Description Usage Arguments Details Value References See Also Examples
results
extracts a result table from a DESeq analysis giving base means across samples,
log2 fold changes, standard errors, test statistics, pvalues and adjusted pvalues;
resultsNames
returns the names of the estimated effects (coefficents) of the model;
removeResults
returns a DESeqDataSet
object with results columns removed.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26  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(),
minmu = 0.5
)
resultsNames(object)
removeResults(object)

object 
a DESeqDataSet, on which one
of the following functions has already been called:

contrast 
this argument specifies what comparison to extract from
the
If specified, the 
name 
the name of the individual effect (coefficient) for
building a results table. Use this argument rather than 
lfcThreshold 
a nonnegative 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 
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

listValues 
only used if a list is provided to 
cooksCutoff 
theshold on Cook's distance, such that if one or more
samples for a row have a distance higher, the pvalue for the row is
set to NA. The default cutoff is the .99 quantile of the F(p, mp) distribution,
where p is the number of coefficients being fitted and m is the number of samples.
Set to 
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 pvalue cutoff (FDR) will be a
value other than 0.1, 
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 pvalues, see 
filterFun 
an optional custom function for performing independent filtering
and pvalue adjustment, with arguments 
format 
character, either 
test 
this is automatically detected internally if not provided.
the one exception is after 
addMLE 
if 
tidy 
whether to output the results table with rownames as a first column 'row'.
the table will also be coerced to 
parallel 
if FALSE, no parallelization. if TRUE, parallel
execution using 
BPPARAM 
an optional parameter object passed internally
to 
minmu 
lower bound on the estimated count (used when calculating contrasts) 
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 twogroup
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.
Note that contrast
will set to 0 the estimated LFC in a
comparison of two groups, where all of the counts in the two groups
are equal to 0 (while other groups have positive counts), while
name
will not automatically set these LFC to 0.
The test statistic used for a contrast is:
c' beta / sqrt( c' 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 pvalues (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 pvalues:
By default, independent filtering is performed to select a set of genes
for multiple test correction which maximizes the number of adjusted
pvalues 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 pvalues for the genes which do not pass the filter threshold
are set to NA
.
By default, results
assigns a pvalue 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 pvalues 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 pvalues 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)
.
If useT=TRUE
was specified when running DESeq
or nbinomWaldTest
,
then the pvalue generated by results
will also make use of the
t distribution for the Wald statistic, using the degrees of freedom
in mcols(object)$tDegreesFreedom
.
For results
: a DESeqResults
object, which is
a simple subclass of DataFrame. This object contains the results columns:
baseMean
, log2FoldChange
, lfcSE
, stat
,
pvalue
and padj
,
and also includes metadata columns of variable information.
The lfcSE
gives the standard error of the log2FoldChange
.
For the Wald test, stat
is the Wald statistic: the log2FoldChange
divided by lfcSE
, which is compared to a standard Normal distribution
to generate a twotailed pvalue
. For the likelihood ratio test (LRT),
stat
is the difference in deviance between the reduced model and the full model,
which is compared to a chisquared distribution to generate a pvalue
.
For resultsNames
: the names of the columns available as results,
usually a combination of the variable name and a level
For removeResults
: the original DESeqDataSet
with results metadata columns removed
Richard Bourgon, Robert Gentleman, Wolfgang Huber: Independent filtering increases detection power for highthroughput experiments. PNAS (2010), http://dx.doi.org/10.1073/pnas.0914005107
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72  ## Example 1: twogroup comparison
dds < makeExampleDESeqDataSet(m=4)
dds < DESeq(dds)
res < results(dds, contrast=c("condition","B","A"))
# with more than two groups, the call would look similar, e.g.:
# results(dds, contrast=c("condition","C","A"))
# etc.
## Example 2: two conditions, two genotypes, with an interaction term
dds < makeExampleDESeqDataSet(n=100,m=12)
dds$genotype < factor(rep(rep(c("I","II"),each=3),2))
design(dds) < ~ genotype + condition + genotype:condition
dds < DESeq(dds)
resultsNames(dds)
# 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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