results: Extract results from a DESeq analysis

Description

results extracts a result table from a DESeq analysis giving base means across samples, log2 fold changes, standard errors, test statistics, p-values and adjusted p-values; resultsNames returns the names of the estimated effects (coefficents) of the model; removeResults returns a DESeqDataSet object with results columns removed.

Usage

results(object, contrast, name, lfcThreshold = 0, altHypothesis = c("greaterAbs", "lessAbs", "greater", "less"), listValues = c(1, -1), cooksCutoff, independentFiltering = TRUE, alpha = 0.1, filter, theta, pAdjustMethod = "BH", filterFun, format = c("DataFrame", "GRanges", "GRangesList"), test, addMLE = FALSE, tidy = FALSE, parallel = FALSE, BPPARAM = bpparam(), ...)
resultsNames(object)
removeResults(object)

Arguments

object

a DESeqDataSet, on which one of the following functions has already been called: DESeq, nbinomWaldTest, or nbinomLRT

contrast

this argument specifies what comparison to extract from the object to build a results table. one of either:

a character vector with exactly three elements: the name of a factor in the design formula, the name of the numerator level for the fold change, and the name of the denominator level for the fold change (simplest case)
a list of 2 character vectors: the names of the fold changes for the numerator, and the names of the fold changes for the denominator. these names should be elements of resultsNames(object). if the list is length 1, a second element is added which is the empty character vector, character(). (more general case, can be to combine interaction terms and main effects)
a numeric contrast vector with one element for each element in resultsNames(object) (most general case)

If specified, the name argument is ignored.

name

the name of the individual effect (coefficient) for building a results table. Use this argument rather than contrast for continuous variables, individual effects or for individual interaction terms. The value provided to name must be an element of resultsNames(object).

lfcThreshold

a non-negative value which specifies a log2 fold change threshold. The default value is 0, corresponding to a test that the log2 fold changes are equal to zero. The user can specify the alternative hypothesis using the altHypothesis argument, which defaults to testing for log2 fold changes greater in absolute value than a given threshold. If lfcThreshold is specified, the results are for Wald tests, and LRT p-values will be overwritten.

altHypothesis

character which specifies the alternative hypothesis, i.e. those values of log2 fold change which the user is interested in finding. The complement of this set of values is the null hypothesis which will be tested. If the log2 fold change specified by name or by contrast is written as $ beta $, then the possible values for altHypothesis represent the following alternate hypotheses:

greaterAbs: $ |beta| > lfcThreshold $, and p-values are two-tailed
lessAbs: $ |beta| < lfcThreshold $, NOTE: this requires that betaPrior=FALSE has been specified in the previous DESeq call. p-values are the maximum of the upper and lower tests.
greater: $ beta > lfcThreshold $
less: $ beta < -lfcThreshold $

listValues

only used if a list is provided to contrast: a numeric of length two: the log2 fold changes in the list are multiplied by these values. the first number should be positive and the second negative. by default this is c(1,-1)

cooksCutoff

theshold on Cook's distance, such that if one or more samples for a row have a distance higher, the p-value for the row is set to NA. The default cutoff is the .99 quantile of the F(p, m-p) distribution, where p is the number of coefficients being fitted and m is the number of samples. Set to Inf or FALSE to disable the resetting of p-values to NA. Note: this test excludes the Cook's distance of samples belonging to experimental groups with only 2 samples.

independentFiltering

logical, whether independent filtering should be applied automatically

alpha

the significance cutoff used for optimizing the independent filtering (by default 0.1). If the adjusted p-value cutoff (FDR) will be a value other than 0.1, alpha should be set to that value.

filter

the vector of filter statistics over which the independent filtering will be optimized. By default the mean of normalized counts is used.

