ExperimentList: S4 Class for handling lists of Experiments

Abstract

The ExperimentList package defines S4 classes to handle data from multiple experiments or studies by providing features of lists as well as those of a concatenated experiment. Individual experiments can be in the form of SummarizedExperiment, Ranged SummarizedExperiment, SingleCellExperiment, or SpatialExperiment objects. Annotations specific to each experiment are stored thus providing a unified interface to dealing with data from multiple studies. Specialised functions to access experiment data, and to apply functions across experiments are implemented. Existing functions implemented for each individual experiment (e.g., SingleCellExperiment::reducedDim()) can be readily applied across the entire list of experiments.

Motivation

The advent of high throughput molecular measurement technologies has resulted in the generation of vast amounts of data. The SummarizedExperiment object and its derivates have assisted in hosting data from these technologies. The SingleCellExperiment and SpatialExperiment objects are able to store even higher resolution single-cell and spatial transcriptomics measurements from a single biological sample respectively. Reduced costs has enabled the generation of these data from multiple biological samples. Such data is not easily stored and manupilated within a single object. Concatenation of objects can partially resolve this data since it can be analysed in unison, however, prevents for sample-wise analysis. Maintaining a list of objects would allow object-wise analysis, but would hinder collective analysis.

The ExperimentList object is designed to fill in this gap and allows storage and manipulation of multiple SpatialExperiment, SingleCellExperiment, RangedSummarizedExperiment or SummarizedExperiment objects. It provides both list-like and object-like functionality thus providing dynamic access to the data as the needs arise. For example, when analysing multiple spatial transcriptomic datasets, users may wish to compute reduced dimensions (e.g., PCA) on each individual object before dataset integration and compute a combined reduced dimension after. In such scenarios, having a hybrid interface to the data is beneficial.

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("ExperimentList")

Anatomy of an `ExperimentList`

The ExperimentList object borrows its structure from the object it hosts. When dealing with a list of SpatialExperiment objects, all slots from the object are made available therefore the rows contain feature (e.g., gene/transcript) information, the columns represent individual observations (e.g., visium spots), and multiple assays can be stored. The individual objects (different shades in the schematic) are concatenated to create a single SpatialExperiment object. The mapping between the original sample and the concatenated columns are maintained using and internal slot (experimentIndex). Sample-specific annotation (NOT spot-specific) are held in a DataFrame and are linked to the columns of the new ExperimentList object.

The following schematic demonstrates the structure of the ExperimentList object and lists some common accessors to interact with the object.

ExperimentList anatomy

Under the hood, a separate class is used to hold the list of objects as described in the table below.

Object class	Matched `ExperimentList` class
`SummarizedExperiment`	`SummarizedExperimentList`
`RangedSummarizedExperiment`	`RangedSummarizedExperimentList`
`SingleCellExperiment`	`SingleCellExperimentList`
`SpatialExperiment`	`SpatialExperimentList`

Constructing an `ExperimentList`

The TENxVisiumData package contains 10X Visium data from various human and mouse tissues. We will use the breast cancer IDC and ILC datasets from this package to demonstrate the ExperimentList container. These data contain measurements of 36601 transcripts measured across 7785 and 4325 spots respectively.

library(ExperimentList)
library(TENxVisiumData)

#download data
spe1 = TENxVisiumData::HumanBreastCancerIDC()
spe2 = TENxVisiumData::HumanBreastCancerILC()

#remove alt exps - these should be matched across exps (likewise for rownames)
altExps(spe2) = list()

#create a list of objects
spe_list = list(
  'HumanBreastCancerIDC' = spe1,
  'HumanBreastCancerILC' = spe2
)
spe_list
#> $HumanBreastCancerIDC
#> class: SpatialExperiment 
#> dim: 36601 7785 
#> metadata(0):
#> assays(1): counts
#> rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
#>   ENSG00000277196
#> rowData names(1): symbol
#> colnames(7785): AAACAAGTATCTCCCA-1 AAACACCAATAACTGC-1 ...
#>   TTGTTTGTATTACACG-1 TTGTTTGTGTAAATTC-1
#> colData names(1): sample_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor
#> 
#> $HumanBreastCancerILC
#> class: SpatialExperiment 
#> dim: 36601 4325 
#> metadata(0):
#> assays(1): counts
#> rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
#>   ENSG00000277196
#> rowData names(1): symbol
#> colnames(4325): AAACAACGAATAGTTC-1 AAACAAGTATCTCCCA-1 ...
#>   TTGTTTCCATACAACT-1 TTGTTTGTGTAAATTC-1
#> colData names(1): sample_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor

Given the individual objects, we first create a sample-specific annotation table and subsequently use it to create an ExperimentList object. With the data prepared, the object can be constructed using the ExperimentList() function.

