Package 'biobroom' reference manual

Title:	Turn Bioconductor objects into tidy data frames
Description:	This package contains methods for converting standard objects constructed by bioinformatics packages, especially those in Bioconductor, and converting them to tidy data. It thus serves as a complement to the broom package, and follows the same the tidy, augment, glance division of tidying methods. Tidying data makes it easy to recombine, reshape and visualize bioinformatics analyses.
Authors:	Andrew J. Bass, David G. Robinson, Steve Lianoglou, Emily Nelson, John D. Storey, with contributions from Laurent Gatto
Maintainer:	John D. Storey <[email protected]> and Andrew J. Bass <[email protected]>
License:	LGPL
Version:	1.39.0
Built:	2024-11-27 05:13:03 UTC
Source:	https://github.com/bioc/biobroom

Tidying methods for a sva list

Description

These are methods for turning a sva list, from the sva package, into a tidy data frame. tidy returns a data.frame of the estimated surrogate variables, glance returns a data.frame of the posterior probabilities, and glance returns a data.frame with only the number of surrogate variables.

Usage

augment_sva(x, data, ...)

tidy_sva(x, addVar = NULL, ...)

glance_sva(x, ...)
augment_sva(x, data, ...)

tidy_sva(x, addVar = NULL, ...)

glance_sva(x, ...)

Arguments

`x`	sva list
`data`	Original data
`...`	extra arguments (not used)
`addVar`	add additional coefficients to the estimated surrogate variables

Value

All tidying methods return a data.frame without rownames. The structure depends on the method chosen.

augment returns one row per gene. It always contains the columns

`pprob.gam`	Posterior probability each gene is affected by heterogeneity
`pprob.b`	Posterior probability each gene is affected by model

tidy returns the estimate surrogate variables.

glance returns the estimate surrogate variables.

Convert Bioconductor Object into Tidy Data Frames

Description

This package contains methods for converting standard objects constructed by bioinformatics packages, especially those in Bioconductor, and converting them to tidy data. It thus serves as a complement to the broom package, and follows the same the tidy, augment, glance division of tidying methods. Tidying data makes it easy to recombine, reshape and visualize bioinformatics analyses.

Tidying methods for DESeq2 DESeqDataSet objects

Description

This reshapes a DESeq2 expressionset object into a tidy format. If the dataset contains hypothesis test results (p-values and estimates), this summarizes one row per gene per possible contrast.

Usage

## S3 method for class 'DESeqDataSet'
tidy(x, colData = FALSE, intercept = FALSE, ...)

## S3 method for class 'DESeqResults'
tidy(x, ...)
## S3 method for class 'DESeqDataSet'
tidy(x, colData = FALSE, intercept = FALSE, ...)

## S3 method for class 'DESeqResults'
tidy(x, ...)

Arguments

`x`	DESeqDataSet object
`colData`	whether colData should be included in the tidied output for those in the DESeqDataSet object. If dataset includes hypothesis test results, this is ignored
`intercept`	whether to include hypothesis test results from the (Intercept) term. If dataset does not include hypothesis testing, this is ignored
`...`	extra arguments (not used)

Details

colDat=TRUE adds covariates from colData to the data frame.

Value

If the dataset contains results (p-values and log2 fold changes), the result is a data frame with the columns

`term`	The contrast being tested, as given to `results`
`gene`	gene ID
`baseMean`	mean abundance level
`estimate`	estimated log2 fold change
`stderror`	standard error in log2 fold change estimate
`statistic`	test statistic
`p.value`	p-value
`p.adjusted`	adjusted p-value

If the dataset does not contain results (DESeq has not been run on it), tidy defaults to tidying the counts in the dataset:

`gene`	gene ID
`sample`	sample ID
`count`	number of reads in this gene in this sample

If colData = TRUE, it also merges this with the columns present in colData(x).

