context('normalize: nmr')
test_that('normalize_nmr produces the correct output', {
# Load the nmr data frames ---------------------------------------------------
load(system.file('testdata',
'nmrData.RData',
package = 'pmartR'
))
# Create an nmrData object.
nmrdata <- as.nmrData(
e_data = edata,
f_data = fdata,
e_meta = emeta,
edata_cname = "Metabolite",
fdata_cname = "SampleID",
emeta_cname = "Bin"
)
# Natural logate the data.
lognmrdata <- edata_transform(
omicsData = nmrdata,
data_scale = "log"
)
# Take the natural log of the Concentration column in f_data. This will be
# used for an nmrData object whose original scale is the log scale.
logfdata <- fdata
logfdata$Concentration <- log(fdata$Concentration)
# Create an nmrData object whose original scale is the log scale.
lnmrdata <- as.nmrData(
e_data = lognmrdata$e_data,
f_data = logfdata,
e_meta = emeta,
edata_cname = "Metabolite",
fdata_cname = "SampleID",
emeta_cname = "Bin",
data_scale = "log"
)
# Abundate the logged nmrData object.
anmrdata <- edata_transform(
omicsData = lnmrdata,
data_scale = "abundance"
)
# Calculate the normalization standards --------------------------------------
# Grab all of the row indices with the reference metabolite.
ref_idx <- which(edata[, "Metabolite"] == "unkm1.53")
# Extract the reference metabolite rows from edata.
ref_spec1 <- as.numeric(edata[ref_idx, -1])
# Natural logate the reference metabolite.
ref_spec1_log <- log(ref_spec1)
# Normalize the data in an nmr fashion to the reference metabolite.
norm_spec1 <- (nmrdata$e_data[-ref_idx, -1] /
rep(ref_spec1, each = 37))
# Back transmogrify spec1 abundance scale.
norm_spec1_bt <- norm_spec1 * median(ref_spec1)
# Normalize the log data in an nmr fashion to the reference metabolite.
norm_spec1_log <- (lognmrdata$e_data[-ref_idx, -1] -
rep(ref_spec1_log, each = 37))
# Back transmute spec1 log scale.
norm_spec1_log_bt <- norm_spec1_log + median(ref_spec1_log)
# Pluck out the normalizing values from f_data like an annoying chin whisker.
ref_spec2 <- fdata$Concentration
# Take the natural log of the concentration values.
ref_spec2_log <- log(ref_spec2)
# Normalize the data in an nmr fashion to the concentration values.
norm_spec2 <- (nmrdata$e_data[, -1] /
rep(ref_spec2, each = 38))
# Back transmogrify spec2 abundance scale.
norm_spec2_bt <- norm_spec2 * median(ref_spec2)
# Normalize the log data in an nmr fashion to the concentration values.
norm_spec2_log <- (lognmrdata$e_data[, -1] -
rep(ref_spec2_log, each = 38))
# Back transmute spec2 log scale.
norm_spec2_log_bt <- norm_spec2_log + median(ref_spec2_log)
# Test normalize_nmr: nmrnormRes ---------------------------------------------
# Abundance scale ---------------
# Use the first specification for identifying the reference samples.
spec1 <- normalize_nmr(nmrdata,
apply_norm = FALSE,
metabolite_name = "unkm1.53"
)
# Use the second specification for identifying the reference samples.
spec2 <- normalize_nmr(nmrdata,
apply_norm = FALSE,
sample_property_cname = "Concentration"
)
# Inspect the class of the two specifications.
expect_s3_class(spec1, c("nmrnormRes", "list"))
expect_s3_class(spec2, c("nmrnormRes", "list"))
