Package 'Rnits'

Title: R Normalization and Inference of Time Series data
Description: R/Bioconductor package for normalization, curve registration and inference in time course gene expression data.
Authors: Dipen P. Sangurdekar <[email protected]>
Maintainer: Dipen P. Sangurdekar <[email protected]>
License: GPL-3
Version: 1.39.0
Built: 2024-09-15 04:33:44 UTC
Source: https://github.com/bioc/Rnits

Help Index

replace slot of Rnits

Description

replace slot of Rnits

Input the RGlist raw data, build a Rnits object and perform filtering and normalization

Description

This function takes high-dimensional expression data as a RGList, creates a Rnits object for subsequent filtering and normalization

Usage

build.Rnits(obj, probedata = NULL, phenodata = NULL, filter = NULL,
  normalize = NULL, normmethod = NULL, plot = FALSE, center = FALSE,
  background = NULL, threshold = 0.8, logscale = FALSE)

Arguments

obj

Raw expression data in RGlist, AffyBatch or simple data frame format
probedata

A data frame containing the probe names that should match the probe names in raw data (optional)
phenodata

A data frame with information about sample names. The rownames of the data frame must match column names of the expression values. If input data is data frame of log ratios, this is required.
filter

An argument to perform background filtering of probes. If NULL, no filtering is done. If an integer (0-500), probes are flagged based on raw channel intensity. If a vector of two numbers is provided, the first will be used for red channel and the second for green channel. If 'background', probes whose intensities are lower than 2 standard deviations less than the mean of the background intensity for the channel are flagged.
normalize

Character string specifying the normalization method for raw data. If Intensity, the reference channels for all arrays are used to construct an array-specific smoothing function which is then applied to normalize the sample channel. If Between, the normalization method normalizeBetweenArrays in the LIMMA package is used (use normmethod to further specify normalization methods. See packaged LIMMA for details.). If Within, the normalization method normalizeWithinArrays in the LIMMA package is used.
normmethod

Normalization method for input data. Default NULL. Can be one of 'quantile', 'vsn', 'Between'
background

Only for AffyBatch data. If TRUE, background filtering will be done on Affy data.
center

If TRUE, the log-ratio data will be mean centered to 0 in the column space.
plot

If TRUE, boxplots of normalized channel intensities and log-ratios are drawn.
threshold

Default 0.8. Fraction of samples with missing data for individual probes to be filtered out.
logscale

Default FALSE. Is the data in logscale? If FALSE, log2 transformation is done on the data.

Details

See the Limma User's Guide for more details on read.maimages, normalizeBetweenArrays, normalizeWithinArrays and RGList. For importing microarray raw data, use the 'Targets file' to specify experimental design. The target file has columns SlideNumber, FileName, Cy3 (description of Cy3 channel ref/control/treatment), Cy5 (description of Cy3 channel ref/control/treatment) and Time. Time values should be identical for control and treatment.

Value

An object of S4 class Rnits (which is derived from class exprSet), containing the probe data, design data, expression data, phenotypical data (i.e. Time).

See Also

ExpressionSet

Examples

# load pre-compiled expressionSet object for Ronen and Botstein yeast chemostat data
data(yeastchemostat)
rnitsobj = build.Rnits(yeastchemostat, logscale = TRUE, normmethod = 'Between')

Calculate the optimal B-spline model using generalized cross-validation

Description

Calculate the optimal B-spline model using generalized cross-validation

Usage

calculateGCV(object, topcomp = 5)

## S4 method for signature 'Rnits'
calculateGCV(object, topcomp = 5)

Arguments

object

Rnits object
topcomp

The number of top eigenvectors to be used for computation

Details

The optimal B-spline model is chosen as the largest model that minimizes the cross validation error of the top N eigenvectors of each time series data.

Value

A list object with fields 'degree', 'df' for each time series data set.

