The TreatmentResponseExperiment Class
1 November 2022
Package
CoreGx 2.2.0
Contents
1 Why Do We Need A New Class?
The current implementation for the @treatmentResponse slot in a
PharmacoSet has some limitations.
Firstly, it does not natively support dose-response experiments with
multiple drugs and/or cancer cell lines. As a result we have not been
able to include this data into a PharmacoSet thus far.
Secondly, drug combination data has the potential to scale to high dimensionality. As a result we need an object that is highly performant to ensure computations on such data can be completed in a timely manner.
To resolve these issues, we designed and implement the
TreamtentResponseExperiment (or TRE for short)!
2 Design Philosophy
The current use case is supporting drug combinations experiments in
PharmacoGx, but we wanted to create something flexible
enough to fit other use cases. As such, we have used the generic term
‘treatment’ to refer to any experimental intervention one can conduct on a
set of samples. In context of PharmacoGx, a
treatment represents application of one or more anti-cancer compounds to a
cancer cell-line. The resulting viability for this cell-line
after treatment is the response metric. We hope that the implementation of
our class is general enough to support other use cases. For example, the
TreatmentResponseExperiment class is also being adopted for radiation
dose-response experiments in cancer cell-lines in
RadioGx as well as for investigating compound
toxicity in healthy human and rat cell-lines in
ToxicoGx.
Our design takes the aspects of the SummarizedExperiment and
MultiAssayExperiment classes and implements them using the data.table
package, which provides an R API to a rich set of tools for scalable,
high performance data processing implemented in C.
3 Anatomy of a TreatmentResponseExperiment
3.1 Class Diagram
We have borrowed directly from the SummarizedExperiment class
for the rowData, colData, metadata and assays slot names.
We also implemented the SummarizedExperiment accessor methods for the
TreatmentResponseExperiment. Therefore the interface should be familiar to
users of common Bioconductor packages.
3.2 Object Structure and Cardinality
There are, however, some important differences which make this object more flexible when dealing with high dimensional data.
Unlike a SummarizedExperiment, there are three distinct
subgroups of columns in rowData and colData.
The first are the rowKey and colKey which are implemented internally to
map between each assay observation and its associated treatments or samples
(rows or columns); these will not be returned by the accessors by default.
The second are the rowIDs and colIDs, these hold all of the information
necessary to uniquely identify a row or column and are used to generate the
rowKey and colKey. Finally, there are the rowMeta and colMeta columns,
which store any additional data about treatments or samples not required to
uniquely identify a row in either table.
Within the TreatmentResponseExperiment, an assayIndex is stored in the
@.intern slot which maps between unique combinations of rowKey and colKey
and the experimental observations in each assay. This relationship is maintained
using a separate primary key for each assay, which can map to one or more rowKey
and colKey combination. For assays containing raw experimental observations,
generally each assay row will map to one and only one combination of rowKey
and colKey. However, for metrics computed over experimental observations, It
may be desirable to summarized over some of the rowID and/or colID columns.
In this case, the relationship between the summarized rows and the metadata
stored in the rowData and colData slots are retained in the assayIndex,
allowing
Also worth noting is the cardinality between rowData and colData for a given
assay within the assays list. As indicated by the lower connection between these
tables and an assay, for each row or column key there may be zero or more rows in
the assay table. Conversely for each row in the assay there may be zero or one key
in colData or rowData. When combined, the rowKey and colKey for a given
row in an assay become a composite key which uniquely identify an observation.
4 Constructing a TreatmentResponseExperiment
To deal with the complex kinds of experimental designs which can be stored
in a LongTable, we have engineered a new object to help document and validate
the way data is mapped from raw data files, as a single large data.frame or
data.table, to the various slots of a TreatmentResponseExperiment object.
4.1 The DataMapper Class
The DataMapper is an abstract class, which means in cannot be instatiated.
Its purpose is to provide a description of the concept of a DataMapper and
define a basic interface for any classes inheriting from it. A DataMapper is
simply a way to map columns from some raw data file to the slots of an S4 class.
