Changes in version 3.62.0
o
New function fitFDistUnequalDF1() for improved empirical Bayes
hyperparameter estimation. The new function is an alternative
to the long-standing limma functions fitFDist() and
fitFDistRobustly(). The new function gives special attention
to contexts where the df1 argument is unequal between cases or
can include small fractional values, such as those resulting
from edgeR's voomLmFit() or from the edgeR 4.0 quasi-likelihood
pipeline. The new function uses profile maximum likelihood
instead of moment estimation to estimate df.prior, and so may
be more accurate than fitFDist() or fitFDistRobustly() even
when the df1 values are all equal. The new hyperpameter
estimation will be used by default by eBayes(), treat() and
squeezeVar() whenever the residual degrees of freedom are
unequal, but users can request legacy behavior for those
functions by setting `legacy=TRUE`.
o
New argument `adaptive.span` for voomWithQualityWeights() and
normalizeCyclicLoess(), with the same purpose as the same
argument for voom(). If `TRUE` then an optimal value for the
span is chosen based on the number of genes in the dataset.
voom() and voomWithQualityWeights() now return the chosen
`span` as part of the output object if `adaptive.span=TRUE`.
o
contrasts.fit() now converts the coefficient name "(Intercept)"
created automatically by model.matrix() to "Intercept", same as
is done by makeContrasts(). This should avoid inconsistent
coefficient names between the fitted model object and contrast
matrices created by makeContrasts().
o
New argument `keep.EList` for voomaLmFit(), similar to the same
argument for edgeR::voomLmFit(). The observation weights
generated by voomaLmFit() are now stored only if
`keep.EList=TRUE`.
o
voomaByGroup() now uses the overall mean-variance trend for
groups with only one sample, for which a separate trend is not
estimable.
o
Add Ravindra et al (2023) as a reference and Mengbo Li as an
author to the voomaByGroup() help page. Emphasize in the help
details that only simple design matrices are supported by this
function.
o
Revise the eBayes/treat help page to further clarify the
`legacy` and `fc` arguments.
o
Bug fix to voomaByGroup() to save variance plot parameters
correctly when `save.plot=TRUE`.
o
Remove argument `zero.weights` from the MArrayLM method for
plotMD() and plotMA().
o
Fix bug in the MArrayLM methods for plotMD() and plotMA(),
which were attempting to access a `weights` component of the
fitted model object, analogously to what plotMD() does with the
EList or MAList objects, even though MArrayLM objects do not
contain a `weights` component.
o
Use of the stats package digamma() function replaced with
statmod's logmdigamma() in fitFDist(), fitFDistUnequalDF1() and
intraspotCorrelation(). Slight improvements to speed, accuracy
and code simplicity, but results should remain the same to at
least 13 significant figures.
Changes in version 3.60.0
o
The default settings for the `small.n` and `min.span` arguments
of chooseLowessSpan() have been increased, increasing the
chosen span value.
o
New argument `adaptive.span` for voom(). If `TRUE`, then
chooseLowessSpan() is used to select an optimal `span` value
depending on the number of genes, same as is done for vooma(),
voomaByGroup() and voomaLmFit().
o
The plot information saved by voom() when `save.plot=TRUE` now
includes `pch` and `cex` plotting character settings.
o
The default `span` set by vooma() is increased slightly to the
value given by `chooseLowessSpan(ngenes, small.n=50,
min.span=0.3, power=1/3)`. A new argument `legacy.span` had
been added to optionally restore the old default settings for
`span` for users who want backward compatibility.
o
vooma() argument `covariate` renamed to `predictor`.
o
vooma() has been revised in several other ways to match the
behavior of voom() more closely. The order of arguments has
been adjusted to match voom() and lmFit(). A new argument
`save.plot` has been added similar to the same argument for
voom(). Changes to have also been made to the title and xlab
for the variance trend plot when `predictor` is non-NULL.
o
New function voomaLmFit() with the same functionality as
vooma() but which automates the estimation of sample weights
and intrablock correlation from vooma(). It produces an
MArrayLM fit object instead of an EList object. It is the
analogous to edgeR::voomLmFit() but for continuous
microarray-like data.
o
Edits to the help pages for decideTests(), voom(),
voomWithQualityWeights(), vooma() and "11RNAseq". Example added
to the vooma() help page.
o
Add RNA-seq to package description.
o
Add Charity Law, Goknur Giner and Mengbo Li to author list in
package description.
Changes in version 3.58.0
o
New argument `covariate` for vooma() that allows the variance
trend to depend on an extra covariate in addition to average
log-expression. This allows vooma() to take account of any
predictor that is correlated with the precision of each
expression value.
o
New argument 'group' to simplify the typical calling sequence
for removeBatchEffect(). The `group` argument provides an
alternative and possibly simpler way to specify the
experimental conditions to be preserved.
o
treat() now works when there are NA coefficients. Previously it
gave an error if any estimated coefficient was NA.
o
Fix behaviour of topTable() with `adjust.method=NULL` so that
it gives "BH" adjustment as documented. Previously the argument
was passed to p.adjust(), which gave "holm" adjustment.
o
Convert Introduction vignette from Sweave/pdf from to
Rmarkdown/html. The Introduction vignette is also somewhat
expanded.
o
cameraPR() has been addeed to the `10GeneSetTests.Rd` help
topic.
o
The pdf limma User's Guide is now formally listed as a
vignette.
o
Non-user-visible changes: limma now imports statmod rather than
suggesting it. BiocStyle has been added to the Suggests field
of DESCRIPTION.
Changes in version 3.56.0
o
Rename readSampleInfoFromGEO() to sampleInfoFromGEO().
o
Add new argument `p.value` for topGO() and topKEGG().
o
Fix a problem with inconsistent pathway names from KEGG. Due to
a change on the KEGG website, pathway names from
getGeneKEGGLinks() have "path:" prefixes but pathway names from
getKEGGPathwayNames() do not. To make the pathway names
consistent, getGeneKEGGLinks() now removes the "path:" prefix.
o
eBayes() now checks whether `fit` is a list object before
undertaking other tests.
o
Revise voom.Rd to specify that y-values in voom plot are sqrt
residual standard deviations.
o
Update out-of-date URLs in the help pages and the User's Guide.
Convert reference URLs to DOIs.
o
Convert CITATION to use the newer bibentry() instead of
citEntry().
Changes in version 3.54.0
o
New function goanaTrend() to estimate a covariate-dependent
trend in the probability of differential expression for use in
goana() and kegga() with `trend=TRUE`. The trend estimated by
goanaTrend() is squeezed slightly towards constancy to provide
stability when the number of DE genes is small. The amount of
squeezing decreases with the number of DE genes.
goana() and kegga() now call goanaTrend() when the `trend`
argument is used. When `plot=TRUE` the plot is now created by
goanaTrend() instead of by barcodeplot().
o
Rename argument `prior.prob` to `null.prob` in goana() and
kegga().
o
Update goana() and kegga() code to catch NA gene IDs or
covariate values.
o
goana() and kegga() no longer give an error when a trend is
estimated but there are no DE genes.
o
kegga() now uses https instead of http links when reading from
rest.kegg.jp.
o
Add new argument `fc` to topTable() and topTableF(). Change
default for `lfc` to NULL instead of 0.
o
lmFit() now checks explicitly for NAs in the design matrix.
o
Update references listed on help pages to use DOIs instead of
URLs.
Changes in version 3.52.0
o
New function readSampleInfoFromGEO().
o
Allow the `trend` argument of eBayes() to specify a general
variance covariate.
o
More detailed checking and error messages when the `object`
input to lmFit() is a data.frame.
o
Improve checking for incorrectly specified `contrasts` argument
in contrasts.fit().
