The NanoStringGeoMxSet was inherited from Biobase’s ExpressionSet class. The NanoStringGeoMxSet class was designed to encapsulate data and corresponding methods for NanoString DCC files generated from the NanoString GeoMx Digital Spatial Profiling (DSP) platform.
There are numerous functions that NanoStringGeoMxSet inherited from ExpressionSet class. You can find these in this link: https://www.bioconductor.org/packages/release/bioc/vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf
Loading the NanoStringNCTools and GeoMxTools packages allow users access to the NanoStringGeoMxSet class and corresponding methods.
Use the readNanoStringGeoMxSet function to read in your DCC files.
The phenoDataFile variable takes in the annotation file, the phenoDataDccColName is to specify which column from your annotation contains the DCC file names. The protocolDataColNames are the columns in your annotation file that you want to put in the protocol data slot.
datadir <- system.file("extdata", "DSP_NGS_Example_Data",
package="GeomxTools")
DCCFiles <- dir(datadir, pattern=".dcc$", full.names=TRUE)
PKCFiles <- unzip(zipfile = file.path(datadir, "/pkcs.zip"))
SampleAnnotationFile <- file.path(datadir, "annotations.xlsx")
demoData <-
suppressWarnings(readNanoStringGeoMxSet(dccFiles = DCCFiles,
pkcFiles = PKCFiles,
phenoDataFile = SampleAnnotationFile,
phenoDataSheet = "CW005",
phenoDataDccColName = "Sample_ID",
protocolDataColNames = c("aoi",
"cell_line",
"roi_rep",
"pool_rep",
"slide_rep")))
class(demoData)
#> [1] "NanoStringGeoMxSet"
#> attr(,"package")
#> [1] "GeomxTools"
isS4(demoData)
#> [1] TRUE
is(demoData, "ExpressionSet")
#> [1] TRUE
demoData
#> NanoStringGeoMxSet (storageMode: lockedEnvironment)
#> assayData: 8707 features, 88 samples
#> element names: exprs
#> protocolData
#> sampleNames: DSP-1001250002642-A02.dcc DSP-1001250002642-A03.dcc ...
#> DSP-1001250002642-H05.dcc (88 total)
#> varLabels: FileVersion SoftwareVersion ... NTC (21 total)
#> varMetadata: labelDescription
#> phenoData
#> sampleNames: DSP-1001250002642-A02.dcc DSP-1001250002642-A03.dcc ...
#> DSP-1001250002642-H05.dcc (88 total)
#> varLabels: slide name scan name ... area (6 total)
#> varMetadata: labelDescription
#> featureData
#> featureNames: RTS0039454 RTS0039455 ... RTS0995671 (8707 total)
#> fvarLabels: RTS_ID TargetName ... Negative (8 total)
#> fvarMetadata: labelDescription
#> experimentData: use 'experimentData(object)'
#> Annotation: Six-gene_test_v1_v1.1.pkc VnV_GeoMx_Hs_CTA_v1.2.pkc
#> signature: none
#> feature: Probe
#> analyte: RNA# access the count matrix
assayData(demoData)[["exprs"]][1:3, 1:3]
#> DSP-1001250002642-A02.dcc DSP-1001250002642-A03.dcc
#> RTS0039454 294 239
#> RTS0039455 270 281
#> RTS0039456 255 238
#> DSP-1001250002642-A04.dcc
#> RTS0039454 6
#> RTS0039455 6
#> RTS0039456 3
# access pheno data
pData(demoData)[1:3, ]
#> slide name
#> DSP-1001250002642-A02.dcc 6panel-old-slide1 (PTL-10891)
#> DSP-1001250002642-A03.dcc 6panel-old-slide1 (PTL-10891)
