In syma-research/microTensor: Tensor Decomposition for Longitudinal Microbiomes
knitr::opts_chunk$set(echo=FALSE)
library(magrittr)
# Registry
# batchtools::makeRegistry(file.dir = "r_batchtools_reg/sim/MisMod",
# package = c("magrittr"))
batchtools::loadRegistry(file.dir = "r_batchtools_reg/sim/MisMod",
writeable = TRUE)
rm(list = ls())
dir_output <- "results/Simulation/MisMod"
dir.create(dir_output, showWarnings = TRUE, recursive = TRUE)
m1 <- rep(c(1, 0, -1), each = 66)
m1 <- m1 / sqrt(sum(m1^2))
s1 <- rep(1, length = 100)
s1 <- s1 / sqrt(sum(s1^2))
t1 <- rep(1, length = 10)
t1 <- t1 / sqrt(sum(t1^2))
lambda1 <- 1000
m2 <- rep(rep(c(-1, 0, 1), each = 22), 3)
m2 <- m2 / sqrt(sum(m2^2))
s2 <- rep(c(0, 1), each = 50)
s2 <- s2 / sqrt(sum(s2^2))
t2 <- rep(c(0, 1), each = 5)
t2 <- t2 / sqrt(sum(t2^2))
lambda2 <- 500
Y <- lambda1 * outer(m1, outer(s1, t1)) + lambda2 * outer(m2, outer(s2, t2))
p <- apply(Y, c(2, 3), function(x) exp(x) / sum(exp(x)))
depth_range <- c(0.1, 10)
params <- list(m = cbind(m1, m2),
s = cbind(s1, s2),
t = cbind(t1, t2),
lambda = c(lambda1, lambda2),
Y = Y,
p = p,
depth_range = depth_range)
save(params, file = paste0(dir_output, "/params_n1.RData"))
# smaller sample size
m1 <- rep(c(1, 0, -1), each = 66)
m1 <- m1 / sqrt(sum(m1^2))
s1 <- rep(1, length = 30)
s1 <- s1 / sqrt(sum(s1^2))
t1 <- rep(1, length = 10)
t1 <- t1 / sqrt(sum(t1^2))
lambda1 <- 1000 / sqrt(10/3)
m2 <- rep(rep(c(-1, 0, 1), each = 22), 3)
m2 <- m2 / sqrt(sum(m2^2))
s2 <- rep(c(0, 1), each = 15)
s2 <- s2 / sqrt(sum(s2^2))
t2 <- rep(c(0, 1), each = 5)
t2 <- t2 / sqrt(sum(t2^2))
lambda2 <- 500 / sqrt(10/3)
Y <- lambda1 * outer(m1, outer(s1, t1)) + lambda2 * outer(m2, outer(s2, t2))
p <- apply(Y, c(2, 3), function(x) exp(x) / sum(exp(x)))
depth_range <- c(0.1, 10)
params <- list(m = cbind(m1, m2),
s = cbind(s1, s2),
t = cbind(t1, t2),
lambda = c(lambda1, lambda2),
Y = Y,
p = p,
depth_range = depth_range)
save(params, file = paste0(dir_output, "/params_n2.RData"))
rm(list = c("m1", "m2", "s1", "s2", "t1", "t2", "lambda1", "lambda2",
"Y", "p", "depth_range", "params"))
tb_job <- tidyr::crossing(
n = c(1, 2),
sd = seq(0.25, 2.25, by = 0.5),
t_missing = c(0, 4),
median_depth = c(1e4, 1e5),
R = 3,
rep = seq(1, 1000),
method = c("microTensor", "ctf")
) %>%
dplyr::mutate(i_job = seq(1, dplyr::n()))
save(tb_job, file = paste0(dir_output, "/tb_job.RData"))
n_job <- nrow(tb_job)
n_job_each <- 200
# fit decomposition methods to simulated data
one_job <- function(i_job) {
library(magrittr)
load(paste0(dir_output, "/tb_job.RData"))
l_statistics <- list()
for(ii_job in seq((i_job - 1) * n_job_each + 1, i_job * n_job_each)) {
i_tb_job <- tb_job[ii_job, ]
load(paste0(dir_output, "/params_n", i_tb_job$n, ".RData"))
set.seed(ii_job + 1)
Ytilde <- params$Y
Ytilde <- Ytilde + rnorm(n = length(Ytilde), mean = 0, sd = i_tb_job$sd)
ptilde <- apply(exp(Ytilde), c(2, 3), function(x) x / sum(x))
ptilde_missing <- ptilde
seq_depth <-
runif(n = dim(ptilde)[2] * dim(ptilde)[3],
min = log(params$depth_range[1] * i_tb_job$median_depth),
max = log(params$depth_range[2] * i_tb_job$median_depth)) %>%
exp() %>%
round()
N <- matrix(seq_depth, nrow = dim(ptilde)[2], ncol = dim(ptilde)[3])
X_array <- ptilde
for(j in seq(1, dim(ptilde)[2]))
for(k in seq(1, dim(ptilde)[3]))
X_array[, j, k] <- rmultinom(n = 1, size = N[j, k], prob = ptilde[, j, k])
# setting some time points to NA
if(i_tb_job$t_missing > 0) {
