Class "CountDataSet" -- a container for count data from HTS experiments

Description

This is the main class for the present package.

Note

Note: This is a summary for reference. For an explanation of the actual usage, see the vignette.

A CountDataSet object stores counts from an HTS data set and offers further slots which are populated during the analysis.

After creation with newCountDataSet , a CountDataSet typically contains a count table, i.e., a matrix of integer data, that is accessible with the accessor function counts . Each row of the matrix corresponds to a gene (or binding region, or the like), and each colum to an experimental sample. The experimental conditions of the samples are stored in a factor (with one element for each row of the counts matrix), which can be read with the accessor function conditions .

In the following analysis steps, further data slots are populated. First, the size factors can be estimated with estimateSizeFactors , which are afterwards accessible via sizeFactors . Then, the dispersions (variance fits) are estimated with estimateDispersions . The resulting estimates are stored in phenoData columns, accessible via pData , with the column names staring with disp_ . The intermediate steps of the fit are stored in the environment-values slot fitInfo (see estimateDispersions for details).

Internally, the mentioned data is stored in slots as follows:

As CountDataSet is derived from eSet , it has a phenoData slot which allows to store sample annotation. This is used to store the factor with the conditions, as a data frame column named condition , and to store the size factors, as an numeric data frame column named sizeFactor . If the user creates an object with multivariate design, i.e., passes a data frame instead of a factor for conditions , this data frame's columns are placed in the phenoData slot instead of the condition column. Furthermore, the function estimateDispersions adds columns with the dispersion values to be used by nbinomTest and fitNbinomGLMs . These columns have names starting with disp_ .

The user may add further columns to the phenoData AnnotatedDataFrame.

The counts table is stored in the eSet 's assayData locked environment with the name counts .

The slot dispInfo is an environment containing lists, one for each set of estimated dispersion values and the slot dispTable (with accessor dispTable shows the assignment of conditions to dispersion estimates. See estimateDispersions

Examples

# See the vignette

Adjust an SCV value for the bias arising when it is calculated from unbiased estimates of mean and variance.

Description

Assume that a small sample of i.i.d. random variables from a negative binomial distribution is given, and you have obtained unbiased estimates of mean and raw variance. Then, a new bias is introduced when the squared coefficient of variation (SCV, a.k.a. dispersion) is calculated from these unbiased estimates by dividing the raw variance by the square of the mean. This bias can be calculated by numerical simulation and a pre-calculated adjustment table (or rather a fit through tabulated values) is supplied with the package. The present function uses this to remove the bias from a raw SCV estimate.

This function is used internally in nbinomTest . You will rarely need to call it directly.

Usage

adjustScvForBias(scv, nsamples)

Arguments

Value

an unbiased estimate of the raw SCV

Author

Simon Anders

Examples

true_mean <- 100
true_scv <- .1
nsamples <- 3
res <- replicate( 1000, {
mySample <- rnbinom( nsamples, mu=true_mean, size=1/true_scv )
mu_est <- mean( mySample )
raw_var_est <- var( mySample ) - mean( mySample )
raw_scv_est <- raw_var_est / mu_est^2
unbiased_raw_scv_est <- adjustScvForBias( raw_scv_est, 4 )
c( raw_scv_est = raw_scv_est, unbiased_raw_scv_est = unbiased_raw_scv_est ) } )
rowMeans( res )

Accessor functions for the 'conditions' information in a CountDataSet object.

Description

The conditions vector is a factor that assigns to each column of the count data a condition (or treatment, or phenotype, or the like). This information is stored in the CountDataSet's "phenoData" slot as a row named "condition".

Usage

list(list("conditions"), list("CountDataSet"))(object, ...)
list(list("conditions"), list("CountDataSet"))(object) <- value

Arguments

Author

Simon Anders, sanders@fs.tum.de

Examples

cds <- makeExampleCountDataSet()
conditions( cds )

Accessors for the 'counts' slot of a CountDataSet object.

Description

The counts slot holds the count data as a matrix of non-negative integer count values, one row for each observational unit (gene or the like), and one column for each sample.

