Gene expression from real-time quantitative PCR

Description

Gene expression levels from real-time quantitative polymerase chain reaction (qPCR) experiments on two different plant lines. Each line was used for 7 experiments each with 45 cycles.

Format

A data frame with 630 observations on the following 4 variables.

Source

Data provided by Kirsten Jorgensen <kij@life.ku.dk>.
Added by Claus Ekstrom <ekstrom@life.ku.dk>

References

Morant, M. et al. (2010). Metabolomic, Transcriptional, Hormonal and Signaling Cross-Talk in Superroot2. Molecular Plant. 3, p.192–211.

Examples

data(qpcr)

#
# Analyze a single run for the wt line, transcript 1
#
run1 <- subset(qpcr, transcript==1 & line=="wt")

model <- nls(flour ~ fmax/(1+exp(-(cycle-c)/b))+fb,
             start=list(c=25, b=1, fmax=100, fb=0), data=run1)

print(model)

plot(run1$cycle, run1$flour, xlab="Cycle", ylab="Fluorescence")
lines(run1$cycle, predict(model))

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(MESS)
Loading required package: geepack

Attaching package: 'MESS'

The following object is masked from 'package:stats':

    power.t.test

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MESS/qpcr.Rd_%03d_medium.png", width=480, height=480)
> ### Name: qpcr
> ### Title: Gene expression from real-time quantitative PCR
> ### Aliases: qpcr
> ### Keywords: datasets
> 
> ### ** Examples
> 
> 
> data(qpcr)
> 
> #
> # Analyze a single run for the wt line, transcript 1
> #
> run1 <- subset(qpcr, transcript==1 & line=="wt")
> 
> model <- nls(flour ~ fmax/(1+exp(-(cycle-c)/b))+fb,
+              start=list(c=25, b=1, fmax=100, fb=0), data=run1)
> 
> print(model)
Nonlinear regression model
  model: flour ~ fmax/(1 + exp(-(cycle - c)/b)) + fb
   data: run1
      c       b    fmax      fb 
29.5932  0.8406 96.7559  3.7226 
 residual sum-of-squares: 53.79

Number of iterations to convergence: 9 
Achieved convergence tolerance: 7.746e-06
> 
> plot(run1$cycle, run1$flour, xlab="Cycle", ylab="Fluorescence")
> lines(run1$cycle, predict(model))
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>