Changes in version 3.50.0
o
Improve help pages for fry() and barcodeplot().
o
Revise Section 18.1.10 of User's Guide so that coloring of X
and Y genes is consistent between plots.
o
Fix bug in the MDS class method of plotMDS() (introduced in the
previous release) when new `dim.plot` values are set.
o
Fix bug in read.idat() when `annotation` contains NA values.
Changes in version 3.48.0
New functionality
o
Explicitly setting `weights=NULL` in a call to lmFit() no
longer over-rides the `weights` value found in `object`.
Default settings in lmFit() changed from `ndups=` and
`spacing=1` to `ndups=NULL` and `spacing=NULL`, although this
doesn't change function behavior from a user point of view.
o
A number of improvements to duplicateCorrelation() to make the
results more robust and to make the interface consistent with
lmFit(). duplicateCorrelation() sets `weights` same as
lmFit(). Setting `weights=NULL` in the function call no longer
overwrites weights found in `object`. duplicateCorrelation()
now checks whether the block factor is spanned by the design
matrix. If so, it returns intrablock correlations of zero with
a warning. Previously this usage error was not specifically
trapped and could lead to correlations that were or NA or close
to 1 depending on floating point errors.
duplicateCorrelation() now issues a simplified message when
design is not of full rank and uses message() to do so instead
of cat(). In terms of output, duplicateCorrelation() now
bounds the genewise correlations away from the upper and lower
bounds by 0.01 so that the correlation matrix will always be
positive-definite. There is also a fix to the value returned
by duplicateCorrelation() when no blocks or duplicates are
present.
o
New argument `fc` for treat() so that the fold-change threshold
can optionally be specified on the fold-change scale rather
than as a log2-fold-change.
o
plotMDS() no longer calls cmdscale() but instead performs the
necessary eigenvector computations directly. Proportion of
variance explained by each dimension is now computed and is
optionally added to the dimension labels. The `ndim` argument
is now removed. All eigenvectors are now stored so that
plotMDS.MDS does not need to recompute them when different
dimensions are plotted.
o
New arguments `path` and `bgxpath` for read.idat().
read.idat() now checks for gzipped IDAT files and, if detected,
gives an informative error message. read.idat() now checks for
existence of input files before calling illuminaio read
functions.
Other code improvements
o
The average log-expression column written by write.fit() now
has column heading "AveExpr" to match the output from
topTable(). The column was previously called "A".
Documentation
o
Additional documentation for the `design` argument of `lmFit`
using the term "samples" instead of "arrays" and mentioning
that the design matrix defaults to `object$design` when that
component is not NULL.
o
duplicateCorrelation() help page revised including new code
example.
o
Help page for voom() now explains that the design matrix will
be set from the `group` factor of the DGEList object if
available.
o
coolmap() help page now clarifies which heatmap.2() arguments
are reserved and which can be included in the coolmap call.
Bug fixes
o
Fix typo in voom() warning when negative counts are detected.
Changes in version 3.46.0
New functionality
o
New function chooseLowessSpan().
o
fitFDist() with a non-NULL `covariate` now fits a smoother
trend (with fewer spline knots) than before unless there are at
least 30 observations. This also affects squeezeVar() and
eBayes() with `trend=TRUE`.
o
New argument `output.style` to weightedLowess().
o
write.fit() now outputs a blank column heading for the row
names so that the number of column names in the delimited
output file will agree with the number of columns (similar to
write.csv in the base package).
Code improvements
o
Subsetting and contrasts.fit() now work on MArrayLM objects
even if the cov.coefficients component is absent.
o
volcanoplot() now prompts user to run eBayes() if the object
doesn't contain p-values.
o
Add checks and more informative error messages to getEAWP() and
lmFit() when the data object is missing, NULL or has zero rows.
o
Subsetting for RGList, MAList, EListRaw, EList, MArrayLM or
TestResults objects now accepts arguments other than `i` or `j`
but ignores them without an error. This is relevant if a user
adds a `drop` argument by analogy with matrix subsetting.
Previously that produced an error.
o
Subsetting of TestResults now requires two arguments, same as
all of the other data object classes listed above. Previously
single index subsetting (for example object[i]) was allowed.
o
Improved treatment of zero weights by weighted.median().
o
More consistent use of rep.int(), rep_len() and rep()
throughout the package.
o
Replace NA with NA_real_ where appropriate.
o
Add a message to topTableF() to warn that the function is
obsolete and will be removed in a future version of limma.
Remove usages of topTableF() from the User's Guide.
o
Remove obsolete function toptable(), which has been officially
deprecated in favor of topTable() since 1 Feb 2018.
Documentation
o
Revise and expand the weightedLowess help page.
o
Add more explanation about "heirarchical" method in the details
section of the decideTests() help page.
o
Add Note to voom help page to emphasise that voom is not an
appropriate tool to analyse expresssion measures that have been
normalized for library size.
o
Revise the help page for plotMDS() to clarify that the function
can produce PCA plots as well as PCoA plots.
o
Add example to EList-class help page.
o
Edits to the eBayes help page.
o
Edit concept entries for help files (for reference manual
index).
Bug fixes
o
Revise write.fit() so that column names of the output file are
correct when `digits=NULL` and the `fit` object contains only
one coefficient column. Previously the same contrast name was
repeated as the column name for the coefficients, t-statistics
and p.values, making them hard to distinguish.
o
Bug fix to arrayWeights() when `y` contains NAs, the design
matrix has several columns and some but not all genes have no
residual df.
Changes in version 3.44.0
New functionality
o
Add new argument `package` to changeLog(). changeLog() now
works for any package rather than only for limma.
Code improvements
o
Improved treatment of NA coefficients by contrasts.fit().
contrasts.fit() has always removed coefficients that were NA
because of singularities in the design matrix, but any extra NA
coefficients caused by NA expression values would cause all the
stdev.unscaled values returned by contrasts.fit() to be NA for
those genes. contrasts.fit() now returns non-NA coefficients
and non-NA stdev.unscaled values if all the NA coefficients are
multiplied by zero contrast multipliers.
o
Speed improvement for contrasts.fit() if some input
coefficients are not involved (have zero multipliers) in any of
the contrasts.
o
contrasts.fit() now preserves the `pivot` component of the fit
object instead of removing it.
o
voom now checks explicitly for NA or negative counts and gives
an informative error message.
o
fitFDist() now allows a covariate trend even with fewer than 4
useable observations. (Useable means positive residual df and
finite variance and covariate values.)
o
Internal code cleaned up to avoid partial matching of function
arguments, attributes or list component names. The automatic
package tests are now run with the warnPartialMatchArgs,
warnPartialMatchAttr and warnPartialMatchDollar options all set
to TRUE.
Documentation
o
More explanation of `covariates` argument to
removeBatchEffects().
Bug fixes
o
Fix bug in contrasts.fit() when some coefficients are NA due to
NA expression values and the corresponding contrast multipliers
are zero. The new code fixes the improvement originally
introduced in limma 3.35.9. The new code will return non-NA
contrasts if the NA coefficients all get 0 weight in the
contrast. Previous code was correct if all genes had the same
NA coefficients but could give an avoidable NA result if some
genes had NA coefficients but the first gene did not.
o
Improvements to subsetting MArrayLM objects by column.
Previously the presence of non-estimable coefficients sometimes
caused the cov.coefficient matrix to be subsetted incorrectly;
that is now fixed. Subsetting zero columns is now allowed even
when F-statistics are present.
o
Fix to classifyTestsF() when one or more of the coefficients
have zero variance. Previously the zero variance will introduce
NAs into the correlation matrix and hence cause an error. This
is now avoided. This also fixes eBayes() when one or more of
the coefficients are identically zero (usually caused by an
all-zero contrast).
