inst/unitTests/test_NanoStringRccSet-summary.R
In Nanostring-Biostats/NanoStringNCTools: NanoString nCounter Tools
rcc <-
NanoStringRccSet(assayData =
matrix(0:11, 4L, 3L,
dimnames = list(letters[1:4], sprintf("%s.RCC", LETTERS[1:3]))),
phenoData =
AnnotatedDataFrame(data.frame(Treatment = c("A", "A", "B"),
Age = c(58L, 42L, 27L),
row.names = sprintf("%s.RCC", LETTERS[1:3]),
stringsAsFactors = FALSE),
dimLabels = c("sampleNames", "sampleColumns")),
featureData =
AnnotatedDataFrame(data.frame(CodeClass = c("Endogenous", "Positive", "Negative", "Housekeeping"),
GeneName = letters[1:4],
Accession = letters[1:4],
IsControl = c(FALSE, TRUE, TRUE, TRUE),
ControlConc = c(NA_real_, 0.125, 0, NA_real_),
row.names = letters[1:4],
stringsAsFactors = FALSE),
dimLabels = c("featureNames", "featureColumns")),
annotation = "rlffile",
protocolData =
AnnotatedDataFrame(data.frame(FileVersion = numeric_version(rep("1.7", 3L)),
SoftwareVersion = numeric_version(rep("4.0.0.3", 3L)),
SystemType = rep("Gen2", 3L),
SampleID = letters[1:3],
SampleOwner = rep("", 3L),
SampleComments = rep("DNA-RNA-Protein", 3L),
SampleDate = as.Date(rep("1999-12-31", 3L)),
SystemAPF = rep("n6_vDV1", 3L),
AssayType = rep(NA_character_, 3L),
LaneID = 1:3,
FovCount = rep(280L, 3L),
FovCounted = 1:3,
ScannerID = rep("a", 3L),
StagePosition = 1:3,
BindingDensity = c(0.75, 1, 1.25),
CartridgeID = letters[1:3],
CartridgeBarcode = rep("", 3L),
row.names = sprintf("%s.RCC", LETTERS[1:3]),
stringsAsFactors = FALSE),
NanoStringNCTools:::.rccMetadata[["protocolData"]],
dimLabels = c("sampleNames", "sampleColumns"))
)
# Sumarizing
test_NanoStringRccSet_summary <- function() {
rcc2 <- transform(rcc, log2_exprs = log2t(exprs))
# Marginal summaries by Feature
checkEquals(cbind(GeomMean = c(2.519842100, 3.556893304, 4.932424149, 6.135792440),
SizeFactor = c(0.6209112338, 0.8764497626, 1.2153926486, 1.5119131688),
MeanLog2 = c(1.333333333, 1.830617699, 2.302296865, 2.617249680),
SDLog2 = c(2.0816659995, 1.6410806067, 1.1864916416, 0.9515847943),
Min = structure(0:3, names = letters[1:4]),
Q1 = 2:5,
Median = 4:7,
Q3 = 6:9,
Max = 8:11),
summary(rcc2, 1L))
checkEquals(cbind(Mean = c(1.333333333, 1.830617699, 2.302296865, 2.617249680),
SD = c(2.0816659995, 1.6410806067, 1.1864916416, 0.9515847943),
Skewness = c(-1.2933427807, -1.2264691300, -1.0112175576, -0.8631188095),
Kurtosis = NA_real_,
Min = structure(c(-1, 0, 1, 1.584962501), names = letters[1:4]),
Q1 = c(0.5, 1.160964047, 1.792481250, 2.196158711),
Median = c(2, 2.321928095, 2.584962501, 2.807354922),
Q3 = c(2.5, 2.745926548, 2.953445298, 3.133393270),
Max = c(3, 3.169925001, 3.321928095, 3.459431619)),
summary(rcc2, 1L, elt = "log2_exprs", log2scale = FALSE))
# Marginal summaries by Sample
checkEquals(cbind(GeomMean = c(1.316074013, 5.383563271, 9.433683366),