# Put on our sleuth hat and investigate the nmrnormRes attributes.
expect_identical(
attr(spec1, "cnames"),
list(
edata_cname = "Metabolite",
fdata_cname = "SampleID"
)
)
expect_identical(
attr(spec1, "nmr_info"),
list(
metabolite_name = "unkm1.53",
sample_property_cname = NULL
)
)
expect_identical(
attr(spec2, "cnames"),
list(
edata_cname = "Metabolite",
fdata_cname = "SampleID"
)
)
expect_identical(
attr(spec2, "nmr_info"),
list(
metabolite_name = NULL,
sample_property_cname = "Concentration"
)
)
# Sleuth around the list elements.
expect_identical(
spec1$Sample,
fdata$SampleID
)
expect_identical(
spec1$Metabolite,
"unkm1.53"
)
expect_identical(
spec1$value,
ref_spec1
)
expect_identical(
spec2$Sample,
fdata$SampleID
)
expect_identical(
spec2$Property,
"Concentration"
)
expect_identical(
spec2$value,
fdata$Concentration
)
# Log scale ---------------
# Use the first specification for identifying the reference samples.
spec1log <- normalize_nmr(lognmrdata,
apply_norm = FALSE,
metabolite_name = "unkm1.53"
)
# Use the second specification for identifying the reference samples.
spec2log <- normalize_nmr(lognmrdata,
apply_norm = FALSE,
sample_property_cname = "Concentration"
)
# Examine the class of the two specifications.
expect_s3_class(spec1log, c("nmrnormRes", "list"))
expect_s3_class(spec2log, c("nmrnormRes", "list"))
# Scrutinize the nmrnormRes objects after they have been logated. The objects
# created with the second specification should be identical whether or not a
# log transformation has bee applied.
expect_identical(attributes(spec1log), attributes(spec1))
expect_identical(spec1log$Sample, spec1$Sample)
expect_identical(spec1log$Metabolite, spec1$Metabolite)
expect_identical(spec1log$value, ref_spec1_log)
expect_identical(spec2log, spec2)
# Test normalize_nmr: apply_norm, no backtransmogrification ------------------
# Abundance scale ---------------
# Use the first specification for identifying the reference samples.
expect_message(
spec1 <- normalize_nmr(nmrdata,
apply_norm = TRUE,
metabolite_name = "unkm1.53"
),
paste("backtransform is set to FALSE. Examine the",
"distribution of your data to ensure this is",
"reasonable.",
sep = " "
)
)
# Use the second specification for identifying the reference samples.
expect_message(
spec2 <- normalize_nmr(nmrdata,
apply_norm = TRUE,
sample_property_cname = "Concentration"
),
paste("backtransform is set to FALSE. Examine the",
"distribution of your data to ensure this is",
"reasonable.",
sep = " "
)
)
# Compare the normalization to the standards.
expect_identical(
spec1$e_data[, -1],
norm_spec1
)
expect_identical(
spec2$e_data[, -1],
norm_spec2
)
# Have a looksie at the class of the output for the two specifications.
expect_s3_class(spec1, "nmrData")
expect_s3_class(spec2, "nmrData")
