The NanoStringRCCSet was inherited from Biobase’s ExpressionSet class. The NanoStringRCCSet class was designed to encapsulate data and corresponding methods for Nanostring RCC files generated from the NanoString nCounter platform.
Loading the NanoStringNCTools package allows users access to the NanoStringRCCSet class.
Load additional packages for vignette plotting.
datadir <- system.file("extdata", "3D_Bio_Example_Data",
package = "NanoStringNCTools")
rcc_files <- dir(datadir, pattern = "SKMEL.*\\.RCC$", full.names = TRUE)
rlf_file <- file.path(datadir, "3D_SolidTumor_Sig.rlf")
sample_annotation <- file.path(datadir, "3D_SolidTumor_PhenoData.csv")
demoData <- readNanoStringRccSet(rcc_files, rlfFile = rlf_file,
phenoDataFile = sample_annotation)
class( demoData )
#> [1] "NanoStringRccSet"
#> attr(,"package")
#> [1] "NanoStringNCTools"
isS4( demoData )
#> [1] TRUE
is( demoData, "ExpressionSet" )
#> [1] TRUE
demoData
#> NanoStringRccSet (storageMode: lockedEnvironment)
#> assayData: 397 features, 12 samples
#> element names: exprs
#> protocolData
#> sampleNames: SKMEL2-DMSO-8h-R1_04.RCC SKMEL2-DMSO-8h-R2_04.RCC ...
#> SKMEL28-VEM-8h-R3_10.RCC (12 total)
#> varLabels: FileVersion SoftwareVersion ... CartridgeBarcode (17
#> total)
#> varMetadata: labelDescription
#> phenoData
#> sampleNames: SKMEL2-DMSO-8h-R1_04.RCC SKMEL2-DMSO-8h-R2_04.RCC ...
#> SKMEL28-VEM-8h-R3_10.RCC (12 total)
#> varLabels: Treatment BRAFGenotype
#> varMetadata: labelDescription
#> featureData
#> featureNames: Endogenous_TP53_NM_000546.2
#> Endogenous_IL22RA2_NM_181310.1 ... SNV_REF_PIK3CA Ref (exon
#> 10)|hg19|+|chr3:178936060-178936141_nRef_00032.1 (397 total)
#> fvarLabels: CodeClass GeneName ... BarcodeComments (10 total)
#> fvarMetadata: labelDescription
#> experimentData: use 'experimentData(object)'
#> Annotation: 3D_SolidTumor_Sig
#> signature: noneAlongside the accessors associated with the ExpressionSet class, NanoStringRCCSet objects have unique additional assignment and accessor methods faciliting common ways to view nCounter data and associated labels.
head( pData( demoData ), 2 )
#> Treatment BRAFGenotype
#> SKMEL2-DMSO-8h-R1_04.RCC DMSO wt/wt
#> SKMEL2-DMSO-8h-R2_04.RCC DMSO wt/wt
protocolData( demoData )
#> An object of class 'AnnotatedDataFrame'
#> sampleNames: SKMEL2-DMSO-8h-R1_04.RCC SKMEL2-DMSO-8h-R2_04.RCC ...
