Package 'antiProfiles'

Gene Expression Anti-profiles

Description

This package implements the anti-profile method of Corrada Bravo et al., BMC Bioinformatics 2012, 13:272 doi:10.1186/1471-2105-13-272.

Author(s)

Hector Corrada Bravo [email protected]

---

A gene expression anti-profile

Description

This class encapsulates anti-profiles. Objects of this class should be built from AntiProfileStats objects using the buildAntiProfile method. Anti-profile scores can be computed for new samples using the apCount method.

Usage

## Accessors

  getProbesetIds(object) 
  getNormalRegions(object)

Arguments

object

Object of class (or inheriting from) AntiProfile

Accessors

In the following code object is a AntiProfile object.

getProbesetIds:

vector of Affymetrix ids for probesets included in the anti-profile (character)

getNormalRegions:

median and upper boundary of normal expression regions (numeric matrix of dimension s-by-2, where s is the size of the anti-profile)

Author(s)

Hector Corrada Bravo [email protected]

See Also

AntiProfileStats for the class of objects from which anti-profiles are built. buildAntiProfile for the method used to construct objects of this class. apCount for the function used to calculate anti-profile scores from objects of this class.

Examples

if (require(antiProfilesData)) {
    data(apColonData)
    colonStats=apStats(exprs(apColonData), pData(apColonData)$Status)
    colonAP = buildAntiProfile(colonStats, tissueSpec=FALSE, sigsize=10)
    show(colonAP)

    head(getProbesetIds(colonAP))
    head(getNormalRegions(colonAP))
  }

---

Statistics used to build anti-profiles

Description

This class stores statistics required to build anti-profiles. Objects of this class should be built using the apStats function. To build anti-profiles from objects of this class, use the buildAntiProfile function.

Usage

## Accessors 
  getProbeStats(object)

Arguments

object

Object of class AntiProfileStats

Accessors

In the following code object is a AntiProfileStats object.

getProbeStats:

Gets probeset statistics. A data.frame with columns

affyid:

Affymetrix probeset id (character)

SD0:

Normal expression standard deviation aggregated over tissue types (numeric)

SD1:

Cancer expression standard deviation aggregated over tumor types (numeric)

stat:

The log2-variance ratio statistic (numeric)

meds0:

Median normal expression aggregated over tissue types (numeric)

mads0:

Median absolute deviation of normal expression aggregate over tissue types (numeric)

Author(s)

Hector Corrada Bravo [email protected]

See Also

apStats to construct objects of this class, buildAntiProfile to build anti-profiles from objects of this class.

Examples

if (require(antiProfilesData)) {
   data(apColonData)
   colonStats = apStats(exprs(apColonData), pData(apColonData)$Status)
   show(getProbeStats(colonStats))
 }

---

Obtain the anti-profile score for a set of samples

Description

This function applies the given anti-profile to a new set of samples. Rownames in the expression matrix are used to match probenames in the AntiProfile object.

Arguments

fit	an object of class AntiProfile as produced by the buildAntiProfile method
expr	a matrix of gene expression, rownames are used as identifiers

Value

a numeric vector of anti-profile scores

Author(s)

Hector Corrada Bravo [email protected]

Examples

if (require(antiProfilesData)) {
    data(apColonData)

    # compute statistics
    colonStats = apStats(exprs(apColonData), pData(apColonData)$Status)

    # create an anti-profile, ignoring tissue-specificity of probesets, with 10 probesets
    ap = buildAntiProfile(colonStats, tissueSpec=FALSE, sigsize=10)

    # get counts for the original dataset
    counts =apCount(ap, exprs(apColonData))
 }

---

Reorder an AntiProfileStats object

Description

Reorders given AntiProfileStats object using provided ordering o

Arguments

stats	An object of class AntiProfileStats
o	A numeric vector giving new probe ordering

Value

A reordered AntiProfileStats object

Author(s)

Hector Corrada Bravo [email protected]

Examples

if (require(antiProfilesData)) {
  data(apColonData)
  colonStats = apStats(exprs(apColonData), pData(apColonData)$Status)
  o = sample(seq(len=nrow(slot(colonStats,"probes"))))
  newStats = apReorder(colonStats, o)
}

---

Compute statistics used to construct antiProfile

Description

This function calculates normal ranges of expressions and variance ratios for all probesets. To create an anti-profile, call buildAntiProfile on the output of this function

Usage

apStats(e, cl, tiss = NULL, minL = 10, cutoff = 5,
    OnCutoff = 2.54)

Arguments

e	matrix of gene expression, with one column per sample
cl	vector of normal/cancer indicators as 0/1
tiss	vector of tissue types for each sample
minL	minimum number of samples of a given tissue/class to compute stats
cutoff	median absolute deviation multiplier used to determine proportion of samples within normal range of expression
OnCutoff	gene expression barcode z-score to determine if a gene is expressed

Value

An object of class SuccsStats

Author(s)

Hector Corrada Bravo [email protected]

See Also

AntiProfileStats for the type of object returned. buildAntiProfile to construct anti-profiles with objects returned by this function.

