Vignette runtime:
-#> Time difference of 2.47674 mins#> Time difference of 2.191318 mins
sessionInfo()
#> R version 4.3.2 (2023-10-31)
@@ -293,118 +293,118 @@ Session information#> [1] ExperimentHub_2.10.0 AnnotationHub_3.10.0
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-#> [15] Seurat_5.0.1 SeuratObject_5.0.0
-#> [17] sp_2.1-1 SpatialExperiment_1.12.0
+#> [7] cowplot_1.1.3 scater_1.30.1
+#> [9] ggplot2_3.4.4 harmony_1.2.0
+#> [11] Rcpp_1.0.12 data.table_1.15.0
+#> [13] scran_1.30.2 scuttle_1.12.0
+#> [15] Seurat_5.0.1 SeuratObject_5.0.1
+#> [17] sp_2.1-3 SpatialExperiment_1.12.0
#> [19] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
#> [21] Biobase_2.62.0 GenomicRanges_1.54.1
-#> [23] GenomeInfoDb_1.38.1 IRanges_2.36.0
-#> [25] S4Vectors_0.40.1 BiocGenerics_0.48.1
-#> [27] MatrixGenerics_1.14.0 matrixStats_1.1.0
-#> [29] Banksy_0.99.2 BiocStyle_2.30.0
+#> [23] GenomeInfoDb_1.38.6 IRanges_2.36.0
+#> [25] S4Vectors_0.40.2 BiocGenerics_0.48.1
+#> [27] MatrixGenerics_1.14.0 matrixStats_1.2.0
+#> [29] Banksy_0.99.7 BiocStyle_2.30.0
#>
#> loaded via a namespace (and not attached):
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#> [7] tools_4.3.2 sctransform_0.4.1
-#> [9] DT_0.30 utf8_1.2.4
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#> [11] R6_2.5.1 lazyeval_0.2.2
-#> [13] uwot_0.1.16 withr_2.5.2
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#> [15] gridExtra_2.3 progressr_0.14.0
-#> [17] cli_3.6.1 textshaping_0.3.7
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-#> [21] labeling_0.4.3 sass_0.4.7
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-#> [35] maps_3.4.1.1 limma_3.58.1
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-#> [39] RSQLite_2.3.3 BiocIO_1.12.0
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#> [61] lattice_0.22-5 beachmat_2.18.0
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-#> [73] glue_1.6.2 vctrs_0.6.4
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#> [77] gtable_0.3.4 rematch2_2.1.2
-#> [79] cachem_1.0.8 xfun_0.41
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#> [81] S4Arrays_1.2.0 mime_0.12
-#> [83] ggside_0.2.2 survival_3.5-7
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-#> [121] spatstat.utils_3.0-4 rmarkdown_2.25
-#> [123] benchmarkmeData_1.0.4 XVector_0.42.0
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+#> [103] DBI_1.2.1 tidyselect_1.2.0
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+#> [107] compiler_4.3.2 BiocNeighbors_1.20.2
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+#> [111] plotly_4.10.4 rtracklayer_1.62.0
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-#> [129] fastmap_1.1.1 rlang_1.1.2
-#> [131] htmlwidgets_1.6.2 shiny_1.7.5.1
