![[Stable]](figures/lifecycle-stable.svg)
These methods access and set the counts assay in a AnyHermesData object.
Usage
# S4 method for AnyHermesData
counts(object, ...)
# S4 method for AnyHermesData,matrix
counts(object, ..., withDimnames = TRUE) <- valueArguments
- object
(
AnyHermesData)
object to access the counts from.- ...
not used.
- withDimnames
(
flag)
settingwithDimnames =FALSEin the setter (counts<-) is required when thedimnameson the supplied counts assay are not identical to thedimnameson theAnyHermesDataobject; it does not influence actual assignment ofdimnamesto the assay (they're always stored as-is).- value
(
matrix)
what should the counts assay be replaced with.
Examples
a <- hermes_data
result <- counts(a)
class(result)
#> [1] "matrix" "array"
head(result)
#> 06520011B0023R 06520067C0018R 06520063C0043R 06520105C0017R
#> GeneID:11185 3 66 35 10
#> GeneID:10677 1668 236 95 1945
#> GeneID:101928428 0 0 0 0
#> GeneID:100422835 0 0 0 0
#> GeneID:102466731 0 0 0 0
#> GeneID:64881 113 11 153 22
#> 06520092C0017R 06520103C0017R 06520001B0023R 06520022C0017R
#> GeneID:11185 68 123 65 10
#> GeneID:10677 570 149 216 182
#> GeneID:101928428 0 0 0 0
#> GeneID:100422835 0 0 0 0
#> GeneID:102466731 0 0 0 0
#> GeneID:64881 0 20 1 5
#> 06520062C0017R 06520046C0018R 06520101B0017R 06520047C0017R
#> GeneID:11185 106 23 72 4
#> GeneID:10677 138 748 314 189
#> GeneID:101928428 0 0 0 0
#> GeneID:100422835 0 0 0 0
#> GeneID:102466731 0 0 0 0
#> GeneID:64881 131 586 0 119
#> 06520024B0014R 06520080B0023R 06520093C0017R 06520070C0018R
#> GeneID:11185 36 0 50 479
#> GeneID:10677 176 213 313 345
#> GeneID:101928428 0 0 0 0
#> GeneID:100422835 0 0 0 0
#> GeneID:102466731 0 0 0 0
#> GeneID:64881 9 62 0 482
#> 06520023C0018R 06520099B0017R 06520015C0016R 06520019C0023R
#> GeneID:11185 235 35 75 47
#> GeneID:10677 1922 269 385 687
#> GeneID:101928428 0 0 0 0
#> GeneID:100422835 0 0 0 0
#> GeneID:102466731 0 0 0 0
#> GeneID:64881 1315 0 3047 135
counts(a) <- counts(a) + 100L
head(counts(a))
#> 06520011B0023R 06520067C0018R 06520063C0043R 06520105C0017R
#> GeneID:11185 103 166 135 110
#> GeneID:10677 1768 336 195 2045
#> GeneID:101928428 100 100 100 100
#> GeneID:100422835 100 100 100 100
#> GeneID:102466731 100 100 100 100
#> GeneID:64881 213 111 253 122
#> 06520092C0017R 06520103C0017R 06520001B0023R 06520022C0017R
#> GeneID:11185 168 223 165 110
#> GeneID:10677 670 249 316 282
#> GeneID:101928428 100 100 100 100
#> GeneID:100422835 100 100 100 100
#> GeneID:102466731 100 100 100 100
#> GeneID:64881 100 120 101 105
#> 06520062C0017R 06520046C0018R 06520101B0017R 06520047C0017R
#> GeneID:11185 206 123 172 104
#> GeneID:10677 238 848 414 289
#> GeneID:101928428 100 100 100 100
#> GeneID:100422835 100 100 100 100
#> GeneID:102466731 100 100 100 100
#> GeneID:64881 231 686 100 219
#> 06520024B0014R 06520080B0023R 06520093C0017R 06520070C0018R
#> GeneID:11185 136 100 150 579
#> GeneID:10677 276 313 413 445
#> GeneID:101928428 100 100 100 100
#> GeneID:100422835 100 100 100 100
#> GeneID:102466731 100 100 100 100
#> GeneID:64881 109 162 100 582
#> 06520023C0018R 06520099B0017R 06520015C0016R 06520019C0023R
#> GeneID:11185 335 135 175 147
#> GeneID:10677 2022 369 485 787
#> GeneID:101928428 100 100 100 100
#> GeneID:100422835 100 100 100 100
#> GeneID:102466731 100 100 100 100
#> GeneID:64881 1415 100 3147 235