biotidy package offers useful utilities for integrating Bioinformatic
objects such as SummarizedExperiment and Seurat into the tidy(verse)
framework.
It is important to note that biotidy serves a different purpose
compared to tidyomics, which primarily
utilizes a pipe-based workflow for managing bioinformatic objects.
Specifically, biotidy provides a method for extracting a data frame
from bioinformatic objects, similar to how the broom::tidy function
operates on statistical model objects. The inspiration for biotidy
came from the functionality of the scuttle::makePerCellDF and
scuttle::makePerFeatureDF functions on the SingleCellExperiment
object. The biotidy package extends these functions to work with other
bioinformatic objects, such as SummarizedExperiment, ExpressionSet,
and Seurat.
library(biotidy)
mocked_se <- mockSE(50, 100)
mocked_sce <- mockSCE(50, 100)
mocked_es <- mockES(50, 100)
mocked_seurat <- mockSeurat(50, 100)makePerCellDF creates a per-cell data.frame (i.e., where each row
represents a sample / cell) from the Bioinformatic objects.
# SummarizedExperiment method
makePerCellDF(mocked_se)[1:5, 1:5]
#> Gene0001 Gene0002 Gene0003 Gene0004 Gene0005
#> Cell001 119 169 7 935 0
#> Cell002 106 21 0 749 0
#> Cell003 416 52 12 1186 0
#> Cell004 113 24 1 586 0
#> Cell005 25 386 1 468 137makePerCellDF(mocked_se, melt = TRUE)[1:5, 1:5]
#> Mutation_Status Cell_Cycle Treatment .features .assay
#> 1 negative S treat1 Gene0001 119
#> 2 negative G2M treat1 Gene0001 106
#> 3 negative G0 treat2 Gene0001 416
#> 4 positive G2M treat2 Gene0001 113
#> 5 negative G1 treat2 Gene0001 25makePerCellDF(mocked_se, melt = TRUE, keep_rownames = TRUE)[1:5, 1:5]
#> .id Mutation_Status Cell_Cycle Treatment .features
#> 1 Cell001 negative S treat1 Gene0001
#> 2 Cell002 negative G2M treat1 Gene0001
#> 3 Cell003 negative G0 treat2 Gene0001
#> 4 Cell004 positive G2M treat2 Gene0001
#> 5 Cell005 negative G1 treat2 Gene0001makePerCellDF(mocked_se, features = FALSE, use_coldata = FALSE)
#> data frame with 0 columns and 50 rows# SingleCellExperiment method
makePerCellDF(mocked_sce)[1:5, 1:5]
#> Gene0001 Gene0002 Gene0003 Gene0004 Gene0005
#> Cell001 270 1229 0 62 206
#> Cell002 773 1413 1 88 216
#> Cell003 583 1386 0 16 432
#> Cell004 389 2198 0 0 13
#> Cell005 808 1651 0 93 21makePerCellDF(mocked_sce, melt = TRUE)[1:5, 1:5]
#> Mutation_Status Cell_Cycle Treatment .features .assay
#> 1 positive G1 treat1 Gene0001 270
#> 2 negative G2M treat2 Gene0001 773
#> 3 positive G2M treat2 Gene0001 583
#> 4 negative G2M treat1 Gene0001 389
#> 5 negative G2M treat1 Gene0001 808makePerCellDF(mocked_sce, melt = TRUE, keep_rownames = TRUE)[1:5, 1:5]
#> .id Mutation_Status Cell_Cycle Treatment .features
#> 1 Cell001 positive G1 treat1 Gene0001
#> 2 Cell002 negative G2M treat2 Gene0001
#> 3 Cell003 positive G2M treat2 Gene0001
#> 4 Cell004 negative G2M treat1 Gene0001
