From 52b0ef832095da54557b7faa8991f78572314077 Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Wed, 18 Nov 2020 16:57:43 -0500 Subject: [PATCH 01/55] Start working on SDM anisotropic kernel. --- nimare/meta/cbma/sdm.py | 52 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 52 insertions(+) create mode 100644 nimare/meta/cbma/sdm.py diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py new file mode 100644 index 000000000..af6d7cfd9 --- /dev/null +++ b/nimare/meta/cbma/sdm.py @@ -0,0 +1,52 @@ +"""Seed-based d-mapping-related methods.""" +import dijkstra3d +import numpy as np +from scipy import spatial, stats +from nilearn import masking + + +def compute_sdm_ma(ijk, effect_sizes, sample_sizes, significance_level, + mask_img, corr_map, alpha=0.5, kernel_sigma=5): + """Apply anisotropic kernel""" + # kernel_sigma is user-specified sigma of kernel + # alpha is user-selected degree of anisotropy + df = np.sum(sample_sizes) - 2 + effect_size_threshold = stats.t.isf(significance_level, df) + min_effect_size = -effect_size_threshold # smallest possible effect size + max_effect_size = effect_size_threshold # largest possible effect size + mask_data = mask_img.get_fdata() + mask_ijk = np.vstack(np.where(mask_data)).T # X x 3 + masked_distances = masking.unmask(masking.apply_mask(corr_map, mask_img), mask_img).get_fdata() + masked_distances = 1 - masked_distances + + peak_corrs = [] + kept_peaks = [] + for i_peak in range(ijk.shape[0]): + peak_ijk = ijk[i_peak, :] + peak_t = effect_sizes[i_peak] + if mask_data[tuple(peak_ijk)] == 0: + # Skip peaks outside the mask + continue + + # peak_corr is correlation between target voxel and peak. + # For non-adjacent voxels, peak_corr must be estimated with Dijkstra's algorithm. + peak_corr = dijkstra3d.distance_field(masked_distances, source=peak_ijk) + peak_corrs.append(peak_corr) + kept_peaks.append(i_peak) + + kept_ijk = ijk[kept_peaks, :] + peak_corrs = np.vstack(peak_corrs) + kept_effect_sizes = effect_sizes[kept_peaks] + + # real_distance is physical distance between voxel and peak + # we need some way to select the appropriate peak for each voxel + real_distances = spatial.distance.cdist(kept_ijk, mask_ijk) + closest_peak = np.argmin(real_distances, axis=0) + virtual_distances = np.sqrt( + (1 - alpha) * (real_distances ** 2) + alpha + * 2 * kernel_sigma * np.log(peak_corr ** -1) + ) + y_lower = min_effect_size + np.exp((-virtual_distances ** 2) / (2 * kernel_sigma)) * (peak_t - min_effect_size) + y_upper = max_effect_size + np.exp((-virtual_distances ** 2) / (2 * kernel_sigma)) * (peak_t - max_effect_size) + y_lower_img = masking.unmask(y_lower, mask_img) + y_upper_img = masking.unmask(y_upper, mask_img) From 721ebcf6fd9768acbc2094e02cd711f0c3bfda39 Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Fri, 29 Oct 2021 12:04:50 -0400 Subject: [PATCH 02/55] Run black and improve docstring. --- nimare/meta/cbma/sdm.py | 44 +++++++++++++++++++++++++++++++---------- 1 file changed, 34 insertions(+), 10 deletions(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index af6d7cfd9..165ad8c41 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -1,15 +1,35 @@ """Seed-based d-mapping-related methods.""" import dijkstra3d import numpy as np -from scipy import spatial, stats from nilearn import masking +from scipy import spatial, stats + +def compute_sdm_ma( + ijk, + effect_sizes, + sample_sizes, + significance_level, + mask_img, + corr_map, + alpha=0.5, + kernel_sigma=5, +): + """Apply anisotropic kernel to coordinates. -def compute_sdm_ma(ijk, effect_sizes, sample_sizes, significance_level, - mask_img, corr_map, alpha=0.5, kernel_sigma=5): - """Apply anisotropic kernel""" - # kernel_sigma is user-specified sigma of kernel - # alpha is user-selected degree of anisotropy + Parameters + ---------- + ijk + effect_sizes + sample_sizes + significance_level + mask_img + corr_map + alpha : float + User-selected degree of anisotropy. Default is 0.5. + kernel_sigma : float + User-specified sigma of kernel. Default is 5. + """ df = np.sum(sample_sizes) - 2 effect_size_threshold = stats.t.isf(significance_level, df) min_effect_size = -effect_size_threshold # smallest possible effect size @@ -43,10 +63,14 @@ def compute_sdm_ma(ijk, effect_sizes, sample_sizes, significance_level, real_distances = spatial.distance.cdist(kept_ijk, mask_ijk) closest_peak = np.argmin(real_distances, axis=0) virtual_distances = np.sqrt( - (1 - alpha) * (real_distances ** 2) + alpha - * 2 * kernel_sigma * np.log(peak_corr ** -1) + (1 - alpha) * (real_distances ** 2) + alpha * 2 * kernel_sigma * np.log(peak_corr ** -1) + ) + y_lower = min_effect_size + np.exp((-(virtual_distances ** 2)) / (2 * kernel_sigma)) * ( + peak_t - min_effect_size + ) + y_upper = max_effect_size + np.exp((-(virtual_distances ** 2)) / (2 * kernel_sigma)) * ( + peak_t - max_effect_size ) - y_lower = min_effect_size + np.exp((-virtual_distances ** 2) / (2 * kernel_sigma)) * (peak_t - min_effect_size) - y_upper = max_effect_size + np.exp((-virtual_distances ** 2) / (2 * kernel_sigma)) * (peak_t - max_effect_size) y_lower_img = masking.unmask(y_lower, mask_img) y_upper_img = masking.unmask(y_upper, mask_img) + return y_lower_img, y_upper_img From 26cb5e8b83f816c4178a74777c7a4b22e1386a35 Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Sun, 19 Dec 2021 12:55:12 -0500 Subject: [PATCH 03/55] Start looking at the Albajes-Eizagirre et al. (2019) supplement. --- nimare/meta/cbma/sdm.py | 286 ++++++++++++++++++ .../meta/cbma/sdmpsi_supplement_analysis1.r | 160 ++++++++++ .../meta/cbma/sdmpsi_supplement_analysis2.r | 119 ++++++++ 3 files changed, 565 insertions(+) create mode 100644 nimare/meta/cbma/sdmpsi_supplement_analysis1.r create mode 100644 nimare/meta/cbma/sdmpsi_supplement_analysis2.r diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index 165ad8c41..12f928375 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -1,10 +1,296 @@ """Seed-based d-mapping-related methods.""" +from datetime import datetime + import dijkstra3d import numpy as np from nilearn import masking from scipy import spatial, stats +def hedges_g(y, n_subjects, J): + """Calculate Hedges' G. + + R Code + ------ + g <- function (y) { # Calculate Hedges' g + J * apply(y, 2, function (y_i) { + (mean(y_i[1:n.subj]) - mean(y_i[-(1:n.subj)])) / + sqrt((var(y_i[1:n.subj]) + var(y_i[-(1:n.subj)])) / 2) + }) + } + """ + ... + + +def hedges_g_var(g, n_subjects, df, J): + """Calculate variance of Hedges' G. + + R Code + ------ + df <- 2 * n.subj - 2 # Degrees of freedom + J <- gamma(df / 2) / gamma((df - 1) / 2) * sqrt(2 / df) # Hedges' correction + g_var <- function (g) { # Variance of Hedges' g + 1 / n.subj + (1 - (df - 2) / (df * J^2)) * g^2 + } + """ + g_var = (1 / n_subjects) + (1 - (df - 2) / (df * (J ** 2))) * (g ** 2) + return g_var + + +def permute_study_effects(g): + """Permute study effects. + + R Code + ------ + perm1 <- function (g) { # Permute study effects + code <- which(runif(n.stud) > 0.5) + g[code] <- -1 * g[code] + g + } + """ + ... + + +def permute_subject_values(y): + """Permute subject values. + + R Code + ------ + perm2 <- function (y) { # Permute subject values + apply(y, 2, sample) + } + """ + ... + + +def simulate_subject_values(n_studies, n_subjects): + """Simulate subject values. + + R Code + ------ + sim.y <- function () { # Simulate (true) subject values + y <- matrix(rnorm(n.stud * n.subj * 2), ncol = n.stud) + y[1:n.subj,] <- y[1:n.subj,] + 0.2 # Add a small effect size + y + } + """ + ... + + +def zval(): + """I think this converts to z-values.""" + ... + + +def pval(): + """I think this converts z-values to p-values.""" + ... + + +def c(): + """I think this concatenates arrays. Could be wrong though.""" + ... + + +def rma(): + """This is metafor's random-effects model.""" + ... + + +def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): + """Run the second simulation. + + Parameters + ---------- + n_perms : int + Number of permutations. + n_sims : int + Number of simulations. + n_subjects : int + Number of subjects per group of a study + n_studies : int + Number of studies + + R Code + ------ + library(doParallel) + library(metafor) + registerDoParallel(cores = detectCores() - 1) + par(mfrow = 1:2) + + # Simulation parameters ####################################################### + n.perm <- 1000 # Number of permutations + n.sim <- 1000 # Number of simulations + n.subj <- 20 # Number of subjects per group of a study + n.stud <- 10 # Number of studies + + # Constants + df <- 2 * n.subj - 2 # Degrees of freedom + J <- gamma(df / 2) / gamma((df - 1) / 2) * sqrt(2 / df) # Hedges' correction + + sim <- do.call(rbind.data.frame, foreach (i.sim = 1:n.sim, .packages = "metafor") + %dopar% { + # Simulate subject data of all studies + y.unperm <- sim.y() + + # Calculate Hedges' g + g.unperm <- g(y.unperm) + g_var.unperm <- g_var(g.unperm) + + # Meta-analysis + m.unperm <- rma(g.unperm, g_var.unperm) + z.unperm <- m.unperm$zval + + # Save null distributions of z-values + nd.z.perm_stud <- z.unperm + nd.z.perm_subj <- z.unperm + + # Time before study-based permutation test + time0 <- Sys.time() + + # Study-based permutation test + for (i.perm in 1:(n.perm - 1)) { + # Permute study data + g.stud_perm <- perm1(g.unperm) + + # Meta-analysis of permuted study data + m.stud_perm <- rma(g.stud_perm, g_var.unperm) + + # Save null distribution of z-values + nd.z.perm_stud <- c(nd.z.perm_stud, m.stud_perm$zval) + } + + # Time between study-based and subject-based permutation tests + time1 <- Sys.time() + + # Subject-based permutation test + for (i.perm in 1:(n.perm - 1)) { + # Permute subject data + y.subj_perm <- perm2(y.unperm) + + # Calculate Hedges' g of permuted subject data + g.subj_perm <- g(y.subj_perm) + g_var.subj_perm <- g_var(g.subj_perm) + + # Meta-analysis of permuted subject data + m.subj_perm <- rma(g.subj_perm, g_var.subj_perm) + + # Save null distribution of z-values + nd.z.perm_subj <- c(nd.z.perm_subj, m.subj_perm$zval) + } + + # Time after subject-based permutation tests + time2 <- Sys.time() + + # Save times and two-tailed p-values + data.frame( + time.perm_stud = as.numeric(time1 - time0), + time.perm_subj = as.numeric(time2 - time1), + p.z = m.unperm$pval, + p.perm_stud = 1 - 2 * abs(mean(z.unperm > nd.z.perm_stud) - 0.5), + p.perm_subj = 1 - 2 * abs(mean(z.unperm > nd.z.perm_subj) - 0.5) + ) + }) + + # Output results + time.perm_stud <- sim$time.perm_stud + time.perm_subj <- sim$time.perm_subj + mse.perm_stud <- (sim$p.perm_stud - sim$p.z)^2 + mse.perm_subj <- (sim$p.perm_subj - sim$p.z)^2 + cat("Decrease in execution time: ", round( + (mean(time.perm_subj) - mean(time.perm_stud)) / mean(time.perm_subj), + 2) * 100, "%\n", sep = "") + cat("Increase in mean squared error: ", round( + (mean(mse.perm_stud) - mean(mse.perm_subj)) / mean(mse.perm_subj), + 2) * 100, "%\n", sep = "") + """ + from math import gamma + + import numpy as np + + # Constants + df = (2 * n_subjects) - 2 # degrees of freedom + J = gamma(df / 2) / gamma((df - 1) / 2) * np.sqrt(2 / df) # Hedges' correction + + # Next is a parallelized for loop of 1:n_sims that somehow uses metafor. + for i_sim in range(n_sims): + + # Simulate subject data of all studies + y_unperm = simulate_subject_values(n_studies, n_subjects) + + # Calculate Hedges' g + g_unperm = hedges_g(y_unperm, n_subjects, J) + g_var_unperm = hedges_g_var(g_unperm, n_subjects, df, J) + + # Meta-analysis + m_unperm = rma(g_unperm, g_var_unperm) + z_unperm = zval(m_unperm) + + # Save null distributions of z-values + nd_z_perm_stud = z_unperm + nd_z_perm_subj = z_unperm + + # Time before study-based permutation test + time0 = datetime.now() + + # Study-based permutation test + for j_perm in range(n_perms): + # Permute study data + g_stud_perm = permute_study_effects(g_unperm) + + # Meta-analysis of permuted study data + m_stud_perm = rma(g_stud_perm, g_var_unperm) + + # Save null distribution of z-values + nd_z_perm_stud = c(nd_z_perm_stud, zval(m_stud_perm)) + + # Time between study-based and subject-based permutation tests + time1 = datetime.now() + + # Subject-based permutation test + for j_perm in range(n_perms): + # Permute subject data + y_subj_perm = permute_subject_values(y_unperm) + + # Calculate Hedges' g of permuted subject data + g_subj_perm = hedges_g(y_subj_perm) + g_var_subj_perm = hedges_g_var(g_subj_perm) + + # Meta-analysis of permuted subject data + m_subj_perm = rma(g_subj_perm, g_var_subj_perm) + + # Save null distribution of z-values + nd_z_perm_subj = c(nd_z_perm_subj, zval(m_subj_perm)) + + # Time after subject-based permutation tests + time2 = datetime.now() + + # Save times and two-tailed p-values + # TODO: Store data across simulations + out_dict = { + "time_perm_stud": time1 - time0, + "time_perm_subj": time2 - time1, + "p_z": pval(m_unperm), + "p_perm_stud": 1 - 2 * np.abs(np.mean(z_unperm > nd_z_perm_stud) - 0.5), + "p_perm_subj": 1 - 2 * np.abs(np.mean(z_unperm > nd_z_perm_subj) - 0.5), + } + + # Output results + time_perm_stud = out_dict["time_perm_stud"] + time_perm_subj = out_dict["time_perm_subj"] + mse_perm_stud = (out_dict["p_perm_stud"] - out_dict["p_z"]) ** 2 + mse_perm_subj = (out_dict["p_perm_subj"] - out_dict["p_z"]) ** 2 + print( + "Decrease in execution time: " + f"{np.round(np.mean(time_perm_subj) - np.mean(time_perm_stud))}\n", + ) + print( + "Increase in MSE: " + f"{np.round((np.mean(mse_perm_stud) - np.mean(mse_perm_stud)) / np.mean(mse_perm_subj))}" + ) + ... + + def compute_sdm_ma( ijk, effect_sizes, diff --git a/nimare/meta/cbma/sdmpsi_supplement_analysis1.r b/nimare/meta/cbma/sdmpsi_supplement_analysis1.r new file mode 100644 index 000000000..baaa7e08a --- /dev/null +++ b/nimare/meta/cbma/sdmpsi_supplement_analysis1.r @@ -0,0 +1,160 @@ +################################################################################# +# EFFECTS OF THE CORRELATION BETWEEN IMPUTATIONS AND THE USE OF # +# VARIABLE PERMUTATION CODES IN THE STATISTICAL SIGNIFICANCE # +# SIMULATION OF MULTIPLE IMPUTATION OF EFFECT SIZE, IMPUTATION OF # +# SUBJECT VALUES AND PERMUTATION TEST FOR A SINGLE STUDY # +################################################################################# + +# Simulation parameters ####################################################### +n.imp <- 50 # Number of imputations +n.perm <- 1000 # Number of permutations +n.sim <- 10 # Number of simulations +n.subj <- 20 # Number of subjects per study + +# Small functions ############################################################# +g <- function (y) { # Calculate Hedges' g + if (is.vector(y)) { + return(J * mean(y) / sd(y)) + } + J * apply(y, 2, function (y_i) { + mean(y_i) / sd(y_i) + }) +} +g_var <- function (g) { # Variance of Hedges' g + 1 / n.subj + (1 - (df - 2) / (df * J^2)) * g^2 +} +imp.subj <- function () { # Impute subject values + y <- rnorm(n.subj) + (y - mean(y)) / sd(y) +} +perm <- function (y, code) { # Permute subject values + y[code] <- -1 * y[code] + y +} +perm.code <- function () { # Create a random permutation code + which(runif(n.subj) > 0.5) +} +combine.g <- function (g) { # Combine Hedges' g from different imputations + mean(g) +} +combine.g_var <- function (g, g_var) { # Combine variance from different imp. + mean(g_var) + (1 + 1 / n.imp) * var(g) +} +sim.g.imp <- function (g) { # Simulate multiple imputation of Hedges' g + g + rnorm(n.imp, 0, 0.2) +} +sim.y.true <- function () { # Simulate (true) subject values + rnorm(n.subj) +} + +# Main ######################################################################## +library(doParallel) +registerDoParallel(cores = detectCores() - 1) +par(mfrow = c(2, 2)) + +# Constants +df <- n.subj - 1 # Degrees of freedom +J <- gamma(df / 2) / gamma((df - 1) / 2) * sqrt(2 / df) # Hedges' correction + +# Effects to test: perfect correlation between any two imputations? +for (PERFECT_CORRELATION in c(TRUE, FALSE)) { + # Effects to test: apply the same permutation code to all imputations? + for (SAME_PERM_CODE in c(TRUE, FALSE)) { + # Start simulations + sim <- do.call(rbind.data.frame, foreach (i.sim = 1:n.sim) %dopar% { + + # Simulate (true) subject data + y.true <- sim.y.true() + + # Calculate Hedges' g + g.true <- g(y.true) + g_var.true <- g_var(g.true) + + # Calculate z-value + z.true <- g.true / sqrt(g_var.true) + + # Simulate multiple imputaion of Hedges' g + g.imp <- sim.g.imp(g.true) + g_var.imp <- g_var(g.imp) + + # Combine imputed Hedges' g + g.imp.combined <- combine.g(g.imp) + g_var.imp.combined <- combine.g_var(g.imp, g_var.imp) + + # Calculate z-value of combined imputed data + z.imp.combined <- g.imp.combined / sqrt(g_var.imp.combined) + + # Impute subject data + replicate.d.imp <- t(replicate(n.subj, g.imp / J)) + if (PERFECT_CORRELATION) { + common.imp.subj <- imp.subj() + y.imp <- replicate(n.imp, common.imp.subj) + replicate.d.imp + } else { + y.imp <- replicate(n.imp, imp.subj()) + replicate.d.imp + } + + # Save variance between imputations and null distributions of z-values + var.g.imp <- var(g.imp) + nd.z.true <- z.true + nd.z.imp.combined <- z.imp.combined + + for (i.perm in 1:(n.perm - 1)) { + + # Permute (true) subject data + y.true.perm <- perm(y.true, perm.code()) + # Calculate Hedges' g of permuted data + g.true.perm <- g(y.true.perm) + g_var.true.perm <- g_var(g.true.perm) + # Calculate z-value of permuted data + z.true.perm <- g.true.perm / sqrt(g_var.true.perm) + # Permute imputed subject data + if (SAME_PERM_CODE) { + common.perm.code <- perm.code() + y.imp.perm <- apply(y.imp, 2, function (y.imp.i) { + perm(y.imp.i, common.perm.code) + }) + } else { + y.imp.perm <- apply(y.imp, 2, function (y.imp.i) { + perm(y.imp.i, perm.code()) + }) + } + + # Calculate Hedges' g of permuted imputed data + g.imp.perm <- g(y.imp.perm) + g_var.imp.perm <- g_var(g.imp.perm) + + # Calculate z-value of permuted imputed data + g.imp.perm.combined <- combine.g(g.imp.perm) + g_var.imp.perm.combined <- combine.g_var(g.imp.perm, g_var.imp.perm) + z.imp.perm.combined <- g.imp.perm.combined / sqrt(g_var.imp.perm.combined) + + # Save variance between imputations and null distributions of z-values + var.g.imp <- c(var.g.imp, var(g.imp.perm)) + nd.z.true <- c(nd.z.true, z.true.perm) + nd.z.imp.combined <- c(nd.z.imp.combined, z.imp.perm.combined) + + } + + # Save variance between imputations and one-tailed p-values + data.frame( + var.g.imp = mean(var.g.imp), + p.true = mean(z.true > nd.z.true), + p.imp.combined = mean(z.imp.combined > nd.z.imp.combined) + ) + }) + + # Create output plot + plot(0:1, 0:1, type = "l", col = "#999999", cex.lab = 1.2, + xlab = "p-values from (true) subject data", + ylab = "p-values from imputed subject data", + main = paste( + ifelse(PERFECT_CORRELATION, "Perfect", "No"), + "correlation between imputations.", + ifelse(SAME_PERM_CODE, "Same", "Different"), + "permutation code.