np.transpose(fm_std, [1, 0, 2]).astype("float32"))
tifffile.imsave(os.path.join(pvaldst, "mf_mean.tif"),
np.transpose(mf_mean, [1, 0, 2]).astype("float32"))
tifffile.imsave(os.path.join(pvaldst, "mf_std.tif"),
np.transpose(mf_std, [1, 0, 2]).astype("float32"))
#Generate the p-values map
##########################
cutoff = 0.05 #set p-value cutoff
#first comparison: mm vs. fm
comparison = "fm_v_mm"
#pcutoff: only display pixels below this level of significance
pvals, psign = stat.tTestVoxelization(mm.astype("float"),
fm.astype("float"),
signed=True,
pcutoff=cutoff)
#color the p-values according to their sign
#(defined by the sign of the difference of the means between the 2 groups)
pvalsc = stat.colorPValues(pvals, psign, positive=[0, 1], negative=[1, 0])
tifffile.imsave(os.path.join(pvaldst, "pvalues_%s.tif" % comparison),
np.transpose(pvalsc, [1, 0, 2, 3]).astype("float32"),
photometric="minisblack",
planarconfig="contig",
bigtiff=True)
#second comparison: mm vs. mf
comparison = "mf_v_mm"
pvals, psign = stat.tTestVoxelization(mm.astype("float"),
mf.astype("float"),
signed=True,
g2s = numpy.std(g2,axis = 0);
io.writeData(os.path.join(baseDirectory, 'group1_mean.raw'), rsp.sagittalToCoronalData(g1a));
io.writeData(os.path.join(baseDirectory, 'group1_std.raw'), rsp.sagittalToCoronalData(g1s));
io.writeData(os.path.join(baseDirectory, 'group2_fast_mean.raw'), rsp.sagittalToCoronalData(g2a));
io.writeData(os.path.join(baseDirectory, 'group2_fast_std.raw'), rsp.sagittalToCoronalData(g2s));
#Generate the p-values map
##########################
#pcutoff: only display pixels below this level of significance
pvals, psign = stat.tTestVoxelization(g1.astype('float'), g2.astype('float'), signed = True, pcutoff = 0.05);
#color the p-values according to their sign (defined by the sign of the difference of the means between the 2 groups)
pvalsc = stat.colorPValues(pvals, psign, positive = [0,1], negative = [1,0]);
io.writeData(os.path.join(baseDirectory, 'pvalues.tif'), rsp.sagittalToCoronalData(pvalsc.astype('float32')));
#############################################################################
# Regions-based statistics:
###########################
group1 = ['/home/yourname/experiment/sample1/cells_transformed_to_Atlas.npy',
def generate_p_value_maps(src):
"""
generates p-value maps as per ClearMap/analysis.py
#TODO: generalise function
"""
#Load the data (heat maps generated previously )
#make groups
groupA = [
os.path.join(flds, fld) for fld in os.listdir(flds)
if conditions[os.path.basename(fld)] == "homecage_control"
]
groupA.sort()
groupB = [
os.path.join(flds, fld) for fld in os.listdir(flds)
if conditions[os.path.basename(fld)] == "CNO_control_no_reversal"
]
groupB.sort()
groupC = [
os.path.join(flds, fld) for fld in os.listdir(flds)
if conditions[os.path.basename(fld)] == "CNO_control_reversal"
]
groupC.sort()
groupD = [
os.path.join(flds, fld) for fld in os.listdir(flds)
if conditions[os.path.basename(fld)] == "DREADDs"
]
groupC.sort()
group_a = [xx + "/cells_heatmap_60um_erosion.tif" for xx in groupA]
group_b = [xx + "/cells_heatmap_60um_erosion.tif" for xx in groupB]
group_c = [xx + "/cells_heatmap_60um_erosion.tif" for xx in groupC]
group_d = [xx + "/cells_heatmap_60um_erosion.tif" for xx in groupD]
grp_a = stat.readDataGroup(group_a)
grp_b = stat.readDataGroup(group_b)
grp_c = stat.readDataGroup(group_c)
grp_d = stat.readDataGroup(group_d)
#Generated average and standard deviation maps
##############################################
grp_aa = np.mean(grp_a, axis=0)
grp_as = np.std(grp_a, axis=0)
grp_ba = np.mean(grp_b, axis=0)
grp_bs = np.std(grp_b, axis=0)
grp_ca = np.mean(grp_c, axis=0)
grp_cs = np.std(grp_c, axis=0)
grp_da = np.mean(grp_d, axis=0)
grp_ds = np.std(grp_d, axis=0)
io.writeData(os.path.join(src, "group_a_mean.raw"),
rsp.sagittalToCoronalData(grp_aa))
io.writeData(os.path.join(src, "group_a_std.raw"),
rsp.sagittalToCoronalData(grp_as))
io.writeData(os.path.join(src, "group_b_mean.raw"),
rsp.sagittalToCoronalData(grp_ba))
io.writeData(os.path.join(src, "group_b_std.raw"),
rsp.sagittalToCoronalData(grp_bs))
io.writeData(os.path.join(src, "group_c_mean.raw"),
rsp.sagittalToCoronalData(grp_ca))
io.writeData(os.path.join(src, "group_c_std.raw"),
