Accessing and modifying information in a CoreSet
CoreSet-accessors.RdDocumentation for the various setters and getters which allow manipulation
of data in the slots of a CoreSet object.
Usage
# S4 method for CoreSet
annotation(object)
# S4 method for CoreSet,list
annotation(object) <- value
# S4 method for CoreSet
dateCreated(object)
# S4 method for CoreSet,character
dateCreated(object) <- value
# S4 method for CoreSet
name(object)
# S4 method for CoreSet
name(object) <- value
# S4 method for CoreSet
sampleInfo(object)
# S4 method for CoreSet,data.frame
sampleInfo(object) <- value
# S4 method for CoreSet
sampleNames(object)
# S4 method for CoreSet,character
sampleNames(object) <- value
# S4 method for CoreSet
treatmentInfo(object)
# S4 method for CoreSet,data.frame
treatmentInfo(object) <- value
# S4 method for CoreSet
treatmentNames(object)
# S4 method for CoreSet,character
treatmentNames(object) <- value
# S4 method for CoreSet
curation(object)
# S4 method for CoreSet,list
curation(object) <- value
# S4 method for CoreSet
datasetType(object)
# S4 method for CoreSet,character
datasetType(object) <- value
# S4 method for CoreSet
molecularProfiles(object, mDataType, assay)
# S4 method for CoreSet,character,character,matrix
molecularProfiles(object, mDataType, assay) <- value
# S4 method for CoreSet,character,missing,matrix
molecularProfiles(object, mDataType, assay) <- value
# S4 method for CoreSet,missing,missing,list_OR_MAE
molecularProfiles(object, mDataType, assay) <- value
# S4 method for CoreSet
featureInfo(object, mDataType)
# S4 method for CoreSet,character,data.frame
featureInfo(object, mDataType) <- value
# S4 method for CoreSet,character
phenoInfo(object, mDataType)
# S4 method for CoreSet,character,data.frame
phenoInfo(object, mDataType) <- value
# S4 method for CoreSet,character
fNames(object, mDataType)
# S4 method for CoreSet,character,character
fNames(object, mDataType) <- value
# S4 method for CoreSet
mDataNames(object)
# S4 method for CoreSet
mDataNames(object) <- value
# S4 method for CoreSet
molecularProfilesSlot(object)
# S4 method for CoreSet,list_OR_MAE
molecularProfilesSlot(object) <- value
# S4 method for CoreSet
sensitivityInfo(object, dimension, ...)
# S4 method for CoreSet,data.frame
sensitivityInfo(object, dimension, ...) <- value
# S4 method for CoreSet
sensitivityMeasures(object)
# S4 method for CoreSet,character
sensitivityMeasures(object) <- value
# S4 method for CoreSet
sensitivityProfiles(object)
# S4 method for CoreSet,data.frame
sensitivityProfiles(object) <- value
# S4 method for CoreSet
sensitivityRaw(object)
# S4 method for CoreSet,array
sensitivityRaw(object) <- value
# S4 method for CoreSet
treatmentResponse(object)
# S4 method for CoreSet,list_OR_LongTable
treatmentResponse(object) <- value
# S4 method for CoreSet
sensNumber(object)
# S4 method for CoreSet,matrix
sensNumber(object) <- value
# S4 method for CoreSet
pertNumber(object)
# S4 method for CoreSet,array
pertNumber(object) <- valueArguments
- object
A
CoreSetobject.- value
See details.
- mDataType
character(1)The name of a molecular datatype to access from themolecularProfilesof aCoreSetobject.- assay
character(1)A valid assay name in theSummarizedExperimentof@molecularProfilesof a CoreSet object for data typemDataType.- dimension
See details.
- ...
See details.
Details
@annotation
annotation: A list of CoreSet annotations with items: 'name',
the name of the object; 'dateCreated', date the object was created; 'sessionInfo',
the sessionInfo() when the object was created; 'call', the R constructor call;
and 'version', the object version.
annotation<-: Setter method for the annotation slot. Arguments:
value: a
listof annotations to update the CoreSet with.
@dateCreated
dateCreated: character(1) The date the CoreSet object was
created, as returned by the date() function.
dateCreated<-: Update the 'dateCreated' item in the annotation slot of
a CoreSet object. Arguments:
value: A
character(1)vector, as returned by thedate()function.
name: character(1) The name of the CoreSet, retreived from
the @annotation slot.
name<-: Update the @annotation$name value in a CoreSet object.
value:
character(1)The name of theCoreSetobject.
cellInfo: data.frame Metadata for all sample in a CoreSet object.
sampleInfo<-: assign updated sample annotations to the CoreSet
object.
Arguments:
value: a
data.frameobject.
sampleNames: character Retrieve the rownames of the data.frame in
the sample slot from a CoreSet object.
sampleNames<-: assign new rownames to the sampleInfo data.frame for
a CoreSet object.
Arguments:
value:
charactervector of rownames for thesampleInfo(object)data.frame.
treatmentInfo: data.frame Metadata for all treatments in a CoreSet
object. Arguments:
object:
CoreSetAn object to retrieve treatment metadata from.
treatmentInfo<-: CoreSet object with updated treatment metadata.
object. Arguments:
object:
CoreSetAn object to set treatment metadata for.value:
data.frameA new table of treatment metadata forobject.
treatmentNames: character Names for all treatments in a CoreSet
object. Arguments:
object:
CoreSetAn object to retrieve treatment names from.
treatmentNames<-: CoreSet Object with updates treatment names.
object. Arguments:
object:
CoreSetAn object to set treatment names from.value:
characterA character vector of updated treatment names.
@curation
curation: A list of curated mappings between identifiers in the
CoreSet object and the original data publication. Contains two data.frames, 'sample' with sample ids and
'tissue' with tissue ids.
curation<-: Update the curation slot of a CoreSet object. Arugments:
value: A
listofdata.frames, one for each type of curated identifier. For aCoreSetobject the slot should contain tissue and sample iddata.frames.
datasetType slot
datasetType: character(1) The type treatment response in the
sensitivity slot. Valid values are 'sensitivity', 'perturbation' or 'both'.
datasetType<-: Update the datasetType slot of a CoreSet object. Arguments:
value: A
character(1)vector with one of 'sensitivity', 'perturbation' or 'both'
@molecularProfiles
molecularProfiles: matrix() Retrieve an assay in a
SummarizedExperiment from the molecularProfiles slot of a CoreSet
object with the specified mDataType. Valid mDataType arguments can be
found with mDataNames(object). Exclude mDataType and assay to
access the entire slot. Arguments:
assay: Optional
character(1)vector specifying an assay in theSummarizedExperimentof themolecularProfilesslot of theCoreSetobject for the specifiedmDataType. If excluded, defaults to modifying the first assay in theSummarizedExperimentfor the givenmDataType.
molecularProfiles<-: Update an assay in a SummarizedExperiment from
the molecularProfiles slot of a CoreSet object with the specified
mDataType. Valid mDataType arguments can be found with
mDataNames(object). Omit mDataType and assay to update the slot.
assay: Optional
character(1)vector specifying an assay in theSummarizedExperimentof themolecularProfilesslot of theCoreSetobject for the specifiedmDataType. If excluded, defaults to modifying the first assay in theSummarizedExperimentfor the givenmDataType.value: A
matrixof values to assign to theassayslot of theSummarizedExperimentfor the selectedmDataType. The rownames and column names must match the associatedSummarizedExperiment.
featureInfo: Retrieve a DataFrame of feature metadata for the specified
mDataType from the molecularProfiles slot of a CoreSet object. More
specifically, retrieve the @rowData slot from the SummarizedExperiment
from the @molecularProfiles of a CoreSet object with the name
mDataType.
featureInfo<-: Update the featureInfo(object, mDataType) DataFrame
with new feature metadata. Arguments:
value: A
data.frameorDataFramewith updated feature metadata for the specified molecular profile in themolecularProfilesslot of aCoreSetobject.
phenoInfo: Return the @colData slot from the SummarizedExperiment of
mDataType, containing sample-level metadata, from a CoreSet object.
phenoInfo<-: Update the @colData slot of the SummarizedExperiment
of mDataType in the @molecularProfiles slot of a CoreSet object.
This updates the sample-level metadata in-place.
value: A
data.frameorDataFrameobject where rows are samples and columns are sample metadata.
fNames: character() The features names from the rowData slot of a
SummarizedExperiment of mDataType within a CoreSet object.
fNames: Updates the rownames of the feature metadata (i.e., rowData)
for a SummarizedExperiment of mDataType within a CoreSet object.
value:
character()A character vector of new features names for therowDataof theSummarizedExperimentofmDataTypein the@molecularProfilesslot of aCoreSetobject. Must be the same length asnrow(featureInfo(object, mDataType)), the number of rows in the feature metadata.
mDataNames: character Retrieve the names of the molecular data types
available in the molecularProfiles slot of a CoreSet object. These
are the options which can be used in the mDataType parameter of various
molecularProfiles slot accessors methods.
mDataNames: Update the molecular data type names of the
molecularProfiles slot of a CoreSet object. Arguments:
value:
charactervector of molecular datatype names, with length equal tolength(molecularProfilesSlot(object)).
molecularProfilesSlot: Return the contents of the @molecularProfiles
slot of a CoreSet object. This will either be a list or
MultiAssayExperiment of SummarizedExperiments.
molecularProfilesSlot<-: Update the contents of the @molecularProfiles
slot of a CoreSet object. Arguemnts:
value: A
listorMultiAssayExperimentofSummarizedExperiments. Thelistandassaysshould be named for the molecular datatype in eachSummarizedExperiment.