Usage

list(list("counts"), list("CountDataSet"))(object, normalized=FALSE)
list(list("counts"), list("CountDataSet,matrix"))(object) <- value

Arguments

Author

Simon Anders, sanders@fs.tum.de

Examples

cds <- makeExampleCountDataSet()
head( counts( cds ) )

Accessor function for the dispTable information in a CountDataSet

Description

The dispersion table ("dispTable") is a named vector that assigns to each condition (as name) a dispersion column (as value). If nbinomTest is called to compare two conditions, say "A" and "B", DESeq looks up in the dispTable, which dispersion columns to use. In the standard case (see example), these are just the dispersions for "A" and "B", i.e., the columns disp_A and disp_B in fData(object) . If the "pooled" or "blind" variance estimation is used, all conditions are assigned the same column.

Usage

dispTable(object,...)

Arguments

Seealso

estimateDispersions , nbinomTest

Author

Simon Anders, sanders@fs.tum.de

Examples

cds <- makeExampleCountDataSet()
cds <- estimateSizeFactors( cds )
cds <- estimateDispersions( cds )
dispTable( cds )

Estimate and fit dispersions for a CountDataSet.

Description

This function obtains dispersion estimates for a count data set. For each condition (or collectively for all conditions, see 'method' argument below) it first computes for each gene an empirical dispersion value (a.k.a. a raw SCV value), then fits by regression a dispersion-mean relationship and finally chooses for each gene a dispersion parameter that will be used in subsequent tests from the empirical and the fitted value according to the 'sharingMode' argument.

Usage

list(list("estimateDispersions"), list("CountDataSet"))( object,
   method = c( "pooled", "pooled-CR", "per-condition", "blind" ),
   sharingMode = c( "maximum", "fit-only", "gene-est-only" ),
   fitType = c("parametric", "local"),
   locfit_extra_args=list(), lp_extra_args=list(),
   modelFrame = NULL, modelFormula = count ~ condition, ... )

Arguments

• pooled - Use the samples from all conditions with replicates to estimate a single pooled empirical dispersion value, called "pooled", and assign it to all samples.

• pooled-CR - Fit models according to modelFormula and estimate the dispersion by maximizing a Cox-Reid adjusted profile likelihood (CR-APL). This method is much slower than method=="pooled" but works also with crossed factors (as may occur, e.g., in designs with paired samples). Usually, you will need to specify the model formula, which should be the same as the one used later in the call to nbinomFitGLMs for fitting the full model. Note: The method of using CR-APL maximization for this application has been developed by McCarthy, Chen and Smyth [Nucl. Acid Res., 2012 and been first implemented in edgeR (in 2010). DESeq optimizes the expression for the CR-APL given in McCarthy et al.'s paper, but does not use the weigthed maximum likelihood scheme proposed there.

