LoomExperiment 1.12.0
LoomExperiment classThe LoomExperiment family of classes inherits from the main class
LoomExperiment as well as the Experiment class that they are named
after. For example, the SingleCellLoomExperiment class inherits from
both LoomExperiment and SingleCellExperiment.
The purpose of the LoomExperiment class is to act as an intermediary
between Bioconductor’s Experiment classes and the Linnarson Lab’s Loom
File Format (http://linnarssonlab.org/loompy/index.html). The Loom
File Format uses HDF5 to store Experiment data.
The LoomExperiment family of classes contain the following slots.
colGraphsrowGraphsBoth of these slots are LoomGraphs objects that describe the
col_graph and row_graph attributes as specified by the Loom File
Format.
There are several ways to create instances of a LoomExperiment class
of object. One can plug an existing SummarizedExperiment type class
into the appropriate constructor:
library(LoomExperiment)
counts <- matrix(rpois(100, lambda = 10), ncol=10, nrow=10)
sce <- SingleCellExperiment(assays = list(counts = counts))
scle <- SingleCellLoomExperiment(sce)
## OR
scle <- LoomExperiment(sce)One can also simply plug the arguments into the appropriate
constructor, since all LoomExperiment constructors call the
applicable class’s constructor
scle <- SingleCellLoomExperiment(assays = list(counts = counts))Also, it is also possible to create a LoomExperiment extending class
via coercion:
scle <- as(sce, "SingleCellLoomExperiment")
scle## class: SingleCellLoomExperiment
## dim: 10 10
## metadata(0):
## assays(1): counts
## rownames: NULL
## rowData names(0):
## colnames: NULL
## colData names(0):
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## rowGraphs(0): NULL
## colGraphs(0): NULLFinally, one can create a LoomExperiment object from importing a
Loom File.
We will use the following SingleCellLoomExperiment for the remainder
of the vignette.
l1_file <-
system.file("extdata", "L1_DRG_20_example.loom", package = "LoomExperiment")
scle <- import(l1_file, type="SingleCellLoomExperiment")
scle## class: SingleCellLoomExperiment
## dim: 20 20
## metadata(4): CreatedWith LOOM_SPEC_VERSION LoomExperiment-class
## MatrixName
## assays(1): matrix
## rownames: NULL
## rowData names(7): Accession Gene ... X_Total X_Valid
## colnames: NULL
## colData names(103): Age AnalysisPool ... cDNA_Lib_Ok ngperul_cDNA
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## rowGraphs(0): NULL
## colGraphs(2): KNN MKNNAll the following methods apply to all LoomExperiment classes.
LoomGraph classThe colGraphs and rowGraphs slots of LoomExperiments correspond to
the col_graphs and row_graphs fields in the Loom File format.
Both of these slots require LoomGraphs objects.
A LoomGraph class extends the SelfHits class from the S4Vectors
package with the requirements that a LoomGraph object must:
integer and non-negativeLoomExperiment object (if attached to a LoomExperiment object)The columns to and from correspond to either row or col
indices in the LoomExperiment object while w is an optional column
that specifies the weight.
A LoomGraph can be constructed in two ways:
a <- c(1, 2, 3)
b <- c(3, 2, 1)
w <- c(100, 10, 1)
df <- DataFrame(a, b, w)
lg <- as(df, "LoomGraph")
## OR
lg <- LoomGraph(a, b, weight = w)
lg## LoomGraph object with 3 hits and 1 metadata column:
## from to | w
## <integer> <integer> | <numeric>
## [1] 1 3 | 100
## [2] 2 2 | 10
## [3] 3 1 | 1
## -------
## nnode: 3LoomGraph objects can be subset by the ‘row’/‘col’ indices.
lg[c(1, 2)]## LoomGraph object with 2 hits and 1 metadata column:
## from to | w
## <integer> <integer> | <numeric>
## [1] 1 3 | 100
## [2] 2 2 | 10
## -------
## nnode: 3lg[-c(2)]## LoomGraph object with 2 hits and 1 metadata column:
## from to | w
## <integer> <integer> | <numeric>
## [1] 1 3 | 100
## [2] 3 1 | 1
## -------
## nnode: 3LoomGraphs classA LoomGraphs object extends the S4Vectors:SimpleList object. It
contains multiple LoomGraph objects with its only requirement being
that it must contain LoomGraph objects.
