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In this article we show some examples of the differences in coding between tidybulk/tidyverse and base R. We noted a decrease > 10x of assignments and a decrease of > 2x of line numbers.
tidybulk
tibble.tt = counts_mini %>% tidybulk(sample, transcript, count)
transcripts
counts
variable transcripts
We may want to identify and filter variable transcripts.
dimensions
dimensions
differential abundance
counts
Cell type composition
samples
redundant
transcriptsheatmap
density plot
sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] tidybulk_1.2.0 ggrepel_0.8.2 ggplot2_3.3.2 magrittr_1.5 tibble_3.0.4
## [6] tidyr_1.1.2 dplyr_1.0.2 knitr_1.30
##
## loaded via a namespace (and not attached):
## [1] backports_1.1.10 tidytext_0.2.6
## [3] plyr_1.8.6 igraph_1.2.6
## [5] lazyeval_0.2.2 splines_4.0.3
## [7] BiocParallel_1.24.0 listenv_0.8.0
## [9] SnowballC_0.7.0 GenomeInfoDb_1.26.0
## [11] sva_3.38.0 digest_0.6.27
## [13] htmltools_0.5.0 fansi_0.4.1
## [15] memoise_1.1.0 tensor_1.5
## [17] cluster_2.1.0 ROCR_1.0-11
## [19] limma_3.46.0 globals_0.13.1
## [21] readr_1.4.0 annotate_1.68.0
## [23] matrixStats_0.57.0 colorspace_1.4-1
## [25] blob_1.2.1 rappdirs_0.3.1
## [27] xfun_0.18 crayon_1.3.4
## [29] RCurl_1.98-1.2 jsonlite_1.7.1
## [31] genefilter_1.72.0 spatstat_1.64-1
## [33] spatstat.data_1.4-3 survival_3.2-7
## [35] zoo_1.8-8 glue_1.4.2
## [37] polyclip_1.10-0 gtable_0.3.0
## [39] zlibbioc_1.36.0 XVector_0.30.0
## [41] leiden_0.3.3 DelayedArray_0.16.0
## [43] future.apply_1.6.0 BiocGenerics_0.36.0
## [45] abind_1.4-5 scales_1.1.1
## [47] DBI_1.1.0 edgeR_3.32.0
## [49] miniUI_0.1.1.1 Rcpp_1.0.5
## [51] widyr_0.1.3 viridisLite_0.3.0
## [53] xtable_1.8-4 reticulate_1.18
## [55] bit_4.0.4 rsvd_1.0.3
## [57] preprocessCore_1.52.0 stats4_4.0.3
## [59] htmlwidgets_1.5.2 httr_1.4.2
## [61] RColorBrewer_1.1-2 ellipsis_0.3.1
## [63] Seurat_3.2.2 ica_1.0-2
## [65] pkgconfig_2.0.3 XML_3.99-0.5
## [67] uwot_0.1.8 deldir_0.1-29
## [69] locfit_1.5-9.4 utf8_1.1.4
## [71] tidyselect_1.1.0 rlang_0.4.8
## [73] reshape2_1.4.4 later_1.1.0.1
## [75] AnnotationDbi_1.52.0 munsell_0.5.0
## [77] tools_4.0.3 cli_2.1.0
## [79] generics_0.0.2 RSQLite_2.2.1
## [81] broom_0.7.2 ggridges_0.5.2
## [83] evaluate_0.14 stringr_1.4.0
## [85] fastmap_1.0.1 goftest_1.2-2
## [87] bit64_4.0.5 fitdistrplus_1.1-1
## [89] purrr_0.3.4 RANN_2.6.1
## [91] pbapply_1.4-3 future_1.19.1
## [93] nlme_3.1-150 mime_0.9
## [95] tokenizers_0.2.1 compiler_4.0.3
## [97] plotly_4.9.2.1 png_0.1-7
## [99] e1071_1.7-4 spatstat.utils_1.17-0
## [101] stringi_1.5.3 lattice_0.20-41
## [103] Matrix_1.2-18 vctrs_0.3.4
## [105] pillar_1.4.6 lifecycle_0.2.0
## [107] lmtest_0.9-38 RcppAnnoy_0.0.16
## [109] data.table_1.13.2 cowplot_1.1.0
## [111] bitops_1.0-6 irlba_2.3.3
## [113] httpuv_1.5.4 patchwork_1.0.1
## [115] GenomicRanges_1.42.0 R6_2.4.1
## [117] promises_1.1.1 KernSmooth_2.23-17
## [119] gridExtra_2.3 janeaustenr_0.1.5
## [121] IRanges_2.24.0 codetools_0.2-16
## [123] MASS_7.3-53 assertthat_0.2.1
## [125] SummarizedExperiment_1.20.0 withr_2.3.0
## [127] sctransform_0.3.1 S4Vectors_0.28.0
## [129] GenomeInfoDbData_1.2.4 mgcv_1.8-33
## [131] parallel_4.0.3 hms_0.5.3
## [133] grid_4.0.3 rpart_4.1-15
## [135] class_7.3-17 MatrixGenerics_1.2.0
## [137] Rtsne_0.15 Biobase_2.50.0
## [139] shiny_1.5.0