Examples


# From DESeq2 documentation

if (require("DESeq2")) {
    dds <- makeExampleDESeqDataSet(betaSD = 1)

    tidy(dds)
    # With design included
    tidy(dds, colData=TRUE)

    # add a noise confounding effect
    colData(dds)$noise <- rnorm(nrow(colData(dds)))
    design(dds) <- (~ condition + noise)

    # perform differential expression tests
    ddsres <- DESeq(dds, test = "Wald")
    # now results are per-gene, per-term
    tidied <- tidy(ddsres)
    tidied

    if (require("ggplot2")) {
        ggplot(tidied, aes(p.value)) + geom_histogram(binwidth = .05) +
            facet_wrap(~ term, scale = "free_y")
    }
}

# From DESeq2 documentation

if (require("DESeq2")) {
    dds <- makeExampleDESeqDataSet(betaSD = 1)

    tidy(dds)
    # With design included
    tidy(dds, colData=TRUE)

    # add a noise confounding effect
    colData(dds)$noise <- rnorm(nrow(colData(dds)))
    design(dds) <- (~ condition + noise)

    # perform differential expression tests
    ddsres <- DESeq(dds, test = "Wald")
    # now results are per-gene, per-term
    tidied <- tidy(ddsres)
    tidied

    if (require("ggplot2")) {
        ggplot(tidied, aes(p.value)) + geom_histogram(binwidth = .05) +
            facet_wrap(~ term, scale = "free_y")
    }
}

Tidiers for edgeR's differential expression objects

Description

Tidy, augment and glance methods for turning edgeR objects into tidy data frames, where each row represents one observation and each column represents one column.

Usage

## S3 method for class 'DGEExact'
tidy(x, ...)

## S3 method for class 'DGEList'
tidy(x, addSamples = FALSE, ...)

## S3 method for class 'DGEList'
augment(x, data = NULL, ...)

## S3 method for class 'DGEExact'
glance(x, alpha = 0.05, p.adjust.method = "fdr",
  ...)
## S3 method for class 'DGEExact'
tidy(x, ...)

## S3 method for class 'DGEList'
tidy(x, addSamples = FALSE, ...)

## S3 method for class 'DGEList'
augment(x, data = NULL, ...)

## S3 method for class 'DGEExact'
glance(x, alpha = 0.05, p.adjust.method = "fdr",
  ...)

Arguments

`x`	DGEExact, DGEList object
`...`	extra arguments (not used)
`addSamples`	Merge information from samples. Default is FALSE.
`data`	merge data to augment. This is particularly useful when merging gene names or other per-gene information. Default is NULL.
`alpha`	Confidence level to test for significance
`p.adjust.method`	Method for adjusting p-values to determine significance; can be any in p.adjust.methods

Value

tidy defaults to tidying the counts in the dataset:

`gene`	gene ID
`sample`	sample ID
`count`	number of reads in this gene in this sample

If addSamples = TRUE, it also merges this with the sample information present in x$samples.

augment returns per-gene information (DGEList only)

glance returns one row with the columns (DGEExact only)

`significant`	number of significant genes using desired adjustment method and confidence level
`comparison`	The pair of groups compared by edgeR, delimited by /

Examples

if (require("edgeR")) {
    library(Biobase)
    data(hammer)
    hammer.counts <- exprs(hammer)[, 1:4]
    hammer.treatment <- phenoData(hammer)$protocol[1:4]

    y <- DGEList(counts=hammer.counts,group=hammer.treatment)
    y <- calcNormFactors(y)
    y <- estimateCommonDisp(y)
    y <- estimateTagwiseDisp(y)
    et <- exactTest(y)

    head(tidy(et))
    head(glance(et))
}

if (require("edgeR")) {
    library(Biobase)
    data(hammer)
    hammer.counts <- exprs(hammer)[, 1:4]
    hammer.treatment <- phenoData(hammer)$protocol[1:4]

    y <- DGEList(counts=hammer.counts,group=hammer.treatment)
    y <- calcNormFactors(y)
    y <- estimateCommonDisp(y)
    y <- estimateTagwiseDisp(y)
    et <- exactTest(y)

    head(tidy(et))
    head(glance(et))
}

Tidying methods for Biobase's ExpressionSet objects

Description

Tidying methods for Biobase's ExpressionSet objects

Usage

## S3 method for class 'ExpressionSet'
tidy(x, addPheno = FALSE,
  assay = Biobase::assayDataElementNames(x)[1L], ...)
## S3 method for class 'ExpressionSet'
tidy(x, addPheno = FALSE,
  assay = Biobase::assayDataElementNames(x)[1L], ...)

Arguments

`x`	ExpressionSet object
`addPheno`	whether columns should be included in the tidied output for those in the ExpressionSet's phenoData
`assay`	The name of the `assayDataElement` to use as the values to tidy. Defaults to `assayDataElementNames(x)[1L]`, which is usually equivalent to `exprs(x)`.
`...`	extra arguments (not used)

Details

addPheno=TRUE adds columns that are redundant (since they add per-sample information to a per-sample-per-gene data frame), but that are useful for some kinds of graphs and analyses.