for(j in seq(1, dim(ptilde)[2])) {
k_missing <- sample.int(n = dim(ptilde)[3], size = i_tb_job$t_missing)
X_array[, j, k_missing] <- NA
ptilde_missing[, j, k_missing] <- NA
}
}
if(i_tb_job$method == "microTensor") {
time <- system.time(
fit_decomp <- microTensor::microTensor(
X = X_array, R = i_tb_job$R,
ortho_m = FALSE,
nn_t = FALSE,
weighted = TRUE,
control = list(
L_init = 3,
gamma = 2,
init = "ctf",
abs_tol = 1e-4,
rel_tol = 1e-4,
maxit = 5000,
verbose = FALSE)))
} else if(i_tb_job$method == "ctf") {
time <- system.time(
fit_decomp <- microTensor::ctf(
X = X_array, R = i_tb_job$R,
control = list(
maxit = 5000,
verbose = FALSE)))
}
fit_decomp$time <- time
## evaluation
results_summary <-
microTensor::evaluate_fit(
fit_decomp = fit_decomp,
p = params$p,
X_array = X_array,
R = 2)
if(i_tb_job$method == "microTensor") {
results_summary_uw <-
microTensor::evaluate_fit(
fit_decomp = fit_decomp$unweighted,
p = params$p,
X_array = X_array,
R = 2)
}
# evaluation of the simulated data
zero_perc <- mean(X_array == 0)
median_depth <- median(apply(X_array, c(2, 3), sum, na.rm = TRUE))
mat_L1_ptilde <-
matrix(NA, nrow = dim(params$p)[2], ncol = dim(params$p)[3])
for(j in seq(1, nrow(mat_L1_ptilde)))
for(k in seq(1, ncol(mat_L1_ptilde)))
mat_L1_ptilde[j, k] <-
mean(abs(ptilde[, j, k] - params$p[, j, k]) / params$p[, j, k])
p_norm <- apply(X_array, c(2, 3),
function(x) {
if(all(x == 0, na.rm = TRUE) )
return(x)
else
return(x / sum(x, na.rm = TRUE))
})
mat_L1_pnorm <-
matrix(NA, nrow = dim(params$p)[2], ncol = dim(params$p)[3])
for(j in seq(1, nrow(mat_L1_pnorm)))
for(k in seq(1, ncol(mat_L1_pnorm)))
mat_L1_pnorm[j, k] <-
mean(abs(p_norm[, j, k] - params$p[, j, k]) / params$p[, j, k])
# save particular runs to calcuate feature level statistics
if(i_tb_job$sd == min(tb_job$sd) &
i_tb_job$median_depth == 1e4 &
i_tb_job$t_missing == 0 &
i_tb_job$n == 1 &
i_tb_job$rep %in% seq(1, 10)) {
save(X_array, file = paste0(dir_output, "/X_array_", ii_job, ".RData"))
save(fit_decomp, file = paste0(dir_output, "/fit_", ii_job, ".RData"))
}
i_l_statistics <- list(mean_L1_relative =
mean(results_summary$mat_L1_relative, na.rm = TRUE),
zero_percentage = zero_perc,
avg_depth = median_depth,
mean_L1_ptilde =
mean(mat_L1_ptilde, na.rm = TRUE),
mean_L1_pnorm =
mean(mat_L1_pnorm, na.rm = TRUE),
time = fit_decomp$time)
if(i_tb_job$method == "microTensor") {
i_l_statistics$mean_l1_relative_uw <-
mean(results_summary_uw$mat_L1_relative, na.rm = TRUE)
}
l_statistics <- c(l_statistics, list(i_l_statistics))
}
save(l_statistics, file = paste0(dir_output, "/l_statistics_", i_job, ".RData"))
return(NULL)
}
batchtools::clearRegistry()
tb_ids <- batchtools::batchMap(one_job,
i_job = seq(1, n_job / n_job_each))
batchtools::batchExport(list("dir_output" = dir_output,
"n_job_each" = n_job_each))
ncpus <- 1
walltime <- 3600
batchtools::submitJobs(ids = seq(1, 10),
resources = list(ncpus = ncpus,
walltime = walltime))
batchtools::submitJobs(ids = seq(11, n_job / n_job_each),
resources = list(ncpus = ncpus,
partition = partition,
walltime = walltime))
library(ggplot2)
load(paste0(dir_output, "/tb_job.RData"))
l_statistics <- seq(1, nrow(tb_job) / n_job_each) %>%
purrr::map(~ {
load(paste0(dir_output, "/l_statistics_", .x, ".RData"))
l_statistics
}) %>%
purrr::reduce(c)
# Main figure
tb_results <- tb_job %>%
dplyr::mutate(mean_L1_relative = l_statistics %>%
purrr::map_dbl("mean_L1_relative")) %>%
rbind(tb_job %>%
