be obtained through:
```r
# install package from github (under development)
devtools::install_github('MahShaaban/pcr')
```
```r
# load required libraries
library(pcr)
```
The following chunk of code locates a dataset of CT values of two genes from 12 different samples and performs a quick analysis to obtain the expression of a target gene **c-myc** normalized by a control GAPDH in the **Kidney** samples relative to the brain samples. `pcr_analyze` provides different methods, the default one that is used here is 'delta_delta_ct' applies the popular Double Delta CT method.
```r
# default mode delta_delta_ct
## locate and read raw ct data
fl <- system.file('extdata', 'ct1.csv', package = 'pcr')
ct1 <- readr::read_csv(fl)
## add grouping variable
group_var <- rep(c('brain', 'kidney'), each = 6)
# calculate all values and errors in one step
## mode == 'separate_tube' default
res <- pcr_analyze(ct1,
group_var = group_var,
reference_gene = 'GAPDH',
reference_group = 'brain')
res
```
The output of `pcr_analyze` is explained in the documentation of the function `?pcr_analyze` and the method it calls `?pcr_ddct`. Briefly, the input includes the CT value of c-myc `normalized` to the control GAPDH, The `calibrated` value of c-myc in the kidney relative to the brain samples and the final `relative_expression` of c-myc. In addition, an `error` term and a `lower` and `upper` intervals are provided.
The previous analysis makes a few assumptions. One of which is a perfect amplification efficiency of the PCR reaction. To assess the validity of this assumption, `pcr_assess` provides a method called `efficiency`. The input `data.frame` is the CT values of c-myc and GAPDH at different input amounts/dilutions.
```r
## locate and read data
fl <- system.file('extdata', 'ct3.csv', package = 'pcr')
ct3 <- readr::read_csv(fl)
## make a vector of RNA amounts
amount <- rep(c(1, .5, .2, .1, .05, .02, .01), each = 3)
## calculate amplification efficiency
res <- pcr_assess(ct3,
amount = amount,
reference_gene = 'GAPDH',
method = 'efficiency')
res
```
In the case of using the Double Delta C_T, the assumption of the amplification efficiency is critical for the reliability of the model. In particular, the *slope* and the *R^2* of the line between the log input amount and Delta C_T or difference between the CT value of the target **c-myc** and GAPDH. Typically, The *slope* should be very small (less than 0.01). The `slope` here is appropriate, so the assumption holds true.
### Other analysis methods `?pcr_analyze`
* Delta CT method
* Relative standard curve method
### Testing statistical significance `?pcr_test`
* Two group testing *t*-test and *wilcoxon* test
* Linear regression testing
## Documnetation
```r
browseVignettes("pcr")
```
Alternatively, the vignette can be found online, [here](http://rpubs.com/MahShaaban/pcr).
## Citation
```r
citation("pcr")
```
## PeerJ Article
For details about the methods and more examples, check out our [PeerJ article](https://peerj.com/articles/4473/?td=bl).