@treatmentResponse
Arguments:
dimension: Optionalcharacter(1)One of 'treatment', 'sample' or 'assay' to retrieverowData,colDataor the 'assay_metadata' assay from theCoreSet@sensitvityLongTableobject, respectively. Ignored with warning if@treatmentResponseis not aLongTableobject....: Additional arguments to therowDataorcolData.LongTablemethods. Only used if the sensitivity slot contains aLongTableobject instead of alistand thedimensionargument is specified.
sensitivityInfo<-: Update the @treatmentResponse slot metadata for a
CoreSet object. When used without the dimension argument is behaves
similar to the old CoreSet implementation, where the @treatmentResponse slot
contained a list with a $info data.frame item. When the dimension
arugment is used, more complicated assignments can occur where 'sample'
modifies the @sensitvity LongTable colData, 'treatment' the rowData and
'assay' the 'assay_metadata' assay.
Arguments:
value: A
data.frameof treatment response experiment metadata, documenting experiment level metadata (mapping to treatments and samples). If the@treatmentResponseslot doesn't contain aLongTableanddimensionis not specified, you can only modify existing columns as returned bysensitivityInfo(object).
sensitivityMeaures: Get the 'sensitivityMeasures' available in a CoreSet
object. Each measure reprents some summary of sample sensitivity to a given
treatment, such as ic50, ec50, AUC, AAC, etc. The results are returned as a
character vector with all available metrics for the PSet object.
sensitivityMeaures: Update the sensitivity meaure in a CoreSet
object. Thesee values are the column names of the 'profiles' assay and
represent various compued sensitviity metrics such as ic50, ec50, AUC, AAC,
etc.
value: A
charactervector of new sensitivity measure names, the then length of the character vector must matcht he number of columns of the 'profiles' assay, excluding metadata and key columns.
sensitivityProfiles: Return the sensitivity profile summaries from the sensitivity slot. This data.frame cotanins vaarious sensitivity summary metrics, such as ic50, amax, EC50, aac, HS, etc as columns, with rows as treatment by sample experiments.
sensitivityProfiles<-: Update the sensitivity profile summaries the
sensitivity slot. Arguments:
-value: A data.frame the the same number of rows as as returned by
sensitivityProfiles(object), but potentially modified columns, such as the
computation of additional summary metrics.
sensitivityRaw: Access the raw sensitiity measurents for a CoreSet
object. A 3D array where rows are experiment_ids, columns are doses
and the third dimension is metric, either 'Dose' for the doses used or
'Viability' for the sample viability at that dose.
sensitvityRaw<-: Update the raw dose and viability data in a CoreSet.
value: A 3D
arrayobject where rows are experiment_ids, columns are replicates and pages are c('Dose', 'Viability'), with the corresponding dose or viability measurement for that experiment_id and replicate.
sensNumber: Return a count of viability observations in a CoreSet
object for each treatment-combo by sample combination.
sensNumber<-: Update the 'n' item, which holds a matrix with a count
of treatment by sample-line experiment counts, in the list in @treatmentResponse
slot of a CoreSet object. Will error when @sensitviity contains
a LongTable object, since the counts are computed on the fly. Arguments:
value: A
matrixwhere rows are samples and columns are treatments, with a count of the number of experiments for each combination as the values.
pertNumber: array Summary of available perturbation experiments
from in a CoreSet object. Returns a 3D array with the number of
perturbation experiments per treatment and sample, and data type.
pertNumber<-: Update the @perturbation$n value in a CoreSet object,
which stores a summary of the available perturbation experiments. Arguments:
value: A new 3D
arraywith the number of perturbation experiments per treatment and sample, and data type
Examples
data(clevelandSmall_cSet)
## @annotation
annotation(clevelandSmall_cSet)
#> $name
#> [1] "Cleveland"
#>
#> $dateCreated
#> [1] "Sat Feb 18 15:10:56 2023"
#>
#> $sessionInfo
#> R Under development (unstable) (2023-02-17 r83862)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.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: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] CoreGx_2.3.1 testthat_3.1.6
#> [3] SummarizedExperiment_1.28.0 Biobase_2.58.0
#> [5] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
#> [7] IRanges_2.32.0 S4Vectors_0.36.1
#> [9] MatrixGenerics_1.10.0 matrixStats_0.63.0
#> [11] BiocGenerics_0.44.0
#>
#> loaded via a namespace (and not attached):
#> [1] rstudioapi_0.14 jsonlite_1.8.4
#> [3] MultiAssayExperiment_1.24.0 magrittr_2.0.3
#> [5] fs_1.6.1 zlibbioc_1.44.0
#> [7] vctrs_0.5.2 memoise_2.0.1
#> [9] RCurl_1.98-1.10 htmltools_0.5.4
#> [11] BiocBaseUtils_1.0.0 usethis_2.1.6
#> [13] curl_5.0.0 KernSmooth_2.23-20
#> [15] htmlwidgets_1.6.1 desc_1.4.2
#> [17] cachem_1.0.6 commonmark_1.8.1
#> [19] igraph_1.4.0 mime_0.12
#> [21] lifecycle_1.0.3 piano_2.14.0
#> [23] pkgconfig_2.0.3 Matrix_1.5-3
#> [25] R6_2.5.1 fastmap_1.1.0
#> [27] rcmdcheck_1.4.0 GenomeInfoDbData_1.2.9
#> [29] shiny_1.7.4 digest_0.6.31
#> [31] colorspace_2.1-0 ps_1.7.2
#> [33] rprojroot_2.0.3 pkgload_1.3.2
#> [35] SnowballC_0.7.0 fansi_1.0.4
#> [37] compiler_4.3.0 remotes_2.4.2
#> [39] withr_2.5.0 marray_1.76.0
#> [41] backports_1.4.1 BiocParallel_1.32.5
#> [43] bench_1.1.2 pak_0.4.0
#> [45] pkgbuild_1.4.0 gplots_3.1.3
#> [47] DelayedArray_0.24.0 sessioninfo_1.2.2
#> [49] gtools_3.9.4 caTools_1.18.2
#> [51] tools_4.3.0 httpuv_1.6.9
#> [53] relations_0.6-12 glue_1.6.2
#> [55] callr_3.7.3 promises_1.2.0.1
#> [57] grid_4.3.0 checkmate_2.1.0
#> [59] cluster_2.1.4 fgsea_1.24.0
#> [61] generics_0.1.3 gtable_0.3.1
#> [63] data.table_1.14.8 xml2_1.3.3
#> [65] utf8_1.2.3 XVector_0.38.0
#> [67] pillar_1.8.1 stringr_1.5.0
#> [69] limma_3.54.1 BumpyMatrix_1.6.0
#> [71] later_1.3.0 dplyr_1.1.0
#> [73] lattice_0.20-45 tidyselect_1.2.0
#> [75] miniUI_0.1.1.1 knitr_1.42
#> [77] xfun_0.37 shinydashboard_0.7.2
#> [79] devtools_2.4.5 brio_1.1.3
#> [81] DT_0.27 visNetwork_2.1.2
#> [83] stringi_1.7.12 xopen_1.0.0
#> [85] codetools_0.2-19 lsa_0.73.3
#> [87] tibble_3.1.8 cli_3.6.0
#> [89] xtable_1.8-4 munsell_0.5.0
#> [91] processx_3.8.0 roxygen2_7.2.3
#> [93] Rcpp_1.0.10 parallel_4.3.0
#> [95] sets_1.0-22 ellipsis_0.3.2
#> [97] ggplot2_3.4.1 prettyunits_1.1.1
#> [99] profvis_0.3.7 urlchecker_1.0.1
#> [101] bitops_1.0-7 slam_0.1-50
#> [103] scales_1.2.1 purrr_1.0.1
#> [105] crayon_1.5.2 rlang_1.0.6
#> [107] cowplot_1.1.1 fastmatch_1.1-3
#> [109] shinyjs_2.1.0
#>
#> $call
#> CoreSet2(name = name(cs), treatment = treatmentInfo(cs), sample = sampleInfo(cs),
#> molecularProfiles = molecularProfiles(cs), treatmentResponse = as(treatmentResponse(cs),
#> "TreatmentResponseExperiment"), curation = cur)
#>
annotation(clevelandSmall_cSet) <- annotation(clevelandSmall_cSet)
dateCreated(clevelandSmall_cSet)
#> [1] "Sat Feb 18 15:10:56 2023"
## dateCreated
dateCreated(clevelandSmall_cSet) <- date()
name(clevelandSmall_cSet)
#> [1] "Cleveland"
name(clevelandSmall_cSet) <- 'new_name'
sampleInfo(clevelandSmall_cSet) <- sampleInfo(clevelandSmall_cSet)
sampleNames(clevelandSmall_cSet)
#> [1] "SK-N-FI" "IMR-32" "SK-N-AS" "CHP-212" "KP-N-S19s" "MHH-NB-11"
#> [7] "SK-N-SH" "NB1" "SNU-245" "SNU-869"
sampleNames(clevelandSmall_cSet) <- sampleNames(clevelandSmall_cSet)
#> Warning:
#> [CoreGx::assay] Cannot use key=TRUE when summarize=TRUE. Ignoring the key argument.
#> Warning: column(s) not removed because not found: [.colnames]
#> Warning: column(s) not removed because not found: [.colnames]
#> Warning: column(s) not removed because not found: [.colnames]
#> Warning: column(s) not removed because not found: [.colnames]
treatmentInfo(clevelandSmall_cSet)
#> data frame with 0 columns and 0 rows
treatmentInfo(clevelandSmall_cSet) <- treatmentInfo(clevelandSmall_cSet)
treatmentNames(clevelandSmall_cSet)
#> character(0)
treatmentNames(clevelandSmall_cSet) <- treatmentNames(clevelandSmall_cSet)
#> No treatments in this object! Returning without modification.