dplyr::filter(method == "microTensor") %>%
{dplyr::mutate(
.,
mean_L1_relative =
l_statistics[.$i_job] %>%
purrr::map_dbl("mean_l1_relative_uw"),
method = "microTensor (uw)")})
tb_plot_results <- tb_results %>%
dplyr::group_by(sd, t_missing, median_depth, method, n) %>%
dplyr::summarise(log10_L1_relative = mean(log10(mean_L1_relative)),
sd_log10_L1_relative = sd(log10(mean_L1_relative)) / sqrt(dplyr::n())) %>%
dplyr::ungroup()
colors <- smar:::gg_color_hue(
values = c("CTF",
"microTensor (uw)",
"microTensor"))
tb_plot_results <- tb_plot_results %>%
dplyr::mutate(method = method %>%
dplyr::recode("ctf" = "CTF") %>%
factor(levels = names(colors))) %>%
dplyr::mutate(Missing =
dplyr::case_when(
t_missing == 0 ~ "Fully Observed",
t_missing == 4 ~ "40% Samples Missing"
) %>%
factor(levels = c("Fully Observed", "40% Samples Missing"))) %>%
dplyr::mutate(Depth =
dplyr::case_when(
median_depth == 1e4 ~ "Median 10,000 Reads",
TRUE ~ "Median 100,000 Reads"
) %>%
factor(levels = c("Median 10,000 Reads", "Median 100,000 Reads"))) %>%
dplyr::arrange(Depth, Missing) %>%
dplyr::mutate(level_plot = paste0(Depth, ";\n ",
Missing) %>%
forcats::as_factor()) %>%
dplyr::mutate(level_plot_label =
c("A", "B", "C", "D")[as.integer(level_plot)] %>%
paste0(") ", level_plot) %>%
forcats::as_factor())
p_figure <- tb_plot_results %>%
dplyr::filter(n == 1) %>%
ggplot(aes(x = sd, y = log10_L1_relative, color = method, shape = method)) +
geom_point(position = position_dodge(width = 0.05), size = 2) +
geom_errorbar(aes(ymin = log10_L1_relative - sd_log10_L1_relative,
ymax = log10_L1_relative + sd_log10_L1_relative),
position = position_dodge(width = 0.05)) +
geom_line(position = position_dodge(width = 0.05)) +
scale_color_manual(values = colors) +
facet_wrap(~ level_plot_label, nrow = 2) +
theme_bw() +
theme(legend.position = "bottom",
legend.title = element_blank(),
legend.direction = "horizontal",
strip.background = element_blank()) +
xlab(expression(paste("Dispersion (", sigma, " for ", tilde(Y)[ijk], ')'))) +
ylab(expression(paste(log[10], " Relative ", L[1], " Difference (|", p[ijk], "-", hat(p)[ijk],
"|/", p[ijk], ")")))
p_figure2 <- tb_plot_results %>%
dplyr::filter(n == 2) %>%
ggplot(aes(x = sd, y = log10_L1_relative, color = method, shape = method)) +
geom_point(position = position_dodge(width = 0.05), size = 2) +
geom_errorbar(aes(ymin = log10_L1_relative - sd_log10_L1_relative,
ymax = log10_L1_relative + sd_log10_L1_relative),
position = position_dodge(width = 0.05)) +
geom_line(position = position_dodge(width = 0.05)) +
scale_color_manual(values = colors) +
facet_wrap(~ level_plot_label, nrow = 2) +
theme_bw() +
theme(legend.position = "bottom",
legend.title = element_blank(),
legend.direction = "horizontal",
strip.background = element_blank()) +
xlab(expression(paste("Dispersion (", sigma, " for ", tilde(Y)[ijk], ')'))) +
ylab(expression(paste(log[10], " Relative ", L[1], " Difference (|", p[ijk], "-", hat(p)[ijk],
"|/", p[ijk], ")")))
ggsave(p_figure, filename = "figures/figure4_simulation2/figure4.pdf",
width = 6, height = 6)
ggsave(p_figure2, filename = "figures/figure4_simulation2/figure4_lessSamples.pdf",
width = 6, height = 6)
# diagnostics plots
tb_plot_diagnostics <- tb_job %>%
dplyr::mutate(
zero_percentage = l_statistics %>%
purrr::map_dbl("zero_percentage"),
avg_depth = l_statistics %>%
purrr::map_dbl("avg_depth"),
mean_L1_ptilde = l_statistics %>%