• per-condition - For each condition with replicates, compute a gene's empirical dispersion value by considering the data from samples for this condition. For samples of unreplicated conditions, the maximum of empirical dispersion values from the other conditions is used. If object has a multivariate design (i.e., if a data frame was passed instead of a factor for the condition argument in newCountDataSet ), this method is not available. (Note: This method was called `normal'' in previous versions.) *blind- Ignore the sample labels and compute a gene's empirical dispersion value as if all samples were replicates of a single condition. This can be done even if there are no biological replicates. This method can lead to loss of power; see the vignette for details. The single estimated dispersion condition is called "blind" and used for all samples. |sharingMode| After the empirical dispersion values have been computed for each gene, a dispersion-mean relationship is fitted for sharing information across genes in order to reduce variability of the dispersion estimates. After that, for each gene, we have two values: the empirical value (derived only from this gene's data), and the fitted value (i.e., the dispersion value typical for genes with an average expression similar to those of this gene). ThesharingModeargument specifies which of these two values will be written to thefeatureData'sdispcolumns and hence will be used by the functions [nbinomTest](#nbinomtest) and [fitNbinomGLMs](#fitnbinomglms) . | *fit-only- use only the fitted value, i.e., the empirical value is used only as input to the fitting, and then ignored. Use this only with very few replicates, and when you are not too concerned about false positives from dispersion outliers, i.e. genes with an unusually high variability. *maximum- take the maximum of the two values. This is the conservative or prudent choice, recommended once you have at least three or four replicates and maybe even with only two replicates. *gene-est-only- No fitting or sharing, use only the empirical value. This method is preferable when the number of replicates is large and the empirical dispersion values are sufficiently reliable. If the number of replicates is small, this option may lead to many cases where the dispersion of a gene is accidentally underestimated and a false positive arises in the subsequent testing. |fitType| | *parametric- Fit a dispersion-mean relation of the formdispersion = asymptDisp + extraPois / meanvia a robust gamma-family GLM. The coefficientsasymptDispandextraPoisare given in the attributecoefficientsof thedispFuncin thefitInfo(see below). *local- Use the locfit package to fit a dispersion-mean relation, as described in the DESeq paper. |locfit_extra_args, lp_extra_args| (only forfitType=local) Options to be passed to thelocfitand to thelpfunction of the locfit package. Use this to adjust the local fitting. For example, you may pass a value fornndifferent from the default (0.7) if the fit seems too smooth or too rough by settinglp_extra_agrs=list(nn=0.9). As another example, you can setlocfit_extra_args=list(maxk=200)if you get the error that locfit ran out of nodes. See the documentation of thelocfitpackage for details. In most cases, you will not need to provide these parameters, as the defaults seem to work quite well.| |modelFrame| By default, the information inconditions(object)orpData(object)is used to determine which samples are replicates (see [newCountDataSet](#newcountdataset) ). Formethod="pooled", a data frame can be passed here, and all rows that are identical in this data frame are considered to indicate replicate samples inobject. Formethod="pooled-CR", the data frame is used in the fits. For the other methods, this argument is ignored. | |modelFormula| Formethod="pooled-CR", this is the formual used for the dispersion fits. For all other methods, this argument is ignored. | |...| extra arguments are ignored| ## Details Behaviour formethod="per-condition": For each replicated condition, a list, named with the condition's name, is placed in the environmentobject@fitInfo. This list has five named elements: The vectorperGeneDispEstscontains the empirical dispersions. The functiondispFuncis the fitted function, i.e., it takes as its argument a normalized mean expression value and returns the corresponding fitted dispersion. The values fitted according to this function are in the third elementfittedDispEst, a vector of the same length asperGeneDispEsts. The fourt element,df, is an integer, indicating the number of degrees of freedom of the per-gene estimation. The fifth element,sharingMode, stores the value of thesharingModeargument toesimateDispersions. Behaviour formethod="blind"andmethod="pooled": Only one list is produced, named"blind"or"pooled"and placed inobject@fitInfo. For each list in thefitInfoenvironment, the dispersion values that are intended to be used in subsequent testing are computed according to the value ofsharingModeand are placed in thefeatureDatadata frame, in a column named with the same name, prefixed with "disp". Then, the [dispTable](#disptable) (see there) is filled to assign to each condition the appropriate dispersion column in the phenoData frame. Note: Up to DESeq version 1.4.x (Bioconductor release 2.8), this function was calledestimateVarianceFunctions, stored its result differently and did not have the argumentssharingModeandfitType.estimatevarianceFunction's behaviour corresponded to the settingssharingMode="fit-only"andfitType="local". Note that these are not the default, because the new defaultssharingMode="maximum"andfitType="parametric"are more robust and tend to give better results. ## Value TheCountDataSetcds, with the slotsfitInfoandfeatureData` updated as described in Details. ## Author Simon Anders, sanders@fs.tum.de ## Examples r cds <- makeExampleCountDataSet() cds <- estimateSizeFactors( cds ) cds <- estimateDispersions( cds ) str( fitInfo( cds ) ) head( fData( cds ) )

Estimate the size factors for a CountDataSet

Description

Estimate the size factors for a CountDataSet

Usage

list(list("estimateSizeFactors"), list("CountDataSet"))( object, locfunc=median, ... )

Arguments

Details

You need to call this function right after newCountDataSet unless you have manually specified size factors.

