def plot_markers_vs_reads(adata):
utils.general_scatter(adata.obs["fucci_time"], adata.X[:,list(adata.var_names).index("ENSG00000112312")],
"Fucci Pseudotime", "GMNN log10 TPM", "figures/GMNN_timeVsReadsScatter.png", False)
utils.general_scatter(adata.obs["Green530"], adata.X[:,list(adata.var_names).index("ENSG00000112312")],
"GMNN FUCCI Marker Intensity", "GMNN log10 TPM", "figures/GMNN_markerVsReadsScatter.png", False)
utils.general_scatter(adata.obs["fucci_time"], adata.X[:,list(adata.var_names).index("ENSG00000167513")],
"Fucci Pseudotime", "GMNN log10 TPM", "figures/CDT1_timeVsReadsScatter.png", False)
utils.general_scatter(adata.obs["Red585"], adata.X[:,list(adata.var_names).index("ENSG00000167513")],
"CDT1 FUCCI Marker Intensity", "CDT1 log10 TPM", "figures/CDT1_markerVsReadsScatter.png", False)
def calculate_variation(use_log, u_well_plates, wp_iscell, wp_isnuc, wp_iscyto,
pol_sort_well_plate, pol_sort_ab_cell, pol_sort_ab_nuc,
pol_sort_ab_cyto, pol_sort_mt_cell,
pol_sort_well_plate_imgnb):
'''Calculate overall variation of protein staining intensity in single cells'''
var_cell, var_nuc, var_cyto, var_mt = [], [], [], [
] # mean intensity variances per antibody
cv_cell, cv_nuc, cv_cyto, cv_mt = [], [], [], []
gini_cell, gini_nuc, gini_cyto, gini_mt = [], [], [], [
] # mean intensity ginis per antibody
mean_mean_cell, mean_mean_nuc, mean_mean_cyto, mean_mean_mt = [], [], [], [
] # mean mean-intensity
cell_counts = []
wpi_img = []
gini_cell_img, gini_nuc_img, gini_cyto_img, gini_mt_img = [], [], [], [
] # mean intensity g per field of view
var_cell_img, var_nuc_img, var_cyto_img, var_mt_img = [], [], [], [
] # mean intensity variances per field of view
cv_cell_img, cv_nuc_img, cv_cyto_img, cv_mt_img = [], [], [], []
# The variance needs to be calculated separately for each well because they all have different numbers of cells
for well in u_well_plates:
curr_well_inds = pol_sort_well_plate == well
curr_ab_cell = pol_sort_ab_cell[
curr_well_inds] if not use_log else np.log10(
pol_sort_ab_cell[curr_well_inds])
curr_ab_nuc = pol_sort_ab_nuc[
curr_well_inds] if not use_log else np.log10(
pol_sort_ab_nuc[curr_well_inds])
curr_ab_cyto = pol_sort_ab_cyto[
curr_well_inds] if not use_log else np.log10(
pol_sort_ab_cyto[curr_well_inds])
curr_mt_cell = pol_sort_mt_cell[
curr_well_inds] if not use_log else np.log10(
pol_sort_mt_cell[curr_well_inds])
cell_counts.append(len(curr_ab_cell))
var_cell.append(np.var(curr_ab_cell))
var_nuc.append(np.var(curr_ab_nuc))
var_cyto.append(np.var(curr_ab_cyto))
var_mt.append(np.var(curr_mt_cell))
cv_cell.append(scipy.stats.variation(curr_ab_cell))
cv_nuc.append(scipy.stats.variation(curr_ab_nuc))
