Package 'INSPEcT'

Description

This method is used to retrieve AIC values for all models tested for all genes.

Usage

## S4 method for signature 'INSPEcT_model'
AIC(object, ..., k = 2)

## S4 method for signature 'INSPEcT'
AIC(object, ..., k = 2)

Arguments

Value

A matrix of AIC values

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
AIC(nascentInspObj10)

Description

A list containing mature and nascent counts for exons and introns, three replicates and 11 time points: 0,1/6,1/3,1/2,1,1.5,2,4,8,12,16 hours.

Format

A list of 4 matrices 500 x 33

Description

This method is used to calculate all the p-values relative to the variability of synthesis, processing and degradation rates. For object modeled with nascent RNA or when non-functional modeling was used, the variability is calculated using the confidence intervals. For objects modeled without nascent RNA, model selection is performed by comparing the likelihood of different (nested) models.

Usage

calculateRatePvals(
  object,
  modelSelection = c("aic", "llr", "hib"),
  preferPValue = TRUE,
  padj = TRUE,
  p_goodness_of_fit = 0.1,
  p_variability = rep(0.05, 3),
  limitModelComplexity = FALSE
)

## S4 method for signature 'INSPEcT'
calculateRatePvals(
  object,
  modelSelection = c("aic", "llr", "hib"),
  preferPValue = TRUE,
  padj = TRUE,
  p_goodness_of_fit = 0.1,
  p_variability = rep(0.05, 3),
  limitModelComplexity = FALSE
)

Arguments

Details

ratePvals retrieve a single p-value for each rate and gene associated to its variability (null hypothesis = the rate is not changing between the conditions)

Value

A matrix containing p-values calculated for each rate

See Also

makeSimModel, makeSimDataset

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
# Set the chi-squared threshold at .2 for nascentInspObj10 object
nascentInspObj10 <- calculateRatePvals(nascentInspObj10, p_goodness_of_fit=.2)

Description

This method is used to retrieve the chi-squared test results for the models that have been selected to better represent the behavior of each gene.

Usage

chisqmodel(object, gc = NULL, tpts = NULL, ...)

## S4 method for signature 'INSPEcT'
chisqmodel(object, gc = NULL, tpts = NULL, ...)

Arguments

Value

A vector of chi-squared test results

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
chisqmodel(nascentInspObj10)

Description

This method is used to retrieve all the chi-squared test results for all models tested for all genes.

Usage

chisqtest(object, ...)

## S4 method for signature 'INSPEcT_model'
chisqtest(object, ...)

## S4 method for signature 'INSPEcT'
chisqtest(object, ...)

Arguments

Value

A matrix of chi-squared test results for all the tested models

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
chisqtest(nascentInspObj10)

Description

This method combines the information coming from different Objects of INSPEcT class. Requirements for two or more object to be combined together are:

• they must be either modeled or either not modeled

• they must have the same time points

• they must have the same modeling parameters

Usage

## S4 method for signature 'INSPEcT,INSPEcT'
combine(x, y, ...)

Arguments

Details

In case the same gene is contained in more than one object that the user tries to combine, the information from one object will be used and a warning will be reported

Value

An Object of class INSPEcT

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
nascentInspObj10_2genes <- nascentInspObj10[1:2]
nascentInspObj10_5genes <- nascentInspObj10[6:10]
nascentInspObj10_7genes <- combine(nascentInspObj10_2genes, nascentInspObj10_5genes)

Description

This method compares two object of class INSPEcT in order to identify differential usage of synthesis, processing or degradation rates in two different steady-state conditions. The two INSPEcT objects must have been profiled with replicates in order to provide a statistical significance to the differences between their rates.

Usage

compareSteady(inspectIds, BPPARAM = SerialParam())

## S4 method for signature 'INSPEcT'
compareSteady(inspectIds, BPPARAM = SerialParam())

Arguments

Value

An object of class INSPEcT_diffsteady which contains both the absolute quantification of the rates as well as the comparison with the statistical significance associated for each gene and rate. (See INSPEcT_diffsteady-class)

Examples

if( Sys.info()["sysname"] != "Windows" ) {
  data('allcounts', package='INSPEcT')
  data('featureWidths', package='INSPEcT')
  data('libsizes', package='INSPEcT')
  
  nascentCounts<-allcounts$nascent
  matureCounts<-allcounts$mature
  conditions<-letters[1:11]
  expDes<-rep(conditions,3)
  tL<-1/6
  
  nasExp_DESeq2<-quantifyExpressionsFromTrCounts(
        allcounts=nascentCounts
        ,libsize=nascentLS
        ,exonsWidths=exWdths
        ,intronsWidths=intWdths
        ,experimentalDesign=expDes)
  
  matExp_DESeq2<-quantifyExpressionsFromTrCounts(
        allcounts=matureCounts
        ,libsize=totalLS
        ,exonsWidths=exWdths
        ,intronsWidths=intWdths
        ,experimentalDesign=expDes)
 
  nasFullObj <- newINSPEcT(
        tpts=conditions
        ,labeling_time=tL
        ,nascentExpressions=nasExp_DESeq2
        ,matureExpressions=matExp_DESeq2)
  
  diffrates = compareSteady(nasFullObj[,c(1,11)])
}

Description

This function compare exons and introns expression level matrices, from two up to an arbitrary number of samples, in order to identify genes which are oddly regluated, compared to an expected standard behaviour, from the post transcriptional point of view.

Usage

compareSteadyNoNascent(
  inspectIds,
  expressionThreshold = 0.25,
  log2FCThreshold = 2,
  trivialAngle = NaN,
  returnNormScores = FALSE,
  referenceCondition = "median"
)

## S4 method for signature 'INSPEcT_steadyNoNascent'
compareSteadyNoNascent(
  inspectIds,
  expressionThreshold = 0.25,
  log2FCThreshold = 2,
  trivialAngle = NaN,
  returnNormScores = FALSE,
  referenceCondition = "median"
)

Arguments

Examples

data('allcounts', package='INSPEcT')
data('featureWidths', package='INSPEcT')
data('libsizes', package='INSPEcT')

nascentCounts<-allcounts$nascent
matureCounts<-allcounts$mature
conditions<-letters[1:11]
expDes<-rep(conditions,3)

matExp_DESeq2<-quantifyExpressionsFromTrCounts(
      allcounts=matureCounts
      ,libsize=totalLS
      ,exonsWidths=exWdths
      ,intronsWidths=intWdths
      ,experimentalDesign=expDes)

matureInspObj <- newINSPEcT(tpts=conditions,matureExpressions=matExp_DESeq2)

matureInspObj<-compareSteadyNoNascent(inspectIds=matureInspObj
								 ,expressionThreshold=0.25
							 ,log2FCThreshold=.5)
regGenes <- PTreg(matureInspObj)
head(regGenes)
table(regGenes)

Description

This function is used to compute the confidence intervals for a given set of modeled genes in the NoNascent scenario.

Usage

computeConfidenceIntervals(object, BPPARAM = SerialParam())

## S4 method for signature 'INSPEcT'
computeConfidenceIntervals(object, BPPARAM = SerialParam())

Arguments

Value

An object of class INSPEcT.