SizeFactor = c(0.3242922004, 1.3265572922, 2.3245424696),
MeanLog2 = c(0.3962406252, 2.4285613794, 3.2378211787),
SDLog2 = c(1.1378458590, 0.3478416172, 0.1977826790),
Min = structure(c(0, 4, 8), names = sampleNames(rcc)),
Q1 = c(0.75, 4.75, 8.75),
Median = c(1.5, 5.5, 9.5),
Q3 = c(2.25, 6.25, 10.25),
Max = c(3, 7, 11)),
summary(rcc2, 2L))
checkEquals(cbind(Mean = c(0.3962406252, 2.4285613794, 3.2378211787),
SD = c(1.1378458590, 0.3478416172, 0.1977826790),
Skewness = c(-0.4002032906, -0.3444852394, -0.1969256558),
Kurtosis = c(-1.6584093446, -0.9658168584, -1.1224275064),
Min = structure(c(-1, 2, 3), names = sampleNames(rcc)),
Q1 = c(-0.25, 2.241446071, 3.127443751),
Median = c(0.5, 2.453445298, 3.245926548),
Q3 = c(1.146240625, 2.640560606, 3.356303976),
Max = c(1.584962501, 2.807354922, 3.459431619)),
summary(rcc2, 2L, elt = "log2_exprs", log2scale = FALSE))
}
test_NanoStringRccSet_summary_GROUP <- function() {
rcc2 <- transform(rcc, log2_exprs = log2t(exprs))
# Marginal summaries by Feature
checkEquals(list(A =
cbind(GeomMean = c(1.414213562, 2.236067977, 3.464101615, 4.582575695),
SizeFactor = c(0.5313001399, 0.8400592816, 1.3014142429, 1.7216092197),
MeanLog2 = c(0.5, 1.160964047, 1.792481250, 2.196158711),
SDLog2 = c(2.1213203436, 1.6418511013, 1.1207377322, 0.8643619704),
Min = structure(0:3, names = letters[1:4]),
Q1 = 1:4,
Median = 2:5,
Q3 = 3:6,
Max = 4:7),
B =
cbind(GeomMean = 8:11,
SizeFactor = c(0.8480250704, 0.9540282042, 1.0600313380, 1.1660344717),
MeanLog2 = c(3, 3.169925001, 3.321928095, 3.459431619),
SDLog2 = NA_real_,
Min = structure(8:11, names = letters[1:4]),
Q1 = 8:11,
Median = 8:11,
Q3 = 8:11,
Max = 8:11)),
summary(rcc2, 1L, "Treatment"))
checkEquals(list("1" =
cbind(Mean = c(-1, 0, 1, 1.584962501),
SD = NA_real_,
Skewness = NA_real_,
Kurtosis = NA_real_,
Min = structure(c(-1, 0, 1, 1.584962501), names = letters[1:4]),
Q1 = c(-1, 0, 1, 1.584962501),
Median = c(-1, 0, 1, 1.584962501),
Q3 = c(-1, 0, 1, 1.584962501),
Max = c(-1, 0, 1, 1.584962501)),
"2" =
cbind(Mean = c(2, 2.321928095, 2.584962501, 2.807354922),
SD = NA_real_,
Skewness = NA_real_,
Kurtosis = NA_real_,
Min = structure(c(2, 2.321928095, 2.584962501, 2.807354922), names = letters[1:4]),
Q1 = c(2, 2.321928095, 2.584962501, 2.807354922),
Median = c(2, 2.321928095, 2.584962501, 2.807354922),
Q3 = c(2, 2.321928095, 2.584962501, 2.807354922),
Max = c(2, 2.321928095, 2.584962501, 2.807354922)),
"3" =
cbind(Mean = c(3, 3.169925001, 3.321928095, 3.459431619),
SD = NA_real_,
Skewness = NA_real_,
Kurtosis = NA_real_,
Min = structure(c(3, 3.169925001, 3.321928095, 3.459431619), names = letters[1:4]),
Q1 = c(3, 3.169925001, 3.321928095, 3.459431619),
Median = c(3, 3.169925001, 3.321928095, 3.459431619),
Q3 = c(3, 3.169925001, 3.321928095, 3.459431619),
