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biotidy

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Introduction

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.

Prepare data

library(biotidy)
mocked_se <- mockSE()
mocked_sce <- mockSCE()
mocked_es <- mockES()
mocked_seurat <- mockSeurat()

makePerCellDF

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      117        7      182       57        0
#> Cell002       70       16      957       86      147
#> Cell003       69      103       53        4      123
#> Cell004      226       14       50        0      144
#> Cell005      276       31      215       58       10
makePerCellDF(mocked_se, melt = TRUE)[1:5, 1:5]
#>   Mutation_Status Cell_Cycle Treatment .features .assay
#> 1        negative          S    treat1  Gene0001    117
#> 2        negative          S    treat1  Gene0001     70
#> 3        positive          S    treat1  Gene0001     69
#> 4        negative        G2M    treat1  Gene0001    226
#> 5        negative         G0    treat1  Gene0001    276
makePerCellDF(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          S    treat1  Gene0001
#> 3 Cell003        positive          S    treat1  Gene0001
#> 4 Cell004        negative        G2M    treat1  Gene0001
#> 5 Cell005        negative         G0    treat1  Gene0001
makePerCellDF(mocked_se, features = FALSE, use_coldata = FALSE)
#> data frame with 0 columns and 200 rows
# SingleCellExperiment method
makePerCellDF(mocked_sce)[1:5, 1:5]
#>         Gene0001 Gene0002 Gene0003 Gene0004 Gene0005
#> Cell001      134        0        0      665        0
#> Cell002      170       53        0     1132        3
#> Cell003      161        7        0       35        0
#> Cell004      158        0        0      250        0
#> Cell005      201       19        0      272        0
makePerCellDF(mocked_sce, melt = TRUE)[1:5, 1:5]
#>   Mutation_Status Cell_Cycle Treatment .features .assay
#> 1        negative          S    treat2  Gene0001    134
#> 2        negative          S    treat1  Gene0001    170
#> 3        negative         G1    treat2  Gene0001    161
#> 4        positive          S    treat1  Gene0001    158
#> 5        positive         G0    treat1  Gene0001    201
makePerCellDF(mocked_sce, melt = TRUE, keep_rownames = TRUE)[1:5, 1:5]
#>       .id Mutation_Status Cell_Cycle Treatment .features
#> 1 Cell001        negative          S    treat2  Gene0001
#> 2 Cell002        negative          S    treat1  Gene0001
#> 3 Cell003        negative         G1    treat2  Gene0001
#> 4 Cell004        positive          S    treat1  Gene0001
#> 5 Cell005        positive         G0    treat1  Gene0001
makePerCellDF(mocked_sce, features = FALSE, use_coldata = FALSE)
#> data frame with 0 columns and 200 rows
# ExpressionSet method
makePerCellDF(mocked_es)[1:5, 1:5]
#>         Gene0001 Gene0002 Gene0003 Gene0004 Gene0005
#> Cell001      788      201      277      447      224
#> Cell002      737     1130      182      111        9
#> Cell003     1481       33      902      161       48
#> Cell004     1445      448      245      281        0
#> Cell005      225       75      433      335        0
makePerCellDF(mocked_es, melt = TRUE)[1:5, 1:5]
#>   Mutation_Status Cell_Cycle Treatment .features .assay
#> 1        positive          S    treat2  Gene0001    788
#> 2        positive         G0    treat2  Gene0001    737
#> 3        negative          S    treat2  Gene0001   1481
#> 4        negative        G2M    treat1  Gene0001   1445
#> 5        negative          S    treat1  Gene0001    225
makePerCellDF(mocked_es, melt = TRUE, keep_rownames = TRUE)[1:5, 1:5]
#>       .id Mutation_Status Cell_Cycle Treatment .features
#> 1 Cell001        positive          S    treat2  Gene0001
#> 2 Cell002        positive         G0    treat2  Gene0001
#> 3 Cell003        negative          S    treat2  Gene0001
#> 4 Cell004        negative        G2M    treat1  Gene0001
#> 5 Cell005        negative          S    treat1  Gene0001
makePerCellDF(mocked_es, features = FALSE, use_coldata = FALSE)
#> data frame with 0 columns and 200 rows
# Seurat method
makePerCellDF(mocked_seurat, layer = "counts")[1:5, 1:5]
#>         Gene0001 Gene0002 Gene0003 Gene0004 Gene0005
#> Cell001       54        0        6        8       78
#> Cell002       23       18       10       12        0
#> Cell003       20       69        5        0        0
#> Cell004        5        0       36       16       36
#> Cell005        5       60        0        9        0
makePerCellDF(mocked_seurat, layer = "counts", melt = TRUE)[1:5, 1:5]
#>      orig.ident nCount_RNA nFeature_RNA Mutation_Status Cell_Cycle
#> 1 SeuratProject     356745         1475        positive         G0
#> 2 SeuratProject     361987         1493        positive         G0
#> 3 SeuratProject     341572         1466        positive         G1
#> 4 SeuratProject     332788         1497        positive         G0
#> 5 SeuratProject     350858         1490        positive        G2M
makePerCellDF(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     356745         1475        positive
#> 2 Cell002 SeuratProject     361987         1493        positive
#> 3 Cell003 SeuratProject     341572         1466        positive
#> 4 Cell004 SeuratProject     332788         1497        positive
#> 5 Cell005 SeuratProject     350858         1490        positive
makePerCellDF(mocked_seurat,
  layer = "counts", features = FALSE, use_coldata =
    FALSE
)
#> data frame with 0 columns and 200 rows

