Package 'NBAMSeq'

Make an example NBAMSeqDataSet

Description

This function makes an example NBAMSeqDataSet

Usage

makeExample(n = 200, m = 30)

Arguments

n	number of genes
m	number of samples

Value

a NBAMSeqDataSet object

References

Love, M.I., Huber, W., Anders, S. (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15:550. https://doi.org/10.1186/s13059-014-0550-8

Examples

gsd = makeExample()

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Making plots to visualize nonlinear associations

Description

This function makes plots to visualize nonlinear associations.

Usage

makeplot(object, phenoname, genename, ...)

Arguments

object	a NBAMSeqDataSet object
phenoname	the name of nonlinear variable to be visualized
genename	the name of gene to be visualized
...	additional arguments provided to plot.gam

Value

the plot made by plot.gam() function

Examples

gsd = makeExample(n = 3, m = 10)
gsd = NBAMSeq(gsd)
makeplot(gsd, "pheno", "gene3", main = "gene10")

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Differential expression analysis based on negative binomial additive model

Description

This function performs differential expression analysis based on negative binomial additive model.

Usage

NBAMSeq(object, gamma = 2.5, parallel = FALSE, fitlin = FALSE,
  BPPARAM = bpparam(), ...)

Arguments

object	a NBAMSeqDataSet object
gamma	a number greater or equal to 1. Increase gamma to create smoother models. Default gamma is 2.5. See gam for details.
parallel	either TRUE or FALSE indicating whether parallel should be used. Default is FALSE
fitlin	either TRUE or FALSE indicating whether linear model should be fitted. Default is FALSE
BPPARAM	an argument provided to bplapply. See register for details.
...	additional arguments provided to gam

Value

a NBAMSeqDataSet object

References

Love, M.I., Huber, W., Anders, S. (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15:550. https://doi.org/10.1186/s13059-014-0550-8

Examples

gsd = makeExample(n = 3, m = 10)
gsd = NBAMSeq(gsd)

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Accessor functions and replace methods for NBAMSeqDataSet object

Description

Accessor functions and replace methods for NBAMSeqDataSet object

For getDesign(): accessor to the design formula

For getsf(): accessor to the size factors

Replace methods for NBAMSeqDataSet object

For setsf(): replace size factors

Usage

getDesign(theObject)

## S4 method for signature 'NBAMSeqDataSet'
getDesign(theObject)

getsf(theObject)

## S4 method for signature 'NBAMSeqDataSet'
getsf(theObject)

setsf(theObject) <- value

## S4 replacement method for signature 'NBAMSeqDataSet,numeric'
setsf(theObject) <- value

Arguments

theObject	a NBAMSeqDataSet object
value	the values to be included in the object

Value

For getDesign(): design formula

For getsf(): size factor

For setsf(): NBAMSeq object

References

Love, M.I., Huber, W., Anders, S. (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15:550. https://doi.org/10.1186/s13059-014-0550-8

Examples

## For getDesign() ##
gsd = makeExample()
design_gsd = getDesign(gsd)
## For getsf() ##
gsd = makeExample()
sf = getsf(gsd)
## For setsf() ##
n = 100
m = 50
gsd = makeExample(n = n, m = m)
sf = sample(1:5, m, replace = TRUE)
setsf(gsd) = sf

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NBAMSeqDataSet constructor

Description

NBAMSeqDataSet constructor

Usage

NBAMSeqDataSet(countData, colData, design, ...)

Arguments

countData	a matrix or data frame contains gene count
colData	a DataFrame or data.frame
design	a mgcv type design. e.g. ~ s(pheno) or ~ s(pheno) + var1 + var2
...	optional arguments passed to SummarizedExperiment

Value

a NBAMSeqDataSet object

Examples

n = 100  ## n stands for number of genes
m = 20   ## m stands for sample size
countData = matrix(rnbinom(n*m, mu=100, size=1/3), ncol = m)
mode(countData) = "integer"
colnames(countData) = paste0("sample", 1:m)
rownames(countData) = paste0("gene", 1:n)
pheno = runif(m, 20, 80)
colData = data.frame(pheno = pheno)
rownames(colData) = paste0("sample", 1:m)
gsd = NBAMSeqDataSet(countData = countData,
colData = colData, design = ~s(pheno))

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NBAMSeqDataSet class

Description

NBAMSeqDataSet is a class inherited from SummarizedExperiment. It is used to store the count matrix, colData, and design formula in differential expression analysis.

Slots

design

a mgcv-type design formula

References

Martin Morgan, Valerie Obenchain, Jim Hester and Hervé Pagès (2018). SummarizedExperiment: SummarizedExperiment container. R package version 1.12.0.

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Pulling out result

Description

This function pulls out result from NBAMSeqDataSet object returned by NBAMSeq

Usage

results(object, name, contrast, indepfilter = TRUE, alpha = 0.1,
  pAdjustMethod = "BH", parallel = FALSE, BPPARAM = bpparam(), ...)

Arguments

object	a NBAMSeqDataSet object returned by NBAMSeq
name	the name of nonlinear variable or continuous linear variable
contrast	a character of length 3. 1st element: name of factor variable; 2nd element: name of numerator level; 3rd element: name of denominator level. contrast = c("group", "treatment", "control") means comparing treatment vs control for group variable.
indepfilter	either TRUE or FALSE indicating whether independent filtering should be performed. Default is TRUE.
alpha	significant threhold for declaring genes as differentially expressed. Default is 0.1.
pAdjustMethod	pvalue adjustment method. Default is "BH". See p.adjust for details.
parallel	either TRUE or FALSE indicating whether parallel should be used. Default is FALSE.
BPPARAM	an argument provided to bplapply. See register for details.
...	additional arguments provided to pvalueAdjustment function in DESeq2. See results for details.

Value

a DataFrame which contains the result

References

Love, M.I., Huber, W., Anders, S. (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15:550. https://doi.org/10.1186/s13059-014-0550-8

Examples

gsd = makeExample(n = 3, m = 10)
gsd = NBAMSeq(gsd)
res = results(gsd, name = "pheno")

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