# Call in the big dogs (Holmes and Watson) to investigate the nmrData
# attributes.
expect_identical(
attr(spec1, "cnames"),
list(
edata_cname = "Metabolite",
emeta_cname = "Bin",
fdata_cname = "SampleID",
techrep_cname = NULL
)
)
expect_equal(
attr(spec1, "data_info"),
list(
data_scale_orig = "abundance",
data_scale = "abundance",
norm_info = list(is_normalized = FALSE),
num_edata = 37,
num_miss_obs = 0,
prop_missing = 0,
num_samps = 41,
data_types = NULL,
batch_info = list(is_bc = FALSE)
)
)
expect_equal(
attr(spec1, "nmr_info"),
list(
metabolite_name = "unkm1.53",
sample_property_cname = NULL,
norm_info = list(
is_normalized = TRUE,
backtransform = FALSE,
norm_method = paste("nmrObject was normalized",
"using metabolite_name:",
"unkm1.53",
sep = " "
),
norm_params = ref_spec1
)
)
)
expect_equal(
attr(spec1, "meta_info"),
list(
meta_data = TRUE,
num_emeta = 37
)
)
expect_identical(attr(spec1, "filters"), list())
expect_identical(
attr(spec2, "cnames"),
list(
edata_cname = "Metabolite",
emeta_cname = "Bin",
fdata_cname = "SampleID",
techrep_cname = NULL
)
)
expect_equal(
attr(spec2, "data_info"),
list(
data_scale_orig = "abundance",
data_scale = "abundance",
norm_info = list(is_normalized = FALSE),
num_edata = 38,
num_miss_obs = 0,
prop_missing = 0,
num_samps = 41,
data_types = NULL,
batch_info = list(is_bc = FALSE)
)
)
expect_equal(
attr(spec2, "nmr_info"),
list(
metabolite_name = NULL,
sample_property_cname = "Concentration",
norm_info = list(
is_normalized = TRUE,
backtransform = FALSE,
norm_method = paste("nmrObject was normalized",
"using sample property:",
"Concentration",
sep = " "
),
norm_params = ref_spec2
)
)
)
expect_equal(
attr(spec2, "meta_info"),
list(
meta_data = TRUE,
num_emeta = 38
)
)
expect_identical(attr(spec2, "filters"), list())
# Log scale ---------------
# Use the first specification for identifying the reference samples.
expect_message(
spec1_log <- normalize_nmr(lognmrdata,
apply_norm = TRUE,
metabolite_name = "unkm1.53"
),
paste("backtransform is set to FALSE. Examine the",
"distribution of your data to ensure this is",
"reasonable.",
sep = " "
)
)
# Use the second specification for identifying the reference samples.
expect_message(
spec2_log <- normalize_nmr(lognmrdata,
apply_norm = TRUE,
sample_property_cname = "Concentration"
),
paste("backtransform is set to FALSE. Examine the",
"distribution of your data to ensure this is",
"reasonable.",
sep = " "
)
)
# Compare the normalization to the standards.
expect_identical(
spec1_log$e_data[, -1],
norm_spec1_log
)
expect_equal(
spec2_log$e_data[, -1],
norm_spec2_log
)
# Have a looksie at the class of the output for the two specifications.
expect_s3_class(spec1_log, "nmrData")
expect_s3_class(spec2_log, "nmrData")
# Call in the big dogs (Holmes and Watson) to investigate the nmrData
# attributes.
expect_identical(
attr(spec1_log, "cnames"),
list(
edata_cname = "Metabolite",
emeta_cname = "Bin",
fdata_cname = "SampleID",
techrep_cname = NULL
)
)
expect_equal(
attr(spec1_log, "data_info"),
list(
data_scale_orig = "abundance",
data_scale = "log",
norm_info = list(is_normalized = FALSE),
num_edata = 37,
num_miss_obs = 0,
prop_missing = 0,
num_samps = 41,
data_types = NULL,
batch_info = list(is_bc = FALSE)
)
)
expect_equal(
attr(spec1_log, "nmr_info"),
list(
metabolite_name = "unkm1.53",
sample_property_cname = NULL,
norm_info = list(
is_normalized = TRUE,
backtransform = FALSE,
norm_method = paste("nmrObject was normalized",
"using metabolite_name:",
"unkm1.53",
sep = " "
),
norm_params = ref_spec1_log
)
)
)
expect_equal(
attr(spec1_log, "meta_info"),
list(
meta_data = TRUE,
num_emeta = 37
)
)
expect_identical(attr(spec1_log, "filters"), list())
expect_identical(
attr(spec2_log, "cnames"),
list(
edata_cname = "Metabolite",
emeta_cname = "Bin",
fdata_cname = "SampleID",
techrep_cname = NULL
)
)
expect_equal(
attr(spec2_log, "data_info"),
list(
data_scale_orig = "abundance",
data_scale = "log",
norm_info = list(is_normalized = FALSE),
num_edata = 38,
num_miss_obs = 0,
prop_missing = 0,
num_samps = 41,
data_types = NULL,
batch_info = list(is_bc = FALSE)
)
)
expect_equal(
attr(spec2_log, "nmr_info"),
list(
metabolite_name = NULL,
sample_property_cname = "Concentration",
norm_info = list(
is_normalized = TRUE,
backtransform = FALSE,
norm_method = paste("nmrObject was normalized",
"using sample property:",
"Concentration",
sep = " "
),
norm_params = ref_spec2_log
)
)
)
expect_equal(
attr(spec2_log, "meta_info"),
list(
meta_data = TRUE,
num_emeta = 38
)
)
expect_identical(attr(spec2_log, "filters"), list())