It can be created simply by using LoomGraph objects in the
LoomGraphs constructor
lgs <- LoomGraphs(lg, lg)
names(lgs) <- c('lg1', 'lg2')
lgs## LoomGraphs of length 2
## names(2): lg1 lg2LoomExperimentThe LoomGraphs assigned to these colGraphs and rowGraphs slots
can be obtained by their eponymous methods:
colGraphs(scle)## LoomGraphs of length 2
## names(2): KNN MKNNrowGraphs(scle)## LoomGraphs of length 0The same symbols can also be used to replace the respective LoomGraphs
colGraphs(scle) <- lgs
rowGraphs(scle) <- lgs
colGraphs(scle)## LoomGraphs of length 2
## names(2): lg1 lg2rowGraphs(scle)## LoomGraphs of length 2
## names(2): lg1 lg2colGraphs(scle)[[1]]## LoomGraph object with 3 hits and 1 metadata column:
## from to | w
## <integer> <integer> | <numeric>
## [1] 1 3 | 100
## [2] 2 2 | 10
## [3] 3 1 | 1
## -------
## nnode: 20rowGraphs(scle)[[1]]## LoomGraph object with 3 hits and 1 metadata column:
## from to | w
## <integer> <integer> | <numeric>
## [1] 1 3 | 100
## [2] 2 2 | 10
## [3] 3 1 | 1
## -------
## nnode: 20LoomExperiment objects can be subsetting in such a way that the
assays, colGraphs, and rowGraphs will all be subsetted.
assays will will be subsetted as any matrix would. The i
element in the subsetting operation will subset the rowGraphs slot
and the j element in the subsetting operation will subset the
colGraphs slot, as we’ve seen from the subsetting method from
LoomGraphs.
scle2 <- scle[c(1, 3), 1:2]
colGraphs(scle2)[[1]]## LoomGraph object with 1 hit and 1 metadata column:
## from to | w
## <integer> <integer> | <numeric>
## [1] 2 2 | 10
## -------
## nnode: 2rowGraphs(scle2)[[1]]## LoomGraph object with 2 hits and 1 metadata column:
## from to | w
## <integer> <integer> | <numeric>
## [1] 1 2 | 100
## [2] 2 1 | 1
## -------
## nnode: 2scle3 <- rbind(scle, scle)
scle3## class: SingleCellLoomExperiment
## dim: 40 20
## metadata(8): CreatedWith LOOM_SPEC_VERSION ... LoomExperiment-class
## MatrixName
## assays(1): matrix
## rownames: NULL
## rowData names(7): Accession Gene ... X_Total X_Valid
## colnames: NULL
## colData names(103): Age AnalysisPool ... cDNA_Lib_Ok ngperul_cDNA
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## rowGraphs(2): lg1 lg2
## colGraphs(4): lg1 lg2 lg1 lg2colGraphs(scle3)## LoomGraphs of length 4
## names(4): lg1 lg2 lg1 lg2rowGraphs(scle3)## LoomGraphs of length 2
## names(2): lg1 lg2colGraphs(scle3)[[1]]## LoomGraph object with 3 hits and 1 metadata column:
## from to | w
## <integer> <integer> | <numeric>
## [1] 1 3 | 100
## [2] 2 2 | 10
## [3] 3 1 | 1
## -------
## nnode: 20rowGraphs(scle3)[[1]]## LoomGraph object with 6 hits and 1 metadata column:
## from to | w
## <integer> <integer> | <numeric>
## [1] 1 3 | 100
## [2] 2 2 | 10
## [3] 3 1 | 1
## [4] 21 23 | 100
## [5] 22 22 | 10
## [6] 23 21 | 1
## -------
## nnode: 40Finally, the LoomExperiment object can be exported.
temp <- tempfile(fileext='.loom')
export(scle2, temp)sessionInfo()## R version 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB 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] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] LoomExperiment_1.12.0 BiocIO_1.4.0
## [3] rhdf5_2.38.0 SingleCellExperiment_1.16.0
## [5] SummarizedExperiment_1.24.0 Biobase_2.54.0
## [7] GenomicRanges_1.46.0 GenomeInfoDb_1.30.0
## [9] IRanges_2.28.0 MatrixGenerics_1.6.0
## [11] matrixStats_0.61.0 S4Vectors_0.32.0
## [13] BiocGenerics_0.40.0 BiocStyle_2.22.0
##
## loaded via a namespace (and not attached):
## [1] bslib_0.3.1 compiler_4.1.1 BiocManager_1.30.16
## [4] jquerylib_0.1.4 XVector_0.34.0 rhdf5filters_1.6.0
## [7] bitops_1.0-7 tools_4.1.1 zlibbioc_1.40.0
## [10] digest_0.6.28 jsonlite_1.7.2 evaluate_0.14
## [13] lattice_0.20-45 rlang_0.4.12 Matrix_1.3-4
## [16] DelayedArray_0.20.0 yaml_2.2.1 xfun_0.27
## [19] fastmap_1.1.0 GenomeInfoDbData_1.2.7 stringr_1.4.0
## [22] knitr_1.36 sass_0.4.0 grid_4.1.1
## [25] R6_2.5.1 HDF5Array_1.22.0 rmarkdown_2.11
## [28] bookdown_0.24 Rhdf5lib_1.16.0 magrittr_2.0.1
## [31] htmltools_0.5.2 stringi_1.7.5 RCurl_1.98-1.5