#> SKMEL28-VEM-8h-R3_10.RCC (12 total)
#> varLabels: FileVersion SoftwareVersion ... CartridgeBarcode (17
#> total)
#> varMetadata: labelDescription
svarLabels( demoData )
#> [1] "Treatment" "BRAFGenotype" "FileVersion" "SoftwareVersion"
#> [5] "SystemType" "SampleID" "SampleOwner" "SampleComments"
#> [9] "SampleDate" "SystemAPF" "AssayType" "LaneID"
#> [13] "FovCount" "FovCounted" "ScannerID" "StagePosition"
#> [17] "BindingDensity" "CartridgeID" "CartridgeBarcode"
head( sData(demoData), 2 )
#> Treatment BRAFGenotype FileVersion SoftwareVersion
#> SKMEL2-DMSO-8h-R1_04.RCC DMSO wt/wt 1.7 4.0.0.3
#> SKMEL2-DMSO-8h-R2_04.RCC DMSO wt/wt 1.7 4.0.0.3
#> SystemType SampleID SampleOwner SampleComments
#> SKMEL2-DMSO-8h-R1_04.RCC Gen2 SKMEL2-DMSO-8hr DNA-RNA-Protein
#> SKMEL2-DMSO-8h-R2_04.RCC Gen2 SKMEL2-DMSO-8hr DNA-RNA-Protein
#> SampleDate SystemAPF AssayType LaneID FovCount
#> SKMEL2-DMSO-8h-R1_04.RCC 2017-01-13 n6_vDV1 <NA> 4 280
#> SKMEL2-DMSO-8h-R2_04.RCC 2017-02-07 n6_vDV1 <NA> 4 280
#> FovCounted ScannerID StagePosition BindingDensity
#> SKMEL2-DMSO-8h-R1_04.RCC 268 1207C0049 3 0.80
#> SKMEL2-DMSO-8h-R2_04.RCC 280 1207C0049 6 1.03
#> CartridgeID CartridgeBarcode
#> SKMEL2-DMSO-8h-R1_04.RCC AGBT-3DBio-C1-SKMEL2
#> SKMEL2-DMSO-8h-R2_04.RCC AGBT-3DBio-3-C1-SKMEL2Design information can be assigned to the NanoStringRCCSet object, as well as feature and sample labels to use for NanoStringRCCSet plotting methods.
Easily summarize count results using the summary method. Data summaries can be generated across features or samples. Labels can be used to generate summaries based on feature or sample groupings.
head( summary( demoData , MARGIN = 1 ), 2 )
#> GeomMean SizeFactor MeanLog2 SDLog2 Min
#> Endogenous_TP53_NM_000546.2 547.134439 5.88657439 9.095752 0.4357479 270
#> Endogenous_IL22RA2_NM_181310.1 4.842601 0.05210115 2.275782 1.1326693 1
#> Q1 Median Q3 Max
#> Endogenous_TP53_NM_000546.2 519.00 602 611.25 810
#> Endogenous_IL22RA2_NM_181310.1 2.75 6 9.25 11
head( summary( demoData , MARGIN = 2 ), 2 )
#> GeomMean SizeFactor MeanLog2 SDLog2 Min Q1 Median
#> SKMEL2-DMSO-8h-R1_04.RCC 81.91473 0.8813138 6.356051 3.548682 0 11 42
#> SKMEL2-DMSO-8h-R2_04.RCC 111.61733 1.2008817 6.802417 3.571600 2 13 54
#> Q3 Max
#> SKMEL2-DMSO-8h-R1_04.RCC 525 30467
#> SKMEL2-DMSO-8h-R2_04.RCC 786 79362
unique( sData( demoData )$"Treatment" )
#> [1] "DMSO" "VEM"
head( summary( demoData , MARGIN = 2, GROUP = "Treatment" )$VEM, 2 )
#> GeomMean SizeFactor MeanLog2 SDLog2 Min Q1 Median
#> SKMEL2-VEM-8h-R1_10.RCC 105.0406 1.113396 6.714804 3.543809 0 14 52
#> SKMEL2-VEM-8h-R2_10.RCC 105.2725 1.115854 6.717985 3.652488 1 11 56
#> Q3 Max
#> SKMEL2-VEM-8h-R1_10.RCC 789 40330
#> SKMEL2-VEM-8h-R2_10.RCC 1004 44614
head( summary( demoData , MARGIN = 2, GROUP = "Treatment" )$"DMSO", 2 )
#> GeomMean SizeFactor MeanLog2 SDLog2 Min Q1 Median
#> SKMEL2-DMSO-8h-R1_04.RCC 81.91473 0.8945544 6.356051 3.548682 0 11 42
#> SKMEL2-DMSO-8h-R2_04.RCC 111.61733 1.2189233 6.802417 3.571600 2 13 54
#> Q3 Max
#> SKMEL2-DMSO-8h-R1_04.RCC 525 30467
#> SKMEL2-DMSO-8h-R2_04.RCC 786 79362
head( summary( demoData , MARGIN = 2, GROUP = "Treatment", log2 = FALSE )$"DMSO", 2 )
#> Mean SD Skewness Kurtosis Min Q1 Median Q3
#> SKMEL2-DMSO-8h-R1_04.RCC 1335.766 3763.740 4.327986 21.46799 0 11 42 525
#> SKMEL2-DMSO-8h-R2_04.RCC 1828.778 6226.835 7.636919 75.77340 2 13 54 786
#> Max
#> SKMEL2-DMSO-8h-R1_04.RCC 30467
#> SKMEL2-DMSO-8h-R2_04.RCC 79362Common subsetting methods including those to separate endogenous features from controls are provided with NanoStringRCCSet objects. In addition, users can use the subset or select arguments to further subset by feature or sample, respectively.