#> DSP-1001250002642-A04.dcc 6panel-old-slide1 (PTL-10891)
#> scan name
#> DSP-1001250002642-A02.dcc cw005 (PTL-10891) Slide1
#> DSP-1001250002642-A03.dcc cw005 (PTL-10891) Slide1
#> DSP-1001250002642-A04.dcc cw005 (PTL-10891) Slide1
#> panel
#> DSP-1001250002642-A02.dcc (v1.2) VnV Cancer Transcriptome Atlas, (v1.0) Six gene test custom
#> DSP-1001250002642-A03.dcc (v1.2) VnV Cancer Transcriptome Atlas, (v1.0) Six gene test custom
#> DSP-1001250002642-A04.dcc (v1.2) VnV Cancer Transcriptome Atlas, (v1.0) Six gene test custom
#> roi segment area
#> DSP-1001250002642-A02.dcc 1 Geometric Segment 31318.73
#> DSP-1001250002642-A03.dcc 2 Geometric Segment 31318.73
#> DSP-1001250002642-A04.dcc 3 Geometric Segment 31318.73
# access the protocol data
pData(protocolData(demoData))[1:3, ]
#> FileVersion SoftwareVersion Date
#> DSP-1001250002642-A02.dcc 0.1 1.0.0 2020-07-14
#> DSP-1001250002642-A03.dcc 0.1 1.0.0 2020-07-14
#> DSP-1001250002642-A04.dcc 0.1 1.0.0 2020-07-14
#> SampleID Plate_ID Well
#> DSP-1001250002642-A02.dcc DSP-1001250002642-A02 1001250002642 A02
#> DSP-1001250002642-A03.dcc DSP-1001250002642-A03 1001250002642 A03
#> DSP-1001250002642-A04.dcc DSP-1001250002642-A04 1001250002642 A04
#> SeqSetId Raw Trimmed Stitched Aligned
#> DSP-1001250002642-A02.dcc VH00121:3:AAAG2YWM5 646250 646250 616150 610390
#> DSP-1001250002642-A03.dcc VH00121:3:AAAG2YWM5 629241 629241 603243 597280
#> DSP-1001250002642-A04.dcc VH00121:3:AAAG2YWM5 831083 831083 798188 791804
#> umiQ30 rtsQ30 DeduplicatedReads
#> DSP-1001250002642-A02.dcc 0.9785 0.9804 312060
#> DSP-1001250002642-A03.dcc 0.9784 0.9811 305528
#> DSP-1001250002642-A04.dcc 0.9785 0.9801 394981
#> aoi cell_line roi_rep pool_rep
#> DSP-1001250002642-A02.dcc Geometric Segment-aoi-001 HS578T 1 1
#> DSP-1001250002642-A03.dcc Geometric Segment-aoi-001 HS578T 2 1
#> DSP-1001250002642-A04.dcc Geometric Segment-aoi-001 HEL 1 1
#> slide_rep NTC_ID NTC
#> DSP-1001250002642-A02.dcc 1 DSP-1001250002642-A01.dcc 7
#> DSP-1001250002642-A03.dcc 1 DSP-1001250002642-A01.dcc 7
#> DSP-1001250002642-A04.dcc 1 DSP-1001250002642-A01.dcc 7
# access the probe information
fData(demoData)[1:3, ]
#> RTS_ID TargetName Module CodeClass
#> RTS0039454 RTS0039454 ACTA2 VnV_GeoMx_Hs_CTA_v1.2 Endogenous
#> RTS0039455 RTS0039455 ACTA2 VnV_GeoMx_Hs_CTA_v1.2 Endogenous
#> RTS0039456 RTS0039456 ACTA2 VnV_GeoMx_Hs_CTA_v1.2 Endogenous
#> ProbeID GeneID SystematicName Negative
#> RTS0039454 NM_001141945.1:460_5p 59 ACTA2 FALSE
#> RTS0039455 NM_001141945.1:460_3p 59 ACTA2 FALSE
#> RTS0039456 NM_001613.2:154_3p 59 ACTA2 FALSE
# check feature type
featureType(demoData)
#> [1] "Probe"
# access PKC information
annotation(demoData)
#> [1] "Six-gene_test_v1_v1.1.pkc" "VnV_GeoMx_Hs_CTA_v1.2.pkc"Alongside the accessors associated with the ExpressionSet class, NanoStringGeoMxSet objects have unique additional assignment and accessor methods facilitating common ways to view DSP data and associated labels.