+#> [129] fastmap_1.1.1 rlang_1.1.3
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-#> [157] ggrepel_0.9.4 deldir_1.0-9
-#> [159] Biostrings_2.70.1 sccore_1.0.4
+#> [145] dotCall64_1.1-1 patchwork_1.2.0
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-#> [163] locfit_1.5-9.8 igraph_1.5.1
-#> [165] spatstat.geom_3.2-7 RcppHNSW_0.5.0
+#> [163] locfit_1.5-9.8 igraph_2.0.1.1
+#> [165] spatstat.geom_3.2-8 RcppHNSW_0.6.0
#> [167] reshape2_1.4.4 ScaledMatrix_1.10.0
-#> [169] XML_3.99-0.15 BiocVersion_3.18.0
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-#> [177] tidyr_1.3.0 purrr_1.0.2
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#> [179] polyclip_1.10-6 benchmarkme_1.0.8
-#> [181] future_1.33.0 scattermore_1.2
+#> [181] future_1.33.1 scattermore_1.2
#> [183] rsvd_1.0.5 xtable_1.8-4
#> [185] restfulr_0.0.15 RSpectra_0.16-1
-#> [187] later_1.3.1 viridisLite_0.4.2
-#> [189] ragg_1.2.6 tibble_3.2.1
-#> [191] GenomicAlignments_1.38.0 AnnotationDbi_1.64.1
+#> [187] later_1.3.2 viridisLite_0.4.2
+#> [189] ragg_1.2.7 tibble_3.2.1
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#> [193] memoise_2.0.1 beeswarm_0.4.0
-#> [195] cluster_2.1.4 shinyWidgets_0.8.0
+#> [195] cluster_2.1.6 shinyWidgets_0.8.1
#> [197] globals_0.16.2
Vignette runtime:
-#> Time difference of 2.466589 mins#> Time difference of 2.836741 mins
sessionInfo()
#> R version 4.3.2 (2023-10-31)
@@ -362,115 +362,114 @@ Session information#> [1] ExperimentHub_2.10.0 AnnotationHub_3.10.0
#> [3] BiocFileCache_2.10.1 dbplyr_2.4.0
#> [5] spatialLIBD_1.14.1 ggspavis_1.8.0
-#> [7] cowplot_1.1.1 scater_1.30.0
+#> [7] cowplot_1.1.3 scater_1.30.1
#> [9] ggplot2_3.4.4 scuttle_1.12.0
-#> [11] Seurat_5.0.1 SeuratObject_5.0.0
-#> [13] sp_2.1-1 SpatialExperiment_1.12.0
+#> [11] Seurat_5.0.1 SeuratObject_5.0.1
+#> [13] sp_2.1-3 SpatialExperiment_1.12.0
#> [15] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
#> [17] Biobase_2.62.0 GenomicRanges_1.54.1
-#> [19] GenomeInfoDb_1.38.1 IRanges_2.36.0
-#> [21] S4Vectors_0.40.1 BiocGenerics_0.48.1
-#> [23] MatrixGenerics_1.14.0 matrixStats_1.1.0
-#> [25] Banksy_0.99.2 BiocStyle_2.30.0
+#> [19] GenomeInfoDb_1.38.6 IRanges_2.36.0
+#> [21] S4Vectors_0.40.2 BiocGenerics_0.48.1
+#> [23] MatrixGenerics_1.14.0 matrixStats_1.2.0
+#> [25] Banksy_0.99.7 BiocStyle_2.30.0
#>
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#> [3] bitops_1.0-7 doParallel_1.0.17
#> [5] httr_1.4.7 RColorBrewer_1.1-3
#> [7] tools_4.3.2 sctransform_0.4.1
-#> [9] DT_0.30 utf8_1.2.4
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-#> [21] labeling_0.4.3 sass_0.4.7
-#> [23] spatstat.data_3.0-3 ggridges_0.5.4
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#> [27] Rsamtools_2.18.0 systemfonts_1.0.5
