Description Usage Arguments Details Value Author(s) References Examples

Calculates and plots posterior means with 95% credible intervals for specified fixed effects (or their combination) for all genes

1 2 3 |

`model` |
The output of mcmc.qpcr function. |

`factors` |
A vector of names of fixed effects of interest; see details. |

`factors2` |
A second vector of fixed effect names to be subtracted from the first; see details. |

`ylimits` |
Y-limits for the plot such as c(-3,6); autoscale by default. |

`hpdtype` |
Specify hpdtype="l" to plot the upper and lower 95% credible limits as a continuous dashed line across all genes. This is useful to compare credible intervals among several models on the same plot. By default (hpdtype="w") the limits are plotted as whiskers around each point. |

`inverse` |
Plot the inverse of the result. |

`jitter` |
For hpdtype="w", shifts the plotted values and whiskers by the specified distance along the x axis (reasonable jitter values are 0.15 or -0.15, for example). This helps plot several results (different models or factor combinations) on the same plot (use HPDpoints to add to existing plot) |

`plot` |
if plot = FALSE the function returns a table of calculated posterior modes, means, upper and lower 95% credible limits (all on log(2) scale), and two types of p-values: derived from Bayesian z-scores, and derived directly from MCMC sample. All such outputs for a given experiment should be concatenated with rbind and processed by padj.qpcr() function to adjust the p-values for multiple comparisons (disregarding the entries corresponding to control genes) |

`grid` |
Whether to draw vertical grid lines to separate genes. |

`zero` |
Whether to draw a horizontal line at 0. |

`...` |
Various plot() options; such as col (color of lines and symbols), pch (type of symbol), main (plot title) etc. |

Use summary(MCMCglmm object) first to see what fixed effect names are actually used in the output. For example, if summary shows:

gene1:conditionheat

gene2:conditionheat

....

gene1:timepointtwo

gene2:timepointtwo

....

gene1:conditionheat:timepointtwo

gene2:conditionheat:timepointtwo

, it is possible to specify factors="conditionheat" to plot only the effects of the heat.

If a vector of several fixed effect names is given, for example: factors=c("timepointtwo","treatmentheat:timepointtwo") the function will plot the posterior mean and credible interval for the sum of these effects.

If a second vector is also given, for example,

factors=c("f1","f2"), factors2=c("f3","f4")

the function will plot the difference between the sums of these two groups of factors.
This is useful for pairwise analysis of differences in complicated designs.

A plot or a table (plot = F).

Use the function HPDpoints() if you need to add graphs to already existing plot.

Mikhail V. Matz, UT Austin <[email protected]>

Matz MV, Wright RM, Scott JG (2013) No Control Genes Required: Bayesian Analysis of qRT-PCR Data. PLoS ONE 8(8): e71448. doi:10.1371/journal.pone.0071448

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
# loading Cq data and amplification efficiencies
data(coral.stress)
data(amp.eff)
# extracting a subset of data
cs.short=subset(coral.stress, timepoint=="one")
genecolumns=c(5,6,16,17) # specifying columns corresponding to genes of interest
conditions=c(1:4) # specifying columns containing factors
# calculating molecule counts and reformatting:
dd=cq2counts(data=cs.short,genecols=genecolumns,
condcols=conditions,effic=amp.eff,Cq1=37)
# fitting the model
mm=mcmc.qpcr(
fixed="condition",
data=dd,
controls=c("nd5","rpl11"),
nitt=3000,burnin=2000 # remove this line when analyzing real data!
)
# plotting log2(fold change) in response to heat stress for all genes
HPDplot(model=mm,factors="conditionheat",main="response to heat stress")
``` |

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