Examples

# load pre-compiled expressionSet object for Ronen and Botstein yeast chemostat data
data(yeastchemostat)
rnitsobj = build.Rnits(yeastchemostat, logscale = TRUE, normmethod = 'Between')
opt_model <- calculateGCV(rnitsobj)
## Not run: 
rnitsobj <- fit(rnitsobj, gene.level = TRUE, model = opt.model)

## End(Not run)

Fit model on time series data

Description

Fit a model comparing time series data set Rnits objects

Usage

fit(object, cluster = TRUE, B = 100, verbatim = FALSE, nclus = NULL,
  modelhistplot = FALSE, seed = 123, gene.level = TRUE,
  clusterallsamples = FALSE, model = NULL)

## S4 method for signature 'Rnits'
fit(object, cluster = TRUE, B = 100, verbatim = FALSE,
  nclus = NULL, modelhistplot = FALSE, seed = 123, gene.level = TRUE,
  clusterallsamples = FALSE, model = NULL)

Arguments

object

Rnits object
cluster

if TRUE, perform clustering to identify groups of genes/probes with similar expression profiles.
B

Default 100. Number of bootstrap iterations for p-value calculation
verbatim

If FALSE, print out details of fitting models.
nclus

Default NULL. Number of clusters to use for k-means clustering.
modelhistplot

If TRUE, p-value histograms of multiple models are plotted.
seed

Random seed for bootstrap iterations
gene.level

If TRUE, collapse probes to gene level information.
clusterallsamples

If TRUE, Use all time series for clustering. By default, only the sample labeled 'control' is used or the lexically first sample is used.
model

A data frame with fields 'degree' and 'df' indicating a specific B-spline model to be used. If provided, model selection is not run.

Details

The function compares multiple time-series expression data sets by i) (optional) summarizing probes into gene-level information ii) (optional) identifying a set of co-expressed genes by clustering iii) For each cluster (or for all genes /probes), fit a series of B-splines with varying curvature and degrees of freedom. Under the null hypothesis H_0, a single model is fit for all data sets, while under H_1, each data set is fit separately. P-values from the hypothesis test are then plotted and the least complex spline parameters that result in uniformly distributed null p-values are automatically chosen.

Value

An object of S4 class Rnits with fitted results data containing cluster information, ratio statistics and p-values.

Examples

# load pre-compiled expressionSet object for Ronen and Botstein yeast  chemostat data
data(yeastchemostat)
rnitsobj = build.Rnits(yeastchemostat, logscale = TRUE, normmethod = 'Between')
## Not run: 
# Fit model using gene-level summarization
rnitsobj <- fit(rnitsobj, gene.level = TRUE, clusterallsamples = FALSE)

## End(Not run)

Cluster IDs of probes/genes from fitted Rnits

Description

Retrieve cluster IDs of probes/genes from fitted Rnits object after fit has been run.

Usage

getCID(object)

## S4 method for signature 'Rnits'
getCID(object)

Arguments

object

Rnits

Details

If cluster = False during fitting, a vector of 1s will be returned.

Value

A vector of cluster IDs corresponding to gene/probe names

Examples

# load pre-compiled expressionSet object for Ronen and Botstein yeast  chemostat data
data(yeastchemostat)
rnitsobj = build.Rnits(yeastchemostat, logscale = TRUE, normmethod = 'Between')
## Not run: 
# Fit model using gene-level summarization
rnitsobj <- fit(rnitsobj, gene.level = TRUE, clusterallsamples = FALSE)

# Get cluster IDs from fitted model
cid <- getCID(rnitsobj)

## End(Not run)

Extract fit data from Rnits object

Description

Retrieve model fit data from Rnits object after fit has been run.

Usage

getFitModel(object)

## S4 method for signature 'Rnits'
getFitModel(object)

Arguments

object

Rnits

Details

Contains Ratio statistic, p-value and cluster ID data

Value

A data frame containing the model fit results for all genes

Examples

# load pre-compiled expressionSet object for Ronen and Botstein yeast  chemostat data
data(yeastchemostat)
rnitsobj = build.Rnits(yeastchemostat, logscale = TRUE, normmethod = 'Between')
## Not run: 
# Fit model using gene-level summarization
rnitsobj <- fit(rnitsobj, gene.level = TRUE, clusterallsamples = FALSE)

# P-values, ratio statistics and cluster ID's can be retrieved for all genes together
fitdata <- getFitModel(rnitsobj)

## End(Not run)

Get log-ratios

Description

Extract normalized log-ratios from Rnits object

Usage

getLR(object, impute = FALSE)

## S4 method for signature 'Rnits'
getLR(object, impute = FALSE)

Arguments

object

Rnits object
impute

If TRUE, perform K-NN imputation to fill missing values

Value

A matrix of normalized log-ratios.