length( sampleNames( demoData ) )
#> [1] 12
length( sampleNames( subset( demoData ,
select = phenoData( demoData )[["Treatment"]] == "VEM" ) ) )
#> [1] 6
dim( exprs( demoData ) )
#> [1] 397 12
dim( exprs( endogenousSubset( demoData,
select = phenoData( demoData )[["Treatment"]] == "VEM" ) ) )
#> [1] 180 6
with( housekeepingSubset( demoData ) , table( CodeClass ) )
#> CodeClass
#> Housekeeping
#> 12
with( negativeControlSubset( demoData ) , table( CodeClass ) )
#> CodeClass
#> Negative
#> 6
with( positiveControlSubset( demoData ) , table( CodeClass ) )
#> CodeClass
#> Positive
#> 6
with( controlSubset( demoData ) , table( CodeClass ) )
#> CodeClass
#> Housekeeping Negative PROTEIN_CELL_NORM PROTEIN_NEG
#> 12 6 1 2
#> Positive SNV_INPUT_CTL SNV_NEG SNV_PCR_CTL
#> 6 3 6 3
#> SNV_POS SNV_UDG_CTL
#> 6 2
with( nonControlSubset( demoData ) , table( CodeClass ) )
#> CodeClass
#> Endogenous PROTEIN SNV_REF SNV_VAR
#> 180 26 40 104Similar to the ExpressionSet’s esApply function, an equivalent method is available with NanoStringRCCSet objects. Functions can be applied to assay data feature- or sample-wise.
assayDataElement( demoData, "demoElem" ) <-
assayDataApply( demoData, MARGIN=2, FUN=log, base=10, elt="exprs" )
assayDataElement( demoData, "demoElem" )[1:3, 1:2]
#> SKMEL2-DMSO-8h-R1_04.RCC
#> Endogenous_TP53_NM_000546.2 2.540329
#> Endogenous_IL22RA2_NM_181310.1 0.698970
#> Endogenous_IL2_NM_000586.2 0.301030
#> SKMEL2-DMSO-8h-R2_04.RCC
#> Endogenous_TP53_NM_000546.2 2.7788745
#> Endogenous_IL22RA2_NM_181310.1 0.9542425
#> Endogenous_IL2_NM_000586.2 0.9030900
assayDataApply( demoData, MARGIN=1, FUN=mean, elt="demoElem")[1:5]
#> Endogenous_TP53_NM_000546.2 Endogenous_IL22RA2_NM_181310.1
#> 2.7380941 0.6850787
#> Endogenous_IL2_NM_000586.2 Endogenous_CCR5_NM_000579.1
#> 0.5595554 -Inf
#> Endogenous_PRLR_NM_001204318.1
#> 0.9411847
head( esBy( demoData,
GROUP = "Treatment",
FUN = function( x ) {
assayDataApply( x, MARGIN = 1, FUN=mean, elt="demoElem" )
} ) )
#> DMSO VEM
#> Endogenous_TP53_NM_000546.2 2.6962710 2.7799171
#> Endogenous_IL22RA2_NM_181310.1 0.5927171 0.7774403
#> Endogenous_IL2_NM_000586.2 0.4808935 0.6382173
#> Endogenous_CCR5_NM_000579.1 -Inf 0.7821118
#> Endogenous_PRLR_NM_001204318.1 1.0082754 0.8740940
#> Endogenous_LIF_NM_002309.3 1.8067671 1.4661876There is also a preloaded nCounter normalization method that comes with the NanoStringRCCSet class. This includes the default normalization performed in nSolver.