Typically, the function is called with the idiom

cds <- estimateSizeFactors( cds )

This estimates the size factors and stores the information in the object.

Internally, the function calls estimateSizeFactorsForMatrix . See there for more details on the calculation.

Value

The CountDataSet passed as parameters, with the size factors filled in.

Seealso

estimateSizeFactorsForMatrix

Author

Simon Anders, sanders@fs.tum.de

Examples

cds <- makeExampleCountDataSet()
cds <- estimateSizeFactors( cds )
sizeFactors( cds )

Low-level function to estimate size factors with robust regression.

Description

Given a matrix or data frame of count data, this function estimates the size factors as follows: Each column is divided by the geometric means of the rows. The median (or, ir requested, another location estimator) of these ratios (skipping the genes with a geometric mean of zero) is used as the size factor for this column.

Typically, you will not call this function directly, but use estimateSizeFactors .

Usage

estimateSizeFactorsForMatrix( counts, locfunc=median)

Arguments

Value

a vector with the estimates size factors, one element per column

Seealso

estimateSizeFactors

Author

Simon Anders, sanders@fs.tum.de

Examples

cds <- makeExampleCountDataSet()
estimateSizeFactorsForMatrix( counts(cds) )

REMOVED

Description

This function has been removed. Instead, use estimateDispersions .

Accessor function for the fitInfo objects in a CountDataSet

Description

After calling estimateDispersions , a CountDataSet object is populated with one or (in case of a `per-condition'' estimation) several fitInfo objects, which can be accessed with this function. ## Usage ```r fitInfo( cds, name=NULL ) ``` ## Arguments |Argument |Description| |------------- |----------------| |cds| a CountDataSet| |name| ifestimateDispersionwas called withmethod="per-condition"a name hasd to specified. Tryls(cds@fitInfo.| ## Seealso [estimateDispersions`](#estimatedispersions) ## Author Simon Anders, sanders@fs.tum.de ## Examples r cds <- makeExampleCountDataSet() cds <- estimateSizeFactors( cds ) cds <- estimateDispersions( cds ) str( fitInfo( cds ) )

Fit a generalized linear model (GLM) for each gene.

Description

Use this function to estimate coefficients and calculate deviance from a GLM for each gene. The GLM uses the nbkd.sf family, with the dispersion estimate according to getVarianceFunction(cds) . Note that this requires that the variance functions were estimated with method "pooled" or "blind".

Usage

fitNbinomGLMs( cds, modelFormula, glmControl=list() )

Arguments

Value

A data frame with one row for each gene and columns as follows:

• list(" one column for each estimated coefficient, on a log2 scale (i.e., the natural ", "log reported by ", list(list("glm")), " is rescaled to base 2) ")

• list(" a column 'deviance', with the deviance of the fit ")

• list(" a boolean column 'converged', indicating whether the fit converged ")
  Furthermore, the data frame has a scalar attribute 'df.residual' that contains the number of residual degrees of freedom.

Seealso

newCountDataSet , nbinomGLMTest , nbkd.sf

Author

Simon Anders (sanders@fs.tum.de)

Examples

# see nbinomGLMTest for an example

Fit negative binomial GLMs to a count matrix.

Description

This is a low-level function that is wrapped by nbinomGLMTest .

Usage

fitNbinomGLMsForMatrix(counts, sizeFactors, rawScv, modelFormula, 
   modelFrame, quiet = FALSE, reportLog2 = TRUE, glmControl = list() )

Arguments

Value

A data frame with one row for each gene and columns as follows:

• list(" one column for each estimated coefficient, on a log2 scale (i.e., the natural ", "log reported by ", list(list("glm")), " is rescaled to base 2) ")

• list(" a column 'deviance', with the deviance of the fit ")

• list(" a boolean column 'converged', indicating whether the fit converged ")
  Furthermore, the data frame has a scalar attribute 'df.residual' that contains the number of residual degrees of freedom.