#> 5 Cell005 negative G2M treat1 Gene0001makePerCellDF(mocked_sce, features = FALSE, use_coldata = FALSE)
#> data frame with 0 columns and 50 rows# ExpressionSet method
makePerCellDF(mocked_es)[1:5, 1:5]
#> Gene0001 Gene0002 Gene0003 Gene0004 Gene0005
#> Cell001 213 0 1 149 20
#> Cell002 678 3 0 372 0
#> Cell003 25 12 61 728 7
#> Cell004 707 14 0 556 0
#> Cell005 469 19 0 65 23makePerCellDF(mocked_es, melt = TRUE)[1:5, 1:5]
#> Mutation_Status Cell_Cycle Treatment .features .assay
#> 1 negative S treat1 Gene0001 213
#> 2 positive G0 treat2 Gene0001 678
#> 3 negative G2M treat2 Gene0001 25
#> 4 negative G1 treat1 Gene0001 707
#> 5 positive G2M treat1 Gene0001 469makePerCellDF(mocked_es, melt = TRUE, keep_rownames = TRUE)[1:5, 1:5]
#> .id Mutation_Status Cell_Cycle Treatment .features
#> 1 Cell001 negative S treat1 Gene0001
#> 2 Cell002 positive G0 treat2 Gene0001
#> 3 Cell003 negative G2M treat2 Gene0001
#> 4 Cell004 negative G1 treat1 Gene0001
#> 5 Cell005 positive G2M treat1 Gene0001makePerCellDF(mocked_es, features = FALSE, use_coldata = FALSE)
#> data frame with 0 columns and 50 rows# Seurat method
makePerCellDF(mocked_seurat, layer = "counts")[1:5, 1:5]
#> Gene0001 Gene0002 Gene0003 Gene0004 Gene0005
#> Cell001 0 438 0 162 201
#> Cell002 1 299 0 0 499
#> Cell003 0 325 0 55 496
#> Cell004 15 442 0 0 304
#> Cell005 3 1536 1 0 427makePerCellDF(mocked_seurat, layer = "counts", melt = TRUE)[1:5, 1:5]
#> orig.ident nCount_RNA nFeature_RNA Mutation_Status Cell_Cycle
#> 1 SeuratProject 24500 76 negative S
#> 2 SeuratProject 18808 80 negative S
#> 3 SeuratProject 22796 81 negative G2M
#> 4 SeuratProject 21535 74 positive S
#> 5 SeuratProject 22934 73 positive G2MmakePerCellDF(mocked_seurat,
layer = "counts", melt = TRUE, keep_rownames = TRUE
)[1:5, 1:5]
#> .id orig.ident nCount_RNA nFeature_RNA Mutation_Status
#> 1 Cell001 SeuratProject 24500 76 negative
#> 2 Cell002 SeuratProject 18808 80 negative
#> 3 Cell003 SeuratProject 22796 81 negative
#> 4 Cell004 SeuratProject 21535 74 positive
#> 5 Cell005 SeuratProject 22934 73 positivemakePerCellDF(mocked_seurat,
layer = "counts", features = FALSE,
use_coldata = FALSE
)
#> data frame with 0 columns and 50 rowsmakePerFeatureDF Create a per-feature data.frame (i.e., where each row
represents a feature / gene).
# SummarizedExperiment method
makePerFeatureDF(mocked_se)[1:5, 1:5]
#> Cell001 Cell002 Cell003 Cell004 Cell005
#> Gene0001 119 106 416 113 25
#> Gene0002 169 21 52 24 386
#> Gene0003 7 0 12 1 1
#> Gene0004 935 749 1186 586 468
#> Gene0005 0 0 0 0 137makePerFeatureDF(mocked_se, melt = TRUE)[1:5, ]
#> .cells .assay
#> 1 Cell001 119
#> 2 Cell001 169
#> 3 Cell001 7
#> 4 Cell001 935
#> 5 Cell001 0makePerFeatureDF(mocked_se, melt = TRUE, keep_rownames = TRUE)[1:5, ]
#> .id .cells .assay
#> 1 Gene0001 Cell001 119
#> 2 Gene0002 Cell001 169
#> 3 Gene0003 Cell001 7