cv_cyto.append(scipy.stats.variation(curr_ab_cyto))
cv_mt.append(scipy.stats.variation(curr_mt_cell))
gini_cell.append(utils.gini(curr_ab_cell))
gini_nuc.append(utils.gini(curr_ab_nuc))
gini_cyto.append(utils.gini(curr_ab_cyto))
gini_mt.append(utils.gini(curr_mt_cell))
# Save the mean mean intensities
mean_mean_cell.append(np.mean(curr_ab_cell))
mean_mean_nuc.append(np.mean(curr_ab_nuc))
mean_mean_cyto.append(np.mean(curr_ab_cyto))
mean_mean_mt.append(np.mean(curr_mt_cell))
curr_well_plate_imgnbs = pol_sort_well_plate_imgnb[curr_well_inds]
curr_wpi_img = []
curr_gini_cell_img, curr_gini_nuc_img, curr_gini_cyto_img, curr_gini_mt_img = [],[],[],[] # mean intensity variances per field of view
curr_var_cell_img, curr_var_nuc_img, curr_var_cyto_img, curr_var_mt_img = [],[],[],[] # mean intensity variances per field of view
curr_cv_cell_img, curr_cv_nuc_img, curr_cv_cyto_img, curr_cv_mt_img = [], [], [], []
for wpi in np.unique(curr_well_plate_imgnbs):
curr_wpis = pol_sort_well_plate_imgnb == wpi
curr_ab_cell = pol_sort_ab_cell[
curr_wpis] if not use_log else np.log10(
pol_sort_ab_cell[curr_wpis])
curr_ab_nuc = pol_sort_ab_nuc[
curr_wpis] if not use_log else np.log10(
pol_sort_ab_nuc[curr_wpis])
curr_ab_cyto = pol_sort_ab_cyto[
curr_wpis] if not use_log else np.log10(
pol_sort_ab_cyto[curr_wpis])
curr_mt_cell = pol_sort_mt_cell[
curr_wpis] if not use_log else np.log10(
pol_sort_mt_cell[curr_wpis])
curr_wpi_img.append(wpi)
curr_var_cell_img.append(np.var(curr_ab_cell))
curr_var_nuc_img.append(np.var(curr_ab_nuc))
curr_var_cyto_img.append(np.var(curr_ab_cyto))
curr_var_mt_img.append(np.var(curr_mt_cell))
curr_gini_cell_img.append(utils.gini(curr_ab_cell))
curr_gini_nuc_img.append(utils.gini(curr_ab_nuc))
curr_gini_cyto_img.append(utils.gini(curr_ab_cyto))
curr_gini_mt_img.append(utils.gini(curr_mt_cell))
curr_cv_cell_img.append(scipy.stats.variation(curr_ab_cell))
curr_cv_nuc_img.append(scipy.stats.variation(curr_ab_nuc))
curr_cv_cyto_img.append(scipy.stats.variation(curr_ab_cyto))
curr_cv_mt_img.append(scipy.stats.variation(curr_mt_cell))
wpi_img.append(curr_wpi_img)
var_cell_img.append(curr_var_cell_img)
var_nuc_img.append(curr_var_nuc_img)
var_cyto_img.append(curr_var_cyto_img)
var_mt_img.append(curr_var_mt_img)
gini_cell_img.append(curr_gini_cell_img)
gini_nuc_img.append(curr_gini_nuc_img)
gini_cyto_img.append(curr_gini_cyto_img)
gini_mt_img.append(curr_gini_mt_img)
cv_cell_img.append(curr_cv_cell_img)
cv_nuc_img.append(curr_cv_nuc_img)
cv_cyto_img.append(curr_cv_cyto_img)
cv_mt_img.append(curr_cv_mt_img)
print(
"Plotting average intensities of proteins and microtubules by batch.")