## curation
curation(clevelandSmall_cSet)
#> $sample
#> sample tissue
#> SK-N-FI SK-N-FI SKNFI
#> IMR-32 IMR-32 IMR32
#> SK-N-AS SK-N-AS SKNAS
#> CHP-212 CHP-212 CHP212
#> KP-N-S19s KP-N-S19s KPNSI9S
#> MHH-NB-11 MHH-NB-11 MHHNB11
#> SK-N-SH SK-N-SH SKNSH
#> NB1 NB1 NB1
#> SNU-245 SNU-245 SNU245
#> SNU-869 SNU-869 SNU869
#>
#> $tissue
#> unique.tissueid Cleveland.tissueid
#> SK-N-FI autonomic_ganglia autonomic_ganglia
#> IMR-32 autonomic_ganglia autonomic_ganglia
#> SK-N-AS autonomic_ganglia autonomic_ganglia
#> CHP-212 autonomic_ganglia autonomic_ganglia
#> KP-N-S19s autonomic_ganglia autonomic_ganglia
#> MHH-NB-11 autonomic_ganglia autonomic_ganglia
#> SK-N-SH autonomic_ganglia autonomic_ganglia
#> NB1 autonomic_ganglia autonomic_ganglia
#> SNU-245 biliary_tract biliary_tract
#> SNU-869 pancreas biliary_tract
#>
#> $treatment
#> data frame with 0 columns and 0 rows
#>
curation(clevelandSmall_cSet) <- curation(clevelandSmall_cSet)
datasetType(clevelandSmall_cSet)
#> [1] "sensitivity"
datasetType(clevelandSmall_cSet) <- 'both'
# No assay specified
molecularProfiles(clevelandSmall_cSet, 'rna') <- molecularProfiles(clevelandSmall_cSet, 'rna')
# Specific assay
molecularProfiles(clevelandSmall_cSet, 'rna', 'exprs') <-
molecularProfiles(clevelandSmall_cSet, 'rna', 'exprs')
# Replace the whole slot
molecularProfiles(clevelandSmall_cSet) <- molecularProfiles(clevelandSmall_cSet)
featureInfo(clevelandSmall_cSet, 'rna')
#> DataFrame with 1000 rows and 8 columns
#> Probe EnsemblGeneId EntrezGeneId Symbol
#> <character> <character> <integer> <character>
#> ENSG00000000003 ENSG00000000003_at ENSG00000000003 7105 TSPAN6
#> ENSG00000000005 ENSG00000000005_at ENSG00000000005 64102 TNMD
#> ENSG00000000419 ENSG00000000419_at ENSG00000000419 8813 DPM1
#> ENSG00000000457 ENSG00000000457_at ENSG00000000457 57147 SCYL3
#> ENSG00000000460 ENSG00000000460_at ENSG00000000460 55732 C1orf112
#> ... ... ... ... ...
#> ENSG00000068308 ENSG00000068308_at ENSG00000068308 55593 OTUD5
#> ENSG00000068323 ENSG00000068323_at ENSG00000068323 7030 TFE3
#> ENSG00000068354 ENSG00000068354_at ENSG00000068354 4943 TBC1D25
#> ENSG00000068383 ENSG00000068383_at ENSG00000068383 3632 INPP5A
#> ENSG00000068394 ENSG00000068394_at ENSG00000068394 27238 GPKOW
#> GeneBioType BEST rownames rownames.1
#> <character> <logical> <character> <character>
#> ENSG00000000003 protein_coding TRUE ENSG00000000003 ENSG00000000003
#> ENSG00000000005 protein_coding TRUE ENSG00000000005 ENSG00000000005
#> ENSG00000000419 protein_coding TRUE ENSG00000000419 ENSG00000000419
#> ENSG00000000457 protein_coding TRUE ENSG00000000457 ENSG00000000457
#> ENSG00000000460 protein_coding TRUE ENSG00000000460 ENSG00000000460
#> ... ... ... ... ...
#> ENSG00000068308 protein_coding TRUE ENSG00000068308 ENSG00000068308
#> ENSG00000068323 protein_coding TRUE ENSG00000068323 ENSG00000068323
#> ENSG00000068354 protein_coding TRUE ENSG00000068354 ENSG00000068354
#> ENSG00000068383 protein_coding TRUE ENSG00000068383 ENSG00000068383
#> ENSG00000068394 protein_coding TRUE ENSG00000068394 ENSG00000068394
featureInfo(clevelandSmall_cSet, 'rna') <- featureInfo(clevelandSmall_cSet, 'rna')
phenoInfo(clevelandSmall_cSet, 'rna')
#> DataFrame with 9 rows and 24 columns
#> samplename
#> <character>
#> NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 NIECE_p_NCLE_RNA3_HG..
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 GILDS_p_NCLE_RNA11_R..
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 BUNDS_p_NCLE_RNA5_HG..
#> SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 SILOS_p_NCLE_RNA9_HG..
#> WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 WATCH_p_NCLE_RNA8_HG..
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 CASED_p_NCLE_RNA4_HG..
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 CASED_p_NCLE_RNA4_HG..
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 BUNDS_p_NCLE_RNA5_HG..
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 GILDS_p_NCLE_RNA11_R..
#> filename
#> <character>
#> NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 NIECE_p_NCLE_RNA3_HG..
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 GILDS_p_NCLE_RNA11_R..
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 BUNDS_p_NCLE_RNA5_HG..
#> SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 SILOS_p_NCLE_RNA9_HG..
#> WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 WATCH_p_NCLE_RNA8_HG..
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 CASED_p_NCLE_RNA4_HG..
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 CASED_p_NCLE_RNA4_HG..
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 BUNDS_p_NCLE_RNA5_HG..
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 GILDS_p_NCLE_RNA11_R..
#> chiptype
#> <character>
#> 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
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 HG-U133_Plus_2
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 HG-U133_Plus_2
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 HG-U133_Plus_2
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 HG-U133_Plus_2
#> hybridization.date
#> <character>
#> 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
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 10/29/08
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 10/29/08
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 12/19/08
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 2010-05-21
#> hybridization.hour
#> <character>
#> 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 17:15:45Z
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 07:52:47
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 08:04:03
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 11:30:25
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 17:07:46Z
#> file.day file.hour
#> <character> <character>
#> NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 2008-07-24 14:23:47
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 2010-05-26 16:35:21
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 2009-01-07 13:06:03
#> SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 2009-12-11 14:20:50
#> WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 2009-08-19 16:16:45
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 2008-11-04 14:19:49
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 2008-11-04 14:19:49
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 2009-01-07 13:05:45
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 2010-05-26 16:35:08
#> batch sampleid
#> <logical> <character>
#> NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 NA CHP-212
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 NA IMR-32
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 NA KP-N-S19s
#> SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 NA MHH-NB-11
#> WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 NA NB1
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 NA SK-N-AS
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 NA SK-N-FI
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 NA SK-N-SH
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 NA SNU-245
#> CCLE.name
#> <character>
#> NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 CHP212_AUTONOMIC_GAN..
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 IMR32_AUTONOMIC_GANG..
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 KPNSI9S_AUTONOMIC_GA..
#> SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 MHHNB11_AUTONOMIC_GA..
#> WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 NB1_AUTONOMIC_GANGLIA
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 SKNAS_AUTONOMIC_GANG..
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 SKNFI_AUTONOMIC_GANG..
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 SKNSH_AUTONOMIC_GANG..
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 SNU245_BILIARY_TRACT
#> Cell.line.primary.name
#> <character>
#> NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 CHP-212
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 IMR-32
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 KP-N-SI9s
#> SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 MHH-NB-11
#> WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 NB-1
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 SK-N-AS
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 SK-N-FI
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 SK-N-SH
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 SNU-245
#> Cell.line.aliases Gender
#> <character> <character>
#> NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 NA NA
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 NA M
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 KP-N-S19s M
#> SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 NA M
#> WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 NA M
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 NA F
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 SK-N-F1 M
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 NA F
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 NCI-SNU-245 NA
#> Site.Primary
#> <character>
#> NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 autonomic_ganglia
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 autonomic_ganglia
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 autonomic_ganglia
#> SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 autonomic_ganglia
#> WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 autonomic_ganglia
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 autonomic_ganglia
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 autonomic_ganglia
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 autonomic_ganglia
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 biliary_tract
#> Histology Hist.Subtype1
#> <character> <character>
#> NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 neuroblastoma NS
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 neuroblastoma NS
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 neuroblastoma NS
#> SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 neuroblastoma NS
#> WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 neuroblastoma NS
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 neuroblastoma NS
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 neuroblastoma NS
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 neuroblastoma NS
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 carcinoma NS
#> Notes
#> <character>
#> NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 NA
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 NA
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 NA
#> SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 NA
#> WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 NA
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 NA
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 NA
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 Identical lines: SH-..
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 NA
#> Source
#> <character>
#> NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 ATCC
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 ATCC
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 HSRRB
#> SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 DSMZ
#> WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 HSRRB
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 ATCC
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 ATCC
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 ATCC
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 KCLB
#> Expression.arrays
#> <character>
#> NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 NIECE_p_NCLE_RNA3_HG..
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 GILDS_p_NCLE_RNA11_R..
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 BUNDS_p_NCLE_RNA5_HG..
#> SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 SILOS_p_NCLE_RNA9_HG..
#> WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 WATCH_p_NCLE_RNA8_HG..
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 CASED_p_NCLE_RNA4_HG..
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 CASED_p_NCLE_RNA4_HG..
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 BUNDS_p_NCLE_RNA5_HG..
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 GILDS_p_NCLE_RNA11_R..
#> SNP.arrays
#> <character>
#> NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 FASTS_p_NCLE_DNAAffy..
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 LOBBY_p_NCLE_DNAAffy..
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 FIEFS_p_NCLE_DNA_Aff..
#> SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 AWASH_p_NCLE_DNAAffy..
#> WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 AWASH_p_NCLE_DNAAffy..
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 FIEFS_p_NCLE_DNA_Aff..
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 FIEFS_p_NCLE_DNA_Aff..
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 BOWER_p_NCLE_DNAAffy..
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 QUAFF_p_NCLE_DNAAffy..
#> Oncomap
#> <character>
#> NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 yes
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 yes
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 yes
#> SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 yes
#> WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 yes
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 yes
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 yes
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 yes
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 yes
#> Hybrid.Capture.Sequencing
#> <character>
#> NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 yes
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 yes
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 yes
#> SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 yes
#> WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 yes
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 yes
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 yes
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 yes
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 yes
#> batchid
#> <logical>
#> NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 NA
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 NA
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 NA
#> SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 NA
#> WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 NA
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 NA
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 NA
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 NA
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 NA
#> rownames
#> <character>
#> NIECE_P_NCLE_RNA3_HG-U133_PLUS_2_G10_296152 NIECE_P_NCLE_RNA3_HG..
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_G02_587654 GILDS_P_NCLE_RNA11_R..
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_B11_419860 BUNDS_P_NCLE_RNA5_HG..
#> SILOS_P_NCLE_RNA9_HG-U133_PLUS_2_A04_523474 SILOS_P_NCLE_RNA9_HG..
#> WATCH_P_NCLE_RNA8_HG-U133_PLUS_2_B04_474582 WATCH_P_NCLE_RNA8_HG..
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G03_383634 CASED_P_NCLE_RNA4_HG..
#> CASED_P_NCLE_RNA4_HG-U133_PLUS_2_G02_383638 CASED_P_NCLE_RNA4_HG..
#> BUNDS_P_NCLE_RNA5_HG-U133_PLUS_2_F07_419790 BUNDS_P_NCLE_RNA5_HG..
#> GILDS_P_NCLE_RNA11_REDO_HG-U133_PLUS_2_C05_587588 GILDS_P_NCLE_RNA11_R..
phenoInfo(clevelandSmall_cSet, 'rna') <- phenoInfo(clevelandSmall_cSet, 'rna')
fNames(clevelandSmall_cSet, 'rna')
#> [1] "ENSG00000000003" "ENSG00000000005" "ENSG00000000419" "ENSG00000000457"
#> [5] "ENSG00000000460" "ENSG00000000938" "ENSG00000000971" "ENSG00000001036"
#> [9] "ENSG00000001084" "ENSG00000001167" "ENSG00000001460" "ENSG00000001461"
#> [13] "ENSG00000001497" "ENSG00000001561" "ENSG00000001617" "ENSG00000001626"
#> [17] "ENSG00000001629" "ENSG00000001631" "ENSG00000002016" "ENSG00000002079"
#> [21] "ENSG00000002330" "ENSG00000002549" "ENSG00000002586" "ENSG00000002587"
#> [25] "ENSG00000002726" "ENSG00000002745" "ENSG00000002746" "ENSG00000002822"
#> [29] "ENSG00000002834" "ENSG00000002919" "ENSG00000002933" "ENSG00000003056"
#> [33] "ENSG00000003096" "ENSG00000003137" "ENSG00000003147" "ENSG00000003249"
#> [37] "ENSG00000003393" "ENSG00000003400" "ENSG00000003402" "ENSG00000003436"
#> [41] "ENSG00000003509" "ENSG00000003756" "ENSG00000003987" "ENSG00000003989"
#> [45] "ENSG00000004059" "ENSG00000004139" "ENSG00000004142" "ENSG00000004399"
#> [49] "ENSG00000004455" "ENSG00000004468" "ENSG00000004478" "ENSG00000004487"
#> [53] "ENSG00000004534" "ENSG00000004660" "ENSG00000004700" "ENSG00000004766"
#> [57] "ENSG00000004776" "ENSG00000004777" "ENSG00000004779" "ENSG00000004799"
#> [61] "ENSG00000004809" "ENSG00000004838" "ENSG00000004846" "ENSG00000004848"
#> [65] "ENSG00000004864" "ENSG00000004866" "ENSG00000004897" "ENSG00000004939"
#> [69] "ENSG00000004948" "ENSG00000004961" "ENSG00000004975" "ENSG00000005001"
#> [73] "ENSG00000005007" "ENSG00000005020" "ENSG00000005022" "ENSG00000005059"
#> [77] "ENSG00000005073" "ENSG00000005075" "ENSG00000005102" "ENSG00000005108"
#> [81] "ENSG00000005156" "ENSG00000005175" "ENSG00000005187" "ENSG00000005189"
#> [85] "ENSG00000005194" "ENSG00000005206" "ENSG00000005238" "ENSG00000005243"
#> [89] "ENSG00000005249" "ENSG00000005302" "ENSG00000005339" "ENSG00000005379"
#> [93] "ENSG00000005381" "ENSG00000005421" "ENSG00000005436" "ENSG00000005448"
#> [97] "ENSG00000005469" "ENSG00000005471" "ENSG00000005483" "ENSG00000005486"
#> [101] "ENSG00000005513" "ENSG00000005700" "ENSG00000005801" "ENSG00000005810"
#> [105] "ENSG00000005812" "ENSG00000005844" "ENSG00000005882" "ENSG00000005884"
#> [109] "ENSG00000005889" "ENSG00000005893" "ENSG00000005961" "ENSG00000005981"
#> [113] "ENSG00000006007" "ENSG00000006015" "ENSG00000006016" "ENSG00000006025"
#> [117] "ENSG00000006042" "ENSG00000006047" "ENSG00000006062" "ENSG00000006071"
#> [121] "ENSG00000006116" "ENSG00000006118" "ENSG00000006128" "ENSG00000006194"
#> [125] "ENSG00000006210" "ENSG00000006282" "ENSG00000006283" "ENSG00000006327"
#> [129] "ENSG00000006377" "ENSG00000006432" "ENSG00000006451" "ENSG00000006453"
#> [133] "ENSG00000006459" "ENSG00000006468" "ENSG00000006530" "ENSG00000006534"
#> [137] "ENSG00000006555" "ENSG00000006576" "ENSG00000006606" "ENSG00000006607"
#> [141] "ENSG00000006611" "ENSG00000006625" "ENSG00000006634" "ENSG00000006638"
#> [145] "ENSG00000006652" "ENSG00000006659" "ENSG00000006695" "ENSG00000006704"
#> [149] "ENSG00000006712" "ENSG00000006715" "ENSG00000006740" "ENSG00000006744"
#> [153] "ENSG00000006747" "ENSG00000006756" "ENSG00000006757" "ENSG00000006788"
#> [157] "ENSG00000006831" "ENSG00000007001" "ENSG00000007038" "ENSG00000007047"
#> [161] "ENSG00000007062" "ENSG00000007080" "ENSG00000007168" "ENSG00000007171"
#> [165] "ENSG00000007174" "ENSG00000007202" "ENSG00000007216" "ENSG00000007237"
#> [169] "ENSG00000007255" "ENSG00000007264" "ENSG00000007306" "ENSG00000007312"
#> [173] "ENSG00000007314" "ENSG00000007341" "ENSG00000007350" "ENSG00000007372"
#> [177] "ENSG00000007376" "ENSG00000007384" "ENSG00000007392" "ENSG00000007402"
#> [181] "ENSG00000007516" "ENSG00000007520" "ENSG00000007541" "ENSG00000007866"
#> [185] "ENSG00000007908" "ENSG00000007923" "ENSG00000007933" "ENSG00000007944"
#> [189] "ENSG00000007952" "ENSG00000007968" "ENSG00000008018" "ENSG00000008056"
#> [193] "ENSG00000008083" "ENSG00000008086" "ENSG00000008118" "ENSG00000008130"
#> [197] "ENSG00000008196" "ENSG00000008197" "ENSG00000008226" "ENSG00000008256"
#> [201] "ENSG00000008277" "ENSG00000008282" "ENSG00000008283" "ENSG00000008294"
#> [205] "ENSG00000008300" "ENSG00000008311" "ENSG00000008323" "ENSG00000008324"
#> [209] "ENSG00000008382" "ENSG00000008394" "ENSG00000008405" "ENSG00000008438"
#> [213] "ENSG00000008441" "ENSG00000008513" "ENSG00000008516" "ENSG00000008517"
#> [217] "ENSG00000008710" "ENSG00000008735" "ENSG00000008838" "ENSG00000008853"
