def ccd_analysis_of_spikeins(adata_spikeins, perms):
'''ERCC spikeins were used as an internal control. We can use them to get an idea of the noise for this analysis.'''
expression_data_spike = adata_spikeins.X # log normalized
normalized_exp_data_spike = (expression_data_spike.T / np.max(expression_data_spike, axis=0)[:,None]).T
fucci_time_inds_spike = np.argsort(adata_spikeins.obs["fucci_time"])
# fucci_time_sort_spike = np.take(np.array(adata_spikeins.obs["fucci_time"]), fucci_time_inds_spike)
norm_exp_sort_spike = np.take(normalized_exp_data_spike, fucci_time_inds_spike, axis=0)
moving_averages_spike = np.apply_along_axis(MovingAverages.mvavg, 0, norm_exp_sort_spike, 100)
cell_cycle_variance_spike = np.apply_along_axis(np.var, 0, moving_averages_spike)
total_variance_spike = np.apply_along_axis(np.var, 0, norm_exp_sort_spike)
total_cv_spike = np.apply_along_axis(scipy.stats.variation, 0, norm_exp_sort_spike)
percent_ccd_variance_spike = cell_cycle_variance_spike / total_variance_spike
# avg_expression_spike = np.apply_along_axis(np.median, 0, norm_exp_sort_spike)
print("Percent variance of spike-in:")
print(f"mean +/- stdev of spike-in variance explained by cell cycle: {np.mean(percent_ccd_variance_spike)} +/- {np.std(percent_ccd_variance_spike)}")
print(f"median of spike-in variance explained by cell cycle: {np.median(percent_ccd_variance_spike)}")
percent_ccd_variance_rng_spike = []
for iii, perm in enumerate(perms):
if iii % 1000 == 0: print(f"permutation {iii}")
norm_exp_sort_perm_spike = np.take(normalized_exp_data_spike, perm, axis=0)
moving_averages_perm_spike = np.apply_along_axis(MovingAverages.mvavg, 0, norm_exp_sort_perm_spike, WINDOW)
percent_ccd_variance_rng_spike.append(
np.var(moving_averages_perm_spike, axis=0) / np.var(norm_exp_sort_perm_spike, axis=0))
percent_ccd_variance_rng_spike = np.asarray(percent_ccd_variance_rng_spike)
mean_diff_from_rng_spike = np.mean((percent_ccd_variance_spike - percent_ccd_variance_rng_spike).T, 1)
print("Percent additional variance CCD than random of spike-in")
print(f"mean +/- stdev of spike-in mean additional percent variance from random: {np.mean(mean_diff_from_rng_spike)} +/- {np.std(mean_diff_from_rng_spike)}")
print(f"median of spike-in addtional variance explained by cell cycle than random: {np.median(mean_diff_from_rng_spike)}")
utils.general_boxplot((percent_ccd_variance_spike, mean_diff_from_rng_spike), ("Percent Variance\nCCD Spike-In", "Percent Additional\nCCD Variance Spike-In"), "", "Percent Variance CCD", "", True, "figures/RNASpikeinVarianceBoxplot.png")
def analyze_cnv_calls(adata, ccdtranscript):
'''Take results from cnvkit calls to analyze effects of copy number variation'''
cnsresults = pd.read_csv("input/RNAData/CnsCallSummary.tsv", sep="\t")
cnsresults_gene = cnsresults["gene"]
cnsresults_allgenes = np.concatenate([g.split(',') for g in cnsresults_gene])
genenamedict = utils.getGeneNameDict()
adata_names = np.array(utils.ccd_gene_names_gapped(adata.var_names[ccdtranscript], genenamedict))
