def contrast(universe, studies, outroot, **kwargs):
# Split universe into two sets
print "Splitting the universe in two..."
inc_studies = list(set(universe.studies) & set(studies))
inc_idx = np.in1d(universe.studies, inc_studies)
# Note: sparse matrices don't support proper logical indexing,
# so get indexes with np.where.
inc_imgs = universe.data[:,np.where(inc_idx)[0]]
no_inc_imgs = universe.data[:,np.where(inc_idx==0)[0]]
n_studies = len(universe.studies)
# Calculate different count variables
print "Calculating count variables..."
n_inc = inc_imgs.shape[1]
print "\t# of studies with inc: %d" % n_inc
n_no_inc = no_inc_imgs.shape[1]
print "\t# of studies without inc: %d" % n_no_inc
n_inc_act = np.asarray(spmatrix.sum(inc_imgs, 1))
n_no_inc_act = np.asarray(spmatrix.sum(no_inc_imgs, 1))
p_inc = (n_inc+1.0)/(n_studies+2)
# P(A|T), P(A|~T), and P(T|A), with Laplace smoothing
print "Calculating conditional probabilities..."
p_act = np.asarray(spmatrix.mean(universe.data, 1)) + 1.0/n_studies
p_act_inc = (n_inc_act+1.0)/(n_inc+2)
p_act_no_inc = (n_no_inc_act+1.0)/(n_no_inc+2)
pp = p_act_inc * p_inc / p_act
# Recompute P(A) and P(T|A) with uniform priors
print "Recomputing with uniform priors..."
pt_unif = kwargs.get('prior', 0.5)
pa_unif = pt_unif * p_act_inc + 0.5 * p_act_no_inc
pp_unif = p_act_inc * pt_unif / pa_unif
# Get p and z values
print "Computing and saving statistical maps..."
# Posterior prob. w/ and w/o uniform prior
imageutils.save_img(pp, outroot + '_pp.nii.gz', mask_inds=universe.vox_kept) # Ugly; need to make masking object
imageutils.save_img(pp_unif, outroot + '_pp_unif.nii.gz', mask_inds=universe.vox_kept)
fdr_q = kwargs.get('fdr_q', 0.05)
# One-way chi-square test for consistency of activation
p_vals = stats.one_way(np.squeeze(n_inc_act), n_inc)
p_vals[p_vals < 1e-240] = 1e-240 # prevents overflow
z_sign = np.sign(n_inc_act - np.mean(n_inc_act)).ravel()
z_vals = np.abs(norm.ppf(p_vals/2)) * z_sign
imageutils.save_img(z_vals, outroot + '_fi_z.nii.gz', mask_inds=universe.vox_kept)
below = np.where(stats.fdr(p_vals, fdr_q)==False)
z_vals[below] = 0
imageutils.save_img(z_vals, outroot + '_fi_z_fdr.nii.gz', mask_inds=universe.vox_kept)
# Two-way chi-square for specificity of activation
cells = np.squeeze(np.array([[n_inc_act, n_no_inc_act], [n_inc-n_inc_act, n_no_inc-n_no_inc_act]]).T)
p_vals = stats.two_way(cells)
p_vals[p_vals < 1e-240] = 1e-240 # prevents overflow
z_sign = np.sign(p_act_inc - p_act_no_inc).ravel()
z_vals = np.abs(norm.ppf(p_vals/2)) * z_sign
imageutils.save_img(z_vals, outroot + '_ri_z.nii.gz', mask_inds=universe.vox_kept)
below = np.where(stats.fdr(p_vals, fdr_q)==False)
z_vals[below] = 0
imageutils.save_img(z_vals, outroot + '_ri_z_fdr.nii.gz', mask_inds=universe.vox_kept)
pp_unif[below] = 0
imageutils.save_img(pp_unif, outroot + '_pp_unif_fdr.nii.gz', mask_inds=universe.vox_kept)
def trim_voxels(self, min_studies=0.03):
means = np.asarray(spmatrix.mean(self.data, 1))
self.vox_kept = np.where(means >= min_studies)[0]
self.data = self.data[self.vox_kept, :]
def contrast(universe, studies, outroot, **kwargs):
# Split universe into two sets
print "Splitting the universe in two..."