Description

This method is used to retrieve the convergence of the models that have been selected to better represent the behavior of each gene. 0 is converged, 1 not converged, 10 degenerated

Usage

convergence(object)

## S4 method for signature 'INSPEcT'
convergence(object)

Arguments

Value

A vector of numeric

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
convergence(nascentInspObj10)

Description

This function plot the rates of a simulated dataset against the modeled ones and compute their correlations.

Usage

correlationPlot(object, object2, plot = TRUE)

## S4 method for signature 'INSPEcT_model,INSPEcT'
correlationPlot(object, object2, plot = TRUE)

Arguments

Value

An list with the correlation values.

Description

A method to obtain the dimension of the object of class INSPEcT reported as a vector containing of the genes and the number of time points

Usage

## S4 method for signature 'INSPEcT'
dim(x)

Arguments

Value

A numeric that indicates the number of genes within the object and the number of time points contained the object

See Also

nGenes, nTpts

Description

Operators acting on INSPEcT, INSPEcT_model or INSPEcT_diffsteady objects to extract parts. INSPEcT_model objects can be subsetted only by gene. INSPEcT objects can be subsetted either by gene id or time point. In case of subsetting an INSPEcT object by time point, the model should be empty.

Usage

## S4 method for signature 'INSPEcT_model,ANY,ANY,ANY'
x[i]

## S4 method for signature 'INSPEcT,ANY,ANY,ANY'
x[i, j]

## S4 method for signature 'INSPEcT_diffsteady,ANY,ANY,ANY'
x[i, j]

Arguments

Value

An Object of class INSPEcT

See Also

removeModel

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
nascentInspObj10_5genes <- nascentInspObj10[1:5]
## Not run: 
## This will turn out into an error:
nascentInspObj10_5genes_5tpts <- nascentInspObj10[1:5, 1:5]

## End(Not run)
## Before subsetting time points, the model should be removed:
nascentInspObj10_5genes_5tpts <- removeModel(nascentInspObj10)[1:5, 1:5]

Description

A method to visualize gene names associated with the object of class INSPEcT

Usage

## S4 method for signature 'INSPEcT'
featureNames(object)

## S4 replacement method for signature 'INSPEcT'
featureNames(object) <- value

Arguments

Value

A character that contains gene names associated with the object of class INSPEcT

Description

Contains two variables: "exWdths" and "intWdths" containing the lenght of the exons and introns, respectively, relative to the genes in "allcounts"

Format

numeric vector of length 500

Description

This method returns a factor that summarise the gene class (transcriptional regulatory mechanism) that INSPEcT has assigned to each gene. The variability of each rate is indicated with a letter, 's' for synthesis, 'p' for processing and 'd' for degradation. In case more than one rate is variable, the letters associated to each variable rate are merged, for example 'sd' stands for a gene where synthesis and degradation cotributed to transcriptional changes. 'no-reg' is associated to genes with no change in transcription. The classification depends on the thresholds of the goodness-of-fit and rate variability tests that can be changed via the method calculateRatePvals.

Usage

geneClass(object, ...)

## S4 method for signature 'INSPEcT'
geneClass(object, ...)

## S4 method for signature 'INSPEcT_model'
geneClass(object, ...)

## S4 method for signature 'INSPEcT_diffsteady'
geneClass(object, ...)

Arguments

Value

A character containing the regulatory class for each gene

See Also

ratePvals

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
geneClass(nascentInspObj10)
# see the classification with another threshold for rate variability
nascentInspObj10 <- calculateRatePvals(nascentInspObj10, p_variability=rep(1,3))
geneClass(nascentInspObj10)

Description

A method to see as an heatmap the logRatios of synthesis, degradation and processing rates and pre-RNA and total RNA concentration of a population of genes, either at the level of etimated or modeled rates.

Usage

inHeatmap(
  object,
  type = "pre-model",
  breaks = seq(-1, 1, length.out = 51),
  palette = colorRampPalette(c("green", "black", "firebrick3")),
  plot_matureRNA = FALSE,
  absoluteExpression = TRUE,
  show_rowLabels = TRUE,
  clustering = TRUE,
  clustIdx = 3:5
)

## S4 method for signature 'INSPEcT'
inHeatmap(
  object,
  type = "pre-model",
  breaks = seq(-1, 1, length.out = 51),
  palette = colorRampPalette(c("green", "black", "firebrick3")),
  plot_matureRNA = FALSE,
  absoluteExpression = TRUE,
  show_rowLabels = TRUE,
  clustering = TRUE,
  clustIdx = 3:5
)

Arguments

Value

A list of matrices containing the logRatios for total RNA levels, pre-RNA levels, synthesis rates, degradation rates and processing rates. Matrices are ordered according to the clustering.

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
inHeatmap(nascentInspObj10, 'pre-model')
inHeatmap(nascentInspObj10, 'model')

Description

INSPEcT (INference of Synthesis, Processing and dEgradation rates from Transcriptomic data), is a package that analyse RNA-seq data in order to evaluate synthesis, processing and degradation rates and asses via modeling the rates that determines changes in RNA levels.

To see how the typical workflow of INSPEcT works, type:
vignette('INSPEcT')

INSPEcT implements two main classes (INSPEcT and INSPEcT_diffsteady) and their corresponding methods. To have a detailed description of how the two classes are structured and which methods apply on, type:

?'INSPEcT-class'
?'INSPEcT_diffsteady-class'

To obtain the citation, type:
citation('INSPEcT')

Description

INSPEcT_diffsteady is a class able to store the results of the comparisons between two steady states. An object of class INSPEcT_diffsteady is created with the method "compareSteady" applied on two "INSPEcT" objects (see compareSteady).

Usage

synthesis(object)

processing(object)

degradation(object)

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

## S4 method for signature 'INSPEcT_diffsteady'
synthesis(object)

## S4 method for signature 'INSPEcT_diffsteady'
processing(object)

## S4 method for signature 'INSPEcT_diffsteady'
degradation(object)

## S4 method for signature 'INSPEcT_diffsteady'
featureNames(object)

Arguments

Details

Methods associated to the class INSPEcT_diffsteady are:

• synthesis: Accessor to the synthesis rates and their comparisons.

• degradation: Accessor to the degradation rates and their comparisons.

• processing: Accessor to the processing rates and their comparisons.

