CoreGx: Class and Function Abstractions for PharmacoGx, RadioGx and ToxicoGx
Petr Smirnov
Ian Smith
Christopher Eeles
Benjamin Haibe-Kains
coreGx.RmdCoreGx
This package provides a foundation for the PharmacoGx, RadioGx and ToxicoGx packages. It is not intended for standalone use, only as a dependency for the aforementioned software. Its existence allows abstracting generic definitions, method definitions and class structures common to all three of the Gx suite packages.
Importing and Using CoreGx
Load the pacakge:
library(CoreGx)
library(Biobase)
library(SummarizedExperiment)The CoreSet Class
The CoreSet class is intended as a general purpose data
structure for storing multiomic treatment response data. Extensions of
this class have been customized for their respective fields of study.
For example, the PharmacoSet class inherits from the
CoreSet and is specialized for storing and analyzing drug
sensitivity and perturbation experiments on cancer cell lines together
with associated multiomic data for each treated sample. The RadioSet
class serves a role similar to the PharmacoSet with radiation instead of
drug treatments. Finally, the ToxicoSet class is used to store toxicity
data for healthy human and rat hepatocytes along with the associated
multiomic profile for each treatment.
getClass("CoreSet")## Class "CoreSet" [package "CoreGx"]
##
## Slots:
##
## Name: treatmentResponse annotation molecularProfiles sample
## Class: list_OR_LongTable list list_OR_MAE data.frame
##
## Name: treatment datasetType perturbation curation
## Class: data.frame character list listThe annotation slot holds the CoreSet name, the original
constructor call, and a range of metadata about the R session in which
the constructor was called. This allows easy comparison of
CoreSet versions across time and ensures the code used to
generate a CoreSet is well-documented and reproducible.
The molecularProfiles slot contains a list of
SummarizedExperiment objects for each multi-omic molecular
datatype available for a given experiment. Within the
SummarizedExperiments are feature and sample annotations
for each data type. We are currently in the process of adopting the
MultiAssayExperiment class instead of a list
for storing molecular profile SummarizedExperiments.
However, the list version of the
molecularProfiles slot is still supported for backwards
compatability.
The sample slot contains a data.frame with
annotations for samples used in the molecularProfiles or sensitivity
slot. It should at minimum have the standardized column ‘sampleid’, with
a unique identifier for each sample in the CoreSet.
The treatment slot contains a data.frame of
metadata for treatments applied to samples in the
molecularProfiles or treatmentResponse slot.
It should at minimum have the standarized column ‘treatmentid’,
containing a unique identifier for each treatment in the
CoreSet.
The datasetType slot contains a character vector
indicating the experiment type the CoreSet contains. This
slot is soft deprecated and may be removed in future updates.
The treatmentResponse slot contains a list of raw,
curated and meta data for treatment-response experiments. We are
currently in the process of adopting our new S4-class, the
TreamtentResponseExperiment to store treatment-response
data within a CoreSet and inheriting classes. However, the
old list format for sensitivity experiments will continue
to be support for backwards compatability.
The perturbation slot contains a list of raw, curated
and meta data for perturbation experiments. This slot is soft-deprecated
and may be removed in the future. The reason is that treatment
perturbation experiments can be efficiently stored in the
colData slot of their respective
SummarizedExperiment objects and thus no longer require
their own space within a CoreSet.
The curation slot contains a list of manually curated
identifiers such as standardized cell-line, tissue and treatment names.
Inclusion of such identifiers ensures a consistent nomenclature is used
across all datasets curated into the classes inheriting from the
CoreSet, enabling results from such datasets to be easily
compared to validate results from published studies or combine them for
use in larger meta-analyses. The slot contains a list of
data.frames, one for each entity, and should at minimum
include a mapping from curated identifiers used throughout the object to
those used in the original dataset publication.
The CoreSet class provides a set of standardized
accessor methods which simplify curation, annotation, and retrieval of
data associated with a specfic treatment response experiment. All
accessors are implemented as generics to allow new methods to be defined
on classes inheriting from the CoreSet.
methods(class="CoreSet")## [1] annotation annotation<- curation
## [4] curation<- datasetType datasetType<-
## [7] dateCreated dateCreated<- featureInfo
## [10] featureInfo<- fNames fNames<-
## [13] mDataNames mDataNames<- molecularProfiles
## [16] molecularProfiles<- molecularProfilesSlot molecularProfilesSlot<-
## [19] name name<- pertNumber
## [22] pertNumber<- phenoInfo phenoInfo<-
## [25] sampleInfo sampleInfo<- sampleNames
## [28] sampleNames<- sensitivityInfo sensitivityInfo<-
## [31] sensitivityMeasures sensitivityMeasures<- sensitivityProfiles
## [34] sensitivityProfiles<- sensitivityRaw sensitivityRaw<-
## [37] sensNumber sensNumber<- show
## [40] subsetByFeature subsetBySample subsetByTreatment
## [43] treatmentInfo treatmentInfo<- treatmentNames
## [46] treatmentNames<- treatmentResponse treatmentResponse<-
## [49] updateObject
## see '?methods' for accessing help and source codeWe have provided a sample CoreSet (cSet) in this
package. In the below code we load the example cSet and demonstrate a
few of the accessor methods.
