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Simulate a dataset of genomic timecourses
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| library(dplyr) | |
| library(tidyr) | |
| library(impulse) # from github/shackett | |
| timepts <- c(0, 5, 10, 20, 30, 40, 60, 90) # time points measured | |
| measurement_sd <- 0.5 # standard deviation of Gaussian noise added to each observation | |
| total_measurements <- 10000 # total number of genes | |
| signal_frac <- 0.2 # what fraction of genes contain real signal | |
| set.seed(1234) | |
| # simulate timecourses containing signal | |
| alt_timecourses <- impulse::simulate_timecourses(n = total_measurements * signal_frac * 2, | |
| timepts = timepts, | |
| prior_pars = c(v_sd = 0.8, | |
| rate_shape = 2, | |
| rate_scale = 0.25, | |
| time_shape = 1, | |
| time_scale = 30), | |
| measurement_sd = measurement_sd) %>% | |
| unnest_legacy(measurements) %>% | |
| select(-true_model) %>% | |
| mutate(signal = "contains signal") %>% | |
| # drop timecourses where no true value's magnitude is greater than 1 (these | |
| # aren't really signal containing | |
| # and timecourses where the initial value isn't ~zero | |
| group_by(tc_id) %>% | |
| filter(any(abs(sim_fit) > 1), | |
| abs(sim_fit[time == 0]) < 0.1) %>% | |
| ungroup() | |
| # only retain the target number of signal containing timecourses | |
| alt_timecourses <- alt_timecourses %>% | |
| semi_join( | |
| alt_timecourses %>% | |
| distinct(tc_id) %>% | |
| sample_n(min(n(), total_measurements * signal_frac)), | |
| by = "tc_id") | |
| null_timecourses <- crossing(tc_id = seq(max(alt_timecourses$tc_id) + 1, | |
| max(alt_timecourses$tc_id) + total_measurements * (1-signal_frac)), | |
| time = timepts) %>% | |
| mutate(signal = "no signal", | |
| sim_fit = 0, | |
| abundance = rnorm(n(), 0, measurement_sd)) | |
| simulated_timecourses <- bind_rows(alt_timecourses, null_timecourses) %>% | |
| mutate(signal = factor(signal, levels = c("contains signal", "no signal"))) %>% | |
| group_by(tc_id) %>% | |
| mutate(fold_change = abundance - abundance[time == 0]) %>% | |
| ungroup() |
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