qpcr: Gene expression from real-time quantitative PCR

In kulife: Datasets and functions from the (now non-existing) Faculty of Life Sciences, University of Copenhagen

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.

Usage

data(qpcr)

Format

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

flour	numeric	Fluorescence level
line	factor	Plant lines rnt (mutant) and wt (wildtype)
cycle	numeric	Cycle number for the experiment
transcript	factor	Transcript used for the different runs

Source

Data provided by Kirsten Jorgensen <[e-mail]>.
Added by Claus Ekstrom <[e-mail]>

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))

Example output

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: 7.728e-06

kulife documentation built on May 2, 2019, 9:53 a.m.