context("higherOrderNormMethods.R")
data("example_design")
data("example_data_only_values")
data("example_data")
data("example_stat_data")
# Subset the data to only look at first three conditions
test_design <- example_design[example_design$group %in% c("1", "2", "3"), ]
test_data <- example_data_only_values[, as.character(test_design$sample)]
# Remove rows with only NA-values
non_na_rows <- which(rowSums(is.na(test_data)) != ncol(test_data))
test_data <- test_data[non_na_rows, ]
real_rt_vals <- round(example_stat_data$Average.RT, 5)[non_na_rows]
test_that("getRTNormalizedMatrix_global_intensity", {
expect_dim <- c(98, 9)
expect_colsum <- c(
2482.77553, 2420.75378, 2497.33571,
2464.09042, 2424.41947, 2472.58042,
2515.43627, 2419.77311, 2393.76557
)
out <- getRTNormalizedMatrix(
rawMatrix=as.matrix(test_data),
retentionTimes=real_rt_vals,
normMethod=globalIntensityNormalization,
stepSizeMinutes=1,
windowMinCount=10)
out_dim <- dim(out)
out_colsum <- round(colSums(out, na.rm=TRUE), 5)
expect_true(
all.equal(
expect_dim,
out_dim
)
)
expect_true(
all(
expect_colsum == out_colsum
)
)
})
test_that("getRTNormalizedMatrix_loess", {
expect_dim <- c(98, 9)
expect_colsum <- c(
2472.8547, 2412.4723, 2496.2553,
2458.90508, 2419.86546, 2471.89521,
2515.73799, 2413.87385, 2392.8269
)
out <- getRTNormalizedMatrix(
rawMatrix=as.matrix(test_data),
retentionTimes=real_rt_vals,
normMethod=performCyclicLoessNormalization,
stepSizeMinutes=1,
windowMinCount=10)
out_dim <- dim(out)
out_colsum <- round(colSums(out, na.rm=TRUE), 5)
expect_true(
all.equal(
expect_dim,
out_dim
)
)
expect_true(
all(
expect_colsum == out_colsum
)
)
})
test_that("getSmoothedRTNormalizedMatrix_global_intensity", {
expect_dim <- c(98, 9)
expect_colsum <- c(
2483.01579, 2420.61195, 2497.10932,
2463.82244, 2424.36461, 2472.49291,
2515.79797, 2421.82197, 2393.95915
)
out <- getSmoothedRTNormalizedMatrix(
rawMatrix=as.matrix(test_data),
retentionTimes=real_rt_vals,
normMethod=globalIntensityNormalization,
stepSizeMinutes=1,
windowMinCount=10,
mergeMethod="median",
windowShifts=4
)
out_dim <- dim(out)
out_colsum <- round(colSums(out, na.rm=TRUE), 5)
expect_true(
all.equal(
expect_dim,
out_dim
)
)
expect_true(
all(
expect_colsum == out_colsum
)
)
})
test_that("getSmoothedRTNormalizedMatrix_loess", {
expect_dim <- c(98, 9)
expect_colsum <- c(
2473.03148, 2412.86092, 2496.37835,
2458.6256, 2419.13168, 2471.7457,
2514.70862, 2413.37041, 2392.00512
)
out <- getSmoothedRTNormalizedMatrix(
rawMatrix=as.matrix(test_data),
retentionTimes=real_rt_vals,
normMethod=performCyclicLoessNormalization,
stepSizeMinutes=1,
windowMinCount=10,
mergeMethod="mean",
windowShifts=3
)
out_dim <- dim(out)
out_colsum <- round(colSums(out, na.rm=TRUE), 5)
expect_true(
all.equal(
expect_dim,
out_dim
)
)
expect_true(
all(
expect_colsum == out_colsum
)
)
})
test_that("getWidenedRTRange_natural_numbers", {
rt_times <- seq_len(10)
expect_true(
all.equal(