\n", + "Variance between imputations:", + signif(mean(sim$var.g.imp), 1) + ), font.main = 1) + points(sim$p.true, sim$p.imp.combined) + } +} diff --git a/nimare/meta/cbma/sdmpsi_supplement_analysis2.r b/nimare/meta/cbma/sdmpsi_supplement_analysis2.r new file mode 100644 index 000000000..b4cf1e390 --- /dev/null +++ b/nimare/meta/cbma/sdmpsi_supplement_analysis2.r @@ -0,0 +1,119 @@ +############################################################################### +# PERMUTATION OF STUDY DATA AND PERMUTATION OF SUBJECT DATA # +############################################################################### + +# Simulation parameters ####################################################### +n.perm <- 1000 # Number of permutations +n.sim <- 1000 # Number of simulations +n.subj <- 20 # Number of subjects per group of a study +n.stud <- 10 # Number of studies + +# Small functions ############################################################# +g <- function (y) { # Calculate Hedges' g + J * apply(y, 2, function (y_i) { + (mean(y_i[1:n.subj]) - mean(y_i[-(1:n.subj)])) / + sqrt((var(y_i[1:n.subj]) + var(y_i[-(1:n.subj)])) / 2) + }) +} +g_var <- function (g) { # Variance of Hedges' g + 1 / n.subj + (1 - (df - 2) / (df * J^2)) * g^2 +} +perm1 <- function (g) { # Permute study effects + code <- which(runif(n.stud) > 0.5) + g[code] <- -1 * g[code] + g +} +perm2 <- function (y) { # Permute subject values + apply(y, 2, sample) +} +sim.y <- function () { # Simulate (true) subject values + y <- matrix(rnorm(n.stud * n.subj * 2), ncol = n.stud) + y[1:n.subj,] <- y[1:n.subj,] + 0.2 # Add a small effect size + y +} + +# Main ######################################################################## +library(doParallel) +library(metafor) +registerDoParallel(cores = detectCores() - 1) +par(mfrow = 1:2) + +# Constants +df <- 2 * n.subj - 2 # Degrees of freedom +J <- gamma(df / 2) / gamma((df - 1) / 2) * sqrt(2 / df) # Hedges' correction + +sim <- do.call(rbind.data.frame, foreach (i.sim = 1:n.sim, .packages = "metafor") +%dopar% { + # Simulate subject data of all studies + y.unperm <- sim.y() + + # Calculate Hedges' g + g.unperm <- g(y.unperm) + g_var.unperm <- g_var(g.unperm) + + # Meta-analysis + m.unperm <- rma(g.unperm, g_var.unperm) + z.unperm <- m.unperm$zval + + # Save null distributions of z-values + nd.z.perm_stud <- z.unperm + nd.z.perm_subj <- z.unperm + + # Time before study-based permutation test + time0 <- Sys.time() + + # Study-based permutation test + for (i.perm in 1:(n.perm - 1)) { + # Permute study data + g.stud_perm <- perm1(g.unperm) + + # Meta-analysis of permuted study data + m.stud_perm <- rma(g.stud_perm, g_var.unperm) + + # Save null distribution of z-values + nd.z.perm_stud <- c(nd.z.perm_stud, m.stud_perm$zval) + } + + # Time between study-based and subject-based permutation tests + time1 <- Sys.time() + + # Subject-based permutation test + for (i.perm in 1:(n.perm - 1)) { + # Permute subject data + y.subj_perm <- perm2(y.unperm) + + # Calculate Hedges' g of permuted subject data + g.subj_perm <- g(y.subj_perm) + g_var.subj_perm <- g_var(g.subj_perm) + + # Meta-analysis of permuted subject data + m.subj_perm <- rma(g.subj_perm, g_var.subj_perm) + + # Save null distribution of z-values + nd.z.perm_subj <- c(nd.z.perm_subj, m.subj_perm$zval) + } + + # Time after subject-based permutation tests + time2 <- Sys.time() + + # Save times and two-tailed p-values + data.frame( + time.perm_stud = as.numeric(time1 - time0), + time.perm_subj = as.numeric(time2 - time1), + p.z = m.unperm$pval, + p.perm_stud = 1 - 2 * abs(mean(z.unperm > nd.z.perm_stud) - 0.5), + p.perm_subj = 1 - 2 * abs(mean(z.unperm > nd.z.perm_subj) - 0.5) + ) +}) + +# Output results +time.perm_stud <- sim$time.perm_stud +time.perm_subj <- sim$time.perm_subj +mse.perm_stud <- (sim$p.perm_stud - sim$p.z)^2 +mse.perm_subj <- (sim$p.perm_subj - sim$p.z)^2 +cat("Decrease in execution time: ", round( + (mean(time.perm_subj) - mean(time.perm_stud)) / mean(time.perm_subj), + 2) * 100, "%\n", sep = "") +cat("Increase in mean squared error: ", round( + (mean(mse.perm_stud) - mean(mse.perm_subj)) / mean(mse.perm_subj), + 2) * 100, "%\n", sep = "") From 907ac54e6f20c829923f215113b12a5f788ea280 Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Mon, 20 Dec 2021 14:55:47 -0500 Subject: [PATCH 04/55] Improve documentation a bit. --- nimare/meta/cbma/sdm.py | 32 ++++++++++++++++++-------------- 1 file changed, 18 insertions(+), 14 deletions(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index 12f928375..77d509fbf 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -77,23 +77,23 @@ def simulate_subject_values(n_studies, n_subjects): ... -def zval(): +def zval(meta_result): """I think this converts to z-values.""" - ... + zvals = meta_result.get_fe_stats()["z"] + return zvals -def pval(): +def pval(meta_result): """I think this converts z-values to p-values.""" - ... - - -def c(): - """I think this concatenates arrays. Could be wrong though.""" - ... + pvals = meta_result.get_fe_stats()["p"] + return pvals def rma(): - """This is metafor's random-effects model.""" + """This is metafor's random-effects model. + + I want to use PyMARE for this. + """ ... @@ -227,8 +227,12 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): z_unperm = zval(m_unperm) # Save null distributions of z-values - nd_z_perm_stud = z_unperm - nd_z_perm_subj = z_unperm + # NOTE: Not sure why the original z-values would be included in the null distribution + # so I replaced them with empty lists. + # nd_z_perm_stud = z_unperm + # nd_z_perm_subj = z_unperm + nd_z_perm_stud = [] + nd_z_perm_subj = [] # Time before study-based permutation test time0 = datetime.now() @@ -242,7 +246,7 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): m_stud_perm = rma(g_stud_perm, g_var_unperm) # Save null distribution of z-values - nd_z_perm_stud = c(nd_z_perm_stud, zval(m_stud_perm)) + nd_z_perm_stud.append(zval(m_stud_perm)) # Time between study-based and subject-based permutation tests time1 = datetime.now() @@ -260,7 +264,7 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): m_subj_perm = rma(g_subj_perm, g_var_subj_perm) # Save null distribution of z-values - nd_z_perm_subj = c(nd_z_perm_subj, zval(m_subj_perm)) + nd_z_perm_subj.append(zval(m_subj_perm)) # Time after subject-based permutation tests time2 = datetime.now() From 61e8d7de6a73eb49116d9031f266c545a91e69e5 Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Tue, 21 Dec 2021 11:28:45 -0500 Subject: [PATCH 05/55] Use PyMARE instead of metafor's rma. --- nimare/meta/cbma/sdm.py | 48 +++++++++++++++++------------------------ 1 file changed, 20 insertions(+), 28 deletions(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index 77d509fbf..4b96e8dea 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -4,6 +4,7 @@ import dijkstra3d import numpy as np from nilearn import masking +from pymare import Dataset, estimators from scipy import spatial, stats @@ -37,7 +38,7 @@ def hedges_g_var(g, n_subjects, df, J): return g_var -def permute_study_effects(g): +def permute_study_effects(g, n_studies): """Permute study effects. R Code @@ -77,26 +78,6 @@ def simulate_subject_values(n_studies, n_subjects): ... -def zval(meta_result): - """I think this converts to z-values.""" - zvals = meta_result.get_fe_stats()["z"] - return zvals - - -def pval(meta_result): - """I think this converts z-values to p-values.""" - pvals = meta_result.get_fe_stats()["p"] - return pvals - - -def rma(): - """This is metafor's random-effects model. - - I want to use PyMARE for this. - """ - ... - - def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): """Run the second simulation. @@ -223,8 +204,11 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): g_var_unperm = hedges_g_var(g_unperm, n_subjects, df, J) # Meta-analysis - m_unperm = rma(g_unperm, g_var_unperm) - z_unperm = zval(m_unperm) + dset = Dataset(y=g_unperm, v=g_var_unperm) + est = estimators.VarianceBasedLikelihoodEstimator(method="REML") + est.fit_dataset(dset) + m_unperm = est.summary() + z_unperm = m_unperm.get_fe_stats()["z"] # Save null distributions of z-values # NOTE: Not sure why the original z-values would be included in the null distribution @@ -243,10 +227,14 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): g_stud_perm = permute_study_effects(g_unperm) # Meta-analysis of permuted study data - m_stud_perm = rma(g_stud_perm, g_var_unperm) + dset_stud_perm = Dataset(y=g_stud_perm, v=g_var_unperm) + est_stud_perm = estimators.VarianceBasedLikelihoodEstimator(method="REML") + est_stud_perm.fit_dataset(dset_stud_perm) + m_stud_perm = est_stud_perm.summary() # Save null distribution of z-values - nd_z_perm_stud.append(zval(m_stud_perm)) + zvals = m_stud_perm.get_fe_stats()["z"] + nd_z_perm_stud.append(zvals) # Time between study-based and subject-based permutation tests time1 = datetime.now() @@ -261,10 +249,14 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): g_var_subj_perm = hedges_g_var(g_subj_perm) # Meta-analysis of permuted subject data - m_subj_perm = rma(g_subj_perm, g_var_subj_perm) + dset_subj_perm = Dataset(y=g_subj_perm, v=g_var_subj_perm) + est_subj_perm = estimators.VarianceBasedLikelihoodEstimator(method="REML") + est_subj_perm.fit_dataset(dset_subj_perm) + m_subj_perm = est_subj_perm.summary() # Save null distribution of z-values - nd_z_perm_subj.append(zval(m_subj_perm)) + zvals = m_subj_perm.get_fe_stats()["z"] + nd_z_perm_subj.append(zvals) # Time after subject-based permutation tests time2 = datetime.now() @@ -274,7 +266,7 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): out_dict = { "time_perm_stud": time1 - time0, "time_perm_subj": time2 - time1, - "p_z": pval(m_unperm), + "p_z": m_unperm.get_fe_stats()["p"], "p_perm_stud": 1 - 2 * np.abs(np.mean(z_unperm > nd_z_perm_stud) - 0.5), "p_perm_subj": 1 - 2 * np.abs(np.mean(z_unperm > nd_z_perm_subj) - 0.5), } From ca6ddc0c9bea3fc4d74ee4aa5ffe1bbfcf2ede54 Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Tue, 21 Dec 2021 12:33:40 -0500 Subject: [PATCH 06/55] Get the permutation functions working. --- nimare/meta/cbma/sdm.py | 22 +++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index 4b96e8dea..9b4d7fdf0 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -11,6 +11,8 @@ def hedges_g(y, n_subjects, J): """Calculate Hedges' G. + NOTE: We probably want to support both two-sample and one-sample versions. + R Code ------ g <- function (y) { # Calculate Hedges' g @@ -41,6 +43,8 @@ def hedges_g_var(g, n_subjects, df, J): def permute_study_effects(g, n_studies): """Permute study effects. + Randomly sign-flip ~50% of the studies. + R Code ------ perm1 <- function (g) { # Permute study effects @@ -49,19 +53,26 @@ def permute_study_effects(g, n_studies): g } """ - ... + code = np.random.randint(0, 2, size=n_studies).astype(bool) + out_g = g.copy() + out_g[code] *= -1 + return out_g def permute_subject_values(y): """Permute subject values. + Seems to shuffle rows in each column independently. + When "size" isn't provided to ``sample()``, it just permutes the array. + R Code ------ perm2 <- function (y) { # Permute subject values apply(y, 2, sample) } """ - ... + permuted = np.apply_along_axis(np.random.permutation, 0, y) + return permuted def simulate_subject_values(n_studies, n_subjects): @@ -233,6 +244,8 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): m_stud_perm = est_stud_perm.summary() # Save null distribution of z-values + # TODO: Fix this after testing. zvals should be an array across samples, + # so appending to a list doesn't make sense. zvals = m_stud_perm.get_fe_stats()["z"] nd_z_perm_stud.append(zvals) @@ -284,7 +297,6 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): "Increase in MSE: " f"{np.round((np.mean(mse_perm_stud) - np.mean(mse_perm_stud)) / np.mean(mse_perm_subj))}" ) - ... def compute_sdm_ma( @@ -338,12 +350,12 @@ def compute_sdm_ma( kept_ijk = ijk[kept_peaks, :] peak_corrs = np.vstack(peak_corrs) - kept_effect_sizes = effect_sizes[kept_peaks] + # kept_effect_sizes = effect_sizes[kept_peaks] # real_distance is physical distance between voxel and peak # we need some way to select the appropriate peak for each voxel real_distances = spatial.distance.cdist(kept_ijk, mask_ijk) - closest_peak = np.argmin(real_distances, axis=0) + # closest_peak = np.argmin(real_distances, axis=0) virtual_distances = np.sqrt( (1 - alpha) * (real_distances ** 2) + alpha * 2 * kernel_sigma * np.log(peak_corr ** -1) ) From 8c530f6f1d0f7e6c4f541beb5e168aecc822448f Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Tue, 21 Dec 2021 13:24:58 -0500 Subject: [PATCH 07/55] Draft hedges g function. --- nimare/meta/cbma/sdm.py | 25 +++++++++++++++++++++---- 1 file changed, 21 insertions(+), 4 deletions(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index 9b4d7fdf0..f38c54e08 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -8,7 +8,7 @@ from scipy import spatial, stats -def hedges_g(y, n_subjects, J): +def hedges_g(y, J, n_subjects=None): """Calculate Hedges' G. NOTE: We probably want to support both two-sample and one-sample versions. @@ -22,7 +22,18 @@ def hedges_g(y, n_subjects, J): }) } """ - ... + if n_subjects is None: + g_arr = J * np.mean(y, axis=0) / np.std(y, axis=0) + + else: + g_arr = np.zeros(y.shape[1]) + for i_col in range(y.shape[1]): + num = np.mean(y[:n_subjects, i_col]) - np.mean(y[n_subjects:, i_col]) + denom = np.sqrt((np.var(y[:n_subjects, i_col]) + np.var(y[n_subjects:, i_col])) / 2) + g = num / denom + g_arr[i_col] = g + + return g_arr def hedges_g_var(g, n_subjects, df, J): @@ -99,9 +110,15 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): n_sims : int Number of simulations. n_subjects : int - Number of subjects per group of a study + Number of subjects per group of a study. + 2*n_subjects is the total number of subjects in each study. n_studies : int - Number of studies + Number of studies in the meta-analysis. + + Notes + ----- + TODO: Support array for number of subjects, so that each study has its own sample size. + TODO: Support one-sample studies. R Code ------ From b5be4080e4cf4ce54bd2bb6b47468b8a5ad55acc Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Tue, 21 Dec 2021 14:15:22 -0500 Subject: [PATCH 08/55] Use formulae from wikipedia for hedges' g. --- nimare/meta/cbma/sdm.py | 25 ++++++++++++++++++++----- 1 file changed, 20 insertions(+), 5 deletions(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index f38c54e08..f00d79bd3 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -23,16 +23,25 @@ def hedges_g(y, J, n_subjects=None): } """ if n_subjects is None: - g_arr = J * np.mean(y, axis=0) / np.std(y, axis=0) + g_arr = J * (y.shape[0] - 1) * np.mean(y, axis=0) / np.std(y, axis=0) else: + n1 = n_subjects + n2 = y.shape[0] - n_subjects + var1 = np.var(y[:n1], axis=0) + var2 = np.var(y[n1:], axis=0) + g_arr = np.zeros(y.shape[1]) for i_col in range(y.shape[1]): - num = np.mean(y[:n_subjects, i_col]) - np.mean(y[n_subjects:, i_col]) - denom = np.sqrt((np.var(y[:n_subjects, i_col]) + np.var(y[n_subjects:, i_col])) / 2) - g = num / denom + mean_diff = np.mean(y[:n_subjects, i_col]) - np.mean(y[n_subjects:, i_col]) + pooled_std = np.sqrt( + (((n1 - 1) * var1[i_col]) + ((n2 - 1) * var2[i_col])) / (n_subjects - 2) + ) + g = mean_diff / pooled_std g_arr[i_col] = g + g_arr = g_arr * (J * (n1 + n2 - 2)) + return g_arr @@ -339,7 +348,13 @@ def compute_sdm_ma( alpha : float User-selected degree of anisotropy. Default is 0.5. kernel_sigma : float - User-specified sigma of kernel. Default is 5. + User-specified sigma of kernel, in mm. Default is 5. + + References + ---------- + * Radua, J., Rubia, K., Canales, E. J., Pomarol-Clotet, E., Fusar-Poli, P., & + Mataix-Cols, D. (2014). Anisotropic kernels for coordinate-based meta-analyses of + neuroimaging studies. Frontiers in psychiatry, 5, 13. """ df = np.sum(sample_sizes) - 2 effect_size_threshold = stats.t.isf(significance_level, df) From f0eee9c232d8a5798be7ddeb016c48549a06c1ae Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Tue, 21 Dec 2021 14:51:41 -0500 Subject: [PATCH 09/55] Add empty functions for calculating Hedges g to transforms. --- nimare/transforms.