Description Usage Arguments Details Value Author(s) Examples

Simulates gene intensities and also applies DDHFm to them

1 | ```
hftrialdatagen(nreps = 4, nps = 128, plot.it = FALSE, uvp = 0.8)
``` |

`nreps` |
Number of replicates |

`nps` |
Number of genes |

`plot.it` |
Takes TRUE to activate the command of the respective plot and FALSE to deactivate it |

`uvp` |
a parameter for the denoising |

The code is well commented for further information.

First, `genesimulator`

is called to obtain a vector of
mean gene intensities (for a number of genes and a number of replicates
for each gene.

Then `link{simdurbin2}`

simulates a series of gene intensities
using the (log-normal type) model as described in Durbin and Rocke
(2001,2002).

Then for each gene the mean of replicates for that gene is computed.

Optionally, if `plot.it`

is `TRUE`

then the mean
is plotted against its standard deviation (over
replicates).

Then the intensities are sorted according to increasing replicate mean.

Optionally, if `plot.it`

is `TRUE`

then a plot of the
intensities values as a vector (sorted according to increasing
replicate mean) is plotted in black, and then the true mean plotted
in colour 2 (on my screen this is red) and the computed replicate
mean plotted in green.

The DDHF transform of the sorted intensities is computed.

Optionally, if `plot.it`

is `TRUE`

then a plot of the
transformed means versus the transformed standard deviations is plotted.
Followed by a time series plot of the transformed sorted intensities.
These can be studied to see how well DDHF has done the transformation.

Then two smoothing methods are applied the the DDHF transformed data.
One method is translation invariant, Haar wavelet universal thresholding.
The other method is the classical smoothing spline. If `plot.it`

is
`TRUE`

then these smoothed estimates are plotted in different
colours.

Then the mean estimated intensity for each gene is computed and this
is returned as the first column of a two-column matrix (`ansm`

).
The second column is the true underlying mean. The object
`hftssq`

contains a measure of error between the estimated
and true gene means.

`ansm` |
Two column matrix containing the estimated gene intensities and the true ones |

`hftssq` |
Sum of squares between estimated means and true means |

`yhf` |
Simulated gene intensities |

Guy Nason <g.p.nason@bris.ac.uk>

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ```
#
# First run hftrialdatagen
#
## Not run: v <- hftrialdatagen()
#
# Now plot the Haar-Fisz transformed intensities.
#
## Not run: ts.plot(v$yhf)
#
# Now plot the denoised intensities
#
# Note that above we have 128 genes and 4 replicates and so there are
# 4*128 = 512 intensities to plot.
#
# However, there are only 128 gene intensities, and estimates. So, for this
# plot we choose to plot the noisy intensities and then for each replicate
# group (which are colocated on the plot) plot the (necessarily constant)
# true and estimated intensities (ie we plot each true/estimated intensity
# 4 times, once for each replicate).
#
# First estimates...
#
## Not run: lines(1:512, rep(v$ansm[,1], rep(4,128)), col=2)
#
# Now plot the truth
#
## Not run: lines(1:512, rep(v$ansm[,2], rep(4,128)), col=3)
``` |

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