adata_ccd_isInCns = adata[np.isin(adata.obs["Well_Plate"], cnsresults.columns),
np.arange(len(ccdtranscript))[ccdtranscript][np.isin(adata_names, cnsresults_allgenes)]]
adata_ccd_isInCns_names = utils.ccd_gene_names_gapped(adata_ccd_isInCns.var_names, genenamedict)
cnsresultIdx = np.array([[n in genelist for genelist in cnsresults_gene] for n in adata_ccd_isInCns_names])
geneInJustOneList = np.array([sum(x) == 1 for x in cnsresultIdx])
adata_ccd_isInCns_inJustOneList = adata_ccd_isInCns[:, geneInJustOneList]
adata_ccd_isInCns_inJustOneList_names = utils.ccd_gene_names_gapped(adata_ccd_isInCns_inJustOneList.var_names, genenamedict)
cnsresultIdx_inJustOneList = cnsresultIdx[geneInJustOneList]
cnsResultsCellData = np.array(cnsresults)[:, np.isin(cnsresults.columns, adata_ccd_isInCns_inJustOneList.obs["Well_Plate"])]
# evaluate consistency of CNVs
heatmap = np.zeros(cnsResultsCellData.T.shape)
heatmap[cnsResultsCellData.T == -5] = -1
heatmap[(cnsResultsCellData.T > -5) & (cnsResultsCellData.T < 1)] = 0
heatmap[cnsResultsCellData.T == 1] = 1
heatmap[cnsResultsCellData.T == 2] = 2
heatmap[cnsResultsCellData.T > 2] = 3
clustergrid = sbn.clustermap(heatmap[:,:-8], col_cluster=False)
plt.savefig("figures/CnvConsistency.pdf")
plt.close()
# heatmaps for phases
adata_idx = np.array([list(adata.obs["Well_Plate"]).index(wp) for wp in cnsresults.columns[np.isin(cnsresults.columns,
adata_ccd_isInCns_inJustOneList.obs["Well_Plate"])]])
sbn.heatmap([adata_ccd_isInCns.obs["phase"][np.asarray(clustergrid.dendrogram_row.reordered_ind)] == "G1",
adata_ccd_isInCns.obs["phase"][np.asarray(clustergrid.dendrogram_row.reordered_ind)] == "S-ph",
adata_ccd_isInCns.obs["phase"][np.asarray(clustergrid.dendrogram_row.reordered_ind)] == "G2M"],
yticklabels=["G1", "S", "G2"])
plt.savefig("figures/CnvConsistencyPhases.pdf")
plt.close()
# is there enrichment for phase in the highly amplified genes?
# print(adata_ccd_isInCns.obs["phase"][clustergrid.dendrogram_row.reordered_ind[:100]].value_counts())
# yes, so is there correlation?
x = adata_ccd_isInCns.obs["fucci_time"]
y = np.mean(cnsResultsCellData, axis=0)
linearModel = scipy.stats.linregress(np.asarray(x).astype(float), np.asarray(y).astype(float))
plt.scatter(x * fucci.TOT_LEN, y)
plt.scatter(x * fucci.TOT_LEN, linearModel.intercept + x * linearModel.slope)
plt.xlabel("Cell Division Time, hrs")
plt.ylabel("Mean CNV of All Chromosome Arms")
plt.savefig("figures/CnvCorrelation.pdf")
plt.close()
print(f"{linearModel[3]}: p-value for nonzero slope by two-sided t test")
residualLinearModel = scipy.stats.linregress(np.asarray(x).astype(float), np.asarray(y - (linearModel.intercept + x * linearModel.slope)).astype(float))
residualNormality = scipy.stats.normaltest(np.asarray(y - (linearModel.intercept + x * linearModel.slope)))
print(f"{residualLinearModel[3]}: p-value for nonzero slope of residuals by two-sided t-test")
print(f"{residualNormality[1]}: p-value for normality of residuals")