• plotMA: visualization fuction for rates comparisons, see plotMA

Value

Method show for objects of class INSPEcT_model returns the number of the genes that have been modeled

Slots

synthesis

A data.frame which contains both input data and comparisons results regarding synthesis rates

degradation

A data.frame which contains both input data and comparisons results regarding degradation rates

processing

A data.frame which contains both input data and comparisons results regarding processing rates

modeling_res

A data.frame which contains modeling results

Examples

if( Sys.info()["sysname"] != "Windows" ) {
  data('allcounts', package='INSPEcT')
  data('featureWidths', package='INSPEcT')
  data('libsizes', package='INSPEcT')
  
  nascentCounts<-allcounts$nascent
  matureCounts<-allcounts$mature
  conditions<-letters[1:11]
  expDes<-rep(conditions,3)
  tL<-1/6
  
  nasExp_DESeq2<-quantifyExpressionsFromTrCounts(
        allcounts=nascentCounts
        ,libsize=nascentLS
        ,exonsWidths=exWdths
        ,intronsWidths=intWdths
        ,experimentalDesign=expDes)
  
  matExp_DESeq2<-quantifyExpressionsFromTrCounts(
        allcounts=matureCounts
        ,libsize=totalLS
        ,exonsWidths=exWdths
        ,intronsWidths=intWdths
        ,experimentalDesign=expDes)
 
  nasFullObj <- newINSPEcT(tpts=conditions,labeling_time=tL
        ,nascentExpressions=nasExp_DESeq2,matureExpressions=matExp_DESeq2)
  
  diffrates = compareSteady(nasFullObj[,c(1,11)])
  head(synthesis(diffrates))
}
if( Sys.info()["sysname"] != "Windows" ) {
  head(processing(diffrates))
}
if( Sys.info()["sysname"] != "Windows" ) {
  head(degradation(diffrates))
}
if( Sys.info()["sysname"] != "Windows" ) {
  featureNames(diffrates)
}

Description

INSPEcT_model is a class able to store all the results of the modeling of synthesis, processing and degradation rates made via the method modelRates (slot ratesSpecs). It also stores the criteria (slot parameter) to choose between the many models tested for each gene the one that better describes the data and the results. The slot simple is a flag that distinguish wheter the model contains the information of the introns or not. In case not, the flag simple is set to TRUE. Also the method makeSimModel of class INSPEcT-class creates an object of class INSPEcT_model. This object will be used by makeSimDataset to generate a complete simulated data-set, whose classification performance can be tested.

Usage

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

Arguments

Details

Methods that apply to INSPEcT_model class are [
AIC
chisqtest
correlationPlot
geneClass
logLik
makeModelRates
makeSimDataset
modelSelection
rocCurve
rocThresholds
show

Value

Method show for objects of class INSPEcT_model returns the number of the genes that have been modeled

Slots

params

A list that defines thresholds and how to perform log likelihood ratio tests

ratesSpecs

A list containing the modeling output

simple

A logical that indicates whether the mode of INSPEcT is simple (no pre-mRNA and degradation rates) or not.

Description

INSPEcT_steadyNoNascent is a class able to store data and arguments that are necessary to make the analysis concerning premature and mature expressions in different samples. In particular, the ratio between mature and premature can be calculated, which reflects the ratio between the rates of processing and degradation in individaul genes (see PTratio), and the analysis of post-transcriptionally regualted genes can be run to identify genes that in specfic samples show a trand which cannot be attributed to transcriptional regulation alone (see PTreg).

Usage

## S4 method for signature 'INSPEcT_steadyNoNascent,ANY,ANY,ANY'
x[i, j]

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

Arguments

Value

Method show for objects of class INSPEcT_steadyNoNascent

Slots

sampleNames

Vector with the names of the samples (columns of the dataset)

geneNames

Vector with the names of the genes (rows of the dataset)

premature

Matrix containing the expressions of the premature RNAs (row=genes, columns=samples)

mature

Matrix containing the expressions of the emature RNAs (row=genes, columns=samples)

prematureVar

Matrix containing the expressions variances of the premature RNAs (row=genes, columns=samples)

matureVar

Matrix containing the expressions variances of the emature RNAs (row=genes, columns=samples)

trivialAngle

Numeric that indicates the angle (slope) of the linear model between mature and premature expressions

log2FCThreshold

Numeric that describes the threshold of the variation to be considered significant

expressionThreshold

Numeric that describes the threshold of the expression to consider the gene expressed

referenceCondition

A sample identifier that set the reference for the post-transcriptional regulation analysis, if NULL the median of all samples is used

ptreg

Matrix containing the post-transcriptioanl regulation state of each gene in the different samples (row=genes, columns=samples)

Description

INSPEcT is a class able to store all the estimated rates and concentrations (slot ratesFirstGuess), the modeled rates and concentrations (slot modelRates) and the model themselves (slot model). Within the class INSPEcT other information regarding the experimental design are stored, such as the time points where experiments were performed (slot tpts) and, if provided, the nascent RNA collecting time (slot tL) and the normalization scale fators used for nascent (labeledSF) RNA-seq libraries. A list of parameters that will be used during the modeling process is stored within the slot params and can be accessed by modelingParams. A new instance of the class INSPEcT can be generated by the constructor function newINSPEcT.

Usage

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

Arguments

Details

Methods that apply to INSPEcT class are AIC
[
calculateDelta
calculateRatePvals
calculateTau
chisqmodel
chisqtest
combine
compareSteady
compareSteadyNoNascent
computeConfidenceIntervals
correlationPlot
dim
featureNames
geneClass
inHeatmap
labeledSF
logLik
makeModelRates
makeOscillatorySimModel
makeSimModel
modelRates
modelRatesNF
modelSelection
modelingParams
nGenes
nTpts
plotGene
processingDelay
ratePvals
ratesFirstGuess
ratesFirstGuessVar
removeModel
rocCurve
rocThresholds
setConfidenceIntervals
show
split
tpts
viewConfidenceIntervals
viewModelRates

Value

Method show for objects of class INSPEcT displays the main features of the slots ratesFirstGuess, model and modelRates

Slots

params

A list of parameters of the modeling part

ratesFirstGuess

An object of class ExpressionSet that contains all the rates and concentrations guessed from the first part of INSPEcT analysis (before modeling)

ratesFirstGuessVar

An object of class ExpressionSet that contains the variances related to rates and concentrations guessed from the first part of INSPEcT analysis (before modeling)

confidenceIntervals

An object of class ExpressionSet that contains the confidence intervals.

model

An object of class INSPEcT_model that contains the output of the mdoeling.

modelRates

An object of class ExpressionSet that contains all modeled the rates and concentrations.

ratePvals

A matrix containing the p-value relative to the variability of synthesis, processing and degradation for each gene.

tpts

A numeric vector of the time-points.

labeledSF

A numeric vector of the scaling factor used for inter time-point normalization of Nascent-seq libraries.

tL

A numeric containing the length of the Nascent pulse.

NoNascent

A logical indicating if the nascent RNA was included into the analysis.

NF

A logical indicating if the modeling approach is Non-Functional

degDuringPulse

A logical indicating if degradation of RNA during the 4sU pulse was considered.

version

A character indicating the version of INSPEcT that created the object

Description

Two shiny apps are encoded into the package inspect: - runProcessingRateDelay: plots single genes as well as genome wide plots associated to the processing induced delay loading the data from an INSPEcT object. - runINSPEcTGUI: is a way to visualize and interact with the RNAdynamics at the level of a single gene, either loading the data from ad INSPEcT object or from scratch.

Usage

runProcessingRateDelay()

runINSPEcTGUI()

Description

Function to run the whole INSPEcT differential rate analysis procedure with a single line. The function save the output analysis to file that can be later loaded in the R environment or in the INSPEcT-GUI.