#> [221] "ENSG00000008869" "ENSG00000008952" "ENSG00000008988" "ENSG00000009307"
#> [225] "ENSG00000009335" "ENSG00000009413" "ENSG00000009694" "ENSG00000009709"
#> [229] "ENSG00000009724" "ENSG00000009765" "ENSG00000009780" "ENSG00000009790"
#> [233] "ENSG00000009830" "ENSG00000009844" "ENSG00000009950" "ENSG00000009954"
#> [237] "ENSG00000010017" "ENSG00000010030" "ENSG00000010072" "ENSG00000010165"
#> [241] "ENSG00000010244" "ENSG00000010256" "ENSG00000010270" "ENSG00000010278"
#> [245] "ENSG00000010292" "ENSG00000010295" "ENSG00000010310" "ENSG00000010318"
#> [249] "ENSG00000010319" "ENSG00000010322" "ENSG00000010327" "ENSG00000010361"
#> [253] "ENSG00000010379" "ENSG00000010404" "ENSG00000010438" "ENSG00000010539"
#> [257] "ENSG00000010610" "ENSG00000010626" "ENSG00000010671" "ENSG00000010704"
#> [261] "ENSG00000010803" "ENSG00000010810" "ENSG00000010818" "ENSG00000010932"
#> [265] "ENSG00000011007" "ENSG00000011009" "ENSG00000011021" "ENSG00000011028"
#> [269] "ENSG00000011083" "ENSG00000011105" "ENSG00000011114" "ENSG00000011132"
#> [273] "ENSG00000011143" "ENSG00000011198" "ENSG00000011201" "ENSG00000011243"
#> [277] "ENSG00000011258" "ENSG00000011260" "ENSG00000011275" "ENSG00000011295"
#> [281] "ENSG00000011304" "ENSG00000011332" "ENSG00000011347" "ENSG00000011376"
#> [285] "ENSG00000011405" "ENSG00000011422" "ENSG00000011426" "ENSG00000011451"
#> [289] "ENSG00000011454" "ENSG00000011465" "ENSG00000011478" "ENSG00000011485"
#> [293] "ENSG00000011523" "ENSG00000011566" "ENSG00000011590" "ENSG00000011600"
#> [297] "ENSG00000011638" "ENSG00000011677" "ENSG00000012048" "ENSG00000012061"
#> [301] "ENSG00000012124" "ENSG00000012171" "ENSG00000012174" "ENSG00000012211"
#> [305] "ENSG00000012223" "ENSG00000012232" "ENSG00000012504" "ENSG00000012660"
#> [309] "ENSG00000012779" "ENSG00000012817" "ENSG00000012822" "ENSG00000012963"
#> [313] "ENSG00000012983" "ENSG00000013016" "ENSG00000013275" "ENSG00000013288"
#> [317] "ENSG00000013293" "ENSG00000013297" "ENSG00000013306" "ENSG00000013364"
#> [321] "ENSG00000013374" "ENSG00000013375" "ENSG00000013392" "ENSG00000013441"
#> [325] "ENSG00000013503" "ENSG00000013523" "ENSG00000013561" "ENSG00000013563"
#> [329] "ENSG00000013573" "ENSG00000013583" "ENSG00000013588" "ENSG00000013619"
#> [333] "ENSG00000013725" "ENSG00000013810" "ENSG00000014123" "ENSG00000014138"
#> [337] "ENSG00000014164" "ENSG00000014216" "ENSG00000014257" "ENSG00000014641"
#> [341] "ENSG00000014824" "ENSG00000014914" "ENSG00000014919" "ENSG00000015133"
#> [345] "ENSG00000015153" "ENSG00000015171" "ENSG00000015285" "ENSG00000015413"
#> [349] "ENSG00000015475" "ENSG00000015479" "ENSG00000015520" "ENSG00000015532"
#> [353] "ENSG00000015592" "ENSG00000015676" "ENSG00000016082" "ENSG00000016391"
#> [357] "ENSG00000016402" "ENSG00000016490" "ENSG00000016602" "ENSG00000016864"
#> [361] "ENSG00000017260" "ENSG00000017427" "ENSG00000017483" "ENSG00000017797"
#> [365] "ENSG00000018189" "ENSG00000018236" "ENSG00000018280" "ENSG00000018408"
#> [369] "ENSG00000018510" "ENSG00000018610" "ENSG00000018625" "ENSG00000018699"
#> [373] "ENSG00000018869" "ENSG00000019102" "ENSG00000019144" "ENSG00000019169"
#> [377] "ENSG00000019186" "ENSG00000019485" "ENSG00000019505" "ENSG00000019549"
#> [381] "ENSG00000019582" "ENSG00000019991" "ENSG00000019995" "ENSG00000020129"
#> [385] "ENSG00000020181" "ENSG00000020256" "ENSG00000020577" "ENSG00000020633"
#> [389] "ENSG00000020922" "ENSG00000021300" "ENSG00000021355" "ENSG00000021461"
#> [393] "ENSG00000021488" "ENSG00000021574" "ENSG00000021645" "ENSG00000021762"
#> [397] "ENSG00000021776" "ENSG00000021826" "ENSG00000021852" "ENSG00000022267"
#> [401] "ENSG00000022277" "ENSG00000022355" "ENSG00000022556" "ENSG00000022567"
#> [405] "ENSG00000022840" "ENSG00000022976" "ENSG00000023041" "ENSG00000023171"
#> [409] "ENSG00000023191" "ENSG00000023228" "ENSG00000023287" "ENSG00000023318"
#> [413] "ENSG00000023330" "ENSG00000023445" "ENSG00000023516" "ENSG00000023572"
#> [417] "ENSG00000023608" "ENSG00000023697" "ENSG00000023734" "ENSG00000023839"
#> [421] "ENSG00000023892" "ENSG00000023902" "ENSG00000023909" "ENSG00000024048"
#> [425] "ENSG00000024422" "ENSG00000024526" "ENSG00000024862" "ENSG00000025039"
#> [429] "ENSG00000025156" "ENSG00000025293" "ENSG00000025423" "ENSG00000025434"
#> [433] "ENSG00000025708" "ENSG00000025770" "ENSG00000025772" "ENSG00000025796"
#> [437] "ENSG00000025800" "ENSG00000026025" "ENSG00000026103" "ENSG00000026508"
#> [441] "ENSG00000026559" "ENSG00000026652" "ENSG00000026751" "ENSG00000026950"
#> [445] "ENSG00000027001" "ENSG00000027075" "ENSG00000027644" "ENSG00000027697"
#> [449] "ENSG00000027847" "ENSG00000027869" "ENSG00000028116" "ENSG00000028137"
#> [453] "ENSG00000028203" "ENSG00000028277" "ENSG00000028310" "ENSG00000028528"
#> [457] "ENSG00000028839" "ENSG00000029153" "ENSG00000029363" "ENSG00000029364"
#> [461] "ENSG00000029534" "ENSG00000029559" "ENSG00000029639" "ENSG00000029725"
#> [465] "ENSG00000029993" "ENSG00000030066" "ENSG00000030304" "ENSG00000030582"
#> [469] "ENSG00000031003" "ENSG00000031081" "ENSG00000031691" "ENSG00000031698"
#> [473] "ENSG00000031823" "ENSG00000032219" "ENSG00000032389" "ENSG00000032444"
#> [477] "ENSG00000032742" "ENSG00000033011" "ENSG00000033030" "ENSG00000033050"
#> [481] "ENSG00000033100" "ENSG00000033122" "ENSG00000033170" "ENSG00000033178"
#> [485] "ENSG00000033327" "ENSG00000033627" "ENSG00000033800" "ENSG00000033867"
#> [489] "ENSG00000034053" "ENSG00000034152" "ENSG00000034239" "ENSG00000034510"
#> [493] "ENSG00000034533" "ENSG00000034677" "ENSG00000034693" "ENSG00000034713"
#> [497] "ENSG00000034971" "ENSG00000035115" "ENSG00000035141" "ENSG00000035403"
#> [501] "ENSG00000035499" "ENSG00000035664" "ENSG00000035681" "ENSG00000035687"
#> [505] "ENSG00000035720" "ENSG00000035862" "ENSG00000035928" "ENSG00000036054"
#> [509] "ENSG00000036257" "ENSG00000036448" "ENSG00000036530" "ENSG00000036549"
#> [513] "ENSG00000036565" "ENSG00000036672" "ENSG00000036828" "ENSG00000037042"
#> [517] "ENSG00000037280" "ENSG00000037474" "ENSG00000037637" "ENSG00000037749"
#> [521] "ENSG00000037757" "ENSG00000037897" "ENSG00000038002" "ENSG00000038210"
#> [525] "ENSG00000038219" "ENSG00000038274" "ENSG00000038295" "ENSG00000038382"
#> [529] "ENSG00000038427" "ENSG00000038532" "ENSG00000038945" "ENSG00000039068"
#> [533] "ENSG00000039123" "ENSG00000039139" "ENSG00000039319" "ENSG00000039523"
#> [537] "ENSG00000039537" "ENSG00000039560" "ENSG00000039600" "ENSG00000039650"
#> [541] "ENSG00000039987" "ENSG00000040199" "ENSG00000040275" "ENSG00000040341"
#> [545] "ENSG00000040487" "ENSG00000040531" "ENSG00000040608" "ENSG00000040633"
#> [549] "ENSG00000040731" "ENSG00000040933" "ENSG00000041353" "ENSG00000041357"
#> [553] "ENSG00000041515" "ENSG00000041802" "ENSG00000041880" "ENSG00000041982"
#> [557] "ENSG00000041988" "ENSG00000042062" "ENSG00000042088" "ENSG00000042286"
#> [561] "ENSG00000042304" "ENSG00000042317" "ENSG00000042429" "ENSG00000042445"
#> [565] "ENSG00000042493" "ENSG00000042753" "ENSG00000042781" "ENSG00000042813"
#> [569] "ENSG00000042832" "ENSG00000042980" "ENSG00000043039" "ENSG00000043093"
#> [573] "ENSG00000043143" "ENSG00000043355" "ENSG00000043462" "ENSG00000043514"
#> [577] "ENSG00000043591" "ENSG00000044012" "ENSG00000044090" "ENSG00000044115"
#> [581] "ENSG00000044446" "ENSG00000044459" "ENSG00000044524" "ENSG00000044574"
#> [585] "ENSG00000046604" "ENSG00000046647" "ENSG00000046651" "ENSG00000046653"
#> [589] "ENSG00000046774" "ENSG00000046889" "ENSG00000047056" "ENSG00000047188"
#> [593] "ENSG00000047230" "ENSG00000047249" "ENSG00000047315" "ENSG00000047346"
#> [597] "ENSG00000047365" "ENSG00000047410" "ENSG00000047457" "ENSG00000047578"
#> [601] "ENSG00000047579" "ENSG00000047617" "ENSG00000047621" "ENSG00000047634"
#> [605] "ENSG00000047644" "ENSG00000047648" "ENSG00000047662" "ENSG00000047849"
#> [609] "ENSG00000047932" "ENSG00000047936" "ENSG00000048028" "ENSG00000048052"
#> [613] "ENSG00000048140" "ENSG00000048162" "ENSG00000048342" "ENSG00000048392"
#> [617] "ENSG00000048405" "ENSG00000048462" "ENSG00000048471" "ENSG00000048540"