getWidenedRTRange(4, 5, 2, rt_times),
c(4, 5)
)
)
expect_true(
all.equal(
getWidenedRTRange(4, 5, 3, rt_times),
c(3, 5)
)
)
expect_true(
all.equal(
getWidenedRTRange(4, 5, 4, rt_times),
c(3, 6)
)
)
})
test_that("getWidenedRTRange_float_numbers", {
rt_times <- c(1.1, 2.2, 3.3, 4.2, 5.4, 6.7, 7.1, 8.2, 9.2, 10.3)
expect_true(
all.equal(
getWidenedRTRange(2.2, 3.3, 2, rt_times),
c(2.2, 3.3)
)
)
expect_true(
all.equal(
getWidenedRTRange(2.2, 3.3, 6, rt_times),
c(1.1, 6.7)
)
)
expect_true(
all.equal(
getWidenedRTRange(2.2, 3.3, 10, rt_times),
c(1.1, 10.3)
)
)
expect_error(
getWidenedRTRange(2.2, 3.3, 11, rt_times)
)
})
test_that("getWidenedRTRange_real_numbers", {
expect_true(
all.equal(
getWidenedRTRange(34.497, 45.081, 20, round(real_rt_vals, 3)),
c(30.937, 45.335)
)
)
expect_true(
all.equal(
getWidenedRTRange(34.497, 45.082, 30, round(real_rt_vals, 3)),
c(28.211, 48.111)
)
)
expect_true(
all.equal(
getWidenedRTRange(34.49672, 45.08198, 98, round(real_rt_vals, 3)),
c(9.024, 128.702)
)
)
expect_error(
getWidenedRTRange(34.49672, 45.08198, 100, round(real_rt_vals, 3))
)
expect_error(
getWidenedRTRange(34.49672, 45.08198, 10, round(real_rt_vals, 3))
)
expect_true(
all.equal(
getWidenedRTRange(34.497, 45.082, 10, round(real_rt_vals, 3), allowTooWideData=TRUE),
c(34.497, 45.082)
)
)
})
test_that("getCombinedMatrix_minimal_symmetric", {
expect_out <- as.matrix(data.frame(
a=c(1.5, 1.5),
b=c(1.5, 1.5)))
df1 <- as.matrix(data.frame(a=c(1, 1), b=c(1, 1)))
df2 <- as.matrix(data.frame(a=c(2, 2), b=c(2, 2)))
l <- list(df1, df2)
out <- getCombinedMatrix(l, mean)
expect_true(
all.equal(
expect_out,
out
)
)
})
test_that("getCombinedMatrix_minimal_asymmetric", {
expect_out <- as.matrix(data.frame(
a=c(0.5, 1, 1.5),
b=c(2, 2.5, 3)))
df1 <- as.matrix(data.frame(a=c(0, 0, 0), b=c(0, 0, 0)))
df2 <- as.matrix(data.frame(a=c(1, 2, 3), b=c(4, 5, 6)))
l <- list(df1, df2)
out <- getCombinedMatrix(l, mean)
expect_true(
all.equal(
expect_out,
out
)
)
})
test_that("getCombinedMatrix_three_layer_mean", {
expect_out <- as.matrix(data.frame(
a=c(1.00000, 3.33333, 5.66667),
b=c(8.00000, 10.33333, 12.66667)))
df1 <- as.matrix(data.frame(a=c(1, 2, 3), b=c(4, 5, 6)))
df2 <- as.matrix(data.frame(a=c(1, 3, 5), b=c(7, 9, 11)))
df3 <- as.matrix(data.frame(a=c(1, 5, 9), b=c(13, 17, 21)))
l <- list()
l[[1]] <- df1
l[[2]] <- df2
l[[3]] <- df3
out <- round(getCombinedMatrix(l, mean), 5)
expect_true(
all.equal(
expect_out,
out
)
)
})
test_that("getCombinedMatrix_three_layer_median", {
expect_out <- as.matrix(data.frame(a=c(1, 3, 5), b=c(7, 9, 11)))
df1 <- as.matrix(data.frame(a=c(1, 2, 3), b=c(4, 5, 6)))
df2 <- as.matrix(data.frame(a=c(1, 3, 5), b=c(7, 9, 11)))
df3 <- as.matrix(data.frame(a=c(1, 5, 9), b=c(13, 17, 21)))
l <- list()
l[[1]] <- df1
l[[2]] <- df2
l[[3]] <- df3
out <- getCombinedMatrix(l, median)
expect_true(
all.equal(
expect_out,
out
)
)
})
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