plot_average_intensities_by_batch(u_well_plates, mean_mean_cell,
mean_mean_nuc, mean_mean_cyto,
mean_mean_mt, wp_iscell, wp_isnuc,
wp_iscyto)
print("Making general plots for variance, CV, and gini by compartment")
var_cell, var_nuc, var_cyto, var_mt = np.array(var_cell), np.array(
var_nuc), np.array(var_cyto), np.array(var_mt)
gini_cell, gini_nuc, gini_cyto, gini_mt = np.array(gini_cell), np.array(
gini_nuc), np.array(gini_cyto), np.array(gini_mt)
cv_cell, cv_nuc, cv_cyto, cv_mt = np.array(cv_cell), np.array(
cv_nuc), np.array(cv_cyto), np.array(cv_mt)
utils.general_boxplot(
(var_cell, var_cyto, var_nuc, var_mt),
("var_cell", "var_cyto", "var_nuc", "var_mt"), "Metacompartment",
f"Variance using {'log' if use_log else 'natural'} intensity values",
"", True, "figures/VarianceBoxplot.png")
utils.general_boxplot(
(cv_cell, cv_cyto, cv_nuc, cv_mt),
("cv_cell", "cv_cyto", "cv_nuc", "cv_mt"), "Metacompartment",
f"Coeff. of Var. using {'log' if use_log else 'natural'} intensity values",
"", True, "figures/CVBoxplot.png")
utils.general_boxplot(
(gini_cell, gini_cyto, gini_nuc, gini_mt),
("gini_cell", "gini_cyto", "gini_nuc", "gini_mt"), "Metacompartment",
f"Gini using {'log' if use_log else 'natural'} intensity values", "",
True, "figures/GiniBoxplot.png")
print(
"Making general plots for variance, CV, and gini in the compartment the protein localizes to"
)
mean_mean_comp = utils.values_comp(mean_mean_cell, mean_mean_nuc,
mean_mean_cyto, wp_iscell, wp_isnuc,
wp_iscyto)
cv_comp = utils.values_comp(cv_cell, cv_nuc, cv_cyto, wp_iscell, wp_isnuc,
wp_iscyto)
gini_comp = utils.values_comp(gini_cell, gini_nuc, gini_cyto, wp_iscell,
wp_isnuc, wp_iscyto)
var_comp = utils.values_comp(var_cell, var_nuc, var_cyto, wp_iscell,
wp_isnuc, wp_iscyto)
utils.general_scatter(var_comp, var_mt, "var_comp", "var_mt",
"figures/var_comp_mt.png")
utils.general_scatter(cv_comp, cv_mt, "cv_comp", "cv_mt",
"figures/cv_comp_mt.png")
utils.general_scatter(gini_comp, gini_mt, "gini_comp", "gini_mt",
"figures/gini_comp_mt.png")
utils.general_scatter(var_comp, mean_mean_comp, "var_comp",
f"{'log10' if use_log else 'natural'} intensity",
"figures/VarianceVsIntensityComp.png")
print("Comparing image to sample variance")
var_comp_img = utils.values_comp(var_cell_img, var_nuc_img, var_cyto_img,
wp_iscell, wp_isnuc, wp_iscyto)
gini_comp_img = utils.values_comp(gini_cell_img, gini_nuc_img,
gini_cyto_img, wp_iscell, wp_isnuc,
wp_iscyto)
cv_comp_img = utils.values_comp(cv_cell_img, cv_nuc_img, cv_cyto_img,
wp_iscell, wp_isnuc, wp_iscyto)
utils.general_scatter(
np.concatenate([[var_comp[i]] * len(vvv)
for i, vvv in enumerate(var_comp_img)]),
np.concatenate(var_comp_img), "variance within compartment",
"variance for each image", "figures/VarianceByImage.png")
utils.general_scatter(
np.concatenate([[gini_comp[i]] * len(vvv)
for i, vvv in enumerate(gini_comp_img)]),
np.concatenate(gini_comp_img), "gini within compartment",
"gini for each image", "figures/GiniByImage.png")
utils.general_scatter(
np.concatenate([[cv_comp[i]] * len(vvv)
for i, vvv in enumerate(cv_comp_img)]),
np.concatenate(cv_comp_img), "cv within compartment",
"cv for each image", "figures/CVByImage.png")
print(
np.concatenate(wpi_img)[np.argmax(np.concatenate(var_comp_img))] +
": the image with the max variance")
plt.hist(
np.concatenate(
[vvv / var_comp[i] for i, vvv in enumerate(var_comp_img)]))
# plt.show()
plt.close()
high_var_img = np.concatenate(wpi_img)[np.concatenate(
[vvv > 4 * var_comp[i] for i, vvv in enumerate(var_comp_img)])]