theta

the quantiles at which to assess the number of rejections from independent filtering

pAdjustMethod

the method to use for adjusting p-values, see ?p.adjust

filterFun

an optional custom function for performing independent filtering and p-value adjustment, with arguments res (a DESeqResults object), filter (the quantitity for filtering tests), alpha (the target FDR), pAdjustMethod. This function should return a DESeqResults object with a padj column.

format

character, either "DataFrame", "GRanges", or "GRangesList", whether the results should be printed as a DESeqResults DataFrame, or if the results DataFrame should be attached as metadata columns to the GRanges or GRangesList rowRanges of the DESeqDataSet. If the rowRanges is a GRangesList, and GRanges is requested, the range of each gene will be returned

test

this is automatically detected internally if not provided. the one exception is after nbinomLRT has been run, test="Wald" will generate Wald statistics and Wald test p-values.

addMLE

whether the "unshrunken" maximum likelihood estimates (MLE) of log2 fold change should be added as a column to the results table (default is FALSE). only applicable when a beta prior was used during the model fitting. only implemented for 'contrast' for three element character vectors or 'name' for interactions.

tidy

whether to output the results table with rownames as a first column 'row'. the table will also be coerced to data.frame

parallel

if FALSE, no parallelization. if TRUE, parallel execution using BiocParallel, see next argument BPPARAM

BPPARAM

an optional parameter object passed internally to bplapply when parallel=TRUE. If not specified, the parameters last registered with register will be used.

...

optional arguments passed to filterFun

Value

For results: a DESeqResults object, which is a simple subclass of DataFrame. This object contains the results columns: baseMean, log2FoldChange, lfcSE, stat, pvalue and padj, and also includes metadata columns of variable information. The lfcSE gives the standard error of the log2FoldChange. For the Wald test, stat is the Wald statistic: the log2FoldChange divided by lfcSE, which is compared to a standard Normal distribution to generate a two-tailed pvalue. For the likelihood ratio test (LRT), stat is the difference in deviance between the reduced model and the full model, which is compared to a chi-squared distribution to generate a pvalue.For resultsNames: the names of the columns available as results, usually a combination of the variable name and a levelFor removeResults: the original DESeqDataSet with results metadata columns removed

Details

The results table when printed will provide the information about the comparison, e.g. "log2 fold change (MAP): condition treated vs untreated", meaning that the estimates are of log2(treated / untreated), as would be returned by contrast=c("condition","treated","untreated"). Multiple results can be returned for analyses beyond a simple two group comparison, so results takes arguments contrast and name to help the user pick out the comparisons of interest for printing a results table. The use of the contrast argument is recommended for exact specification of the levels which should be compared and their order.

If results is run without specifying contrast or name, it will return the comparison of the last level of the last variable in the design formula over the first level of this variable. For example, for a simple two-group comparison, this would return the log2 fold changes of the second group over the first group (the reference level). Please see examples below and in the vignette.

The argument contrast can be used to generate results tables for any comparison of interest, for example, the log2 fold change between two levels of a factor, and its usage is described below. It can also accomodate more complicated numeric comparisons. The test statistic used for a contrast is:

$$ c^t \beta / \sqrt{c^t \Sigma c } $$

The argument name can be used to generate results tables for individual effects, which must be individual elements of resultsNames(object). These individual effects could represent continuous covariates, effects for individual levels, or individual interaction effects.

Information on the comparison which was used to build the results table, and the statistical test which was used for p-values (Wald test or likelihood ratio test) is stored within the object returned by results. This information is in the metadata columns of the results table, which is accessible by calling mcols on the DESeqResults object returned by results.

On p-values:

By default, independent filtering is performed to select a set of genes for multiple test correction which maximizes the number of adjusted p-values less than a given critical value alpha (by default 0.1). See the reference in this man page for details on independent filtering. The filter used for maximizing the number of rejections is the mean of normalized counts for all samples in the dataset. Several arguments from the filtered_p function of the genefilter package (used within the results function) are provided here to control the independent filtering behavior. In DESeq2 version >= 1.10, the threshold that is chosen is the lowest quantile of the filter for which the number of rejections is close to the peak of a curve fit to the number of rejections over the filter quantiles. 'Close to' is defined as within 1 residual standard deviation. The adjusted p-values for the genes which do not pass the filter threshold are set to NA.