Max = c(3, 3.169925001, 3.321928095, 3.459431619))),
summary(rcc2, 1L, "LaneID", elt = "log2_exprs", log2scale = FALSE))
# Marginal summaries by Sample
checkEquals(list(Endogenous =
cbind(GeomMean = c(0.5, 4, 8),
SizeFactor = c(0.1984251315, 1.5874010520, 3.1748021039),
MeanLog2 = c(-1, 2, 3),
SDLog2 = NA_real_,
Min = structure(c(0, 4, 8), names = sampleNames(rcc)),
Q1 = c(0, 4, 8),
Median = c(0, 4, 8),
Q3 = c(0, 4, 8),
Max = c(0, 4, 8)),
Housekeeping =
cbind(GeomMean = c(3, 7, 11),
SizeFactor = c(0.4889344008, 1.1408469352, 1.7927594696),
MeanLog2 = c(1.584962501, 2.807354922, 3.459431619),
SDLog2 = NA_real_,
Min = structure(c(3, 7, 11), names = sampleNames(rcc)),
Q1 = c(3, 7, 11),
Median = c(3, 7, 11),
Q3 = c(3, 7, 11),
Max = c(3, 7, 11)),
Negative =
cbind(GeomMean = c(2, 6, 10),
SizeFactor = c(0.405480133, 1.216440399, 2.027400665),
MeanLog2 = c(1, 2.584962501, 3.321928095),
SDLog2 = NA_real_,
Min = structure(c(2, 6, 10), names = sampleNames(rcc)),
Q1 = c(2, 6, 10),
Median = c(2, 6, 10),
Q3 = c(2, 6, 10),
Max = c(2, 6, 10)),
Positive =
cbind(GeomMean = c(1, 5, 9),
SizeFactor = c(0.2811442218, 1.4057211088, 2.5302979959),
MeanLog2 = c(0, 2.321928095, 3.169925001),
SDLog2 = NA_real_,
Min = structure(c(1, 5, 9), names = sampleNames(rcc)),
Q1 = c(1, 5, 9),
Median = c(1, 5, 9),
Q3 = c(1, 5, 9),
Max = c(1, 5, 9))),
summary(rcc2, 2L, "CodeClass"))
}
Nanostring-Biostats/NanoStringNCTools documentation built on April 19, 2024, 8:21 p.m.
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rcc <-
NanoStringRccSet(assayData =
matrix(0:11, 4L, 3L,
dimnames = list(letters[1:4], sprintf("%s.RCC", LETTERS[1:3]))),
phenoData =
AnnotatedDataFrame(data.frame(Treatment = c("A", "A", "B"),
Age = c(58L, 42L, 27L),
row.names = sprintf("%s.RCC", LETTERS[1:3]),
stringsAsFactors = FALSE),
dimLabels = c("sampleNames", "sampleColumns")),
featureData =
AnnotatedDataFrame(data.frame(CodeClass = c("Endogenous", "Positive", "Negative", "Housekeeping"),
GeneName = letters[1:4],
Accession = letters[1:4],
IsControl = c(FALSE, TRUE, TRUE, TRUE),
ControlConc = c(NA_real_, 0.125, 0, NA_real_),
row.names = letters[1:4],
stringsAsFactors = FALSE),
dimLabels = c("featureNames", "featureColumns")),
annotation = "rlffile",
protocolData =
AnnotatedDataFrame(data.frame(FileVersion = numeric_version(rep("1.7", 3L)),
SoftwareVersion = numeric_version(rep("4.0.0.3", 3L)),
SystemType = rep("Gen2", 3L),
SampleID = letters[1:3],
SampleOwner = rep("", 3L),
SampleComments = rep("DNA-RNA-Protein", 3L),
SampleDate = as.Date(rep("1999-12-31", 3L)),
SystemAPF = rep("n6_vDV1", 3L),
AssayType = rep(NA_character_, 3L),
LaneID = 1:3,
FovCount = rep(280L, 3L),
FovCounted = 1:3,
ScannerID = rep("a", 3L),
StagePosition = 1:3,
BindingDensity = c(0.75, 1, 1.25),
CartridgeID = letters[1:3],
CartridgeBarcode = rep("", 3L),