Usage

inspectFromBAM(
  txdb,
  annotation_table,
  labeling_time = NULL,
  strandSpecific = 0,
  isPairedEnd = FALSE,
  estimateRatesWith = "der",
  useSigmoidFun = TRUE,
  file = NULL
)

Arguments

Description

Function to run the whole INSPEcT differential rate analysis procedure with a single line. The function save the output analysis to file that can be later loaded in the R environment or in the INSPEcT-GUI.

Usage

inspectFromPCR(
  totalRNA_table,
  nascentRNA_table = NULL,
  labeling_time = NULL,
  estimateRatesWith = "der",
  useSigmoidFun = TRUE,
  file = NULL
)

Arguments

Examples

if( Sys.info()["sysname"] != "Windows" ) {
totalAnnTabPCR <- system.file(package = 'INSPEcT', 'totalAnnTabPCR.csv')
nascentAnnTabPCR <- system.file(package = 'INSPEcT', 'nascentAnnTabPCR.csv')
inspectFromPCR(totalAnnTabPCR, nascentAnnTabPCR, labeling_time=1/6)
}

Description

Accessor to obtain the labeledSF slot associated with the object of class INSPEcT

Usage

labeledSF(object)

## S4 method for signature 'INSPEcT'
labeledSF(object)

Arguments

Value

A numeric that indicates the scaling factors applied between time points of the data coming from Nascent-seq library (applies directly to synthesis rates and indirectly to degradation rates)

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
labeledSF(nascentInspObj10)

Description

Contains two variables: "nascentLS" and "totalLS" containing the sequencing depth of nascent and total libraries respectively, relative to the experiments in "allcounts"

Format

numeric vector of length 33

Description

This method is used to retrieve all the log likelihood ratio test results for all pairs tested for all genes.

Usage

logLik(object, ...)

## S4 method for signature 'INSPEcT_model'
logLik(object, ...)

## S4 method for signature 'INSPEcT'
logLik(object, ...)

Arguments

Value

A matrix of log likelihood test results for all the tested model comparisons

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
logLik(nascentInspObj10)

Description

This function is used to evaluate rates and concentrations after modeling of the rates has been run with modelRates. The modeled rates are in functional form and can be evaluated at any time points.

This method can be used to regenerate the rates assiciated to the modeling, in case some testing parameters has changed.

Usage

makeModelRates(object, ...)

## S4 method for signature 'INSPEcT_model'
makeModelRates(object, ...)

## S4 method for signature 'INSPEcT'
makeModelRates(object, ...)

Arguments

Value

An object of class ExpressionSet containing the modeled rates and concentrations

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
viewModelRates(nascentInspObj10, 'degradation')
## force every degradation rate to be accepted as variable (makeModelRates is called internally)
nascentInspObj10 <- calculateRatePvals(nascentInspObj10, p_variability = c(.05,.05,1))
viewModelRates(nascentInspObj10, 'degradation')

Description

This method allow the creation of synthesis, degradation and processing rates that generate an oscillatory expression with a period of 24 hours. Two modes are available: one where oscillations arise just by oscillations in the synthesis of the genes (oscillatoryk3=FALSE, default) and another one where both synthesis and degradation rates oscillates (oscillatoryk3=TRUE). In this latter case, the oscillations of the two rates can be coupled by a cetrain delay (parametrer k3delay). After the creation of the synthetic rates, a dataset with noise and contamination added can be made by makeSimDataset.

Usage

makeOscillatorySimModel(
  object,
  nGenes,
  oscillatoryk3 = FALSE,
  k3delay = NULL,
  na.rm = TRUE,
  seed = NULL
)

## S4 method for signature 'INSPEcT'
makeOscillatorySimModel(
  object,
  nGenes,
  oscillatoryk3 = FALSE,
  k3delay = NULL,
  na.rm = TRUE,
  seed = NULL
)

Arguments

Value

An object of class INSPEcT_model with synthetic rates

See Also

makeSimModel

Examples

nascentInspObj <- readRDS(system.file(package='INSPEcT', 'nascentInspObj.rds'))
simRates<-makeOscillatorySimModel(nascentInspObj, 1000, seed=1)
table(geneClass(simRates))

Description

This method generates rates and concentrations where noise is added according to the desired number of replicates that the user set as an arguments from the INSPEcT_model object that has been created by the method of the class INSPEcT makeSimModel. Rates and concentrations can be generated at the time-points of interest within the original time window. This method generates an INSPEcT object that can be modeled and the performance of the modeling can be tested directly aginst the INSPEcT_model object created by makeSimModel.

Usage

makeSimDataset(
  object,
  tpts,
  nRep,
  NoNascent = FALSE,
  seed = NULL,
  b = 0.3,
  tL = 1/6,
  noise_sd = 4
)

## S4 method for signature 'INSPEcT_model'
makeSimDataset(
  object,
  tpts,
  nRep,
  NoNascent = FALSE,
  seed = NULL,
  b = 0.3,
  tL = 1/6,
  noise_sd = 4
)

Arguments

Value

An object of the class ExpressionSet containing rates and concentrations

See Also

makeSimModel

Examples

if( Sys.info()["sysname"] != "Windows" ) {
  nascentInspObj <- readRDS(system.file(package='INSPEcT', 'nascentInspObj.rds'))
  simRates<-makeSimModel(nascentInspObj, 1000, seed=1)
  tpts <- tpts(nascentInspObj)
  nascentSim2replicates <- makeSimDataset(object=simRates,tpts=tpts,nRep=3,NoNascent=FALSE,seed=1)
}

Description

This method allow the creation of synthesis, degradation and processing rates for a certain number of genes. The rates are created according to the distributions of the real data-set which is given as an input of the method. Different proportions of constant varying rates can be set and a new vector of time points can be provided. This method has to be used before the makeSimDataset method.

Usage

makeSimModel(
  object,
  nGenes,
  newTpts = NULL,
  probs = c(constant = 0.5, sigmoid = 0.3, impulse = 0.2),
  na.rm = TRUE,
  seed = NULL
)

## S4 method for signature 'INSPEcT'
makeSimModel(
  object,
  nGenes,
  probs = rbind(synthesis = c(constant = 0.5, sigmoid = 0.3, impulse = 0.2), processing
    = c(constant = 0.5, sigmoid = 0.3, impulse = 0.2), degradation = c(constant = 0.5,
    sigmoid = 0.3, impulse = 0.2)),
  na.rm = TRUE,
  seed = NULL
)

Arguments

Details

The method makeSimModel generates an object of class INSPEcT_model that stores the parametric functions to genrate clean rates of a time-course. To any of the rates also a noise variance is associate but not used yet. In a typical workflow the output of makeSimModel is the input of the method makeSimDataset, that build the noisy rates and concentrations, given a specified number of replicates.