It is similar to a schema in SQL in that it defines the valid parts of an
object (analogously a SQL table), but differs in that no types are specified or
enforced at this time.
This object is not important for general users, but may be useful for other
developers who want to map from some raw data to some S4 class. In this case,
any derived data mapper should inherit from the DataMapper abstract class.
Only one slot is defined by default, a list or List in the @rawdata slot.
An accessor method, rawdata(DataMapper), is defined to assign and retrieve
the raw data from your mapper object.
4.2 The TREDataMapper Class
The TREDataMapper class is the first concrete sub-class of a
DataMapper. It is the object which defines how to go from a single
data.frame or data.table of raw experimental data to a properly formatted
and valid TreatmentResponseExperiment object. This is accomplished by defining
various mappings, which let the the user decide which columns from rawdata
should go into which slots of the object. Each slot mapping is implemented as a
list of character vectors specifying the column names from rawdata to assign
to each slot.
Additionally, a helper method has been included, guessMapping, that will
try to determine which columns of a TreatmentResponseExperiments rawdata
should be assigned to which slots, and therefore which maps.
To get started making a TreatmentResponseExperiment lets have a look at some
rawdata which is a subset of the data from Oneil et al., 2016. The full set
of rawdata is available for exploration and download from
SynergxDB.ca, a free and open source web-app and
database of publicly available drug combination sensitivity experiments which we
created and released (Seo et al., 2019).
The data was generated as part of the commercial activities of the pharmaceutical company Merck, and is thus named according.
filePath <- system.file('extdata', 'merckLongTable.csv', package='CoreGx',
mustWork=TRUE)
merckDT <- fread(filePath, na.strings=c('NULL'))
colnames(merckDT)## [1] "drug1id" "drug2id" "drug1dose"
## [4] "drug2dose" "combination_name" "cellid"
## [7] "batchid" "viability1" "viability2"
## [10] "viability3" "viability4" "mu/muMax_published"
## [13] "X/X0_published"| drug1id | drug2id | drug1dose | drug2dose | combination_name |
|---|---|---|---|---|
| 5-FU | Bortezomib | 0.35 | 0.00045 | 5-FU & Bortezomib |
| 5-FU | Bortezomib | 0.35 | 0.00200 | 5-FU & Bortezomib |
| 5-FU | Bortezomib | 0.35 | 0.00900 | 5-FU & Bortezomib |
| 5-FU | Bortezomib | 0.35 | 0.04000 | 5-FU & Bortezomib |
| 5-FU | L778123 | 0.35 | 0.32500 | 5-FU & L778123 |
| 5-FU | L778123 | 0.35 | 0.80000 | 5-FU & L778123 |
| combination_name | cellid | batchid | viability1 | viability2 | viability3 | viability4 | mu/muMax_published | X/X0_published |
|---|---|---|---|---|---|---|---|---|
| 5-FU & Bortezomib | A2058 | 1 | 0.814 | 0.754 | 0.765 | 0.849 | 0.880 | 0.847 |
| 5-FU & Bortezomib | A2058 | 1 | 0.792 | 0.788 | 0.840 | 0.852 | 0.897 | 0.867 |
| 5-FU & Bortezomib | A2058 | 1 | 0.696 | 0.831 | 0.690 | 0.806 | 0.854 | 0.817 |
| 5-FU & Bortezomib | A2058 | 1 | 0.637 | 0.678 | 0.625 | 0.627 | 0.767 | 0.724 |
| 5-FU & L778123 | A2058 | 1 | 0.679 | 0.795 | 0.731 | 0.700 | 0.830 | 0.790 |
| 5-FU & L778123 | A2058 | 1 | 0.667 | 0.734 | 0.596 | 0.613 | 0.773 | 0.730 |
We can see that all the data related to the treatment response experiment is contained within this table.
To get an idea of where in a TreatmentResponseExperiment this data should go, lets come up
with some guesses for mappings.