# Test normalize_nmr: apply_norm, with backtransmogrification ----------------
# Abundance scale ---------------
# Use the first specification for identifying the reference samples.
spec1_bt <- normalize_nmr(nmrdata,
apply_norm = TRUE,
backtransform = TRUE,
metabolite_name = "unkm1.53"
)
# Use the second specification for identifying the reference samples
spec2_bt <- normalize_nmr(nmrdata,
apply_norm = TRUE,
backtransform = TRUE,
sample_property_cname = "Concentration"
)
# Compare the normalization to the standards.
expect_identical(
spec1_bt$e_data[, -1],
norm_spec1_bt
)
expect_equal(
spec2_bt$e_data[, -1],
norm_spec2_bt
)
# Have a looksie at the class of the output for the two specifications.
expect_s3_class(spec1_bt, "nmrData")
expect_s3_class(spec2_bt, "nmrData")
# Call in the big dogs (Holmes and Watson) to investigate the nmrData
# attributes.
expect_identical(
attr(spec1_bt, "cnames"),
list(
edata_cname = "Metabolite",
emeta_cname = "Bin",
fdata_cname = "SampleID",
techrep_cname = NULL
)
)
expect_equal(
attr(spec1_bt, "data_info"),
list(
data_scale_orig = "abundance",
data_scale = "abundance",
norm_info = list(is_normalized = FALSE),
num_edata = 37,
num_miss_obs = 0,
prop_missing = 0,
num_samps = 41,
data_types = NULL,
batch_info = list(is_bc = FALSE)
)
)
expect_equal(
attr(spec1_bt, "nmr_info"),
list(
metabolite_name = "unkm1.53",
sample_property_cname = NULL,
norm_info = list(
is_normalized = TRUE,
backtransform = TRUE,
norm_method = paste("nmrObject was normalized",
"using metabolite_name:",
"unkm1.53",
sep = " "
),
norm_params = ref_spec1
)
)
)
expect_equal(
attr(spec1_bt, "meta_info"),
list(
meta_data = TRUE,
num_emeta = 37
)
)
expect_identical(attr(spec1_bt, "filters"), list())
expect_identical(
attr(spec2_bt, "cnames"),
list(
edata_cname = "Metabolite",
emeta_cname = "Bin",
fdata_cname = "SampleID",
techrep_cname = NULL
)
)
expect_equal(
attr(spec2_bt, "data_info"),
list(
data_scale_orig = "abundance",
data_scale = "abundance",
norm_info = list(is_normalized = FALSE),
num_edata = 38,
num_miss_obs = 0,
prop_missing = 0,
num_samps = 41,
data_types = NULL,
batch_info = list(is_bc = FALSE)
)
)
expect_equal(
attr(spec2_bt, "nmr_info"),
list(
metabolite_name = NULL,
sample_property_cname = "Concentration",
norm_info = list(
is_normalized = TRUE,
backtransform = TRUE,
norm_method = paste("nmrObject was normalized",
"using sample property:",
"Concentration",
sep = " "
),
norm_params = ref_spec2
)
)
)
expect_equal(
attr(spec2_bt, "meta_info"),
list(
meta_data = TRUE,
num_emeta = 38
)
)
expect_identical(attr(spec2_bt, "filters"), list())