Value

An object of class INSPEcT_model with synthetic rates

See Also

makeSimDataset

Examples

nascentInspObj <- readRDS(system.file(package='INSPEcT', 'nascentInspObj.rds'))
simRates<-makeSimModel(nascentInspObj, 1000, seed=1)
table(geneClass(simRates))

Description

Extract mature RNA expressions

Usage

mature(object)

## S4 method for signature 'INSPEcT_steadyNoNascent'
mature(object)

Arguments

Value

A matrix containing mature RNA expressions

Description

Extract mature RNA expressions variances

Usage

matureVar(object)

## S4 method for signature 'INSPEcT_steadyNoNascent'
matureVar(object)

Arguments

Value

A matrix containing mature RNA expressions variances

Description

A method to get the parameters used for modeling rates and concentrations by the method modelRates

Usage

modelingParams(object)

## S4 method for signature 'INSPEcT'
modelingParams(object)

Arguments

Value

List of parameters and their values

• estimateRatesWith Either "int" or "der". With "int" the degradation and processing rates are estimated integrating the system between one time point and the following. With "der" degradation and processing rates are estimated using the derivative of total and pre mRNA.

• useSigmoidFun A logical, whether to choose between sigmoid and impulse function to fit rates and concentrations. In case not, always impulse function is used.

• testOnSmooth A logical, wheter models should be tested on smoothed pre-mRNA, total mRNA and eventually synthesis rates or not.

• nInit number of optimization to find the best functional representation of each rate

• nIter number of max iteration during optimization

• Dmin lower bondary for degradation rates in the NoNascent mode

• Dmax upper bondary for degradation rates in the NoNascent mode

• seed A numeric, indicatindg the seed set for reproducible results.

See Also

modelRates

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
modelingParams(nascentInspObj10)

Description

Launch the modeling process with parameters set with modelingParams

This method model the synthesis, degradation and processing rates after their estimation by the constructor function newINSPEcT. Estimated rates are not guaranteed to optimally describes provided input data yet. To this purpose, modeled rates can be generated and genes can be assigned to a transcriptional regulatory mechanism. Modeled rates can be accessed via the method viewModelRates and gene classification according to the regulatory mechanism can be accessed by geneClass. The modeling options used for the modeling can be later accessed by the user via modelingParams. After modeling, model selection is run by the method calculateRatePvals with default parameters.

Usage

modelRates(
  object,
  estimateRatesWith = c("der", "int"),
  useSigmoidFun = TRUE,
  nInit = 10,
  nIter = 300,
  Dmin = 1e-06,
  Dmax = 10,
  seed = NULL,
  BPPARAM = SerialParam()
)

## S4 method for signature 'INSPEcT'
modelRates(
  object,
  estimateRatesWith = c("der", "int"),
  useSigmoidFun = TRUE,
  nInit = 10,
  nIter = 300,
  Dmin = 1e-06,
  Dmax = 10,
  seed = NULL,
  BPPARAM = SerialParam()
)

Arguments

Value

An object of class INSPEcT with modeled rates

See Also

viewModelRates, calculateRatePvals, geneClass

Examples

if( Sys.info()["sysname"] != "Windows" ) {
	nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
	## models removal
	nascentInspObjThreeGenes <- removeModel(nascentInspObj10[1:3])
	nascentInspObjThreeGenes <- modelRates(nascentInspObjThreeGenes, 
	  seed=1, BPPARAM=SerialParam())
	## view modeled synthesis rates
	viewModelRates(nascentInspObjThreeGenes, 'synthesis')
	## view gene classes
	geneClass(nascentInspObjThreeGenes)
}

Description

This method compute confidence intervals for the rates of synthesis, degradation and processing estimated by newINSPEcT that will be used to estimate the variability of each rate in ratePvals method.

Usage

modelRatesNF(object, BPPARAM = SerialParam())

## S4 method for signature 'INSPEcT'
modelRatesNF(object, BPPARAM = SerialParam())

Arguments

Value

An object of class INSPEcT with modeled rates

Examples

if( Sys.info()["sysname"] != "Windows" ) {
	nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
	## models removal
	nascentInspObjThreeGenes <- removeModel(nascentInspObj10[1:3])
	nascentInspObjThreeGenes <- modelRatesNF(nascentInspObjThreeGenes, 
	  BPPARAM=SerialParam())
	## view modeled synthesis rates
	viewModelRates(nascentInspObjThreeGenes, 'synthesis')
	## view gene classes
	geneClass(nascentInspObjThreeGenes)
}

Description

Method to visualize the criteria used to assess variability of rates.

Usage

modelSelection(object)

## S4 method for signature 'INSPEcT'
modelSelection(object)

## S4 method for signature 'INSPEcT_model'
modelSelection(object)

Arguments

Value

• modelSelection 'aic' compares nested models closest to the one with lowest AIC, 'llr' compares all nested models, 'hib' is a mix between the previous two. (default 'aic')

• preferPValue a logical, if TRUE (default) limit the search for best models among the ones with succeded the goodness of fit test.

• padj a logical, if TRUE (default) correct the p-values for multiple testing

• goodness_of_fit a numeric, the threshold for the goodness-of-fit test (default = .1)

• variability a numeric, a vector with the thresholds for the variability test (one threshold for each rate, default = c('s'=05, 'p'=.05, 'd'=.05))

• limitModelComplexity a logical that limits the complexity of the function used to describe dynamics to the length of the time-course (default = FALSE)

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
modelSelection(nascentInspObj10)

Description

The function newINSPEcT creates a new instance of the class INSPEcT provided the experimental time points, expression data (like RPKMs) of mature and eventually nascent RNA. For the nascent analysis, it is also requires a collecting time and the scaling factor to normalize the nascent RNA-seq libraries. This latter parameter can also be calculated by the function itself if both exonic and intronic expression data are provided; otherwise it must be given as an input and it is essential to guarantee the robustness of the analysis.

Usage

newINSPEcT(
  tpts,
  labeling_time = NULL,
  nascentExpressions = NULL,
  matureExpressions,
  preexisting = FALSE,
  BPPARAM = SerialParam(),
  labeledSF = NULL,
  simulatedData = FALSE,
  degDuringPulse = FALSE,
  Dmin = 1e-06,
  Dmax = 10,
  genesFilter = TRUE,
  genesFilterThreshold = 2/3,
  imputeNAs = TRUE
)

Arguments

Value

An object of class INSPEcT with a first estimation of the rates which can be accessed by the method ratesFirstGuess

Examples

data('allcounts', package='INSPEcT')
data('featureWidths', package='INSPEcT')
data('libsizes', package='INSPEcT')

matureCounts<-allcounts$mature
tpts <- c(0,1/6,1/3,1/2,1,1.5,2,4,8,12,16)
expDes<-rep(tpts,3)

matExp_DESeq2<-quantifyExpressionsFromTrCounts(
 	allcounts=matureCounts
 	,libsize=totalLS
 	,exonsWidths=exWdths
 	,intronsWidths=intWdths
 	,experimentalDesign=expDes)

matureInspObj<-newINSPEcT(tpts=tpts
                         ,labeling_time=NULL
                         ,nascentExpressions=NULL
                         ,matureExpressions=matExp_DESeq2)

Description

A method to obtain the number of the genes associated with the object of class INSPEcT

Usage

nGenes(object)

## S4 method for signature 'INSPEcT'
nGenes(object)

Arguments

Value

A numeric that indicates the number of genes within the object

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
nGenes(nascentInspObj10)

Description

A method to obtain the number of the tpts associated with the object of class INSPEcT

Usage

nTpts(object)

## S4 method for signature 'INSPEcT'
nTpts(object)

Arguments

Value

A numeric that indicates the number of time points contained the object

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
nTpts(nascentInspObj10)

Description

A method to see the shapes of the estimated synthesis, degradation and processing rates, pre-RNA and total RNA concentrations (solid thin lines) their variances (dashed lines) and the modeled rates and concentrations (ticker solid line) of a single gene.