makePerFeatureDF

makePerFeatureDF 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     117      70      69     226     276
#> Gene0002       7      16     103      14      31
#> Gene0003     182     957      53      50     215
#> Gene0004      57      86       4       0      58
#> Gene0005       0     147     123     144      10
makePerFeatureDF(mocked_se, melt = TRUE)[1:5, ]
#>    .cells .assay
#> 1 Cell001    117
#> 2 Cell001      7
#> 3 Cell001    182
#> 4 Cell001     57
#> 5 Cell001      0
makePerFeatureDF(mocked_se, melt = TRUE, keep_rownames = TRUE)[1:5, ]
#>        .id  .cells .assay
#> 1 Gene0001 Cell001    117
#> 2 Gene0002 Cell001      7
#> 3 Gene0003 Cell001    182
#> 4 Gene0004 Cell001     57
#> 5 Gene0005 Cell001      0
makePerFeatureDF(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 Cell051 Cell052 Cell053 Cell054
#>  [55] Cell055 Cell056 Cell057 Cell058 Cell059 Cell060 Cell061 Cell062 Cell063
#>  [64] Cell064 Cell065 Cell066 Cell067 Cell068 Cell069 Cell070 Cell071 Cell072
#>  [73] Cell073 Cell074 Cell075 Cell076 Cell077 Cell078 Cell079 Cell080 Cell081
#>  [82] Cell082 Cell083 Cell084 Cell085 Cell086 Cell087 Cell088 Cell089 Cell090
#>  [91] Cell091 Cell092 Cell093 Cell094 Cell095 Cell096 Cell097 Cell098 Cell099
#> [100] Cell100 Cell101 Cell102 Cell103 Cell104 Cell105 Cell106 Cell107 Cell108
#> [109] Cell109 Cell110 Cell111 Cell112 Cell113 Cell114 Cell115 Cell116 Cell117
#> [118] Cell118 Cell119 Cell120 Cell121 Cell122 Cell123 Cell124 Cell125 Cell126
#> [127] Cell127 Cell128 Cell129 Cell130 Cell131 Cell132 Cell133 Cell134 Cell135
#> [136] Cell136 Cell137 Cell138 Cell139 Cell140 Cell141 Cell142 Cell143 Cell144
#> [145] Cell145 Cell146 Cell147 Cell148 Cell149 Cell150 Cell151 Cell152 Cell153
#> [154] Cell154 Cell155 Cell156 Cell157 Cell158 Cell159 Cell160 Cell161 Cell162
#> [163] Cell163 Cell164 Cell165 Cell166 Cell167 Cell168 Cell169 Cell170 Cell171
#> [172] Cell172 Cell173 Cell174 Cell175 Cell176 Cell177 Cell178 Cell179 Cell180
#> [181] Cell181 Cell182 Cell183 Cell184 Cell185 Cell186 Cell187 Cell188 Cell189
#> [190] Cell190 Cell191 Cell192 Cell193 Cell194 Cell195 Cell196 Cell197 Cell198
#> [199] Cell199 Cell200
#> <0 rows> (or 0-length row.names)
# SingleCellExperiment method
makePerFeatureDF(mocked_sce)[1:5, 1:5]
#>          Cell001 Cell002 Cell003 Cell004 Cell005
#> Gene0001     134     170     161     158     201
#> Gene0002       0      53       7       0      19
#> Gene0003       0       0       0       0       0
#> Gene0004     665    1132      35     250     272
#> Gene0005       0       3       0       0       0
makePerFeatureDF(mocked_sce, melt = TRUE)[1:5, ]
#>    .cells .assay
#> 1 Cell001    134
#> 2 Cell001      0
#> 3 Cell001      0
#> 4 Cell001    665
#> 5 Cell001      0
makePerFeatureDF(mocked_sce, melt = TRUE, keep_rownames = TRUE)[1:5, ]
#>        .id  .cells .assay
#> 1 Gene0001 Cell001    134
#> 2 Gene0002 Cell001      0
#> 3 Gene0003 Cell001      0
#> 4 Gene0004 Cell001    665
#> 5 Gene0005 Cell001      0