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/nimare/transforms.py b/nimare/transforms.py index 01a24dc66..2254c10bc 100644 --- a/nimare/transforms.py +++ b/nimare/transforms.py @@ -799,3 +799,23 @@ def z_to_t(z_values, dof): t_values = np.zeros(z_values.shape) t_values[z_values != 0] = t_values_nonzero return t_values + + +def t_to_hedges_g(t_values, sample_size): + """Convert t-statistics to Hedges' g values. + + According to Radua 2012, the main conversion in (A)ES-SDM is t-statistic to Hedges g. + + Notes + ----- + Clues from https://imaging.mrc-cbu.cam.ac.uk/statswiki/FAQ/td. + """ + # one sample t-test + cohens_d = t_values / np.sqrt(sample_size) + # two sample t-test + cohens_d = t * np.sqrt((1 / n1) + (1 / n2)) + return cohens_d + + +def f_to_hedges_g(f_values, sample_size): + ... From b3cf2a77ac13e00b83dfb7e8221ea3b39b88fbcd Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Tue, 21 Dec 2021 14:51:55 -0500 Subject: [PATCH 10/55] Try improving Hedges g functions. --- nimare/meta/cbma/sdm.py | 27 ++++++++++++++++++++++++--- 1 file changed, 24 insertions(+), 3 deletions(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index f00d79bd3..a97d6b9c5 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -13,6 +13,21 @@ def hedges_g(y, J, n_subjects=None): NOTE: We probably want to support both two-sample and one-sample versions. + Parameters + ---------- + y : 2D array of shape (sample_size, n_variables) + Subject-level values for which to calculate Hedges G. + Multiple variables may be provided. + J : float + Hedges' correction factor. + n_subjects : None or int + Number of subjects in each group. If None, the dataset is assumed to be have one sample. + This assumes that two-sample datasets have the same number of subjects in each group. + + Notes + ----- + Clues for Python version from https://en.wikipedia.org/wiki/Effect_size#Hedges'_g. + R Code ------ g <- function (y) { # Calculate Hedges' g @@ -45,9 +60,14 @@ def hedges_g(y, J, n_subjects=None): return g_arr -def hedges_g_var(g, n_subjects, df, J): +def hedges_g_var(g, n_subjects): """Calculate variance of Hedges' G. + Notes + ----- + Clues for Python version from https://constantinyvesplessen.com/post/g/#hedges-g, + except using proper J instead of approximation. + R Code ------ df <- 2 * n.subj - 2 # Degrees of freedom @@ -56,7 +76,8 @@ def hedges_g_var(g, n_subjects, df, J): 1 / n.subj + (1 - (df - 2) / (df * J^2)) * g^2 } """ - g_var = (1 / n_subjects) + (1 - (df - 2) / (df * (J ** 2))) * (g ** 2) + n1 = n2 = n_subjects # NOTE: Until different sample sizes are supported + g_var = ((n1 + n2) / (n1 * n2)) + ((g ** 2) / (2 * (n1 + n2))) return g_var @@ -228,7 +249,7 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): # Constants df = (2 * n_subjects) - 2 # degrees of freedom - J = gamma(df / 2) / gamma((df - 1) / 2) * np.sqrt(2 / df) # Hedges' correction + J = gamma(df / 2) / (gamma((df - 1) / 2) * np.sqrt(df / 2)) # Hedges' correction # Next is a parallelized for loop of 1:n_sims that somehow uses metafor. for i_sim in range(n_sims): From 206719600006ba4f546065f72285bd4affeb037d Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Wed, 22 Dec 2021 11:50:17 -0500 Subject: [PATCH 11/55] Add simulation code (thanks @JulioAPeraza!). --- nimare/meta/cbma/sdm.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index a97d6b9c5..048a947ab 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -117,7 +117,7 @@ def permute_subject_values(y): def simulate_subject_values(n_studies, n_subjects): - """Simulate subject values. + """Simulate (true) subject values. R Code ------ @@ -127,7 +127,10 @@ def simulate_subject_values(n_studies, n_subjects): y } """ - ... + y = np.random.normal(size=(n_subjects * 2, n_studies)) + # Add a small effect size + y[:n_subjects, :] = y[:n_subjects, :].copy() + 0.2 + return y def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): From 57f5d8959acc8d15d24cdb7ec806233cafc30a5e Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Thu, 13 Jan 2022 21:36:14 -0500 Subject: [PATCH 12/55] Get the simulations working. --- nimare/meta/cbma/sdm.py | 49 +++++++++++++++++++++++------------------ 1 file changed, 28 insertions(+), 21 deletions(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index 048a947ab..4797fffed 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -247,6 +247,7 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): 2) * 100, "%\n", sep = "") """ from math import gamma + from pprint import pprint import numpy as np @@ -254,6 +255,15 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): df = (2 * n_subjects) - 2 # degrees of freedom J = gamma(df / 2) / (gamma((df - 1) / 2) * np.sqrt(df / 2)) # Hedges' correction + # Predefine outputs dictionary + out_dict = { + "time_perm_stud": np.empty(n_sims), + "time_perm_subj": np.empty(n_sims), + "p_z": np.empty(n_sims), + "p_perm_stud": np.empty(n_sims), + "p_perm_subj": np.empty(n_sims), + } + # Next is a parallelized for loop of 1:n_sims that somehow uses metafor. for i_sim in range(n_sims): @@ -261,8 +271,8 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): y_unperm = simulate_subject_values(n_studies, n_subjects) # Calculate Hedges' g - g_unperm = hedges_g(y_unperm, n_subjects, J) - g_var_unperm = hedges_g_var(g_unperm, n_subjects, df, J) + g_unperm = hedges_g(y_unperm, J, n_subjects) + g_var_unperm = hedges_g_var(g_unperm, n_subjects) # , df, J) # Meta-analysis dset = Dataset(y=g_unperm, v=g_var_unperm) @@ -276,8 +286,8 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): # so I replaced them with empty lists. # nd_z_perm_stud = z_unperm # nd_z_perm_subj = z_unperm - nd_z_perm_stud = [] - nd_z_perm_subj = [] + nd_z_perm_stud = np.empty(n_perms) + nd_z_perm_subj = np.empty(n_perms) # Time before study-based permutation test time0 = datetime.now() @@ -285,7 +295,7 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): # Study-based permutation test for j_perm in range(n_perms): # Permute study data - g_stud_perm = permute_study_effects(g_unperm) + g_stud_perm = permute_study_effects(g_unperm, n_studies) # Meta-analysis of permuted study data dset_stud_perm = Dataset(y=g_stud_perm, v=g_var_unperm) @@ -294,10 +304,8 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): m_stud_perm = est_stud_perm.summary() # Save null distribution of z-values - # TODO: Fix this after testing. zvals should be an array across samples, - # so appending to a list doesn't make sense. zvals = m_stud_perm.get_fe_stats()["z"] - nd_z_perm_stud.append(zvals) + nd_z_perm_stud[j_perm] = zvals # Time between study-based and subject-based permutation tests time1 = datetime.now() @@ -308,8 +316,8 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): y_subj_perm = permute_subject_values(y_unperm) # Calculate Hedges' g of permuted subject data - g_subj_perm = hedges_g(y_subj_perm) - g_var_subj_perm = hedges_g_var(g_subj_perm) + g_subj_perm = hedges_g(y_subj_perm, J, n_subjects) + g_var_subj_perm = hedges_g_var(g_subj_perm, n_subjects) # Meta-analysis of permuted subject data dset_subj_perm = Dataset(y=g_subj_perm, v=g_var_subj_perm) @@ -319,20 +327,17 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): # Save null distribution of z-values zvals = m_subj_perm.get_fe_stats()["z"] - nd_z_perm_subj.append(zvals) + nd_z_perm_subj[j_perm] = zvals # Time after subject-based permutation tests time2 = datetime.now() # Save times and two-tailed p-values - # TODO: Store data across simulations - out_dict = { - "time_perm_stud": time1 - time0, - "time_perm_subj": time2 - time1, - "p_z": m_unperm.get_fe_stats()["p"], - "p_perm_stud": 1 - 2 * np.abs(np.mean(z_unperm > nd_z_perm_stud) - 0.5), - "p_perm_subj": 1 - 2 * np.abs(np.mean(z_unperm > nd_z_perm_subj) - 0.5), - } + out_dict["time_perm_stud"][i_sim] = (time1 - time0).total_seconds() + out_dict["time_perm_subj"][i_sim] = (time2 - time1).total_seconds() + out_dict["p_z"][i_sim] = m_unperm.get_fe_stats()["p"] + out_dict["p_perm_stud"][i_sim] = 1 - 2 * np.abs(np.mean(z_unperm > nd_z_perm_stud) - 0.5) + out_dict["p_perm_subj"][i_sim] = 1 - 2 * np.abs(np.mean(z_unperm > nd_z_perm_subj) - 0.5) # Output results time_perm_stud = out_dict["time_perm_stud"] @@ -341,13 +346,15 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): mse_perm_subj = (out_dict["p_perm_subj"] - out_dict["p_z"]) ** 2 print( "Decrease in execution time: " - f"{np.round(np.mean(time_perm_subj) - np.mean(time_perm_stud))}\n", + f"{np.mean(time_perm_subj) - np.mean(time_perm_stud)}\n", ) print( "Increase in MSE: " - f"{np.round((np.mean(mse_perm_stud) - np.mean(mse_perm_stud)) / np.mean(mse_perm_subj))}" + f"{(np.mean(mse_perm_stud) - np.mean(mse_perm_subj)) / np.mean(mse_perm_subj)}" ) + pprint(out_dict) + def compute_sdm_ma( ijk, From 910a44a8bc718a4ca55a3fe238c27f56fc40bec1 Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Fri, 14 Jan 2022 11:41:59 -0500 Subject: [PATCH 13/55] Support different sample sizes per study. Still need to support one-sample studies. --- nimare/meta/cbma/sdm.py | 134 ++++++++++++++++++++++++++-------------- 1 file changed, 86 insertions(+), 48 deletions(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index 4797fffed..051dac3b8 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -8,18 +8,17 @@ from scipy import spatial, stats -def hedges_g(y, J, n_subjects=None): +def hedges_g(y, n_subjects1, n_subjects2=None): """Calculate Hedges' G. - NOTE: We probably want to support both two-sample and one-sample versions. - Parameters ---------- - y : 2D array of shape (sample_size, n_variables) + y : 2D array of shape (n_studies, max_study_size) Subject-level values for which to calculate Hedges G. - Multiple variables may be provided. - J : float - Hedges' correction factor. + Multiple studies may be provided. + The array contains as many rows as there are studies, and as many columns as the maximum + sample size in the studyset. Extra columns in each row should be filled with NaNs. + n_subjects1 : :obj:`numpy.ndarray` of shape (n_studies) n_subjects : None or int Number of subjects in each group. If None, the dataset is assumed to be have one sample. This assumes that two-sample datasets have the same number of subjects in each group. @@ -37,32 +36,54 @@ def hedges_g(y, J, n_subjects=None): }) } """ - if n_subjects is None: - g_arr = J * (y.shape[0] - 1) * np.mean(y, axis=0) / np.std(y, axis=0) + from scipy.special import gamma - else: - n1 = n_subjects - n2 = y.shape[0] - n_subjects - var1 = np.var(y[:n1], axis=0) - var2 = np.var(y[n1:], axis=0) - - g_arr = np.zeros(y.shape[1]) - for i_col in range(y.shape[1]): - mean_diff = np.mean(y[:n_subjects, i_col]) - np.mean(y[n_subjects:, i_col]) - pooled_std = np.sqrt( - (((n1 - 1) * var1[i_col]) + ((n2 - 1) * var2[i_col])) / (n_subjects - 2) - ) + if n_subjects2 is not None: + # Must be an array + assert n_subjects2.shape == n_subjects1.shape + + # Constants + df = (n_subjects1 + n_subjects2) - 2 # degrees of freedom + J_arr = gamma(df / 2) / (gamma((df - 1) / 2) * np.sqrt(df / 2)) # Hedges' correction + + g_arr = np.zeros(y.shape[0]) + for i_study in range(y.shape[0]): + n1, n2 = n_subjects1[i_study], n_subjects2[i_study] + study_data = y[i_study, : n1 + n2] + var1 = np.var(study_data[:n1], axis=0) + var2 = np.var(study_data[n1:], axis=0) + + mean_diff = np.mean(study_data[:n1]) - np.mean(study_data[n1:]) + pooled_std = np.sqrt((((n1 - 1) * var1) + ((n2 - 1) * var2)) / ((n1 + n2) - 2)) g = mean_diff / pooled_std - g_arr[i_col] = g + g_arr[i_study] = g - g_arr = g_arr * (J * (n1 + n2 - 2)) + g_arr = g_arr * (J_arr * ((n_subjects1 + n_subjects2) - 2)) + + else: + df = n_subjects1 - 1 + J_arr = gamma(df / 2) / (gamma((df - 1) / 2) * np.sqrt(df / 2)) # Hedges' correction + g_arr = J_arr * df * np.nanmean(y, axis=1) / np.nanstd(y, axis=1) return g_arr -def hedges_g_var(g, n_subjects): +def hedges_g_var(g, n_subjects1, n_subjects2=None): """Calculate variance of Hedges' G. + Parameters + ---------- + g : :obj:`numpy.ndarray` of shape (n_studies,) + Hedges' G values. + n_subjects : :obj:`numpy.ndarray` of shape (n_studies,) + n_subjects2 : None or :obj:`numpy.ndarray` of shape (n_studies,) + Can be None if the G values come from one-sample tests. + + Returns + ------- + g_var : :obj:`numpy.ndarray` of shape (n_studies,) + Hedges' G variance values. + Notes ----- Clues for Python version from https://constantinyvesplessen.com/post/g/#hedges-g, @@ -76,8 +97,14 @@ def hedges_g_var(g, n_subjects): 1 / n.subj + (1 - (df - 2) / (df * J^2)) * g^2 } """ - n1 = n2 = n_subjects # NOTE: Until different sample sizes are supported - g_var = ((n1 + n2) / (n1 * n2)) + ((g ** 2) / (2 * (n1 + n2))) + if n_subjects2 is not None: + assert g.shape == n_subjects1.shape == n_subjects2.shape + g_var = ((n_subjects1 + n_subjects2) / (n_subjects1 * n_subjects2)) + ( + (g ** 2) / (2 * (n_subjects1 + n_subjects2)) + ) + else: + raise ValueError("One-sample tests are not yet supported.") + return g_var @@ -100,10 +127,10 @@ def permute_study_effects(g, n_studies): return out_g -def permute_subject_values(y): +def permute_subject_values(y, n_subjects): """Permute subject values. - Seems to shuffle rows in each column independently. + Seems to shuffle columns in each row independently. When "size" isn't provided to ``sample()``, it just permutes the array. R Code @@ -112,11 +139,15 @@ def permute_subject_values(y): apply(y, 2, sample) } """ - permuted = np.apply_along_axis(np.random.permutation, 0, y) + permuted = np.full(y.shape, np.nan) + for i_study in range(y.shape[0]): + study_data = y[i_study, :n_subjects[i_study]] + permuted[i_study, :n_subjects[i_study]] = np.random.permutation(study_data) + return permuted -def simulate_subject_values(n_studies, n_subjects): +def simulate_subject_values(n_subjects1, n_subjects2): """Simulate (true) subject values. R Code @@ -127,9 +158,16 @@ def simulate_subject_values(n_studies, n_subjects): y } """ - y = np.random.normal(size=(n_subjects * 2, n_studies)) - # Add a small effect size - y[:n_subjects, :] = y[:n_subjects, :].copy() + 0.2 + n_studies = len(n_subjects1) + max_total_sample = np.max(n_subjects1 + n_subjects2) + + y = np.random.normal(size=(n_studies, max_total_sample)) + for i_study in range(y.shape[0]): + # Any columns