The package provide functions to get the annotations of the data
Access the available pheno and protocol data variables
svarLabels(demoData)
#> [1] "slide name" "scan name" "panel"
#> [4] "roi" "segment" "area"
#> [7] "FileVersion" "SoftwareVersion" "Date"
#> [10] "SampleID" "Plate_ID" "Well"
#> [13] "SeqSetId" "Raw" "Trimmed"
#> [16] "Stitched" "Aligned" "umiQ30"
#> [19] "rtsQ30" "DeduplicatedReads" "aoi"
#> [22] "cell_line" "roi_rep" "pool_rep"
#> [25] "slide_rep" "NTC_ID" "NTC"
head(sData(demoData), 2)
#> slide name
#> DSP-1001250002642-A02.dcc 6panel-old-slide1 (PTL-10891)
#> DSP-1001250002642-A03.dcc 6panel-old-slide1 (PTL-10891)
#> scan name
#> DSP-1001250002642-A02.dcc cw005 (PTL-10891) Slide1
#> DSP-1001250002642-A03.dcc cw005 (PTL-10891) Slide1
#> panel
#> DSP-1001250002642-A02.dcc (v1.2) VnV Cancer Transcriptome Atlas, (v1.0) Six gene test custom
#> DSP-1001250002642-A03.dcc (v1.2) VnV Cancer Transcriptome Atlas, (v1.0) Six gene test custom
#> roi segment area FileVersion
#> DSP-1001250002642-A02.dcc 1 Geometric Segment 31318.73 0.1
#> DSP-1001250002642-A03.dcc 2 Geometric Segment 31318.73 0.1
#> SoftwareVersion Date SampleID
#> DSP-1001250002642-A02.dcc 1.0.0 2020-07-14 DSP-1001250002642-A02
#> DSP-1001250002642-A03.dcc 1.0.0 2020-07-14 DSP-1001250002642-A03
#> Plate_ID Well SeqSetId Raw Trimmed
#> DSP-1001250002642-A02.dcc 1001250002642 A02 VH00121:3:AAAG2YWM5 646250 646250
#> DSP-1001250002642-A03.dcc 1001250002642 A03 VH00121:3:AAAG2YWM5 629241 629241
#> Stitched Aligned umiQ30 rtsQ30 DeduplicatedReads
#> DSP-1001250002642-A02.dcc 616150 610390 0.9785 0.9804 312060
#> DSP-1001250002642-A03.dcc 603243 597280 0.9784 0.9811 305528
#> aoi cell_line roi_rep pool_rep
#> DSP-1001250002642-A02.dcc Geometric Segment-aoi-001 HS578T 1 1
#> DSP-1001250002642-A03.dcc Geometric Segment-aoi-001 HS578T 2 1
#> slide_rep NTC_ID NTC
#> DSP-1001250002642-A02.dcc 1 DSP-1001250002642-A01.dcc 7
#> DSP-1001250002642-A03.dcc 1 DSP-1001250002642-A01.dcc 7Design information can be assigned to the NanoStringGeoMxSet object, as well as feature and sample labels to use for NanoStringGeoMxSet 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 Q1 Median Q3 Max
#> RTS0039454 11.41376 1.196060 3.512703 2.287478 1 4 9 16.75 344
#> RTS0039455 10.35145 1.084739 3.371761 2.228309 0 4 7 21.00 315
head(summary(demoData, MARGIN = 2), 2)
#> GeomMean SizeFactor MeanLog2 SDLog2 Min Q1 Median Q3
#> DSP-1001250002642-A02.dcc 9.929751 1.0405489 3.311758 1.94747 0 4 7 23
#> DSP-1001250002642-A03.dcc 9.280617 0.9725255 3.214221 1.98530 0 4 7 22
#> Max
#> DSP-1001250002642-A02.dcc 8137
#> DSP-1001250002642-A03.dcc 9147
unique(sData(demoData)$"cell_line")
#> [1] "HS578T" "HEL" "U118MG" "HDLM2" "THP1" "H596" "OPM2"
#> [8] "DAUDI" "MALME3M" "COLO201" "HUT78"
head(summary(demoData, MARGIN = 2, GROUP = "cell_line")$"HS578T", 2)
#> GeomMean SizeFactor MeanLog2 SDLog2 Min Q1 Median Q3
#> DSP-1001250002642-A02.dcc 9.929751 1.507066 3.311758 1.94747 0 4 7 23
#> DSP-1001250002642-A03.dcc 9.280617 1.408545 3.214221 1.98530 0 4 7 22
#> Max
#> DSP-1001250002642-A02.dcc 8137
#> DSP-1001250002642-A03.dcc 9147
head(summary(demoData, MARGIN = 2, GROUP = "cell_line")$"COLO201", 2)
#> GeomMean SizeFactor MeanLog2 SDLog2 Min Q1 Median
#> DSP-1001250002642-B08.dcc 3.683270 0.5817191 1.880987 1.815589 0 2 3
#> DSP-1001250002642-B09.dcc 4.385107 0.6925640 2.132612 1.879853 0 2 4
#> Q3 Max
#> DSP-1001250002642-B08.dcc 8 1146
#> DSP-1001250002642-B09.dcc 10 1372
head(summary(demoData, MARGIN = 2, GROUP = "cell_line", log2 = FALSE)$"COLO201", 2)
#> Mean SD Skewness Kurtosis Min Q1 Median Q3
#> DSP-1001250002642-B08.dcc 9.859538 31.49779 14.13199 312.7038 0 2 3 8
#> DSP-1001250002642-B09.dcc 12.517400 40.84549 13.33816 264.5914 0 2 4 10
#> Max
#> DSP-1001250002642-B08.dcc 1146
#> DSP-1001250002642-B09.dcc 1372NanoStringGeoMxSet provides subsetting methods including bracket subsetting and subset functions. Users can use the subset or select arguments to further subset by feature or sample, respectively.