Author

Simon Anders, sanders@fs.tum.de

Examples

# See the code of fitNbinomGLMs for an example.

Perform row-wise estimates of base-level means and variances for count data.

Description

This function is called internally by a number of other functions. You will need to call it directly only in very special cases.

Usage

getBaseMeansAndVariances(counts, sizeFactors)

Arguments

Details

This function is kept for backwards compatibility. See the example below for an alternative and more self-explanatory way to get the same data.

Value

A data frame with one row for each row in 'counts' and two columns:

*

Author

Simon Anders, sanders@fs.tum.de

Examples

cds <- makeExampleCountDataSet()
cds <- estimateSizeFactors( cds )
head( getBaseMeansAndVariances( counts(cds), sizeFactors(cds) ) )

# You can get the same as follows
head( rowMeans( counts( cds, normalized=TRUE ) ) )
head( genefilter::rowVars( counts( cds, normalized=TRUE ) ) )

Apply a variance stabilizing transformation (VST) to the count data

Description

This function calculates a variance stabilizing transformation (VST) from the fitted dispersion-mean relation(s) and then transforms the count data (normalized by division by the size factor), yielding a matrix of values which are now approximately homoskedastic. This is useful as input to statistical analyses requiring homoskedasticity.

Usage

varianceStabilizingTransformation(cds)
getVarianceStabilizedData(cds)

Arguments

Details

For each sample (i.e., column of counts(cds) ), the full variance function is calculated from the raw variance (by scaling according to the size factor and adding the shot noise). The function requires a blind estimate of the variance function, i.e., one ignoring conditions. Usually, this is achieved by calling estimateDispersions with method="blind" before calling it. A typical workflow is shown in Section Variance stabilizing transformation in the package vignette.

If estimateDispersions was called with fitType="parametric" , a closed-form expression for the variance stabilizing transformation is used on the normalized count data. The expression can be found in the file vst.pdf which is distributed with the vignette.

If estimateDispersions was called with fitType="locfit" , the reciprocal of the square root of the variance of the normalized counts, as derived from the dispersion fit, is then numerically integrated, and the integral (approximated by a spline function) is evaluated for each count value in the column, yielding a transformed value.

In both cases, the transformation is scaled such that for large counts, it becomes asymptotically (for large values) equal to the logarithm to base 2.

Limitations: In order to preserve normalization, the same transformation has to be used for all samples. This results in the variance stabilizition to be only approximate. The more the size factors differ, the more residual dependence of the variance on the mean you will find in the transformed data. As shown in the vignette, you can use the function meanSdPlot from the package vsn to see whether this is a problem for your data.

Value

For varianceStabilizingTransformation , an ExpressionSet .

For getVarianceStabilizedData , a matrix of the same dimension as the count data, containing the transformed values.

Author

Simon Anders sanders@fs.tum.de

Examples

cds <- makeExampleCountDataSet()
cds <- estimateSizeFactors( cds )
cds <- estimateDispersions( cds, method="blind" )
vsd <- getVarianceStabilizedData( cds )
colsA <- conditions(cds) == "A"
plot( rank( rowMeans( vsd[,colsA] ) ), genefilter::rowVars( vsd[,colsA] ) )

make a simple example CountDataSet with random data

Description

This function returns an example CountDataSet. It is used for the examples in the package help pages.

Usage

makeExampleCountDataSet()

Value

a CountDataSet that has been constructed as follows: First, true base mean values for 10,000 genes are drawn from an exponential distribution with rate 1/250. Then, certain genes are declared (with probability 0.3 per gene) as truly differentially expressed (tDE). For these genes, the true base mean is split into two values, one for condition "A" and one for condition "B", such that the log2 fold change from "A" to "B" follows a zero-centred normal distribution with standard deviation 2. Then, counts are drawn for each gene for 5 samples, the first three corresponding to condition "A" and the remaining two for condition "B". The counts are drawn from a negative binomial with the specified mean, multiplied by the size factor for the sample, with a constant raw SCV (dispersion) of 0.2 (i.e., a 'size' parameter of 1/0.2). The true size factors are fixed to c( 1., 1.3, .7, .9, 1.6 ).