purrr::map_dbl("mean_L1_ptilde"),
mean_L1_pnorm = l_statistics %>%
purrr::map_dbl("mean_L1_pnorm")) %>%
dplyr::mutate(method = method %>%
dplyr::recode("ctf" = "CTF") %>%
factor(levels = names(colors)))
tb_plot_diagnostics <- tb_plot_diagnostics %>%
dplyr::filter(t_missing == 0,
n == 1) %>%
dplyr::group_by(sd) %>%
dplyr::mutate(L1_ptilde = mean(log10(mean_L1_ptilde))) %>%
dplyr::group_by(sd, median_depth, L1_ptilde) %>%
dplyr::summarise(zero_percentage = mean(zero_percentage)) %>%
dplyr::ungroup()
# histogram of log p
load(paste0(dir_output, "/params_n1.RData"))
p_hist <- data.frame(logp = log10(as.vector(params$p))) %>%
ggplot(aes(x = logp)) +
geom_histogram() +
theme_bw() +
xlab(expression(paste(log[10], p[ijk]))) +
ylab("Count")
# difference between p and ptilde
p_L1_ptilde <- tb_plot_diagnostics %>%
dplyr::filter(median_depth == 10000) %>%
ggplot(aes(x = sd, y = L1_ptilde)) +
geom_point(position = position_dodge(width = 0.05)) +
geom_line(position = position_dodge(width = 0.05)) +
theme_bw() +
xlab(expression(paste("Dispersion (", sigma, " for ", tilde(Y)[ijk], ')'))) +
ylab(expression(paste(log[10], " Relative ", L[1], " Difference (|", p[ijk], "-", tilde(p)[ijk],
"|/", p[ijk], ")")))
# ZI characteristics
p_ZI <- tb_plot_diagnostics %>%
dplyr::mutate(Depth =
dplyr::case_when(
median_depth == 1e4 ~ "Median 10,000 Reads",
TRUE ~ "Median 100,000 Reads"
) %>%
factor(levels = c("Median 10,000 Reads", "Median 100,000 Reads"))) %>%
ggplot(aes(x = sd, y = zero_percentage)) +
geom_point(position = position_dodge(width = 0.05)) +
geom_line(position = position_dodge(width = 0.05)) +
facet_grid(~ Depth) +
theme_bw() +
xlab(expression(paste("Dispersion (", sigma, " for ", tilde(Y)[ijk], ')'))) +
ylab(expression(paste("Zero proportion in ", X[ijk])))
p_Supp <- cowplot::plot_grid(p_hist, p_L1_ptilde, nrow = 1,
labels = c("A", "B")) %>%
cowplot::plot_grid(p_ZI, ncol = 1, labels = c("", "C"))
ggsave(p_Supp, filename = "figures/figure4_simulation2/figure4_Supp.pdf",
width = 8, height = 8)
# sparsity evaluation
tb_eval <- tb_job %>%
dplyr::filter(sd == min(sd),
median_depth == 1e4,
t_missing == 0,
n == 1,
rep %in% seq(1, 10))
tb_eval <- tb_eval$i_job %>%
purrr::map_dfr(function(i_job) {
i_tb_job <- tb_job[i_job, ]
load(paste0(dir_output, "/fit_", i_tb_job$i_job, ".RData"))
load(paste0(dir_output, "/params_n", i_tb_job$n, ".RData"))
load(paste0(dir_output, "/X_array_", i_tb_job$i_job, ".RData"))
Yhat <- microTensor::get_Yhat(fit_decomp, R = 2)
phat <- apply(Yhat, c(2, 3), function(x) {
x <- x - max(x)
return(exp(x)/sum(exp(x)))
})
array_L1_relative <- abs(phat - params$p) / params$p
rel_diff <- apply(log10(array_L1_relative), 1, mean)
mean_x_zero <- apply(X_array == 0, 1, mean)
tibble::tibble(rel_diff = rel_diff,
mean_x_zero = mean_x_zero,
method = i_tb_job$method)
})
p_sparse <- tb_eval %>%
dplyr::mutate(method = method %>%
dplyr::recode("ctf" = "CTF") %>%
factor(levels = names(colors))) %>%
ggplot(aes(x = mean_x_zero,
y = rel_diff,
color = method,
shape = method)) +
geom_point() +
scale_color_manual(values = colors[-2], name = "Method") +
scale_shape_manual(values = c("CTF" = 19, "microTensor" = 15), name = "Method") +
xlab("Per-feature sparsity") +
ylab("Per-feature log10 relative difference") +
theme_bw()
ggsave(p_sparse, filename = "figures/figure4_simulation2/figure4_sparsity.pdf",
width = 5, height = 4)
syma-research/microTensor documentation built on July 1, 2022, 6:30 a.m.