makePerFeatureDF(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 Cell051 Cell052 Cell053 Cell054
#>  [55] Cell055 Cell056 Cell057 Cell058 Cell059 Cell060 Cell061 Cell062 Cell063
#>  [64] Cell064 Cell065 Cell066 Cell067 Cell068 Cell069 Cell070 Cell071 Cell072
#>  [73] Cell073 Cell074 Cell075 Cell076 Cell077 Cell078 Cell079 Cell080 Cell081
#>  [82] Cell082 Cell083 Cell084 Cell085 Cell086 Cell087 Cell088 Cell089 Cell090
#>  [91] Cell091 Cell092 Cell093 Cell094 Cell095 Cell096 Cell097 Cell098 Cell099
#> [100] Cell100 Cell101 Cell102 Cell103 Cell104 Cell105 Cell106 Cell107 Cell108
#> [109] Cell109 Cell110 Cell111 Cell112 Cell113 Cell114 Cell115 Cell116 Cell117
#> [118] Cell118 Cell119 Cell120 Cell121 Cell122 Cell123 Cell124 Cell125 Cell126
#> [127] Cell127 Cell128 Cell129 Cell130 Cell131 Cell132 Cell133 Cell134 Cell135
#> [136] Cell136 Cell137 Cell138 Cell139 Cell140 Cell141 Cell142 Cell143 Cell144
#> [145] Cell145 Cell146 Cell147 Cell148 Cell149 Cell150 Cell151 Cell152 Cell153
#> [154] Cell154 Cell155 Cell156 Cell157 Cell158 Cell159 Cell160 Cell161 Cell162
#> [163] Cell163 Cell164 Cell165 Cell166 Cell167 Cell168 Cell169 Cell170 Cell171
#> [172] Cell172 Cell173 Cell174 Cell175 Cell176 Cell177 Cell178 Cell179 Cell180
#> [181] Cell181 Cell182 Cell183 Cell184 Cell185 Cell186 Cell187 Cell188 Cell189
#> [190] Cell190 Cell191 Cell192 Cell193 Cell194 Cell195 Cell196 Cell197 Cell198
#> [199] Cell199 Cell200
#> <0 rows> (or 0-length row.names)
# ExpressionSet method
makePerFeatureDF(mocked_es)[1:5, 1:5]
#>          Cell001 Cell002 Cell003 Cell004 Cell005
#> Gene0001     788     737    1481    1445     225
#> Gene0002     201    1130      33     448      75
#> Gene0003     277     182     902     245     433
#> Gene0004     447     111     161     281     335
#> Gene0005     224       9      48       0       0
makePerFeatureDF(mocked_es, melt = TRUE)[1:5, ]
#>    .cells .assay
#> 1 Cell001    788
#> 2 Cell001    201
#> 3 Cell001    277
#> 4 Cell001    447
#> 5 Cell001    224
makePerFeatureDF(mocked_es, melt = TRUE, keep_rownames = TRUE)[1:5, ]
#>        .id  .cells .assay
#> 1 Gene0001 Cell001    788
#> 2 Gene0002 Cell001    201
#> 3 Gene0003 Cell001    277
#> 4 Gene0004 Cell001    447
#> 5 Gene0005 Cell001    224
makePerFeatureDF(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 Cell051 Cell052 Cell053 Cell054
#>  [55] Cell055 Cell056 Cell057 Cell058 Cell059 Cell060 Cell061 Cell062 Cell063
#>  [64] Cell064 Cell065 Cell066 Cell067 Cell068 Cell069 Cell070 Cell071 Cell072
#>  [73] Cell073 Cell074 Cell075 Cell076 Cell077 Cell078 Cell079 Cell080 Cell081
#>  [82] Cell082 Cell083 Cell084 Cell085 Cell086 Cell087 Cell088 Cell089 Cell090
#>  [91] Cell091 Cell092 Cell093 Cell094 Cell095 Cell096 Cell097 Cell098 Cell099
#> [100] Cell100 Cell101 Cell102 Cell103 Cell104 Cell105 Cell106 Cell107 Cell108
#> [109] Cell109 Cell110 Cell111 Cell112 Cell113 Cell114 Cell115 Cell116 Cell117
#> [118] Cell118 Cell119 Cell120 Cell121 Cell122 Cell123 Cell124 Cell125 Cell126
#> [127] Cell127 Cell128 Cell129 Cell130 Cell131 Cell132 Cell133 Cell134 Cell135