Usage

plotGene(
  object,
  ix,
  relative_expression = FALSE,
  fix.yaxis = FALSE,
  priors = TRUE,
  constantModel = FALSE
)

## S4 method for signature 'INSPEcT'
plotGene(
  object,
  ix,
  relative_expression = FALSE,
  fix.yaxis = FALSE,
  priors = TRUE,
  constantModel = FALSE
)

Arguments

Value

A list containing total RNA levels and their confidence interval (levels plus and minus one standard deviation), pre-RNA lelevs and their confidence intervals, synthsis rates and their confidence intervals, degradation rates and processing rates of the selected gene.

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
plotGene(nascentInspObj10, 1)

Description

Visualize the comparison between the rates calculated from two different INSPEcT objects profiled in steady-state conditions.

Usage

## S4 method for signature 'INSPEcT_diffsteady'
plotMA(object, ...)

Arguments

Details

Possible arguments to "plotMA":

• "rate" - A character, which represent the rate to be visualized, either "synthesis", "processing" or "degradation". By default, "synthesis" is chosen.

• "padj" - A numeric, The p-adjusted threshold for significance. Genes with p-adjusted lower than the threshold will be depicted as orange triangles. By default set to -Inf, meaning that no genes will be highlighted.

• "xlim" - A numeric vector of length 2, limits of x-axis, by default the range of the data.

• "xlab" - A character, the label of x-axis, by default "log2 geometric mean"

• "ylim" - A numeric vector of length 2, limits of y-axis, by default the range of the data.

• "ylab" - A character, the label of y-axis, by default "log2 fold change"

• "main" - A character, the title of the plot, by default the name of the visualized rate.

See Also

http://en.wikipedia.org/wiki/MA_plot

Examples

if( Sys.info()["sysname"] != "Windows" ) {
  data('allcounts', package='INSPEcT')
  data('featureWidths', package='INSPEcT')
  data('libsizes', package='INSPEcT')
  
  nascentCounts<-allcounts$nascent
  matureCounts<-allcounts$mature
  conditions<-letters[1:11]
  expDes<-rep(conditions,3)
  tL<-1/6
  
  nasExp_DESeq2<-quantifyExpressionsFromTrCounts(
        allcounts=nascentCounts
        ,libsize=nascentLS
        ,exonsWidths=exWdths
        ,intronsWidths=intWdths
        ,experimentalDesign=expDes)
  
  matExp_DESeq2<-quantifyExpressionsFromTrCounts(
        allcounts=matureCounts
        ,libsize=totalLS
        ,exonsWidths=exWdths
        ,intronsWidths=intWdths
        ,experimentalDesign=expDes)
 
  nasFullObj <- newINSPEcT(tpts=conditions
        ,labeling_time=tL
        ,nascentExpressions=nasExp_DESeq2
        ,matureExpressions=matExp_DESeq2)
  
  diffrates = compareSteady(nasFullObj[,c(1,11)])
 
  plotMA(diffrates, padj=.01)
}

Description

Plot the premature and mature expressions of a specific gene in the different samples of the dataset along with the null model and the log2 fold change threshold. Individal observations that fall outside of the dashed lines are considered post-transcriptional events.

Usage

plotPMgene(object, gene_id, samples_colors = 1)

## S4 method for signature 'INSPEcT_steadyNoNascent'
plotPMgene(object, gene_id, samples_colors = 1)

Arguments

Description

Plot the null model estimated for the specific dataset. The null model is the trend between premature and mature expression, which is usually linear in the log-log scale and generally points to an increase in the ratio between premature and mature RNA at increased levels of expression

Usage

plotPMtrend(inspectIds)

## S4 method for signature 'INSPEcT_steadyNoNascent'
plotPMtrend(inspectIds)

Arguments

Description

Extract premature RNA expressions

Usage

premature(object)

## S4 method for signature 'INSPEcT_steadyNoNascent'
premature(object)

Arguments

Value

A matrix containing premature RNA expressions

Description

Extract premature RNA expressions variances

Usage

prematureVar(object)

## S4 method for signature 'INSPEcT_steadyNoNascent'
prematureVar(object)

Arguments

Value

A matrix containing premature RNA expressions variances

Description

These functions calculates the tau and delta metrics for all genes with introns and exons in an oblect of class INSPEcT. If the INSPEcT dataset was obtained with nascent RNA the metrics are caluclated using RNA dynamics and solving numerically the system of equations. If the INSPEcT dataset was obtained without nascent RNA the metrics are approximated using premature and mature levels.

Usage

processingDelay(
  inspectIds,
  tauThreshold = 1.2,
  deltaThreshold = 1,
  silent = TRUE
)

calculateDelta(inspectIds, silent = FALSE)

calculateTau(inspectIds, silent = FALSE)

## S4 method for signature 'INSPEcT'
processingDelay(
  inspectIds,
  tauThreshold = 1.2,
  deltaThreshold = 1,
  silent = TRUE
)

## S4 method for signature 'INSPEcT'
calculateTau(inspectIds, silent = FALSE)

## S4 method for signature 'INSPEcT'
calculateDelta(inspectIds, silent = FALSE)

Arguments

Examples

data('allcounts', package='INSPEcT')
data('featureWidths', package='INSPEcT')
data('libsizes', package='INSPEcT')

nascentCounts<-allcounts$nascent
matureCounts<-allcounts$mature
conditions<-c(0,1/6,1/3,1/2,1,1.5,2,4,8,12,16)
expDes<-rep(conditions,3)
tL <- 1/6

nasExp_DESeq2<-quantifyExpressionsFromTrCounts(
      allcounts=matureCounts
      ,libsize=totalLS
      ,exonsWidths=exWdths
      ,intronsWidths=intWdths
      ,experimentalDesign=expDes)

matExp_DESeq2<-quantifyExpressionsFromTrCounts(
      allcounts=matureCounts
      ,libsize=totalLS
      ,exonsWidths=exWdths
      ,intronsWidths=intWdths
      ,experimentalDesign=expDes)

matureInspObj <- newINSPEcT(
      tpts=conditions
      ,labeling_time=tL
      ,nascentExpressions=nasExp_DESeq2
      ,matureExpressions=matExp_DESeq2)

procDelay<- processingDelay(inspectIds=matureInspObj
      ,tauThreshold=1.2
      ,deltaThreshold=1.0)

head(procDelay)
table(procDelay)

head(calculateTau(matureInspObj))

head(calculateDelta(matureInspObj))

Description

Extract the ratio between mature and premature RNAs

Usage

PTratio(object, infToNA = TRUE)

## S4 method for signature 'INSPEcT_steadyNoNascent'
PTratio(object, infToNA = TRUE)

Arguments

Value

A matrix containing the PTratios

Description

Extract the post-transcriptional regulation matrix

Usage

PTreg(object)

## S4 method for signature 'INSPEcT_steadyNoNascent'
PTreg(object)

Arguments

Value

A matrix containing the post-transcriptional regulated genes. This matrix is generated by the method compareSteadyStateNoNascent. It generally report 1 for regulated genes in specific samples, 0 for non regulated genes and NA for genes that do not passed the expression threshold. In case the argument returnNormScores was set to TRUE, instead of discretes values, the deviations from the expected model normalized by the experimental standard deviation is reported.