demoData <- normalize( demoData , type="nSolver", fromELT = "exprs" , toELT = "exprs_norm" )
assayDataElement( demoData , elt = "exprs_norm" )[1:3, 1:2]
#> SKMEL2-DMSO-8h-R1_04.RCC
#> Endogenous_TP53_NM_000546.2 8.753132
#> Endogenous_IL22RA2_NM_181310.1 2.895151
#> Endogenous_IL2_NM_000586.2 1.895151
#> SKMEL2-DMSO-8h-R2_04.RCC
#> Endogenous_TP53_NM_000546.2 8.983110
#> Endogenous_IL22RA2_NM_181310.1 3.071419
#> Endogenous_IL2_NM_000586.2 2.919416The NanoStringRCCSet munge function helps users generate data frames for downstream modeling and visualization. There is also a transform method, which functions similarly to the base transform function.
neg_set <- negativeControlSubset( demoData )
class( neg_set )
#> [1] "NanoStringRccSet"
#> attr(,"package")
#> [1] "NanoStringNCTools"
neg_ctrls <- munge( neg_set )
head( neg_ctrls, 2 )
#> FeatureName SampleName exprs Treatment
#> 1 Negative_NEG_C(0)_ERCC_00019.1 SKMEL2-DMSO-8h-R1_04.RCC 9 DMSO
#> 2 Negative_NEG_D(0)_ERCC_00076.1 SKMEL2-DMSO-8h-R1_04.RCC 9 DMSO
class( neg_ctrls )
#> [1] "data.frame"
head( munge( demoData ), 2 )
#> FeatureName SampleName exprs Treatment
#> 1 Endogenous_TP53_NM_000546.2 SKMEL2-DMSO-8h-R1_04.RCC 347 DMSO
#> 2 Endogenous_IL22RA2_NM_181310.1 SKMEL2-DMSO-8h-R1_04.RCC 5 DMSO
munge( demoData, mapping = ~`BRAFGenotype` + GeneMatrix )
#> DataFrame with 12 rows and 2 columns
#> BRAFGenotype GeneMatrix
#> <character> <matrix>
#> SKMEL2-DMSO-8h-R1_04.RCC wt/wt 347:5:2:...
#> SKMEL2-DMSO-8h-R2_04.RCC wt/wt 601:9:8:...
#> SKMEL2-DMSO-8h-R3_04.RCC wt/wt 270:2:1:...
#> SKMEL2-VEM-8h-R1_10.RCC wt/wt 588:10:5:...
#> SKMEL2-VEM-8h-R2_10.RCC wt/wt 810:3:5:...
#> ... ... ...
#> SKMEL28-DMSO-8h-R2_04.RCC mut/mut 722:5:3:...
#> SKMEL28-DMSO-8h-R3_04.RCC mut/mut 608:1:4:...
#> SKMEL28-VEM-8h-R1_10.RCC mut/mut 603:11:6:...
#> SKMEL28-VEM-8h-R2_10.RCC mut/mut 618:10:9:...
#> SKMEL28-VEM-8h-R3_10.RCC mut/mut 519:7:1:...
exprs_df <- transform( assayData( demoData )[["exprs_norm"]] )
class( exprs_df )
#> [1] "data.frame"
exprs_df[1:3, 1:2]
#> SKMEL2.DMSO.8h.R1_04.RCC
#> Endogenous_TP53_NM_000546.2 8.753132
#> Endogenous_IL22RA2_NM_181310.1 2.895151
#> Endogenous_IL2_NM_000586.2 1.895151
#> SKMEL2.DMSO.8h.R2_04.RCC
#> Endogenous_TP53_NM_000546.2 8.983110
#> Endogenous_IL22RA2_NM_181310.1 3.071419
#> Endogenous_IL2_NM_000586.2 2.919416Users can flag samples that fail QC thresholds or have borderline results based on housekeeper and ERCC expression, imaging quality, and binding density. Additionally, QC results can be visualized using the NanoStringRCCSet autoplot method.