Changes in version 3.42.0
New functionality
o
New head() and tail() methods for all limma data classes.
o
New unique() and subsetting methods for TestResults objects.
Previously a subsetted TestResults object became an ordinary
numeric matrix, but now the TestResults class is preserved.
Single index subsetting is also allowed but produces a numeric
vector.
o
New argument `gene.weights` for fry() so as to match the
arguments and behavior of mroast(). Previously gene weights
could be input to fry() only through the `index` argument.
fry's mixed p-values now take account of gene weights.
Previously gene weights were not used in fry's mixed p-value
calculation.
o
Add arguments `block`, `correlation` and `weights` to voom()
and remove the `...` argument.
o
Update the arguments of voomWithQualityWeights() to match the
revisions to arrayWeights() in limma 3.40.0. Add new argument
`var.group`.
o
New arguments `xlab`, `ylab` and `main` for plotFB(). The
default plot title now includes the column name from the data
object. Default for `pch` increased to 0.3.
o
plotWithHighlights and plotMD.MArrayLM now gives special
treatment to the case that `status` is a TestResults object.
Code improvements
o
roast() and mroast() now use less memory. Memory usage now
remains bounded regardless of the number of rotations used.
Both functions are faster than before when approx.zscore=TRUE.
A new argument `legacy` is added to allow users to turn these
improvements off if they wish to reproduce numerical results
from limma 3.40.0 exactly. The default number of rotations
`nrot` is increased from 999 to 1999. A bug has been fixed for
roast() and mroast() when `index` is a character vector of
geneids or rownames. Previously this usage failed.
o
Complete rewrite of zscoreT(), which is now somewhat faster and
offer two new options for the normalizing transformation. The
new argument `method` indicates which transformation is used
when `approx=TRUE`. The default approximation method is changed
from "hill" to "bailey". zscoreT() now allows NA values when
`approx=TRUE` and works correctly for all valid df values.
Previously `approx=TRUE` returned NA results for `df` infinite
or <= 0.05. A simple robust approximation from Wallace (1959)
has been introduced to handle very large or very small `df`
values. Previously `approx=FALSE` treated any `df` greater than
10000 as infinite; this threshold is now raised to 1e300.
o
tZScore() is slightly faster.
o
All uses of the approx() function now set
`ties=list("ordered",mean)`, except for fitFDistRobustly()
which uses `ties=mean`. The change was prompted by a change in
R 3.6.0 whereby using the default for `ties` generates a
warning message whenever ties are present in `x`. The new code
gives the same results but is very slightly faster and avoids
the warning message. limma now depends on R >= 3.6.0 because
of this change.
Documentation
o
Update Yoruba case study in User's Guide to call voom() and
duplicateCorrelation() twice each instead of once.
o
Update Users Guide: update Rsubread reference, correct remarks
about prior distribution for 1/sigma^2 in Section 13.2 and
rerun the Yoruba and Pasilla case studies.
o
Use "RNA-seq" and "ChIP-seq" consistently in the documentation
instead of "RNA-Seq" and "ChIP-Seq".
Bug fixes
o
Fix bug in barcodeplot() when `index` or `index2` are character
vectors.
Changes in version 3.40.0
o
Major rewrite of arrayWeights() to improve speed and stability.
The arrayWeightsSimple() function has been removed and its
functionality incorporated into arrayWeights(). A new
'var.group' argument has been added to simplify specification
of the variance design matrix. The weights are now squeezed
slightly towards unit and a new argument 'prior.n' has been
added to control the prior weight with which the array weights
are squeezed. arrayWeights() now chooses between the REML and
gene-by-gene algorithms automatically by default. REML is
chosen when there are no prior weights or missing values and
gene-by-gene is used otherwise. arrayWeights() now checks for
and skips over any genes with zero residual variances.
o
voom() now checks for experiments that have no replication. In
this case it now returns weights that are all 1, with an
informative warning but no error. Two references have been
added to the voom help page.
o
classifyTests.Rd now focuses on nested F-tests. The function
FStat() is now removed. It was a convenience wrapper for
classifyTestsF() with fstat.only=TRUE but is not used often
enough to justify itself. classifyTestsP() is no longer
exported as it is not intended to be called by users. The
function classifyTestsT() is now removed, having been
superceded by more sophisticated multiple testing methods.
o
contrasts.fit() now removes any test statistics or p-values
found in the fit object. This avoids any potential mis-match
between coefficients and test statistics if eBayes() has been
run on the object previously.
More details have been added to the warning about
approximations in the contrasts.fit() help page.
o
The old function ebayes(), which was deprecated a year ago in
favor of eBayes(), is now removed.
o
The fitted() and residuals() methods for MArrayLM objects now
give an informative error message if the object contains
contrasts instead of the original coefficients.
o
Rewrite Filtering chapter of User's Guide.
o
Add Research Fields to the biocViews entry in the package
DESCRIPTION file.
Changes in version 3.38.0
o
New function plotExonJunc() to plot results from diffSplice().
o
New function logsumexp().
o
New argument hl.col for volcanoplot(), allowing users to
specify the color for the gene names when highlight > 0.
o
barcodeplot() no longer assumes that 'statistic' has unique
names. Previously it returned an error if names(statistic)
contained any duplicated values.
o
The colors "blue", "red" and "yellow" used by coolmap() changed
to "blue2", "red2" and "yellow2" when used in a color panel
with white.
o
goana.Rd now explains more explicitly that p-values are
unadjusted for multiple testing.
o
arrayWeights.Rd now mentions minimum dimensions for expression
object.
o
More advice on how to choose 'lfc' added to the treat() help
page.
o
Minor bug fix to the mixed p-value from roast() and mroast()
when set.statistic="floormean".
o
Bug fix for cumOverlap(), which was under-counting overlaps in
some cases.
Changes in version 3.36.0
o
New arguments 'quote' and 'row.names' for write.fit().
Write.fit() now outputs row.names by default, which previously
were suppressed.
o
coolmap() will now accept an arbitrary vector of colors. A new
preset "whitered" panel is now also supported.
o
In the past, contrasts.fit() always returned NA contrasts for
genes with any NA fitted coefficients. contrasts.fit() will now
ignore an NA coefficient if the contrast multiplier is zero for
that coefficient.
o
The treat() default for 'lfc' changed from lfc=0 to
lfc=log2(1.2).
o
kegga() and goana() now check whether a data.frame has been
input by mistake and generates an error. Previously a
data.frame value for 'de' was interpreted as a list of gene
sets without any error.
o
voomWithQualityWeights() now returns 'sample.weights' as a
column of the 'targets' data.frame instead of as a separate
vector.
o
"TestResults" objects now include a 'labels' attribute,
defaulting to c("Down","NotSig","Up").
o
Functions ebayes() and toptable(), long ago replaced by
eBayes() and topTable(), are now formally deprecated.
o
Update the User's Guide case study that analyses Lrp- E. Coli
samples and profiled by Affymetrix arrays.
o
Update the Agilent single-channel case study in Section 17.4 of
the Users' Guide to use the Agilent gIsWellAboveBG detection
column.
o
Comments added to voom.Rd and eBayes.Rd to clarify the
relationship between limma-trend and voom.
o
In the kegga help page, clarify default gene ID system used by
kegga() when species="dme".
o
Update wsva() help page to describe correct number of columns
for output.
o
bug fix for goana() when 'covariate' is specified and some of
the 'universe' genes don't have GO annotation.
o
Fix to how roast(), mroast() and fry() handle weights. The
'weight' argument can now be a matrix, or a vector of length
nrow(y), or a vector of length ncol(y). This allows the
functions to accept precision weights, or gene weights, or
sample weights.