#create some artificial experiment annotations
experimentAnnotation = data.frame(
  sex = c('Female', 'Female'),
  age = c(65, 68),
  row.names = c('HumanBreastCancerIDC', 'HumanBreastCancerILC')
)
experimentAnnotation
#>                         sex age
#> HumanBreastCancerIDC Female  65
#> HumanBreastCancerILC Female  68

#create ExperimentList objects
el = ExperimentList(experiments = spe_list, experimentData = experimentAnnotation)
el
#> ExperimentList with 2 SpatialExperiments
#> class: SpatialExperimentList 
#> dim: 36601 12110 
#> metadata(0):
#> assays(1): counts
#> rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
#>   ENSG00000277196
#> rowData names(1): symbol
#> colnames(12110): HumanBreastCancerIDC.AAACAAGTATCTCCCA-1
#>   HumanBreastCancerIDC.AAACACCAATAACTGC-1 ...
#>   HumanBreastCancerILC.TTGTTTCCATACAACT-1
#>   HumanBreastCancerILC.TTGTTTGTGTAAATTC-1
#> colData names(1): sample_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor
#> experiments: 2
#> experimentNames (2): HumanBreastCancerIDC HumanBreastCancerILC
#> experimentData names (2): sex age

The ExperimentList object can be created without experimentData and with an unnamed list. When using a named list, names of the list should be matched with rownames of experimentData.

Common operations on `ExperimentList`

Subsetting

Subsetting can be performed in a similar way to the parental SummarizedExperiment object using the [ function.

#subset the first five features and first three samples
el[1:5, 1:3]
#> ExperimentList with 2 SpatialExperiments
#> class: SpatialExperimentList 
#> dim: 5 3 
#> metadata(0):
#> assays(1): counts
#> rownames(5): ENSG00000243485 ENSG00000237613 ENSG00000186092
#>   ENSG00000238009 ENSG00000239945
#> rowData names(1): symbol
#> colnames(3): HumanBreastCancerIDC.AAACAAGTATCTCCCA-1
#>   HumanBreastCancerIDC.AAACACCAATAACTGC-1
#>   HumanBreastCancerIDC.AAACAGAGCGACTCCT-1
#> colData names(1): sample_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor
#> experiments: 2
#> experimentNames (2): HumanBreastCancerIDC HumanBreastCancerILC
#> experimentData names (2): sex age

In addition to this, entire experiments can be subsetted using the [ function as below.

#subset the first five features and all columns from the second experiment
el[1:5, , exp = 2]
#> ExperimentList with 1 SpatialExperiments
#> class: SpatialExperimentList 
#> dim: 5 4325 
#> metadata(0):
#> assays(1): counts
#> rownames(5): ENSG00000243485 ENSG00000237613 ENSG00000186092
#>   ENSG00000238009 ENSG00000239945
#> rowData names(1): symbol
#> colnames(4325): HumanBreastCancerILC.AAACAACGAATAGTTC-1
#>   HumanBreastCancerILC.AAACAAGTATCTCCCA-1 ...
#>   HumanBreastCancerILC.TTGTTTCCATACAACT-1
#>   HumanBreastCancerILC.TTGTTTGTGTAAATTC-1
#> colData names(1): sample_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor
#> experiments: 1
#> experimentNames (1): HumanBreastCancerILC
#> experimentData names (2): sex age

Getters and setters

The functions below can be used to access and set data in the object.

nexp(), experimentNames()

#number of experiments
nexp(el)
#> [1] 2
#names of experiments
experimentNames(el)
#> [1] "HumanBreastCancerIDC" "HumanBreastCancerILC"

experiments()