#> 4 Gene0004 Cell001 935
#> 5 Gene0005 Cell001 0makePerFeatureDF(mocked_se, features = FALSE, use_rowdata = FALSE)
#> [1] Cell001 Cell002 Cell003 Cell004 Cell005 Cell006 Cell007 Cell008 Cell009
#> [10] Cell010 Cell011 Cell012 Cell013 Cell014 Cell015 Cell016 Cell017 Cell018
#> [19] Cell019 Cell020 Cell021 Cell022 Cell023 Cell024 Cell025 Cell026 Cell027
#> [28] Cell028 Cell029 Cell030 Cell031 Cell032 Cell033 Cell034 Cell035 Cell036
#> [37] Cell037 Cell038 Cell039 Cell040 Cell041 Cell042 Cell043 Cell044 Cell045
#> [46] Cell046 Cell047 Cell048 Cell049 Cell050
#> <0 rows> (or 0-length row.names)# SingleCellExperiment method
makePerFeatureDF(mocked_sce)[1:5, 1:5]
#> Cell001 Cell002 Cell003 Cell004 Cell005
#> Gene0001 270 773 583 389 808
#> Gene0002 1229 1413 1386 2198 1651
#> Gene0003 0 1 0 0 0
#> Gene0004 62 88 16 0 93
#> Gene0005 206 216 432 13 21makePerFeatureDF(mocked_sce, melt = TRUE)[1:5, ]
#> .cells .assay
#> 1 Cell001 270
#> 2 Cell001 1229
#> 3 Cell001 0
#> 4 Cell001 62
#> 5 Cell001 206makePerFeatureDF(mocked_sce, melt = TRUE, keep_rownames = TRUE)[1:5, ]
#> .id .cells .assay
#> 1 Gene0001 Cell001 270
#> 2 Gene0002 Cell001 1229
#> 3 Gene0003 Cell001 0
#> 4 Gene0004 Cell001 62
#> 5 Gene0005 Cell001 206makePerFeatureDF(mocked_sce, features = FALSE, use_rowdata = FALSE)
#> [1] Cell001 Cell002 Cell003 Cell004 Cell005 Cell006 Cell007 Cell008 Cell009
#> [10] Cell010 Cell011 Cell012 Cell013 Cell014 Cell015 Cell016 Cell017 Cell018
#> [19] Cell019 Cell020 Cell021 Cell022 Cell023 Cell024 Cell025 Cell026 Cell027
#> [28] Cell028 Cell029 Cell030 Cell031 Cell032 Cell033 Cell034 Cell035 Cell036
#> [37] Cell037 Cell038 Cell039 Cell040 Cell041 Cell042 Cell043 Cell044 Cell045
#> [46] Cell046 Cell047 Cell048 Cell049 Cell050
#> <0 rows> (or 0-length row.names)# ExpressionSet method
makePerFeatureDF(mocked_es)[1:5, 1:5]
#> Cell001 Cell002 Cell003 Cell004 Cell005
#> Gene0001 213 678 25 707 469
#> Gene0002 0 3 12 14 19
#> Gene0003 1 0 61 0 0
#> Gene0004 149 372 728 556 65
#> Gene0005 20 0 7 0 23makePerFeatureDF(mocked_es, melt = TRUE)[1:5, ]
#> .cells .assay
#> 1 Cell001 213
#> 2 Cell001 0
#> 3 Cell001 1
#> 4 Cell001 149
#> 5 Cell001 20makePerFeatureDF(mocked_es, melt = TRUE, keep_rownames = TRUE)[1:5, ]
#> .id .cells .assay
#> 1 Gene0001 Cell001 213
#> 2 Gene0002 Cell001 0
#> 3 Gene0003 Cell001 1
#> 4 Gene0004 Cell001 149
#> 5 Gene0005 Cell001 20makePerFeatureDF(mocked_es, features = FALSE, use_rowdata = FALSE)
#> [1] Cell001 Cell002 Cell003 Cell004 Cell005 Cell006 Cell007 Cell008 Cell009
#> [10] Cell010 Cell011 Cell012 Cell013 Cell014 Cell015 Cell016 Cell017 Cell018
#> [19] Cell019 Cell020 Cell021 Cell022 Cell023 Cell024 Cell025 Cell026 Cell027
#> [28] Cell028 Cell029 Cell030 Cell031 Cell032 Cell033 Cell034 Cell035 Cell036
#> [37] Cell037 Cell038 Cell039 Cell040 Cell041 Cell042 Cell043 Cell044 Cell045
#> [46] Cell046 Cell047 Cell048 Cell049 Cell050