Examples

# load pre-compiled expressionSet object for Ronen and Botstein yeast  chemostat data
data(yeastchemostat)
rnitsobj = build.Rnits(yeastchemostat, logscale = TRUE, normmethod = 'Between')
## Not run: 
# Fit model using gene-level summarization
rnitsobj <- fit(rnitsobj, gene.level = TRUE, clusterallsamples = FALSE)

# Get logratios
lr <- getLR(rnitsobj)

## End(Not run)

Get Normalized channel data for two channel arrays

Description

For two color data, extract normalized channel data from Rnits object

Usage

getNormTwoChannel(object)

## S4 method for signature 'Rnits'
getNormTwoChannel(object)

Arguments

object

Rnits object

Value

A list containing R and G fields for normalized Red and Green channel data respectively.

Get p-values

Description

Extract p-values from fitted Rnits object

Usage

getPval(object)

## S4 method for signature 'Rnits'
getPval(object)

Arguments

object

Rnits object

Value

An vector of p-values

Examples

# load pre-compiled expressionSet object for Ronen and Botstein yeast  chemostat data
data(yeastchemostat)
rnitsobj = build.Rnits(yeastchemostat, logscale = TRUE, normmethod = 'Between')
## Not run: 
# Fit model using gene-level summarization
rnitsobj <- fit(rnitsobj, gene.level = TRUE, clusterallsamples = FALSE)

#Get pvalues from fitted model
pval <- getPval(rnitsobj)


## End(Not run)

Retrieve ratio statistics

Description

Extract ratio statistics from fitted Rnits object

Usage

getStat(object)

## S4 method for signature 'Rnits'
getStat(object)

Arguments

object

Rnits object

Value

An vector of ratio statistics

Examples

# load pre-compiled expressionSet object for Ronen and Botstein yeast  chemostat data
data(yeastchemostat)
rnitsobj = build.Rnits(yeastchemostat, logscale = TRUE, normmethod = 'Between')
## Not run: 
# Fit model using gene-level summarization
rnitsobj <- fit(rnitsobj, gene.level = TRUE, clusterallsamples = FALSE)

# Get ratio statistics from fitted model
stat <- getStat(rnitsobj)

## End(Not run)

Plot profiles of top genes/probes

Description

After fit has been applied on Rnits object, plot the profiles of N top ranking genes/probes.

Usage

plotResults(object, id = NULL, fdr = NULL, top = 48, pdf = FALSE,
  sort.by = "p-value", filename = "TopPlots.pdf", scale_y = NULL)

## S4 method for signature 'Rnits'
plotResults(object, id = NULL, fdr = NULL, top = 48,
  pdf = FALSE, sort.by = "p-value", filename = "TopPlots.pdf",
  scale_y = NULL)

Arguments

object

Rnits object.
id

Names of specific genes or probes to be plotted. Overrides fdr and top argument.
fdr

FDR cut-off plotting top probes or genes. Overrides top argument.
top

Number of top genes or probes whose profile is to be plotted. Default 48.
pdf

Save plot as pdf? Default FALSE.
sort.by

Criteria for sorting top genes or probes. Default 'p-value'.
filename

Name of pdf file. Default topplots.pdf.
scale_y

If 'free', use free scales for plots. Default NULL.
...

Optional arguments to plot

Examples

# load pre-compiled expressionSet object for Ronen and Botstein yeast  chemostat data
data(yeastchemostat)
rnitsobj = build.Rnits(yeastchemostat, logscale = TRUE, normmethod = 'Between')
## Not run: 
# Fit model using gene-level summarization
rnitsobj <- fit(rnitsobj, gene.level = TRUE, clusterallsamples = FALSE)

# Plot top results
plotResults(rnitsobj, top = 16)


## End(Not run)

rnits.

Description

rnits.

rnits class

Description

Class rnits for time series

Details

Some details

Summarize probe level data to gene level data

Description

The code utilizes the probe-gene mapping from features file to summarize probe-level log ratios to gene level ratios.