use the bracket notation
dim(demoData[, demoData$`slide name` == "6panel-old-slide1 (PTL-10891)"])
#> Features Samples
#> 8707 22Or use subset method to subset demoData object by selecting only certain slides
dim(subset(demoData, select = phenoData(demoData)[["slide name"]] == "6panel-old-slide1 (PTL-10891)"))
#> Features Samples
#> 8707 22Subset by selecting specific targets and slide name
dim(subset(demoData, TargetName == "ACTA2", `slide name` == "6panel-old-slide1 (PTL-10891)"))
#> Features Samples
#> 5 22
dim(subset(demoData, CodeClass == "Control", `slide name` == "6panel-old-slide1 (PTL-10891)"))
#> Features Samples
#> 154 22use endogenousSubset and negativeControlSubset function to subset the demodata and include only features that belong to endogenous code class or negative code class.
dim(endogenousSubset(demoData))
#> Features Samples
#> 8470 88
dim(negativeControlSubset(demoData))
#> Features Samples
#> 83 88endogenousSubset function also takes select arguments to further subset by phenodata
dim(endogenousSubset(demoData,
select = phenoData(demoData)[["slide name"]] == "6panel-old-slide1 (PTL-10891)"))
#> Features Samples
#> 8470 22
# tally the number of samples according to their protocol or phenodata grouping
with(endogenousSubset(demoData), table(`slide name`))
#> slide name
#> 6panel-new-slide3 (PTL-10891) 6panel-new-slide4 (PTL-10891)
#> 22 22
#> 6panel-old-slide1 (PTL-10891) 6panel-old-slide2 (PTL-10891)
#> 22 22
with(demoData [1:10, 1:10], table(cell_line))
#> cell_line
#> HDLM2 HEL HS578T THP1 U118MG
#> 2 2 2 2 2
with(negativeControlSubset(demoData), table(CodeClass))
#> CodeClass
#> Negative
#> 83Similar to the ExpressionSet’s esApply function, an equivalent method is available with NanoStringGeoMxSet objects. Functions can be applied to assay data feature-wise or sample-wise.
Add the demoElem data which is computed as the logarithm of the count matrix (exprs) into the demoData by using assayDataApply function. The accessor function assayDataElement from eSet returns matrix element from assayData slot of object. Elt refers to the element in the assayData.
