R package for the joint analysis of multiple single-cell RNA-seq datasets
Version 1.4.1 published on CRAN, 15 May 2021
getOdGenesUniformly
and con$correctGenes
collapseCellsByType
and colSumByFactor
are moved to sccore
The initial CRAN version for conos 1.4.0 was published on Feb 23 2021: https://cran.r-project.org/web/packages/conos/index.html
plotDEheatmap
functionbalancing.factor.per.sample
in buildGraph
std::cout
to Rcpp::Rcout
(July 2020)ht_opt$message = FALSE
for ComplexHeatmap (July 2020)getPerCellTypeDE()
for errors, removing NAs (July 2020)ht_opt$message = FALSE
for ComplexHeatmap (July 2020)saveConosForScanPy()
(July 2020)getCorrectionVector()
and getPerCellTypeDECorrected
(2 July 2020)neighborhood.average
(4 July 2020)Tag to update the docker version build
sccore
buildGraph
k.same.factor
and balancing.factor.per.sample
to buildGraph
.
It can be used to improve alignment between different conditions: with same.factor.downweight
it gives the system similar to k.self
and k.self.weight
convertToPagoda2
to create Pagoda 2 from Conos. Helpful for PagodaWebApp.getDifferentialGenes
uses first clustering by defaultcollapseCellsByType
. Note: probably will affect DE results.n_sgd_threads
from uwot
to n.cores
by default. It gives much better parallelization, but kills reproducibility.
Use n.sgd.cores=1
to get reproducible embeddings.target.dims
in UMAP embeddingcor.based
with alingnment.strength == 0
. It removes edges with negative correlation and reduce down-weight of inter-sample edges, which can change results of the alignment.fixed.initial.labels
in propagateLabels
from FALSE
to TRUE
. Presumably, FALSE
should never be used.saveConosForScanPy
getDifferentialGenes
(parameters append.specifisity.metrics
and append.auc
)findSubcommunities
to increase resolution for specific clusterssubgroups
to embeddingPlot
. It allows to plot only cells, belonging to the specified subgroupskeep.limits
to embeddingPlot
getDifferentialGenes
(parameters append.specifisity.metrics
and append.auc
)velocityInfoConos
function for RNA velocity analysis on samples integrated with conos (together with supplementary functions prepareVelocity
and pcaFromConos
)velocityInfoConos
function)getJointCountMatrix
to conos obectsaveConosForScanPy
parseCellGroups
to parse properly cell groupings depending on user settingsestimteWeightEntropyPerCell
to visualize alignment quality per cell