Changes in version 3.34.0
o
read.idat() can now handle idat files in SNP format.
o
New argument 'sort.by' for topSplice().
o
diffSplice() now treats any NA geneid as a separate gene with
one exon. Previously all NA geneids were treated as the same
gene.
o
Bug fixes for plotSplice() which, in some circumstances, was
not highlight significant exons.
o
Default value for 'style' in volcanoplot() changed to "p-value"
instead of "p". This doesn't change the behavior of the
function.
o
volcanoplot() didn't work with MArrayLM objects produced by
treat(). Now fixed.
o
The gene.info.file argument of alias2SymbolUsingNCBI() will now
optionally accept a data.frame instead of a file name.
o
alias2SymbolUsingNCBI() now always produces a data.frame.
Previously a single-column data.frame was returned as a vector.
o
Bug fixes to camera() and cameraPR() when 'index' contains
character row names.
o
New argument 'restrict.universe' for the default kegga()
method. The default is now not to the restrict the universe to
genes with KEGG annotation.
o
goana.default() code is rewritten to more closely match
kegga.default. Slight speed improvement. Prior probabilities
are now computed using the restricted universe with GO
annotation instead of the whole universe.
o
Bug fix for kegga() when trend=TRUE or a prior.prob or
covariate is specified. Previously the Down p-values were
substantially too small and the Up p-values were too large.
o
plotWithHighlights() checks whether 'status' is a factor and
converts to character.
o
Bug fixes for beadCountWeights(). Default is now set correct
for 'design' and the function now works correctly when 'y' is
an EList object contain bead standard errors but not standard
deviations.
Changes in version 3.32.0
o
New function cameraPR(), which implemented a pre-ranked version
of camera().
o
New function alias2SymbolUsingNCBI(), which converts gene
aliases or synonyms into official gene symbols using an NCBI
gene-info file.
o
New function wsva() for weighted surrogate variable analysis.
o
New function coolmap(). This is essentially a wrapper for the
heatmap.2() function in the ggplots package, but with sensible
default settings for genomic log-expression data.
o
decideTests() is now an S3 generic function with a default
method and a method for MArrayLM objects. decideTests() now
selects all null hypotheses as rejected if p.value=1.
o
length() methods removed all limma data objects (objects of
class EList, EListRaw, RGList, MAList or MArrayLM). length(x)
will now return the number of list components in the object
rather than the number of elements in the expression matrix.
o
New argument 'style' for volcanoplot(). The default is now to
use -log10(p-value) for the y-axis instead of the B-statistic.
o
New argument 'xlab' for barcodeplot().
o
New argument 'col' for plotSA(). plotSA() now longer plots a
lowess curve trend, but if appropriate both high and low
outlier variances are highlighted in a different color.
o
Argument 'replace.weights' removed from
voomWithQualityWeights(). The function now always produces an
EList, similar to voom(). The default behavior of the function
is unchanged.
o
barcodeplot() now ranks statistics from low to high, instead of
from high to low, following the usual style of axes in R plots.
This means that left and right are now interchanged.
o
plotSA() now plots quarter-root variances instead of
log2(variances).
o
Default for 'legend' argument of plotWithHighlights() changed
from "topleft" to "topright".
o
fitFDist() now estimates the scale by mean(x) when df2 is
estimated to be Inf. This will make the results from eBayes()
less conservative than before when df.prior=Inf.
o
plotSA() now indicates, by way of an open plotting symbol, any
points that have low robust df.prior values.
o
Clearer error message from fitFDistRobustly() when some
variances are zero.
o
C functions are now registered using R_registerRoutines.
o
Bug fix for contrastAsCoef() when there is more than one
contrast. Previously the coefficients for the transformed
design matrix were correct only for the first contrast.
o
Bug fix for kegga() when the universe is explicitly specified.
o
Bug fix for fitFDistRobustly() when there is an extreme
outlier. Previously floating point underflow for the outlier
p-value could cause an error.
o
Bug fix to mroast(), which was ignoring 'geneid' argument.
o
Bug fix to printHead() for arrays with 1 column.
Changes in version 3.30.0
o
New function cumOverlap() to analyse the overlap between two
ordered lists.
o
New function detectionPValues() to compute detection p-values
from negative control probes.
o
New function fitmixture() to estimate genewise fold changes and
expression values from mixture experiments. Previously this
funtion was only available as part of the illumina package
available from http://bioinf.wehi.edu.au/illumina
o
New function logcosh() to compute log(cosh(x)) accurately
without floating underflow or overflow.
o
The default settings for the 'inter.gene.cor' and 'use.neg.cor'
arguments of camera() have been changed. camera() now uses by
default a preset value for the inter-gene correlation. This has
the effect that it tends to rank highly co-regulated,
biologically-interpretable more highly than before.
o
New flexibility for the roast() and mroast() functions, similar
to that previously implemented for fry(). The index vector for
each gene set can now be a data.frame, allowing each gene set
to have its own set of gene weights. The indices can now
optionally be a vector of gene names instead of a vector of
indices. roast() and mroast() now support the robust empirical
Bayes option of squeezeVar(). roast() and mroast() can now
accept, via '...', any argument that would be normally passed
to lmFit() or eBayes().
o
Slight change to the standardize="posterior.sd" method for
fry().
o
goana(), alias2Symbol() and alias2SymbolTable() now work for
any species for which an Entrez Gene based organism package
exists.
o
The 'species' argument of kegga() can now accept any
Bioconductor species abbreviation.
o
topGO() now breaks ties by the number of genes in the GO Term
and by the name of the Term if the p-values are equal. (This is
the same behavior as topKEGG.)
o
New argument 'plot' for plotMDS(), to optionally allow an MDS
object to be returned without making a plot.
o
New arguments 'annotation' and 'verbose' for read.idat(). The
first of these to allows users to read any required columns
from the manifest file.
o
New arguments 'pch.y' and 'pch.z' for plotRLDF.
o
Unnecessary argument 'design' removed from fitted.MArrayLM.
o
normalizeBetweenArrays() now checks whether the input 'object'
is a data.frame, and converts to a matrix if possible.
o
duplicateCorrelation() now expands weights using
expandAsWeights(), making it consistent with lmFit().
o
The lowess line drawn by plotSA() is now more robust with
respect to NA variances.
o
More informative error message from voom() when there is only
one row of data.
o
More informative error message from getEAWP() and lmFit() when
'object' is a non-normalized data object.
o
Update Phipson et al (2016) reference for robust empirical
Bayes.
o
Bug fix to fitFDistRobustly(), which in some circumstances was
trying to save extra (undocumented) results that had not been
computed.
o
Bug fixes for fry() with robust=TRUE or when 'index' has NULL
names.
o
Bug fix to propTrueNull() when method="hist" and all the
p-values are less than 1/nbins.
o
Bug fix to alias2Symbol() with expand.symbol=TRUE.
