Package: BEARscc 1.27.1

Benjamin Schuster-Boeckler

BEARscc: BEARscc (Bayesian ERCC Assesstment of Robustness of Single Cell Clusters)

BEARscc is a noise estimation and injection tool that is designed to assess putative single-cell RNA-seq clusters in the context of experimental noise estimated by ERCC spike-in controls.

Authors:David T. Severson <[email protected]>

BEARscc_1.27.1.tar.gz
BEARscc_1.27.1.zip(r-4.5)BEARscc_1.27.1.zip(r-4.4)BEARscc_1.27.1.zip(r-4.3)
BEARscc_1.27.1.tgz(r-4.4-any)BEARscc_1.27.1.tgz(r-4.3-any)
BEARscc_1.27.1.tar.gz(r-4.5-noble)BEARscc_1.27.1.tar.gz(r-4.4-noble)
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BEARscc.pdf |BEARscc.html
BEARscc/json (API)
NEWS

# Install 'BEARscc' in R:
install.packages('BEARscc', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:

On BioConductor:BEARscc-1.27.0(bioc 3.21)BEARscc-1.25.0(bioc 3.20)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

immunooncologysinglecellclusteringtranscriptomics

4.48 score 1 scripts 172 downloads 2 mentions 40 exports 56 dependencies

Last updated 3 days agofrom:d6c8ab70f3. Checks:OK: 1 ERROR: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 18 2024
R-4.5-winERRORDec 18 2024
R-4.5-linuxERRORDec 18 2024
R-4.4-winERRORDec 18 2024
R-4.4-macERRORDec 18 2024
R-4.3-winERRORDec 18 2024
R-4.3-macERRORDec 18 2024

Exports:apply_bayesbuild_dropoutmodelcalculate_cell_metricscalculate_cell_metrics_by_clustercalculate_cluster_metricscalculate_cluster_metrics_by_clustercleanup_model_namescluster_consensuscompute_alphacompute_consensuscompute_genewise_dropoutscounts2mpccreate_cmcreate_null_dropout_modelestimate_missingdataestimate_mu2sigmaestimate_noiseparametersestimate_undetected2molpercellexecute_sim_replicatesfill_out_count_probability_tablegenewise_dropoutsHPC_simulate_replicatesiterate_alphasiterate_spikeinsmelt_spikeinspermute_count_in_dropout_rangeplot_alpha2muplot_cluster_metricsplot_mu2sigmaplot_obs2actualplot_spikein_fitsprepare_datarandomizerreport_cell_metricsreport_cluster_metricssample_modelssimulate_replicatessubcompute_sample_modelssubplot_spikein_fitswrite_noise_model

Dependencies:abindaskpassBiobaseBiocGenericsclicolorspacecrayoncurldata.tableDelayedArrayfansifarvergenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2gluegtablehttrIRangesisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvmimemunsellnlmeopensslpillarpkgconfigR6RColorBrewerrlangS4ArraysS4VectorsscalesSingleCellExperimentSparseArraySummarizedExperimentsystibbleUCSC.utilsutf8vctrsviridisLitewithrXVectorzlibbioc

BEARscc: Using spike-ins to assess single cell cluster robustness

Rendered fromBEARscc.Rmdusingknitr::rmarkdownon Dec 18 2024.

Last update: 2020-06-03
Started: 2017-12-11

Readme and manuals

Help Manual

Help pageTopics
BEARscc (Bayesian ERCC Assesstment of Robustness of Single Cell Clusters)BEARscc-package BEARscc
BEARscc downstream example objects..Random.seed analysis_examples BEARscc_clusts.df BEAR_analyzed.sce clusters.df noise_consensus recluster
Example data for BEARscc.BEARscc_examples BEAR_examples.sce data.counts.df ERCC.counts.df ERCC.meta.df
Cluster the consensus matrix.cluster_consensus
Compute consensus matrix.compute_consensus create_cm
Estimates noise in single cell data.apply_bayes build_dropoutmodel cleanup_model_names compute_alpha compute_genewise_dropouts counts2mpc create_null_dropout_model estimate_missingdata estimate_mu2sigma estimate_noiseparameters estimate_undetected2molpercell iterate_alphas iterate_spikeins melt_spikeins plot_alpha2mu plot_mu2sigma plot_obs2actual plot_spikein_fits prepare_data sample_models subcompute_sample_models subplot_spikein_fits transcripts V1 write_noise_model
Reports BEARscc metrics for cells.. calculate_cell_metrics calculate_cell_metrics_by_cluster report_cell_metrics rn
Reports BEARscc metrics for clusters.calculate_cluster_metrics calculate_cluster_metrics_by_cluster L1 mean.prom Overall.mean plot_cluster_metrics report_cluster_metrics size value
Computes 'BEARscc' simulated technical replicates.example_code execute_noiseinjected_counts execute_sim_replicates fill_out_count_probability_table genewise_dropouts gene_name HPC_simulate_replicates permute_count_in_dropout_range randomizer simulate_replicates