assayDataElement(demoData, "demoElem") <-
assayDataApply(demoData, MARGIN=2, FUN=log, base=10, elt="exprs")
assayDataElement(demoData, "demoElem")[1:3, 1:2]
#> DSP-1001250002642-A02.dcc DSP-1001250002642-A03.dcc
#> RTS0039454 2.468347 2.378398
#> RTS0039455 2.431364 2.448706
#> RTS0039456 2.406540 2.376577
# loop over the features(1) or samples(2) of the assayData element and get the mean
assayDataApply(demoData, MARGIN=1, FUN=mean, elt="demoElem")[1:5]
#> RTS0039454 RTS0039455 RTS0039456 RTS0039457 RTS0039458
#> 1.0574289 -Inf 0.9943958 1.4974429 -Inf
# split the data by group column with feature, pheno or protocol data then get the mean
head(esBy(demoData,
GROUP = "cell_line",
FUN = function(x) {
assayDataApply(x, MARGIN = 1, FUN=mean, elt="demoElem")
}))
#> COLO201 DAUDI H596 HDLM2 HEL HS578T HUT78
#> RTS0039454 0.3910499 0.7918610 0.7070841 1.235112 0.7436161 2.335504 2.3005684
#> RTS0039455 0.4139162 0.6934411 0.7044355 1.121399 0.7169818 2.250828 2.2031171
#> RTS0039456 0.3571666 0.5259476 0.7796930 1.153415 0.7048680 2.196286 2.1858906
#> RTS0039457 1.1237902 1.2848894 1.2285543 1.416938 1.2153038 2.611181 2.4856476
#> RTS0039458 0.4942803 0.8141501 0.7048680 1.216667 0.8127214 2.394991 2.3079279
#> RTS0039459 1.1052012 0.6291780 0.5407100 -Inf 0.5160499 -Inf 0.4350727
#> MALME3M OPM2 THP1 U118MG
#> RTS0039454 1.1950246 0.5140756 0.3692803 1.0485415
#> RTS0039455 1.1766227 0.4643331 -Inf 1.0308625
#> RTS0039456 1.1870660 0.4885606 0.3910499 0.9684105
#> RTS0039457 1.5631204 1.1072751 1.0942654 1.3409072
#> RTS0039458 1.2459613 0.7235564 -Inf 1.0365510
#> RTS0039459 0.4984583 0.5684411 0.5019619 -InfUsers can flag samples that fail QC thresholds or have borderline results based on expression. The setQC Flags will set the QC flags in the protocolData for the samples and probes that are low in count and saturation levels. It will also set flags for probe local outliers (low and high) and Global Outliers
demoData <- shiftCountsOne(demoData, useDALogic=TRUE)
demoData <- setSegmentQCFlags(demoData)
head(protocolData(demoData)[["QCFlags"]])
#> LowReads LowTrimmed LowStitched LowAligned
#> DSP-1001250002642-A02.dcc FALSE FALSE FALSE FALSE
#> DSP-1001250002642-A03.dcc FALSE FALSE FALSE FALSE
#> DSP-1001250002642-A04.dcc FALSE FALSE FALSE FALSE
#> DSP-1001250002642-A05.dcc FALSE FALSE FALSE FALSE
#> DSP-1001250002642-A06.dcc FALSE FALSE FALSE FALSE
#> DSP-1001250002642-A07.dcc FALSE FALSE FALSE FALSE
#> LowSaturation LowNegatives HighNTC LowArea
#> DSP-1001250002642-A02.dcc TRUE TRUE FALSE FALSE
#> DSP-1001250002642-A03.dcc TRUE TRUE FALSE FALSE
#> DSP-1001250002642-A04.dcc FALSE TRUE FALSE FALSE
#> DSP-1001250002642-A05.dcc TRUE TRUE FALSE FALSE
#> DSP-1001250002642-A06.dcc FALSE TRUE FALSE FALSE
#> DSP-1001250002642-A07.dcc TRUE TRUE FALSE FALSE
demoData <- setBioProbeQCFlags(demoData)
featureData(demoData)[["QCFlags"]][1:5, 1:4]
#> LowProbeRatio GlobalGrubbsOutlier
#> RTS0039454 FALSE FALSE
#> RTS0039455 FALSE FALSE
#> RTS0039456 FALSE FALSE
#> RTS0039457 FALSE FALSE
#> RTS0039458 FALSE FALSE
#> LocalGrubbsOutlier.DSP-1001250002642-A02.dcc
#> RTS0039454 FALSE
#> RTS0039455 FALSE
#> RTS0039456 FALSE
#> RTS0039457 FALSE
#> RTS0039458 FALSE
#> LocalGrubbsOutlier.DSP-1001250002642-A03.dcc
#> RTS0039454 FALSE
#> RTS0039455 FALSE
#> RTS0039456 FALSE
#> RTS0039457 FALSE
#> RTS0039458 FALSEProbes and Samples that were flagged can be removed from analysis by subsetting.
Subset object to exclude all that did not pass Sequencing and background QC.