Value

tidy returns a data frame with one row per gene-sample combination, with columns

`gene`	gene name
`sample`	sample name (from column names)
`value`	expressions on log2 scale

Examples

library(Biobase)
# import ExpressionSet object
data(hammer)

# Use tidy to extract genes, sample ids and measured value
tidy(hammer)
# add phenoType data
tidy(hammer, addPheno=TRUE)

library(Biobase)
# import ExpressionSet object
data(hammer)

# Use tidy to extract genes, sample ids and measured value
tidy(hammer)
# add phenoType data
tidy(hammer, addPheno=TRUE)

ExpressionSet results from Hammer et al 2010

Description

An ExpressionSet containing the results of the Hammer et al 2010 RNA-Seq study on the nervous system of rats (Hammer et al 2010).

This was downloaded from the ReCount database of analysis-ready RNA-Seq datasets (Frazee et al 2011).

Hammer, P., Banck, M. S., Amberg, R., Wang, C., Petznick, G., Luo, S., Khrebtukova, I., Schroth, G. P., Beyerlein, P., and Beutler, A. S. (2010). mRNA-seq with agnostic splice site discovery for nervous system transcriptomics tested in chronic pain. Genome research, 20(6), 847-860. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2877581/

Frazee, A. C., Langmead, B., and Leek, J. T. (2011). ReCount: a multi-experiment resource of analysis-ready RNA-seq gene count datasets. BMC Bioinformatics, 12, 449. http://bowtie-bio.sourceforge.net/recount/

Usage

hammer
hammer

Format

An object of class ExpressionSet with 29516 rows and 8 columns.

Value

ExpressionSet

Tidiers for the output of limma (linear models for microarray analysis)

Description

Tidy, augment, and glance methods for MArrayLM objects, which contain the results of gene-wise linear models to microarray datasets. This class is the output of the lmFit and eBayes functions.

Usage

## S3 method for class 'MArrayLM'
tidy(x, intercept = FALSE, ...)

## S3 method for class 'MArrayLM'
augment(x, data, ...)

## S3 method for class 'MArrayLM'
glance(x, ...)

## S3 method for class 'MAList'
tidy(x, ...)

## S3 method for class 'EList'
tidy(x, addTargets = FALSE, ...)
## S3 method for class 'MArrayLM'
tidy(x, intercept = FALSE, ...)

## S3 method for class 'MArrayLM'
augment(x, data, ...)

## S3 method for class 'MArrayLM'
glance(x, ...)

## S3 method for class 'MAList'
tidy(x, ...)

## S3 method for class 'EList'
tidy(x, addTargets = FALSE, ...)

Arguments

`x`	`MArrayLM`, `MAList`, `Elist` object
`intercept`	whether the `(Intercept)` term should be included (default FALSE)
`...`	extra arguments, not used
`data`	original expression matrix; if missing, `augment` returns only the computed per-gene statistics
`addTargets`	Add sample level information. Default is FALSE.

Details

Tidying this fit computes one row per coefficient per gene, while augmenting returns one row per gene, with per-gene statistics included. (This is thus a rare case where the augment output has more rows than the tidy output. This is a side effect of the fact that the input to limma is not tidy but rather a one-row-per-gene matrix).

Value

The output of tidying functions is always a data frame without rownames.

tidy returns one row per gene per coefficient. It always contains the columns

`gene`	The name of the gene (extracted from the rownames of the input matrix)
`term`	The coefficient being estimated
`estimate`	The estimate of each per-gene coefficient

Depending on whether the object comes from eBayes, it may also contain

`statistic`	Empirical Bayes t-statistic
`p.value`	p-value computed from t-statistic
`lod`	log-of-odds score

augment returns one row per gene, containing the original gene expression matrix if provided. It then adds columns containing the per-gene statistics included in the MArrayLM object, each prepended with a .:

`.gene`	gene ID, obtained from the rownames of the input
`.sigma`	per-gene residual standard deviation
`.df.residual`	per-gene residual degrees of freedom

The following columns may also be included, depending on which have been added by lmFit and eBayes:

`.AMean`	average intensity across probes
`.statistic`	moderated F-statistic
`.p.value`	p-value generated from moderated F-statistic
`.df.total`	total degrees of freedom per gene
`.df.residual`	residual degrees of freedom per gene
`.s2.prior`	prior estimate of residual variance
`.s2.post`	posterior estimate of residual variance

glance returns one row, containing

`rank`	rank of design matrix
`df.prior`	empirical Bayesian prior degrees of freedom
`s2.prior`	empirical Bayesian prior residual standard deviation

tidy returns a data frame with one row per gene-sample combination, with columns

`gene`	gene name
`sample`	sample name (from column names)
`value`	expressions on log2 scale

tidy returns a data frame with one row per gene-sample combination, with columns

`gene`	gene name
`sample`	sample name (from column names)
`value`	expressions on log2 scale
`weight`	present if `weights` is set
`other columns`	if present and if `addTargets` is set

Examples

if (require("limma")) {
    # create random data and design
    set.seed(2014)
    dat <- matrix(rnorm(1000), ncol=4)
    dat[, 1:2] <- dat[, 1:2] + .5  # add an effect
    rownames(dat) <- paste0("g", 1:nrow(dat))
    des <- data.frame(treatment = c("a", "a", "b", "b"),
                      confounding = rnorm(4))

    lfit <- lmFit(dat, model.matrix(~ treatment + confounding, des))
    eb <- eBayes(lfit)
    head(tidy(lfit))
    head(tidy(eb))

    if (require("ggplot2")) {
        # the tidied form puts it in an ideal form for plotting
        ggplot(tidy(lfit), aes(estimate)) + geom_histogram(binwidth=1) +
            facet_wrap(~ term)
        ggplot(tidy(eb), aes(p.value)) + geom_histogram(binwidth=.2) +
            facet_wrap(~ term)
    }
}
if (require("limma")) {
    # create random data and design
    set.seed(2014)
    dat <- matrix(rnorm(1000), ncol=4)
    dat[, 1:2] <- dat[, 1:2] + .5  # add an effect
    rownames(dat) <- paste0("g", 1:nrow(dat))
    des <- data.frame(treatment = c("a", "a", "b", "b"),
                      confounding = rnorm(4))

    lfit <- lmFit(dat, model.matrix(~ treatment + confounding, des))
    eb <- eBayes(lfit)
    head(tidy(lfit))
    head(tidy(eb))

    if (require("ggplot2")) {
        # the tidied form puts it in an ideal form for plotting
        ggplot(tidy(lfit), aes(estimate)) + geom_histogram(binwidth=1) +
            facet_wrap(~ term)
        ggplot(tidy(eb), aes(p.value)) + geom_histogram(binwidth=.2) +
            facet_wrap(~ term)
    }
}

Tidiers for return values from functions that aren't S3 objects

Description

This method handles the return values of functions that return lists rather than S3 objects, such as sva, and therefore cannot be handled by S3 dispatch.

Usage

## S3 method for class 'list'
tidy(x, ...)

## S3 method for class 'list'
glance(x, ...)
## S3 method for class 'list'
tidy(x, ...)

## S3 method for class 'list'
glance(x, ...)

Arguments

`x`	list object
`...`	extra arguments, passed to the tidying function

Details

Those tiders themselves are implemented as functions of the form tidy_<function> that are not exported.

Tidying methods for Biobase's ExpressionSet objects

Description

Tidying methods for Biobase's ExpressionSet objects

Usage

## S3 method for class 'MSnSet'
tidy(x, addPheno = FALSE, ...)
## S3 method for class 'MSnSet'
tidy(x, addPheno = FALSE, ...)

Arguments

`x`	MSnSet object
`addPheno`	whether columns should be included in the tidied output for those in the MSnSet's phenoData
`...`	extra arguments (not used)

Details

addPheno=TRUE adds columns that are redundant (since they add per-sample information to a per-sample-per-gene data frame), but that are useful for some kinds of graphs and analyses.

Value

tidy returns a data frame with one row per gene-sample combination, with columns

`protein`	protein name
`sample`	sample name (from column names)
`value`	protein quantitation data

Examples

if (require("MSnbase")) {
  library(MSnbase)
  # import MSnSet object
  data(msnset)

  # Use tidy to extract genes, sample ids and measured value
  tidy(msnset)
  # add phenoType data
  tidy(msnset, addPheno=TRUE)
}
if (require("MSnbase")) {
  library(MSnbase)
  # import MSnSet object
  data(msnset)

  # Use tidy to extract genes, sample ids and measured value
  tidy(msnset)
  # add phenoType data
  tidy(msnset, addPheno=TRUE)
}

Tidying methods for a qvalue object

Description

These are methods for turning a qvalue object, from the qvalue package for false discovery rate control, into a tidy data frame. augment returns a data.frame of the original p-values combined with the computed q-values and local false discovery rates, tidy constructs a table showing how the estimate of pi0 (the proportion of true nulls) depends on the choice of the tuning parameter lambda, and glance returns a data.frame with only the chosen pi0 value.

