Get and set feature-level meta data
# S3 method for Assay
[[(x, i, ..., drop = FALSE)
# S4 method for Assay,ANY,ANY,ANY
[[(x, i, j, ...) <- value
# S3 method for Assay
head(x, n = 10L, ...)
# S3 method for Assay
tail(x, n = 10L, ...)
# S4 method for Assay,missing,missing,data.frame
[[(x, i, j, ...) <- value[[: The feature-level meta data for i
[[<-: x with value added as i
in feature-level meta data
head: The first n rows of feature-level meta data
tail: the last n rows of feature-level meta data
v3 Assay object, validity, and interaction methods:
$.Assay(),
Assay-class,
Assay-validity,
CreateAssayObject(),
[.Assay(),
dim.Assay(),
dimnames.Assay(),
merge.Assay(),
split.Assay(),
subset.Assay()
rna <- pbmc_small[["RNA"]]
# Pull the entire feature-level meta data data frame
head(rna[[]])
#> vst.mean vst.variance vst.variance.expected vst.variance.standardized
#> MS4A1 0.3875 1.0251582 1.1411616 0.8983463
#> CD79B 0.6000 1.2810127 2.7076228 0.4731134
#> CD79A 0.7000 4.3645570 4.0178895 1.0862810
#> HLA-DRA 13.4250 725.4626582 745.5260337 0.9730883
#> TCL1A 0.3000 0.8708861 0.6423114 1.3558627
#> HLA-DQB1 1.7125 16.4099684 16.4951770 0.9948343
#> vst.variable
#> MS4A1 FALSE
#> CD79B FALSE
#> CD79A FALSE
#> HLA-DRA FALSE
#> TCL1A FALSE
#> HLA-DQB1 FALSE
# Pull a specific column of feature-level meta data
head(rna[["vst.mean"]])
#> vst.mean
#> MS4A1 0.3875
#> CD79B 0.6000
#> CD79A 0.7000
#> HLA-DRA 13.4250
#> TCL1A 0.3000
#> HLA-DQB1 1.7125
head(rna[["vst.mean", drop = TRUE]])
#> MS4A1 CD79B CD79A HLA-DRA TCL1A HLA-DQB1
#> 0.3875 0.6000 0.7000 13.4250 0.3000 1.7125
# `head` and `tail` can be used to quickly view feature-level meta data
head(rna)
#> vst.mean vst.variance vst.variance.expected vst.variance.standardized
#> MS4A1 0.3875 1.02515823 1.14116156 0.8983463
#> CD79B 0.6000 1.28101266 2.70762281 0.4731134
#> CD79A 0.7000 4.36455696 4.01788953 1.0862810
#> HLA-DRA 13.4250 725.46265823 745.52603372 0.9730883
#> TCL1A 0.3000 0.87088608 0.64231140 1.3558627
#> HLA-DQB1 1.7125 16.40996835 16.49517698 0.9948343
#> HVCN1 0.2000 0.31392405 0.33043460 0.9500338
#> HLA-DMB 0.5875 1.28338608 2.57396962 0.4986019
#> LTB 2.0500 11.71898734 20.46768888 0.5725604
#> LINC00926 0.0750 0.09556962 0.09211918 1.0374562
#> vst.variable
#> MS4A1 FALSE
#> CD79B FALSE
#> CD79A FALSE
#> HLA-DRA FALSE
#> TCL1A FALSE
#> HLA-DQB1 FALSE
#> HVCN1 FALSE
#> HLA-DMB FALSE
#> LTB FALSE
#> LINC00926 FALSE
tail(rna)
#> vst.mean vst.variance vst.variance.expected vst.variance.standardized
#> PPP1R18 0.4000 0.4962025 1.2694690 0.3908741
#> CD247 0.3125 0.4707278 0.6625599 0.7104683
#> ALOX5AP 0.4000 0.5215190 1.2694690 0.4108166
#> XCL2 0.1375 0.2466772 0.1999215 1.2338705
#> C12orf75 0.2625 0.5757911 0.5534245 1.0404150
#> RARRES3 0.6875 1.4833861 3.8269090 0.3876199
#> PCMT1 1.0750 42.1208861 8.4027747 1.1438602
#> LAMP1 0.3375 0.4795886 0.7618688 0.6294897
#> SPON2 0.4000 0.9265823 1.2694690 0.7298975
#> S100B 0.1500 1.2683544 0.2189476 1.1963621
#> vst.variable
#> PPP1R18 FALSE
#> CD247 FALSE
#> ALOX5AP FALSE
#> XCL2 FALSE
#> C12orf75 FALSE
#> RARRES3 FALSE
#> PCMT1 FALSE
#> LAMP1 FALSE
#> SPON2 FALSE
#> S100B FALSE