demoData <- setQCFlags( demoData )
tail( svarLabels( demoData ) )
#> [1] "BindingDensity" "CartridgeID" "CartridgeBarcode"
#> [4] "Sample ID" "QCFlags" "QCBorderlineFlags"
head( protocolData( demoData )[["QCFlags"]], 2 )
#> Imaging Binding Linearity LoD Housekeeping
#> SKMEL2-DMSO-8h-R1_04.RCC FALSE FALSE FALSE FALSE FALSE
#> SKMEL2-DMSO-8h-R2_04.RCC FALSE FALSE FALSE FALSE FALSE
head( protocolData( demoData )[["QCBorderlineFlags"]], 2 )
#> Imaging Binding Linearity LoD Housekeeping
#> SKMEL2-DMSO-8h-R1_04.RCC FALSE FALSE FALSE FALSE FALSE
#> SKMEL2-DMSO-8h-R2_04.RCC FALSE FALSE FALSE FALSE FALSEBinding Density QC
theme_set( theme_gray( base_family = "Arial" ) )
girafe( ggobj = autoplot( demoData , "bindingDensity-mean" ) )
#> Warning: Ignoring unknown parameters: tooltip_fillQC by Lane
girafe( ggobj = autoplot( demoData , "lane-bindingDensity" ) )
#> Warning: Ignoring unknown parameters: tooltip_fillHousekeeping Genes QC
subData <- subset( demoData, select = phenoData( demoData )[["Treatment"]] == "DMSO" )
girafe( ggobj = autoplot( subData, "housekeep-geom" ) )
#> Warning: Ignoring unknown parameters: tooltip_fillERCC QC
girafe( ggobj = autoplot( subData , "ercc-lod" ) ) #> Warning: Ignoring unknown parameters: tooltip_fill #> Warning: Transformation introduced infinite values in continuous y-axis #> Warning: Transformation introduced infinite values in continuous y-axis #> Warning: Removed 1 rows containing non-finite values (stat_boxplot). #> Warning: Removed 1 rows containing non-finite values (stat_boxplot).Further data exploration can be performed by visualizing a select feature’s expression or by getting a bird’s eye view with expression heatmaps auto-generated with unsupervised clustering dendrograms.
#girafe( ggobj = autoplot( demoData , "boxplot-feature" , index = featureNames(demoData)[3] , elt = "exprs" ) )
#autoplot( demoData , "heatmap-genes" , elt = "exprs_norm" )sessionInfo()
#> R version 4.1.0 (2021-05-18)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.2 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.13-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 parallel stats graphics grDevices utils datasets
#> [8] methods base
#>
#> other attached packages:
#> [1] ggiraph_0.7.10 ggthemes_4.2.4 NanoStringNCTools_1.0.0
#> [4] ggplot2_3.3.3 S4Vectors_0.30.0 Biobase_2.52.0
#> [7] BiocGenerics_0.38.0
#>
#> loaded via a namespace (and not attached):
#> [1] beeswarm_0.3.1 tidyselect_1.1.1 xfun_0.23
#> [4] bslib_0.2.5.1 purrr_0.3.4 colorspace_2.0-1
#> [7] vctrs_0.3.8 generics_0.1.0 htmltools_0.5.1.1
#> [10] yaml_2.2.1 utf8_1.2.1 rlang_0.4.11
#> [13] jquerylib_0.1.4 pillar_1.6.1 glue_1.4.2
#> [16] withr_2.4.2 DBI_1.1.1 RColorBrewer_1.1-2
#> [19] uuid_0.1-4 GenomeInfoDbData_1.2.6 lifecycle_1.0.0
#> [22] stringr_1.4.0 zlibbioc_1.38.0 Biostrings_2.60.0
#> [25] munsell_0.5.0 gtable_0.3.0 htmlwidgets_1.5.3
#> [28] evaluate_0.14 labeling_0.4.2 knitr_1.33
#> [31] IRanges_2.26.0 GenomeInfoDb_1.28.0 vipor_0.4.5
#> [34] fansi_0.4.2 Rcpp_1.0.6 scales_1.1.1
#> [37] jsonlite_1.7.2 XVector_0.32.0 farver_2.1.0
#> [40] systemfonts_1.0.2 digest_0.6.27 stringi_1.6.2
#> [43] dplyr_1.0.6 grid_4.1.0 tools_4.1.0
#> [46] bitops_1.0-7 magrittr_2.0.1 sass_0.4.0
#> [49] RCurl_1.98-1.3 tibble_3.1.2 crayon_1.4.1
#> [52] pkgconfig_2.0.3 pheatmap_1.0.12 ellipsis_0.3.2
#> [55] ggbeeswarm_0.6.0 assertthat_0.2.1 rmarkdown_2.8
#> [58] R6_2.5.0 compiler_4.1.0