# Our guesses of how we may identify rows, columns and assays
groups <- list(
justDrugs=c('drug1id', 'drug2id'),
drugsAndDoses=c('drug1id', 'drug2id', 'drug1dose', 'drug2dose'),
justCells=c('cellid'),
cellsAndBatches=c('cellid', 'batchid'),
assays1=c('drug1id', 'drug2id', 'cellid'),
assays2=c('drug1id', 'drug2id', 'drug1dose', 'drug2dose', 'cellid', 'batchid')
)
# Decide if we want to subset out mapped columns after each group
subsets <- c(FALSE, TRUE, FALSE, TRUE, FALSE, TRUE)
# First we put our data in the `TRE`
TREdataMapper <- TREDataMapper(rawdata=merckDT)
# Then we can test our hypotheses, subset=FALSE means we don't remove mapped
# columns after each group is mapped
guess <- guessMapping(TREdataMapper, groups=groups, subset=subsets)## [CoreGx::guessMapping,LongTableDataMapper-method]
## Mapping for group justDrugs: drug1id, drug2id## [CoreGx::guessMapping,LongTableDataMapper-method]
## Mapping for group drugsAndDoses: drug1id, drug2id, drug1dose, drug2dose## [CoreGx::guessMapping,LongTableDataMapper-method]
## Mapping for group justCells: cellid## [CoreGx::guessMapping,LongTableDataMapper-method]
## Mapping for group cellsAndBatches: cellid, batchid## [CoreGx::guessMapping,LongTableDataMapper-method]
## Mapping for group assays1: drug1id, drug2id, cellid## [CoreGx::guessMapping,LongTableDataMapper-method]
## Mapping for group assays2: drug1id, drug2id, drug1dose, drug2dose, cellid, batchidguess## $metadata
## $metadata$id_columns
## [1] NA
##
## $metadata$mapped_columns
## character(0)
##
##
## $justDrugs
## $justDrugs$id_columns
## [1] "drug1id" "drug2id"
##
## $justDrugs$mapped_columns
## [1] "combination_name"
##
##
## $drugsAndDoses
## $drugsAndDoses$id_columns
## [1] "drug1id" "drug2id" "drug1dose" "drug2dose"
##
## $drugsAndDoses$mapped_columns
## [1] "combination_name"
##
##
## $justCells
## $justCells$id_columns
## [1] "cellid"
##
## $justCells$mapped_columns
## character(0)
##
##
## $cellsAndBatches
## $cellsAndBatches$id_columns
## [1] "cellid" "batchid"
##
## $cellsAndBatches$mapped_columns
## character(0)
##
##
## $assays1
## $assays1$id_columns
## [1] "drug1id" "drug2id" "cellid"
##
## $assays1$mapped_columns
## character(0)
##
##
## $assays2
## $assays2$id_columns
## [1] "drug1id" "drug2id" "drug1dose" "drug2dose" "cellid" "batchid"
##
## $assays2$mapped_columns
## [1] "viability1" "viability2" "viability3"
## [4] "viability4" "mu/muMax_published" "X/X0_published"
##
##
## $unmapped
## character(0)Since we want our TreatmentResponseExperiment to have drugs as rows and
samples as columns, we see that both justDrug and drugsAndDoses yield the
same result. So we do not yet prefer one over the other. Looking at justCells
and cellsAndBatches, we see one column maps to each of them and therefore still
have no preference. For assay1 however, we see that no columns mapped, while
assay2 maps many of raw data columns.
Since assays will be subset based on the rowKey and colKey, we know that
the rowIDs must be drugsAndDose and the the colIDs must be cellsAndBatches.
Therefore, to uniquely identify an observation in any given assay we need
all of these columns. We can use this information to assign maps to our
TREDataMapper.
rowDataMap(TREdataMapper) <- guess$drugsAndDose
colDataMap(TREdataMapper) <- guess$cellsAndBatchesLooking at our mapped columns for assay2, we must decide if we want these
to go into more than one assay. If we do, we should name each item of our
assayMap for the TREDataMapper and specify it in a list of
character vectors, one for each assay. Since viability is the raw experimental
measurement and the final two columns are summaries of it, we will assign them
to two assays:sensitivity and profiles.