All these values were chosen to give data that at least somewhat resembles what one might encounter in an actual experiment. Note that this function is not meant to verify the package by simulation. For this purpose the parameters and distribution choices should be more varied.

Author

Simon Anders, anders@embl.de

Examples

cds <- makeExampleCountDataSet()

Perform chi-squared tests comparing two sets of GLM fits

Description

For each gene, the function calculates a chi-square p value by simply calculating: 1 - pchisq(resReduced$deviance - resFull$deviance, attr(resReduced,

Usage

nbinomGLMTest(resFull, resReduced)

Arguments

Value

a vector of p values

Seealso

fitNbinomGLMs

Author

Simon Anders, anders@embl.de

Examples

cds <- makeExampleCountDataSet()[ 1:100, ]
cds <- estimateSizeFactors( cds )
cds <- estimateDispersions( cds, method="pooled" )
fit1 <- fitNbinomGLMs( cds, count ~ condition )
fit0 <- fitNbinomGLMs( cds, count ~ 1 )
nbinomGLMTest( fit1, fit0 )

Test for differences between the base means for two conditions

Description

This function tests for differences between the base means of two conditions (i.e., for differential expression in the case of RNA-Seq).

Usage

nbinomTest(cds, condA, condB, pvals_only = FALSE, eps=NULL )

Arguments

Details

See nbinomTestForMatrices for more technical informations

Value

A data frame with the following columns:

*

Author

Simon Anders, sanders@fs.tum.de

Examples

cds <- makeExampleCountDataSet()
cds <- estimateSizeFactors( cds )
cds <- estimateDispersions( cds )
head( nbinomTest( cds, "A", "B" ) )

Perform row-wise tests for differences between the base means of two count matrices.

Description

This function is called by nbinomTest . Call it directly only if the S4 interface is unsuitable for your task.

Usage

nbinomTestForMatrices(countsA, countsB, sizeFactorsA, sizeFactorsB, dispsA, dispsB )

Arguments

Details

See the vignette for an exact description of the null hypothesis tested.

Value

A vector of unadjusted p values, one for each row in the counts matrices.

Author

Simon Anders, sanders@fs.tum.de

Examples

cds <- makeExampleCountDataSet()
cds <- estimateSizeFactors( cds )
cds <- estimateDispersions( cds, method="per-condition" )
colsA <- conditions(cds) == "A"
colsB <- conditions(cds) == "B"
bmvA <- getBaseMeansAndVariances( counts(cds)[,colsA], sizeFactors(cds)[colsA] )
bmvB <- getBaseMeansAndVariances( counts(cds)[,colsB], sizeFactors(cds)[colsB] )
pvals <- nbinomTestForMatrices(
counts(cds)[,colsA],
counts(cds)[,colsB],
sizeFactors(cds)[colsA],
sizeFactors(cds)[colsB],
fitInfo(cds,"A")$dispFunc( rowMeans( counts( cds, normalized=TRUE ) ) ),
fitInfo(cds,"B")$dispFunc( rowMeans( counts( cds, normalized=TRUE ) ) ) )
names( pvals ) <- row.names( counts(cds) )
head( pvals )

# This here should give the same results:
head( nbinomTest( cds, "A", "B" )$pval )

GLM family for a negative binomial with known dispersion and log link with size factors

Description

A distribution family for use with glm . It describes a negative binomial (as negative.binomial in the MASS package), but with a special link function, namely eta[i] = log( mu[i] / sf[i] ), i.e., each count value is divided by its size factor before the log is taken. This is used internally by fitNbinomGLMs .

Usage

nbkd.sf(r, sf)

Arguments

Value

A GLM family object.

Author

Simon Anders, anders@embl.de

Create a CountDataSet object

Description

This function creates a CountDataSet object from a matrix or data frame of count data.