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-#> [79] ggside_0.2.2 survival_3.5-7
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-#> [119] XVector_0.42.0 htmltools_0.5.7
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-#> [143] Rcpp_1.0.11 viridis_0.6.4
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-#> [147] stringi_1.7.12 zlibbioc_1.48.0
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-#> [155] Biostrings_2.70.1 sccore_1.0.4
-#> [157] splines_4.3.2 tensor_1.5
-#> [159] locfit_1.5-9.8 igraph_1.5.1
-#> [161] spatstat.geom_3.2-7 RcppHNSW_0.5.0
-#> [163] reshape2_1.4.4 ScaledMatrix_1.10.0
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-#> [177] future_1.33.0 scattermore_1.2
-#> [179] rsvd_1.0.5 xtable_1.8-4
-#> [181] restfulr_0.0.15 RSpectra_0.16-1
-#> [183] later_1.3.1 viridisLite_0.4.2
-#> [185] ragg_1.2.6 tibble_3.2.1
-#> [187] GenomicAlignments_1.38.0 memoise_2.0.1
-#> [189] beeswarm_0.4.0 AnnotationDbi_1.64.1
-#> [191] cluster_2.1.4 shinyWidgets_0.8.0
-#> [193] globals_0.16.2
Vignette runtime:
-#> Time difference of 44.3582 secs#> Time difference of 47.01152 secs
sessionInfo()
#> R version 4.3.2 (2023-10-31)
@@ -346,65 +346,64 @@ Session information#> [8] base
#>
#> other attached packages:
-#> [1] cowplot_1.1.1 scater_1.30.0
+#> [1] cowplot_1.1.3 scater_1.30.1
#> [3] ggplot2_3.4.4 scuttle_1.12.0
#> [5] SpatialExperiment_1.12.0 SingleCellExperiment_1.24.0
#> [7] SummarizedExperiment_1.32.0 Biobase_2.62.0
-#> [9] GenomicRanges_1.54.1 GenomeInfoDb_1.38.1
-#> [11] IRanges_2.36.0 S4Vectors_0.40.1
+#> [9] GenomicRanges_1.54.1 GenomeInfoDb_1.38.6
+#> [11] IRanges_2.36.0 S4Vectors_0.40.2
#> [13] BiocGenerics_0.48.1 MatrixGenerics_1.14.0
-#> [15] matrixStats_1.1.0 Banksy_0.99.2
+#> [15] matrixStats_1.2.0 Banksy_0.99.7
#> [17] BiocStyle_2.30.0
#>
#> loaded via a namespace (and not attached):
#> [1] bitops_1.0-7 gridExtra_2.3
-#> [3] rlang_1.1.2 magrittr_2.0.3
+#> [3] rlang_1.1.3 magrittr_2.0.3
#> [5] compiler_4.3.2 sccore_1.0.4
#> [7] DelayedMatrixStats_1.24.0 systemfonts_1.0.5
-#> [9] vctrs_0.6.4 stringr_1.5.0
+#> [9] vctrs_0.6.5 stringr_1.5.1
#> [11] pkgconfig_2.0.3 crayon_1.5.2
-#> [13] fastmap_1.1.1 magick_2.8.1
+#> [13] fastmap_1.1.1 magick_2.8.2
#> [15] XVector_0.42.0 labeling_0.4.3
#> [17] utf8_1.2.4 rmarkdown_2.25
-#> [19] ggbeeswarm_0.7.2 ragg_1.2.6
-#> [21] purrr_1.0.2 xfun_0.41
+#> [19] ggbeeswarm_0.7.2 ragg_1.2.7
+#> [21] purrr_1.0.2 xfun_0.42
#> [23] zlibbioc_1.48.0 cachem_1.0.8
-#> [25] beachmat_2.18.0 jsonlite_1.8.7
+#> [25] beachmat_2.18.0 jsonlite_1.8.8
#> [27] highr_0.10 DelayedArray_0.28.0
#> [29] BiocParallel_1.36.0 irlba_2.3.5.1
#> [31] parallel_4.3.2 aricode_1.0.3
-#> [33] R6_2.5.1 bslib_0.5.1
-#> [35] stringi_1.7.12 leidenAlg_1.1.2