# Log scale ---------------
# Use the first specification for identifying the reference samples.
spec1_log_bt <- normalize_nmr(lognmrdata,
apply_norm = TRUE,
backtransform = TRUE,
metabolite_name = "unkm1.53"
)
# Use the second specification for identifying the reference samples
spec2_log_bt <- normalize_nmr(lognmrdata,
apply_norm = TRUE,
backtransform = TRUE,
sample_property_cname = "Concentration"
)
# Compare the normalization to the standards.
expect_identical(
spec1_log_bt$e_data[, -1],
norm_spec1_log_bt
)
expect_equal(
spec2_log_bt$e_data[, -1],
norm_spec2_log_bt
)
# Have a looksie at the class of the output for the two specifications.
expect_s3_class(spec1_log_bt, "nmrData")
expect_s3_class(spec2_log_bt, "nmrData")
# Call in the big dogs (Holmes and Watson) to investigate the nmrData
# attributes.
expect_identical(
attr(spec1_log_bt, "cnames"),
list(
edata_cname = "Metabolite",
emeta_cname = "Bin",
fdata_cname = "SampleID",
techrep_cname = NULL
)
)
expect_equal(
attr(spec1_log_bt, "data_info"),
list(
data_scale_orig = "abundance",
data_scale = "log",
norm_info = list(is_normalized = FALSE),
num_edata = 37,
num_miss_obs = 0,
prop_missing = 0,
num_samps = 41,
data_types = NULL,
batch_info = list(is_bc = FALSE)
)
)
expect_equal(
attr(spec1_log_bt, "nmr_info"),
list(
metabolite_name = "unkm1.53",
sample_property_cname = NULL,
norm_info = list(
is_normalized = TRUE,
backtransform = TRUE,
norm_method = paste("nmrObject was normalized",
"using metabolite_name:",
"unkm1.53",
sep = " "
),
norm_params = ref_spec1_log
)
)
)
expect_equal(
attr(spec1_log_bt, "meta_info"),
list(
meta_data = TRUE,
num_emeta = 37
)
)
expect_identical(attr(spec1_log_bt, "filters"), list())
expect_identical(
attr(spec2_log_bt, "cnames"),
list(
edata_cname = "Metabolite",
emeta_cname = "Bin",
fdata_cname = "SampleID",
techrep_cname = NULL
)
)
expect_equal(
attr(spec2_log_bt, "data_info"),
list(
data_scale_orig = "abundance",
data_scale = "log",
norm_info = list(is_normalized = FALSE),
num_edata = 38,
num_miss_obs = 0,
prop_missing = 0,
num_samps = 41,
data_types = NULL,
batch_info = list(is_bc = FALSE)
)
)
expect_equal(
attr(spec2_log_bt, "nmr_info"),
list(
metabolite_name = NULL,
sample_property_cname = "Concentration",
norm_info = list(
is_normalized = TRUE,
backtransform = TRUE,
norm_method = paste("nmrObject was normalized",
"using sample property:",
"Concentration",
sep = " "
),
norm_params = ref_spec2_log
)
)
)
expect_equal(
attr(spec2_log_bt, "meta_info"),
list(
meta_data = TRUE,
num_emeta = 38
)
)
expect_identical(attr(spec2_log_bt, "filters"), list())
# Test normalize_nmr: when original data scale is log ------------------------
# No backtransform ---------------
# Use the second specification for identifying the reference samples
expect_message(
spec2_l <- normalize_nmr(lnmrdata,
apply_norm = TRUE,
backtransform = FALSE,
sample_property_cname = "Concentration"
),
paste("backtransform is set to FALSE. Examine the",
"distribution of your data to ensure this is",
"reasonable.",
sep = " "
)
)
# Use the second specification for identifying the reference samples
expect_message(
spec2_a <- normalize_nmr(anmrdata,
apply_norm = TRUE,
backtransform = FALSE,
sample_property_cname = "Concentration"
),
paste("backtransform is set to FALSE. Examine the",
"distribution of your data to ensure this is",
"reasonable.",
sep = " "
)
)