print(
f"{high_var_img}: the images with greater than 4x the variance of the whole sample"
)
norm_cv_img = np.concatenate(
[vvv / cv_comp[i] for i, vvv in enumerate(cv_comp_img)])
plt.hist(norm_cv_img)
# plt.show()
plt.close()
cutoff = np.mean(norm_cv_img) + 3 * np.std(norm_cv_img)
high_cv_img = np.concatenate(wpi_img)[norm_cv_img > cutoff]
print(
f"{high_cv_img}: the images with greater than 4x the variance of the whole sample"
)
np.intersect1d(high_var_img, high_cv_img)
# Pickle and return main results
utils.np_save_overwriting("output/pickles/mean_mean_comp.npy",
mean_mean_comp)
utils.np_save_overwriting("output/pickles/cv_comp.npy", cv_comp)
utils.np_save_overwriting("output/pickles/gini_comp.npy", gini_comp)
utils.np_save_overwriting("output/pickles/var_comp.npy", var_comp)
utils.np_save_overwriting("output/pickles/cv_cell.npy", cv_cell)
utils.np_save_overwriting("output/pickles/gini_cell.npy", gini_cell)
utils.np_save_overwriting("output/pickles/var_cell.npy", var_cell)
return mean_mean_comp, var_comp, gini_comp, cv_comp, var_cell, gini_cell, cv_cell, var_mt, gini_mt, cv_mt
def compare_variances_prot_v_rna(adata, norm_exp_sort, wp_ensg, var_comp_prot,
gini_comp_prot, cv_comp_prot, var_cell_prot,
gini_cell_prot, cv_cell_prot):
'''Compare the measures of variance for RNA and protein and make scatterplots'''
total_variance_rna = np.var(norm_exp_sort, 0)
total_gini_rna = np.apply_along_axis(utils.gini, 0, norm_exp_sort)
total_cv_rna = np.apply_along_axis(scipy.stats.variation, 0, norm_exp_sort)
prot_ensg = list(wp_ensg)
rna_ensg = list(adata.var_names)
both_ensg = np.intersect1d(prot_ensg, rna_ensg)
both_prot_idx = np.array([prot_ensg.index(ensg) for ensg in both_ensg])
both_rna_idx = np.array([rna_ensg.index(ensg) for ensg in both_ensg])
insct_prot_variance_comp = var_comp_prot[both_prot_idx]
insct_prot_gini_comp = gini_comp_prot[both_prot_idx]
insct_prot_cv_comp = cv_comp_prot[both_prot_idx]
insct_prot_variance_cell = var_cell_prot[both_prot_idx]
insct_prot_gini_cell = gini_cell_prot[both_prot_idx]
insct_prot_cv_cell = cv_cell_prot[both_prot_idx]
insct_rna_variance = total_variance_rna[both_rna_idx]
insct_rna_gini = total_gini_rna[both_rna_idx]
insct_rna_cv = total_cv_rna[both_rna_idx]
utils.general_scatter(insct_rna_variance, insct_prot_variance_cell,
"Total RNA Variance",
"Total Protein Variance (Cell)",
"figures/ProteinRNAVariance.png")
utils.general_scatter(insct_rna_gini, insct_prot_gini_cell, "RNA Gini",
"Protein Gini (Cell)", "figures/ProteinRNAGini.png")
utils.general_scatter(insct_rna_cv, insct_prot_cv_cell, "RNA CV",
"Protein CV (Cell)", "figures/ProteinRNACV.png")
utils.general_scatter(insct_rna_variance, insct_prot_variance_comp,
"Total RNA Variance",
"Total Protein Variance (Compartment)",
"figures/ProteinRNAVariance.png")
utils.general_scatter(insct_rna_gini, insct_prot_gini_comp, "RNA Gini",
"Protein Gini (Compartment)",
"figures/ProteinRNAGini.png")
utils.general_scatter(insct_rna_cv, insct_prot_cv_comp, "RNA CV",
"Protein CV (Compartment)",
"figures/ProteinRNACV.png")
pd.DataFrame({
"gene": both_ensg,
"variance_rna": insct_rna_variance,
"gini_rna": insct_rna_gini,
"cv_rna": insct_rna_cv,
"variance_comp_prot": insct_prot_variance_comp,
"gini_comp_prot": insct_prot_gini_comp,
"cv_comp_prot": insct_prot_cv_comp,
"variance_cell_prot": insct_prot_variance_cell,
"gini_cell_prot": insct_prot_gini_cell,
"cv_cell_prot": insct_prot_cv_cell,
}).to_csv("output/VarianceRNAProtein.csv", index=False)