#> [621] "ENSG00000048544" "ENSG00000048545" "ENSG00000048649" "ENSG00000048707"
#> [625] "ENSG00000048740" "ENSG00000048828" "ENSG00000048991" "ENSG00000049089"
#> [629] "ENSG00000049130" "ENSG00000049167" "ENSG00000049192" "ENSG00000049239"
#> [633] "ENSG00000049245" "ENSG00000049246" "ENSG00000049247" "ENSG00000049249"
#> [637] "ENSG00000049283" "ENSG00000049323" "ENSG00000049540" "ENSG00000049541"
#> [641] "ENSG00000049618" "ENSG00000049656" "ENSG00000049759" "ENSG00000049768"
#> [645] "ENSG00000049769" "ENSG00000049860" "ENSG00000049883" "ENSG00000050030"
#> [649] "ENSG00000050130" "ENSG00000050165" "ENSG00000050327" "ENSG00000050344"
#> [653] "ENSG00000050393" "ENSG00000050405" "ENSG00000050426" "ENSG00000050438"
#> [657] "ENSG00000050555" "ENSG00000050628" "ENSG00000050730" "ENSG00000050748"
#> [661] "ENSG00000050767" "ENSG00000050820" "ENSG00000051009" "ENSG00000051108"
#> [665] "ENSG00000051128" "ENSG00000051180" "ENSG00000051341" "ENSG00000051382"
#> [669] "ENSG00000051523" "ENSG00000051620" "ENSG00000051825" "ENSG00000052126"
#> [673] "ENSG00000052344" "ENSG00000052723" "ENSG00000052749" "ENSG00000052795"
#> [677] "ENSG00000052802" "ENSG00000052841" "ENSG00000052850" "ENSG00000053108"
#> [681] "ENSG00000053254" "ENSG00000053371" "ENSG00000053372" "ENSG00000053438"
#> [685] "ENSG00000053501" "ENSG00000053524" "ENSG00000053702" "ENSG00000053747"
#> [689] "ENSG00000053770" "ENSG00000053900" "ENSG00000053918" "ENSG00000054116"
#> [693] "ENSG00000054118" "ENSG00000054179" "ENSG00000054267" "ENSG00000054277"
#> [697] "ENSG00000054282" "ENSG00000054356" "ENSG00000054392" "ENSG00000054523"
#> [701] "ENSG00000054598" "ENSG00000054611" "ENSG00000054654" "ENSG00000054690"
#> [705] "ENSG00000054793" "ENSG00000054796" "ENSG00000054803" "ENSG00000054938"
#> [709] "ENSG00000054965" "ENSG00000054967" "ENSG00000054983" "ENSG00000055044"
#> [713] "ENSG00000055070" "ENSG00000055118" "ENSG00000055130" "ENSG00000055147"
#> [717] "ENSG00000055163" "ENSG00000055208" "ENSG00000055211" "ENSG00000055332"
#> [721] "ENSG00000055483" "ENSG00000055609" "ENSG00000055732" "ENSG00000055917"
#> [725] "ENSG00000055950" "ENSG00000055955" "ENSG00000055957" "ENSG00000056050"
#> [729] "ENSG00000056097" "ENSG00000056277" "ENSG00000056291" "ENSG00000056487"
#> [733] "ENSG00000056558" "ENSG00000056586" "ENSG00000056736" "ENSG00000056972"
#> [737] "ENSG00000056998" "ENSG00000057019" "ENSG00000057149" "ENSG00000057252"
#> [741] "ENSG00000057294" "ENSG00000057468" "ENSG00000057593" "ENSG00000057608"
#> [745] "ENSG00000057657" "ENSG00000057663" "ENSG00000057704" "ENSG00000057757"
#> [749] "ENSG00000057935" "ENSG00000058056" "ENSG00000058063" "ENSG00000058085"
#> [753] "ENSG00000058091" "ENSG00000058262" "ENSG00000058272" "ENSG00000058335"
#> [757] "ENSG00000058404" "ENSG00000058453" "ENSG00000058600" "ENSG00000058668"
#> [761] "ENSG00000058673" "ENSG00000058729" "ENSG00000058799" "ENSG00000058804"
#> [765] "ENSG00000058866" "ENSG00000059122" "ENSG00000059145" "ENSG00000059377"
#> [769] "ENSG00000059378" "ENSG00000059573" "ENSG00000059588" "ENSG00000059691"
#> [773] "ENSG00000059728" "ENSG00000059758" "ENSG00000059804" "ENSG00000059915"
#> [777] "ENSG00000060069" "ENSG00000060138" "ENSG00000060140" "ENSG00000060237"
#> [781] "ENSG00000060303" "ENSG00000060339" "ENSG00000060491" "ENSG00000060558"
#> [785] "ENSG00000060566" "ENSG00000060642" "ENSG00000060656" "ENSG00000060688"
#> [789] "ENSG00000060709" "ENSG00000060718" "ENSG00000060749" "ENSG00000060762"
#> [793] "ENSG00000060971" "ENSG00000060982" "ENSG00000061273" "ENSG00000061337"
#> [797] "ENSG00000061455" "ENSG00000061492" "ENSG00000061656" "ENSG00000061676"
#> [801] "ENSG00000061794" "ENSG00000061918" "ENSG00000061936" "ENSG00000061938"
#> [805] "ENSG00000061987" "ENSG00000062038" "ENSG00000062096" "ENSG00000062194"
#> [809] "ENSG00000062282" "ENSG00000062370" "ENSG00000062524" "ENSG00000062598"
#> [813] "ENSG00000062650" "ENSG00000062716" "ENSG00000062725" "ENSG00000063015"
#> [817] "ENSG00000063127" "ENSG00000063176" "ENSG00000063177" "ENSG00000063180"
#> [821] "ENSG00000063241" "ENSG00000063244" "ENSG00000063245" "ENSG00000063322"
#> [825] "ENSG00000063438" "ENSG00000063515" "ENSG00000063587" "ENSG00000063601"
#> [829] "ENSG00000063660" "ENSG00000063761" "ENSG00000063854" "ENSG00000063978"
#> [833] "ENSG00000064012" "ENSG00000064042" "ENSG00000064102" "ENSG00000064115"
#> [837] "ENSG00000064195" "ENSG00000064199" "ENSG00000064201" "ENSG00000064205"
#> [841] "ENSG00000064218" "ENSG00000064225" "ENSG00000064270" "ENSG00000064300"
#> [845] "ENSG00000064309" "ENSG00000064313" "ENSG00000064393" "ENSG00000064419"
#> [849] "ENSG00000064490" "ENSG00000064545" "ENSG00000064547" "ENSG00000064601"
#> [853] "ENSG00000064607" "ENSG00000064651" "ENSG00000064652" "ENSG00000064655"
#> [857] "ENSG00000064666" "ENSG00000064687" "ENSG00000064692" "ENSG00000064703"
#> [861] "ENSG00000064726" "ENSG00000064763" "ENSG00000064787" "ENSG00000064835"
#> [865] "ENSG00000064886" "ENSG00000064932" "ENSG00000064933" "ENSG00000064961"
#> [869] "ENSG00000064989" "ENSG00000064995" "ENSG00000064999" "ENSG00000065000"
#> [873] "ENSG00000065029" "ENSG00000065054" "ENSG00000065057" "ENSG00000065060"
#> [877] "ENSG00000065135" "ENSG00000065150" "ENSG00000065154" "ENSG00000065183"
#> [881] "ENSG00000065243" "ENSG00000065268" "ENSG00000065308" "ENSG00000065320"
#> [885] "ENSG00000065325" "ENSG00000065328" "ENSG00000065357" "ENSG00000065361"
#> [889] "ENSG00000065371" "ENSG00000065413" "ENSG00000065427" "ENSG00000065485"
#> [893] "ENSG00000065491" "ENSG00000065518" "ENSG00000065526" "ENSG00000065534"
#> [897] "ENSG00000065548" "ENSG00000065559" "ENSG00000065600" "ENSG00000065609"
#> [901] "ENSG00000065613" "ENSG00000065615" "ENSG00000065618" "ENSG00000065621"
#> [905] "ENSG00000065665" "ENSG00000065675" "ENSG00000065717" "ENSG00000065802"
#> [909] "ENSG00000065809" "ENSG00000065833" "ENSG00000065882" "ENSG00000065883"
#> [913] "ENSG00000065911" "ENSG00000065923" "ENSG00000065970" "ENSG00000065989"
#> [917] "ENSG00000066027" "ENSG00000066032" "ENSG00000066044" "ENSG00000066056"
#> [921] "ENSG00000066084" "ENSG00000066117" "ENSG00000066135" "ENSG00000066136"
#> [925] "ENSG00000066185" "ENSG00000066230" "ENSG00000066248" "ENSG00000066279"
#> [929] "ENSG00000066294" "ENSG00000066322" "ENSG00000066336" "ENSG00000066379"
#> [933] "ENSG00000066382" "ENSG00000066405" "ENSG00000066422" "ENSG00000066427"
#> [937] "ENSG00000066455" "ENSG00000066468" "ENSG00000066557" "ENSG00000066583"
#> [941] "ENSG00000066629" "ENSG00000066651" "ENSG00000066654" "ENSG00000066735"
#> [945] "ENSG00000066739" "ENSG00000066777" "ENSG00000066827" "ENSG00000066855"
#> [949] "ENSG00000066923" "ENSG00000066926" "ENSG00000066933" "ENSG00000067048"
#> [953] "ENSG00000067057" "ENSG00000067064" "ENSG00000067066" "ENSG00000067082"
#> [957] "ENSG00000067113" "ENSG00000067141" "ENSG00000067167" "ENSG00000067177"
#> [961] "ENSG00000067182" "ENSG00000067191" "ENSG00000067208" "ENSG00000067221"
#> [965] "ENSG00000067225" "ENSG00000067248" "ENSG00000067334" "ENSG00000067365"
#> [969] "ENSG00000067369" "ENSG00000067445" "ENSG00000067533" "ENSG00000067560"
#> [973] "ENSG00000067596" "ENSG00000067601" "ENSG00000067606" "ENSG00000067646"
#> [977] "ENSG00000067704" "ENSG00000067715" "ENSG00000067798" "ENSG00000067829"
#> [981] "ENSG00000067836" "ENSG00000067840" "ENSG00000067842" "ENSG00000067900"
#> [985] "ENSG00000067955" "ENSG00000067992" "ENSG00000068001" "ENSG00000068024"
#> [989] "ENSG00000068028" "ENSG00000068078" "ENSG00000068079" "ENSG00000068097"
#> [993] "ENSG00000068120" "ENSG00000068137" "ENSG00000068305" "ENSG00000068308"
#> [997] "ENSG00000068323" "ENSG00000068354" "ENSG00000068383" "ENSG00000068394"
fNames(clevelandSmall_cSet, 'rna') <- fNames(clevelandSmall_cSet, 'rna')
mDataNames(clevelandSmall_cSet)
#> [1] "rna" "rnaseq"
mDataNames(clevelandSmall_cSet) <- mDataNames(clevelandSmall_cSet)
molecularProfilesSlot(clevelandSmall_cSet)
#> A MultiAssayExperiment object of 2 listed
#> experiments with user-defined names and respective classes.
