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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 4.0.4 (2021-02-15)
## 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_1.2.1 ggrepel_0.9.1 ggplot2_3.3.3 magrittr_2.0.1 tibble_3.1.0
## [6] tidyr_1.1.3 dplyr_1.0.5 knitr_1.31
##
## loaded via a namespace (and not attached):
## [1] backports_1.2.1 tidytext_0.3.0
## [3] plyr_1.8.6 igraph_1.2.6
## [5] lazyeval_0.2.2 splines_4.0.4
## [7] BiocParallel_1.24.1 listenv_0.8.0
## [9] SnowballC_0.7.0 scattermore_0.7
## [11] GenomeInfoDb_1.26.6 sva_3.38.0
## [13] digest_0.6.27 htmltools_0.5.1.1
## [15] fansi_0.4.2 memoise_2.0.0
## [17] tensor_1.5 cluster_2.1.1
## [19] ROCR_1.0-11 limma_3.46.0
## [21] globals_0.14.0 readr_1.4.0
## [23] annotate_1.68.0 matrixStats_0.58.0
## [25] spatstat.sparse_2.0-0 colorspace_2.0-0
## [27] blob_1.2.1 xfun_0.22
## [29] crayon_1.4.1 RCurl_1.98-1.3
## [31] jsonlite_1.7.2 genefilter_1.72.1
## [33] spatstat.data_2.1-0 survival_3.2-10
## [35] zoo_1.8-9 glue_1.4.2
## [37] polyclip_1.10-0 gtable_0.3.0
## [39] zlibbioc_1.36.0 XVector_0.30.0
## [41] leiden_0.3.7 DelayedArray_0.16.3
## [43] future.apply_1.7.0 BiocGenerics_0.36.0
## [45] abind_1.4-5 scales_1.1.1
## [47] DBI_1.1.1 edgeR_3.32.1
## [49] miniUI_0.1.1.1 Rcpp_1.0.6
## [51] widyr_0.1.3 viridisLite_0.3.0
## [53] xtable_1.8-4 reticulate_1.18
## [55] spatstat.core_2.0-0 bit_4.0.4
## [57] proxy_0.4-25 preprocessCore_1.52.1
## [59] stats4_4.0.4 htmlwidgets_1.5.3
## [61] httr_1.4.2 RColorBrewer_1.1-2
## [63] ellipsis_0.3.1 Seurat_4.0.1
## [65] ica_1.0-2 pkgconfig_2.0.3
## [67] XML_3.99-0.6 uwot_0.1.10
## [69] deldir_0.2-10 locfit_1.5-9.4
## [71] utf8_1.2.1 tidyselect_1.1.0
## [73] rlang_0.4.10 reshape2_1.4.4
## [75] later_1.1.0.1 AnnotationDbi_1.52.0
## [77] munsell_0.5.0 tools_4.0.4
## [79] cachem_1.0.4 cli_2.4.0
## [81] generics_0.1.0 RSQLite_2.2.5
## [83] broom_0.7.6 ggridges_0.5.3
## [85] evaluate_0.14 stringr_1.4.0
## [87] fastmap_1.1.0 goftest_1.2-2
## [89] bit64_4.0.5 fitdistrplus_1.1-3
## [91] purrr_0.3.4 RANN_2.6.1
## [93] pbapply_1.4-3 future_1.21.0
## [95] nlme_3.1-152 mime_0.10
## [97] tokenizers_0.2.1 debugme_1.1.0
## [99] compiler_4.0.4 rstudioapi_0.13
## [101] plotly_4.9.3 png_0.1-7
## [103] e1071_1.7-6 spatstat.utils_2.1-0
## [105] stringi_1.5.3 ps_1.6.0
## [107] lattice_0.20-41 Matrix_1.3-2
## [109] vctrs_0.3.7 pillar_1.5.1
## [111] lifecycle_1.0.0 spatstat.geom_2.0-1
## [113] lmtest_0.9-38 RcppAnnoy_0.0.18
## [115] data.table_1.14.0 cowplot_1.1.1
## [117] bitops_1.0-6 irlba_2.3.3
## [119] httpuv_1.5.5 patchwork_1.1.1
## [121] GenomicRanges_1.42.0 R6_2.5.0
## [123] promises_1.2.0.1 KernSmooth_2.23-18
## [125] gridExtra_2.3 janeaustenr_0.1.5
## [127] IRanges_2.24.1 parallelly_1.24.0
## [129] codetools_0.2-18 MASS_7.3-53.1
## [131] assertthat_0.2.1 SummarizedExperiment_1.20.0
## [133] withr_2.4.1 SeuratObject_4.0.0
## [135] sctransform_0.3.2 S4Vectors_0.28.1
## [137] GenomeInfoDbData_1.2.4 mgcv_1.8-34
## [139] parallel_4.0.4 hms_1.0.0
## [141] rpart_4.1-15 grid_4.0.4
## [143] class_7.3-18 MatrixGenerics_1.2.1
## [145] Rtsne_0.15 Biobase_2.50.0
## [147] shiny_1.6.0