The goal of concordexR is to replace UMAP as a clustering diagnostic.
This package can be installed from Bioconductor since version 3.17 with
BiocManager::install("concordexR")
This is a basic example which shows you how to solve a common problem:
library(concordexR)
library(BiocNeighbors)
g <- findKNN(iris[, seq_len(4)], k = 10)
#> Warning in (function (to_check, X, clust_centers, clust_info, dtype, nn, :
#> detected tied distances to neighbors, see ?'BiocNeighbors-ties'
res <- calculateConcordex(g$index, labels = iris$Species, k = 10, return.map = TRUE)
plotConcordexSim(res)
heatConcordex(res)
sessionInfo()
#> R version 4.3.2 (2023-10-31)
#> Platform: aarch64-apple-darwin20 (64-bit)
#> Running under: macOS Ventura 13.6.1
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#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
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#> time zone: America/New_York
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#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
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#> [5] bluster_1.12.0 BiocNeighbors_1.20.0
#> [7] TENxPBMCData_1.20.0 HDF5Array_1.30.0
#> [9] rhdf5_2.46.0 DelayedArray_0.28.0
#> [11] SparseArray_1.2.1 S4Arrays_1.2.0
#> [13] abind_1.4-5 Matrix_1.6-1.1
#> [15] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
#> [17] Biobase_2.62.0 GenomicRanges_1.54.1
#> [19] GenomeInfoDb_1.38.0 IRanges_2.36.0
#> [21] S4Vectors_0.40.1 BiocGenerics_0.48.1
#> [23] MatrixGenerics_1.14.0 matrixStats_1.0.0
#> [25] concordexR_1.2.0 BiocStyle_2.30.0
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#> [5] magick_2.8.1 farver_2.1.1
#> [7] rmarkdown_2.25 zlibbioc_1.48.0
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#> [11] DelayedMatrixStats_1.24.0 RCurl_1.98-1.13
#> [13] htmltools_0.5.7 AnnotationHub_3.10.0
#> [15] curl_5.1.0 Rhdf5lib_1.24.0
#> [17] sass_0.4.7 bslib_0.5.1
#> [19] cachem_1.0.8 igraph_1.5.1
#> [21] mime_0.12 lifecycle_1.0.3
#> [23] pkgconfig_2.0.3 rsvd_1.0.5
#> [25] R6_2.5.1 fastmap_1.1.1
#> [27] GenomeInfoDbData_1.2.11 shiny_1.7.5.1
#> [29] digest_0.6.33 colorspace_2.1-0
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