# Compare the normalization to the standards.
expect_identical(
spec2_l$e_data[, -1],
norm_spec2_log
)
expect_equal(
spec2_a$e_data[, -1],
norm_spec2
)
# Have a looksie at the class of the output for the two specifications.
expect_s3_class(spec2_l, "nmrData")
expect_s3_class(spec2_a, "nmrData")
# Sleuth around the nmr_info attribute.
expect_equal(
attr(spec2_l, "nmr_info"),
list(
metabolite_name = NULL,
sample_property_cname = "Concentration",
norm_info = list(
is_normalized = TRUE,
backtransform = FALSE,
norm_method = paste("nmrObject was normalized",
"using sample property:",
"Concentration",
sep = " "
),
norm_params = ref_spec2_log
)
)
)
expect_equal(
attr(spec2_a, "nmr_info"),
list(
metabolite_name = NULL,
sample_property_cname = "Concentration",
norm_info = list(
is_normalized = TRUE,
backtransform = FALSE,
norm_method = paste("nmrObject was normalized",
"using sample property:",
"Concentration",
sep = " "
),
norm_params = ref_spec2
)
)
)
# Poke around the remaining attributes.
expect_identical(
attr(spec2_l, "cnames"),
list(
edata_cname = "Metabolite",
emeta_cname = "Bin",
fdata_cname = "SampleID",
techrep_cname = NULL
)
)
expect_equal(
attr(spec2_l, "data_info"),
list(
data_scale_orig = "log",
data_scale = "log",
norm_info = list(is_normalized = FALSE),
num_edata = 38,
num_miss_obs = 0,
prop_missing = 0,
num_samps = 41,
data_types = NULL,
batch_info = list(is_bc = FALSE)
)
)
expect_equal(
attr(spec2_l, "meta_info"),
list(
meta_data = TRUE,
num_emeta = 38
)
)
expect_identical(attr(spec2_l, "filters"), list())
expect_identical(
attr(spec2_a, "cnames"),
list(
edata_cname = "Metabolite",
emeta_cname = "Bin",
fdata_cname = "SampleID",
techrep_cname = NULL
)
)
expect_equal(
attr(spec2_a, "data_info"),
list(
data_scale_orig = "log",
data_scale = "abundance",
norm_info = list(is_normalized = FALSE),
num_edata = 38,
num_miss_obs = 0,
prop_missing = 0,
num_samps = 41,
data_types = NULL,
batch_info = list(is_bc = FALSE)
)
)
expect_equal(
attr(spec2_a, "meta_info"),
list(
meta_data = TRUE,
num_emeta = 38
)
)
expect_identical(attr(spec2_a, "filters"), list())
# With backtransform ---------------
# Use the second specification for identifying the reference samples
spec2_l_bt <- normalize_nmr(lnmrdata,
apply_norm = TRUE,
backtransform = TRUE,
sample_property_cname = "Concentration"
)
# Use the second specification for identifying the reference samples
spec2_a_bt <- normalize_nmr(anmrdata,
apply_norm = TRUE,
backtransform = TRUE,
sample_property_cname = "Concentration"
)
# Compare the normalization to the standards.
expect_identical(
spec2_l_bt$e_data[, -1],
norm_spec2_log_bt
)
expect_equal(
spec2_a_bt$e_data[, -1],
norm_spec2_bt
)
# Have a looksie at the class of the output for the two specifications.
expect_s3_class(spec2_l_bt, "nmrData")
expect_s3_class(spec2_a_bt, "nmrData")
# Sleuth around the nmr_info attribute.
expect_equal(
attr(spec2_l_bt, "nmr_info"),
list(
metabolite_name = NULL,
sample_property_cname = "Concentration",
norm_info = list(
is_normalized = TRUE,
backtransform = TRUE,
norm_method = paste("nmrObject was normalized",
"using sample property:",
"Concentration",
sep = " "
),
norm_params = ref_spec2_log
)
)
)
expect_equal(
attr(spec2_a_bt, "nmr_info"),
list(
metabolite_name = NULL,
sample_property_cname = "Concentration",
norm_info = list(
is_normalized = TRUE,
backtransform = TRUE,
norm_method = paste("nmrObject was normalized",
"using sample property:",
"Concentration",
sep = " "
),
norm_params = ref_spec2
)
)
)