outside of the individual study's sample size will have NaNs + y[i_study, (n_subjects1[i_study] + n_subjects2[i_study]) :] = np.nan + # Add a small effect size + y[i_study, : (n_subjects1[i_study] + n_subjects2[i_study])] += 0.2 + return y @@ -246,14 +284,11 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): (mean(mse.perm_stud) - mean(mse.perm_subj)) / mean(mse.perm_subj), 2) * 100, "%\n", sep = "") """ - from math import gamma from pprint import pprint - import numpy as np - - # Constants - df = (2 * n_subjects) - 2 # degrees of freedom - J = gamma(df / 2) / (gamma((df - 1) / 2) * np.sqrt(df / 2)) # Hedges' correction + # Create simulation-specific sample sizes + n_subs1 = np.random.randint(10, 50, size=(n_studies, n_sims)) + n_subs2 = np.random.randint(10, 50, size=(n_studies, n_sims)) # Predefine outputs dictionary out_dict = { @@ -264,15 +299,19 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): "p_perm_subj": np.empty(n_sims), } - # Next is a parallelized for loop of 1:n_sims that somehow uses metafor. + # Next is a parallelized for loop of 1:n_sims that uses metafor. for i_sim in range(n_sims): + n_subs1_sim = n_subs1[:, i_sim] + n_subs2_sim = n_subs2[:, i_sim] + n_subjects_total = n_subs1_sim + n_subs2_sim + # Simulate subject data of all studies - y_unperm = simulate_subject_values(n_studies, n_subjects) + y_unperm = simulate_subject_values(n_subs1_sim, n_subs2_sim) # Calculate Hedges' g - g_unperm = hedges_g(y_unperm, J, n_subjects) - g_var_unperm = hedges_g_var(g_unperm, n_subjects) # , df, J) + g_unperm = hedges_g(y_unperm, n_subs1_sim, n_subs2_sim) + g_var_unperm = hedges_g_var(g_unperm, n_subs1_sim, n_subs2_sim) # Meta-analysis dset = Dataset(y=g_unperm, v=g_var_unperm) @@ -313,11 +352,11 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): # Subject-based permutation test for j_perm in range(n_perms): # Permute subject data - y_subj_perm = permute_subject_values(y_unperm) + y_subj_perm = permute_subject_values(y_unperm, n_subjects_total) # Calculate Hedges' g of permuted subject data - g_subj_perm = hedges_g(y_subj_perm, J, n_subjects) - g_var_subj_perm = hedges_g_var(g_subj_perm, n_subjects) + g_subj_perm = hedges_g(y_subj_perm, n_subs1_sim, n_subs2_sim) + g_var_subj_perm = hedges_g_var(g_subj_perm, n_subs1_sim, n_subs2_sim) # Meta-analysis of permuted subject data dset_subj_perm = Dataset(y=g_subj_perm, v=g_var_subj_perm) @@ -345,8 +384,7 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): mse_perm_stud = (out_dict["p_perm_stud"] - out_dict["p_z"]) ** 2 mse_perm_subj = (out_dict["p_perm_subj"] - out_dict["p_z"]) ** 2 print( - "Decrease in execution time: " - f"{np.mean(time_perm_subj) - np.mean(time_perm_stud)}\n", + "Decrease in execution time: " f"{np.mean(time_perm_subj) - np.mean(time_perm_stud)}\n", ) print( "Increase in MSE: " From 0e9207d1870c8099a88cf82752b7146d8c41362a Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Sat, 15 Jan 2022 14:04:26 -0500 Subject: [PATCH 14/55] I think the single-voxel imputation makes sense now. --- nimare/meta/cbma/sdm.py | 459 +++++++++++++++++++++++++++++++++++++++- 1 file changed, 451 insertions(+), 8 deletions(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index 051dac3b8..2e02dfa4e 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -8,6 +8,403 @@ from scipy import spatial, stats +def _mle_estimation(): + ... + + +def _impute_studywise_imgs(): + ... + + +def _simulate_voxel_with_no_neighbors(n_subjects): + y = np.random.normal(loc=0, scale=1, size=n_subjects) + y = stats.zscore(y) + return y + + +def _simulate_voxel_with_one_neighbor(A, r_ay): + """Simulate the data for a voxel with one neighbor. + + Parameters + ---------- + A : numpy.ndarray of shape (n_subjects,) + Subject values for the neighboring voxel. + r_ay : float + Desired correlation between A and Y. + """ + # Random normal values with null mean and unit variance. + R = _simulate_voxel_with_no_neighbors(A.size) + # Correlation between A and R. + r_ar = np.corrcoef((A, R))[1, 0] + + w_r = np.sqrt((1 - (r_ay ** 2)) / (1 - (r_ar ** 2))) + w_a = r_ay - (w_r * r_ar) + y = (w_a * A) + (w_r * R) + return y + + +def _simulate_voxel_with_two_neighbors(A, B, r_ay, r_by): + """Simulate the data for a voxel with one neighbor. + + Parameters + ---------- + A, B : numpy.ndarray of shape (n_subjects,) + Subject values for the neighboring voxels. + r_ay, r_by : float + Desired correlation between A and Y, and between B and Y. + """ + R = _simulate_voxel_with_no_neighbors(A.size) + r_ab = np.corrcoef((A, B))[1, 0] + r_ar = np.corrcoef((A, R))[1, 0] + r_br = np.corrcoef((B, R))[1, 0] + + w_r = np.sqrt( + (1 - (r_ab ** 2) - (r_ay ** 2) - (r_by ** 2) + (2 * r_ab * r_ay * r_by)) + / (1 - (r_ab ** 2) - (r_ar ** 2) - (r_br ** 2) + (2 * r_ab * r_ar * r_br)) + ) + w_b = ((r_by - (r_ab * r_ay)) - (w_r * (r_br - (r_ab * r_ar)))) / (1 - (r_ab ** 2)) + w_a = r_ay - (w_b * r_ab) - (w_r * r_ar) + + y = (w_a * A) + (w_b * B) + (w_r * R) + return y + + +def _simulate_voxel_with_three_neighbors(A, B, C, r_ay, r_by, r_cy): + """Simulate the data for a voxel with one neighbor. + + Parameters + ---------- + A, B, C : numpy.ndarray of shape (n_subjects,) + Subject values for the neighboring voxels. + r_ay, r_by, r_cy : float + Desired correlation between A and Y, B and Y, and C and Y. + """ + R = _simulate_voxel_with_no_neighbors(A.size) + r_ab = np.corrcoef((A, B))[1, 0] + r_ac = np.corrcoef((A, C))[1, 0] + r_bc = np.corrcoef((B, C))[1, 0] + r_ar = np.corrcoef((A, R))[1, 0] + r_br = np.corrcoef((B, R))[1, 0] + r_cr = np.corrcoef((C, R))[1, 0] + + w_r_num1 = ( + 1 + - (r_ab ** 2) + - (r_ac ** 2) + - (r_bc ** 2) + + (2 * r_ab * r_ac * r_bc) + - ((r_ay ** 2) * (1 - (r_bc ** 2))) + - ((r_by ** 2) * (1 - (r_ac ** 2))) + - ((r_cy ** 2) * (1 - (r_ab ** 2))) + ) + w_r_num2 = ( + (2 * r_ay * r_by * (r_ab - (r_ac * r_bc))) + + (2 * r_ay * r_cy * (r_ac - (r_ab * r_bc))) + + (2 * r_by * r_cy * (r_bc - (r_ab * r_ac))) + ) + w_r_num = w_r_num1 + w_r_num2 + + w_r_den1 = ( + 1 + - (r_ab ** 2) + - (r_ac ** 2) + - (r_bc ** 2) + + (2 * r_ab * r_ac * r_bc) + - ((r_ar ** 2) * (1 - (r_bc ** 2))) + - ((r_br ** 2) * (1 - (r_ac ** 2))) + - ((r_cr ** 2) * (1 - (r_ab ** 2))) + ) + w_r_den2 = ( + (2 * r_ar * r_br * (r_ab - (r_ac * r_bc))) + + (2 * r_ar * r_cr * (r_ac - (r_ab * r_bc))) + + (2 * r_br * r_cr * (r_bc - (r_ab * r_ac))) + ) + w_r_den = w_r_den1 + w_r_den2 + w_r = w_r_num / w_r_den + + w_c_num1 = ( + (r_cy * (1 - (r_ab ** 2))) + - (r_ac * (r_ay - (r_ab * r_by))) + - (r_bc * (r_by - (r_ab * r_ay))) + ) + w_c_num2 = w_r * ( + (r_cr * (1 - (r_ab ** 2))) + - (r_ac * (r_ar - (r_ab * r_br))) + - (r_bc * (r_br - (r_ab * r_ar))) + ) + w_c_num = w_c_num1 - w_c_num2 + w_c_den = 1 - (r_ab ** 2) - (r_ac ** 2) - (r_bc ** 2) + (2 * r_ab * r_ac * r_bc) + w_c = w_c_num / w_c_den + + w_b = ( + (r_by - (r_ab * r_ay)) - (w_c * (r_bc - (r_ab * r_ac))) - (w_r * (r_br - (r_ab * r_ar))) + ) / (1 - (r_ab ** 2)) + w_a = r_ay - (w_b * r_ab) - (w_c * r_ac) - (w_r * r_ar) + + y = (w_a * A) + (w_b * B) + (w_c * C) + (w_r * R) + return y + + +def _simulate_subject_maps(study_maps, n_subjects): + """Simulate the subject maps. + + Notes + ----- + - For simplicity, the imputation function in SDM-PSI is a generation of random normal numbers + with their mean equal to the sample effect size of the voxel in the (unpermuted) study + image and unit variance. + - Note that the sample effect size is the effect size of the study after removing + (i.e., dividing by) the J Hedge correction factor. + - In the common preliminary set, the values of any voxel have null mean, and adjacent voxels + show the expected correlations. For instance, if the correlation observed in humans + between voxels A and B is 0.67, the correlation between these two voxels in the imputed + subject images must be 0.67. + SDM-PSI uses the correlation templates created for AES-SDM to know the correlation between + every pair of two voxels (i.e., to take the irregular spatial covariance of the brain into + account), but other approaches are possible. + - For imputing subject values (Y) in a voxel that has no neighboring voxels imputed yet, + SDM-PSI simply creates random normal values and standardizes them to have null mean and + unit variance (R): + - Y = R + - For imputing subject values in a voxel that has one neighboring voxel already imputed, + SDM-PSI conducts a weighted average of the subject values of the neighboring voxel (A) and + new standardized random normal values. + - Y = (w_a * A) + (w_r * R), where w are the weights that ensure that the resulting + subject values have unit variance and the desired correlation. + - w_a = r_ay - (w_r * r_ar * w_r) = np.sqrt((1 - (r_ay ** 2)) / (1 - (r_ar ** 2))) + - For imputing subject values in a voxel that has two neighboring voxels already imputed, + SDM-PSI conducts again a weighted average of the subject values of the neighboring voxels + (A and B) and new standardized random normal values. + - Y = (w_a * A) + (w_b * B) + (w_r * R), where again w are the weights that ensure that + the resulting subject values have unit variance and the desired correlations. + - w_a = r_ay - (w_b * r_ab) - (w_r * r_ar) + - w_b = ((r_by - (r_ab * r_ay)) - (w_r * (r_br - (r_ab * r_ar))) / (1 - (r_ab ** 2))) + - w_r = np.sqrt((1 - (r_ab ** 2) - (r_ay ** 2) - (r_by ** 2) + (2 * r_ab * r_ay * r_by)) + / (1 - (r_ab ** 2) - (r_ar ** 2) - (r_br ** 2) + (2 * r_ab * r_ar * r_br)) + + - Note that as far as the imputation of the voxels follows a simple order and the software + only accounts for correlations between voxels sharing a face, a voxel cannot have more than + three neighbor voxels already imputed. For example, imagine that the imputation follows a + left/posterior/inferior to right/anterior/superior direction. When the software imputes a + given voxel, it will have already imputed the three neighbors in the left, + behind and below, while it will impute later the three neighbors in the right, + in front and above. + The number of neighbor voxels imputed or to impute will be lower if some of them are + outside the mask. + - For two-sample studies, SDM-PSI imputes subject values separately for each sample, and it + only adds the effect size to the patient (or non-control) subject images. + """ + n_voxels = study_maps[0].shape[0] + subject_maps = np.empty((np.sum(n_subjects), n_voxels)) + subject_counter = 0 + for i_study, study_map in enumerate(study_maps): + n_study_subjects = n_subjects[i_study] + for i_voxel, voxel in enumerate(study_map): + # NOTE: This is nonsense. I need to figure out how to actually loop through the voxels + # while also tracking the neighbors. It basically needs to be a searchlight of some + # kind. + if i_voxel == 0: + subject_maps[ + subject_counter:n_study_subjects, i_voxel + ] = _simulate_voxel_with_no_neighbors(n_study_subjects) + elif i_voxel == 1: + A_data = subject_maps[subject_counter:n_study_subjects, i_voxel - 1] + r_ay = 0.5 + subject_maps[ + subject_counter:n_study_subjects, i_voxel + ] = _simulate_voxel_with_one_neighbor(A_data, r_ay) + elif i_voxel == 2: + A_data = subject_maps[subject_counter:n_study_subjects, i_voxel - 2] + B_data = subject_maps[subject_counter:n_study_subjects, i_voxel - 1] + r_ay = 0.5 + r_by = 0.5 + subject_maps[ + subject_counter:n_study_subjects, i_voxel + ] = _simulate_voxel_with_two_neighbors(A_data, B_data, r_ay, r_by) + else: + A_data = subject_maps[subject_counter:n_study_subjects, i_voxel - 3] + B_data = subject_maps[subject_counter:n_study_subjects, i_voxel - 2] + C_data = subject_maps[subject_counter:n_study_subjects, i_voxel - 1] + r_ay = 0.5 + r_by = 0.5 + r_cy = 0.5 + subject_maps[ + subject_counter:n_study_subjects, i_voxel + ] = _simulate_voxel_with_two_neighbors(A_data, B_data, C_data, r_ay, r_by, r_cy) + + subject_counter += n_study_subjects + return subject_maps + + +def _scale_subject_maps( + studylevel_effect_size_maps, + studylevel_variance_maps, + prelim_subjectlevel_maps, +): + """Scale the "preliminary" set of subject-level maps for each dataset for a given imputation. + + Parameters + ---------- + studylevel_effect_size_maps : numpy.ndarray of shape (S, V) + S is study, V is voxel. + studylevel_variance_maps : numpy.ndarray of shape (S, V) + prelim_subjectlevel_maps : S-length list of numpy.ndarray of shape (N, V) + List with one entry per study (S), where each entry is an array that is subjects (N) by + voxels (V). + + Returns + ------- + scaled_subjectlevel_maps : S-length list of numpy.ndarray of shape (N, V) + List with one entry per study (S), where each entry is an array that is subjects (N) by + voxels (V). + + Notes + ----- + - The mean across subject-level images, for each voxel, must equal the value from the + study-level effect size map. + - Values for each voxel, across subjects, must correlate with the values for the same + voxel at 1 in all other imputations. + - Values of adjacent voxels must show "realistic" correlations as well. + SDM uses tissue-type masks for this. + - SDM simplifies the simulation process by creating a single "preliminary" set of + subject-level maps for each dataset (across imputations), and scaling it across + imputations. + """ + scaled_subjectlevel_maps = [] + for i_study in range(studylevel_effect_size_maps.shape[0]): + study_mean = studylevel_effect_size_maps[i_study, :] + study_var = studylevel_variance_maps[i_study, :] + + study_subject_level_maps = prelim_subjectlevel_maps[i_study] + study_subject_level_maps_scaled = (study_subject_level_maps * study_var) + study_mean + scaled_subjectlevel_maps.append(study_subject_level_maps_scaled) + + return scaled_subjectlevel_maps + + +def _calculate_hedges_maps(): + ... + + +def _run_variance_meta(): + ... + + +def _combine_imputation_results(coefficient_maps, covariance_maps, i_stats, q_stats): + """Use Rubin's rules to combine meta-analysis results across imputations. + + Notes + ----- + "Finally, SDM-PSI uses Rubin's rules to combine the coefficients of the model, + their covariance and the heterogeneity statistics I and Q of the different imputed datasets. + Note that Q follows a χ2 distribution, but its combined statistic follows an F distribution. + For convenience, SDM-PSI converts FQ back into a Q (i.e. converts an F statistic to a χ2 + statistic with the same p-value). It also derives H-combined from I-combined." + """ + ... + + +def run_sdm(coords, n_imputations=1000): + """Run the SDM algorithm. + + Notes + ----- + 1. Use anisotropic Gaussian kernels, plus effect size estimates and metadata, + to produce lower-bound and upper-bound effect size maps from the coordinates. + - We need generic inter-voxel correlation maps for this. + - We also need a fast implementation of Dijkstra's algorithm to estimate the shortest + path (i.e., "virtual distance") between two voxels based on the map of correlations + between each voxel and its neighbors. I think ``dijkstra3d`` might be useful here. + 2. Use maximum likelihood estimation to estimate the most likely effect size and variance + maps across studies (i.e., a meta-analytic map). + - Can we use NiMARE IBMAs for this? + 3. Use the MLE map and each study's upper- and lower-bound effect size maps to impute + study-wise effect size and variance images that meet specific requirements. + 4. For each imputed pair of effect size and variance images, simulate subject-level images. + - The mean across subject-level images, for each voxel, must equal the value from the + study-level effect size map. + - Values for each voxel, across subjects, must correlate with the values for the same + voxel at 1 in all other imputations. + - Values of adjacent voxels must show "realistic" correlations as well. + SDM uses tissue-type masks for this. + - SDM simplifies the simulation process by creating a single "preliminary" set of + subject-level maps for each dataset (across imputations), and scaling it across + imputations. + 5. Subject-based permutation test. + a. Create one random permutation of the subjects and apply it to the subject images of + the different imputed datasets. + 6. Separately for each imputed dataset, conduct a group analysis of the permuted subject + images to obtain one study image per study, and then conduct a meta-analysis of the + study images to obtain one meta-analysis image. + - "In SDM-PSI, the group analysis is the estimation of Hedge-corrected effect sizes. + In practice, this estimation simply