assays <- list(
sensitivity=list(
guess$assays2[[1]],
guess$assays2[[2]][seq_len(4)]
),
profiles=list(
guess$assays2[[1]],
guess$assays2[[2]][c(5, 6)]
)
)
assays## $sensitivity
## $sensitivity[[1]]
## [1] "drug1id" "drug2id" "drug1dose" "drug2dose" "cellid" "batchid"
##
## $sensitivity[[2]]
## [1] "viability1" "viability2" "viability3" "viability4"
##
##
## $profiles
## $profiles[[1]]
## [1] "drug1id" "drug2id" "drug1dose" "drug2dose" "cellid" "batchid"
##
## $profiles[[2]]
## [1] "mu/muMax_published" "X/X0_published"assayMap(TREdataMapper) <- assays4.3 metaConstruct Method
The metaConstruct method accepts a DataMapper object as its only argument,
and uses the information in that DataMapper to preprocess all rawdata and
map them to the appropriate slots of an S4 object. In our case, we are mapping
from the merckDT data.table to a TreatmentResponseExperiment.
At minimum, a TREDataMapper must specify the rowDataMap, colDataMap,
and assayMap. Additional maps are available, see ?TREDataMapper-class
and ?TREDataMapper-accessors for more details.
After configuration, creating the object is very straight forward.
tre <- metaConstruct(TREdataMapper)5 TreatmentResponseExperiment Object
As mentioned previously, a TreatmentResponseExperiment has both list and
table like behaviours. For table like operations, a given
TreatmentResponseExperiment can be thought of as a rowKey by
colKey rectangular object.
To support data.frame like sub-setting for this object, the constructor makes
pseudo row and column names, which are the ID columns for each row of
rowData or colData pasted together with a ‘:’. The ordering of these
columns is preserved in the pseudo-dim names, so be sure to arrange them
as desirged before creating the TreatmentResponseExperiment.
5.1 Row and Column Names
head(rownames(tre))## [1] "5-FU:Bortezomib:0.35:0.00045" "5-FU:Bortezomib:0.35:0.002"
## [3] "5-FU:Bortezomib:0.35:0.009" "5-FU:Bortezomib:0.35:0.04"
## [5] "5-FU:L778123:0.35:0.325" "5-FU:L778123:0.35:0.8"We see that the rownames for the Merck TreatmentResponseExperiment are the cell-line name
pasted to the batch id.
head(colnames(tre))## [1] "A2058:1" "A2058:3" "A2780:1" "A2780:2" "A375:1" "A375:2"For the column names, a similar pattern is followed by combining the colID columns in the form ‘drug1:drug2:drug1dose:drug2dose’.
5.2 data.frame Subsetting
We can subset a TreatmentResponseExperiment using the same row and column name syntax as
with a data.frame or matrix.
row <- rownames(tre)[1]
columns <- colnames(tre)[1:2]
tre[row, columns]## <TreatmentResponseExperiment>
## dim: 1 1
## assays(2): sensitivity profiles
## rownames(1): 5-FU:Bortezomib:0.35:0.00045
## rowData(5): drug1id drug2id drug1dose drug2dose combination_name
## colnames(1): A2058:1
## colData(2): cellid batchid
## metadata(0): none5.2.1 Regex Queries
However, unlike a data.frame or matrix this subsetting also accepts partial
row and column names as well as regex queries.
head(rowData(tre), 3)## drug1id drug2id drug1dose drug2dose combination_name
## 1: 5-FU Bortezomib 0.35 0.00045 5-FU & Bortezomib
## 2: 5-FU Bortezomib 0.35 0.00200 5-FU & Bortezomib
## 3: 5-FU Bortezomib 0.35 0.00900 5-FU & Bortezomibhead(colData(tre), 3)## cellid batchid
## 1: A2058 1
## 2: A2058 3
## 3: A2780 1For example, if we want to get all instance where ‘5-FU’ is the drug:
tre['5-FU', ]## <TreatmentResponseExperiment>
## dim: 21 5
## assays(2): sensitivity profiles
## rownames(21): 5-FU:Bortezomib:0.35:0.00045 5-FU:Bortezomib:0.35:0.002 ... 5-FU:geldanamycin:0.35:2 MK-4541:5-FU:0.045:10
## rowData(5): drug1id drug2id drug1dose drug2dose combination_name
## colnames(5): A2058:1 A2780:1 A375:1 A427:1 CAOV3:1
## colData(2): cellid batchid
## metadata(0): noneThis has matched all colnames where 5-FU was in either drug1 or drug2. If we only want to match drug1, we have several options:
all.equal(tre['5-FU:*:*:*', ], tre['^5-FU', ])## [1] TRUEAs a technicaly note, ‘*’ is replaced with ‘.*’ internally for regex queries. This was implemented to mimic the linux shell style patten matching that most command-line users are familiar with.