data(clevelandSmall_cSet)
clevelandSmall_cSet## <CoreSet>
## Name: Cleveland
## Date Created: Sat Feb 18 15:10:56 2023
## Number of samples: 10
## Molecular profiles: <MultiAssayExperiment>
## ExperimentList class object of length 2:
## [1] rna : SummarizedExperiment with 1000 rows and 9 columns
## [2] rnaseq : SummarizedExperiment with 1000 rows and 9 columns
## Treatment response: <TreatmentResponseExperiment>
## dim: 9 10
## assays(2): sensitivity profiles
## rownames(9): radiation:1:1 radiation:1:2 ... radiation:8:1 radiation:10:1
## rowData(3): treatment1id treatment1dose replicate_id
## colnames(10): CHP-212 IMR-32 KP-N-S19s ... SK-N-SH SNU-245 SNU-869
## colData(2): sampleid rn
## metadata(1): experiment_metadataAccess a specific molecular profiles:
mProf <- molecularProfiles(clevelandSmall_cSet, "rna")
mProf[seq_len(5), seq_len(5)]## NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152
## ENSG00000000003 10.280970
## ENSG00000000005 3.647436
## ENSG00000000419 11.883769
## ENSG00000000457 7.515721
## ENSG00000000460 7.808139
## GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654
## ENSG00000000003 10.304971
## ENSG00000000005 4.895494
## ENSG00000000419 11.865191
## ENSG00000000457 7.187144
## ENSG00000000460 7.789921
## BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860
## ENSG00000000003 9.596987
## ENSG00000000005 3.793174
## ENSG00000000419 12.498285
## ENSG00000000457 8.076655
## ENSG00000000460 8.456691
## SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474
## ENSG00000000003 8.620860
## ENSG00000000005 3.674918
## ENSG00000000419 11.674671
## ENSG00000000457 6.790332
## ENSG00000000460 6.663846
## WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582
## ENSG00000000003 9.866551
## ENSG00000000005 3.748959
## ENSG00000000419 12.228260
## ENSG00000000457 7.292420
## ENSG00000000460 8.869378Access cell-line metadata:
cInfo <- sampleInfo(clevelandSmall_cSet)
cInfo[seq_len(5), seq_len(5)]## sampleid tissueid CellLine Primarysite Histology
## SK-N-FI SK-N-FI autonomic_ganglia SKNFI autonomic_ganglia neuroblastoma
## IMR-32 IMR-32 autonomic_ganglia IMR32 autonomic_ganglia neuroblastoma
## SK-N-AS SK-N-AS autonomic_ganglia SKNAS autonomic_ganglia neuroblastoma
## CHP-212 CHP-212 autonomic_ganglia CHP212 autonomic_ganglia neuroblastoma
## KP-N-S19s KP-N-S19s autonomic_ganglia KPNSI9S autonomic_ganglia neuroblastomaAccess treatment-response data:
sensProf <- sensitivityProfiles(clevelandSmall_cSet)
sensProf[seq_len(5), seq_len(5)]## [1] 1 2 3 4 5For more information about the accessor methods available for the
CoreSet class please see the class?CoreSet
help page.
Extending the CoreSet Class
Given that the CoreSet class is intended for extension, we will show some examples of how to define a new class based on it and implement new methods for the generics provided for the CoreSet class.
Here we will define a new class, the DemoSet, with an
additional slot, the demoSlot. We will then view the
available methods for this class as well as define new S4 methods on
it.
DemoSet <- setClass("DemoSet",
representation(demoSlot="character"),
contains="CoreSet")
getClass("DemoSet")## Class "DemoSet" [in ".GlobalEnv"]
##
## Slots:
##
## Name: demoSlot treatmentResponse annotation molecularProfiles
## Class: character list_OR_LongTable list list_OR_MAE
##
## Name: sample treatment datasetType perturbation
## Class: data.frame data.frame character list
##
## Name: curation
## Class: list
##
## Extends: "CoreSet"Here we can see the class extending CoreSet has all of
the same slots as the original CoreSet, plus the new slot
we defined: demoSlot.