# what if we only look at one phase? G1 before doubling? for all genes?
adata_names = np.array(utils.ccd_gene_names_gapped(adata.var_names, genenamedict))
adata_ccd_isInCns = adata[np.isin(adata.obs["Well_Plate"], cnsresults.columns) & (adata.obs["phase"] == "G1"), np.arange(len(adata_names))[np.isin(adata_names, cnsresults_allgenes)]]
adata_ccd_isInCns_names = utils.ccd_gene_names_gapped(adata_ccd_isInCns.var_names, genenamedict)
cnsresultIdx = np.array([[n in genelist for genelist in cnsresults_gene] for n in adata_ccd_isInCns_names])
geneInJustOneList = np.array([sum(x) == 1 for x in cnsresultIdx])
adata_ccd_isInCns_inJustOneList = adata_ccd_isInCns[:, geneInJustOneList]
adata_ccd_isInCns_inJustOneList_names = utils.ccd_gene_names_gapped(adata_ccd_isInCns_inJustOneList.var_names, genenamedict)
cnsresultIdx_inJustOneList = cnsresultIdx[geneInJustOneList]
cnsResultsCellData = np.array(cnsresults)[:, np.isin(cnsresults.columns, adata_ccd_isInCns_inJustOneList.obs["Well_Plate"])]
cnvAmplified, cnvPvalOneSided = [],[]
cnvDeleted, cnvPvalOneSidedDeleted = [],[]
amplifiedTpmsAll, neutralTpmsAll, deletionTpmsAll = [],[],[]
for ii, tpm in enumerate(adata_ccd_isInCns.X.T[geneInJustOneList]):
cnv = np.concatenate(cnsResultsCellData[cnsresultIdx_inJustOneList[ii],:])
missingData = cnv == -5
amplified, amplifiedTpms = cnv[~missingData & (cnv > 1)], tpm[~missingData & (cnv > 1)]
neutral, neutralTpms = cnv[~missingData & (cnv == 1)], tpm[~missingData & (cnv == 1)]
deletion, deletionTpms = cnv[~missingData & (cnv < 1)], tpm[~missingData & (cnv < 1)]
cnvAmplified.append(np.median(amplifiedTpms) > np.median(tpm[~missingData]))
cnvPvalOneSided.append(scipy.stats.kruskal(amplifiedTpms, neutralTpms)[1] * 2)
cnvDeleted.append(np.median(deletionTpms) < np.median(tpm[~missingData]))
cnvPvalOneSidedDeleted.append(scipy.stats.kruskal(deletionTpms, neutralTpms)[1] * 2)
amplifiedTpmsAll.extend(amplifiedTpms)
neutralTpmsAll.extend(neutralTpms)
deletionTpmsAll.extend(deletionTpms)
cnvAmplified = np.asarray(cnvAmplified)
cnvTestPvals_BH, cnvTestPvals_rejectBH = utils.benji_hoch(0.01, cnvPvalOneSided)
cnvTestPvalsDel_BH, cnvTestPvalsDel_rejectBH = utils.benji_hoch(0.01, cnvPvalOneSidedDeleted)
print(f"{sum(cnvAmplified & cnvTestPvals_rejectBH)}: number of novel CCD with significantly higher expression with amplified CNVs than neutral")
print(f"{sum(cnvDeleted & cnvTestPvalsDel_rejectBH)}: number of novel CCD with significantly higher expression with amplified CNVs than neutral")
utils.general_boxplot([amplifiedTpmsAll, neutralTpmsAll, deletionTpmsAll],
["amplified", "neutral", "deletion"], "", "logTPMs", "", False, "figures/CNVStateBoxplot.pdf")
print(f"Of {len(cnvAmplified)} genes:")
print(f"{scipy.stats.kruskal(amplifiedTpmsAll, neutralTpmsAll, deletionTpmsAll)[1]}: kruskal two sided pval that there's a difference between the three")
print(f"{scipy.stats.kruskal(amplifiedTpmsAll, neutralTpmsAll)[1]}: kruskal two sided pval that there's a difference between amplified/neutral")
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_peak_expression_prot_v_rna(adata, wp_ensg, ccd_comp, ccdtranscript,
wp_max_pol, wp_max_pol_ccd,
sorted_maxpol_array, max_moving_avg_pol,
sorted_max_moving_avg_pol_ccd):
'''Compare the time of peak expression of protein and RNA'''
prot_ccd_ensg = list(wp_ensg[ccd_comp])
rna_ccd_ensg = list(adata.var_names[ccdtranscript])
both_ccd_ensg = np.intersect1d(prot_ccd_ensg, rna_ccd_ensg)
both_prot_ccd_idx = np.array(
[prot_ccd_ensg.index(ensg) for ensg in both_ccd_ensg])
both_rna_ccd_idx = np.array(
[rna_ccd_ensg.index(ensg) for ensg in both_ccd_ensg])
insct_prot_max_pol_ccd = wp_max_pol_ccd[both_prot_ccd_idx]
insct_rna_max_pol_ccd = sorted_max_moving_avg_pol_ccd[both_rna_ccd_idx]
diff_max_pol = insct_prot_max_pol_ccd - insct_rna_max_pol_ccd