5.3 data.table Subsetting
In addition to regex queries, a TreatmentResponseExperiment object supports
arbitrarily complex subset queries using the data.table API. To access this
API, you will need to use the . function, which allows you to pass raw R
expressions to be evaluated inside the i and j arguments for
dataTable[i, j].
For example if we want to subset to rows where the cell line is VCAP and columns where drug1 is Temozolomide and drug2 is either Lapatinib or Bortezomib:
tre[
# row query
.(drug1id == 'Temozolomide' & drug2id %in% c('Lapatinib', 'Bortezomib')),
.(cellid == 'CAOV3') # column query
]## <TreatmentResponseExperiment>
## dim: 8 1
## assays(2): sensitivity profiles
## rownames(8): Temozolomide:Bortezomib:2.75:0.00045 Temozolomide:Bortezomib:2.75:0.002 ... Temozolomide:Lapatinib:2.75:1.1 Temozolomide:Lapatinib:2.75:5
## rowData(5): drug1id drug2id drug1dose drug2dose combination_name
## colnames(1): CAOV3:1
## colData(2): cellid batchid
## metadata(0): noneWe can also invert matches or subset on other columns in rowData or colData:
subTRE <- tre[
.(drug1id == 'Temozolomide' & drug2id != 'Lapatinib'),
.(batchid != 2)
]To show that this works as expected:
print(paste0('drug2id: ', paste0(unique(rowData(subTRE)$drug2id),
collapse=', ')))## [1] "drug2id: ABT-888, BEZ-235, Bortezomib, Dasatinib, Erlotinib, MK-2206, MK-5108, MK-8669, MK-8776, PD325901, SN-38, Sorafenib, geldanamycin"print(paste0('batchid: ', paste0(unique(colData(subTRE)$batchid),
collapse=', ')))## [1] "batchid: 1"6 Accessor Methods
6.1 rowData
head(rowData(tre), 3)## drug1id drug2id drug1dose drug2dose combination_name
## 1: 5-FU Bortezomib 0.35 0.00045 5-FU & Bortezomib
## 2: 5-FU Bortezomib 0.35 0.00200 5-FU & Bortezomib
## 3: 5-FU Bortezomib 0.35 0.00900 5-FU & Bortezomibhead(rowData(tre, key=TRUE), 3)## drug1id drug2id drug1dose drug2dose combination_name rowKey
## 1: 5-FU Bortezomib 0.35 0.00045 5-FU & Bortezomib 1
## 2: 5-FU Bortezomib 0.35 0.00200 5-FU & Bortezomib 2
## 3: 5-FU Bortezomib 0.35 0.00900 5-FU & Bortezomib 36.2 colData
head(colData(tre), 3)## cellid batchid
## 1: A2058 1
## 2: A2058 3
## 3: A2780 1head(colData(tre, key=TRUE), 3)## cellid batchid colKey
## 1: A2058 1 1
## 2: A2058 3 2
## 3: A2780 1 36.3 assays
assays <- assays(tre)
assays[[1]]## drug1id drug2id drug1dose drug2dose cellid batchid