#> Containing an ExperimentList class object of length 2:
#> [1] rna: SummarizedExperiment with 1000 rows and 9 columns
#> [2] rnaseq: SummarizedExperiment with 1000 rows and 9 columns
#> Functionality:
#> experiments() - obtain the ExperimentList instance
#> colData() - the primary/phenotype DataFrame
#> sampleMap() - the sample coordination DataFrame
#> `$`, `[`, `[[` - extract colData columns, subset, or experiment
#> *Format() - convert into a long or wide DataFrame
#> assays() - convert ExperimentList to a SimpleList of matrices
#> exportClass() - save data to flat files
molecularProfilesSlot(clevelandSmall_cSet) <- molecularProfilesSlot(clevelandSmall_cSet)
sensitivityInfo(clevelandSmall_cSet)
#> Warning:
#> [CoreGx::assay] Cannot use key=TRUE when summarize=TRUE. Ignoring the key argument.
#> Warning: column(s) not removed because not found: [.colnames]
#> Warning: column(s) not removed because not found: [.colnames]
#> treatment1id replicate_id sampleid rn
#> radiation:1_CHP-212 radiation 1 CHP-212 MHH-NB-11_radiation_6
#> radiation:2_CHP-212 radiation 2 CHP-212 MHH-NB-11_radiation_6
#> radiation:1_IMR-32 radiation 1 IMR-32 IMR-32_radiation_2
#> radiation:2_IMR-32 radiation 2 IMR-32 IMR-32_radiation_2
#> radiation:1_KP-N-S19s radiation 1 KP-N-S19s NB1_radiation_8
#> radiation:2_KP-N-S19s radiation 2 KP-N-S19s NB1_radiation_8
#> radiation:1_MHH-NB-11 radiation 1 MHH-NB-11 SK-N-AS_radiation_3
#> radiation:2_MHH-NB-11 radiation 2 MHH-NB-11 SK-N-AS_radiation_3
#> radiation:1_NB1 radiation 1 NB1 SK-N-SH_radiation_7
#> radiation:2_NB1 radiation 2 NB1 SK-N-SH_radiation_7
#> radiation:1_SK-N-AS radiation 1 SK-N-AS KP-N-S19s_radiation_5
#> radiation:2_SK-N-AS radiation 2 SK-N-AS KP-N-S19s_radiation_5
#> radiation:1_SK-N-FI radiation 1 SK-N-FI CHP-212_radiation_4
#> radiation:2_SK-N-FI radiation 2 SK-N-FI CHP-212_radiation_4
#> radiation:1_SK-N-SH radiation 1 SK-N-SH SK-N-FI_radiation_1
#> radiation:2_SK-N-SH radiation 2 SK-N-SH SK-N-FI_radiation_1
#> radiation:1_SNU-245 radiation 1 SNU-245 SNU-245_radiation_9
#> radiation:2_SNU-245 radiation 2 SNU-245 SNU-245_radiation_9
#> radiation:1_SNU-869 radiation 1 SNU-869 SNU-869_radiation_10
#> radiation:2_SNU-869 radiation 2 SNU-869 SNU-869_radiation_10
#> col Key .rownames Dose1-1Gy-rep1 Dose1-1Gy-rep2 Dose2-2Gy
#> radiation:1_CHP-212 4 CHP-212 1 1 2
#> radiation:2_CHP-212 4 CHP-212 1 1 2
#> radiation:1_IMR-32 2 IMR-32 1 1 2
#> radiation:2_IMR-32 2 IMR-32 1 1 2
#> radiation:1_KP-N-S19s 5 KP-N-S19s 1 1 2
#> radiation:2_KP-N-S19s 5 KP-N-S19s 1 1 2
#> radiation:1_MHH-NB-11 6 MHH-NB-11 1 1 2
#> radiation:2_MHH-NB-11 6 MHH-NB-11 1 1 2
#> radiation:1_NB1 8 NB1 1 1 2
#> radiation:2_NB1 8 NB1 1 1 2
#> radiation:1_SK-N-AS 3 SK-N-AS 1 1 2
#> radiation:2_SK-N-AS 3 SK-N-AS 1 1 2
#> radiation:1_SK-N-FI 1 SK-N-FI 1 1 2
#> radiation:2_SK-N-FI 1 SK-N-FI 1 1 2
#> radiation:1_SK-N-SH 7 SK-N-SH 1 1 2
#> radiation:2_SK-N-SH 7 SK-N-SH 1 1 2
#> radiation:1_SNU-245 9 SNU-245 1 1 2
#> radiation:2_SNU-245 9 SNU-245 1 1 2
#> radiation:1_SNU-869 10 SNU-869 1 1 2
#> radiation:2_SNU-869 10 SNU-869 1 1 2
#> Dose3-3Gy Dose4-4Gy Dose5-5Gy Dose6-6Gy Dose8-8Gy
#> radiation:1_CHP-212 3 4 5 6 8
#> radiation:2_CHP-212 3 4 5 6 8
#> radiation:1_IMR-32 3 4 5 6 8
#> radiation:2_IMR-32 3 4 5 6 8
#> radiation:1_KP-N-S19s 3 4 5 6 8
#> radiation:2_KP-N-S19s 3 4 5 6 8
#> radiation:1_MHH-NB-11 3 4 5 6 8
#> radiation:2_MHH-NB-11 3 4 5 6 8
#> radiation:1_NB1 3 4 5 6 8
#> radiation:2_NB1 3 4 5 6 8
#> radiation:1_SK-N-AS 3 4 5 6 8
#> radiation:2_SK-N-AS 3 4 5 6 8
#> radiation:1_SK-N-FI 3 4 5 6 8
#> radiation:2_SK-N-FI 3 4 5 6 8
#> radiation:1_SK-N-SH 3 4 5 6 8
#> radiation:2_SK-N-SH 3 4 5 6 8
#> radiation:1_SNU-245 3 4 5 6 8
#> radiation:2_SNU-245 3 4 5 6 8
#> radiation:1_SNU-869 3 4 5 6 8
#> radiation:2_SNU-869 3 4 5 6 8
#> Dose10-10Gy treatmentid treatment_uid sample_uid
#> radiation:1_CHP-212 10 radiation radiation:1 CHP-212
#> radiation:2_CHP-212 10 radiation radiation:2 CHP-212
#> radiation:1_IMR-32 10 radiation radiation:1 IMR-32
#> radiation:2_IMR-32 10 radiation radiation:2 IMR-32
#> radiation:1_KP-N-S19s 10 radiation radiation:1 KP-N-S19s
#> radiation:2_KP-N-S19s 10 radiation radiation:2 KP-N-S19s
#> radiation:1_MHH-NB-11 10 radiation radiation:1 MHH-NB-11
#> radiation:2_MHH-NB-11 10 radiation radiation:2 MHH-NB-11
#> radiation:1_NB1 10 radiation radiation:1 NB1
#> radiation:2_NB1 10 radiation radiation:2 NB1
#> radiation:1_SK-N-AS 10 radiation radiation:1 SK-N-AS
#> radiation:2_SK-N-AS 10 radiation radiation:2 SK-N-AS
#> radiation:1_SK-N-FI 10 radiation radiation:1 SK-N-FI
#> radiation:2_SK-N-FI 10 radiation radiation:2 SK-N-FI
#> radiation:1_SK-N-SH 10 radiation radiation:1 SK-N-SH
#> radiation:2_SK-N-SH 10 radiation radiation:2 SK-N-SH
#> radiation:1_SNU-245 10 radiation radiation:1 SNU-245
#> radiation:2_SNU-245 10 radiation radiation:2 SNU-245
#> radiation:1_SNU-869 10 radiation radiation:1 SNU-869
#> radiation:2_SNU-869 10 radiation radiation:2 SNU-869
#> exp_id
#> radiation:1_CHP-212 radiation:1_CHP-212
#> radiation:2_CHP-212 radiation:2_CHP-212
#> radiation:1_IMR-32 radiation:1_IMR-32
#> radiation:2_IMR-32 radiation:2_IMR-32
#> radiation:1_KP-N-S19s radiation:1_KP-N-S19s
#> radiation:2_KP-N-S19s radiation:2_KP-N-S19s
#> radiation:1_MHH-NB-11 radiation:1_MHH-NB-11
#> radiation:2_MHH-NB-11 radiation:2_MHH-NB-11
#> radiation:1_NB1 radiation:1_NB1
#> radiation:2_NB1 radiation:2_NB1
#> radiation:1_SK-N-AS radiation:1_SK-N-AS
#> radiation:2_SK-N-AS radiation:2_SK-N-AS
#> radiation:1_SK-N-FI radiation:1_SK-N-FI
#> radiation:2_SK-N-FI radiation:2_SK-N-FI
#> radiation:1_SK-N-SH radiation:1_SK-N-SH
#> radiation:2_SK-N-SH radiation:2_SK-N-SH
#> radiation:1_SNU-245 radiation:1_SNU-245
#> radiation:2_SNU-245 radiation:2_SNU-245
#> radiation:1_SNU-869 radiation:1_SNU-869
#> radiation:2_SNU-869 radiation:2_SNU-869
sensitivityInfo(clevelandSmall_cSet) <- sensitivityInfo(clevelandSmall_cSet)
#> Warning:
#> [CoreGx::assay] Cannot use key=TRUE when summarize=TRUE. Ignoring the key argument.