QCResultsIndex <- which(apply(protocolData(demoData)[["QCFlags"]],
1L , function(x) sum(x) == 0L))
QCPassed <- demoData[, QCResultsIndex]
dim(QCPassed)
#> Features Samples
#> 8707 0After cleaning the object from low counts, the counts can be collapsed to Target using aggregateCounts function.
Save the new object as target_demoData when you call the aggregateCounts function. This will change the dimension of the features. After aggregating the counts, feature data will contain target counts and not probe counts. To check the feature type, you can use the featureType accessor function.
Note that feature data changed to target.
featureType(target_demoData)
#> [1] "Target"
exprs(target_demoData)[1:5, 1:5]
#> DSP-1001250002642-A02.dcc DSP-1001250002642-A03.dcc
#> ACTA2 328.286182 323.490808
#> FOXA2 4.919019 4.919019
#> NANOG 2.954177 4.128918
#> TRAC 2.992556 4.617893
#> TRBC1/2 2.825235 1.933182
#> DSP-1001250002642-A04.dcc DSP-1001250002642-A05.dcc
#> ACTA2 6.081111 5.304566
#> FOXA2 6.942503 4.208378
#> NANOG 8.359554 7.785262
#> TRAC 4.514402 4.192963
#> TRBC1/2 3.519482 3.807308
#> DSP-1001250002642-A06.dcc
#> ACTA2 15.927470
#> FOXA2 6.470273
#> NANOG 3.981072
#> TRAC 4.643984
#> TRBC1/2 4.535866There is a preloaded GeoMx DSP-DA Normalization that comes with the NanoStringGeoMxSet class. This includes the options to normalize on quantile, housekeeping or negative normalization.
target_demoData <- normalize(target_demoData , norm_method="quant",
desiredQuantile = .9, toElt = "q_norm")
target_demoData <- normalize(target_demoData , norm_method="neg", fromElt="exprs", toElt="neg_norm")
target_demoData <- normalize(target_demoData , norm_method="hk", fromElt="exprs", toElt="hk_norm")
assayDataElement( target_demoData , elt = "q_norm" )[1:3, 1:2]
#> DSP-1001250002642-A02.dcc DSP-1001250002642-A03.dcc
#> ACTA2 326.118346 324.968900
#> FOXA2 4.886536 4.941495
#> NANOG 2.934669 4.147784
assayDataElement( target_demoData , elt = "hk_norm" )[1:3, 1:2]
#> DSP-1001250002642-A02.dcc DSP-1001250002642-A03.dcc
#> ACTA2 265.002676 273.615381
#> FOXA2 3.970783 4.160610
#> NANOG 2.384702 3.492326
assayDataElement( target_demoData , elt = "neg_norm" )[1:3, 1:2]
#> DSP-1001250002642-A02.dcc DSP-1001250002642-A03.dcc
#> ACTA2 288.519851 344.955505
#> FOXA2 4.323163 5.245412
#> NANOG 2.596328 4.402885The NanoStringGeoMxSet munge function generates a data frame object for downstream modeling and visualization. This combines available features and samples into a long format.
neg_set <- negativeControlSubset(demoData)
class(neg_set)
#> [1] "NanoStringGeoMxSet"
#> attr(,"package")
#> [1] "GeomxTools"
neg_ctrls <- munge(neg_set, ~ exprs)
head(neg_ctrls, 2)
#> FeatureName SampleName exprs
#> 1 RTS0047618 DSP-1001250002642-A02.dcc 6
#> 2 RTS0047619 DSP-1001250002642-A02.dcc 4
class(neg_ctrls)
#> [1] "data.frame"
head(munge(demoData, ~ exprs), 2)
#> FeatureName SampleName exprs
#> 1 RTS0039454 DSP-1001250002642-A02.dcc 294
#> 2 RTS0039455 DSP-1001250002642-A02.dcc 270
munge(demoData, mapping = ~`cell_line` + GeneMatrix)
#> DataFrame with 88 rows and 2 columns
#> cell_line GeneMatrix
#> <character> <matrix>
#> DSP-1001250002642-A02.dcc HS578T 294:270:255:...
#> DSP-1001250002642-A03.dcc HS578T 239:281:238:...
#> DSP-1001250002642-A04.dcc HEL 6: 6: 3:...