Usage

newCountDataSet(countData, conditions, sizeFactors = NULL, phenoData = NULL, featureData = NULL)

Arguments

Details

See also CountDataSet-class and the documentation of eSet (package Biobase) for the meaning of the other slots, which CountDataSet inherits from eSet (but which the present package does not use).

Value

an object of class CountDataSet

Author

Simon Anders, sanders@fs.tum.de

Examples

countsTable <- counts( makeExampleCountDataSet() )
cds <- newCountDataSet( countsTable, c( "A", "A", "A", "B", "B" ) )

Create a new CountDataSet from count files generated with htseq-count

Description

Use this function to start a DESeq analysis if you used htseq-count to count your reads.

Usage

newCountDataSetFromHTSeqCount(sampleTable, directory = ".")

Arguments

Value

A CountDataSet object.

Seealso

newCountDataSet

Author

Simon Anders

References

See http://www-huber.embl.de/users/anders/HTSeq/ for htseq-count.

Plot dispersion estimates and fitted values

Description

A simple helper function that plots the per-gene dispersion estimates together with the fitted mean-dispersion relationship.

Usage

plotDispEsts(cds, name=NULL, ymin, linecol="#ff000080",
  xlab = "mean of normalized counts", ylab = "dispersion",
  log = "xy", cex = 0.45, ... )

Arguments

Details

This is a trivial helper function. Do not be afraid to edit and modify it to your needs.

Value

The function is called for its side effect.

Author

Simon Anders, sanders@fs.tum.de

Examples

cds <- makeExampleCountDataSet()
cds <- estimateSizeFactors( cds )
cds <- estimateDispersions( cds )
plotDispEsts(cds)

Makes a so-called "MA-plot"

Description

A simple helper function that makes a so-called "MA-plot", i.e. a scatter plot of logarithmic fold changes (on the y-axis) versus the mean of normalized counts (on the x-axis).

Usage

plotMA(x, ylim,
  col = ifelse(x$padj>=0.1, "gray32", "red3"),
  linecol = "#ff000080",
  xlab = "mean of normalized counts", ylab = expression(log[2]~fold~change),
  log = "x", cex=0.45, ...)

Arguments

Details

This is a trivial helper function. Do not be afraid to edit and modify it to your needs.

Value

The function is called for its side effect.

Author

Wolfgang Huber

Examples

## see vignette

Sample PCA plot from variance-stabilized data

Description

This plot helps to check for batch effects and the like.

Usage

plotPCA(x, intgroup = "condition", ntop = 500)

Arguments

Value

a plot is produced

Seealso

varianceStabilizingTransformation

Author

Wolfgang Huber

Examples

cds <- makeExampleCountDataSet()
cds <- estimateSizeFactors( cds )
cds <- estimateDispersions( cds, method="blind" )
vsd <- varianceStabilizingTransformation( cds )
plotPCA( vsd )

REMOVED

Description

This function has been removed. Please see the vignette for our newer suggestions on how to check fit quality.

Usage

residualsEcdfPlot(...)

Arguments

REMOVED

Description

This function has been removed. Please see the vignette for our newer suggestions on how to check fit quality.

Usage

scvPlot( ... )

Arguments

Accessor functions for the 'sizeFactors' information in a CountDataSet object.

Description

The sizeFactors vector assigns to each column of the count data a value, the size factor, such that count values in the columns can be brought to a common scale by dividing by the corresponding size factor.

Usage

list(list("sizeFactors"), list("CountDataSet"))(object)
list(list("sizeFactors"), list("CountDataSet,numeric"))(object) <- value

Arguments

Seealso

estimateSizeFactors

Author

Simon Anders, sanders@fs.tum.de

Examples

cds <- makeExampleCountDataSet()
cds <- estimateSizeFactors( cds )
sizeFactors(cds)

REMOVED

Description

This function has been removed. Please see the vignette for our newer suggestions on how to check fit quality.

Usage

varianceFitDiagnostics( ... )

Arguments