#get a list of individual experiments
experiments(el)
#> $HumanBreastCancerIDC
#> class: SpatialExperiment 
#> dim: 36601 7785 
#> metadata(0):
#> assays(1): counts
#> rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
#>   ENSG00000277196
#> rowData names(1): symbol
#> colnames(7785): AAACAAGTATCTCCCA-1 AAACACCAATAACTGC-1 ...
#>   TTGTTTGTATTACACG-1 TTGTTTGTGTAAATTC-1
#> colData names(1): sample_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor
#> 
#> $HumanBreastCancerILC
#> class: SpatialExperiment 
#> dim: 36601 4325 
#> metadata(0):
#> assays(1): counts
#> rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
#>   ENSG00000277196
#> rowData names(1): symbol
#> colnames(4325): AAACAACGAATAGTTC-1 AAACAAGTATCTCCCA-1 ...
#>   TTGTTTCCATACAACT-1 TTGTTTGTGTAAATTC-1
#> colData names(1): sample_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor

experimentData(), colData()

#get experiment annotations
experimentData(el)
#> DataFrame with 2 rows and 2 columns
#>                              sex       age
#>                      <character> <numeric>
#> HumanBreastCancerIDC      Female        65
#> HumanBreastCancerILC      Female        68
#get column annotations
head(colData(el))
#> DataFrame with 6 rows and 1 column
#>                                                     sample_id
#>                                                   <character>
#> HumanBreastCancerIDC.AAACAAGTATCTCCCA-1 HumanBreastCancerIDC1
#> HumanBreastCancerIDC.AAACACCAATAACTGC-1 HumanBreastCancerIDC1
#> HumanBreastCancerIDC.AAACAGAGCGACTCCT-1 HumanBreastCancerIDC1
#> HumanBreastCancerIDC.AAACAGGGTCTATATT-1 HumanBreastCancerIDC1
#> HumanBreastCancerIDC.AAACAGTGTTCCTGGG-1 HumanBreastCancerIDC1
#> HumanBreastCancerIDC.AAACATTTCCCGGATT-1 HumanBreastCancerIDC1
#get column annotations merged with experiment annotations
head(colData(el, experimentData = TRUE))
#> DataFrame with 6 rows and 3 columns
#>                                                     sample_id         sex
#>                                                   <character> <character>
#> HumanBreastCancerIDC.AAACAAGTATCTCCCA-1 HumanBreastCancerIDC1      Female
#> HumanBreastCancerIDC.AAACACCAATAACTGC-1 HumanBreastCancerIDC1      Female
#> HumanBreastCancerIDC.AAACAGAGCGACTCCT-1 HumanBreastCancerIDC1      Female
#> HumanBreastCancerIDC.AAACAGGGTCTATATT-1 HumanBreastCancerIDC1      Female
#> HumanBreastCancerIDC.AAACAGTGTTCCTGGG-1 HumanBreastCancerIDC1      Female
#> HumanBreastCancerIDC.AAACATTTCCCGGATT-1 HumanBreastCancerIDC1      Female
#>                                               age
#>                                         <numeric>
#> HumanBreastCancerIDC.AAACAAGTATCTCCCA-1        65
#> HumanBreastCancerIDC.AAACACCAATAACTGC-1        65
#> HumanBreastCancerIDC.AAACAGAGCGACTCCT-1        65
#> HumanBreastCancerIDC.AAACAGGGTCTATATT-1        65
#> HumanBreastCancerIDC.AAACAGTGTTCCTGGG-1        65
#> HumanBreastCancerIDC.AAACATTTCCCGGATT-1        65

Apply

The elapply() function can be used to apply functions to individual objects.

#apply function
elapply(el, dim)
#> $HumanBreastCancerIDC
#> [1] 36601  7785
#> 
#> $HumanBreastCancerILC
#> [1] 36601  4325

If the return type is the same as the type of the individual experiment objects, they will be combined into ExperimentList object.

#get the first 100 spots
elapply(el, function(x) x[, 1:100])
#> ExperimentList with 2 SpatialExperiments
#> class: SpatialExperimentList 
#> dim: 36601 200 
#> metadata(0):
#> assays(1): counts
#> rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
#>   ENSG00000277196
#> rowData names(1): symbol
#> colnames(200): HumanBreastCancerIDC.AAACAAGTATCTCCCA-1
#>   HumanBreastCancerIDC.AAACACCAATAACTGC-1 ...
#>   HumanBreastCancerILC.AACCCGAGCAGAATCG-1
#>   HumanBreastCancerILC.AACCCTACTGTCAATA-1
#> colData names(1): sample_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor
#> experiments: 2
#> experimentNames (2): HumanBreastCancerIDC HumanBreastCancerILC
#> experimentData names (2): sex age

This function allows the ExperimentList object to be treated as a list. Since the parent class of the object is SpatialExperiment, all functions that work for it automatically work for the ExperimentList object.