Usage

summarizeProbes(object)

## S4 method for signature 'Rnits'
summarizeProbes(object)

Arguments

object

Rnits object

Details

Tukey's biweight is used to compute gene level summary

Value

An object of class Rnits with gene level log ratios, which can be retrieved by getLR(object)

Examples

# load pre-compiled expressionSet object for Ronen and Botstein yeast  chemostat data
data(yeastchemostat)
rnitsobj = build.Rnits(yeastchemostat, logscale = TRUE, normmethod = 'Between')

# Summarize gene-level data
rnitsobj <- summarizeProbes(rnitsobj)

Summary of fit

Description

Summarize top genes or probes from Rnits fit method

Usage

## S4 method for signature 'Rnits'
summary(object, top = 48, fdr = NULL, plot = FALSE,
  sort.by = "p-value")

Arguments

object

Rnits object on which fit has been applied
top

Display results for top N genes/probes. Default 50
fdr

Display results for genes/probes less than FDR (%) cutoff (if provided). Overrides top argument
plot

If TRUE, plot histogram of p-values
sort.by

Sort top results by either p-value or FDR

Value

A table of top genes/profiles listing the ratio statistics, p-values, q-values and cluster information.

Examples

# load pre-compiled expressionSet object for Ronen and Botstein yeast  chemostat data
data(yeastchemostat)
rnitsobj = build.Rnits(yeastchemostat, logscale = TRUE, normmethod = 'Between')
## Not run: 
# Fit model using gene-level summarization
rnitsobj <- fit(rnitsobj, gene.level = TRUE, clusterallsamples = FALSE)

# Get summary of top genes
summary(rnitsobj, FDR = 5)


## End(Not run)

Curve registration of time series curves

Description

Align multiple time series to the average seris

Usage

timeAlign(object, iterMax = 5, seed = 123, null.frac = 0.75,
  anchor = NULL, rerun = FALSE, plot = FALSE)

## S4 method for signature 'Rnits'
timeAlign(object, iterMax = 5, seed = 123,
  null.frac = 0.75, anchor = NULL, rerun = FALSE, plot = FALSE)

Arguments

object

rnits object
iterMax

Maximum iterations to be performed
seed

Random seed
null.frac

Fraction of genes that are considered as null
anchor

Sample to be considerded as base for aligning time series. If not provided, the average is used
rerun

If TRUE, re-align previously aligned data
plot

If TRUE, plot results

Examples

# load pre-compiled expressionSet object for Ronen and Botstein yeast  chemostat data
data(yeastchemostat)
rnitsobj = build.Rnits(yeastchemostat, logscale = TRUE, normmethod = 'Between')

# Do curve-registration on data
rnitsobj <- timeAlign(rnitsobj)

Data of top genes/probes

Description

Extract expression data for top genes/probes

Usage

topData(object, id = NULL, fdr = NULL, top = 16, sort.by = "p-value")

## S4 method for signature 'Rnits'
topData(object, id = NULL, fdr = NULL, top = 16,
  sort.by = "p-value")

Arguments

object

Rnits object on which fit has been applied
id

Names of probes or genes
top

Display results for top N genes/probes. Default 50
fdr

Display results for genes/probes less than FDR cutoff (if provided). Overrides top argument
sort.by

Sort top results by either p-value or FDR

Value

A table of expression values of top genes/profiles

Examples

# load pre-compiled expressionSet object for Ronen and Botstein yeast  chemostat data
data(yeastchemostat)
rnitsobj = build.Rnits(yeastchemostat, logscale = TRUE, normmethod = 'Between')
## Not run: 
# Fit model using gene-level summarization
rnitsobj <- fit(rnitsobj, gene.level = TRUE, clusterallsamples = FALSE)

#Get data for top genes
td <- topData(rnitsobj, FDR = 5)


## End(Not run)

Yeast chemostat data from Ronen and Botstein (Proc Natl Acad Sci U S A. 2006 Jan 10;103(2):389-94. Epub 2005 Dec 28.)

Description

(From author's GEO submission) Transcriptional response of steady-state yeast cultures to transient perturbations in carbon source

Usage

yeastchemostat

Format

An ExpressionSet object with containing 'Sample' and 'Time' columns and replicates removed.

Source

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4158