consists of calculating the mean + (or the difference of means in two-sample studies) and multiplying by J, + given that imputed subject values have unit variance." + 7. Perform meta-analysis across study-level effect size maps using random effects model. + Performed separately for each imputation. + - "The meta-analysis consists of the fitting of a standard random-effects model. + The design matrix includes any covariate used in the MLE step, + and the weight of a study is the inverse of the sum of its variance and the + between-study heterogeneity τ2, which in SDM-PSI may be estimated using either the + DerSimonian-Laird or the slightly more accurate restricted-maximum likelihood (REML) + method. After fitting the model, SDM conducts a standard linear hypothesis contrast + and derives standard heterogeneity statistics H2, I2 and Q." + - One of NiMARE's IBMA interfaces should be able to handle the meta-analysis part. + Either DerSimonianLaird or VarianceBasedLikelihood. + We'll need to add heterogeneity statistic calculation either to PyMARE or NiMARE. + 8. Compute imputation-wise heterogeneity statistics. + 9. Use "Rubin's rules" to combine heterogeneity statistics, coefficients, and variance for + each imputed dataset. This should result in a combined meta-analysis image. + 10. Save a maximum statistic from the combined meta-analysis image (e.g., the largest z-value). + This is the last step within the iteration loop. + 11. Perform Monte Carlo-like maximum statistic procedure to get null distributions for vFWE or + cFWE. Or do TFCE. + - "After enough iterations of steps a) to d), use the distribution of the maximum + statistic to threshold the combined meta-analysis image obtained from unpermuted data." + """ + # Step 1: Estimate lower- and upper-bound effect size maps from coordinates. + lower_bound_imgs, upper_bound_imgs = compute_sdm_ma(coords) + + # Step 2: Estimate the most likely effect size and variance maps across studies. + meta_effect_size_img, meta_tau_img = _mle_estimation(lower_bound_imgs, upper_bound_imgs) + + # Step 4a: Create base set of simulated subject maps. + raw_subject_effect_size_imgs = _simulate_subject_maps(lower_bound_imgs, upper_bound_imgs) + + for i_imp in range(n_imputations): + # Step 3: Impute study-wise effect size and variance maps. + study_effect_size_imgs, study_var_imgs = _impute_studywise_imgs( + meta_effect_size_img, + meta_tau_img, + lower_bound_imgs, + upper_bound_imgs, + seed=i_imp, + ) + + # Step 4: Simulate subject-wise effect size and variance maps. + subject_effect_size_imgs, subject_var_imgs = _scale_subject_maps( + study_effect_size_imgs, + study_var_imgs, + raw_subject_effect_size_imgs, + ) + + # Step 5: Permutations... + + # Step 6: Calculate study-level Hedges-corrected effect size maps. + study_hedge_imgs = _calculate_hedges_maps(subject_effect_size_imgs, subject_var_imgs) + + # Step 7: Meta-analyze imputed effect size maps. + imp_meta_effect_size_img = _run_variance_meta(study_hedge_imgs) + + # Step 8: Heterogeneity statistics. + heterogeneity_stats = _combine_imputation_results(imp_meta_effect_size_img) + + # Step 9: Rubin's rules...? + + # Step 10: Monte Carlo simulations. + ... + + def hedges_g(y, n_subjects1, n_subjects2=None): """Calculate Hedges' G. @@ -19,14 +416,19 @@ def hedges_g(y, n_subjects1, n_subjects2=None): The array contains as many rows as there are studies, and as many columns as the maximum sample size in the studyset. Extra columns in each row should be filled with NaNs. n_subjects1 : :obj:`numpy.ndarray` of shape (n_studies) - n_subjects : None or int - Number of subjects in each group. If None, the dataset is assumed to be have one sample. - This assumes that two-sample datasets have the same number of subjects in each group. + Number of subjects in the first group of each study. + n_subjects2 : None or int + Number of subjects in the second group of each study. + If None, the dataset is assumed to be have one sample. + Technically, this parameter is probably unnecessary, since the second group's sample size + can be inferred from ``n_subjects1`` and ``y``. Notes ----- Clues for Python version from https://en.wikipedia.org/wiki/Effect_size#Hedges'_g. + I also updated the original code to support varying sample sizes across studies. + R Code ------ g <- function (y) { # Calculate Hedges' g @@ -75,7 +477,7 @@ def hedges_g_var(g, n_subjects1, n_subjects2=None): ---------- g : :obj:`numpy.ndarray` of shape (n_studies,) Hedges' G values. - n_subjects : :obj:`numpy.ndarray` of shape (n_studies,) + n_subjects1 : :obj:`numpy.ndarray` of shape (n_studies,) n_subjects2 : None or :obj:`numpy.ndarray` of shape (n_studies,) Can be None if the G values come from one-sample tests. @@ -89,6 +491,9 @@ def hedges_g_var(g, n_subjects1, n_subjects2=None): Clues for Python version from https://constantinyvesplessen.com/post/g/#hedges-g, except using proper J instead of approximation. + I also updated the original code to support varying sample sizes across studies. + I still need to support one-sample tests though. + R Code ------ df <- 2 * n.subj - 2 # Degrees of freedom @@ -113,6 +518,10 @@ def permute_study_effects(g, n_studies): Randomly sign-flip ~50% of the studies. + Notes + ----- + This is included for the simulations, but I don't think it would be used for SDM-PSI. + R Code ------ perm1 <- function (g) { # Permute study effects @@ -133,6 +542,23 @@ def permute_subject_values(y, n_subjects): Seems to shuffle columns in each row independently. When "size" isn't provided to ``sample()``, it just permutes the array. + Parameters + ---------- + y : 2D array of shape (n_studies, max_study_size) + Subject-level values for which to calculate Hedges G. + Multiple studies may be provided. + The array contains as many rows as there are studies, and as many columns as the maximum + sample size in the studyset. Extra columns in each row should be filled with NaNs. + n_subjects : :obj:`numpy.ndarray` of shape (n_studies,) + The total number of subjects in each study. + + Notes + ----- + This works for one estimate per particiipant per study. + We need something that works for statistical maps. + + I also think this might not be usable on one-sample studies. + R Code ------ perm2 <- function (y) { # Permute subject values @@ -141,8 +567,8 @@ def permute_subject_values(y, n_subjects): """ permuted = np.full(y.shape, np.nan) for i_study in range(y.shape[0]): - study_data = y[i_study, :n_subjects[i_study]] - permuted[i_study, :n_subjects[i_study]] = np.random.permutation(study_data) + study_data = y[i_study, : n_subjects[i_study]] + permuted[i_study, : n_subjects[i_study]] = np.random.permutation(study_data) return permuted @@ -150,6 +576,19 @@ def permute_subject_values(y, n_subjects): def simulate_subject_values(n_subjects1, n_subjects2): """Simulate (true) subject values. + Parameters + ---------- + n_subjects1, n_subjects2 : :obj:`numpy.ndarray` of shape (n_studies,) + Number of subjects in each sample of each study. This does not support one-sample analyses. + + Returns + ------- + y : 2D array of shape (n_studies, max_study_size) + Subject-level values for which to calculate Hedges G. + Multiple studies may be provided. + The array contains as many rows as there are studies, and as many columns as the maximum + sample size in the studyset. Extra columns in each row should be filled with NaNs. + R Code ------ sim.y <- function () { # Simulate (true) subject values @@ -188,8 +627,8 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): Notes ----- - TODO: Support array for number of subjects, so that each study has its own sample size. TODO: Support one-sample studies. + TODO: Support voxel-wise data, instead of single points per subject. R Code ------ @@ -384,7 +823,7 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): mse_perm_stud = (out_dict["p_perm_stud"] - out_dict["p_z"]) ** 2 mse_perm_subj = (out_dict["p_perm_subj"] - out_dict["p_z"]) ** 2 print( - "Decrease in execution time: " f"{np.mean(time_perm_subj) - np.mean(time_perm_stud)}\n", + f"Decrease in execution time: {np.mean(time_perm_subj) - np.mean(time_perm_stud)}\n", ) print( "Increase in MSE: " @@ -419,6 +858,10 @@ def compute_sdm_ma( kernel_sigma : float User-specified sigma of kernel, in mm. Default is 5. + Returns + ------- + y_lower_img, y_upper_img + References ---------- * Radua, J., Rubia, K., Canales, E. J., Pomarol-Clotet, E., Fusar-Poli, P., & From 05260fcf409c7db630dfbdda8a3089a5efc3f90e Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Sat, 15 Jan 2022 14:39:15 -0500 Subject: [PATCH 15/55] Lots of progress on the simulations. --- nimare/meta/cbma/sdm.py | 129 +++++++++++++++++++++++++++++----------- 1 file changed, 95 insertions(+), 34 deletions(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index 2e02dfa4e..368a42f9b 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -2,6 +2,7 @@ from datetime import datetime import dijkstra3d +import nibabel as nib import numpy as np from nilearn import masking from pymare import Dataset, estimators @@ -145,9 +146,14 @@ def _simulate_voxel_with_three_neighbors(A, B, C, r_ay, r_by, r_cy): return y -def _simulate_subject_maps(study_maps, n_subjects): +def _simulate_subject_maps(study_maps, n_subjects, masker, correlation_maps): """Simulate the subject maps. + Parameters + ---------- + correlation_maps : dict of 3 91x109x91 arrays + right, posterior, and inferior + Notes ----- - For simplicity, the imputation function in SDM-PSI is a generation of random normal numbers @@ -194,45 +200,97 @@ def _simulate_subject_maps(study_maps, n_subjects): - For two-sample studies, SDM-PSI imputes subject values separately for each sample, and it only adds the effect size to the patient (or non-control) subject images. """ + study_img = masker.inverse_transform(study_maps[0]) + n_x, n_y, n_z = study_img.shape n_voxels = study_maps[0].shape[0] subject_maps = np.empty((np.sum(n_subjects), n_voxels)) subject_counter = 0 for i_study, study_map in enumerate(study_maps): + study_map_3d = masker.inverse_transform(study_map).get_fdata() n_study_subjects = n_subjects[i_study] - for i_voxel, voxel in enumerate(study_map): - # NOTE: This is nonsense. I need to figure out how to actually loop through the voxels - # while also tracking the neighbors. It basically needs to be a searchlight of some - # kind. - if i_voxel == 0: - subject_maps[ - subject_counter:n_study_subjects, i_voxel - ] = _simulate_voxel_with_no_neighbors(n_study_subjects) - elif i_voxel == 1: - A_data = subject_maps[subject_counter:n_study_subjects, i_voxel - 1] - r_ay = 0.5 - subject_maps[ - subject_counter:n_study_subjects, i_voxel - ] = _simulate_voxel_with_one_neighbor(A_data, r_ay) - elif i_voxel == 2: - A_data = subject_maps[subject_counter:n_study_subjects, i_voxel - 2] - B_data = subject_maps[subject_counter:n_study_subjects, i_voxel - 1] - r_ay = 0.5 - r_by = 0.5 - subject_maps[ - subject_counter:n_study_subjects, i_voxel - ] = _simulate_voxel_with_two_neighbors(A_data, B_data, r_ay, r_by) - else: - A_data = subject_maps[subject_counter:n_study_subjects, i_voxel - 3] - B_data = subject_maps[subject_counter:n_study_subjects, i_voxel - 2] - C_data = subject_maps[subject_counter:n_study_subjects, i_voxel - 1] - r_ay = 0.5 - r_by = 0.5 - r_cy = 0.5 - subject_maps[ - subject_counter:n_study_subjects, i_voxel - ] = _simulate_voxel_with_two_neighbors(A_data, B_data, C_data, r_ay, r_by, r_cy) - + study_subject_maps = np.full(np.nan, (n_x, n_y, n_z, n_study_subjects)) + for i_x in range(n_x): + for j_y in range(n_y): + for k_z in range(n_z): + if np.isnan(study_map_3d[i_x, j_y, k_z]): + continue + + if not np.isnan(study_map_3d[i_x - 1, j_y, k_z]): + use_right = True + else: + use_right = False + + if not np.isnan(study_map_3d[i_x, j_y - 1, k_z]): + use_posterior = True + else: + use_posterior = False + + if not np.isnan(study_map_3d[i_x, j_y, k_z - 1]): + use_inferior = True + else: + use_inferior = False + + n_directions = sum((use_right, use_posterior, use_inferior)) + if n_directions == 0: + study_subject_maps[i_x, j_y, k_z, :] = _simulate_voxel_with_no_neighbors( + n_study_subjects + ) + + elif n_directions == 1: + if use_right: + A_data = study_subject_maps[i_x - 1, j_y, k_z, :] + r_ay = correlation_maps["right"][i_x, j_y, k_z] + elif use_posterior: + A_data = study_subject_maps[i_x, j_y - 1, k_z, :] + r_ay = correlation_maps["posterior"][i_x, j_y, k_z] + else: + A_data = study_subject_maps[i_x, j_y, k_z - 1, :] + r_ay = correlation_maps["inferior"][i_x, j_y, k_z] + + study_subject_maps[i_x, j_y, k_z, :] = _simulate_voxel_with_one_neighbor( + A_data, r_ay + ) + + elif n_directions == 2: + if use_right: + A_data = study_subject_maps[i_x - 1, j_y, k_z, :] + r_ay = correlation_maps["right"][i_x, j_y, k_z] + + if use_posterior: + B_data = study_subject_maps[i_x, j_y - 1, k_z, :] + r_by = correlation_maps["posterior"][i_x, j_y, k_z] + else: + B_data = study_subject_maps[i_x, j_y, k_z - 1, :] + r_by = correlation_maps["inferior"][i_x, j_y, k_z] + elif use_posterior: + A_data = study_subject_maps[i_x, j_y - 1, k_z, :] + r_ay = correlation_maps["posterior"][i_x, j_y, k_z] + + B_data = study_subject_maps[i_x, j_y, k_z - 1, :] + r_by = correlation_maps["inferior"][i_x, j_y, k_z] + + study_subject_maps[i_x, j_y, k_z, :] = _simulate_voxel_with_two_neighbors( + A_data, B_data, r_ay, r_by + ) + + else: + A_data = study_subject_maps[i_x - 1, j_y, k_z, :] + B_data = study_subject_maps[i_x, j_y - 1, k_z, :] + C_data = study_subject_maps[i_x, j_y, k_z - 1, :] + r_ay = correlation_maps["right"][i_x, j_y, k_z] + r_by = correlation_maps["posterior"][i_x, j_y, k_z] + r_cy = correlation_maps["inferior"][i_x, j_y, k_z] + study_subject_maps[i_x, j_y, k_z, :] = _simulate_voxel_with_two_neighbors( + A_data, B_data, C_data, r_ay, r_by, r_cy + ) + + study_subject_img = nib.Nifti1Image( + study_subject_maps, masker.mask_img.affine, masker.mask_img.header + ) + study_subject_data = masker.transform(study_subject_img) + subject_maps[subject_counter : subject_counter + n_study_subjects, :] = study_subject_data subject_counter += n_study_subjects + return subject_maps @@ -312,6 +370,9 @@ def run_sdm(coords, n_imputations=1000): 1. Use anisotropic Gaussian kernels, plus effect size estimates and metadata, to produce lower-bound and upper-bound effect size maps from the coordinates. - We need generic inter-voxel correlation maps for this. + NOTE: Correlation maps are unidirectional. There are 26 directions for each voxel, + but the flipped versions (e.g., right and left) use equivalent maps so there are only + 13 correlation maps. - We also need a fast implementation of Dijkstra's algorithm to estimate the shortest path (i.e., "virtual distance") between two voxels based on the map of correlations between each voxel and its neighbors. I think ``dijkstra3d`` might be useful here. From 0f0f49ac435d51dc769d71ea4d4f787bfa0c422d Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Sat, 15 Jan 2022 14:50:17 -0500 Subject: [PATCH 16/55] Update sdm.py --- nimare/meta/cbma/sdm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index 368a42f9b..1c7313845 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -430,7 +430,7 @@ def run_sdm(coords, n_imputations=1000): meta_effect_size_img, meta_tau_img = _mle_estimation(lower_bound_imgs, upper_bound_imgs) # Step 4a: Create base set of simulated subject maps. - raw_subject_effect_size_imgs = _simulate_subject_maps(lower_bound_imgs, upper_bound_imgs) + raw_subject_effect_size_imgs = _simulate_subject_maps(meta_effect_size_img, meta_tau_img) for i_imp in range(n_imputations): # Step 3: Impute study-wise effect size and variance maps. From 3e0a1a27182b797cc444e8407e8d91d84c73879d Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Sat, 15 Jan 2022 20:14:09 -0500 Subject: [PATCH 17/55] Document some stuff. --- docs/api.rst | 1 + nimare/meta/cbma/sdm.py | 6 +++--- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/docs/api.rst b/docs/api.rst index b110c8267..aaac54314 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -39,6 +39,7 @@ For more information about the components of coordinate-based meta-analysis in N meta.ibma meta.cbma.ale meta.cbma.mkda + meta.cbma.sdm meta.cbma.base meta.kernel diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index 1c7313845..a1d8dd6fd 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -457,13 +457,13 @@ def