#% Sanity check: double check that the names line up
prot_names = np.array(prot_ccd_ensg)[both_prot_ccd_idx]
rna_names = np.array(rna_ccd_ensg)[both_rna_ccd_idx]
print(f"The name arrays are the same: {all(prot_names == rna_names)}")
# Alluvial plot showing RNA-protein phase of peak expression for each genes
peak_expression_alluvial(diff_max_pol, insct_rna_max_pol_ccd,
insct_prot_max_pol_ccd)
# Histogram for peak expression
utils.general_histogram(
diff_max_pol * fucci.TOT_LEN,
"Delay in peak protein expression from peak RNA expression, hrs",
"Count of CCD Proteins", 0.5, "figures/DelayPeakProteinRNA.pdf")
# Scatter for peak expression with colorbar for the delay
peak_expression_delay_scatter(insct_rna_max_pol_ccd,
insct_prot_max_pol_ccd, diff_max_pol)
# Boxplot for delay of peak expression
utils.general_boxplot((insct_prot_max_pol_ccd * fucci.TOT_LEN,
insct_rna_max_pol_ccd * fucci.TOT_LEN),
("Protein", "RNA"), "", "Peak Expression, hrs", "",
True, "figures/DelayPeakProteinRNA_boxplot.png")
print(f"Count of prot CCD genes: {len(prot_ccd_ensg)}")
print(f"Count of CCD RNA genes: {len(rna_ccd_ensg)}")
print(
f"Count of intersection betweeen CCD prot and CCD RNA: {len(both_ccd_ensg)}"
)
print(
f"Median delay of RNA and protein expression time for CCD proteins: {fucci.TOT_LEN * np.median(diff_max_pol)}"
)
print(
f"Median RNA expression time for CCD proteins: {fucci.TOT_LEN * np.median(insct_rna_max_pol_ccd)}"
)
print(
f"Median protein expression time for CCD proteins: {fucci.TOT_LEN * np.median(insct_prot_max_pol_ccd)}"
)
t, p = scipy.stats.kruskal(insct_rna_max_pol_ccd, insct_prot_max_pol_ccd)
print(
f"One-sided kruskal for median protein expression time higher than median RNA expression time: {2*p}"
)
t, p = scipy.stats.ttest_1samp(diff_max_pol, 0)
print(
f"One-sided, one-sample t-test for mean delay in protein expression larger than zero: {2*p}"
)
#% Output tables
pd.DataFrame({
"gene": wp_ensg,
"max_pol_protein": wp_max_pol,
"max_time_protein": wp_max_pol * fucci.TOT_LEN
}).to_csv("output/max_pol_protein.csv", index=False)
pd.DataFrame({
"gene": adata.var_names,
"max_pol_rna": max_moving_avg_pol,
"max_time_rna": max_moving_avg_pol * fucci.TOT_LEN
}).to_csv("output/max_pol_rna.csv", index=False)
pd.DataFrame({
"gene": both_ccd_ensg,
"insct_prot_max_pol_ccd": insct_prot_max_pol_ccd,
"insct_rna_max_pol_ccd": insct_rna_max_pol_ccd,
"diff_max_pol": diff_max_pol
}).to_csv("output/diff_max_pol.csv", index=False)
#% Figures of merit
peaked_after_g1_prot = sorted_maxpol_array * fucci.TOT_LEN > fucci.G1_LEN
wp_ensg_counts_ccd = np.array([
sum([eeee == ensg for eeee in wp_ensg[ccd_comp]])
for ensg in wp_ensg[ccd_comp]
])
with open("output/figuresofmerit.txt", "a") as file:
fom = "--- temporal delay\n\n"
fom += f"significant delay in peak protein expression compared to transcript expression, {fucci.TOT_LEN * np.median(diff_max_pol)} hours on average" + "\n\n"
fom += f"G1 is the longest period of the cell cycle, in which the majority of RNAs ({100 * sum(sorted_max_moving_avg_pol_ccd * fucci.TOT_LEN <=fucci. G1_LEN) / len(sorted_max_moving_avg_pol_ccd)}%) peak in expression" + "\n\n"
fom += f"However, the majority ({100 * sum(peaked_after_g1_prot) / len(np.unique(wp_ensg[ccd_comp]))}%) of the proteins peaked towards the end of the cell cycle corresponding to the S&G2 phases" + "\n\n"
fom += f"The delay between peak RNA and protein expression for the 50 CCD proteins that also had CCD transcripts was {fucci.TOT_LEN * np.median(diff_max_pol)} hrs on average " + "\n\n"
fom += f"this delay indicates that it may take a little less than the same amount of time ({12 - fucci.TOT_LEN * np.median(diff_max_pol)} hrs) to produce a target metabolite after peak expression of an enzyme." + "\n\n"
fom += f"" + "\n\n"
fom += f"" + "\n\n"
fom += f"" + "\n\n"
print(fom)
file.write(fom)