row.names = sprintf("%s.RCC", LETTERS[1:3]),
stringsAsFactors = FALSE),
NanoStringNCTools:::.rccMetadata[["protocolData"]],
dimLabels = c("sampleNames", "sampleColumns"))
)
# Sumarizing
test_NanoStringRccSet_summary <- function() {
rcc2 <- transform(rcc, log2_exprs = log2t(exprs))
# Marginal summaries by Feature
checkEquals(cbind(GeomMean = c(2.519842100, 3.556893304, 4.932424149, 6.135792440),
SizeFactor = c(0.6209112338, 0.8764497626, 1.2153926486, 1.5119131688),
MeanLog2 = c(1.333333333, 1.830617699, 2.302296865, 2.617249680),
SDLog2 = c(2.0816659995, 1.6410806067, 1.1864916416, 0.9515847943),
Min = structure(0:3, names = letters[1:4]),
Q1 = 2:5,
Median = 4:7,
Q3 = 6:9,
Max = 8:11),
summary(rcc2, 1L))
checkEquals(cbind(Mean = c(1.333333333, 1.830617699, 2.302296865, 2.617249680),
SD = c(2.0816659995, 1.6410806067, 1.1864916416, 0.9515847943),
Skewness = c(-1.2933427807, -1.2264691300, -1.0112175576, -0.8631188095),
Kurtosis = NA_real_,
Min = structure(c(-1, 0, 1, 1.584962501), names = letters[1:4]),
Q1 = c(0.5, 1.160964047, 1.792481250, 2.196158711),
Median = c(2, 2.321928095, 2.584962501, 2.807354922),
Q3 = c(2.5, 2.745926548, 2.953445298, 3.133393270),
Max = c(3, 3.169925001, 3.321928095, 3.459431619)),
summary(rcc2, 1L, elt = "log2_exprs", log2scale = FALSE))
# Marginal summaries by Sample
checkEquals(cbind(GeomMean = c(1.316074013, 5.383563271, 9.433683366),
SizeFactor = c(0.3242922004, 1.3265572922, 2.3245424696),
MeanLog2 = c(0.3962406252, 2.4285613794, 3.2378211787),
SDLog2 = c(1.1378458590, 0.3478416172, 0.1977826790),
Min = structure(c(0, 4, 8), names = sampleNames(rcc)),
Q1 = c(0.75, 4.75, 8.75),
Median = c(1.5, 5.5, 9.5),
Q3 = c(2.25, 6.25, 10.25),
Max = c(3, 7, 11)),
summary(rcc2, 2L))
checkEquals(cbind(Mean = c(0.3962406252, 2.4285613794, 3.2378211787),
SD = c(1.1378458590, 0.3478416172, 0.1977826790),
Skewness = c(-0.4002032906, -0.3444852394, -0.1969256558),
Kurtosis = c(-1.6584093446, -0.9658168584, -1.1224275064),
Min = structure(c(-1, 2, 3), names = sampleNames(rcc)),
Q1 = c(-0.25, 2.241446071, 3.127443751),
Median = c(0.5, 2.453445298, 3.245926548),
Q3 = c(1.146240625, 2.640560606, 3.356303976),
Max = c(1.584962501, 2.807354922, 3.459431619)),
summary(rcc2, 2L, elt = "log2_exprs", log2scale = FALSE))
}
test_NanoStringRccSet_summary_GROUP <- function() {
rcc2 <- transform(rcc, log2_exprs = log2t(exprs))
# Marginal summaries by Feature
checkEquals(list(A =
cbind(GeomMean = c(1.414213562, 2.236067977, 3.464101615, 4.582575695),
SizeFactor = c(0.5313001399, 0.8400592816, 1.3014142429, 1.7216092197),
MeanLog2 = c(0.5, 1.160964047, 1.792481250, 2.196158711),
SDLog2 = c(2.1213203436, 1.6418511013, 1.1207377322, 0.8643619704),
Min = structure(0:3, names = letters[1:4]),
Q1 = 1:4,
Median = 2:5,
Q3 = 3:6,
Max = 4:7),
B =