#extract image data for each object separately
elapply(el, imgData)
#> $HumanBreastCancerIDC
#> DataFrame with 2 rows and 4 columns
#>               sample_id    image_id   data scaleFactor
#>             <character> <character> <list>   <numeric>
#> 1 HumanBreastCancerIDC1      lowres   ####   0.0247525
#> 2 HumanBreastCancerIDC2      lowres   ####   0.0247525
#> 
#> $HumanBreastCancerILC
#> DataFrame with 1 row and 4 columns
#>                sample_id    image_id   data scaleFactor
#>              <character> <character> <list>   <numeric>
#> 1 HumanBreastCancerILC..      lowres   ####   0.0247525
#extract image data collectively
imgData(el)
#> DataFrame with 3 rows and 4 columns
#>                sample_id    image_id   data scaleFactor
#>              <character> <character> <list>   <numeric>
#> 1  HumanBreastCancerIDC1      lowres   ####   0.0247525
#> 2  HumanBreastCancerIDC2      lowres   ####   0.0247525
#> 3 HumanBreastCancerILC..      lowres   ####   0.0247525

Coercion

ExperimentList objects can be coerced to their parental classes or ExperimentList versions of their parental classes.

#convert to SpatialExperiment
as(el, 'SpatialExperiment')
#> class: SpatialExperiment 
#> dim: 36601 12110 
#> metadata(0):
#> assays(1): counts
#> rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
#>   ENSG00000277196
#> rowData names(1): symbol
#> colnames(12110): HumanBreastCancerIDC.AAACAAGTATCTCCCA-1
#>   HumanBreastCancerIDC.AAACACCAATAACTGC-1 ...
#>   HumanBreastCancerILC.TTGTTTCCATACAACT-1
#>   HumanBreastCancerILC.TTGTTTGTGTAAATTC-1
#> colData names(1): sample_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor
#convert to SummarizedExperiment
as(el, 'SummarizedExperiment')
#> class: SummarizedExperiment 
#> dim: 36601 12110 
#> metadata(0):
#> assays(1): counts
#> rownames: NULL
#> rowData names(0):
#> colnames(12110): HumanBreastCancerIDC.AAACAAGTATCTCCCA-1
#>   HumanBreastCancerIDC.AAACACCAATAACTGC-1 ...
#>   HumanBreastCancerILC.TTGTTTCCATACAACT-1
#>   HumanBreastCancerILC.TTGTTTGTGTAAATTC-1
#> colData names(1): sample_id
#convert to SummarizedExperimentList
as(el, 'SummarizedExperimentList')
#> ExperimentList with 2 SummarizedExperiments
#> class: SummarizedExperimentList 
#> dim: 36601 12110 
#> metadata(0):
#> assays(1): counts
#> rownames: NULL
#> rowData names(0):
#> colnames(12110): HumanBreastCancerIDC.AAACAAGTATCTCCCA-1
#>   HumanBreastCancerIDC.AAACACCAATAACTGC-1 ...
#>   HumanBreastCancerILC.TTGTTTCCATACAACT-1
#>   HumanBreastCancerILC.TTGTTTGTGTAAATTC-1
#> colData names(1): sample_id
#> experiments: 2
#> experimentNames (2): HumanBreastCancerIDC HumanBreastCancerILC
#> experimentData names (2): sex age

The full coercion hierarchy can be explored using the is() function.

is(el)
#>  [1] "SpatialExperimentList"      "SpatialExperiment"         
#>  [3] "ExperimentList"             "SingleCellExperiment"      
#>  [5] "RangedSummarizedExperiment" "SummarizedExperiment"      
#>  [7] "RectangularData"            "Vector"                    
#>  [9] "Annotated"                  "vector_OR_Vector"