Changes in version 3.28.0
o
Improved capabilities and performance for fry(). fry() has two
new arguments, 'geneid' and 'standardize'. The index argument
of fry() can now be a list of data.frames containing
identifiers and weights for each set. The options introduced
by the standardize argument allows fry() to be more
statistically powerful when genes have unequal variances.
fry() now accepts any arguments that would be suitable for
lmFit() or eBayes(). The 'sort' argument of fry() is now the
same as for mroast().
o
roast(), mroast(), fry() and camera() now require the 'design'
matrix to be set. Previously the design matrix defaulted to a
single intercept column.
o
Two changes to barcodeplot(): new argument 'alpha' to set
semitransparency of positional bars in some circumstances; the
default value for 'quantiles' is increased slightly.
o
kegga() has several new arguments and now supports any species
supported by KEGG. kegga() can now accept annotation as a
data.frame, meaning that it can work from user-supplied
annotation.
o
New functions getGeneKEGGLinks() and getKEGGPathwayNames() to
get pathway annotation from the rest.kegg website.
o
topKEGG() now breaks tied p-values by number of genes in
pathway and by name of pathway.
o
goana() now supports species="Pt" (chimpanzee).
o
plotRLDF() now produces more complete output and the output is
more completely documented. It can now be used as a complete
LDF analysis for classification based on training data. The
argument 'main' has been removed as an explicit argument
because it and other plotting arguments are better passed using
the ... facility. New arguments 'ndim' and 'plot' have been
added. The first allows all possible discriminant functions to
be completed even if only two are plotted. The second allows
dicriminant functions to be computed without a plot.
plotRLDF() also now uses squeezeVar() to estimate how by how
much the within-group covariance matrix is moderated. It has
new arguments 'trend' and 'robust' as options to the empirical
Bayes estimation step. It also has new argument 'var.prior' to
allow the prior variances to be optionally supplied. Argument
'df.prior' can now be a vector of genewise values.
o
New argument 'save.plot' for voom().
o
diffSplice() now returns genewise residual variances.
o
removeExt() has new 'sep' argument.
o
Slightly improved algorithm for producing the df2.shrunk values
returned by fitFDistRobustly(). fitFDistRobustly() now returns
tail.p.value, prob.outlier and df2.outlier as well as
previously returned values. The minimum df.prior value
returned by eBayes(fit, robust=TRUE) may be slightly smaller
than previously.
o
tmixture.vector() now handles unequal df in a better way. This
will be seen in slightly changed B-statistics from topTable
when the residual or prior df are not all equal.
o
If a targets data.frame is provided to read.maimages() through
the 'files' argument, then read.maimages() now overwrites its
rownames on output to agree with the column names of the RGList
object.
o
More explicit handling of namespaces. Functions needed from
the grDevices, graphics, stats and utils packages are now
imported explicitly into the NAMESPACE, avoiding any possible
masking by other packages. goana(), alias2Symbol(),
alias2SymbolTable() now load the name spaces of GO.db the
relevant organism package org.XX.eg.db instead of loading the
packages. This keeps the user search path cleaner.
o
Various minor bug fixes and improvements to error messages.
Changes in version 3.26.0
o
New functions kegga() and topKEGG() to conduct KEGG pathway
over-representation analyses. kegga() is an S3 generic
function with a method for MArrayLM fitted model objects as
well a default method. It uses the KEGGREST package to access
up-to-date KEGG pathways using an internet connection.
o
goana() with trend now chooses the span of the tricube smoother
based on the number of differentially expressed genes. Larger
spans give smoother trends and are used for smaller numbers of
genes. Arguments 'species' and 'plot' are no longer part of
goana.MArrayLM() and are instead passed if present in a call to
goana.default().
o
topGO() has a new argument 'truncate.term' to limit number of
characters appearing in a column of the output data.frame.
o
Improved power for the romer() function. The empirical Bayes
shrinkage of the residuals, removed on 4 April, has been
re-instated in a corrected form. It can optionally be turned
off using the 'shrink.resid' argument.
o
camera() has a new argument inter.gene.cor. This allows a
preset inter gene correlation to be set for each test test,
resulting in potentially less conservative tests. camera() no
longer gives NA results when a gene has a residual variance
exactly zero.
o
mroast() now sorts significant gene sets by proportion of genes
changing as well as by p-value and set size. When p-values are
equal, sets with a higher proportion of changing genes will now
rank higher in ordered results. The contrast argument of
roast() can now optionally be a character string giving the
name of a column of the design matrix.
o
fry() now produces "mixed" (non-directional) p-values and FDRs
as well as directional p-values and FDRs. Bug fix for fry()
when the gene set has a small number of genes.
o
camera(), fry(), mroast() and romer() now give more informative
error messages when the index list is empty.
o
Speedup for ids2indices().
o
tricubeMovingAverage() has new argument 'power' and old
argument 'full.length' is removed. tricubeMovingAverage() now
checks whether span is correctly specified. It now gives
identical results for any span>=1 or any span<=0.
o
lmFit() and friends now always produce an output component
'rank' that returns the column rank of the design matrix.
Previously it depended on the options used whether this
component was given.
o
lmFit() now assumes that if the 'object' of expression values
is a simple vector, that it represents a single gene rather
than a single sample.
o
lmFit() now always treats infinite expression values as NA. In
the past, this was done somewhat inconsistently.
o
Checking for NA coefficients in lmFit() is now done more
efficiently, meaning that lmFit() is now faster on large
datasets.
o
More informative error message from lmscFit() when correlation
is NA.
o
getEAWP() now works on all eSet objects, not just
ExpressionSet, provided there is an data element called
"exprs". This allows all the linear modelling functions to
handle general eSet objects.
o
plotMD() can now accept arguments 'array' or 'coef' as
appropriate as synonyms for 'column'. This may help users
transitioning from plotMA ot plotMD.
o
Arguments hi.pch, hi.col and hi.cex of plotWithHighlights()
renamed to hl.pch, hl.col and hl.cex. ('hl' is short for
'highlight'.)
o
New 'tolerance' argument for read.idat() to allow manifest and
idat to differ by a certain number of probes
o
The 'genes' argument of controlStatus() now accepts EListRaw or
Elist objects.
o
All data classes (EListRaw, EList, RGList, MAList, MArrayLM)
now require two arguments for subsetting. This restores the
behavior in limma version 3.19.10 and earlier. In limma
3.19.11 through 3.25.13, subsetting by one argument y[i] was
equivalent to y[i,]. This will now give an error message.
o
The dimnames<- method for EListRaw objects now sets rownames
for the background matrix Eb as well as for the foreground
matrix E.
o
avearrays() now gives a more informative error message if input
argument 'x' is not a matrix.
o
auROC() now allows for tied stat values.
o
Bug fix to write.fit() when method="global" and fit contains
more than one column.
Changes in version 3.24.0
o
Limma review article (Ritchie et al, Nucleic Acids Research,
2015) is now published, and becomes the primary citation for
limma. References in User's Guide, help files and CITATION have
been updated to refer to this article.
o
New function plotMD() for mean-difference plots. This will
take over the functionality of the plotMA() function.
o
mdplot() has new arguments 'columns' and 'main'.
o
Arguments 'pch', 'cex' and 'col' of plotWithHighlights()
renamed to 'hi.pch', 'hi.cex' and 'hi.col'. Improved help page
for plotWithHighlights().
o
plot() methods removed for EList, MAList and MArrayLM objects.
Use plotMD() instead.
o
New function fry() provides a fast version of mroast() when
there is little heteroscedasticity between genes.
o
Minor change to mroast() output so that FDR values are never
smaller than p-values, even when mid-p-values are used.
o
camera(), roast(), mroast() and romer() now give a warning if
any arguments found in ... are not used.
o
romer() is now an S3 generic function. Speed improvements for
romer() when the "mean" or "floormean" set statistics are used.