We can see which methods are available for this new class.
methods(class="DemoSet")## [1] annotation annotation<- curation
## [4] curation<- datasetType datasetType<-
## [7] dateCreated dateCreated<- featureInfo
## [10] featureInfo<- fNames fNames<-
## [13] mDataNames mDataNames<- molecularProfiles
## [16] molecularProfiles<- molecularProfilesSlot molecularProfilesSlot<-
## [19] name name<- pertNumber
## [22] pertNumber<- phenoInfo phenoInfo<-
## [25] sampleInfo sampleInfo<- sampleNames
## [28] sampleNames<- sensitivityInfo sensitivityInfo<-
## [31] sensitivityMeasures sensitivityMeasures<- sensitivityProfiles
## [34] sensitivityProfiles<- sensitivityRaw sensitivityRaw<-
## [37] sensNumber sensNumber<- show
## [40] subsetByFeature subsetBySample subsetByTreatment
## [43] treatmentInfo treatmentInfo<- treatmentNames
## [46] treatmentNames<- treatmentResponse treatmentResponse<-
## [49] updateObject
## see '?methods' for accessing help and source codeWe see that all the accessors defined for the CoreSet
are also defined for the inheriting DemoSet. These methods
all assume the inherit slots have the same structure as the
CoreSet. If this is not true, for example, if
molecularProfiles holds ExpressionSets instead of
SummarizedExperiments, we can redefine existing methods as
follows:
clevelandSmall_dSet <- DemoSet(clevelandSmall_cSet)
class(clevelandSmall_dSet@molecularProfiles[['rna']])## [1] "SummarizedExperiment"
## attr(,"package")
## [1] "SummarizedExperiment"
expressionSets <- lapply(molecularProfilesSlot(clevelandSmall_dSet), FUN=as,
'ExpressionSet')
molecularProfilesSlot(clevelandSmall_dSet) <- expressionSets
# Now this will error
tryCatch({molecularProfiles(clevelandSmall_dSet, 'rna')},
error=function(e)
print(paste("Error: ", e$message)))## [1] "Error: unable to find an inherited method for function 'assay' for signature '\"ExpressionSet\", \"numeric\"'"Since we changed the data in the molecularProfiles slot
of the DemoSet, the original method from
CoreGx no longer works. Thus we get an error when trying to
access that slot. To fix this we will need to set a new S4 method for
the molecularProfiles generic function defined in
CoreGx.
setMethod(molecularProfiles,
signature("DemoSet"),
function(object, mDataType) {
pData(object@molecularProfiles[[mDataType]])
})This new method is now called whenever we use the
molecularProfiles method on a DemoSet. Since
the new method uses ExpressionSet accessor methods instead
of SummarizedExperiment accessor methods, we now expect to
be able to access the data in our modified slot.
# Now we test our new method
mProf <- molecularProfiles(clevelandSmall_dSet, 'rna')
head(mProf)[seq_len(5), seq_len(5)]## samplename
## NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 NIECE_p_NCLE_RNA3_HG-U133_Plus_2_G10_296152
## GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 GILDS_p_NCLE_RNA11_Redo_HG-U133_Plus_2_G02_587654
## BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 BUNDS_p_NCLE_RNA5_HG-U133_Plus_2_B11_419860
## SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 SILOS_p_NCLE_RNA9_HG-U133_Plus_2_A04_523474
## WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 WATCH_p_NCLE_RNA8_HG-U133_Plus_2_B04_474582
## filename
## NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 NIECE_p_NCLE_RNA3_HG-U133_Plus_2_G10_296152.CEL.gz
## GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 GILDS_p_NCLE_RNA11_Redo_HG-U133_Plus_2_G02_587654.CEL.gz
## BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 BUNDS_p_NCLE_RNA5_HG-U133_Plus_2_B11_419860.CEL.gz
## SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 SILOS_p_NCLE_RNA9_HG-U133_Plus_2_A04_523474.CEL.gz
## WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 WATCH_p_NCLE_RNA8_HG-U133_Plus_2_B04_474582.CEL.gz
## chiptype
## NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 HG-U133_Plus_2
## GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 HG-U133_Plus_2
## BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 HG-U133_Plus_2
## SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 HG-U133_Plus_2
## WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 HG-U133_Plus_2
## hybridization.date
## NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 07/15/08
## GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 2010-05-21
## BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 12/19/08
## SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 2009-12-08
## WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 2009-08-14
## hybridization.hour
## NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 12:54:10
## GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 16:45:06Z
## BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 11:43:19
## SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 20:44:59Z
## WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 17:15:45ZWe can see our new method works! In order to finish updating the methods for our new class, we would have to redefine all the methods which access the modified slot.
However, additional work needs to be done to define accessors for the
new demoSlot. Since no generics are available in CoreGx to
access this slot, we need to first define a generic, then implement
methods which dispatch on the ‘DemoSet’ class to retrieve data in the
slot.