Usage

## S3 method for class 'qvalue'
tidy(x, ...)

## S3 method for class 'qvalue'
augment(x, data, ...)

## S3 method for class 'qvalue'
glance(x, ...)
## S3 method for class 'qvalue'
tidy(x, ...)

## S3 method for class 'qvalue'
augment(x, data, ...)

## S3 method for class 'qvalue'
glance(x, ...)

Arguments

`x`	qvalue object
`...`	extra arguments (not used)
`data`	Original data

Value

All tidying methods return a data.frame without rownames. The structure depends on the method chosen.

tidy returns one row for each choice of the tuning parameter lambda that was considered (argument lambda to qvalue), containing

`lambda`	the tuning parameter
`pi0`	corresponding estimate of pi0
`smoothed`	whether the estimate has been spline-smoothed)

If pi0.method="smooth", the pi0 estimates and smoothed values both appear in the table. If pi0.method="bootstrap", smoothed is FALSE for all entries.

augment returns a data.frame with

`p.value`	the original p-values given to `qvalue`
`q.value`	the computed q-values
`lfdr`	the local false discovery rate

glance returns a one-row data.frame containing

`pi0`	the estimated pi0 (proportion of nulls)
`lambda`	lambda used to compute pi0. Note that if pi0 is 1, this may be NA since it can be ambiguous which lambda was used

Examples


library(ggplot2)
if (require("qvalue")) {
set.seed(2014)

# generate p-values from many one sample t-tests: half of them null
oracle <- rep(c(0, .5), each=1000)
pvals <- sapply(oracle, function(mu) t.test(rnorm(15, mu))$p.value)
qplot(pvals)

q <- qvalue(pvals)

tidy(q)
head(augment(q))
glance(q)

# use augmented data to compare p-values to q-values
ggplot(augment(q), aes(p.value, q.value)) + geom_point()

# use tidy see how pi0 estimate changes with lambda, comparing
# to smoothed version
g <- ggplot(tidy(q), aes(lambda, pi0, color=smoothed)) + geom_line()
g

# show the chosen value
g + geom_hline(yintercept=q$pi0, lty=2)
}
library(ggplot2)
if (require("qvalue")) {
set.seed(2014)

# generate p-values from many one sample t-tests: half of them null
oracle <- rep(c(0, .5), each=1000)
pvals <- sapply(oracle, function(mu) t.test(rnorm(15, mu))$p.value)
qplot(pvals)

q <- qvalue(pvals)

tidy(q)
head(augment(q))
glance(q)

# use augmented data to compare p-values to q-values
ggplot(augment(q), aes(p.value, q.value)) + geom_point()

# use tidy see how pi0 estimate changes with lambda, comparing
# to smoothed version
g <- ggplot(tidy(q), aes(lambda, pi0, color=smoothed)) + geom_line()
g

# show the chosen value
g + geom_hline(yintercept=q$pi0, lty=2)
}

Tidying methods for Biobase's SummarizedExperiment objects

Description

Tidying methods for Biobase's SummarizedExperiment objects

Usage

## S3 method for class 'RangedSummarizedExperiment'
tidy(x, addPheno = FALSE,
  assay = SummarizedExperiment::assayNames(x)[1L], ...)
## S3 method for class 'RangedSummarizedExperiment'
tidy(x, addPheno = FALSE,
  assay = SummarizedExperiment::assayNames(x)[1L], ...)

Arguments

`x`	SummarizedExperiment object
`addPheno`	whether columns should be included in the tidied output for those in the SummarizedExperiment colData
`assay`	Which assay to return as the `value` column. Defaults to `assays(x)[[1L]]`
`...`	extra arguments (not used)

Details

addPheno=TRUE adds columns that are redundant (since they add per-sample information to a per-sample-per-gene data frame), but that are useful for some kinds of graphs and analyses.

Value

tidy returns a data frame with one row per gene-sample combination, with columns

`gene`	gene name
`sample`	sample name (from column names)
`value`	expressions

If addPheno is TRUE then information from colData is added.

