classify_spectrum | R Documentation |
Spectra can be classified based on the aggregate spectrum classifier score.
If sum(score) == 3
spectrum considered non-random, random otherwise.
classify_spectrum(
adj_r_squared,
degree,
slope,
consistency_score_n,
n_significant,
n_bins
)
adj_r_squared |
adjusted |
degree |
degree of polynomial, returned by score_spectrum |
slope |
coefficient of the linear term of the polynomial model (spectrum "direction"), returned by score_spectrum |
consistency_score_n |
number of performed permutations before early stopping, returned by score_spectrum |
n_significant |
number of bins with statistically significant enrichment |
n_bins |
number of bins |
a three-dimensional binary vector with the following components:
coordinate 1 | adj_r_squared >= 0.4 |
coordinate 2 | consistency_score_n > 1000000 |
coordinate 3 | n_significant >= floor(n_bins / 10)
|
Other SPMA functions:
run_kmer_spma()
,
run_matrix_spma()
,
score_spectrum()
,
subdivide_data()
n_bins <- 40
# random spectrum
random_sp <- score_spectrum(runif(n = n_bins, min = -1, max = 1),
max_model_degree = 1)
score <- classify_spectrum(
get_adj_r_squared(random_sp), get_model_degree(random_sp),
get_model_slope(random_sp), get_consistency_score_n(random_sp), 0, n_bins
)
sum(score)
# non-random linear spectrum with strong noise component
signal <- seq(-1, 0.99, 2 / 40)
noise <- rnorm(n = 40, mean = 0, sd = 0.5)
linear_sp <- score_spectrum(signal + noise, max_model_degree = 1,
max_cs_permutations = 100000)
score <- classify_spectrum(
get_adj_r_squared(linear_sp), get_model_degree(linear_sp),
get_model_slope(linear_sp), get_consistency_score_n(linear_sp), 10, n_bins
)
sum(score)
## Not run:
# non-random linear spectrum with weak noise component
signal <- seq(-1, 0.99, 2 / 40)
noise <- rnorm(n = 40, mean = 0, sd = 0.2)
linear_sp <- score_spectrum(signal + noise, max_model_degree = 1,
max_cs_permutations = 100000)
score <- classify_spectrum(
get_adj_r_squared(linear_sp), get_model_degree(linear_sp),
get_model_slope(linear_sp), get_consistency_score_n(linear_sp), 10, n_bins
)
sum(score)
## End(Not run)
# non-random quadratic spectrum with strong noise component
signal <- seq(-1, 0.99, 2 / 40)^2 - 0.5
noise <- rnorm(n = 40, mean = 0, sd = 0.2)
quadratic_sp <- score_spectrum(signal + noise, max_model_degree = 2,
max_cs_permutations = 100000)
score <- classify_spectrum(
get_adj_r_squared(quadratic_sp), get_model_degree(quadratic_sp),
get_model_slope(quadratic_sp),
get_consistency_score_n(quadratic_sp), 10, n_bins
)
sum(score)
## Not run:
# non-random quadratic spectrum with weak noise component
signal <- seq(-1, 0.99, 2 / 40)^2 - 0.5
noise <- rnorm(n = 40, mean = 0, sd = 0.1)
quadratic_sp <- score_spectrum(signal + noise, max_model_degree = 2)
score <- classify_spectrum(
get_adj_r_squared(quadratic_sp), get_model_degree(quadratic_sp),
get_model_slope(quadratic_sp),
get_consistency_score_n(quadratic_sp), 10, n_bins
)
sum(score)
## End(Not run)
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