The empirical Bayes shrinkage of the residuals, introduced 5
October 2009, has been removed - this means that romer()
results will revert to being somewhat more conservative.
o
topGO() can now sort gene ontology results by several p-value
columns at once, using the minimum of the columns. The default
is now to sort results using all the p-value columns found in
the results instead of just by the first column.
o
goana() has new arguments 'plot' and 'covariate'. The plot
argument allows users to view any estimated trend in the
genewise significance values. The help files goana.Rd and
goana.MArrayLM.Rd are consolidated into one file.
o
plotDensities() has a new argument 'legend', allowing user to
reposition the legend.
o
voomWithQualityWeights() has a new argument 'col', allowing the
barplot of sample weights to be colour-coded.
o
Improvements and bug fixes to plotMDS(). plotMDS now stores
'axislabel', depending on the method used to compute distances,
and uses this to make more informative axis labels. plotMDS now
traps problems when the distances are all zero or if one or
more of the dimensions are degenerate. Also, a bug when
gene.selection="common" and top=nrow(x) has been fixed.
o
The arguments for predFCm() have been renamed. Old argument
'VarRel' replaced by logical argument 'var.indep.of.fc'. Old
argument 'prop' replaced by 'all.de'. New argument
'prop.true.null.method' passes through method to
propTrueNull().
o
vennDiagram() now fills circles with specified colors.
o
plotMA() now has a method for EListRaw objects.
o
voomWithQualityWeights() now stores the sample weights in the
output EList object. The values stored in $weights are
unchanged, they are still the combined observation and sample
weights.
o
nec() and neqc() now remove the background Eb values if they
exist in the EListRaw object.
o
Acknowledgements edited in User's Guide.
o
fix URLs for a number of references in help pages.
o
Improvements to lowess C code.
o
When loading suggested packages within function calls, usages
of require() have been converted to requireNamespace() wherever
possible. Functions from suggested packages are called using
`::'.
o
topTable() now gives an information error message if eBayes()
or treat() have not already been run on the fitted model
object.
o
genas() now gives an error message when applied to fitted model
for which the coefficient correlations are gene-specific.
o
Fix a bug in the MAList method for plotDensities that was
introduced with the use of NextMethod() in version 3.21.16.
Changes in version 3.22.0
o
New functions goana() and topGO() provide gene ontology
analyses of differentially genes from a linear model fit. The
tests include the ability to adjust for gene length or
abundance biases in differential expression detection, similar
to the goseq package.
o
Improvements to diffSplice. diffSplice() now calculates Simes
adjusted p-values for gene level inferences, in addition to the
exon level t-tests and gene level F-tests. topSplice() now has
three ranking methods ("simes", "F" or "t"), with "simes" now
becoming the default. diffSplice() also has a new argument
'robust' giving access to robust empirical Bayes variance
moderation.
o
New function plotExons() to plot log-fold-changes by exon for a
given gene.
o
New function voomWithQualityWeights() allows users to estimate
sample quality weights or allow for heteroscedasticity between
treatment groups when doing an RNA-seq analysis.
o
Improvement to arrayQualightyWeights(). It now has a new
argument 'var.design' which allows users to model variability
by treatment group or other covariates.
o
Improved plotting for voomaByGroup().
o
barcodeplot() can now plot different weights for different
genes in the set.
o
Improvements to roast() and mroast(). The directional (up and
down) tests done by roast() now use both the original rotations
and their opposite signs, effectively doubling the number of
effective rotations for no additional computational cost. The
two-sided tests are now done explicitly by rotation instead of
doubling the smallest one-sided p-value. The two-sided p-value
is now called "UpOrDown" in the roast() output. Both functions
now use a fast approximation to convert t-statistics into
z-scores, making the functions much faster when the number of
rotations or the number of genes is large. The contrast
argument can now optionally be a character string giving a
column name of the design matrix.
o
zscoreT() can optionally use a fast approximation instead of
the slower exact calculation.
o
symbols2indices() renamed to ids2indices().
o
Improvements to removeBatchEffect(). It can now take into
account weights and other arguments that will affect the linear
model fit. It can now accept any arguments that would be
acceptable for lmFit(). The behavior of removeBatchEffect()
with design supplied has also changed so that it is now
consistent with that of lmFit() when modelling batches as
additive effects. Previously batch adjustments were made only
within the treatment levels defined by the design matrix.
o
New function plotWithHighlights(), which is now used as the
low-level function for plotMA() and plot() methods for limma
data objects.
o
The definition of the M and A axes for an MA-plot of single
channel data is changed slightly. Previously the A-axis was
the average of all arrays in the dataset - this has been
definition since MA-plots were introduced for single channel
data in April 2003. Now an artificial array is formed by
averaging all arrays other than the one to be plotted. Then a
mean-difference plot is formed from the specified array and the
artificial array. This change ensures the specified and
artificial arrays are computed from independent data, and
ensures the MA-plot will reduce to a correct mean-difference
plot when there are just two arrays in the dataset.
o
plotMDS() can now optionally plot samples using symbols instead
of text labels. It no longer has a 'col' argument, which
instead is handled by ....
o
vennDiagram() now supports circles of different colors for any
number of circles. Previously this was supported only up to
three sets.
o
getEAWP() will now find a weights matrix in an ExpressionSet
object if it exists.
o
update to helpMethods().
o
Substantial updates to the two RNA-seq case studies in the
User's Guide. In both cases, the short read data has been
realigned and resummarized.
o
Improvements to many Rd files. Many keyword entries have been
revised. Many usage and example lines been reformated to avoid
over long lines.
o
biocViews keywords updated.
o
Subsetting columns of a MArrayLM object no longer subsets the
design matrix.
o
Bug fix for read.maimages: default value for 'quote' was not
being set correctly for source="agilent.mean" or
source="agilent.median".
o
Bug fix to topTableF() and topTable(). The ordering of Amean
values was sometimes incorrect when sorting by F-statistic and
a lfc or p.value filter had been set.
o
Bug fix to read.ilmn() when sep=",".
Changes in version 3.20.0
o
New functions diffSplice(), topSplice() and plotSplice()
provide functionality to analyse differential splicing using
exon-level expression data from either microarrays or RNA-seq.
o
New Pasilla case study added to User's Guide, demonstrating
differential splicing analysis of RNA-seq data.
o
new function weightedLowess() which fits a lowess curve with
prior weights. Unlike previous implementations of lowess or
loess, the weights are used in calculating which neighbouring
points to include in each local regression as well as in the
local regression itself.
o
weightedLoess() now becomes the default method used by
loessFit() to fit the loess curve when there are weights. The
previous locfit and loess() methods are offered as options.
o
linear model fit functions lm.series(), mrlm.series() and
gls.series() no longer drop the dimensions of the components of
the fitted object when there is just coefficient or just one
gene. Previously this was done inconsistently in some cases but
not others. Now the matrix components always keep dimensions.
o
The functions lmFit(), eBayes() and tmixture.vector() now work
even when there is just one gene (one row of data).
o
New function subsetListOfArrays(), which is used to simplify
the subsetting code for RGList, MAList, EList, EListRaw and
MArrayLM objects.
o
new function tricubeMovingAverage() for smoothing a time
series.
o
barcodeplot() has a new option to add enrichment worms to the
plot, making use of tricubeMovingAverage().
o
New plot() methods for RGList, MAList, EList and MArrayLM class
objects. In each case, this produces a similar result to
plotMA(). When using plot() or plotMA() on an MArrayLM object,
the column is now specified by the 'coef' argument instead of
by 'array'.
o
plotMA3by2() now works on single channel data objects as well
as on MAList objects.
o
New function read.idat() to read files from Illumina expression
beadarrays in IDAT format.
o
The ctrlpath argument of read.ilmn() now defaults to the same
as path for regular probes. This means that only one path
setting is required if the regular and control probe profiles
are in the same directory.
o
read.ilmn() now sets the same probe IDs as rownames for both
the expression matrix E and the annotation data.frame genes,
providing that the probe IDs are unique.
o
beadCountWeights() can now work with either probe-wise standard
errors or probe-wise standard deviations.