By default, results assigns a p-value of NA to genes containing count outliers, as identified using Cook's distance. See the cooksCutoff argument for control of this behavior. Cook's distances for each sample are accessible as a matrix "cooks" stored in the assays() list. This measure is useful for identifying rows where the observed counts might not fit to a Negative Binomial distribution.

For analyses using the likelihood ratio test (using nbinomLRT), the p-values are determined solely by the difference in deviance between the full and reduced model formula. A single log2 fold change is printed in the results table for consistency with other results table outputs, however the test statistic and p-values may nevertheless involve the testing of one or more log2 fold changes. Which log2 fold change is printed in the results table can be controlled using the name argument, or by default this will be the estimated coefficient for the last element of resultsNames(object).

References

Richard Bourgon, Robert Gentleman, Wolfgang Huber: Independent filtering increases detection power for high-throughput experiments. PNAS (2010), http://dx.doi.org/10.1073/pnas.0914005107

Examples

Run this code


## Example 1: two-group comparison

dds <- makeExampleDESeqDataSet(m=4)

dds <- DESeq(dds)
res <- results(dds, contrast=c("condition","B","A"))

# with more than two groups, the call would look similar, e.g.:
# results(dds, contrast=c("condition","C","A"))
# etc.

## Example 2: two conditions, two genotypes, with an interaction term

dds <- makeExampleDESeqDataSet(n=100,m=12)
dds$genotype <- factor(rep(rep(c("I","II"),each=3),2))

design(dds) <- ~ genotype + condition + genotype:condition
dds <- DESeq(dds) 
resultsNames(dds)

# Note: design with interactions terms by default have betaPrior=FALSE

# the condition effect for genotype I (the main effect)
results(dds, contrast=c("condition","B","A"))

# the condition effect for genotype II
# this is, by definition, the main effect *plus* the interaction term
# (the extra condition effect in genotype II compared to genotype I).
results(dds, list( c("condition_B_vs_A","genotypeII.conditionB") ))

# the interaction term, answering: is the condition effect *different* across genotypes?
results(dds, name="genotypeII.conditionB")
 
## Example 3: two conditions, three genotypes

# ~~~ Using interaction terms ~~~

dds <- makeExampleDESeqDataSet(n=100,m=18)
dds$genotype <- factor(rep(rep(c("I","II","III"),each=3),2))
design(dds) <- ~ genotype + condition + genotype:condition
dds <- DESeq(dds)
resultsNames(dds)

# the condition effect for genotype I (the main effect)
results(dds, contrast=c("condition","B","A"))

# the condition effect for genotype III.
# this is the main effect *plus* the interaction term
# (the extra condition effect in genotype III compared to genotype I).
results(dds, contrast=list( c("condition_B_vs_A","genotypeIII.conditionB") ))
 
# the interaction term for condition effect in genotype III vs genotype I.
# this tests if the condition effect is different in III compared to I
results(dds, name="genotypeIII.conditionB")

# the interaction term for condition effect in genotype III vs genotype II.
# this tests if the condition effect is different in III compared to II
results(dds, contrast=list("genotypeIII.conditionB", "genotypeII.conditionB"))

# Note that a likelihood ratio could be used to test if there are any
# differences in the condition effect between the three genotypes.

# ~~~ Using a grouping variable ~~~

# This is a useful construction when users just want to compare
# specific groups which are combinations of variables.

dds$group <- factor(paste0(dds$genotype, dds$condition))
design(dds) <- ~ group
dds <- DESeq(dds)
resultsNames(dds)

# the condition effect for genotypeIII
results(dds, contrast=c("group", "IIIB", "IIIA"))

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