ax.set(xlabel='Doubling Time (min)', ylabel='CTD repeats')
# assign intelligible strain names
strain_labels = ['26', '14', '12', '10', '9', '8']
plt.yticks(plt.yticks()[0], strain_labels)
plt.tight_layout()
plt.savefig('./figures/output/Fig2_growthrates.png')
###############################################################################
# RPB1/RPB3 nuclear fluorescence
###############################################################################
rpb1fluor = pd.read_csv('./data/mScarRPB1_052019_mean.csv')
fig, ax = plt.subplots(figsize=(14, 8))
ax = plot.ecdfbystrain('mean_intensity_nuc',
rpb1fluor,
ax=ax,
formal=True,
line_kws={
'alpha': 0.8,
'rasterized': True
})
ax.set(xlabel='RPB1 Nuclear Fluorescence (a.u.)',
ylabel='ECDF',
xlim=(230, 900))
plt.tight_layout()
plt.savefig('./figures/output/Fig2_rpb1fluor.svg')
rpb3fluor = pd.read_csv('./data/mScarRPB3_022020_mean.csv')
rpb3fluor = rpb3fluor[~(rpb3fluor.strain.isin([15, 16]))]
fig, ax = plt.subplots(figsize=(10, 8))
ax = plot.ecdfbystrain('mean_intensity_nuc',
rpb3fluor,
ax=ax,
growth,
ax=ax,
colors=[colors_ctd[c] for c in colors_ctd])
ax.set(xlabel='Doubling Time (min)', ylabel='CTD repeats')
# assign intelligible strain names
strain_labels = ['26', '14', '12', '10', '9', '8']
plt.yticks(plt.yticks()[0], strain_labels)
plt.tight_layout()
plt.savefig('./figures/output/Fig2_growthrates.png')
###############################################################################
# RPB1 nuclear fluorescence
###############################################################################
rpb1fluor = pd.read_csv('./data/mScarRPB1_05212019_quantiles.csv')
fig, ax = plt.subplots(figsize=(14, 8))
ax = plot.ecdfbystrain('0.95_quant_nc', rpb1fluor, ax=ax)
ax.set(xlabel='Nuclear Fluorescence (a.u.)', ylabel='ECDF', xlim=(100, 1250))
plt.tight_layout()
plt.savefig('./figures/output/Fig2_rpb1fluor_.svg')
###############################################################################
# RNA-seq
###############################################################################
# get sleuth data
sleuth = pd.read_csv('./data/sleuth_all.csv').dropna(axis=0)
# get unique coefficient id, as they occur in multiple models
sleuth['cid'] = sleuth['coef'] + sleuth['model']
# get q-value cumulative distribution by model and coefficient
qvalecdf_series = Parallel(n_jobs=n_jobs)(
delayed(ecdftools.compute_qvalecdf)(model, coef, _df)
vmin=np.min(traces.values), vmax=np.max(traces.values),
cbar_kws={'label': 'Normalized Fluorescence'})
plt.xticks(rotation=60)
ax.set(xlabel='', ylabel=plot.CTDr_dict[strain], yticks=[])
ax.set(xlabel='Time (min)')
plt.tight_layout()
plt.subplots_adjust(hspace=0.01)
plt.savefig('./figures/output/Fig3_pp7traces_afmhot.png')
###############################################################################
# Distribution of burst fluorescence for strains yQC21, yQC22 and yQC23
###############################################################################
parts_max = pp7data.groupby(['particle','mov_name','strain','CTDr']).mass_norm.max().reset_index()
fig, ax = plt.subplots(figsize=(10, 8))
ax = plot.ecdfbystrain('mass_norm', parts_max, ax=ax, formal=True, line_kws={'alpha':1, 'rasterized':True})
ax.set(xlabel='Normalized Fluorescence', ylabel='ECDF', xlim=(5.5, 10.5))
ax.set_xticks(np.arange(6, 11))
plt.tight_layout()
plt.savefig('./figures/output/Fig3_pp7movfluor.svg')
###############################################################################
# Distribution of interburst times for strains yQC21, yQC22 and yQC23
###############################################################################
interpeak = pd.read_csv('./data/pp7_interpeaktime.csv')
fig, ax = plt.subplots(figsize=(10, 8))