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knitr::opts_chunk$set(echo=FALSE) library(magrittr)
# Registry # batchtools::makeRegistry(file.dir = "r_batchtools_reg/sim/MisMod", # package = c("magrittr")) batchtools::loadRegistry(file.dir = "r_batchtools_reg/sim/MisMod", writeable = TRUE) rm(list = ls()) dir_output <- "results/Simulation/MisMod" dir.create(dir_output, showWarnings = TRUE, recursive = TRUE)
m1 <- rep(c(1, 0, -1), each = 66) m1 <- m1 / sqrt(sum(m1^2)) s1 <- rep(1, length = 100) s1 <- s1 / sqrt(sum(s1^2)) t1 <- rep(1, length = 10) t1 <- t1 / sqrt(sum(t1^2)) lambda1 <- 1000 m2 <- rep(rep(c(-1, 0, 1), each = 22), 3) m2 <- m2 / sqrt(sum(m2^2)) s2 <- rep(c(0, 1), each = 50) s2 <- s2 / sqrt(sum(s2^2)) t2 <- rep(c(0, 1), each = 5) t2 <- t2 / sqrt(sum(t2^2)) lambda2 <- 500 Y <- lambda1 * outer(m1, outer(s1, t1)) + lambda2 * outer(m2, outer(s2, t2)) p <- apply(Y, c(2, 3), function(x) exp(x) / sum(exp(x))) depth_range <- c(0.1, 10) params <- list(m = cbind(m1, m2), s = cbind(s1, s2), t = cbind(t1, t2), lambda = c(lambda1, lambda2), Y = Y, p = p, depth_range = depth_range) save(params, file = paste0(dir_output, "/params_n1.RData")) # smaller sample size m1 <- rep(c(1, 0, -1), each = 66) m1 <- m1 / sqrt(sum(m1^2)) s1 <- rep(1, length = 30) s1 <- s1 / sqrt(sum(s1^2)) t1 <- rep(1, length = 10) t1 <- t1 / sqrt(sum(t1^2)) lambda1 <- 1000 / sqrt(10/3) m2 <- rep(rep(c(-1, 0, 1), each = 22), 3) m2 <- m2 / sqrt(sum(m2^2)) s2 <- rep(c(0, 1), each = 15) s2 <- s2 / sqrt(sum(s2^2)) t2 <- rep(c(0, 1), each = 5) t2 <- t2 / sqrt(sum(t2^2)) lambda2 <- 500 / sqrt(10/3) Y <- lambda1 * outer(m1, outer(s1, t1)) + lambda2 * outer(m2, outer(s2, t2)) p <- apply(Y, c(2, 3), function(x) exp(x) / sum(exp(x))) depth_range <- c(0.1, 10) params <- list(m = cbind(m1, m2), s = cbind(s1, s2), t = cbind(t1, t2), lambda = c(lambda1, lambda2), Y = Y, p = p, depth_range = depth_range) save(params, file = paste0(dir_output, "/params_n2.RData")) rm(list = c("m1", "m2", "s1", "s2", "t1", "t2", "lambda1", "lambda2", "Y", "p", "depth_range", "params"))
tb_job <- tidyr::crossing( n = c(1, 2), sd = seq(0.25, 2.25, by = 0.5), t_missing = c(0, 4), median_depth = c(1e4, 1e5), R = 3, rep = seq(1, 1000), method = c("microTensor", "ctf") ) %>% dplyr::mutate(i_job = seq(1, dplyr::n())) save(tb_job, file = paste0(dir_output, "/tb_job.RData")) n_job <- nrow(tb_job) n_job_each <- 200
# fit decomposition methods to simulated data one_job <- function(i_job) { library(magrittr) load(paste0(dir_output, "/tb_job.RData")) l_statistics <- list() for(ii_job in seq((i_job - 1) * n_job_each + 1, i_job * n_job_each)) { i_tb_job <- tb_job[ii_job, ] load(paste0(dir_output, "/params_n", i_tb_job$n, ".RData")) set.seed(ii_job + 1) Ytilde <- params$Y Ytilde <- Ytilde + rnorm(n = length(Ytilde), mean = 0, sd = i_tb_job$sd) ptilde <- apply(exp(Ytilde), c(2, 3), function(x) x / sum(x)) ptilde_missing <- ptilde seq_depth <- runif(n = dim(ptilde)[2] * dim(ptilde)[3], min = log(params$depth_range[1] * i_tb_job$median_depth), max = log(params$depth_range[2] * i_tb_job$median_depth)) %>% exp() %>% round() N <- matrix(seq_depth, nrow = dim(ptilde)[2], ncol = dim(ptilde)[3]) X_array <- ptilde for(j in seq(1, dim(ptilde)[2])) for(k in seq(1, dim(ptilde)[3])) X_array[, j, k] <- rmultinom(n = 1, size = N[j, k], prob = ptilde[, j, k]) # setting some time points to NA if(i_tb_job$t_missing > 0) { for(j in