Package: numbat 1.4.1

Teng Gao

numbat: Haplotype-Aware CNV Analysis from scRNA-Seq

A computational method that infers copy number variations (CNVs) in cancer scRNA-seq data and reconstructs the tumor phylogeny. 'numbat' integrates signals from gene expression, allelic ratio, and population haplotype structures to accurately infer allele-specific CNVs in single cells and reconstruct their lineage relationship. 'numbat' can be used to: 1. detect allele-specific copy number variations from single-cells; 2. differentiate tumor versus normal cells in the tumor microenvironment; 3. infer the clonal architecture and evolutionary history of profiled tumors. 'numbat' does not require tumor/normal-paired DNA or genotype data, but operates solely on the donor scRNA-data data (for example, 10x Cell Ranger output). Additional examples and documentations are available at <https://kharchenkolab.github.io/numbat/>. For details on the method please see Gao et al. Nature Biotechnology (2022) <doi:10.1038/s41587-022-01468-y>.

Authors:Teng Gao [cre, aut], Ruslan Soldatov [aut], Hirak Sarkar [aut], Evan Biederstedt [aut], Peter Kharchenko [aut]

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numbat.pdf |numbat.html
numbat/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/kharchenkolab/numbat/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

cancer-genomicscnv-detectionlineage-tracingphylogenysingle-cellsingle-cell-analysissingle-cell-rna-seqspatial-transcriptomics

16 exports 162 stars 5.05 score 115 dependencies 92 scripts 1.1k downloads

Last updated 4 months agofrom:d21a5b1f47. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 02 2024
R-4.5-win-x86_64NOTESep 02 2024
R-4.5-linux-x86_64NOTESep 02 2024
R-4.4-win-x86_64NOTESep 02 2024
R-4.4-mac-x86_64NOTESep 02 2024
R-4.4-mac-aarch64NOTESep 02 2024
R-4.3-win-x86_64NOTESep 02 2024
R-4.3-mac-x86_64NOTESep 02 2024
R-4.3-mac-aarch64NOTESep 02 2024

Exports:aggregate_countsanalyze_bulkannotate_genescnv_heatmapdetect_clonal_lohget_bulkget_gtreeNumbatplot_bulksplot_consensusplot_exp_rollplot_mut_historyplot_phylo_heatmapplot_psbulkplot_sc_treerun_numbat

Dependencies:apeaplotaskpassBiocGenericsbitopscachemcaToolscliclustercodetoolscolorspacecpp11crayoncurldata.tabledendextenddigestdplyrfansifarverfastmapfastmatchfsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesgetoptggforceggfunggplot2ggplotifyggraphggrepelggtreegluegraphlayoutsgridExtragridGraphicsgtablehahmmrhttrigraphIRangesisobandjsonlitelabelinglatticelazyevallifecyclelobstrloggermagrittrMASSMatrixmemoisememusemgcvmimemunsellnlmeopenssloptparseparallelDistpatchworkpermutephangornpillarpinfsc50pkgconfigplyrpolyclipprettyunitspryrpurrrquadprogR.methodsS3R.ooR.utilsR6RColorBrewerRcppRcppArmadilloRcppEigenRcppParallelreshape2RhpcBLASctlrlangroptimS4VectorsscalesscistreerstringistringrsyssystemfontstibbletidygraphtidyrtidyselecttidytreetreeiotweenrUCSC.utilsutf8vcfRvctrsveganviridisviridisLitewithrXVectoryulab.utilszlibbioczoo

Readme and manuals

Help Manual

Help pageTopics
centromere regions (hg19)acen_hg19
centromere regions (hg38)acen_hg38
Utility function to make reference gene expression profilesaggregate_counts
Call CNVs in a pseudobulk profile using the Numbat joint HMManalyze_bulk
example reference cell annotationannot_ref
Annotate genes on allele dataframeannotate_genes
example pseudobulk dataframebulk_example
chromosome sizes (hg19)chrom_sizes_hg19
chromosome sizes (hg38)chrom_sizes_hg38
Plot CNV heatmapcnv_heatmap
example gene expression count matrixcount_mat_example
example reference count matrixcount_mat_ref
Call clonal LOH using SNP density. Rcommended for cell lines or tumor samples with no normal cells.detect_clonal_loh
example allele count dataframedf_allele_example
genome gap regions (hg19)gaps_hg19
genome gap regions (hg38)gaps_hg38
Aggregate single-cell data into combined bulk expression and allele profileget_bulk
Get a tidygraph tree with simplified mutational history.get_gtree
example smoothed gene expression dataframegexp_roll_example
gene model (hg19)gtf_hg19
gene model (hg38)gtf_hg38
gene model (mm10)gtf_mm10
example hclust treehc_example
example joint single-cell cnv posterior dataframejoint_post_example
example mutation graphmut_graph_example
Numbat R6 classNumbat
example single-cell phylogenyphylogeny_example
Plot a group of pseudobulk HMM profilesplot_bulks
Plot consensus CNVsplot_consensus
Plot single-cell smoothed expression magnitude heatmapplot_exp_roll
Plot mutational historyplot_mut_history
Plot single-cell CNV calls along with the clonal phylogenyplot_phylo_heatmap
Plot a pseudobulk HMM profileplot_psbulk
Plot single-cell smoothed expression magnitude heatmapplot_sc_tree
HMM object for unit testspre_likelihood_hmm
reference expression magnitudes from HCAref_hca
reference expression counts from HCAref_hca_counts
Run workflow to decompose tumor subclonesrun_numbat
example CNV segments dataframesegs_example
UPGMA and WPGMA clusteringupgma
example VCF headervcf_meta