# Poke around the remaining attributes.
expect_identical(
attr(spec2_l_bt, "cnames"),
list(
edata_cname = "Metabolite",
emeta_cname = "Bin",
fdata_cname = "SampleID",
techrep_cname = NULL
)
)
expect_equal(
attr(spec2_l_bt, "data_info"),
list(
data_scale_orig = "log",
data_scale = "log",
norm_info = list(is_normalized = FALSE),
num_edata = 38,
num_miss_obs = 0,
prop_missing = 0,
num_samps = 41,
data_types = NULL,
batch_info = list(is_bc = FALSE)
)
)
expect_equal(
attr(spec2_l_bt, "meta_info"),
list(
meta_data = TRUE,
num_emeta = 38
)
)
expect_identical(attr(spec2_l_bt, "filters"), list())
expect_identical(
attr(spec2_a_bt, "cnames"),
list(
edata_cname = "Metabolite",
emeta_cname = "Bin",
fdata_cname = "SampleID",
techrep_cname = NULL
)
)
expect_equal(
attr(spec2_a_bt, "data_info"),
list(
data_scale_orig = "log",
data_scale = "abundance",
norm_info = list(is_normalized = FALSE),
num_edata = 38,
num_miss_obs = 0,
prop_missing = 0,
num_samps = 41,
data_types = NULL,
batch_info = list(is_bc = FALSE)
)
)
expect_equal(
attr(spec2_a_bt, "meta_info"),
list(
meta_data = TRUE,
num_emeta = 38
)
)
expect_identical(attr(spec2_a_bt, "filters"), list())
# Test mutate_fdata ----------------------------------------------------------
# Inspect output when the original data scale is log.
expect_equal(
pmartR:::mutate_fdata(
ds = "log2",
ds_orig = "log",
is_log = TRUE,
is_log_orig = TRUE,
sample_property = log(fdata$Concentration)
),
log2(fdata$Concentration)
)
expect_equal(
pmartR:::mutate_fdata(
ds = "log10",
ds_orig = "log",
is_log = TRUE,
is_log_orig = TRUE,
sample_property = log(fdata$Concentration)
),
log10(fdata$Concentration)
)
# Inspect output when the original data scale is log2.
expect_equal(
pmartR:::mutate_fdata(
ds = "log",
ds_orig = "log2",
is_log = TRUE,
is_log_orig = TRUE,
sample_property = log2(fdata$Concentration)
),
log(fdata$Concentration)
)
expect_equal(
pmartR:::mutate_fdata(
ds = "log10",
ds_orig = "log2",
is_log = TRUE,
is_log_orig = TRUE,
sample_property = log2(fdata$Concentration)
),
log10(fdata$Concentration)
)
# Inspect output when the original data scale is log10.
expect_equal(
pmartR:::mutate_fdata(
ds = "log",
ds_orig = "log10",
is_log = TRUE,
is_log_orig = TRUE,
sample_property = log10(fdata$Concentration)
),
log(fdata$Concentration)
)
expect_equal(
pmartR:::mutate_fdata(
ds = "log2",
ds_orig = "log10",
is_log = TRUE,
is_log_orig = TRUE,
sample_property = log10(fdata$Concentration)
),
log2(fdata$Concentration)
)
load(system.file('testdata',
'little_seqdata.RData',
package = 'pmartR'
))
myseqData <- as.seqData(
e_data = edata,
f_data = fdata,
edata_cname = 'ID_REF',
fdata_cname = 'Samples'
)
err_2 <- "omicsData must be of the class 'nmrData'"
testthat::expect_error(normalize_nmr(myseqData), err_2)
})
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