Package 'profileScoreDist'

Background distribution.

Description

backgroundDist returns the background distribution of a profile object.

Usage

backgroundDist(x)

Arguments

x	A ProfileDist object.

Details

This is a generic function.

Value

The background distribution vector.

Examples

anObject <- ProfileDist()
backgroundDist(anObject)

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Compute exact position weight/count matrix score distribution.

Description

Computes the discretisized score distribution of a position count matrix (PCM) or a position weight matrix (PWM), using the method described by Rahmann et al.

Usage

computeScoreDist(motif, gc, granularity = 0.01, unit = "nat")

Arguments

motif	A matrix representing a PCM or PWM; each column a position and each row a base corresponding to A, C, G, T. This order is assumed, unless the rows are correspondingly named in a different order.
gc	A scalar giving the GC fraction to assume.
granularity	The granularity of the discretization, defaults to 0.01.
unit	The logarithm unit of the score computed from the PCM or PWM, can be "nat" (default, natural logarithm), "bit" (base 2), or "dit" (base 10).

Value

a ProfileDist object

References

Rahmann, S., Mueller, T., and Vingron, M. (2003). On the power of profiles for transcription factor binding site detection. Stat Appl Genet Mol Biol 2, Article7.

Examples

data(INR)
thedist <- computeScoreDist(regularizeMatrix(INR), 0.5)
plotDist(thedist)

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The position count matrix for INR.

Description

The position count matrix for the initiator (INR) core promoter element. This matrix was obtained from the JASPAR public domain database, but was originally published by P. Bucher (1990); in that publication (and elsewhere) it was termed Cap signal.

Usage

INR

Format

A matrix with named rows corresponding to the counts for each of the four nucleotides.

Value

The position count matrix for INR.

Source

http://jaspar.genereg.net

References

Bucher, P. (1990). Weight matrix descriptions of four eukaryotic RNA polymerase II promoter elements derived from 502 unrelated promoter sequences. Journal of Molecular Biology 212, 563???-578.

Mathelier, A., Zhao, X., Zhang, A.W., Parcy, F., Worsley-Hunt, R., Arenillas, D.J., Buchman, S., Chen, C.-Y., Chou, A., Ienasescu, H., et al. (2014). JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles. Nucleic Acids Research 42, D142–D147.

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Plot background and signal distributions.

Description

plotDist creates a rudimentary plot of signals and backgrounds.

Usage

plotDist(x)

Arguments

x	A ProfileDist object.

Details

This is a generic function.

Value

The scores vector.

Examples

data(INR)
thedist <- computeScoreDist(regularizeMatrix(INR), 0.5)
plotDist(thedist)

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ProfileDist

Description

This class represents signal and background score distributions for a profile.

Usage

## S4 method for signature 'ProfileDist'
show(object)

## S4 method for signature 'ProfileDist'
score(x)

## S4 method for signature 'ProfileDist'
signalDist(x)

## S4 method for signature 'ProfileDist'
backgroundDist(x)

## S4 method for signature 'ProfileDist'
plotDist(x)

Arguments

object	A ProfileDist object for the show method.
x	A ProfileDist object.

Value

A ProfileDist object.

Methods (by generic)

• show: Shows useful information

• score: Accessor for the scores

• signalDist: Accessor for the signal distribution

• backgroundDist: Accessor for the background distribution

• plotDist: Simple plot method for signal and background distributions

Slots

f

Signal distribution

g

Background distribution

Scores

Scores for the distributions

Constructor

ProfileDist(f=numeric, g=numeric, Scores=numeric)

---

Careful regularization (pseudocount addition) to a position count matrix.

Description

Carries out the regularization suggested by Rahmann et al. This lets each column in the regularized matrix be a linear combination of the column in the non-regularized matrix and rho, the overall base distribution of all positions. The weighting of the linear combination is determined by the parameter E in a non-trivial way, see Rahmann et al. for more information. A default value E=1.5 usually works well.

Usage

regularizeMatrix(motif, E = 1.5)

Arguments

motif	A position count matrix; each column a position and each row a base corresponding to A, C, G, T. This order is assumed, unless the rows are correspondingly named in a different order.
E	Weighting parameter between 0 and 3 for the regularization.

Value

The regularized matrix

References

Rahmann, S., Mueller, T., and Vingron, M. (2003). On the power of profiles for transcription factor binding site detection. Stat Appl Genet Mol Biol 2, Article7.

Examples

data(INR)
regularizeMatrix(INR)

---

False discovery rate and power for PWM Score distributions.

Description

Computes score cutoffs for a PWM or a PCM, given distributions as calculated with computeScoreDist(). Cutoffs can be computed for a given false discovery rate (FDR), for a given false negative rate (FNR), and the optimal tradeoff between the two, in the sense that $c \times FDR = FNR$ for some $c$ that the user may choose.

Usage

scoreDistCutoffs(scoreDist, n, m = 1, c = 1, cutoff = 0.01)

Arguments

scoreDist	A ProfileDist object, as computed by computeScoreDist()
n	The number of scores considered for the given PWM. If one sequence is considered and a score is computed for all overlapping windows of the same length as the PWM, this will be the length of the sequence, minus the PWM length plus 1. If scanning a sequence and its reverse complement too, this number must be further multiplied by two. The number forms the basis for the FDR, since this is a multiple testing problem.
m	The number of true positives assumed for computing the FNR.
c	A factor expressing how much more important the FDR is compared to the FNR, when computing the tradeoff cutoff that considers both FDR and FNR. See Rahmann et al. for details.
cutoff	The FDR and FNR considered, typically 0.01 or 0.05.

Value

a list with elements:

cutoffa

Score cutoff for FDR=cutoff

cutoffb

Score cutoff for FNR=cutoff

cutoffopt

Score cutoff for c*FDR = FNR

References

Rahmann, S., Mueller, T., and Vingron, M. (2003). On the power of profiles for transcription factor binding site detection. Stat Appl Genet Mol Biol 2, Article7.

Examples

data(INR)
thedist <- computeScoreDist(regularizeMatrix(INR), 0.5)
scoreDistCutoffs(thedist, n=2000, cutoff=0.05)

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Signal distribution.

Description

signalDist returns the signal distribution of a profile object.

Usage

signalDist(x)

Arguments

x	A ProfileDist object.

Details

This is a generic function.

Value

The signal distribution vector.

Examples

anObject <- ProfileDist()
backgroundDist(anObject)

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