Examples

if (require("SummarizedExperiment", "airway")) {
    data(airway)

    se <- airway
    tidy(se)
}

if (require("SummarizedExperiment", "airway")) {
    data(airway)

    se <- airway
    tidy(se)
}

Tidying methods for edge's deSet object

Description

Tidying methods for edge's deSet object

Usage

## S3 method for class 'deSet'
tidy(x, addPheno = FALSE, ...)

## S3 method for class 'deSet'
augment(x, data, ...)

## S3 method for class 'deSet'
glance(x, ...)
## S3 method for class 'deSet'
tidy(x, addPheno = FALSE, ...)

## S3 method for class 'deSet'
augment(x, data, ...)

## S3 method for class 'deSet'
glance(x, ...)

Arguments

`x`	deSet object
`addPheno`	whether columns should be included in the tidied output for those in the ExpressionSet's phenoData
`...`	extra arguments (not used)
`data`	Original data can be added. Default is NULL.

Details

addPheno=TRUE adds columns that are redundant (since they add per-sample information to a per-sample-per-gene data frame), but that are useful for some kinds of graphs and analyses.

Value

tidy returns a data frame with one row per gene-sample combination, with columns

`gene`	gene name
`sample`	sample name (from column names)
`value`	expressions on log2 scale

augment returns a data.frame with

`p.value`	the original p-values given to `qvalue`
`q.value`	the computed q-values
`lfdr`	the local false discovery rate

glance returns a data.frame with the model fits

Tidying methods for GRanges and GRangesList objects.

Description

Tidying methods for GRanges and GRangesList objects.

Usage

## S3 method for class 'GRanges'
tidy(x, ...)

## S3 method for class 'GRangesList'
tidy(x, ...)

## S3 method for class 'GRanges'
glance(x, ...)

## S3 method for class 'GRangesList'
glance(x, ...)
## S3 method for class 'GRanges'
tidy(x, ...)

## S3 method for class 'GRangesList'
tidy(x, ...)

## S3 method for class 'GRanges'
glance(x, ...)

## S3 method for class 'GRangesList'
glance(x, ...)

Arguments

`x`	GRanges or GRangesList object
`...`	Not used.

Value

All tidying methods return a data.frame without rownames. tidy returns one row for each range, which contains

start of the range
end of the range
width (or length) of the range
names of the range
strand
seqname Name of the sequence from which the range comes (usually the chromosome)
metadata Any included metadata, (ie, score, GC content)

For GRangesList, there will also be a column representing which group the ranges comes from. glance returns a data.frame with the number of ranges, the number of sequences, and the number of groups (if applicable).

Examples

if (require("GenomicRanges", "airway")) {
data(airway)

# GRangesList object
air_gr <- rowRanges(airway)

tidy(air_gr)
glance(air_gr)

# GRanges object
air_gr <- rowRanges(airway)@unlistData

tidy(air_gr)
glance(air_gr)


}
if (require("GenomicRanges", "airway")) {
data(airway)

# GRangesList object
air_gr <- rowRanges(airway)

tidy(air_gr)
glance(air_gr)

# GRanges object
air_gr <- rowRanges(airway)@unlistData

tidy(air_gr)
glance(air_gr)


}

Package 'biobroom'

Help Index

Tidying methods for a sva list

Description

Usage

Arguments

Value

Convert Bioconductor Object into Tidy Data Frames

Description

Tidying methods for DESeq2 DESeqDataSet objects

Description

Usage

Arguments

Details

Value

Examples

Tidiers for edgeR's differential expression objects

Description

Usage

Arguments

Value

Examples

Tidying methods for Biobase's ExpressionSet objects

Description

Usage

Arguments

Details

Value

Examples

ExpressionSet results from Hammer et al 2010

Description

Usage

Format

Value

Tidiers for the output of limma (linear models for microarray analysis)

Description

Usage

Arguments

Details

Value

Examples

Tidiers for return values from functions that aren't S3 objects

Description

Usage

Arguments

Details

Tidying methods for Biobase's ExpressionSet objects

Description

Usage

Arguments

Details

Value

Examples

Tidying methods for a qvalue object

Description

Usage

Arguments

Value

Examples

Tidying methods for Biobase's SummarizedExperiment objects

Description

Usage

Arguments

Details

Value

Examples

Tidying methods for edge's deSet object

Description

Usage

Arguments

Details

Value

Tidying methods for GRanges and GRangesList objects.

Description

Usage

Arguments

Value

Examples