o
treat() has new arguments robust and winsor.tail.p which are
passed through to robust empirical Bayes estimation.
o
topTreat() now includes ... argument which is passed to
topTable().
o
topTable() with confint=TRUE now produces confidence intervals
based on the t-distribution instead of on the normal
distribution. It also now accepts a numeric value for the
confint argument to specify a confidence level other the
default of 0.95.
o
topTable() will now work on an MArrayLM fit object that is
missing the lods component, for example as produced by treat().
o
roast() and mroast() now permit array weights and observation
weights to both be specified.
o
camera(), roast() and mroast() now use getEAWP() to interpret
the data object. This means that they now work on any class of
data object that lmFit() will.
o
romer() now uses propTrueNull(method="lfdr") instead of
convest(). This makes it substantially faster when the number
of genes is large.
o
genas() now uses fit$df.total from the MArrayLM object. This
prevents df.total from exceeding the total pooled residual df
for the dataset. The genas() results will change slightly for
datasets for which df.prior was very lage.
o
plotDensities() is now an S3 generic function with methods for
RGList, MAList, EListRaw and EList objects.
o
plotFB is now an S3 generic function with methods for RGList
and EList data objects.
o
New topic help pages 10GeneSetTests.Rd and 11RNAseq.Rd. The
page 10Other.Rd is deleted. All topic help pages are now listed
under 'See also' in the package introduction page accessible by
?limma.
o
avereps() was never intended to be applied to RGList or
EListRaw objects. It now gives an error when applied to these
objects instead of returning a matrix of questionable value.
o
Bug fix: fitFDistRobustly() was failing when there were missing
values or zero df values and covariate was NULL.
o
Bug fix: vennDiagram() wasn't passing extra arguments (...) to
plot() when the number of sets was greater than 3.
o
Bug fix to topTreat(). Rownames were incorrectly ordered when
p<1.
o
bug fix to genas(), which was not handling vector df.prior
correctly when the fit object was generated using robust=TRUE.
o
bug fix to squeezeVar(). Previously there was an error when
robust=TRUE and trend=FALSE and some of the estimated df.prior
were infinite.
o
bug fix to topTable() and topTableF() when sorting by
F-statistic combined with p-value or lfc cutoffs.
Changes in version 3.18.0
o
new function beadCountWeights() to estimate quantitative
weights from the bead counts for each probe for Illumina
BeadArrays.
o
New function contrastAsCoef(), which reforms a design matrix so
that one or more specified contrasts become coefficients. This
function is called by roast().
o
plotMA() is now an S3 generic function.
o
The legend argument to plotMA() can now take a character value
giving the position to place the legend.
o
toptable(), topTable() and topTreat() now preserve the rownames
of the fit object, unless the fit object has duplicated names,
in which case the rownames are copied to the ID column. Empty
rownames are replaced with 1:nrow(fit).
o
read.ilmn() no longer adds the probe IDs to the gene annotation
data.frame, leaving them instead as rownames of the expression
matrix. It longer creates a targets file since the sample
names are already preserved as column names of the expression
matrix.
o
loessFit() now uses the locfit.raw in the locfit package when
weights are provided instead of loess in the stats package.
The function now runs very efficiently even on very long data
vectors. The output results will change slightly when weights
are provided.
o
voom() now outputs lib.size as a column of targets instead of
as a separate component.
o
cbind for EList and EListRaw objects now recognizes a design
matrix if it is present.
o
plotMDS() now checks explicitly that there are at least 3
samples to plot.
o
normexp.fit.detection.p() now tolerates some non-monotonicity
in the detection p-pvalues as a function of expression.
o
fitFDistRobustly() now uses a smoother for the smallest
df.prior values. This may result in smaller tail values than
before when a group of input x values appear to be outliers but
the largest value is not individually a stand-out value.
o
New merge methods for EList and EListRaw objects.
o
topTable() and treat() now give more informative error messages
when the argument fit is not a valid MArrayLM object.
o
roast() now calls mroast() if the index vector is a list. Bug
fix to mroast(), which had been ignoring the weights.
o
Updates to genas() function. argument chooseMethod renamed to
subset and option "n" renamed to "all". Function now returns
NA results and a message when no genes satisfy the criterion
for inclusion in the analysis. Some editing of help page and
streamlining of code.
o
Roles of contributors now specified in author field of
DESCRIPTION file using standard codes.
o
Additions and updates to references in the help pages. Removed
defunct Berkeley Press links to published Smyth (2004) article
in several Rd files. Replacing with link to Preprint. Added
link to Phipson (2013) thesis in two Rd files. Add Majewski et
al reference to genas.Rd. Add Phipson et al and Sartor et al
references to squeezeVar.Rd. Add Phipson et al reference to
eBayes.Rd. Update lmscFit and voom references.
o
Update mammmary stem cell case study in User's Guide. As well
as reflecting changes to read.ilmn() and topTable(), this now
demonstrates how to find signature genes for particular cell
type.
o
documentation about rownames and column names and the use of
rownames(fit) and colnames(fit) added to lmFit.Rd.
o
improvements to help pages for data classes.
o
Edits to normalizeBetweenArrays help page (i) to further
clarify which normalization methods are available for
single-channel data and which are available for two-color data
and (ii) to give a cross citation to the neqc() function for
Illumina BeadChips.
o
Edits to voomaByGroup help page.
o
duplicateCorrelation() now uses the weights matrix when block
is set. Previously the weights were ignored when block was
used.
o
Bug fix to subsetting for MArrayLM objects: the df.total
component was not being subsetted.
o
bug fix to eBayes(robust=TRUE) when some of the df.prior values
are infinite.
o
Bug fix to ebayes(), which was not passing the 'robust'
argument correctly on to squeezeVar().
o
Bug fix to fitFDistRobustly(), which affected the estimated
scale when df2 is estimated to be Inf.
Changes in version 3.16.0
o
New section in User's Guide on time course experiments with
many time points. The RNA-seq case study in User's Guide has
also been revised.
o
Improvements to various help pages including read.maimages.Rd,
squeezeVar.Rd, fitFDist.Rd, trigammaInverse.Rd,
normalizeRobustSpline.Rd, genas.Rd and roast.Rd. Previously the
meaning of source="agilent" was mis-stated in read.maimages.Rd.
o
New robust method for estimating the empirical Bayes prior,
called by specifying robust=TRUE in the call to eBayes(). When
this is TRUE the output df.prior is now a vector instead of a
scalar.
o
New function fitFDistRobustly() estimates the parameters of a
scaled F-distribution robustly using Winsorized values.
Outlier observations receive smaller values for df.prior than
non-outliers. This permits robust methods for squeezeVar(),
ebayes() and eBayes(), all of which now have a new argument
wins.tail.p to specify the tail proportions for Winsorizing.
o
fitFDist() now permits infinite values for the covariate. It
also gracefully handles cases where the covariate takes only a
small number of distinct values. Similarly for eBayes() and
squeezeVar() that call fitFDist().
o
All the functions that perform gene set tests have been revised
to make the input and output formats more consistent.
roast(), mroast() and camera() are now S3 generic functions,
with methods for EList and MAList objects.
The order of arguments has been changed for roast(), mroast()
and camera() so that the first argument is now y.
All functions that perform gene sets now use the argument
'index' to specify which genes are included in the test set.