#> DSP-1001250002642-A05.dcc HEL 7: 5: 2:...
#> DSP-1001250002642-A06.dcc U118MG 13: 11: 16:...
#> ... ... ...
#> DSP-1001250002642-H01.dcc MALME3M 15: 21: 20:...
#> DSP-1001250002642-H02.dcc COLO201 4: 8: 5:...
#> DSP-1001250002642-H03.dcc COLO201 1: 4: 6:...
#> DSP-1001250002642-H04.dcc HUT78 243:218:250:...
#> DSP-1001250002642-H05.dcc HUT78 230:215:222:...Subtract max count from each sample Create log1p transformation of adjusted counts
thresh <- assayDataApply(negativeControlSubset(demoData), 2, max)
demoData <-
transform(demoData,
negCtrlZeroed = sweep(exprs, 2, thresh),
log1p_negCtrlZeroed = log1p(pmax(negCtrlZeroed, 0)))
assayDataElementNames(demoData)
#> [1] "demoElem" "exprs" "log1p_negCtrlZeroed"
#> [4] "negCtrlZeroed" "preLocalRemoval" "rawZero"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] ggiraph_0.8.3 EnvStats_2.7.0 GeomxTools_3.2.0
#> [4] NanoStringNCTools_1.6.0 ggplot2_3.3.6 S4Vectors_0.36.0
#> [7] Biobase_2.58.0 BiocGenerics_0.44.0
#>
#> loaded via a namespace (and not attached):
#> [1] nlme_3.1-160 bitops_1.0-7 RColorBrewer_1.1-3
#> [4] GenomeInfoDb_1.34.0 numDeriv_2016.8-1.1 tools_4.2.1
#> [7] bslib_0.4.0 utf8_1.2.2 R6_2.5.1
#> [10] vipor_0.4.5 rgeos_0.5-9 DBI_1.1.3
#> [13] colorspace_2.0-3 sp_1.5-0 withr_2.5.0
#> [16] tidyselect_1.2.0 GGally_2.1.2 compiler_4.2.1
#> [19] progressr_0.11.0 cli_3.4.1 sass_0.4.2
#> [22] scales_1.2.1 systemfonts_1.0.4 stringr_1.4.1
#> [25] digest_0.6.30 minqa_1.2.5 rmarkdown_2.17
#> [28] XVector_0.38.0 pkgconfig_2.0.3 htmltools_0.5.3
#> [31] parallelly_1.32.1 lme4_1.1-31 fastmap_1.1.0
#> [34] htmlwidgets_1.5.4 rlang_1.0.6 ggthemes_4.2.4
#> [37] readxl_1.4.1 jquerylib_0.1.4 generics_0.1.3
#> [40] jsonlite_1.8.3 dplyr_1.0.10 RCurl_1.98-1.9
#> [43] magrittr_2.0.3 GenomeInfoDbData_1.2.9 Matrix_1.5-1
#> [46] Rcpp_1.0.9 ggbeeswarm_0.6.0 munsell_0.5.0
#> [49] fansi_1.0.3 lifecycle_1.0.3 stringi_1.7.8
#> [52] yaml_2.3.6 MASS_7.3-58.1 zlibbioc_1.44.0
#> [55] plyr_1.8.7 grid_4.2.1 parallel_4.2.1
#> [58] listenv_0.8.0 crayon_1.5.2 lattice_0.20-45
#> [61] Biostrings_2.66.0 splines_4.2.1 knitr_1.40
#> [64] pillar_1.8.1 uuid_1.1-0 boot_1.3-28
#> [67] rjson_0.2.21 future.apply_1.9.1 reshape2_1.4.4
#> [70] codetools_0.2-18 glue_1.6.2 evaluate_0.17
#> [73] SeuratObject_4.1.2 data.table_1.14.4 vctrs_0.5.0
#> [76] nloptr_2.0.3 cellranger_1.1.0 gtable_0.3.1
#> [79] purrr_0.3.5 reshape_0.8.9 future_1.28.0
#> [82] assertthat_0.2.1 cachem_1.0.6 xfun_0.34
#> [85] tibble_3.1.8 pheatmap_1.0.12 lmerTest_3.1-3
#> [88] beeswarm_0.4.0 IRanges_2.32.0 globals_0.16.1