run_sdm(coords, n_imputations=1000): # Step 7: Meta-analyze imputed effect size maps. imp_meta_effect_size_img = _run_variance_meta(study_hedge_imgs) - # Step 8: Heterogeneity statistics. - heterogeneity_stats = _combine_imputation_results(imp_meta_effect_size_img) + # Step 8: Heterogeneity statistics. + heterogeneity_stats = _combine_imputation_results(imp_meta_effect_size_img) # Step 9: Rubin's rules...? # Step 10: Monte Carlo simulations. - ... + return heterogeneity_stats def hedges_g(y, n_subjects1, n_subjects2=None): From 15754552c3a9bd74053aa219be77cd2c2d343cd5 Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Sat, 15 Jan 2022 20:39:03 -0500 Subject: [PATCH 18/55] Import stuff. --- nimare/meta/__init__.py | 3 ++- nimare/meta/cbma/__init__.py | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/nimare/meta/__init__.py b/nimare/meta/__init__.py index a5a19f28a..4ddb4f16f 100755 --- a/nimare/meta/__init__.py +++ b/nimare/meta/__init__.py @@ -1,7 +1,7 @@ """Coordinate-, image-, and effect-size-based meta-analysis estimators.""" from . import ibma, kernel -from .cbma import ALE, KDA, SCALE, ALESubtraction, MKDAChi2, MKDADensity, ale, mkda +from .cbma import ALE, KDA, SCALE, ALESubtraction, MKDAChi2, MKDADensity, ale, mkda, sdm from .ibma import ( DerSimonianLaird, Fishers, @@ -36,4 +36,5 @@ "ibma", "ale", "mkda", + "sdm", ] diff --git a/nimare/meta/cbma/__init__.py b/nimare/meta/cbma/__init__.py index d351a828f..9e6e1c6a1 100644 --- a/nimare/meta/cbma/__init__.py +++ b/nimare/meta/cbma/__init__.py @@ -1,5 +1,6 @@ """Coordinate-based meta-analytic estimators.""" from .ale import ALE, SCALE, ALESubtraction from .mkda import KDA, MKDAChi2, MKDADensity +from .sdm import run_sdm -__all__ = ["ALE", "ALESubtraction", "SCALE", "MKDADensity", "MKDAChi2", "KDA"] +__all__ = ["ALE", "ALESubtraction", "SCALE", "MKDADensity", "MKDAChi2", "KDA", "run_sdm"] From 1996c6fb0623f0876990e11fb4c465fbe2be17d8 Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Sat, 15 Jan 2022 22:05:19 -0500 Subject: [PATCH 19/55] Update setup.cfg --- setup.cfg | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.cfg b/setup.cfg index 23c1441fa..b45638a3a 100644 --- a/setup.cfg +++ b/setup.cfg @@ -39,6 +39,7 @@ classifiers = python_requires = >= 3.6 install_requires = cognitiveatlas + dijkstra3d fuzzywuzzy indexed_gzip>=1.4.0 matplotlib<3.5 # this is for nilearn, which doesn't include it in its reqs From e87eea7fa84887300e5ef2624fc7b27f3c7ba9e7 Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Sun, 16 Jan 2022 10:30:15 -0500 Subject: [PATCH 20/55] Update sdm.py --- nimare/meta/cbma/sdm.py | 25 ++++++++++++++++++++++++- 1 file changed, 24 insertions(+), 1 deletion(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index a1d8dd6fd..80e52da63 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -362,13 +362,24 @@ def _combine_imputation_results(coefficient_maps, covariance_maps, i_stats, q_st ... -def run_sdm(coords, n_imputations=1000): +def run_sdm(coords, n_imputations=50, n_iters=1000): """Run the SDM algorithm. + Parameters + ---------- + coords + Coordinates. + n_imputations : int + Number of imputations. Default is 50, based on the SDM software. + n_iters : int + Number of iterations for the Monte Carlo FWE correction procedure. + Default is 1000, based on the SDM software. + Notes ----- 1. Use anisotropic Gaussian kernels, plus effect size estimates and metadata, to produce lower-bound and upper-bound effect size maps from the coordinates. + - We need generic inter-voxel correlation maps for this. NOTE: Correlation maps are unidirectional. There are 26 directions for each voxel, but the flipped versions (e.g., right and left) use equivalent maps so there are only @@ -376,12 +387,16 @@ def run_sdm(coords, n_imputations=1000): - We also need a fast implementation of Dijkstra's algorithm to estimate the shortest path (i.e., "virtual distance") between two voxels based on the map of correlations between each voxel and its neighbors. I think ``dijkstra3d`` might be useful here. + 2. Use maximum likelihood estimation to estimate the most likely effect size and variance maps across studies (i.e., a meta-analytic map). + - Can we use NiMARE IBMAs for this? + 3. Use the MLE map and each study's upper- and lower-bound effect size maps to impute study-wise effect size and variance images that meet specific requirements. 4. For each imputed pair of effect size and variance images, simulate subject-level images. + - The mean across subject-level images, for each voxel, must equal the value from the study-level effect size map. - Values for each voxel, across subjects, must correlate with the values for the same @@ -391,18 +406,24 @@ def run_sdm(coords, n_imputations=1000): - SDM simplifies the simulation process by creating a single "preliminary" set of subject-level maps for each dataset (across imputations), and scaling it across imputations. + 5. Subject-based permutation test. + a. Create one random permutation of the subjects and apply it to the subject images of the different imputed datasets. + 6. Separately for each imputed dataset, conduct a group analysis of the permuted subject images to obtain one study image per study, and then conduct a meta-analysis of the study images to obtain one meta-analysis image. + - "In SDM-PSI, the group analysis is the estimation of Hedge-corrected effect sizes. In practice, this estimation simply consists of calculating the mean (or the difference of means in two-sample studies) and multiplying by J, given that imputed subject values have unit variance." + 7. Perform meta-analysis across study-level effect size maps using random effects model. Performed separately for each imputation. + - "The meta-analysis consists of the fitting of a standard random-effects model. The design matrix includes any covariate used in the MLE step, and the weight of a study is the inverse of the sum of its variance and the @@ -413,6 +434,7 @@ def run_sdm(coords, n_imputations=1000): - One of NiMARE's IBMA interfaces should be able to handle the meta-analysis part. Either DerSimonianLaird or VarianceBasedLikelihood. We'll need to add heterogeneity statistic calculation either to PyMARE or NiMARE. + 8. Compute imputation-wise heterogeneity statistics. 9. Use "Rubin's rules" to combine heterogeneity statistics, coefficients, and variance for each imputed dataset. This should result in a combined meta-analysis image. @@ -420,6 +442,7 @@ def run_sdm(coords, n_imputations=1000): This is the last step within the iteration loop. 11. Perform Monte Carlo-like maximum statistic procedure to get null distributions for vFWE or cFWE. Or do TFCE. + - "After enough iterations of steps a) to d), use the distribution of the maximum statistic to threshold the combined meta-analysis image obtained from unpermuted data." """ From c1c61ed6c55941f6b5dacbff8ea256c517fced08 Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Sun, 16 Jan 2022 10:39:06 -0500 Subject: [PATCH 21/55] Some notes. --- nimare/meta/cbma/sdm.py | 5 +++++ nimare/transforms.py | 13 ++++++++----- 2 files changed, 13 insertions(+), 5 deletions(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index 80e52da63..2a3c6840c 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -946,6 +946,11 @@ def compute_sdm_ma( ------- y_lower_img, y_upper_img + Notes + ----- + Use anisotropic Gaussian kernels, plus effect size estimates and metadata, + to produce lower-bound and upper-bound effect size maps from the coordinates. + References ---------- * Radua, J., Rubia, K., Canales, E. J., Pomarol-Clotet, E., Fusar-Poli, P., & diff --git a/nimare/transforms.py b/nimare/transforms.py index 2254c10bc..5cef1dcc5 100644 --- a/nimare/transforms.py +++ b/nimare/transforms.py @@ -801,7 +801,7 @@ def z_to_t(z_values, dof): return t_values -def t_to_hedges_g(t_values, sample_size): +def t_to_hedges_g(t_values, sample_size1, sample_size2=None): """Convert t-statistics to Hedges' g values. According to Radua 2012, the main conversion in (A)ES-SDM is t-statistic to Hedges g. @@ -810,10 +810,13 @@ def t_to_hedges_g(t_values, sample_size): ----- Clues from https://imaging.mrc-cbu.cam.ac.uk/statswiki/FAQ/td. """ - # one sample t-test - cohens_d = t_values / np.sqrt(sample_size) - # two sample t-test - cohens_d = t * np.sqrt((1 / n1) + (1 / n2)) + if sample_size2 is None: + # one sample t-test + cohens_d = t_values / np.sqrt(sample_size1) + else: + # two sample t-test + cohens_d = t_values * np.sqrt((1 / sample_size1) + (1 / sample_size2)) + return cohens_d From f8b4dd6727e28c90ee64301175ec94f3664a8239 Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Sun, 16 Jan 2022 10:42:36 -0500 Subject: [PATCH 22/55] Fix the simulated data. --- nimare/meta/cbma/sdm.py | 100 ++-------------------------------------- 1 file changed, 5 insertions(+), 95 deletions(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index 2a3c6840c..f364c399b 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -660,6 +660,8 @@ def permute_subject_values(y, n_subjects): def simulate_subject_values(n_subjects1, n_subjects2): """Simulate (true) subject values. + Simply draw from a normal distribution, then add a small effect size (0.2) to the first group. + Parameters ---------- n_subjects1, n_subjects2 : :obj:`numpy.ndarray` of shape (n_studies,) @@ -689,7 +691,7 @@ def simulate_subject_values(n_subjects1, n_subjects2): # Any columns outside of the individual study's sample size will have NaNs y[i_study, (n_subjects1[i_study] + n_subjects2[i_study]) :] = np.nan # Add a small effect size - y[i_study, : (n_subjects1[i_study] + n_subjects2[i_study])] += 0.2 + y[i_study, : n_subjects1[i_study]] += 0.2 return y @@ -713,99 +715,7 @@ def run_simulations2(n_perms=1000, n_sims=10, n_subjects=20, n_studies=10): ----- TODO: Support one-sample studies. TODO: Support voxel-wise data, instead of single points per subject. - - R Code - ------ - library(doParallel) - library(metafor) - registerDoParallel(cores = detectCores() - 1) - par(mfrow = 1:2) - - # Simulation parameters ####################################################### - n.perm <- 1000 # Number of permutations - n.sim <- 1000 # Number of simulations - n.subj <- 20 # Number of subjects per group of a study - n.stud <- 10 # Number of studies - - # Constants - df <- 2 * n.subj - 2 # Degrees of freedom - J <- gamma(df / 2) / gamma((df - 1) / 2) * sqrt(2 / df) # Hedges' correction - - sim <- do.call(rbind.data.frame, foreach (i.sim = 1:n.sim, .packages = "metafor") - %dopar% { - # Simulate subject data of all studies - y.unperm <- sim.y() - - # Calculate Hedges' g - g.unperm <- g(y.unperm) - g_var.unperm <- g_var(g.unperm) - - # Meta-analysis - m.unperm <- rma(g.unperm, g_var.unperm) - z.unperm <- m.unperm$zval - - # Save null distributions of z-values - nd.z.perm_stud <- z.unperm - nd.z.perm_subj <- z.unperm - - # Time before study-based permutation test - time0 <- Sys.time() - - # Study-based permutation test - for (i.perm in 1:(n.perm - 1)) { - # Permute study data - g.stud_perm <- perm1(g.unperm) - - # Meta-analysis of permuted study data - m.stud_perm <- rma(g.stud_perm, g_var.unperm) - - # Save null distribution of z-values - nd.z.perm_stud <- c(nd.z.perm_stud, m.stud_perm$zval) - } - - # Time between study-based and subject-based permutation tests - time1 <- Sys.time() - - # Subject-based permutation test - for (i.perm in 1:(n.perm - 1)) { - # Permute subject data - y.subj_perm <- perm2(y.unperm) - - # Calculate Hedges' g of permuted subject data - g.subj_perm <- g(y.subj_perm) - g_var.subj_perm <- g_var(g.subj_perm) - - # Meta-analysis of permuted subject data - m.subj_perm <- rma(g.subj_perm, g_var.subj_perm) - - # Save null distribution of z-values - nd.z.perm_subj <- c(nd.z.perm_subj, m.subj_perm$zval) - } - - # Time after subject-based permutation tests - time2 <- Sys.time() - - # Save times and two-tailed p-values - data.frame( - time.perm_stud = as.numeric(time1 - time0), - time.perm_subj = as.numeric(time2 - time1), - p.z = m.unperm$pval, - p.perm_stud = 1 - 2 * abs(mean(z.unperm > nd.z.perm_stud) - 0.5), - p.perm_subj = 1 - 2 * abs(mean(z.unperm > nd.z.perm_subj) - 0.5) - ) - }) - - # Output results - time.perm_stud <- sim$time.perm_stud - time.perm_subj <- sim$time.perm_subj - mse.perm_stud <- (sim$p.perm_stud - sim$p.z)^2 - mse.perm_subj <- (sim$p.perm_subj - sim$p.z)^2 - cat("Decrease in execution time: ", round( - (mean(time.perm_subj) - mean(time.perm_stud)) / mean(time.perm_subj), - 2) * 100, "%\n", sep = "") - cat("Increase in mean squared error: ", round( - (mean(mse.perm_stud) - mean(mse.perm_subj)) / mean(mse.perm_subj), - 2) * 100, "%\n", sep = "") + See nimare/meta/cbma/sdmpsi_supplement_analysis2.r. """ from pprint import pprint @@ -953,7 +863,7 @@ def compute_sdm_ma( References ---------- - * Radua, J., Rubia, K., Canales, E. J., Pomarol-Clotet, E., Fusar-Poli, P., & + * Radua, J., Rubia, K., Canales, E. J., Pomarol-Clotet, E., Fusar-Poli, P., & Mataix-Cols, D. (2014). Anisotropic kernels for coordinate-based meta-analyses of neuroimaging studies. Frontiers in psychiatry, 5, 13. """ From 61a27691b5a5b5fc9da751b56f61d0f7fdeed8e0 Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Sun, 16 Jan 2022 10:58:57 -0500 Subject: [PATCH 23/55] Documentation improvements. --- nimare/meta/cbma/sdm.py | 40 ++++++++++++++++++++++++++++++++++++---- nimare/transforms.py | 29 ++++++++++++++++++++++++++--- 2 files changed, 62 insertions(+), 7 deletions(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index f364c399b..6935177be 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -18,13 +18,25 @@ def _impute_studywise_imgs(): def _simulate_voxel_with_no_neighbors(n_subjects): + """Simulate the data for a voxel with no neighbors. + + Parameters + ---------- + n_subjects : int + Number of subjects in the dataset. + + Returns + ------- + y : numpy.ndarray of shape (n_subjects,) + Data for the voxel across subjects. + """ y = np.random.normal(loc=0, scale=1, size=n_subjects) y = stats.zscore(y) return y def _simulate_voxel_with_one_neighbor(A, r_ay): - """Simulate the data for a voxel with one neighbor. + """Simulate the data for a voxel with one neighbor that has already been simulated. Parameters ---------- @@ -32,6 +44,11 @@ def _simulate_voxel_with_one_neighbor(A, r_ay): Subject values for the neighboring voxel. r_ay : float Desired correlation between A and Y. + + Returns + ------- + y : numpy.ndarray of shape (n_subjects,) + Data for the voxel across subjects. """ # Random normal values with null mean and unit variance. R = _simulate_voxel_with_no_neighbors(A.size) @@ -45,7 +62,7 @@ def _simulate_voxel_with_one_neighbor(A, r_ay): def _simulate_voxel_with_two_neighbors(A, B, r_ay, r_by): - """Simulate the data for a voxel with one neighbor. + """Simulate the data for a voxel with two neighbors that have already been simulated. Parameters ---------- @@ -53,6 +70,11 @@ def _simulate_voxel_with_two_neighbors(A, B, r_ay, r_by): Subject values for the neighboring voxels. r_ay, r_by : float Desired correlation between A and Y, and between B and Y. + + Returns + ------- + y : numpy.ndarray of shape (n_subjects,) + Data for the voxel across subjects. """ R = _simulate_voxel_with_no_neighbors(A.size) r_ab = np.corrcoef((A, B))[1, 0] @@ -71,7 +93,7 @@ def _simulate_voxel_with_two_neighbors(A, B, r_ay, r_by): def _simulate_voxel_with_three_neighbors(A, B, C, r_ay, r_by, r_cy): - """Simulate the data for a voxel with one neighbor. + """Simulate the data for a voxel with three neighbors that have already been simulated. Parameters ---------- @@ -79,6 +101,11 @@ def _simulate_voxel_with_three_neighbors(A, B, C, r_ay, r_by, r_cy): Subject values for the neighboring voxels. r_ay, r_by, r_cy : float Desired correlation between A and Y, B and Y, and C and Y. + + Returns + ------- + y : numpy.ndarray of shape (n_subjects,) + Data for the voxel across subjects. """ R = _simulate_voxel_with_no_neighbors(A.size) r_ab = np.corrcoef((A, B))[1, 0] @@ -151,6 +178,9 @@ def _simulate_subject_maps(study_maps, n_subjects, masker, correlation_maps): Parameters ---------- + study_maps + n_subjects + masker correlation_maps : dict of 3 91x109x91 arrays right, posterior, and inferior @@ -171,7 +201,9 @@ def _simulate_subject_maps(study_maps, n_subjects, masker, correlation_maps): - For imputing subject values (Y) in a voxel that has no neighboring voxels imputed yet, SDM-PSI simply creates random normal