Description

Given a TranscriptDb object and a list of BAM or SAM files "quantifyExpressionsFormBAMs" evaluates exons and introns expressions and the associated variances per each gene.

Usage

quantifyExpressionsFromBAMs(
  txdb,
  BAMfiles,
  experimentalDesign,
  by = c("gene", "tx"),
  countMultiMappingReads = FALSE,
  allowMultiOverlap = FALSE,
  prioritizeExons = TRUE,
  libsize = c("assigned", "all"),
  strandSpecific = 0,
  isPairedEnd = FALSE,
  DESeq2 = TRUE,
  varSamplingCondition = NULL,
  BPPARAM = SerialParam()
)

Arguments

Value

A list containing expressions and associated variances for exons and introns.

Examples

if( Sys.info()["sysname"] != "Windows" ) {
  require(TxDb.Mmusculus.UCSC.mm9.knownGene)
  txdb<-TxDb.Mmusculus.UCSC.mm9.knownGene
  expDes<-c(0,0,1,1)
  
  paths_total<-system.file('extdata/', c('bamRep1.bam'
                                        ,'bamRep2.bam'
                                        ,'bamRep3.bam'
                                        ,'bamRep4.bam')
                          ,package='INSPEcT')
 
  matExp<-quantifyExpressionsFromBAMs(txdb=txdb
                                     ,BAMfiles=paths_total
                                     ,experimentalDesign=expDes)
}

Description

Given a TranscriptDb object and a list of bigWig (BW) files "quantifyExpressionsFormBWs" evaluates exons and introns expressions and the associated variances per each gene.

Usage

quantifyExpressionsFromBWs(
  txdb,
  BWfiles,
  experimentalDesign,
  readLength = 50,
  by = c("gene", "tx"),
  libsize = c("assigned", "all"),
  DESeq2 = TRUE,
  varSamplingCondition = NULL,
  BPPARAM = SerialParam()
)

Arguments

Value

A list containing expressions and associated variances for exons and introns.

Description

Given introns and exons abundances (for example RPKMs) this method returns their variances evaluated thorugh plgem.

Usage

quantifyExpressionsFromTrAbundance(
  trAbundaces,
  experimentalDesign,
  varSamplingCondition = NULL,
  simulatedData = FALSE
)

Arguments

Value

A list containing RPKMs and associated variances for exons and introns.

Description

Evaluates introns and exons RPKMs, per gene, from counts data.

Usage

quantifyExpressionsFromTrCounts(
  allcounts,
  experimentalDesign,
  exonsWidths,
  intronsWidths,
  libsize = NULL,
  DESeq2 = TRUE,
  varSamplingCondition = NULL
)

Arguments

Value

A list containing RPKMs and associated variances for exons and introns.

Examples

data('allcounts', package='INSPEcT')
data('featureWidths', package='INSPEcT')
data('libsizes', package='INSPEcT')

nascentCounts<-allcounts$nascent
matureCounts<-allcounts$mature

expDes<-rep(c(0,1/6,1/3,1/2,1,1.5,2,4,8,12,16),3)

nasExp_DESeq2<-quantifyExpressionsFromTrCounts(libsize=nascentLS
                                              ,exonsWidths=exWdths
                                              ,intronsWidths=intWdths
                                              ,allcounts=nascentCounts
                                              ,experimentalDesign=expDes)

matExp_DESeq2<-quantifyExpressionsFromTrCounts(libsize=totalLS
                                              ,exonsWidths=exWdths
                                              ,intronsWidths=intWdths
                                              ,allcounts=matureCounts
                                              ,experimentalDesign=expDes)

nasExp_plgem<-quantifyExpressionsFromTrCounts(libsize=nascentLS
                                             ,exonsWidths=exWdths
                                             ,intronsWidths=intWdths
                                             ,allcounts=nascentCounts
                                             ,DESeq2=FALSE
                                             ,experimentalDesign=expDes)

matExp_plgem<-quantifyExpressionsFromTrCounts(libsize=totalLS
                                             ,exonsWidths=exWdths
                                             ,intronsWidths=intWdths
                                             ,allcounts=matureCounts
                                             ,DESeq2=FALSE
                                             ,experimentalDesign=expDes)

Description

This method is used to retrieve all the p-values relative to the variability of synthesis, processing and degradation rates.

Usage

ratePvals(object)

## S4 method for signature 'INSPEcT'
ratePvals(object)

Arguments

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
ratePvals(nascentInspObj10)

Description

This method allow to access to the estimated synthesis, degradation, processing rates and pre mRNA and total mRNA concentrations the way they were calculated by the constructor function newINSPEcT.

Usage

ratesFirstGuess(object, feature)

## S4 method for signature 'INSPEcT'
ratesFirstGuess(object, feature)

Arguments

Value

A numeric matrix containing the values for the selected feature

See Also

newINSPEcT, ratesFirstGuessVar

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))

ratesFirstGuess(nascentInspObj10, 'total')
ratesFirstGuess(nascentInspObj10, 'preMRNA')
ratesFirstGuess(nascentInspObj10, 'synthesis')

Description

This method allow to access to the estimated variance of synthesis rates and pre mRNA and total mRNA concentrations the way they were calculated by the constructor function newINSPEcT.

Usage

ratesFirstGuessVar(object, feature)

## S4 method for signature 'INSPEcT'
ratesFirstGuessVar(object, feature)

Arguments

Value

A numeric vector containing the values for the selected feature

See Also

newINSPEcT, ratesFirstGuess

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))

ratesFirstGuessVar(nascentInspObj10, 'total')
ratesFirstGuessVar(nascentInspObj10, 'preMRNA')
ratesFirstGuessVar(nascentInspObj10, 'synthesis')

Description

Remove the model from an INSPEcT object. It is required when subsetting an INSPEcT object per time points because when removing time points the modeling is not valid anymore.