Session information

sessionInfo()
#> R Under development (unstable) (2022-03-10 r81874)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.4 LTS
#> 
#> Matrix products: default
#> BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [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] TENxVisiumData_1.3.0        ExperimentHub_2.3.5        
#>  [3] AnnotationHub_3.3.9         BiocFileCache_2.3.4        
#>  [5] dbplyr_2.1.1                ExperimentList_0.99.0      
#>  [7] SpatialExperiment_1.5.4     SingleCellExperiment_1.17.2
#>  [9] SummarizedExperiment_1.25.3 Biobase_2.55.0             
#> [11] GenomicRanges_1.47.6        GenomeInfoDb_1.31.4        
#> [13] IRanges_2.29.1              MatrixGenerics_1.7.0       
#> [15] matrixStats_0.61.0          S4Vectors_0.33.10          
#> [17] BiocGenerics_0.41.2        
#> 
#> loaded via a namespace (and not attached):
#>  [1] rjson_0.2.21                  ellipsis_0.3.2               
#>  [3] rprojroot_2.0.2               scuttle_1.5.0                
#>  [5] XVector_0.35.0                fs_1.5.2                     
#>  [7] bit64_4.0.5                   interactiveDisplayBase_1.33.0
#>  [9] AnnotationDbi_1.57.1          fansi_1.0.2                  
#> [11] R.methodsS3_1.8.1             sparseMatrixStats_1.7.0      
#> [13] cachem_1.0.6                  knitr_1.37                   
#> [15] jsonlite_1.8.0                png_0.1-7                    
#> [17] R.oo_1.24.0                   shiny_1.7.1                  
#> [19] HDF5Array_1.23.2              BiocManager_1.30.16          
#> [21] compiler_4.2.0                httr_1.4.2                   
#> [23] dqrng_0.3.0                   assertthat_0.2.1             
#> [25] Matrix_1.4-0                  fastmap_1.1.0                
#> [27] limma_3.51.5                  cli_3.2.0                    
#> [29] later_1.3.0                   htmltools_0.5.2              
#> [31] tools_4.2.0                   glue_1.6.2                   
#> [33] GenomeInfoDbData_1.2.7        dplyr_1.0.8                  
#> [35] rappdirs_0.3.3                Rcpp_1.0.8.2                 
#> [37] jquerylib_0.1.4               pkgdown_2.0.2.9000           
#> [39] vctrs_0.3.8                   Biostrings_2.63.1            
#> [41] rhdf5filters_1.7.0            DelayedMatrixStats_1.17.0    
#> [43] xfun_0.30                     stringr_1.4.0                
#> [45] beachmat_2.11.0               mime_0.12                    
#> [47] lifecycle_1.0.1               edgeR_3.37.0                 
#> [49] zlibbioc_1.41.0               BiocStyle_2.23.1             
#> [51] ragg_1.2.2                    promises_1.2.0.1             
#> [53] parallel_4.2.0                rhdf5_2.39.6                 
#> [55] yaml_2.3.5                    curl_4.3.2                   
#> [57] memoise_2.0.1                 sass_0.4.0                   
#> [59] stringi_1.7.6                 RSQLite_2.2.10               
#> [61] BiocVersion_3.15.0            desc_1.4.1                   
#> [63] filelock_1.0.2                BiocParallel_1.29.17         
#> [65] rlang_1.0.2                   pkgconfig_2.0.3              
#> [67] systemfonts_1.0.4             bitops_1.0-7                 
#> [69] evaluate_0.15                 lattice_0.20-45              
#> [71] purrr_0.3.4                   Rhdf5lib_1.17.3              
#> [73] bit_4.0.4                     tidyselect_1.1.2             
#> [75] magrittr_2.0.2                R6_2.5.1                     
#> [77] magick_2.7.3                  generics_0.1.2               
#> [79] DelayedArray_0.21.2           DBI_1.1.2                    
#> [81] withr_2.5.0                   pillar_1.7.0                 
#> [83] prettydoc_0.4.1               KEGGREST_1.35.0              
#> [85] RCurl_1.98-1.6                tibble_3.1.6                 
#> [87] crayon_1.5.0                  DropletUtils_1.15.2          
#> [89] utf8_1.2.2                    rmarkdown_2.13               
#> [91] locfit_1.5-9.5                grid_4.2.0                   
#> [93] blob_1.2.2                    digest_0.6.29                
#> [95] xtable_1.8-4                  httpuv_1.6.5                 
#> [97] R.utils_2.11.0                textshaping_0.3.6            
#> [99] bslib_0.3.1

Dharmesh D. Bhuva

15 March 2022

Motivation

Anatomy of an `ExperimentList`

Constructing an `ExperimentList`

Common operations on `ExperimentList`

Subsetting

Getters and setters

Apply

Coercion

Session information

ExperimentList: S4 Class for handling lists of Experiments

Dharmesh D. Bhuva

15 March 2022

Motivation

Anatomy of an ExperimentList

Constructing an ExperimentList

Common operations on ExperimentList

Subsetting

Getters and setters

Apply

Coercion

Session information

Anatomy of an `ExperimentList`

Constructing an `ExperimentList`

Common operations on `ExperimentList`