seq(1, dim(ptilde)[2])) { k_missing <- sample.int(n = dim(ptilde)[3], size = i_tb_job$t_missing) X_array[, j, k_missing] <- NA ptilde_missing[, j, k_missing] <- NA } } if(i_tb_job$method == "microTensor") { time <- system.time( fit_decomp <- microTensor::microTensor( X = X_array, R = i_tb_job$R, ortho_m = FALSE, nn_t = FALSE, weighted = TRUE, control = list( L_init = 3, gamma = 2, init = "ctf", abs_tol = 1e-4, rel_tol = 1e-4, maxit = 5000, verbose = FALSE))) } else if(i_tb_job$method == "ctf") { time <- system.time( fit_decomp <- microTensor::ctf( X = X_array, R = i_tb_job$R, control = list( maxit = 5000, verbose = FALSE))) } fit_decomp$time <- time ## evaluation results_summary <- microTensor::evaluate_fit( fit_decomp = fit_decomp, p = params$p, X_array = X_array, R = 2) if(i_tb_job$method == "microTensor") { results_summary_uw <- microTensor::evaluate_fit( fit_decomp = fit_decomp$unweighted, p = params$p, X_array = X_array, R = 2) } # evaluation of the simulated data zero_perc <- mean(X_array == 0) median_depth <- median(apply(X_array, c(2, 3), sum, na.rm = TRUE)) mat_L1_ptilde <- matrix(NA, nrow = dim(params$p)[2], ncol = dim(params$p)[3]) for(j in seq(1, nrow(mat_L1_ptilde))) for(k in seq(1, ncol(mat_L1_ptilde))) mat_L1_ptilde[j, k] <- mean(abs(ptilde[, j, k] - params$p[, j, k]) / params$p[, j, k]) p_norm <- apply(X_array, c(2, 3), function(x) { if(all(x == 0, na.rm = TRUE) ) return(x) else return(x / sum(x, na.rm = TRUE)) }) mat_L1_pnorm <- matrix(NA, nrow = dim(params$p)[2], ncol = dim(params$p)[3]) for(j in seq(1, nrow(mat_L1_pnorm))) for(k in seq(1, ncol(mat_L1_pnorm))) mat_L1_pnorm[j, k] <- mean(abs(p_norm[, j, k] - params$p[, j, k]) / params$p[, j, k]) # save particular runs to calcuate feature level statistics if(i_tb_job$sd == min(tb_job$sd) & i_tb_job$median_depth == 1e4 & i_tb_job$t_missing == 0 & i_tb_job$n == 1 & i_tb_job$rep %in% seq(1, 10)) { save(X_array, file = paste0(dir_output, "/X_array_", ii_job, ".RData")) save(fit_decomp, file = paste0(dir_output, "/fit_", ii_job, ".RData")) } i_l_statistics <- list(mean_L1_relative = mean(results_summary$mat_L1_relative, na.rm = TRUE), zero_percentage = zero_perc, avg_depth = median_depth, mean_L1_ptilde = mean(mat_L1_ptilde, na.rm = TRUE), mean_L1_pnorm = mean(mat_L1_pnorm, na.rm = TRUE), time = fit_decomp$time) if(i_tb_job$method == "microTensor") { i_l_statistics$mean_l1_relative_uw <- mean(results_summary_uw$mat_L1_relative, na.rm = TRUE) } l_statistics <- c(l_statistics, list(i_l_statistics)) } save(l_statistics, file = paste0(dir_output, "/l_statistics_", i_job, ".RData")) return(NULL) }
batchtools::clearRegistry() tb_ids <- batchtools::batchMap(one_job, i_job = seq(1, n_job / n_job_each)) batchtools::batchExport(list("dir_output" = dir_output, "n_job_each" = n_job_each)) ncpus <- 1 walltime <- 3600 batchtools::submitJobs(ids = seq(1, 10), resources = list(ncpus = ncpus, walltime = walltime)) batchtools::submitJobs(ids = seq(11, n_job / n_job_each), resources = list(ncpus = ncpus, partition = partition, walltime = walltime))