# Define generic for setter method
setGeneric('demoSlot<-', function(object, value) standardGeneric('demoSlot<-'))## [1] "demoSlot<-"
# Define a setter method
setReplaceMethod('demoSlot',
signature(object='DemoSet', value="character"),
function(object, value) {
object@demoSlot <- value
return(object)
})
# Lets add something to our demoSlot
demoSlot(clevelandSmall_dSet) <- c("This", "is", "the", "demoSlot")
# Define generic for getter method
setGeneric('demoSlot', function(object, ...) standardGeneric("demoSlot"))## [1] "demoSlot"
# Define a getter method
setMethod("demoSlot",
signature("DemoSet"),
function(object) {
paste(object@demoSlot, collapse=" ")
})
# Test our getter method
demoSlot(clevelandSmall_dSet)## [1] "This is the demoSlot"Now you should have all the knowledge you need to extend the CoreSet class for use in other treatment-response experiments!
For more information about this package and the possibility of collaborating on its extension please contact benjamin.haibe.kains@utoronto.ca.
sessionInfo
## R version 4.3.2 (2023-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
##
## 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
##
## time zone: UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CoreGx_2.7.4 SummarizedExperiment_1.32.0
## [3] Biobase_2.62.0 GenomicRanges_1.54.1
## [5] GenomeInfoDb_1.38.8 IRanges_2.36.0
## [7] S4Vectors_0.40.2 MatrixGenerics_1.14.0
## [9] matrixStats_1.2.0 BiocGenerics_0.48.1
## [11] knitr_1.45 BiocStyle_2.30.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 rlang_1.1.3
## [3] magrittr_2.0.3 shinydashboard_0.7.2
## [5] compiler_4.3.2 systemfonts_1.0.6
## [7] vctrs_0.6.5 relations_0.6-13
## [9] pkgconfig_2.0.3 crayon_1.5.2
## [11] fastmap_1.1.1 backports_1.4.1
## [13] XVector_0.42.0 ellipsis_0.3.2
## [15] caTools_1.18.2 utf8_1.2.4
## [17] promises_1.2.1 rmarkdown_2.26
## [19] ragg_1.3.0 purrr_1.0.2
## [21] xfun_0.42 MultiAssayExperiment_1.28.0
## [23] zlibbioc_1.48.2 cachem_1.0.8
## [25] jsonlite_1.8.8 SnowballC_0.7.1
## [27] later_1.3.2 DelayedArray_0.28.0
## [29] BiocParallel_1.36.0 parallel_4.3.2
## [31] sets_1.0-25 cluster_2.1.6
## [33] R6_2.5.1 bslib_0.6.2
## [35] limma_3.58.1 jquerylib_0.1.4
## [37] Rcpp_1.0.12 bookdown_0.38
## [39] BiocBaseUtils_1.4.0 httpuv_1.6.14
## [41] Matrix_1.6-5 igraph_2.0.3
## [43] tidyselect_1.2.1 abind_1.4-5
## [45] yaml_2.3.8 gplots_3.1.3.1
## [47] codetools_0.2-19 lattice_0.22-6
## [49] tibble_3.2.1 shiny_1.8.0
## [51] BumpyMatrix_1.10.0 evaluate_0.23
## [53] desc_1.4.3 bench_1.1.3
## [55] pillar_1.9.0 lsa_0.73.3
## [57] BiocManager_1.30.22 KernSmooth_2.23-22
## [59] checkmate_2.3.1 DT_0.32
## [61] shinyjs_2.1.0 piano_2.18.0
## [63] generics_0.1.3 RCurl_1.98-1.14
## [65] ggplot2_3.5.0 munsell_0.5.0
## [67] scales_1.3.0 gtools_3.9.5
## [69] xtable_1.8-4 marray_1.80.0
## [71] glue_1.7.0 slam_0.1-50
## [73] tools_4.3.2 data.table_1.15.2
## [75] fgsea_1.28.0 visNetwork_2.1.2
## [77] fs_1.6.3 fastmatch_1.1-4
## [79] cowplot_1.1.3 grid_4.3.2
## [81] colorspace_2.1-0 GenomeInfoDbData_1.2.11
## [83] cli_3.6.2 textshaping_0.3.7
## [85] fansi_1.0.6 S4Arrays_1.2.1
## [87] dplyr_1.1.4 gtable_0.3.4
## [89] sass_0.4.9 digest_0.6.35
## [91] SparseArray_1.2.4 htmlwidgets_1.6.4
## [93] memoise_2.0.1 htmltools_0.5.7
## [95] pkgdown_2.0.7 lifecycle_1.0.4
## [97] statmod_1.5.0 mime_0.12