#> <0 rows> (or 0-length row.names)# Seurat method
makePerFeatureDF(mocked_seurat, layer = "counts")[1:5, 1:5]
#> Cell001 Cell002 Cell003 Cell004 Cell005
#> Gene0001 0 1 0 15 3
#> Gene0002 438 299 325 442 1536
#> Gene0003 0 0 0 0 1
#> Gene0004 162 0 55 0 0
#> Gene0005 201 499 496 304 427makePerFeatureDF(mocked_seurat, layer = "counts", melt = TRUE)[1:5, ]
#> .cells .assay
#> 1 Cell001 0
#> 2 Cell001 438
#> 3 Cell001 0
#> 4 Cell001 162
#> 5 Cell001 201makePerFeatureDF(mocked_seurat,
layer = "counts", melt = TRUE, keep_rownames = TRUE
)[1:5, ]
#> .id .cells .assay
#> 1 Gene0001 Cell001 0
#> 2 Gene0002 Cell001 438
#> 3 Gene0003 Cell001 0
#> 4 Gene0004 Cell001 162
#> 5 Gene0005 Cell001 201makePerFeatureDF(mocked_seurat,
layer = "counts", features = FALSE,
use_rowdata = FALSE
)
#> [1] Cell001 Cell002 Cell003 Cell004 Cell005 Cell006 Cell007 Cell008 Cell009
#> [10] Cell010 Cell011 Cell012 Cell013 Cell014 Cell015 Cell016 Cell017 Cell018
#> [19] Cell019 Cell020 Cell021 Cell022 Cell023 Cell024 Cell025 Cell026 Cell027
#> [28] Cell028 Cell029 Cell030 Cell031 Cell032 Cell033 Cell034 Cell035 Cell036
#> [37] Cell037 Cell038 Cell039 Cell040 Cell041 Cell042 Cell043 Cell044 Cell045
#> [46] Cell046 Cell047 Cell048 Cell049 Cell050
#> <0 rows> (or 0-length row.names)sessionInfo()
#> R version 4.4.0 (2024-04-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04 LTS
#>
#> Matrix products: default
#> BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libmkl_rt.so; LAPACK version 3.8.0
#>
#> locale:
#> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
#> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
#> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Asia/Shanghai
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] biotidy_0.99.0
#>
#> loaded via a namespace (and not attached):
#> [1] Matrix_1.7-0 future.apply_1.11.2
#> [3] jsonlite_1.8.8 compiler_4.4.0
#> [5] crayon_1.5.3 Rcpp_1.0.12
#> [7] SeuratObject_5.0.2 SummarizedExperiment_1.34.0
#> [9] Biobase_2.64.0 GenomicRanges_1.56.1
#> [11] parallel_4.4.0 globals_0.16.3
#> [13] IRanges_2.38.0 yaml_2.3.8
#> [15] fastmap_1.2.0 lattice_0.22-6
#> [17] R6_2.5.1 XVector_0.44.0
#> [19] generics_0.1.3 S4Arrays_1.4.1
#> [21] GenomeInfoDb_1.40.1 knitr_1.47
#> [23] BiocGenerics_0.50.0 dotCall64_1.1-1
#> [25] future_1.33.2 DelayedArray_0.30.1
#> [27] MatrixGenerics_1.16.0 GenomeInfoDbData_1.2.12
#> [29] rlang_1.1.4 sp_2.1-4
#> [31] xfun_0.45 SparseArray_1.4.8
#> [33] cli_3.6.3 progressr_0.14.0
#> [35] zlibbioc_1.50.0 digest_0.6.36
#> [37] grid_4.4.0 spam_2.10-0
#> [39] lifecycle_1.0.4 S4Vectors_0.42.0
#> [41] SingleCellExperiment_1.26.0 data.table_1.15.4
#> [43] evaluate_0.24.0 listenv_0.9.1
#> [45] codetools_0.2-20 abind_1.4-5
#> [47] stats4_4.4.0 parallelly_1.37.1
#> [49] rmarkdown_2.27 httr_1.4.7
#> [51] matrixStats_1.3.0 tools_4.4.0
#> [53] htmltools_0.5.8.1 UCSC.utils_1.0.0