#> [136] Cell136 Cell137 Cell138 Cell139 Cell140 Cell141 Cell142 Cell143 Cell144
#> [145] Cell145 Cell146 Cell147 Cell148 Cell149 Cell150 Cell151 Cell152 Cell153
#> [154] Cell154 Cell155 Cell156 Cell157 Cell158 Cell159 Cell160 Cell161 Cell162
#> [163] Cell163 Cell164 Cell165 Cell166 Cell167 Cell168 Cell169 Cell170 Cell171
#> [172] Cell172 Cell173 Cell174 Cell175 Cell176 Cell177 Cell178 Cell179 Cell180
#> [181] Cell181 Cell182 Cell183 Cell184 Cell185 Cell186 Cell187 Cell188 Cell189
#> [190] Cell190 Cell191 Cell192 Cell193 Cell194 Cell195 Cell196 Cell197 Cell198
#> [199] Cell199 Cell200
#> <0 rows> (or 0-length row.names)
# Seurat method
makePerFeatureDF(mocked_seurat, layer = "counts")[1:5, 1:5]
#>          Cell001 Cell002 Cell003 Cell004 Cell005
#> Gene0001      54      23      20       5       5
#> Gene0002       0      18      69       0      60
#> Gene0003       6      10       5      36       0
#> Gene0004       8      12       0      16       9
#> Gene0005      78       0       0      36       0
makePerFeatureDF(mocked_seurat, layer = "counts", melt = TRUE)[1:5, ]
#>    .cells .assay
#> 1 Cell001     54
#> 2 Cell001      0
#> 3 Cell001      6
#> 4 Cell001      8
#> 5 Cell001     78
makePerFeatureDF(mocked_seurat,
  layer = "counts", melt = TRUE, keep_rownames = TRUE
)[1:5, ]
#>        .id  .cells .assay
#> 1 Gene0001 Cell001     54
#> 2 Gene0002 Cell001      0
#> 3 Gene0003 Cell001      6
#> 4 Gene0004 Cell001      8
#> 5 Gene0005 Cell001     78
makePerFeatureDF(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 Cell051 Cell052 Cell053 Cell054
#>  [55] Cell055 Cell056 Cell057 Cell058 Cell059 Cell060 Cell061 Cell062 Cell063
#>  [64] Cell064 Cell065 Cell066 Cell067 Cell068 Cell069 Cell070 Cell071 Cell072
#>  [73] Cell073 Cell074 Cell075 Cell076 Cell077 Cell078 Cell079 Cell080 Cell081
#>  [82] Cell082 Cell083 Cell084 Cell085 Cell086 Cell087 Cell088 Cell089 Cell090
#>  [91] Cell091 Cell092 Cell093 Cell094 Cell095 Cell096 Cell097 Cell098 Cell099
#> [100] Cell100 Cell101 Cell102 Cell103 Cell104 Cell105 Cell106 Cell107 Cell108
#> [109] Cell109 Cell110 Cell111 Cell112 Cell113 Cell114 Cell115 Cell116 Cell117
#> [118] Cell118 Cell119 Cell120 Cell121 Cell122 Cell123 Cell124 Cell125 Cell126
#> [127] Cell127 Cell128 Cell129 Cell130 Cell131 Cell132 Cell133 Cell134 Cell135
#> [136] Cell136 Cell137 Cell138 Cell139 Cell140 Cell141 Cell142 Cell143 Cell144
#> [145] Cell145 Cell146 Cell147 Cell148 Cell149 Cell150 Cell151 Cell152 Cell153
#> [154] Cell154 Cell155 Cell156 Cell157 Cell158 Cell159 Cell160 Cell161 Cell162
#> [163] Cell163 Cell164 Cell165 Cell166 Cell167 Cell168 Cell169 Cell170 Cell171
#> [172] Cell172 Cell173 Cell174 Cell175 Cell176 Cell177 Cell178 Cell179 Cell180
#> [181] Cell181 Cell182 Cell183 Cell184 Cell185 Cell186 Cell187 Cell188 Cell189
#> [190] Cell190 Cell191 Cell192 Cell193 Cell194 Cell195 Cell196 Cell197 Cell198
#> [199] Cell199 Cell200
#> <0 rows> (or 0-length row.names)

Session information

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

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