rsp.sagittalToCoronalData(grp_cs))
io.writeData(os.path.join(src, "group_d_mean.raw"),
rsp.sagittalToCoronalData(grp_da))
io.writeData(os.path.join(src, "group_d_std.raw"),
rsp.sagittalToCoronalData(grp_ds))
#Generate the p-values map
##########################
#first comparison
#pcutoff: only display pixels below this level of significance
pvals, psign = stat.tTestVoxelization(grp_a.astype("float"),
grp_d.astype("float"),
signed=True,
pcutoff=0.05)
#color the p-values according to their sign (defined by the sign of the difference of the means between the 2 groups)
pvalsc = stat.colorPValues(pvals, psign, positive=[0, 1], negative=[1, 0])
io.writeData(os.path.join(src, "pvalues_homecage_control_vs_DREADDs.tif"),
rsp.sagittalToCoronalData(pvalsc.astype("float32")))
#second comparison
pvals, psign = stat.tTestVoxelization(grp_a.astype("float"),
grp_b.astype("float"),
signed=True,
pcutoff=0.05)
pvalsc = stat.colorPValues(pvals, psign, positive=[0, 1], negative=[1, 0])
io.writeData(
os.path.join(
src, "pvalues_homecage_control_vs_CNO_control_no_reversal.tif"),
rsp.sagittalToCoronalData(pvalsc.astype("float32")))
#third comparison
pvals, psign = stat.tTestVoxelization(grp_b.astype("float"),
grp_c.astype("float"),
signed=True,
pcutoff=0.05)
pvalsc = stat.colorPValues(pvals, psign, positive=[0, 1], negative=[1, 0])
io.writeData(
os.path.join(
src,
"pvalues_CNO_control_no_reversal_vs_CNO_control_reversal.tif"),
rsp.sagittalToCoronalData(pvalsc.astype("float32")))
#fourth comparison
pvals, psign = stat.tTestVoxelization(grp_c.astype("float"),
grp_d.astype("float"),
signed=True,
pcutoff=0.05)
pvalsc = stat.colorPValues(pvals, psign, positive=[0, 1], negative=[1, 0])
io.writeData(
os.path.join(src, "pvalues_CNO_control_reversal_vs_DREADDs.tif"),
rsp.sagittalToCoronalData(pvalsc.astype("float32")))
io.writeData(os.path.join(baseDirectory, 'group1_mean.raw'),
rsp.sagittalToCoronalData(g1a))
io.writeData(os.path.join(baseDirectory, 'group1_std.raw'),
rsp.sagittalToCoronalData(g1s))
io.writeData(os.path.join(baseDirectory, 'group2_fast_mean.raw'),
rsp.sagittalToCoronalData(g2a))
io.writeData(os.path.join(baseDirectory, 'group2_fast_std.raw'),
rsp.sagittalToCoronalData(g2s))
#Generate the p-values map
##########################
#pcutoff: only display pixels below this level of significance
pvals, psign = stat.tTestVoxelization(g1.astype('float'),
g2.astype('float'),
signed=True,
pcutoff=0.05)
#color the p-values according to their sign (defined by the sign of the difference of the means between the 2 groups)
pvalsc = stat.colorPValues(pvals, psign, positive=[0, 1], negative=[1, 0])
io.writeData(os.path.join(baseDirectory, 'pvalues.tif'),
rsp.sagittalToCoronalData(pvalsc.astype('float32')))
#############################################################################
# Regions-based statistics:
###########################
group1 = [
'/home/yourname/experiment/sample1/cells_transformed_to_Atlas.npy',
'/home/yourname/experiment/sample2/cells_transformed_to_Atlas.npy',
io.writeData(os.path.join(pvaldst, "dorsal_up/observers_mean.raw"),
rsp.sagittalToCoronalData(oa))
io.writeData(os.path.join(pvaldst, "dorsal_up/observers_std.raw"),
rsp.sagittalToCoronalData(ostd))
io.writeData(os.path.join(pvaldst, "dorsal_up/demonstrators_mean.raw"),
rsp.sagittalToCoronalData(da))
io.writeData(os.path.join(pvaldst, "dorsal_up/demonstrators_std.raw"),
rsp.sagittalToCoronalData(dstd))
#Generate the p-values map
##########################
#first comparison
#pcutoff: only display pixels below this level of significance
pvals, psign = stat.tTestVoxelization(o.astype("float"),
c.astype("float"),
signed=True,
pcutoff=0.05)
#color the p-values according to their sign (defined by the sign of the difference of the means between the 2 groups)
pvalsc = stat.colorPValues(pvals, psign, positive=[0, 1], negative=[1, 0])
dst = os.path.join(
pvaldst, "dorsal_up/observer_v_control/pvalues_observer_v_control.tif")
makedir(os.path.dirname(dst))
io.writeData(dst, rsp.sagittalToCoronalData(pvalsc.astype("float32")))
#second comparison
pvals, psign = stat.tTestVoxelization(d.astype("float"),
c.astype("float"),
signed=True,
pcutoff=0.05)
pvalsc = stat.colorPValues(pvals, psign, positive=[0, 1], negative=[1, 0])