.read_matrix_block()
dreamlet()
gives clearer error message for singular design matrixplotGeneHeatmap()
handles zmax
correctly nowprocessOneAssay
, set rescaleWeightsAfter=FALSE
by defaulttopTable()
with multiple coef
valuesrun_mash()
with multiple coefficients
dreamlet()
allow formula to include only interceptcompositePosteriorTest()
plotPCA()
and outlierByAssay()
list
, not just dreamletProcessedData
pbWeights()
outlierByAssay()
eBayes()
processAssays()
pass argument scaledByLib
to voomWithDreamWeights()
pbWeights()
getExprGeneNames()
seeErrors()
and documentationoutlier()
to compute z-scores. How returns data.frame()
outlierByAssay()
and plotPCA()
compositePosteriorTest()
allows exclude
set to be NULL
meta_analysis()
stackAssays()
now includeds metadata()$aggr_means
correctlycompositePosteriorTest()
get_metadata_aggr_means()
to extract aggr_means
when SCE is produced by cbind'ingaggr_means
in aggregateToPseudoBulk()
rdf
is low for all genesplotBeeswarm()
rowWeightedVarsMatrix()
isFullRank()
check in dreamlet()
run_mash()
pbWeights()
add argument maxRatio
pbWeights()
dreamlet()
pbWeights()
to compute precision weights for pseudobulk countsextractData()
and include it in vignettestackAssays()
diffVar()
getVarFromCounts)
so zeta
is a mean, not a sumcomputeLogCPM()
uses augmentPriorCount()
computeLogCPM()
now returns matrix
instead of sparseMatrix
variancePartition
version dependencygetWeightsList()
processAssays()
setAutoBlockSize()
update within aggregateToPseudoBulk()
processAssays()
and fitVarPart()
styler::style_pkg()
dreamletCompareClusters()
now allows cell-level covariates in response to https://github.com/GabrielHoffman/dreamlet/issues/11dreamlet::residuals()
processAssays()
use voomWithDreamWeights(..., span="auto")
to estimate the lowess tuning parameter merge_metadata()
when a cell type is not observed for all donors.dreamlet()
fix issue when contrasts are specified and formula includes variable from metadata()
assays
argument to buildClusterTreeFromPB()
processAssays()
when assays is droppedzellkonverter (>= 1.10.1)
to avoid issues with previous version
topTable()
for dreamletResult
in the case where one or more cells didn't estimate the coefficient of interestcomputeNormCounts()
and computeLogCPM()
topTable()
to deal with multiple coef
as arraycolData<-
for dreamletProcessedData
topTable()
and plotForest()
aggregateToPseudoBulk()
stores mean of cell-level covariates in metadata(pb)$aggr_means
processAssays()
, dreamlet()
, fitVarPart()
aggregateNonCountSignal()
plotProjection()
outlier()
plotForest()
plotVolcano()
to allow scales="free_y"
aggregateNonCountSignal()
to include filtersaggregateNonCountSignal()
aggregateNonCountSignal()
plotGeneHeatmap()
drop empty genesbuildClusterTreeFromPB()
topTable()
as.dreamletResult()
variancePartition
dependency and sourceprocessAssays()
and processOneAssay()
, add argument min.prop
indicating the minimum proportion of retained samples with non-zero countszenith_gsa()
for few gene setscomputeCellCounts()
transpose
argument to plotGeneHeatmap()
alpha
arugment to plotVoom()
plotVarPart()
totalCPM
column to output of cellTypeSpecificity()
to use for filtering. Functions dreamlet::plotHeatmap()
plotViolin()
and plotPercentBars()
now ignore this column plotGeneHeatmap()
assays
to plotVarPart()
extractData()
aggregateToPseudoBulk()
by speeding up check in .check_arg_assay()
tabToMatrix()
topTable()
when all random effects are droppedaggregateToPseudoBulk()
when summarizing for just 1 samplegetTreat()
for dreamlet()
resultdroplevels
for colData
in processAssays()
processAssays()
to detect issues with SCEaggregateToPseudoBulk()
with sample orderingdreamletCompareClusters()
colsum2()
using beachmat code.aggregateToPseudoBulk()
by fixing `aggregateByColnames()run_mash()
to combine results across coefsdreamlet::colsum_fast()
used in pseudobulkda_to_sparseMatrix()
aggregateToPseudoBulk()
for DelayedArray
now uses colsum_fast()
DelayedArray
dreamletCompareClusters()
:plotZenithResults()
errorsAsWarnings
. If TRUE
warns and returns NULL. dreamletCompareClusters()
to be compatible with zenith_gsa()
formula
in dreamletCompareClusters()
aggregateToPseudoBulk()
when a Seurat object is usedaggregateToPseudoBulk()
when a Seurat object is usedaggregateToPseudoBulk()
when a Seurat object is usedcollapse=TRUE
to dreamletCompareClusters()
dreamletCompareClusters()
dreamletCompareClusters()
min.samples
to processAssays()
, processOneAssay()
dreamletCompareClusters()
and run_mash()
dreamletCompareClusters()
mashr
dependencyrun_mash()
zenith_gsa()
, plotVolcano()
, plotForest()
for resultscellTypeSpecificity()
for genes with zero reads across all cell typesplotForest()
and zenith_gsa()
changed for consistancyremoveConstantTerms()
when excluded variable string (i.e. tissue) is also a substring of other variables (i.e. tissueStatus)residuals()
for dreamlet()
resultdreamletPairs()
removeConstantTerms()
with multiple constant termscellTypeSpecificity()
by adding plotPercentBars()
and plotViolin()
compatabilitytopTable()
when coef
is not estimatedassays
to dreamlet()
, fitVarPart()
, and processAssays()
processOneAssay()
weights by number of cellsvariancePartition >= 1.25.1
to handle weights in voomWithDreamWeights()
topTable()
plotPercentBars()
for class vpDF
applyQualityWeights()
ilr_composition_test.R
dreamletResult
using coefNames()
regModel()
removeConstantTerms()
now doesn't drop terms solely because of NA'saggregateToPseudoBulk()
bug fix in removeConstantTerms()
Sept 30, 2021
zenith_gsa()
adds argument inter.gene.cor
and progressbar
cellTypeCompositionVarPart()
and cellTypeCompositionTest()
topTable()
where FDR was evaluated on only a subset of genesdreamlet()
to handle linear contrastsremoveConstantTerms()
now drops categorical variables with only a max of one example per categorycellTypeCompositionTest()
processAssays()
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