## 1: 5-FU Bortezomib 0.3500 0.00045 A2058 1
## 2: 5-FU Bortezomib 0.3500 0.00045 A2780 1
## 3: 5-FU Bortezomib 0.3500 0.00045 A375 1
## 4: 5-FU Bortezomib 0.3500 0.00045 A427 1
## 5: 5-FU Bortezomib 0.3500 0.00045 CAOV3 1
## ---
## 3796: geldanamycin Topotecan 0.0223 0.07750 A2058 1
## 3797: geldanamycin Topotecan 0.0223 0.07750 A2780 1
## 3798: geldanamycin Topotecan 0.0223 0.07750 A375 1
## 3799: geldanamycin Topotecan 0.0223 0.07750 A427 1
## 3800: geldanamycin Topotecan 0.0223 0.07750 CAOV3 1
## combination_name viability1 viability2 viability3 viability4
## 1: 5-FU & Bortezomib 0.814 0.754 0.765 0.849
## 2: 5-FU & Bortezomib 0.214 0.195 0.186 0.223
## 3: 5-FU & Bortezomib 1.064 1.080 1.082 1.009
## 4: 5-FU & Bortezomib 0.675 0.582 0.482 0.516
## 5: 5-FU & Bortezomib 0.845 0.799 0.799 0.759
## ---
## 3796: geldanamycin & Topotecan 0.090 0.043 0.112 0.103
## 3797: geldanamycin & Topotecan 0.025 0.022 0.029 0.023
## 3798: geldanamycin & Topotecan 0.151 0.146 0.144 0.171
## 3799: geldanamycin & Topotecan 0.142 0.166 0.124 0.175
## 3800: geldanamycin & Topotecan 0.091 0.084 0.134 0.119assays[[2]]## drug1id drug2id drug1dose drug2dose cellid batchid
## 1: 5-FU Bortezomib 0.3500 0.00045 A2058 1
## 2: 5-FU Bortezomib 0.3500 0.00045 A2780 1
## 3: 5-FU Bortezomib 0.3500 0.00045 A375 1
## 4: 5-FU Bortezomib 0.3500 0.00045 A427 1
## 5: 5-FU Bortezomib 0.3500 0.00045 CAOV3 1
## ---
## 3796: geldanamycin Topotecan 0.0223 0.07750 A2058 1
## 3797: geldanamycin Topotecan 0.0223 0.07750 A2780 1
## 3798: geldanamycin Topotecan 0.0223 0.07750 A375 1
## 3799: geldanamycin Topotecan 0.0223 0.07750 A427 1
## 3800: geldanamycin Topotecan 0.0223 0.07750 CAOV3 1
## combination_name mu/muMax_published X/X0_published
## 1: 5-FU & Bortezomib 0.880 0.847
## 2: 5-FU & Bortezomib 0.384 0.426
## 3: 5-FU & Bortezomib 1.033 1.047
## 4: 5-FU & Bortezomib 0.676 0.638
## 5: 5-FU & Bortezomib 0.708 0.667
## ---
## 3796: geldanamycin & Topotecan -0.187 0.193
## 3797: geldanamycin & Topotecan -0.445 0.135
## 3798: geldanamycin & Topotecan 0.090 0.283
## 3799: geldanamycin & Topotecan -0.012 0.246
## 3800: geldanamycin & Topotecan -1.935 0.017assays <- assays(tre, withDimnames=TRUE)
colnames(assays[[1]])## [1] "drug1id" "drug2id" "drug1dose" "drug2dose"
## [5] "cellid" "batchid" "combination_name" "viability1"
## [9] "viability2" "viability3" "viability4"assays <- assays(tre, withDimnames=TRUE, metadata=TRUE)
colnames(assays[[2]])## [1] "drug1id" "drug2id" "drug1dose"
## [4] "drug2dose" "cellid" "batchid"
## [7] "combination_name" "mu/muMax_published" "X/X0_published"assayNames(tre)## [1] "sensitivity" "profiles"Using these names we can access specific assays within a
TreatmentResponseExperiment.
