Prevent an R session crash when an NA is supplied to block= and group= in functions like suggestRnaQcThresholds() and scoreMarkers().
+Now, an error is thrown if a NA value is detected.
Modified default extra.work to respect IRLBA's internal defaults for better performance.
Fix to preserve dimnames during assay extraction in the quick*Qc.se() wrappers, so that they can be matched to character subsets=.
Fix to disable medians in aggregateByCells() by default, as it's slow and not usually desired.
Multiple changes to clusterGraph():
Updates to use the latest version of the igraph library.
method="leiden" now supports the ER objective function in leiden.objective=.
Non-zero status are now directly reported as errors, status is removed from the output.
Multiple additions to scoreMarkers():
Support use of quantiles to average statistics across blocks, via the new block.average.policy= and block.quantile= arguments.
Allow calculation of arbitrary quantiles as a summary statistic for the effect sizes of each group, via the new compute.summary.quantiles= argument.
Output now contains the number of rows, row names and group IDs for easier interpretation by downstream functions.
Added top.index.only= argument to report only the indices of the top genes when all.pairwise= is an integer.
Added arguments to summarizeEffects() to disable calculation of certain summary statistics, e.g., by setting compute.summary.median=FALSE.
Also support calculation of arbitrary quantiles as described for scoreMarkers().
suggest*QcThresholds() functions now return a block.ids field that preserves the type of block=.
This enables easier matching in the corresponding filter*QcMetrics() function.
Multiple changes to modelGeneVariances():
Allow the use of quantiles to average statistics across blocks via the new block.average.policy= and block.quantile= arguments.
Return a block.ids field that preserves the type of block= for easier matching.
Multiple changes to runPca():
Added a subset= argument that performs a PCA on a subset of features but still reports the full rotation matrix for all features.
Return a block.ids field that preserves the type of block= for easier matching.
clusterKmeans() now emits a warning upon convergence failure.
This can be disabled by setting warn=FALSE.
Simplified the automatic naming performed by combineFactors() when factors is unnamed.
Added SummarizedExperiment-compatible wrappers for all functions.
These can be identified by their *.se suffix, e.g., aggregateAcrossCells.se().
Some functions execute multiple steps for convenience, e.g., quickRnaQc.se() runs all of the RNA-related QC functions.
Added analyze.se() to replace the analyze() function.
The former is more convenient it stores most outputs in a SingleCellExperiment and formats the marker tables correctly.
The latter is now deprecated.
compute*QcMetrics() functions now return a DataFrame for more convenient inspection and manipulation.
All functions that previously returned a base data.frame will now return a DataFrame from S4Vectors. This is a little prettier and we already load S4Vectors anyway.
Removed checks on num.neighbors= when pre-computed neighbor search results are supplied in runUmap(), buildSnnGraph() and subsampleByNeighbors().
Any differences between num.neighbors= and the number of neighbors in the pre-computed results are now ignored.
Implemented the LogNormalizedMatrix class for delayed log-transformation.
Modified normalizeCounts() to return instances of this class when log=TRUE and delayed=TRUE.
This allows downstream C++ code to use a more efficient normalization wrapper via initializeCpp().
Note that there are some very minor differences in the log-normalized values when computed in R via extract_array() and those in C++ via tatami extraction.
Added the countGroupsByBlock() function to count the number of cells within each combination of group and block.
This is primarily intended for diagnosing batch effects.
Added options to control which statistics are computed in aggregateAcrossCells().
Also added bindings to compute the per-group medians via the compute.median= option.
Multiple changes to scoreMarkers():
Setting all.pairwise= to an integer will now return the top markers by effect size for each pairwise comparison.
This is more efficient than creating the 3D array of effects and then ranking the genes in each comparison.
The min-rank summary is now limited to the top min.rank.limit= genes, for efficiency.
All other genes will have their reported min-rank set to a maximum placeholder.
Added compute.group.mean= and compute.group.detected= options to disable reporting of the group-wise means.
Multiple changes to testEnrichment():
Setting universe=NULL will automatically define the universe as the union of all genes.
Return value is now a data frame with the number of overlapping genes and the size of each gene set.
Updated correctMnn() to the new algorithm used by the mnncorrect C++ library.
This aims to improve correction accuracy and reduce sensitivity to the choice of reference.
Bugfix to runAllNeighborSteps() to return an empty list when no step is requested.
Set bound=0 by default in chooseHighlyVariableGenes(), for more convenient use with residuals.
Modified scoreGeneSet() to return weights as a data frame containing the row indices of the genes in the set.
Support character and logical vectors in sets= for aggregateAcrossGenes().
Automatically remove empty clusters from the output of clusterKmeans().
Supported blocking and related arguments for weighted averages of distances in scaleByNeighbors().
Added the aggregateAcrossGenes() function, to compute an aggregate expression value for gene sets.
Added compute.cohens.d=, compute.delta.mean= and compute.delta.detected= options to scoreMarkers().
Support top=Inf in chooseHighlyVariableGenes().
Also added the bound= argument to set a hard upper/lower bound.
Bugfix for correct filtering with block= in the various filter*QcMetrics() functions when not all blocking levels are present.
Bugfix to clusterKmeans() to respect the user-supplied seed= in relevant initialization methods.
Added a return.graph= option to return the SNN graph from runAllNeighborSteps().
Added a testEnrichment() function for quick and dirty gene set enrichment testing.
Modified runPca() so that it caps number= to the maximum number of available PCs.
Added an analyze() function that provides a one-click approach for analyzing single-cell data.
Added a reportGroupMarkerStatistics() function to combine all marker statistics for a single group into one data frame.
Switch clusterGraph() to use C++ wrappers around the igraph community detection algorithms via Rigraphlib.
This replaces the dependency on the igraph R package.
Added a delayed= option to avoid wrapping the matrix in a DelayedArray in normalizeCounts().