Examples

if (require(antiProfilesData)) {
   data(apColonData)
   colonStats = apStats(exprs(apColonData), pData(apColonData)$Status)
 }

---

Create an anti-profile from a AntiProfileStats object

Description

This function creates anti-profile using statistics stored in a AntiProfileStats object

Arguments

stats	an object of class AntiProfileStats as produced by the apStats function
tissueSpec	use tissue-specific regions of normal expression
tissueFilter	use only tissue-specific genes in the anti-profile
sigsize	desired size of signature, if NULL, computed from statCutoff
cutoff	median absolute deviation multiplier used to define normal regions of expression
statCutoff	cutoff used to include probesets in anti-profile

Value

an object of class AntiProfilesSig or AntiProfilesTissueSig depending on the tissueSpec argument

Author(s)

Hector Corrada Bravo [email protected]

Examples

if (require(antiProfilesData)) {
  # create an anti-profile, ignoring tissue-specificity of probesets, with 10 probesets
  data(apColonData)
  colonStats = apStats(exprs(apColonData), pData(apColonData)$Status)
  ap = buildAntiProfile(colonStats, tissueSpec=FALSE, sigsize=10)
}

---

Compute expression variability measure

Description

This function computes expression variability in a way that removes dependence on mean expression. It uses a local polynomial likelihood method to estimate variance as gamma distributed around given mean expression for each probeset. This function makes this calculation using all samples in argument. To calculate expression variability for samples in different groups, call this function for each subset of columns separately.

Usage

ev(x, cutoff = NULL, plot = FALSE, ...)

Arguments

x	matrix of gene expression, with one column per sample
cutoff	minimum expression value to be included in computation (for frma normalized data, we find 2.54 to be a good value for determining if a probeset is expressed in a given sample (default NULL)
plot	make a plot of local likelihood model using smoothScatter (default=FALSE)
...	arguments passed to smoothScatter

Value

numeric vector of length equal to number of rows of x

Author(s)

Hector Corrada Bravo [email protected]

References

E. Alemu, H. Corrada Bravo, S. Hannenhalli (2014). Determinants of Expression Variability. Nucleic Acids Research, 42 (6), 3503-14.

See Also

frma for normalization

Examples

if (require(antiProfilesData)) {
  data(apColonData)
  e <- exprs(apColonData)[,pData(apColonData)$Status==1]
  ev <- ev(e, cutoff=2.54)
}

---

A gene expression anti-profile using tissue-specific regions

Description

This class encapsulates anti-profiles with tissue-specific normal expression regions. Objects of this class should be built from AntiProfileStats objects using the buildAntiProfile method. Anti-profile scores can be computed for new samples using the apCount method.

Usage

## Accessors

  ## S4 method for signature 'TissueSpecAntiProfile'
getProbesetIds(object) 
  ## S4 method for signature 'TissueSpecAntiProfile'
getNormalRegions(object)
  getNormalTissueRegions(object)

Arguments

object

Object of class TissueSpecAntiProfile

Accessors

In the following code object is a TissueSpecAntiProfile object.

getProbesetIds:

vector of Affymetrix ids for probesets included in the anti-profile (character)

getNormalRegions:

median and upper boundary of normal expression regions (numeric matrix of dimension s-by-2, where s is the size of the anti-profile)

getNormalTissueRegions:

median and upper boundary of normal expression regions (numeric array of dimension s-by-2-by-t, where s is the size of the anti-profile, and t the number of normal tissues used in the anti-profile)

Author(s)

Hector Corrada Bravo [email protected]

See Also

AntiProfileStats for the class of objects from which anti-profiles are built. buildAntiProfile for the method used to construct objects of this class. apCount for the function used to calculate anti-profile scores from objects of this class. #'

Examples

if (require(antiProfilesData)) {
    data(apColonData)
    # fake tissues
    tissue=rep(c("colon","lung"), len=length(sampleNames(apColonData)))
    tissStats=apStats(exprs(apColonData), pData(apColonData)$Status, tiss=tissue, minL=3)
    tissAP=buildAntiProfile(tissStats, sigsize=10)
    show(tissAP)

    head(getProbesetIds(tissAP))
    head(getNormalRegions(tissAP))
    head(getNormalTissueRegions(tissAP))
  }

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