6.4 assay
colnames(assay(tre, 'sensitivity'))## [1] "drug1id" "drug2id" "drug1dose" "drug2dose"
## [5] "cellid" "batchid" "combination_name" "viability1"
## [9] "viability2" "viability3" "viability4"assay(tre, 'sensitivity')## drug1id drug2id drug1dose drug2dose cellid batchid
## 1: 5-FU Bortezomib 0.3500 0.00045 A2058 1
## 2: 5-FU Bortezomib 0.3500 0.00045 A2780 1
## 3: 5-FU Bortezomib 0.3500 0.00045 A375 1
## 4: 5-FU Bortezomib 0.3500 0.00045 A427 1
## 5: 5-FU Bortezomib 0.3500 0.00045 CAOV3 1
## ---
## 3796: geldanamycin Topotecan 0.0223 0.07750 A2058 1
## 3797: geldanamycin Topotecan 0.0223 0.07750 A2780 1
## 3798: geldanamycin Topotecan 0.0223 0.07750 A375 1
## 3799: geldanamycin Topotecan 0.0223 0.07750 A427 1
## 3800: geldanamycin Topotecan 0.0223 0.07750 CAOV3 1
## combination_name viability1 viability2 viability3 viability4
## 1: 5-FU & Bortezomib 0.814 0.754 0.765 0.849
## 2: 5-FU & Bortezomib 0.214 0.195 0.186 0.223
## 3: 5-FU & Bortezomib 1.064 1.080 1.082 1.009
## 4: 5-FU & Bortezomib 0.675 0.582 0.482 0.516
## 5: 5-FU & Bortezomib 0.845 0.799 0.799 0.759
## ---
## 3796: geldanamycin & Topotecan 0.090 0.043 0.112 0.103
## 3797: geldanamycin & Topotecan 0.025 0.022 0.029 0.023
## 3798: geldanamycin & Topotecan 0.151 0.146 0.144 0.171
## 3799: geldanamycin & Topotecan 0.142 0.166 0.124 0.175
## 3800: geldanamycin & Topotecan 0.091 0.084 0.134 0.119colnames(assay(tre, 'sensitivity', withDimnames=TRUE))## [1] "drug1id" "drug2id" "drug1dose" "drug2dose"
## [5] "cellid" "batchid" "combination_name" "viability1"
## [9] "viability2" "viability3" "viability4"assay(tre, 'sensitivity', withDimnames=TRUE)## drug1id drug2id drug1dose drug2dose cellid batchid
## 1: 5-FU Bortezomib 0.3500 0.00045 A2058 1
## 2: 5-FU Bortezomib 0.3500 0.00045 A2780 1
## 3: 5-FU Bortezomib 0.3500 0.00045 A375 1
## 4: 5-FU Bortezomib 0.3500 0.00045 A427 1
## 5: 5-FU Bortezomib 0.3500 0.00045 CAOV3 1
## ---
## 3796: geldanamycin Topotecan 0.0223 0.07750 A2058 1
## 3797: geldanamycin Topotecan 0.0223 0.07750 A2780 1
## 3798: geldanamycin Topotecan 0.0223 0.07750 A375 1
## 3799: geldanamycin Topotecan 0.0223 0.07750 A427 1
## 3800: geldanamycin Topotecan 0.0223 0.07750 CAOV3 1
## combination_name viability1 viability2 viability3 viability4
## 1: 5-FU & Bortezomib 0.814 0.754 0.765 0.849
## 2: 5-FU & Bortezomib 0.214 0.195 0.186 0.223
## 3: 5-FU & Bortezomib 1.064 1.080 1.082 1.009
## 4: 5-FU & Bortezomib 0.675 0.582 0.482 0.516
## 5: 5-FU & Bortezomib 0.845 0.799 0.799 0.759
## ---
## 3796: geldanamycin & Topotecan 0.090 0.043 0.112 0.103
## 3797: geldanamycin & Topotecan 0.025 0.022 0.029 0.023
## 3798: geldanamycin & Topotecan 0.151 0.146 0.144 0.171
## 3799: geldanamycin & Topotecan 0.142 0.166 0.124 0.175
## 3800: geldanamycin & Topotecan 0.091 0.084 0.134 0.1197 References
O’Neil J, Benita Y, Feldman I, Chenard M, Roberts B, Liu Y, Li J, Kral A, Lejnine S, Loboda A, Arthur W, Cristescu R, Haines BB, Winter C, Zhang T, Bloecher A, Shumway SD. An Unbiased Oncology Compound Screen to Identify Novel Combination Strategies. Mol Cancer Ther. 2016 Jun;15(6):1155-62. doi: 10.1158/1535-7163.MCT-15-0843. Epub 2016 Mar 16. PMID: 26983881.