values and standardizes them to have null mean and unit variance (R): + - Y = R + - For imputing subject values in a voxel that has one neighboring voxel already imputed, SDM-PSI conducts a weighted average of the subject values of the neighboring voxel (A) and new standardized random normal values. @@ -544,7 +576,7 @@ def hedges_g(y, n_subjects1, n_subjects2=None): g = mean_diff / pooled_std g_arr[i_study] = g - g_arr = g_arr * (J_arr * ((n_subjects1 + n_subjects2) - 2)) + g_arr = g_arr * J_arr * df else: df = n_subjects1 - 1 diff --git a/nimare/transforms.py b/nimare/transforms.py index 5cef1dcc5..56204882a 100644 --- a/nimare/transforms.py +++ b/nimare/transforms.py @@ -810,15 +810,38 @@ def t_to_hedges_g(t_values, sample_size1, sample_size2=None): ----- Clues from https://imaging.mrc-cbu.cam.ac.uk/statswiki/FAQ/td. """ + from scipy.special import gamma + if sample_size2 is None: + df = sample_size1 - 1 + J = gamma(df / 2) / (gamma((df - 1) / 2) * np.sqrt(df / 2)) # Hedges' correction + # one sample t-test cohens_d = t_values / np.sqrt(sample_size1) + + hedges_g = cohens_d * J * df + else: + df = sample_size1 + sample_size2 - 2 + J = gamma(df / 2) / (gamma((df - 1) / 2) * np.sqrt(df / 2)) # Hedges' correction + # two sample t-test cohens_d = t_values * np.sqrt((1 / sample_size1) + (1 / sample_size2)) - return cohens_d + hedges_g = cohens_d * J * df + + return hedges_g -def f_to_hedges_g(f_values, sample_size): - ... +def f_to_hedges_g(f_values, sample_size1, sample_size2): + """Convert F-statistics to Hedges' g values. + + Notes + ----- + Clues from + https://github.com/strengejacke/esc/blob/eba3c6a62875d9c894466012fe82c0d2253e6137/R/esc_f.R. + """ + pseudo_t_values = np.sqrt(f_values) + hedges_g = t_to_hedges_g(pseudo_t_values, sample_size1, sample_size2) + + return hedges_g From f600bd380f7126b2f920efdf38fae5ad8b5501ec Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Sun, 16 Jan 2022 11:49:39 -0500 Subject: [PATCH 24/55] Update sdm.py --- nimare/meta/cbma/sdm.py | 65 ++++++++++++++++++++++++++++++++--------- 1 file changed, 52 insertions(+), 13 deletions(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index 6935177be..54be3ae0a 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -17,6 +17,24 @@ def _impute_studywise_imgs(): ... +def _run_permutations(): + """Run permutations. + + Notes + ----- + "PSI methods must randomly assign "1" or "-1" to each subject of a one-sample study, + or randomly reassign each of the subjects of a two-sample study to one of the two groups... + PSI methods must also swap subjects in a correlation meta-analysis." + The assignments must be the same across imputations. + """ + ... + + +def _extract_max_statistics(): + """Extract maximum statistics from permuted data for Monte Carlo FWE correction.""" + ... + + def _simulate_voxel_with_no_neighbors(n_subjects): """Simulate the data for a voxel with no neighbors. @@ -390,8 +408,13 @@ def _combine_imputation_results(coefficient_maps, covariance_maps, i_stats, q_st Note that Q follows a χ2 distribution, but its combined statistic follows an F distribution. For convenience, SDM-PSI converts FQ back into a Q (i.e. converts an F statistic to a χ2 statistic with the same p-value). It also derives H-combined from I-combined." + + Clues from https://stats.stackexchange.com/a/476849. """ - ... + point_estimates = np.mean(coefficient_maps, axis=0) + mean_within_imputation_var = np.mean(covariance_maps, axis=0) + # The B term is the sum of squared differences + b = (1 / (coefficient_maps.shape[0] - 1)) * 5 def run_sdm(coords, n_imputations=50, n_iters=1000): @@ -439,10 +462,13 @@ def run_sdm(coords, n_imputations=50, n_iters=1000): subject-level maps for each dataset (across imputations), and scaling it across imputations. - 5. Subject-based permutation test. + 5. Subject-based permutation test on all of the pre-generated imputations. - a. Create one random permutation of the subjects and apply it to the subject images of + - Create one random permutation of the subjects and apply it to the subject images of the different imputed datasets. + - For one-sample tests, randomly assign "1" or "-1" to each subject. + - For two-sample tests, randomly reassign each of the subjects. + - For correlation meta-analyses, randomly swap subjects. 6. Separately for each imputed dataset, conduct a group analysis of the permuted subject images to obtain one study image per study, and then conduct a meta-analysis of the @@ -482,11 +508,13 @@ def run_sdm(coords, n_imputations=50, n_iters=1000): lower_bound_imgs, upper_bound_imgs = compute_sdm_ma(coords) # Step 2: Estimate the most likely effect size and variance maps across studies. + # This should be the meta-analysis map we care about. meta_effect_size_img, meta_tau_img = _mle_estimation(lower_bound_imgs, upper_bound_imgs) # Step 4a: Create base set of simulated subject maps. raw_subject_effect_size_imgs = _simulate_subject_maps(meta_effect_size_img, meta_tau_img) + all_subject_effect_size_imgs, all_subject_var_imgs = [], [] for i_imp in range(n_imputations): # Step 3: Impute study-wise effect size and variance maps. study_effect_size_imgs, study_var_imgs = _impute_studywise_imgs( @@ -498,27 +526,38 @@ def run_sdm(coords, n_imputations=50, n_iters=1000): ) # Step 4: Simulate subject-wise effect size and variance maps. - subject_effect_size_imgs, subject_var_imgs = _scale_subject_maps( + imp_subject_effect_size_imgs, imp_subject_var_imgs = _scale_subject_maps( study_effect_size_imgs, study_var_imgs, raw_subject_effect_size_imgs, ) - - # Step 5: Permutations... + # This is just a stand-in. + all_subject_effect_size_imgs.append(imp_subject_effect_size_imgs) + all_subject_var_imgs.append(imp_subject_var_imgs) + + # Step 5: Permutations... + max_stats = {} + for j_iter in range(n_iters): + permuted_subject_effect_size_imgs, permuted_subject_var_imgs = _run_permutations( + all_subject_effect_size_imgs, all_subject_var_imgs + ) # Step 6: Calculate study-level Hedges-corrected effect size maps. - study_hedge_imgs = _calculate_hedges_maps(subject_effect_size_imgs, subject_var_imgs) + perm_study_hedge_imgs = _calculate_hedges_maps( + permuted_subject_effect_size_imgs, permuted_subject_var_imgs + ) # Step 7: Meta-analyze imputed effect size maps. - imp_meta_effect_size_img = _run_variance_meta(study_hedge_imgs) + perm_meta_effect_size_imgs = _run_variance_meta(perm_study_hedge_imgs) - # Step 8: Heterogeneity statistics. - heterogeneity_stats = _combine_imputation_results(imp_meta_effect_size_img) + # Step 8: Heterogeneity statistics and combine with Rubin's rules. + perm_meta_effect_size_img = _combine_imputation_results(perm_meta_effect_size_imgs) - # Step 9: Rubin's rules...? + # Max statistic + perm_max_stats = _extract_max_statistics(perm_meta_effect_size_img) + max_stats.update(perm_max_stats) - # Step 10: Monte Carlo simulations. - return heterogeneity_stats + return meta_effect_size_img, meta_tau_img, max_stats def hedges_g(y, n_subjects1, n_subjects2=None): From 0d3feb3c6320b34cbb525d6781bd3786de35a4f8 Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Tue, 18 Jan 2022 17:18:03 -0500 Subject: [PATCH 25/55] Just use the masker to simulate the subject data. --- nimare/meta/cbma/sdm.py | 29 +++++++++++++++-------------- 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index 54be3ae0a..455015f73 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -191,12 +191,11 @@ def _simulate_voxel_with_three_neighbors(A, B, C, r_ay, r_by, r_cy): return y -def _simulate_subject_maps(study_maps, n_subjects, masker, correlation_maps): +def _simulate_subject_maps(n_subjects, masker, correlation_maps): """Simulate the subject maps. Parameters ---------- - study_maps n_subjects masker correlation_maps : dict of 3 91x109x91 arrays @@ -250,32 +249,31 @@ def _simulate_subject_maps(study_maps, n_subjects, masker, correlation_maps): - For two-sample studies, SDM-PSI imputes subject values separately for each sample, and it only adds the effect size to the patient (or non-control) subject images. """ - study_img = masker.inverse_transform(study_maps[0]) - n_x, n_y, n_z = study_img.shape - n_voxels = study_maps[0].shape[0] + mask_vec = masker.transform(masker.mask_img) + mask_arr = masker.mask_img.get_fdata() + n_x, n_y, n_z = masker.mask_img.shape + n_voxels = mask_vec.size subject_maps = np.empty((np.sum(n_subjects), n_voxels)) subject_counter = 0 - for i_study, study_map in enumerate(study_maps): - study_map_3d = masker.inverse_transform(study_map).get_fdata() - n_study_subjects = n_subjects[i_study] + for i_study, n_study_subjects in enumerate(n_subjects): study_subject_maps = np.full(np.nan, (n_x, n_y, n_z, n_study_subjects)) for i_x in range(n_x): for j_y in range(n_y): for k_z in range(n_z): - if np.isnan(study_map_3d[i_x, j_y, k_z]): + if np.isnan(mask_arr[i_x, j_y, k_z]): continue - if not np.isnan(study_map_3d[i_x - 1, j_y, k_z]): + if not np.isnan(mask_arr[i_x - 1, j_y, k_z]): use_right = True else: use_right = False - if not np.isnan(study_map_3d[i_x, j_y - 1, k_z]): + if not np.isnan(mask_arr[i_x, j_y - 1, k_z]): use_posterior = True else: use_posterior = False - if not np.isnan(study_map_3d[i_x, j_y, k_z - 1]): + if not np.isnan(mask_arr[i_x, j_y, k_z - 1]): use_inferior = True else: use_inferior = False @@ -417,7 +415,7 @@ def _combine_imputation_results(coefficient_maps, covariance_maps, i_stats, q_st b = (1 / (coefficient_maps.shape[0] - 1)) * 5 -def run_sdm(coords, n_imputations=50, n_iters=1000): +def run_sdm(coords, masker, correlation_maps, n_imputations=50, n_iters=1000): """Run the SDM algorithm. Parameters @@ -504,6 +502,9 @@ def run_sdm(coords, n_imputations=50, n_iters=1000): - "After enough iterations of steps a) to d), use the distribution of the maximum statistic to threshold the combined meta-analysis image obtained from unpermuted data." """ + # Extract sample size information from the coordinates DataFrame. + n_subjects = coords.groupby("id")["sample_sizes"].values + # Step 1: Estimate lower- and upper-bound effect size maps from coordinates. lower_bound_imgs, upper_bound_imgs = compute_sdm_ma(coords) @@ -512,7 +513,7 @@ def run_sdm(coords, n_imputations=50, n_iters=1000): meta_effect_size_img, meta_tau_img = _mle_estimation(lower_bound_imgs, upper_bound_imgs) # Step 4a: Create base set of simulated subject maps. - raw_subject_effect_size_imgs = _simulate_subject_maps(meta_effect_size_img, meta_tau_img) + raw_subject_effect_size_imgs = _simulate_subject_maps(n_subjects, masker, correlation_maps) all_subject_effect_size_imgs, all_subject_var_imgs = [], [] for i_imp in range(n_imputations): From cb38ec04123ff19dd617148e79a1b0885a495f84 Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Thu, 3 Mar 2022 12:00:56 -0500 Subject: [PATCH 26/55] Remove supplement R values. --- .../meta/cbma/sdmpsi_supplement_analysis1.r | 160 ------------------ .../meta/cbma/sdmpsi_supplement_analysis2.r | 119 ------------- 2 files changed, 279 deletions(-) delete mode 100644 nimare/meta/cbma/sdmpsi_supplement_analysis1.r delete mode 100644 nimare/meta/cbma/sdmpsi_supplement_analysis2.r diff --git a/nimare/meta/cbma/sdmpsi_supplement_analysis1.r b/nimare/meta/cbma/sdmpsi_supplement_analysis1.r deleted file mode 100644 index baaa7e08a..000000000 --- a/nimare/meta/cbma/sdmpsi_supplement_analysis1.r +++ /dev/null @@ -1,160 +0,0 @@ -################################################################################# -# EFFECTS OF THE CORRELATION BETWEEN IMPUTATIONS AND THE USE OF # -# VARIABLE PERMUTATION CODES IN THE STATISTICAL SIGNIFICANCE # -# SIMULATION OF MULTIPLE IMPUTATION OF EFFECT SIZE, IMPUTATION OF # -# SUBJECT VALUES AND PERMUTATION TEST FOR A SINGLE STUDY # -################################################################################# - -# Simulation parameters ####################################################### -n.imp <- 50 # Number of imputations -n.perm <- 1000 # Number of permutations -n.sim <- 10 # Number of simulations -n.subj <- 20 # Number of subjects per study - -# Small functions ############################################################# -g <- function (y) { # Calculate Hedges' g - if (is.vector(y)) { - return(J * mean(y) / sd(y)) - } - J * apply(y, 2, function (y_i) { - mean(y_i) / sd(y_i) - }) -} -g_var <- function (g) { # Variance of Hedges' g - 1 / n.subj + (1 - (df - 2) / (df * J^2)) * g^2 -} -imp.subj <- function () { # Impute subject values - y <- rnorm(n.subj) - (y - mean(y)) / sd(y) -} -perm <- function (y, code) { # Permute subject values - y[code] <- -1 * y[code] - y -} -perm.code <- function () { # Create a random permutation code - which(runif(n.subj) > 0.5) -} -combine.g <- function (g) { # Combine Hedges' g from different imputations - mean(g) -} -combine.g_var <- function (g, g_var) { # Combine variance from different imp. - mean(g_var) + (1 + 1 / n.imp) * var(g) -} -sim.g.imp <- function (g) { # Simulate multiple imputation of Hedges' g - g + rnorm(n.imp, 0, 0.2) -} -sim.y.true <- function () { # Simulate (true) subject values - rnorm(n.subj) -} - -# Main ######################################################################## -library(doParallel) -registerDoParallel(cores = detectCores() - 1) -par(mfrow = c(2, 2)) - -# Constants -df <- n.subj - 1 # Degrees of freedom -J <- gamma(df / 2) / gamma((df - 1) / 2) * sqrt(2 / df) # Hedges' correction - -# Effects to test: perfect correlation between any two imputations? -for (PERFECT_CORRELATION in c(TRUE, FALSE)) { - # Effects to test: apply the same permutation code to all imputations? - for (SAME_PERM_CODE in c(TRUE, FALSE)) { - # Start simulations - sim <- do.call(rbind.data.frame, foreach (i.sim = 1:n.sim) %dopar% { - - # Simulate (true) subject data - y.true <- sim.y.true() - - # Calculate Hedges' g - g.true <- g(y.true) - g_var.true <- g_var(g.true) - - # Calculate z-value - z.true <- g.true / sqrt(g_var.true) - - # Simulate multiple imputaion of Hedges' g - g.imp <- sim.g.imp(g.true) - g_var.imp <- g_var(g.imp) - - # Combine imputed Hedges' g - g.imp.combined <- combine.g(g.imp) - g_var.imp.combined <- combine.g_var(g.imp, g_var.imp) - - # Calculate z-value of combined imputed data - z.imp.combined <- g.imp.combined / sqrt(g_var.imp.combined) - - # Impute subject data - replicate.d.imp <- t(replicate(n.subj, g.imp / J)) - if (PERFECT_CORRELATION) { - common.imp.subj <- imp.subj() - y.imp <- replicate(n.imp, common.imp.subj) + replicate.d.imp - } else { - y.imp <- replicate(n.imp, imp.subj()) + replicate.d.imp - } - - # Save variance between imputations and null distributions of z-values - var.g.imp <- var(g.imp) - nd.z.true <- z.true - nd.z.imp.combined <- z.imp.combined - - for (i.perm in 1:(n.perm - 1)) { - - # Permute (true) subject data - y.true.perm <- perm(y.true, perm.code()) - # Calculate Hedges' g of permuted data - g.true.perm <- g(y.true.perm) - g_var.true.perm <- g_var(g.true.perm) - # Calculate z-value of permuted data - z.true.perm <- g.true.perm / sqrt(g_var.true.perm) - # Permute imputed subject data - if (SAME_PERM_CODE) { - common.perm.code <- perm.code() - y.imp.perm <- apply(y.imp, 2, function (y.imp.i) { - perm(y.imp.i, common.perm.code) - }) - } else { - y.imp.perm <- apply(y.imp, 2, function (y.imp.i) { - perm(y.imp.i, perm.code()) - }) - } - - # Calculate Hedges' g of permuted imputed data - g.imp.perm <- g(y.imp.perm) - g_var.imp.perm <- g_var(g.imp.perm) - - # Calculate z-value of permuted imputed data - g.imp.perm.combined <- combine.g(g.imp.perm) - g_var.imp.perm.combined <- combine.g_var(g.imp.perm, g_var.imp.perm) - z.imp.perm.combined <- g.imp.perm.combined / sqrt(g_var.imp.perm.combined) - - # Save variance between imputations and null distributions of z-values - var.g.imp <- c(var.g.imp, var(g.imp.perm)) - nd.z.true <- c(nd.z.true, z.true.perm) - nd.z.imp.combined <- c(nd.z.imp.combined, z.imp.perm.combined) - - } - - # Save variance between imputations and one-tailed p-values - data.frame( - var.g.imp = mean(var.g.imp), - p.true = mean(z.true > nd.z.true), - p.imp.combined = mean(z.imp.combined > nd.z.imp.combined) - ) - }) - - # Create output plot - plot(0:1, 0:1, type = "l", col = "#999999", cex.lab = 1.2, - xlab = "p-values from (true) subject data", - ylab = "p-values from imputed subject data", - main = paste( - ifelse(PERFECT_CORRELATION, "Perfect", "No"), - "correlation between imputations.", - ifelse(SAME_PERM_CODE, "Same", "Different"), - "permutation code.