-#> [37] jquerylib_0.1.4 Rcpp_1.0.11
+#> [33] R6_2.5.1 bslib_0.6.1
+#> [35] stringi_1.8.3 leidenAlg_1.1.2
+#> [37] jquerylib_0.1.4 Rcpp_1.0.12
#> [39] bookdown_0.37 knitr_1.45
-#> [41] Matrix_1.6-1.1 igraph_1.5.1
-#> [43] tidyselect_1.2.0 viridis_0.6.4
+#> [41] Matrix_1.6-5 igraph_2.0.1.1
+#> [43] tidyselect_1.2.0 viridis_0.6.5
#> [45] rstudioapi_0.15.0 abind_1.4-5
-#> [47] yaml_2.3.7 codetools_0.2-19
+#> [47] yaml_2.3.8 codetools_0.2-19
#> [49] lattice_0.22-5 tibble_3.2.1
-#> [51] withr_2.5.2 evaluate_0.23
-#> [53] desc_1.4.2 mclust_6.0.0
+#> [51] withr_3.0.0 evaluate_0.23
+#> [53] desc_1.4.3 mclust_6.0.1
#> [55] pillar_1.9.0 BiocManager_1.30.22
-#> [57] generics_0.1.3 dbscan_1.1-11
-#> [59] rprojroot_2.0.3 RCurl_1.98-1.13
-#> [61] sparseMatrixStats_1.14.0 munsell_0.5.0
-#> [63] scales_1.2.1 glue_1.6.2
-#> [65] tools_4.3.2 BiocNeighbors_1.20.0
-#> [67] data.table_1.14.8 ScaledMatrix_1.10.0
-#> [69] fs_1.6.3 grid_4.3.2
-#> [71] colorspace_2.1-0 GenomeInfoDbData_1.2.11
-#> [73] RcppHungarian_0.3 beeswarm_0.4.0
-#> [75] BiocSingular_1.18.0 vipor_0.4.5
-#> [77] cli_3.6.1 rsvd_1.0.5
-#> [79] textshaping_0.3.7 fansi_1.0.5
-#> [81] viridisLite_0.4.2 S4Arrays_1.2.0
-#> [83] dplyr_1.1.3 uwot_0.1.16
-#> [85] gtable_0.3.4 sass_0.4.7
-#> [87] digest_0.6.33 ggrepel_0.9.4
-#> [89] SparseArray_1.2.2 farver_2.1.1
-#> [91] rjson_0.2.21 memoise_2.0.1
-#> [93] htmltools_0.5.7 pkgdown_2.0.7
-#> [95] lifecycle_1.0.4
BANKSY is applicable to a wide array of spatial technologies (e.g. 10x Visium, Slide-seq, MERFISH, CosMX, CODEX) and scales well to large datasets. For more details, check out:
+BANKSY is applicable to a wide array of spatial technologies (e.g. 10x Visium, Slide-seq, MERFISH, CosMX, CODEX) and scales well to large datasets. For more details, check out
clusterBanksy(
se,
- use_agf = TRUE,
+ use_agf = FALSE,
lambda = 0.2,
use_pcs = TRUE,
npcs = 20L,
diff --git a/docs/reference/clusterNames.html b/docs/reference/clusterNames.html
index 639c1dd..f3c2af3 100644
--- a/docs/reference/clusterNames.html
+++ b/docs/reference/clusterNames.html
@@ -17,7 +17,7 @@
runBanksyPCA(
se,
- use_agf = TRUE,
+ use_agf = FALSE,
lambda = 0.2,
npcs = 20L,
assay_name = NULL,
diff --git a/docs/reference/runBanksyUMAP.html b/docs/reference/runBanksyUMAP.html
index 6b3c246..10399d8 100644
--- a/docs/reference/runBanksyUMAP.html
+++ b/docs/reference/runBanksyUMAP.html
@@ -17,7 +17,7 @@
runBanksyUMAP(
se,
- use_agf = TRUE,
+ use_agf = FALSE,
lambda = 0.2,
use_pcs = TRUE,
npcs = 20L,
diff --git a/docs/reference/simulateDataset.html b/docs/reference/simulateDataset.html
index c099320..48ce02c 100644
--- a/docs/reference/simulateDataset.html
+++ b/docs/reference/simulateDataset.html
@@ -17,7 +17,7 @@