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pcr

Quality assessing, analyzing and testing the statistical significance of real-time quantitative PCR data

https://github.com/mahshaaban/pcr

Science Score: 23.0%

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Keywords

data-analyses molecular-biology qpcr
Last synced: 6 months ago · JSON representation

Repository

Quality assessing, analyzing and testing the statistical significance of real-time quantitative PCR data

Basic Info
Statistics
  • Stars: 30
  • Watchers: 4
  • Forks: 8
  • Open Issues: 8
  • Releases: 5
Topics
data-analyses molecular-biology qpcr
Created over 8 years ago · Last pushed over 1 year ago
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Readme Changelog Contributing License

README.md

Build Status Build status Build status Coverage Status CRAN version downloads

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 'deltadeltact' applies the popular Double Delta CT method.

```r

default mode deltadeltact

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 <- pcranalyze(ct1, groupvar = groupvar, referencegene = '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 <- pcrassess(ct3, amount = amount, referencegene = 'GAPDH', method = 'efficiency') res ```

In the case of using the Double Delta CT, 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 CT 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.

Citation

r citation("pcr")

PeerJ Article

For details about the methods and more examples, check out our PeerJ article.

Owner

  • Name: Mahmoud Ahmed
  • Login: MahShaaban
  • Kind: user
  • Location: London, UK
  • Company: The Institute of Cancer Research

Postdoc

GitHub Events

Total
  • Issues event: 1
  • Watch event: 4
  • Issue comment event: 1
  • Create event: 1
Last Year
  • Issues event: 1
  • Watch event: 4
  • Issue comment event: 1
  • Create event: 1

Committers

Last synced: about 1 year ago

All Time
  • Total Commits: 99
  • Total Committers: 5
  • Avg Commits per committer: 19.8
  • Development Distribution Score (DDS): 0.172
Past Year
  • Commits: 2
  • Committers: 1
  • Avg Commits per committer: 2.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
MahShaaban m****y@s****g 82
Mahmoud Ahmed m****d@M****l 12
Mahmoud Ahmed m****d@M****l 3
Bisho2122 3****2 1
Arian 1****n 1
Committer Domains (Top 20 + Academic)
students.kasralainy.edu.eg: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 31
  • Total pull requests: 8
  • Average time to close issues: 2 months
  • Average time to close pull requests: 1 day
  • Total issue authors: 25
  • Total pull request authors: 4
  • Average comments per issue: 2.55
  • Average comments per pull request: 0.63
  • Merged pull requests: 6
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 0
  • Average time to close issues: about 1 month
  • Average time to close pull requests: N/A
  • Issue authors: 2
  • Pull request authors: 0
  • Average comments per issue: 0.5
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
  • lmanchon (5)
  • MahShaaban (3)
  • katefgit (1)
  • SimoniMD (1)
  • CamillaMaciel (1)
  • lsilvam (1)
  • sgmccalla (1)
  • JacobRPrice (1)
  • shubham7193 (1)
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  • janstrauss1 (1)
  • IgnaciaMeza (1)
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  • danutasastre (1)
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Pull Request Authors
  • MahShaaban (4)
  • Bisho2122 (1)
  • Neurarian (1)
  • richardstoeckl (1)
Top Labels
Issue Labels
question (12) bug (4) enhancement (3) help wanted (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 380 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 8
  • Total maintainers: 1
cran.r-project.org: pcr

Analyzing Real-Time Quantitative PCR Data

  • Versions: 8
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 380 Last month
Rankings
Forks count: 9.1%
Stargazers count: 12.9%
Downloads: 18.5%
Average: 21.2%
Dependent packages count: 29.8%
Dependent repos count: 35.5%
Maintainers (1)
mahmoud.s.fahmy@students.kasralainy.edu.eg
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.4.0 depends
  • ggplot2 * imports
  • covr * suggests
  • cowplot * suggests
  • knitr * suggests
  • rmarkdown * suggests
  • testthat * suggests
.github/workflows/r.yml actions
  • actions/checkout v4 composite
  • r-lib/actions/setup-r f57f1301a053485946083d7a45022b278929a78a composite