Description Usage Arguments Details Value Author(s) References See Also Examples

Takes a matrix containing the resuls of a (e.g.) microarray experiment (rows are different genes, columns are replicates) and performs a multiscale variance stabilization and Gaussianization transform. Note, you have to have replicates to make this work.

1 | ```
DDHFm(x)
``` |

`x` |
A matrix containing n rows (genes) and k columns (replicates) |

The data-drive Haar-Fisz transform is a multiscale variance stabilization and Gaussianization transform. The algorithm will run on a one-column matrix but the results will NOT be good. For one column of data use some other variance stabilizer (in which case why are you using one column, eh?)

However, if you have replicates then you can use this powerful multiscale algorithm which will stabilize variance and also draw the distribution of the intensities towards Gaussian extremely effectively (so skew and kurtosis will look good too).

This function forms a single vector of intensities by the
following procedure. First, it computes the mean over replicates
for each gene (this is why you need replicates, really).
Then it orders the rows of the matrix into increasing mean
order. Then it converts that matrix into a vector (which is therefore
a sequence of intensities which are grouped according to gene
(according to increasing mean intensity within each group)).
Then this vector is padded up to the next power-of-two in length
with zeroes. Then the padded vector is subjected to the
`ddhft.np.2`

function which actually does the
data-driven Haar-Fisz transform. The results of this are
unpadded and then rearranged into a matrix with an identical
structure to the input matrix. This final matrix contains
stabilized and Gaussianized (is this a word?) intensities.

It is important that you have replicates (ie, the number of columns of the matrix has to be greater than 1). Also, it is scientifically important that the replicates be just that (ie, although the rows might refer to the same genes the columns must be results from repeats experiments held under identical conditions). For example, you shouldn't have results for the gene on one subject and then the same gene on another subject unless I suppose you are considering them to be replicates in some way.

A single matrix with the same dimensions as the input matrix. Each entry in the resultant matrix corresponds to a variance stabilized and Gaussianized version of the entry in the input matrix.

Guy Nason

Motakis, E.S., Nason, G.P., Fryzlewicz, P. and Rutter, G.A.
(2005)
Variance stabilization and normalization for one-color
microarray data using a data-driven multiscale approach.
*Technical Report*, 05:16, Statistics Group,
Department of Mathematics, University of Bristol, UK

Motakis, E.S., Nason, G.P., Fryzlewicz, P. and Rutter, G.A.
(2006)
Variance stabilization and normalization for one-color
microarray data using a data-driven multiscale approach.
*Bioinformatics*, **22**, 2547–2553.

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 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | ```
#
# First, let's make up some "true" intensities (these are gamma)
#
TrueInt <- genesimulator(nreps=5, nps=100)
#
# Now let us simulate data from the Durbin and Rocke (2001) model conditioning
# on the TrueInt
#
MAInts <- simdurbin2(TrueInt,alpha=24800, seta=0.227, seps=4800)
#
# Now put these intensities into the correct format for DDHFm. That is
# replicates across columns
#
MAm <- matrix(MAInts, ncol=5, byrow=TRUE)
#
# Apply DDHFm transform
#
MAmDDHFm <- DDHFm(MAm)
#
# Now look at the variances of each
#
diag(var(MAm))
#
#[1] 22443437 25065729 23430160 22470058 21023183
#
diag(var(MAm))/ mean(diag(var(MAm)))
#
#[1] 0.9806403 1.0952183 1.0237540 0.9818035 0.9185839
#
# Now for the DDHFm version
#
diag(var(MAmDDHFm))
#
# [1] 10938.71 11369.41 11169.50 11144.74 10959.94
#
diag(var(MAmDDHFm))/ mean(diag(var(MAmDDHFm)))
#
# [1] 0.9840099 1.0227549 1.0047711 1.0025441 0.9859200
#
# So stabilization is better with DDHFm
#
``` |

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