library(ggplot2) load(paste0(dir_output, "/tb_job.RData")) l_statistics <- seq(1, nrow(tb_job) / n_job_each) %>% purrr::map(~ { load(paste0(dir_output, "/l_statistics_", .x, ".RData")) l_statistics }) %>% purrr::reduce(c) # Main figure tb_results <- tb_job %>% dplyr::mutate(mean_L1_relative = l_statistics %>% purrr::map_dbl("mean_L1_relative")) %>% rbind(tb_job %>% dplyr::filter(method == "microTensor") %>% {dplyr::mutate( ., mean_L1_relative = l_statistics[.$i_job] %>% purrr::map_dbl("mean_l1_relative_uw"), method = "microTensor (uw)")}) tb_plot_results <- tb_results %>% dplyr::group_by(sd, t_missing, median_depth, method, n) %>% dplyr::summarise(log10_L1_relative = mean(log10(mean_L1_relative)), sd_log10_L1_relative = sd(log10(mean_L1_relative)) / sqrt(dplyr::n())) %>% dplyr::ungroup() colors <- smar:::gg_color_hue( values = c("CTF", "microTensor (uw)", "microTensor")) tb_plot_results <- tb_plot_results %>% dplyr::mutate(method = method %>% dplyr::recode("ctf" = "CTF") %>% factor(levels = names(colors))) %>% dplyr::mutate(Missing = dplyr::case_when( t_missing == 0 ~ "Fully Observed", t_missing == 4 ~ "40% Samples Missing" ) %>% factor(levels = c("Fully Observed", "40% Samples Missing"))) %>% dplyr::mutate(Depth = dplyr::case_when( median_depth == 1e4 ~ "Median 10,000 Reads", TRUE ~ "Median 100,000 Reads" ) %>% factor(levels = c("Median 10,000 Reads", "Median 100,000 Reads"))) %>% dplyr::arrange(Depth, Missing) %>% dplyr::mutate(level_plot = paste0(Depth, ";\n ", Missing) %>% forcats::as_factor()) %>% dplyr::mutate(level_plot_label = c("A", "B", "C", "D")[as.integer(level_plot)] %>% paste0(") ", level_plot) %>% forcats::as_factor()) p_figure <- tb_plot_results %>% dplyr::filter(n == 1) %>% ggplot(aes(x = sd, y = log10_L1_relative, color = method, shape = method)) + geom_point(position = position_dodge(width = 0.05), size = 2) + geom_errorbar(aes(ymin = log10_L1_relative - sd_log10_L1_relative, ymax = log10_L1_relative + sd_log10_L1_relative), position = position_dodge(width = 0.05)) + geom_line(position = position_dodge(width = 0.05)) + scale_color_manual(values = colors) + facet_wrap(~ level_plot_label, nrow = 2) + theme_bw() + theme(legend.position = "bottom", legend.title = element_blank(), legend.direction = "horizontal", strip.background = element_blank()) + xlab(expression(paste("Dispersion (", sigma, " for ", tilde(Y)[ijk], ')'))) + ylab(expression(paste(log[10], " Relative ", L[1], " Difference (|", p[ijk], "-", hat(p)[ijk], "|/", p[ijk], ")"))) p_figure2 <- tb_plot_results %>% dplyr::filter(n == 2) %>% ggplot(aes(x = sd, y = log10_L1_relative, color = method, shape = method)) + geom_point(position = position_dodge(width = 0.05), size = 2) + geom_errorbar(aes(ymin = log10_L1_relative - sd_log10_L1_relative, ymax = log10_L1_relative + sd_log10_L1_relative), position = position_dodge(width = 0.05)) + geom_line(position = position_dodge(width = 0.05)) + scale_color_manual(values = colors) + facet_wrap(~ level_plot_label, nrow = 2) + theme_bw() + theme(legend.position = "bottom", legend.title = element_blank(), legend.direction = "horizontal", strip.background = element_blank()) + xlab(expression(paste("Dispersion (", sigma, " for ", tilde(Y)[ijk], ')'))) + ylab(expression(paste(log[10], " Relative ", L[1], " Difference (|", p[ijk], "-", hat(p)[ijk], "|/", p[ijk], ")"))) ggsave(p_figure, filename = "figures/figure4_simulation2/figure4.pdf", width = 6, height = 6) ggsave(p_figure2, filename = "figures/figure4_simulation2/figure4_lessSamples.pdf", width = 6, height = 6) # diagnostics plots tb_plot_diagnostics <- tb_job %>% dplyr::mutate( zero_percentage = l_statistics %>% purrr::map_dbl("zero_percentage"), avg_depth = l_statistics %>% purrr::map_dbl("avg_depth"), mean_L1_ptilde = l_statistics %>% purrr::map_dbl("mean_L1_ptilde"), mean_L1_pnorm = l_statistics %>% purrr::map_dbl("mean_L1_pnorm")) %>% dplyr::mutate(method = method %>% dplyr::recode("ctf" = "CTF") %>% factor(levels = names(colors))) tb_plot_diagnostics <- tb_plot_diagnostics %>% dplyr::filter(t_missing == 0, n == 1) %>% dplyr::group_by(sd) %>% dplyr::mutate(L1_ptilde = mean(log10(mean_L1_ptilde))) %>% dplyr::group_by(sd, median_depth, L1_ptilde) %>% dplyr::summarise(zero_percentage = mean(zero_percentage)) %>% dplyr::ungroup() # histogram of log p load(paste0(dir_output, "/params_n1.RData")) p_hist <- data.frame(logp = log10(as.vector(params$p))) %>% ggplot(aes(x = logp)) + geom_histogram() + theme_bw() + xlab(expression(paste(log[10], p[ijk]))) + ylab("Count") # difference between p and ptilde p_L1_ptilde <- tb_plot_diagnostics %>% dplyr::filter(median_depth == 10000) %>% ggplot(aes(x = sd, y = L1_ptilde)) + geom_point(position = position_dodge(width = 0.05)) + geom_line(position = position_dodge(width = 0.05)) + theme_bw() + xlab(expression(paste("Dispersion (", sigma, " for ", tilde(Y)[ijk], ')'))) + ylab(expression(paste(log[10], " Relative ", L[1], " Difference (|", p[ijk], "-", tilde(p)[ijk], "|/", p[ijk], ")"))) # ZI characteristics p_ZI <- tb_plot_diagnostics %>% dplyr::mutate(Depth = dplyr::case_when( median_depth == 1e4 ~ "Median 10,000 Reads", TRUE ~ "Median 100,000 Reads" ) %>% factor(levels = c("Median 10,000 Reads", "Median 100,000 Reads"))) %>% ggplot(aes(x = sd, y = zero_percentage)) + geom_point(position = position_dodge(width = 0.05)) + geom_line(position = position_dodge(width = 0.05)) + facet_grid(~ Depth) + theme_bw() + xlab(expression(paste("Dispersion (", sigma, " for ", tilde(Y)[ijk], ')'))) + ylab(expression(paste("Zero proportion in ", X[ijk]))) p_Supp <- cowplot::plot_grid(p_hist, p_L1_ptilde, nrow = 1, labels = c("A", "B")) %>% cowplot::plot_grid(p_ZI, ncol = 1, labels = c("", "C")) ggsave(p_Supp, filename = "figures/figure4_simulation2/figure4_Supp.pdf", width = 8, height = 8) # sparsity evaluation tb_eval <- tb_job %>% dplyr::filter(sd == min(sd), median_depth == 1e4, t_missing == 0, n == 1, rep %in% seq(1, 10)) tb_eval <- tb_eval$i_job %>% purrr::map_dfr(function(i_job) { i_tb_job <- tb_job[i_job, ] load(paste0(dir_output, "/fit_", i_tb_job$i_job, ".RData")) load(paste0(dir_output, "/params_n", i_tb_job$n, ".RData")) load(paste0(dir_output, "/X_array_", i_tb_job$i_job, ".RData")) Yhat <- microTensor::get_Yhat(fit_decomp, R = 2) phat <- apply(Yhat, c(2, 3), function(x) { x <- x - max(x) return(exp(x)/sum(exp(x))) }) array_L1_relative <- abs(phat - params$p) / params$p rel_diff <- apply(log10(array_L1_relative), 1, mean) mean_x_zero <- apply(X_array == 0, 1, mean) tibble::tibble(rel_diff = rel_diff, mean_x_zero = mean_x_zero, method = i_tb_job$method) }) p_sparse <- tb_eval %>% dplyr::mutate(method = method %>% dplyr::recode("ctf" = "CTF") %>% factor(levels = names(colors))) %>% ggplot(aes(x = mean_x_zero, y = rel_diff, color = method, shape = method)) + geom_point() + scale_color_manual(values = colors[-2], name = "Method") + scale_shape_manual(values = c("CTF" = 19, "microTensor" = 15), name = "Method") + xlab("Per-feature sparsity") + ylab("Per-feature log10 relative difference") + theme_bw() ggsave(p_sparse, filename = "figures/figure4_simulation2/figure4_sparsity.pdf", width = 5, height = 4)
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