cbind(GeomMean = 8:11,
SizeFactor = c(0.8480250704, 0.9540282042, 1.0600313380, 1.1660344717),
MeanLog2 = c(3, 3.169925001, 3.321928095, 3.459431619),
SDLog2 = NA_real_,
Min = structure(8:11, names = letters[1:4]),
Q1 = 8:11,
Median = 8:11,
Q3 = 8:11,
Max = 8:11)),
summary(rcc2, 1L, "Treatment"))
checkEquals(list("1" =
cbind(Mean = c(-1, 0, 1, 1.584962501),
SD = NA_real_,
Skewness = NA_real_,
Kurtosis = NA_real_,
Min = structure(c(-1, 0, 1, 1.584962501), names = letters[1:4]),
Q1 = c(-1, 0, 1, 1.584962501),
Median = c(-1, 0, 1, 1.584962501),
Q3 = c(-1, 0, 1, 1.584962501),
Max = c(-1, 0, 1, 1.584962501)),
"2" =
cbind(Mean = c(2, 2.321928095, 2.584962501, 2.807354922),
SD = NA_real_,
Skewness = NA_real_,
Kurtosis = NA_real_,
Min = structure(c(2, 2.321928095, 2.584962501, 2.807354922), names = letters[1:4]),
Q1 = c(2, 2.321928095, 2.584962501, 2.807354922),
Median = c(2, 2.321928095, 2.584962501, 2.807354922),
Q3 = c(2, 2.321928095, 2.584962501, 2.807354922),
Max = c(2, 2.321928095, 2.584962501, 2.807354922)),
"3" =
cbind(Mean = c(3, 3.169925001, 3.321928095, 3.459431619),
SD = NA_real_,
Skewness = NA_real_,
Kurtosis = NA_real_,
Min = structure(c(3, 3.169925001, 3.321928095, 3.459431619), names = letters[1:4]),
Q1 = c(3, 3.169925001, 3.321928095, 3.459431619),
Median = c(3, 3.169925001, 3.321928095, 3.459431619),
Q3 = c(3, 3.169925001, 3.321928095, 3.459431619),
Max = c(3, 3.169925001, 3.321928095, 3.459431619))),
summary(rcc2, 1L, "LaneID", elt = "log2_exprs", log2scale = FALSE))
# Marginal summaries by Sample
checkEquals(list(Endogenous =
cbind(GeomMean = c(0.5, 4, 8),
SizeFactor = c(0.1984251315, 1.5874010520, 3.1748021039),
MeanLog2 = c(-1, 2, 3),
SDLog2 = NA_real_,
Min = structure(c(0, 4, 8), names = sampleNames(rcc)),
Q1 = c(0, 4, 8),
Median = c(0, 4, 8),
Q3 = c(0, 4, 8),
Max = c(0, 4, 8)),
Housekeeping =
cbind(GeomMean = c(3, 7, 11),
SizeFactor = c(0.4889344008, 1.1408469352, 1.7927594696),
MeanLog2 = c(1.584962501, 2.807354922, 3.459431619),
SDLog2 = NA_real_,
Min = structure(c(3, 7, 11), names = sampleNames(rcc)),
Q1 = c(3, 7, 11),
Median = c(3, 7, 11),
Q3 = c(3, 7, 11),
Max = c(3, 7, 11)),
Negative =
cbind(GeomMean = c(2, 6, 10),
SizeFactor = c(0.405480133, 1.216440399, 2.027400665),
MeanLog2 = c(1, 2.584962501, 3.321928095),
SDLog2 = NA_real_,
Min = structure(c(2, 6, 10), names = sampleNames(rcc)),
Q1 = c(2, 6, 10),
Median = c(2, 6, 10),
Q3 = c(2, 6, 10),
Max = c(2, 6, 10)),
Positive =
cbind(GeomMean = c(1, 5, 9),
SizeFactor = c(0.2811442218, 1.4057211088, 2.5302979959),
MeanLog2 = c(0, 2.321928095, 3.169925001),
SDLog2 = NA_real_,
Min = structure(c(1, 5, 9), names = sampleNames(rcc)),
Q1 = c(1, 5, 9),
Median = c(1, 5, 9),
Q3 = c(1, 5, 9),
Max = c(1, 5, 9))),
summary(rcc2, 2L, "CodeClass"))
}
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