inc_studies = list(set(universe.studies) & set(studies))
inc_idx = np.in1d(universe.studies, inc_studies)
# Note: sparse matrices don't support proper logical indexing,
# so get indexes with np.where.
inc_imgs = universe.data[:, np.where(inc_idx)[0]]
no_inc_imgs = universe.data[:, np.where(inc_idx == 0)[0]]
n_studies = len(universe.studies)
# Calculate different count variables
print "Calculating count variables..."
n_inc = inc_imgs.shape[1]
print "\t# of studies with inc: %d" % n_inc
n_no_inc = no_inc_imgs.shape[1]
print "\t# of studies without inc: %d" % n_no_inc
n_inc_act = np.asarray(spmatrix.sum(inc_imgs, 1))
n_no_inc_act = np.asarray(spmatrix.sum(no_inc_imgs, 1))
p_inc = (n_inc + 1.0) / (n_studies + 2)
# P(A|T), P(A|~T), and P(T|A), with Laplace smoothing
print "Calculating conditional probabilities..."
p_act = np.asarray(spmatrix.mean(universe.data, 1)) + 1.0 / n_studies
p_act_inc = (n_inc_act + 1.0) / (n_inc + 2)
p_act_no_inc = (n_no_inc_act + 1.0) / (n_no_inc + 2)
pp = p_act_inc * p_inc / p_act
# Recompute P(A) and P(T|A) with uniform priors
print "Recomputing with uniform priors..."
pt_unif = kwargs.get('prior', 0.5)
pa_unif = pt_unif * p_act_inc + 0.5 * p_act_no_inc
pp_unif = p_act_inc * pt_unif / pa_unif
# Get p and z values
print "Computing and saving statistical maps..."
# Posterior prob. w/ and w/o uniform prior
imageutils.save_img(
pp, outroot + '_pp.nii.gz',
mask_inds=universe.vox_kept) # Ugly; need to make masking object
imageutils.save_img(pp_unif,
outroot + '_pp_unif.nii.gz',
mask_inds=universe.vox_kept)
fdr_q = kwargs.get('fdr_q', 0.05)
# One-way chi-square test for consistency of activation
p_vals = stats.one_way(np.squeeze(n_inc_act), n_inc)
p_vals[p_vals < 1e-240] = 1e-240 # prevents overflow
z_sign = np.sign(n_inc_act - np.mean(n_inc_act)).ravel()
z_vals = np.abs(norm.ppf(p_vals / 2)) * z_sign
imageutils.save_img(z_vals,
outroot + '_fi_z.nii.gz',
mask_inds=universe.vox_kept)
below = np.where(stats.fdr(p_vals, fdr_q) == False)
z_vals[below] = 0
imageutils.save_img(z_vals,
outroot + '_fi_z_fdr.nii.gz',
mask_inds=universe.vox_kept)
# Two-way chi-square for specificity of activation
cells = np.squeeze(
np.array([[n_inc_act, n_no_inc_act],
[n_inc - n_inc_act, n_no_inc - n_no_inc_act]]).T)
p_vals = stats.two_way(cells)
p_vals[p_vals < 1e-240] = 1e-240 # prevents overflow
z_sign = np.sign(p_act_inc - p_act_no_inc).ravel()
z_vals = np.abs(norm.ppf(p_vals / 2)) * z_sign
imageutils.save_img(z_vals,
outroot + '_ri_z.nii.gz',
mask_inds=universe.vox_kept)
below = np.where(stats.fdr(p_vals, fdr_q) == False)
z_vals[below] = 0
imageutils.save_img(z_vals,
outroot + '_ri_z_fdr.nii.gz',
mask_inds=universe.vox_kept)
pp_unif[below] = 0
imageutils.save_img(pp_unif,
outroot + '_pp_unif_fdr.nii.gz',
mask_inds=universe.vox_kept)
def trim_voxels(self, min_studies=0.03): means = np.asarray(spmatrix.mean(self.data, 1)) self.vox_kept = np.where(means >= min_studies)[0] self.data = self.data[self.vox_kept,:]