Previously this argument was called 'iset' for roast() and
romer() and 'indices' for camera().
camera() and mroast() now produce a data.frames. Instead of
separate up and down p-value columns, there is now a two-sided
p-value and a column indicating direction of change. There are
new columns giving FDR values and the number of genes in each
set. There is a new argument 'sort' to indicate whether output
results should be sorted by p-value.
mroast() has a new argument 'weights' for observational
weights, to bring it into line with roast(),
o
vennDiagram() can now plot up to five sets (previously limited
to three).
o
genas() now optionally draws a plot in which ellipses are used
to represent the technical and biological components of
correlation. It also now has the ability to automatically
select which probes are used for the correlation analysis, and
a new argument controls the method used for this selection.
o
New options for the method argument of propTrueNull().
o
New functions vooma() and voomaByGroup() for computing
precision weights based on a mean-variance trend. vooma() is
similar to voom() but for microarray data instead of RNA-Seq.
voomaByGroup() allows different groups to have systematically
different variances.
o
New function predFCm() to compute predictive (shrunk) log fold
changes.
o
New function fitGammaIntercept() for estimating the intercept
of a gamma glm with an offset. Used by genas().
o
New function zscoreHyper() for computing z-score equivalents of
deviates from a hypergeometric distribution.
o
New function qqf() for qq-plots relative to an F-distribution.
o
normalizeWithinArrays() with method="robustspline" now longer
requires the layout argument to be set. The layout argument for
normalizeRobustSpline() now defaults to a single print-tip
group.
o
fitFDist() now coerces degrees of freedom df1 below 1e-15 to
zero.
o
Due to changes in R, loessFit() no longer makes direct calls to
foreign language code in the stats package, and instead calls R
functions. Unfortunately, this makes loessFit() about 25-30%
slower than previously when weights are used.
o
Bug fix to read.maimages(), which was not accepting
source="agilent.mean".
o
Bug fix for contrasts.fit() when the covariance matrix of the
coefficients (cov.coefficients) is not found in the fitted
model object. This situation doesn't arise using any of the
standard limma analysis pipelines.
o
Bug fix to lmscFit() when the residual df = 1.
o
Bug fix to readTargets() to avoid warning message when
targets$Label is used to set row names but targets$Label
contains duplicated entries.
Changes in version 3.14.0
o
limma license upgraded to GPL (>=2) instead of LGPL to match R
itself.
o
Many updates to the User's Guide. Sections have been added on
reading single channel Agilent and Illumina data. The chapter
on experimental designs has been split into three chapters on
single-channel, common reference and two-color designs
respectively. The material on the fixed effect approach to
technical replication has been deleted. There are new sections
on nested interactions for factorial designs and on multi-level
designs.
o
The links to the Apoa1, Weaver and Bob1 datasets in the User's
Guide have been updated to help users download the data
themselves if they wish to repeat the case study analyses.
o
The help page for camera() now cites published paper Wu and
Smyth (NAR, 2012). In view of the results of this paper, the
claim is no longer made on help page for geneSetTest() that
genes might be treated as independent when the experimental
units are genetically identical mice.
o
Minor edits to CITATION file.
o
New function propTrueNull() for fast estimation of the
proportion of true null hypotheses from a vector of p-values.
o
New function zscore() to compute z-score equivalents for
deviates from any continuous distribution. Includes the
functionality of the older functions zscoreGamma() and
zscoreT() as special cases.
o
roast() now accepts observation level weights, through a new
argument 'weights'.
o
loessFit() now applies minimum and maximum bounds by default to
avoid zero or infinite weights. Equal weights are now treated
as if the weights were NULL, even all zero weights, so that the
lowess code is called instead of the loess code.
o
When there are no weights, loessFit() now extracts residuals
directly from the C code output instead of computing in R.
o
fitFDist() now permits missing values for x or zero values for
df1 even when there is a covariate. This means that
squeezeVar() and eBayes() now work with trends even when not
all the data values are informative.
o
New argument 'file' for convest(), implementing edits
contributed by Marcus Davy. Arguments doplot and dereport
renamed to 'plot' and 'report'.
o
Two improvements for plotMDS(). It now coerces labels to be
character, and now makes extra room on the plot when the text
labels are wide.
o
plotMDS() no longer gives an error when the requested number of
top genes is greater than the total number of rows of data.
o
Code speed-up for alias2SymbolTable()
o
any(duplicated()) replaced by anyDuplicated() in several
functions.
o
Fix to voom() so that it computes weights correctly even when
the design matrix is not of full rank.
o
Bug fix for roast() when the fitted model has only one
coefficient.
Changes in version 3.12.0
o
read.maimages() with source="agilent" now reads median
foreground estimates instead of mean foreground. New option
source= "agilent.mean" preserves earlier meaning of
source="agilent".
o
Agilent single-channel case study added to User's Guide.
o
removeBatchEffect() now corrects for continuous covariates as
well as qualitative factors.
o
new function camera() performs competitive gene set tests while
adjusting for inter-gene correlation.
o
new function interGeneCorrelation() estimates the average
intergene correlation for a set of genes.
o
columns in output from roast() have been re-ordered.
o
arguments 'selected' and 'selected2' renamed to 'index' and
'index2' in functions barcodeplot(), geneSetTest() and
wilcoxGST().
o
default labels for barcodeplot() are now somewhat more
explicit.
o
new function rankSumTestWithCorrelation extends the
Wilcoxon-Mann-Whitney test to allow for correlation between
cases in one of the groups. geneSetTest() now calls this
function instead of wilcox.test, with a consequence improvement
in speed.
o
The lfc (log-fold-change) cutoff argument of topTable() is now
applied to the minimum absolute logFC when ranking by
F-statistic. Previously lfc was only used when ranking by
t-statistic.
o
new methods "fast" and "affy" for normalizeCyclicLoess(), with
"fast" becoming the default method. New argument
'cyclic.method' for normalizeBetweenArrays() gives access to
the different cyclic loess methods.
o
There were problems with using the argument gene.weights in
mroast(). This argument is now permitted to be of the same
length as the number of probes in the data set. It is then
automatically subsetted for each gene set.
o
mroast() now uses mid-p-values by default when adjusting for
multiple testing.
o
neqc(), nec() and normexp.fit.control() now give user-friendly
error messages when no negative control probes or no regular
probes are found.
Changes in version 3.10.0
o
New function voom() allows RNA-Seq experiments to be analysed
using the standard limma pipeline. An RNA-Seq case study is
added to User's Guide.
o
treat(), roast() and mroast() can now estimate and work with a
trend on the prior variance, bringing them into line with
eBayes().
o
barcodeplot() and barcodeplot2() merged into one function.
o
removeBatchEffect() can now correct for two batch factors.
o
plotMDS is now an S3 generic function. This allows MDS plots
to be redrawn with new labels without needing to repeat the
distance or scaling calculations. New S4 class "MDS" to hold
the multidimensional scaling information output from plotMDS.
o
getEAWP() now gets probe annotation from the expression
rownames of an EList object, if no other probe annotation is
available.
o
topRomer() now ranks gene sets by secondary columns as well the
primary criterion specified, to give a more meaningful ranking
when the p-values are tied.
o
wilcoxGST() now accepts signed or unsigned test statistics.
Change to p-value calculation in geneSetTest() when
rank.only=FALSE to avoid zero p-values and follow
recommendation of Phipson and Smyth (SAGMB, 2010).
o
plotMA() now recognizes ElistRaw and EList objects
appropriately.
o
Default span for normalizeCyclicLoess increased from 0.4 to
0.7. Speed improved when weights=NULL.
o
Weaver case study (Section 11.5) in User's Guide is updated and
rewritten. Data classes ElistRaw and Elist now described in the
quick start section of the User's Guide. Other minor updates
to User's Guide.
o
Bug fix for normalizeBetweenArrays() when object is an EListRaw
and method="cyclicloess". Previously this function was
applying cyclicloess to the raw intensities, then logging. Now
it logs first, then applies cyclicloess.
o
Bug fix to avereps() for EList objects x when x$other is not
empty.