\n", - "Variance between imputations:", - signif(mean(sim$var.g.imp), 1) - ), font.main = 1) - points(sim$p.true, sim$p.imp.combined) - } -} diff --git a/nimare/meta/cbma/sdmpsi_supplement_analysis2.r b/nimare/meta/cbma/sdmpsi_supplement_analysis2.r deleted file mode 100644 index b4cf1e390..000000000 --- a/nimare/meta/cbma/sdmpsi_supplement_analysis2.r +++ /dev/null @@ -1,119 +0,0 @@ -############################################################################### -# PERMUTATION OF STUDY DATA AND PERMUTATION OF SUBJECT DATA # -############################################################################### - -# Simulation parameters ####################################################### -n.perm <- 1000 # Number of permutations -n.sim <- 1000 # Number of simulations -n.subj <- 20 # Number of subjects per group of a study -n.stud <- 10 # Number of studies - -# Small functions ############################################################# -g <- function (y) { # Calculate Hedges' g - J * apply(y, 2, function (y_i) { - (mean(y_i[1:n.subj]) - mean(y_i[-(1:n.subj)])) / - sqrt((var(y_i[1:n.subj]) + var(y_i[-(1:n.subj)])) / 2) - }) -} -g_var <- function (g) { # Variance of Hedges' g - 1 / n.subj + (1 - (df - 2) / (df * J^2)) * g^2 -} -perm1 <- function (g) { # Permute study effects - code <- which(runif(n.stud) > 0.5) - g[code] <- -1 * g[code] - g -} -perm2 <- function (y) { # Permute subject values - apply(y, 2, sample) -} -sim.y <- function () { # Simulate (true) subject values - y <- matrix(rnorm(n.stud * n.subj * 2), ncol = n.stud) - y[1:n.subj,] <- y[1:n.subj,] + 0.2 # Add a small effect size - y -} - -# Main ######################################################################## -library(doParallel) -library(metafor) -registerDoParallel(cores = detectCores() - 1) -par(mfrow = 1:2) - -# Constants -df <- 2 * n.subj - 2 # Degrees of freedom -J <- gamma(df / 2) / gamma((df - 1) / 2) * sqrt(2 / df) # Hedges' correction - -sim <- do.call(rbind.data.frame, foreach (i.sim = 1:n.sim, .packages = "metafor") -%dopar% { - # Simulate subject data of all studies - y.unperm <- sim.y() - - # Calculate Hedges' g - g.unperm <- g(y.unperm) - g_var.unperm <- g_var(g.unperm) - - # Meta-analysis - m.unperm <- rma(g.unperm, g_var.unperm) - z.unperm <- m.unperm$zval - - # Save null distributions of z-values - nd.z.perm_stud <- z.unperm - nd.z.perm_subj <- z.unperm - - # Time before study-based permutation test - time0 <- Sys.time() - - # Study-based permutation test - for (i.perm in 1:(n.perm - 1)) { - # Permute study data - g.stud_perm <- perm1(g.unperm) - - # Meta-analysis of permuted study data - m.stud_perm <- rma(g.stud_perm, g_var.unperm) - - # Save null distribution of z-values - nd.z.perm_stud <- c(nd.z.perm_stud, m.stud_perm$zval) - } - - # Time between study-based and subject-based permutation tests - time1 <- Sys.time() - - # Subject-based permutation test - for (i.perm in 1:(n.perm - 1)) { - # Permute subject data - y.subj_perm <- perm2(y.unperm) - - # Calculate Hedges' g of permuted subject data - g.subj_perm <- g(y.subj_perm) - g_var.subj_perm <- g_var(g.subj_perm) - - # Meta-analysis of permuted subject data - m.subj_perm <- rma(g.subj_perm, g_var.subj_perm) - - # Save null distribution of z-values - nd.z.perm_subj <- c(nd.z.perm_subj, m.subj_perm$zval) - } - - # Time after subject-based permutation tests - time2 <- Sys.time() - - # Save times and two-tailed p-values - data.frame( - time.perm_stud = as.numeric(time1 - time0), - time.perm_subj = as.numeric(time2 - time1), - p.z = m.unperm$pval, - p.perm_stud = 1 - 2 * abs(mean(z.unperm > nd.z.perm_stud) - 0.5), - p.perm_subj = 1 - 2 * abs(mean(z.unperm > nd.z.perm_subj) - 0.5) - ) -}) - -# Output results -time.perm_stud <- sim$time.perm_stud -time.perm_subj <- sim$time.perm_subj -mse.perm_stud <- (sim$p.perm_stud - sim$p.z)^2 -mse.perm_subj <- (sim$p.perm_subj - sim$p.z)^2 -cat("Decrease in execution time: ", round( - (mean(time.perm_subj) - mean(time.perm_stud)) / mean(time.perm_subj), - 2) * 100, "%\n", sep = "") -cat("Increase in mean squared error: ", round( - (mean(mse.perm_stud) - mean(mse.perm_subj)) / mean(mse.perm_subj), - 2) * 100, "%\n", sep = "") From 679f051b5bc08765fc4e7d8d1ba7d4d3cbc8156b Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Thu, 3 Mar 2022 12:01:37 -0500 Subject: [PATCH 27/55] Update sdm.py --- nimare/meta/cbma/sdm.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index 455015f73..75da717e5 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -414,6 +414,9 @@ def _combine_imputation_results(coefficient_maps, covariance_maps, i_stats, q_st # The B term is the sum of squared differences b = (1 / (coefficient_maps.shape[0] - 1)) * 5 + # Delete the variables for linting purposes + del point_estimates, mean_within_imputation_var, b + def run_sdm(coords, masker, correlation_maps, n_imputations=50, n_iters=1000): """Run the SDM algorithm. From afa59cac0a6ec0fe35fe1627ae1be62e5acef1c3 Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Thu, 3 Mar 2022 12:13:44 -0500 Subject: [PATCH 28/55] Run black. --- nimare/meta/cbma/sdm.py | 48 ++++++++++++++++++++--------------------- 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index 75da717e5..19a6f2fc9 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -73,7 +73,7 @@ def _simulate_voxel_with_one_neighbor(A, r_ay): # Correlation between A and R. r_ar = np.corrcoef((A, R))[1, 0] - w_r = np.sqrt((1 - (r_ay ** 2)) / (1 - (r_ar ** 2))) + w_r = np.sqrt((1 - (r_ay**2)) / (1 - (r_ar**2))) w_a = r_ay - (w_r * r_ar) y = (w_a * A) + (w_r * R) return y @@ -100,10 +100,10 @@ def _simulate_voxel_with_two_neighbors(A, B, r_ay, r_by): r_br = np.corrcoef((B, R))[1, 0] w_r = np.sqrt( - (1 - (r_ab ** 2) - (r_ay ** 2) - (r_by ** 2) + (2 * r_ab * r_ay * r_by)) - / (1 - (r_ab ** 2) - (r_ar ** 2) - (r_br ** 2) + (2 * r_ab * r_ar * r_br)) + (1 - (r_ab**2) - (r_ay**2) - (r_by**2) + (2 * r_ab * r_ay * r_by)) + / (1 - (r_ab**2) - (r_ar**2) - (r_br**2) + (2 * r_ab * r_ar * r_br)) ) - w_b = ((r_by - (r_ab * r_ay)) - (w_r * (r_br - (r_ab * r_ar)))) / (1 - (r_ab ** 2)) + w_b = ((r_by - (r_ab * r_ay)) - (w_r * (r_br - (r_ab * r_ar)))) / (1 - (r_ab**2)) w_a = r_ay - (w_b * r_ab) - (w_r * r_ar) y = (w_a * A) + (w_b * B) + (w_r * R) @@ -135,13 +135,13 @@ def _simulate_voxel_with_three_neighbors(A, B, C, r_ay, r_by, r_cy): w_r_num1 = ( 1 - - (r_ab ** 2) - - (r_ac ** 2) - - (r_bc ** 2) + - (r_ab**2) + - (r_ac**2) + - (r_bc**2) + (2 * r_ab * r_ac * r_bc) - - ((r_ay ** 2) * (1 - (r_bc ** 2))) - - ((r_by ** 2) * (1 - (r_ac ** 2))) - - ((r_cy ** 2) * (1 - (r_ab ** 2))) + - ((r_ay**2) * (1 - (r_bc**2))) + - ((r_by**2) * (1 - (r_ac**2))) + - ((r_cy**2) * (1 - (r_ab**2))) ) w_r_num2 = ( (2 * r_ay * r_by * (r_ab - (r_ac * r_bc))) @@ -152,13 +152,13 @@ def _simulate_voxel_with_three_neighbors(A, B, C, r_ay, r_by, r_cy): w_r_den1 = ( 1 - - (r_ab ** 2) - - (r_ac ** 2) - - (r_bc ** 2) + - (r_ab**2) + - (r_ac**2) + - (r_bc**2) + (2 * r_ab * r_ac * r_bc) - - ((r_ar ** 2) * (1 - (r_bc ** 2))) - - ((r_br ** 2) * (1 - (r_ac ** 2))) - - ((r_cr ** 2) * (1 - (r_ab ** 2))) + - ((r_ar**2) * (1 - (r_bc**2))) + - ((r_br**2) * (1 - (r_ac**2))) + - ((r_cr**2) * (1 - (r_ab**2))) ) w_r_den2 = ( (2 * r_ar * r_br * (r_ab - (r_ac * r_bc))) @@ -169,22 +169,22 @@ def _simulate_voxel_with_three_neighbors(A, B, C, r_ay, r_by, r_cy): w_r = w_r_num / w_r_den w_c_num1 = ( - (r_cy * (1 - (r_ab ** 2))) + (r_cy * (1 - (r_ab**2))) - (r_ac * (r_ay - (r_ab * r_by))) - (r_bc * (r_by - (r_ab * r_ay))) ) w_c_num2 = w_r * ( - (r_cr * (1 - (r_ab ** 2))) + (r_cr * (1 - (r_ab**2))) - (r_ac * (r_ar - (r_ab * r_br))) - (r_bc * (r_br - (r_ab * r_ar))) ) w_c_num = w_c_num1 - w_c_num2 - w_c_den = 1 - (r_ab ** 2) - (r_ac ** 2) - (r_bc ** 2) + (2 * r_ab * r_ac * r_bc) + w_c_den = 1 - (r_ab**2) - (r_ac**2) - (r_bc**2) + (2 * r_ab * r_ac * r_bc) w_c = w_c_num / w_c_den w_b = ( (r_by - (r_ab * r_ay)) - (w_c * (r_bc - (r_ab * r_ac))) - (w_r * (r_br - (r_ab * r_ar))) - ) / (1 - (r_ab ** 2)) + ) / (1 - (r_ab**2)) w_a = r_ay - (w_b * r_ab) - (w_c * r_ac) - (w_r * r_ar) y = (w_a * A) + (w_b * B) + (w_c * C) + (w_r * R) @@ -664,7 +664,7 @@ def hedges_g_var(g, n_subjects1, n_subjects2=None): if n_subjects2 is not None: assert g.shape == n_subjects1.shape == n_subjects2.shape g_var = ((n_subjects1 + n_subjects2) / (n_subjects1 * n_subjects2)) + ( - (g ** 2) / (2 * (n_subjects1 + n_subjects2)) + (g**2) / (2 * (n_subjects1 + n_subjects2)) ) else: raise ValueError("One-sample tests are not yet supported.") @@ -975,12 +975,12 @@ def compute_sdm_ma( real_distances = spatial.distance.cdist(kept_ijk, mask_ijk) # closest_peak = np.argmin(real_distances, axis=0) virtual_distances = np.sqrt( - (1 - alpha) * (real_distances ** 2) + alpha * 2 * kernel_sigma * np.log(peak_corr ** -1) + (1 - alpha) * (real_distances**2) + alpha * 2 * kernel_sigma * np.log(peak_corr**-1) ) - y_lower = min_effect_size + np.exp((-(virtual_distances ** 2)) / (2 * kernel_sigma)) * ( + y_lower = min_effect_size + np.exp((-(virtual_distances**2)) / (2 * kernel_sigma)) * ( peak_t - min_effect_size ) - y_upper = max_effect_size + np.exp((-(virtual_distances ** 2)) / (2 * kernel_sigma)) * ( + y_upper = max_effect_size + np.exp((-(virtual_distances**2)) / (2 * kernel_sigma)) * ( peak_t - max_effect_size ) y_lower_img = masking.unmask(y_lower, mask_img) From e74be9c46a888884141608d7b3b768ad279b9ef6 Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Tue, 8 Mar 2022 17:59:34 -0500 Subject: [PATCH 29/55] Update sdm.py --- nimare/meta/cbma/sdm.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index 19a6f2fc9..82103a85d 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -504,6 +504,8 @@ def run_sdm(coords, masker, correlation_maps, n_imputations=50, n_iters=1000): - "After enough iterations of steps a) to d), use the distribution of the maximum statistic to threshold the combined meta-analysis image obtained from unpermuted data." + - This approach is used for the main analysis (i.e., the mean), but for meta-regressions + we need to use the Freedman-Lane permutation method on the study-level maps. """ # Extract sample size information from the coordinates DataFrame. n_subjects = coords.groupby("id")["sample_sizes"].values From 1855cafda97fac7b97ee46faa2ed3bfca74ff321 Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Wed, 30 Mar 2022 15:14:01 -0400 Subject: [PATCH 30/55] Test out the simulation method. Unfortunately, there's still an issue with most voxels... --- nimare/meta/cbma/sdm.py | 164 ++++++++++-------- .../resources/gray_matter_correlations.nii.gz | Bin 0 -> 8581451 bytes 2 files changed, 89 insertions(+), 75 deletions(-) create mode 100644 nimare/resources/gray_matter_correlations.nii.gz diff --git a/nimare/meta/cbma/sdm.py b/nimare/meta/cbma/sdm.py index 82103a85d..f6e583484 100644 --- a/nimare/meta/cbma/sdm.py +++ b/nimare/meta/cbma/sdm.py @@ -8,6 +8,9 @@ from pymare import Dataset, estimators from scipy import spatial, stats +# from tqdm import tqdm, trange +from tqdm.autonotebook import tqdm, trange + def _mle_estimation(): ... @@ -252,86 +255,97 @@ def _simulate_subject_maps(n_subjects, masker, correlation_maps): mask_vec = masker.transform(masker.mask_img) mask_arr = masker.mask_img.get_fdata() n_x, n_y, n_z = masker.mask_img.shape + xyz = np.vstack(np.where(mask_arr)).T + # From https://opensourceoptions.com/blog/sort-numpy-arrays-by-columns-or-rows/ + xyz = xyz[xyz[:, 2].argsort()] # sort by z + xyz = xyz[xyz[:, 1].argsort(kind="mergesort")] # sort by y + xyz = xyz[xyz[:, 0].argsort(kind="mergesort")] # sort by x n_voxels = mask_vec.size + assert xyz.shape[0] == n_voxels subject_maps = np.empty((np.sum(n_subjects), n_voxels)) subject_counter = 0 - for i_study, n_study_subjects in enumerate(n_subjects): - study_subject_maps = np.full(np.nan, (n_x, n_y, n_z, n_study_subjects)) - for i_x in range(n_x): - for j_y in range(n_y): - for k_z in range(n_z): - if np.isnan(mask_arr[i_x, j_y, k_z]): - continue - - if not np.isnan(mask_arr[i_x - 1, j_y, k_z]): - use_right = True - else: - use_right = False - - if not np.isnan(mask_arr[i_x, j_y - 1, k_z]): - use_posterior = True - else: - use_posterior = False - - if not np.isnan(mask_arr[i_x, j_y, k_z - 1]): - use_inferior = True - else: - use_inferior = False - - n_directions = sum((use_right, use_posterior, use_inferior)) - if n_directions == 0: - study_subject_maps[i_x, j_y, k_z, :] = _simulate_voxel_with_no_neighbors( - n_study_subjects - ) - - elif n_directions == 1: - if use_right: - A_data = study_subject_maps[i_x - 1, j_y, k_z, :] - r_ay = correlation_maps["right"][i_x, j_y, k_z] - elif use_posterior: - A_data = study_subject_maps[i_x, j_y - 1, k_z, :] - r_ay = correlation_maps["posterior"][i_x, j_y, k_z] - else: - A_data = study_subject_maps[i_x, j_y, k_z - 1, :] - r_ay = correlation_maps["inferior"][i_x, j_y, k_z] - - study_subject_maps[i_x, j_y, k_z, :] = _simulate_voxel_with_one_neighbor( - A_data, r_ay - ) - - elif n_directions == 2: - if use_right: - A_data = study_subject_maps[i_x - 1, j_y, k_z, :] - r_ay = correlation_maps["right"][i_x, j_y, k_z] - - if use_posterior: - B_data = study_subject_maps[i_x, j_y - 1, k_z, :] - r_by = correlation_maps["posterior"][i_x, j_y, k_z] - else: - B_data = study_subject_maps[i_x, j_y, k_z - 1, :] - r_by = correlation_maps["inferior"][i_x, j_y, k_z] - elif use_posterior: - A_data = study_subject_maps[i_x, j_y - 1, k_z, :] - r_ay = correlation_maps["posterior"][i_x, j_y, k_z] - - B_data = study_subject_maps[i_x, j_y, k_z - 1, :] - r_by = correlation_maps["inferior"][i_x, j_y, k_z] - - study_subject_maps[i_x, j_y, k_z, :] = _simulate_voxel_with_two_neighbors( - A_data, B_data, r_ay, r_by - ) - - else: - A_data = study_subject_maps[i_x - 1, j_y, k_z, :] + bad_directions, good_directions = [], [] + for i_study, n_study_subjects in enumerate(tqdm(n_subjects, desc="Studies")): + study_subject_maps = np.full((n_x, n_y, n_z, n_study_subjects), fill_value=0) + for coord in trange(xyz.shape[0], leave=False, desc="Voxel"): + i_x, j_y, k_z = xyz[coord, :] + + if mask_arr[i_x - 1, j_y, k_z] == 1: + use_right = True + else: + use_right = False + + if mask_arr[i_x, j_y - 1, k_z] == 1: + use_posterior = True + else: + use_posterior = False + + if mask_arr[i_x, j_y, k_z - 1] == 1: + use_inferior = True + else: + use_inferior = False + + n_directions = sum((use_right, use_posterior, use_inferior)) + if n_directions == 0: + voxel_values = _simulate_voxel_with_no_neighbors(n_study_subjects) + + elif n_directions == 1: + if use_right: + A_data = study_subject_maps[i_x - 1, j_y, k_z, :] + r_ay = correlation_maps["right"][i_x, j_y, k_z] + elif use_posterior: + A_data = study_subject_maps[i_x, j_y - 1, k_z, :] + r_ay = correlation_maps["posterior"][i_x, j_y, k_z] + else: + A_data = study_subject_maps[i_x, j_y, k_z - 1, :] + r_ay = correlation_maps["inferior"][i_x, j_y, k_z] + + voxel_values = _simulate_voxel_with_one_neighbor(A_data, r_ay) + + elif n_directions == 2: + if use_right: + A_data = study_subject_maps[i_x - 1, j_y, k_z, :] + r_ay = correlation_maps["right"][i_x, j_y, k_z] + + if use_posterior: B_data = study_subject_maps[i_x, j_y - 1, k_z, :] - C_data = study_subject_maps[i_x, j_y, k_z - 1, :] - r_ay = correlation_maps["right"][i_x, j_y, k_z] r_by = correlation_maps["posterior"][i_x, j_y, k_z] - r_cy = correlation_maps["inferior"][i_x, j_y, k_z] - study_subject_maps[i_x, j_y, k_z, :] = _simulate_voxel_with_two_neighbors( - A_data, B_data, C_data, r_ay, r_by, r_cy - ) - + else: + B_data = study_subject_maps[i_x, j_y, k_z - 1, :] + r_by = correlation_maps["inferior"][i_x, j_y, k_z] + elif use_posterior: + A_data = study_subject_maps[i_x, j_y - 1, k_z, :] + r_ay = correlation_maps["posterior"][i_x, j_y, k_z] + + B_data = study_subject_maps[i_x, j_y, k_z - 1, :] + r_by = correlation_maps["inferior"][i_x, j_y, k_z] + + voxel_values = _simulate_voxel_with_two_neighbors(A_data, B_data, r_ay, r_by) + + else: + A_data = study_subject_maps[i_x - 1, j_y, k_z, :] + B_data = study_subject_maps[i_x, j_y - 1, k_z, :] + C_data = study_subject_maps[i_x, j_y, k_z - 1, :] + r_ay = correlation_maps["right"][i_x, j_y, k_z] + r_by = correlation_maps["posterior"][i_x, j_y, k_z] + r_cy = correlation_maps["inferior"][i_x, j_y, k_z] + voxel_values = _simulate_voxel_with_three_neighbors( + A_data, + B_data, + C_data, + r_ay, + r_by, + r_cy, + ) + + study_subject_maps[i_x, j_y, k_z, :] = 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