ax = plot.ecdfbystrain('time', interpeak, ax=ax, formal=True, line_kws={'alpha':1, 'rasterized':True})
ax.set(xlabel='Inter-burst time (s)', ylabel='ECDF', xlim=(-100, 2200))
plt.tight_layout()
plt.savefig('./figures/output/Fig3_pp7interburst.svg')
plt.xticks(plt.xticks()[0], strain_labels)
plt.tight_layout()
plt.savefig('./figures/output/FigS4_Kwon2013_DropletBinding.svg')
###############################################################################
# RPB3 nuclear fluorescence
###############################################################################
rpb3fluor = pd.read_csv('./data/mScarRPB3_022020_mean.csv')
rpb3fluor = rpb3fluor[~(rpb3fluor.strain.isin([6]))]
colors, patches = plot.get_palette(['26', '10'])
colors['10_TAF'] = '#823b3b'
colors['10_FUS'] = '#da6363'
patches = plot.get_patches(colors)
fig, ax = plt.subplots(figsize=(15, 8))
ax = plot.ecdfbystrain('mean_intensity_nuc',
rpb3fluor,
ax=ax,
formal=True,
line_kws={
'alpha': 0.8,
'rasterized': True
},
colors=colors,
patches=patches)
ax.set(xlabel='RPB3 Nuclear Fluorescence (a.u.)',
ylabel='ECDF',
xlim=(120, 600))
plt.tight_layout()
plt.savefig('./figures/output/FigS4_rpb3fluor.svg')
# mRNA molecules per cell ECDF from smFISH
###############################################################################
parts = pd.read_csv(
'/Users/porfirio/lab/yeastEP/figures_paper/data/smFISH_GAL10_GAL3_mRNAperCell.csv'
)
parts = parts[parts.strain.isin(['TL47', 'yQC62', 'yQC15', 'yQC16'])]
parts = parts[parts.date == 9132019] # only determined threshold for this exp
order = ['TL47', 'yQC62', 'yQC16', 'yQC15']
colors = {
s: c
for s, c in zip(order, ['#326976', '#98b4ba', '#823b3b', '#da6363'])
}
for gene, _parts in parts.groupby(['gene', 'date']):
fig, ax = plt.subplots(figsize=(9, 8))
ax = plot.ecdfbystrain('num_spots',
_parts,
groupby='strain',
colors=colors,
ax=ax,
formal=True,
line_kws={
'alpha': 1,
'rasterized': True
})
ax.set(xlabel='mRNA molecules per cell',
ylabel='ECDF',
title=gene[0].upper())
plt.tight_layout()
plt.savefig('./figures/output/FigS5_ECDF_mRNAperCell_{}.svg'.format(gene))
ax.plot(prob_f1.prob_thresh, prob_f1.f1score_False, color=colors[False])
ax.plot(prob_f1.prob_thresh, prob_f1.f1score_True, color=colors[True])
ax.set(ylabel='F1 score', xlabel='GPC Probability threshold')
ax.axvline(0.5, ls='--', c='k', alpha=0.5)
plt.legend(handles=plot.get_patches(colors))
plt.tight_layout()
plt.savefig('./figures/output/FigS2_f1score_prob.svg')
###############################################################################
# Nuclear fluorescence by strain
###############################################################################
fig, ax = plt.subplots(figsize=(10, 8))
plot.ecdfbystrain('nuc_fluor',
pp7data[pp7data.laser_power==150].drop_duplicates(['roi','mov_name']),
ax=ax, formal=True, line_kws={'alpha':1, 'rasterized':True})
ax.set(xlabel='Nuclear Fluorescence (a.u.)', ylabel='ECDF')
plt.tight_layout()
plt.savefig('./figures/output/FigS2_pp7Nucfluor.svg')
###############################################################################
# Correlation between nuclear fluorescence and burst fluorescence
###############################################################################
fluor_metrics = (('raw_mass','Raw Fluorescence (a.u.)'),('mass_norm','Normalized Fluorescence'))
for fluor, fluor_name in fluor_metrics:
fig, ax = plt.subplots(figsize=(10, 8))
pp7data.plot(x='nuc_fluor', y=fluor, kind='hexbin', colormap='viridis', ax=ax, rasterized=True)
ax.set(ylabel=fluor_name, xlabel='Nuclear fluorescence (a.u.)', xticks=np.arange(150, 350, 50))
plt.tight_layout()
plt.savefig('./figures/output/FigS2_pp7{}vNucfluor.svg'.format(fluor))