Usage

removeModel(object)

## S4 method for signature 'INSPEcT'
removeModel(object)

Arguments

Value

An Object of class INSPEcT

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
nascentInspObj10_5genes <- nascentInspObj10[1:5]

## This will turn out into an error:
## Not run: nascentInspObj10_5genes_5tpts <- nascentInspObj10[1:5, 1:5]


## Before subsetting time points, the model should be removed:
nascentInspObj10_5genes_5tpts <- removeModel(nascentInspObj10)[1:5, 1:5]

## Also this will turn out into an error:
## Not run: nascentInspObj10 <- modelRates(nascentInspObj10)

## Before running the model again, or changing modeling parameters,
## the previous model should be removed:
nascentInspObj10_old <- nascentInspObj10
nascentInspObj10_new <- removeModel(nascentInspObj10)
## Not run: nascentInspObj10_new <- modelRates(nascentInspObj10_new, useSigmoidFun = FALSE)

Description

A method to visualize the performance in the classification of synthesis, degradation and processing rates based on the comparison of the original simulated rates and the one obtained by the function modelRates. For each rate, classification performance is measured in terms of sensitivity and specificity using a ROC curve analysis. False negatives (FN) represent cases where the rate is identified as constant while it was simulated as varying. False positives (FP) represent cases where INSPEcT identified a rate as varying while it was simulated as constant. On the contrary, true positives (TP) and negatives (TN) are cases of correct classification of varying and constant rates, respectively. Consequently, sensitivity and specificity are computed using increasing thresholds for the brown p-values, and the ability of correctly classifying a rate is measured through the area under the curve (AUC) for each rate.

Usage

rocCurve(object, object2, plot = TRUE, comparative = FALSE)

## S4 method for signature 'INSPEcT_model,INSPEcT'
rocCurve(object, object2, plot = TRUE, comparative = FALSE)

Arguments

Value

A list of objects of class pROC with summary of each roc curve

See Also

makeSimModel, makeSimDataset, rocThresholds

Examples

if( Sys.info()["sysname"] != "Windows" ) {
  nascentInspObj <- readRDS(system.file(package='INSPEcT', 'nascentInspObj.rds'))
 
  simRates<-makeSimModel(nascentInspObj, 1000, seed=1)
   
  # newTpts<-simRates@params$tpts
  # nascentSim2replicates<-makeSimDataset(object=simRates
  #                                    ,tpts=newTpts
  #                                    ,nRep=3
  #                                    ,NoNascent=FALSE
  #                                    ,seed=1)
  # nascentSim2replicates<-modelRates(nascentSim2replicates[1:100]
  #                                ,seed=1)
  # (not evaluated to save computational time)
 
  data("nascentSim2replicates",package='INSPEcT')
 
  rocCurve(simRates[1:100],nascentSim2replicates)
  title("3rep. 11t.p. Total and nascent RNA", line=3)
}

Description

A method to visualize the performance in the classification of synthesis, degradation and processing rates based on the comparison of the original simulated rates and the one obtained by the function modelRates. For each rate, classification performance is measured in terms of sensitivity and specificity using a ROC curve analysis. False negatives (FN) represent cases where the rate is identified as constant while it was simulated as varying. False positives (FP) represent cases where INSPEcT identified a rate as varying while it was simulated as constant. On the contrary, true positives (TP) and negatives (TN) are cases of correct classification of varying and constant rates, respectively. Consequently, at increasing brown p-values different sensitivity and specificity can be achieved.

Usage

rocThresholds(object, object2, xlim = c(1e-05, 1), plot = TRUE)

## S4 method for signature 'INSPEcT_model,INSPEcT'
rocThresholds(object, object2, xlim = c(1e-05, 1), plot = TRUE)

Arguments

Value

The thresholds that maximize both sensitivity and specificity

See Also

makeSimModel, makeSimDataset, rocCurve

Examples

if( Sys.info()["sysname"] != "Windows" ) {
  nascentInspObj <- readRDS(system.file(package='INSPEcT', 'nascentInspObj.rds'))
 
  simRates<-makeSimModel(nascentInspObj, 1000, seed=1)
   
  # newTpts<-simRates@params$tpts
  # nascentSim2replicates<-makeSimDataset(object=simRates
  #                                    ,tpts=newTpts
  #                                    ,nRep=3
  #                                    ,NoNascent=FALSE
  #                                    ,seed=1)
  # nascentSim2replicates<-modelRates(nascentSim2replicates[1:100]
  #                                ,seed=1)
  # (not evaluated to save computational time)
 
  data("nascentSim2replicates",package='INSPEcT')
 
  rocThresholds(simRates[1:100],nascentSim2replicates)
}

Description

This function is used to set the confidence intervals in the nascent RNA mode.

Usage

setConfidenceIntervals(object, confidenceIntervals)

## S4 method for signature 'INSPEcT'
setConfidenceIntervals(object, confidenceIntervals)

Arguments

Value

An object of class ExpressionSet containing the confidence intervals.

Description

A dataset containing the rates and concentrations obtained from the dataset simRates; 3 replicates and time points corresponding to: 0,1/6,1/3,1/2,1,1.5,2,4,8,12,16 hours.

Format

An INSPEcT object

Description

A dataset containing the rates and concentrations obtained from the dataset simRates with 1 replicates and time points corresponding to: 0, 1/6, 1/3, 1/2, 1, 2, 4, 8, 16 hours. On this dataset rates and concentrations have been modeled with the method modelRates

Format

An INSPEcT object

Description

A dataset containing the rates and concentrations obtained from the dataset simRates; 3 replicates and time points corresponding to: 0,1/6,1/3,1/2,1,1.25,1.5,2,3,4,6,8,10,12,16 hours.

Format

An INSPEcT object

Description

A dataset containing the rates and concentrations obtained from the dataset simRates with 1 replicates and time points corresponding to: 0, 1/6, 1/3, 1/2, 1, 2, 4, 8, 16 hours. On this dataset rates and concentrations have been modeled with the method modelRates

Format

An INSPEcT object

Description

Divides the INSPEcT object into the groups defined by 'f',

Usage

## S4 method for signature 'INSPEcT,ANY'
split(x, f, drop = FALSE, ...)

Arguments

Value

A list containing objects of class INSPEcT

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
splitIdx <- c(1,1,1,2,2,2,3,3,3,4)
nascentInspObj10Split <- split(nascentInspObj10, splitIdx)

Description

Accessor to obtain the tpts associated with the object of class INSPEcT

Usage

tpts(object)

## S4 method for signature 'INSPEcT'
tpts(object)

Arguments

Value

A numeric that indicates time points contained the object

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
tpts(nascentInspObj10)

Description

A method to access the modeld confidence intervals computed via the method computeConfidenceIntervals

Usage

viewConfidenceIntervals(object, feature)

## S4 method for signature 'INSPEcT'
viewConfidenceIntervals(object, feature)

Arguments

Value

A numeric matrix containing the values for the selected feature

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
viewConfidenceIntervals(nascentInspObj10, 'synthesis')

Description

A method to access the modeld rates via the method modelRates

Usage

viewModelRates(object, feature)

## S4 method for signature 'INSPEcT'
viewModelRates(object, feature)

Arguments

Value

A numeric matrix containing the values for the selected feature

Examples

nascentInspObj10 <- readRDS(system.file(package='INSPEcT', 'nascentInspObj10.rds'))
viewModelRates(nascentInspObj10, 'synthesis')