Contents

1 The LoomExperiment class

1.1 Definition

The 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.

  • colGraphs
  • rowGraphs

Both of these slots are LoomGraphs objects that describe the col_graph and row_graph attributes as specified by the Loom File Format.

1.2 Create instances of LoomExperiment

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):
## altExpNames(0):
## rowGraphs(0): NULL
## colGraphs(0): NULL

Finally, one can create a LoomExperiment object from importing a Loom File.

1.3 Setting up a simple example

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(0):
## 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):
## altExpNames(0):
## rowGraphs(0): NULL
## colGraphs(2): KNN MKNN

All the following methods apply to all LoomExperiment classes.

2 The LoomGraph class

The 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:

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: 3

LoomGraph 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: 3
lg[-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: 3

3 The LoomGraphs class

A 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 lg2

4 Available methods for the LoomExperiment

The LoomGraphs assigned to these colGraphs and rowGraphs slots can be obtained by their eponymous methods:

colGraphs(scle)
## LoomGraphs of length 2
## names(2): KNN MKNN
rowGraphs(scle)
## LoomGraphs of length 0

The 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 lg2
rowGraphs(scle)
## LoomGraphs of length 2
## names(2): lg1 lg2
colGraphs(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: 20
rowGraphs(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: 20

LoomExperiment 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: 2
rowGraphs(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: 2
scle3 <- rbind(scle, scle)
scle3
## class: SingleCellLoomExperiment 
## dim: 40 20 
## metadata(0):
## 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):
## altExpNames(0):
## rowGraphs(2): lg1 lg2
## colGraphs(4): lg1 lg2 lg1 lg2
colGraphs(scle3)
## LoomGraphs of length 4
## names(4): lg1 lg2 lg1 lg2
rowGraphs(scle3)
## LoomGraphs of length 2
## names(2): lg1 lg2
colGraphs(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: 20
rowGraphs(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: 40

Finally, the LoomExperiment object can be exported.

temp <- tempfile(fileext='.loom')
export(scle2, temp)

5 Session Info

sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        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] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] LoomExperiment_1.8.0        rtracklayer_1.50.0         
##  [3] rhdf5_2.34.0                SingleCellExperiment_1.12.0
##  [5] SummarizedExperiment_1.20.0 Biobase_2.50.0             
##  [7] GenomicRanges_1.42.0        GenomeInfoDb_1.26.0        
##  [9] IRanges_2.24.0              MatrixGenerics_1.2.0       
## [11] matrixStats_0.57.0          S4Vectors_0.28.0           
## [13] BiocGenerics_0.36.0         BiocStyle_2.18.0           
## 
## loaded via a namespace (and not attached):
##  [1] compiler_4.0.3           BiocManager_1.30.10      XVector_0.30.0          
##  [4] bitops_1.0-6             rhdf5filters_1.2.0       tools_4.0.3             
##  [7] zlibbioc_1.36.0          digest_0.6.27            evaluate_0.14           
## [10] lattice_0.20-41          rlang_0.4.8              Matrix_1.2-18           
## [13] DelayedArray_0.16.0      yaml_2.2.1               xfun_0.18               
## [16] GenomeInfoDbData_1.2.4   stringr_1.4.0            knitr_1.30              
## [19] Biostrings_2.58.0        grid_4.0.3               HDF5Array_1.18.0        
## [22] BiocParallel_1.24.0      XML_3.99-0.5             rmarkdown_2.5           
## [25] bookdown_0.21            Rhdf5lib_1.12.0          magrittr_1.5            
## [28] GenomicAlignments_1.26.0 Rsamtools_2.6.0          htmltools_0.5.0         
## [31] stringi_1.5.3            RCurl_1.98-1.2           crayon_1.3.4