Heewon Seo, Denis Tkachuk, Chantal Ho, Anthony Mammoliti, Aria Rezaie, Seyed Ali Madani Tonekaboni, Benjamin Haibe-Kains, SYNERGxDB: an integrative pharmacogenomic portal to identify synergistic drug combinations for precision oncology, Nucleic Acids Research, Volume 48, Issue W1, 02 July 2020, Pages W494–W501, https://doi.org/10.1093/nar/gkaa421
8 sessionInfo
## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-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] data.table_1.14.4 CoreGx_2.2.0
## [3] SummarizedExperiment_1.28.0 Biobase_2.58.0
## [5] GenomicRanges_1.50.0 GenomeInfoDb_1.34.0
## [7] IRanges_2.32.0 S4Vectors_0.36.0
## [9] MatrixGenerics_1.10.0 matrixStats_0.62.0
## [11] BiocGenerics_0.44.0 formatR_1.12
## [13] BiocStyle_2.26.0
##
## loaded via a namespace (and not attached):
## [1] lsa_0.73.3 bitops_1.0-7
## [3] BumpyMatrix_1.6.0 SnowballC_0.7.0
## [5] tools_4.2.1 backports_1.4.1
## [7] bslib_0.4.0 DT_0.26
## [9] utf8_1.2.2 R6_2.5.1
## [11] KernSmooth_2.23-20 DBI_1.1.3
## [13] colorspace_2.0-3 tidyselect_1.2.0
## [15] compiler_4.2.1 cli_3.4.1
## [17] shinyjs_2.1.0 DelayedArray_0.24.0
## [19] bookdown_0.29 slam_0.1-50
## [21] sass_0.4.2 caTools_1.18.2
## [23] scales_1.2.1 bench_1.1.2
## [25] checkmate_2.1.0 relations_0.6-12
## [27] stringr_1.4.1 digest_0.6.30
## [29] rmarkdown_2.17 XVector_0.38.0
## [31] pkgconfig_2.0.3 htmltools_0.5.3
## [33] highr_0.9 fastmap_1.1.0
## [35] limma_3.54.0 htmlwidgets_1.5.4
## [37] rlang_1.0.6 shiny_1.7.3
## [39] visNetwork_2.1.2 generics_0.1.3
## [41] jquerylib_0.1.4 jsonlite_1.8.3
## [43] BiocParallel_1.32.0 gtools_3.9.3
## [45] dplyr_1.0.10 RCurl_1.98-1.9
## [47] magrittr_2.0.3 GenomeInfoDbData_1.2.9
## [49] Matrix_1.5-1 Rcpp_1.0.9
## [51] munsell_0.5.0 fansi_1.0.3
## [53] lifecycle_1.0.3 piano_2.14.0
## [55] stringi_1.7.8 yaml_2.3.6
## [57] zlibbioc_1.44.0 gplots_3.1.3
## [59] grid_4.2.1 parallel_4.2.1
## [61] promises_1.2.0.1 shinydashboard_0.7.2
## [63] crayon_1.5.2 lattice_0.20-45
## [65] cowplot_1.1.1 knitr_1.40
## [67] pillar_1.8.1 fgsea_1.24.0
## [69] igraph_1.3.5 codetools_0.2-18
## [71] marray_1.76.0 fastmatch_1.1-3
## [73] glue_1.6.2 evaluate_0.17
## [75] BiocManager_1.30.19 MultiAssayExperiment_1.24.0
## [77] vctrs_0.5.0 httpuv_1.6.6
## [79] gtable_0.3.1 assertthat_0.2.1
## [81] cachem_1.0.6 ggplot2_3.3.6
## [83] xfun_0.34 mime_0.12
## [85] xtable_1.8-4 later_1.3.0
## [87] tibble_3.1.8 sets_1.0-21
## [89] cluster_2.1.4 ellipsis_0.3.2