#> Warning: column(s) not removed because not found: [.colnames]
#> Warning: column(s) not removed because not found: [.colnames]
#> Warning: column(s) not removed because not found: [.colnames]
#> Warning:
#> [CoreGx::rowData<-] The ID columns treatment1dose are not present in value. The function will attempt to join with existing rowIDs, but this may fail!
#> Warning: column(s) not removed because not found: [.colnames]
sensitivityMeasures(clevelandSmall_cSet) <- sensitivityMeasures(clevelandSmall_cSet)
#> Warning: column(s) not removed because not found: [.colnames]
#> Warning: column(s) not removed because not found: [.colnames]
#> Warning: The CoreSet class structure has been updated! Assignment via sensitivityProfiles no long works, please see vignette('The LongTable Class', package='CoreGx') for more information.
sensitivityMeasures(clevelandSmall_cSet) <- sensitivityMeasures(clevelandSmall_cSet)
#> Warning: column(s) not removed because not found: [.colnames]
#> Warning: column(s) not removed because not found: [.colnames]
#> Warning: The CoreSet class structure has been updated! Assignment via sensitivityProfiles no long works, please see vignette('The LongTable Class', package='CoreGx') for more information.
sensitivityProfiles(clevelandSmall_cSet)
#> Warning: column(s) not removed because not found: [.colnames]
#> exp_id AUC_published AUC_recomputed alpha beta
#> <char> <num> <num> <num> <num>
#> 1: radiation:1_CHP-212 1.833 1.3440925 0.4615728 0.00000000
#> 2: radiation:1_IMR-32 0.634 0.5490508 0.8098218 0.00000000
#> 3: radiation:1_KP-N-S19s 1.477 2.3251731 0.2974555 0.00000000
#> 4: radiation:1_MHH-NB-11 0.545 0.6260366 0.7520926 0.00000000
#> 5: radiation:1_NB1 0.774 0.7906043 0.6551282 0.00000000
#> 6: radiation:1_SK-N-AS 3.789 3.2589305 0.1245542 0.01650291
#> 7: radiation:1_SK-N-FI 3.410 2.8616423 0.1521972 0.01783452
#> 8: radiation:1_SK-N-SH 1.661 1.8720963 0.3584304 0.00000000
#> 9: radiation:1_SNU-245 4.145 4.5066456 0.1371595 0.00000000
#> 10: radiation:1_SNU-869 4.113 4.3476552 0.1450146 0.00000000
#> 11: radiation:2_CHP-212 1.833 1.3440925 0.4615728 0.00000000
#> 12: radiation:2_IMR-32 0.634 0.5490508 0.8098218 0.00000000
#> 13: radiation:2_KP-N-S19s 1.477 2.3251731 0.2974555 0.00000000
#> 14: radiation:2_MHH-NB-11 0.545 0.6260366 0.7520926 0.00000000
#> 15: radiation:2_NB1 0.774 0.7906043 0.6551282 0.00000000
#> 16: radiation:2_SK-N-AS 3.789 3.2589305 0.1245542 0.01650291
#> 17: radiation:2_SK-N-FI 3.410 2.8616423 0.1521972 0.01783452
#> 18: radiation:2_SK-N-SH 1.661 1.8720963 0.3584304 0.00000000
#> 19: radiation:2_SNU-245 4.145 4.5066456 0.1371595 0.00000000
#> 20: radiation:2_SNU-869 4.113 4.3476552 0.1450146 0.00000000
#> exp_id AUC_published AUC_recomputed alpha beta
#> SF2
#> <num>
#> 1: 0.4160333
#> 2: 0.0824000
#> 3: 0.2469118
#> 4: 0.0652000
#> 5: 0.2169389
#> 6: 0.7827120
#> 7: 0.6181713
#> 8: 0.3086860
#> 9: 0.7885541
#> 10: 0.7346687
#> 11: 0.4160333
#> 12: 0.0824000
#> 13: 0.2469118
#> 14: 0.0652000
#> 15: 0.2169389
#> 16: 0.7827120
#> 17: 0.6181713
#> 18: 0.3086860
#> 19: 0.7885541
#> 20: 0.7346687
#> SF2
sensitivityProfiles(clevelandSmall_cSet) <- sensitivityProfiles(clevelandSmall_cSet)
#> Warning: column(s) not removed because not found: [.colnames]
#> Warning: The CoreSet class structure has been updated! Assignment via sensitivityProfiles no long works, please see vignette('The LongTable Class', package='CoreGx') for more information.
head(sensitivityRaw(clevelandSmall_cSet))
#> Warning: column(s) not removed because not found: [.colnames]
#> , , Dose
#>
#> dose1
#> radiation:1:1_CHP-212 1
#> radiation:1:1_IMR-32 1
#> radiation:1:1_KP-N-S19s 1
#> radiation:1:1_MHH-NB-11 1
#> radiation:1:1_NB1 1
#> radiation:1:1_SK-N-AS 1
#>
#> , , Viability
#>
#> dose1
#> radiation:1:1_CHP-212 0.7312816
#> radiation:1:1_IMR-32 0.4154556
#> radiation:1:1_KP-N-S19s 0.4164569
#> radiation:1:1_MHH-NB-11 0.3711540
#> radiation:1:1_NB1 0.2981927
#> radiation:1:1_SK-N-AS 0.9488615
#>
sensitivityRaw(clevelandSmall_cSet) <- sensitivityRaw(clevelandSmall_cSet)
#> Warning: column(s) not removed because not found: [.colnames]
#> Warning: column(s) not removed because not found: [.colnames]
#> Warning: column(s) not removed because not found: [.colnames]
treatmentResponse(clevelandSmall_cSet)
#> <TreatmentResponseExperiment>
#>
#> Warning: column(s) not removed because not found: [.colnames]
#> dim: 9 10
#> assays(2): sensitivity profiles
#> rownames(9): radiation:1:1 radiation:2:1 ... radiation:10:1 radiation:1:2
#> rowData(4): treatment1id treatment1dose replicate_id row Key
#> Warning: column(s) not removed because not found: [.colnames]
#> Warning: column(s) not removed because not found: [.colnames]
#> colnames(10):
#> Warning: column(s) not removed because not found: [.colnames]
#> colData(4): sampleid rn .rownames col Key
#> metadata(1): experiment_metadata
treatmentResponse(clevelandSmall_cSet) <- treatmentResponse(clevelandSmall_cSet)
sensNumber(clevelandSmall_cSet)
#> Warning: column(s) not removed because not found: [.colnames]
#> CHP-212 IMR-32 KP-N-S19s MHH-NB-11 NB1 SK-N-AS SK-N-FI SK-N-SH
#> radiation 9 9 9 9 9 9 9 9
#> SNU-245 SNU-869
#> radiation 9 9
sensNumber(clevelandSmall_cSet) <- sensNumber(clevelandSmall_cSet)
#> Warning: column(s) not removed because not found: [.colnames]
pertNumber(clevelandSmall_cSet)
#> [1] 0 0 0
pertNumber(clevelandSmall_cSet) <- pertNumber(clevelandSmall_cSet)