{"Package":"pcr","Version":"1.2.2","Title":"Analyzing Real-Time Quantitative PCR Data","Description":"Calculates the amplification efficiency and curves from real-time\nquantitative PCR (Polymerase Chain Reaction) data. Estimates the relative\nexpression from PCR data using the double delta CT and the standard curve\nmethods Livak & Schmittgen (2001) . Tests for\nstatistical significance using two-group tests and linear regression\nYuan et al. (2006) .","Authors@R":"person(\"Mahmoud\", \"Ahmed\",\nemail = \"mahmoud.s.fahmy@students.kasralainy.edu.eg\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-4377-6541\"))","Maintainer":"Mahmoud Ahmed ","URL":"https://github.com/MahShaaban/pcr","BugReports":"https://github.com/MahShaaban/pcr/issues","Depends":"R (>= 3.4.0)","Encoding":"UTF-8","LazyData":"true","RoxygenNote":"7.0.2","Imports":"ggplot2","Suggests":"testthat, knitr, rmarkdown, covr, cowplot","VignetteBuilder":"knitr","License":"GPL-3","NeedsCompilation":"no","Packaged":"2020-04-01 05:30:14 UTC; rstudio","Author":"Mahmoud Ahmed [aut, cre] ()","Repository":"CRAN","Date/Publication":"2020-04-01 06:10:02 UTC","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/pcr_1.2.2.tar.gz","jsonAuthors":[{"name":"Mahmoud Ahmed","email":"mahmoud.s.fahmy@students.kasralainy.edu.eg","maintainer":true}],"readme":"[![Build Status](https://travis-ci.org/MahShaaban/pcr.svg?branch=master)](https://travis-ci.org/MahShaaban/pcr)\n[![Build status](https://ci.appveyor.com/api/projects/status/y9hfiwwc390cce28?svg=true)](https://ci.appveyor.com/project/MahShaaban/pcr)\n[![Build status](https://ci.appveyor.com/api/projects/status/y9hfiwwc390cce28/branch/master?svg=true)](https://ci.appveyor.com/project/MahShaaban/pcr/branch/master)\n[![Coverage Status](https://img.shields.io/codecov/c/github/MahShaaban/pcr/master.svg)](https://codecov.io/github/MahShaaban/pcr?branch=master)\n[![CRAN version](https://img.shields.io/badge/CRAN-v1.1.2-blue.svg)](https://CRAN.R-project.org/package=pcr) \n![downloads](https://cranlogs.r-pkg.org/badges/grand-total/pcr) \n\n# pcr \n\n# Overview \n\nQuantitative 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. \n\n# Getting started \n\nThe `pcr` is available on CRAN. To install it, use: \n\n```r\n# install package CRAN\ninstall.packages('pcr')\n```\n\nThe development version of the package can be obtained through: \n\n```r\n# install package from github (under development)\ndevtools::install_github('MahShaaban/pcr')\n```\n\n```r\n# load required libraries\nlibrary(pcr)\n```\n\nThe 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. \n\n```r\n# default mode delta_delta_ct\n## locate and read raw ct data\nfl <- system.file('extdata', 'ct1.csv', package = 'pcr')\nct1 <- readr::read_csv(fl)\n\n## add grouping variable\ngroup_var <- rep(c('brain', 'kidney'), each = 6)\n\n# calculate all values and errors in one step\n## mode == 'separate_tube' default\nres <- pcr_analyze(ct1,\n group_var = group_var,\n reference_gene = 'GAPDH',\n reference_group = 'brain')\n \nres\n```\n\nThe 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. \n\nThe 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. \n\n```r\n## locate and read data\nfl <- system.file('extdata', 'ct3.csv', package = 'pcr')\nct3 <- readr::read_csv(fl)\n\n## make a vector of RNA amounts\namount <- rep(c(1, .5, .2, .1, .05, .02, .01), each = 3)\n\n## calculate amplification efficiency\nres <- pcr_assess(ct3,\n amount = amount,\n reference_gene = 'GAPDH',\n method = 'efficiency')\nres\n```\n\nIn 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. \n\n### Other analysis methods `?pcr_analyze` \n\n* Delta CT method \n* Relative standard curve method \n\n### Testing statistical significance `?pcr_test` \n\n* Two group testing *t*-test and *wilcoxon* test \n* Linear regression testing \n\n## Documnetation \n\n```r\nbrowseVignettes(\"pcr\")\n```\nAlternatively, the vignette can be found online, [here](http://rpubs.com/MahShaaban/pcr).\n\n## Citation \n\n```r\ncitation(\"pcr\")\n```\n\n## PeerJ Article \n\nFor details about the methods and more examples, check out our [PeerJ article](https://peerj.com/articles/4473/?td=bl).\n","jobInfo":{"package":"pcr","version":"1.2.2","parsingStatus":"success","parserVersion":1,"parsedAt":"2020-04-01T08:31:48+0000"}}

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