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foxp3_pileup_plots_for_wei.py
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foxp3_pileup_plots_for_wei.py
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from plotting_functions import init_subplots
import matplotlib.pyplot as plt
from aux_functions import *
import numpy as np
import pandas as pd
import seaborn as sns
def foxp3_at_nearby_atac_for_joris_wei(cols, foxp3_metadata_at_all_atac_sites, foxp3_values_at_all_atac_sites,
my_tss_df, pval_df_all, lfc_df_all, atac_bedtool, col_to_name, slopsize = 10_000,
xlabel = "ATAC peaks near DEGs", ylabel='Foxp3 Binding'):
tmp = foxp3_metadata_at_all_atac_sites['treg_yuri_foxp3_bw'].set_index('name')
treg_col, tcon_col = ['treg_yuri_foxp3_bw', 'tcon_yuri_foxp3_bw']
tss_df_set = set(my_tss_df['gene_name'])
fig, axs = init_subplots(6, 2, space=.4, fgsz=(6, 4))
for c, (wei_col, jor_col) in enumerate(zip(*cols)):
wei_up = pval_df_all.index[(pval_df_all[wei_col] < .05) & (lfc_df_all[wei_col] > 0)]
jor_up = pval_df_all.index[(pval_df_all[jor_col] < .05) & (lfc_df_all[jor_col] > 0)]
jor_down = pval_df_all.index[(pval_df_all[jor_col] < .05) & (lfc_df_all[jor_col] < 0)]
wei_down = pval_df_all.index[(pval_df_all[wei_col] < .05) & (lfc_df_all[wei_col] < 0)]
jor_up = [x for x in jor_up if x in tss_df_set]
wei_up = [x for x in wei_up if x in tss_df_set]
jor_down = [x for x in jor_down if x in tss_df_set]
wei_down = [x for x in wei_down if x in tss_df_set]
jor_up_inds = get_col(atac_bedtool.intersect(pbt.BedTool.from_dataframe(my_tss_df.set_index('gene_name').loc[wei_up]
).slop(b=slopsize, g='./annotations/chr_chromsizes',
), u=True), -1).astype(int)
wei_up_inds = get_col(atac_bedtool.intersect(pbt.BedTool.from_dataframe(my_tss_df.set_index('gene_name').loc[jor_up]
).slop(b=slopsize, g='./annotations/chr_chromsizes',
), u=True), -1).astype(int)
jor_down_inds = get_col(atac_bedtool.intersect(pbt.BedTool.from_dataframe(my_tss_df.set_index('gene_name').loc[jor_down]
).slop(b=slopsize, g='./annotations/chr_chromsizes',
), u = True), -1).astype(int)
wei_down_inds = get_col(atac_bedtool.intersect(pbt.BedTool.from_dataframe(my_tss_df.set_index('gene_name').loc[wei_down]
).slop(b=slopsize, g='./annotations/chr_chromsizes',
), u = True), -1).astype(int)
jor_up_vals = foxp3_values_at_all_atac_sites[treg_col][jor_up_inds] - foxp3_values_at_all_atac_sites[tcon_col][jor_up_inds]
wei_up_vals = foxp3_values_at_all_atac_sites[treg_col][wei_up_inds] - foxp3_values_at_all_atac_sites[tcon_col][wei_up_inds]
jor_down_vals = foxp3_values_at_all_atac_sites[treg_col][jor_down_inds] - foxp3_values_at_all_atac_sites[tcon_col][jor_down_inds]
wei_down_vals = foxp3_values_at_all_atac_sites[treg_col][wei_down_inds] - foxp3_values_at_all_atac_sites[tcon_col][wei_down_inds]
baseline = foxp3_values_at_all_atac_sites[treg_col] - foxp3_values_at_all_atac_sites[tcon_col]
xs = np.linspace(-500, 500, jor_up_vals.shape[1])
plt.sca(axs[c])
plt.plot(xs, np.nanmean(jor_up_vals, axis=0), color='#FF9999', label=f'KO \n(n={len(jor_up_vals)})')
plt.plot(xs, np.nanmean(wei_up_vals, axis=0), color='#FF6633', label=f'Tir1 \n(n={len(wei_up_vals)})')
plt.plot(xs, np.nanmean(baseline, axis=0), color='black')
plt.legend(loc='upper left')
# p_ks = scipy.stats.ks_2samp(jor_up_vals[:, 500], wei_up_vals[:, 500])[1]
# p_t = scipy.stats.ttest_ind(jor_up_vals[:, 500], wei_up_vals[:, 500])[1]
p_t_baseline = scipy.stats.ttest_ind(baseline[:, 500], wei_up_vals[:, 500])[1]
p_t_baseline2 = scipy.stats.ttest_ind(baseline[:, 500], jor_up_vals[:, 500])[1]
# plt.text(.98, 1-.02, f'Jor/Wei_ks={format_pvalue(p_ks)}', ha='right', va='top', transform=plt.gca().transAxes)
# plt.text(.98, 1-.12, f'Jor/Wei_t={format_pvalue(p_t)}', ha='right', va='top', transform=plt.gca().transAxes)
plt.text(.98, 1-.02, f'Wei/Base={format_pvalue(p_t_baseline)}', ha='right', va='top', transform=plt.gca().transAxes)
plt.text(.98, 1-.12, f'Jor/Base={format_pvalue(p_t_baseline2)}', ha='right', va='top', transform=plt.gca().transAxes)
plt.title(col_to_name[wei_col] + " Up")
plt.sca(axs[c+3])
plt.plot(xs, np.nanmean(jor_down_vals, axis=0), color='#9999FF', label=f'KO \n(n={len(jor_down_vals)})')
plt.plot(xs, np.nanmean(wei_down_vals, axis=0), color='#66CCFF', label=f'Tir1 \n(n={len(wei_down_vals)})')
plt.plot(xs, np.nanmean(baseline, axis=0), color='black')
plt.title(col_to_name[wei_col] + " Down")
plt.legend(loc='upper left')
p_t_baseline = scipy.stats.ttest_ind(baseline[:, 500], wei_down_vals[:, 500])[1]
p_t_baseline2 = scipy.stats.ttest_ind(baseline[:, 500], jor_down_vals[:, 500])[1]
# plt.text(.98, 1-.02, f'Jor/Wei_ks={format_pvalue(p_ks)}', ha='right', va='top', transform=plt.gca().transAxes)
# plt.text(.98, 1-.12, f'Jor/Wei_t={format_pvalue(p_t)}', ha='right', va='top', transform=plt.gca().transAxes)
plt.text(.98, 1-.02, f'Wei/Base={format_pvalue(p_t_baseline)}', ha='right', va='top', transform=plt.gca().transAxes)
plt.text(.98, 1-.12, f'Jor/Base={format_pvalue(p_t_baseline2)}', ha='right', va='top', transform=plt.gca().transAxes)
for ax in axs:
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
def foxp3_at_nearest_atac_peaks(cols, foxp3_metadata_at_all_atac_sites, foxp3_values_at_all_atac_sites,
pval_df_all, lfc_df_all, atac_bedtool, col_to_name, my_atac_meta_new,
foxp3_count_set,
xlabel = "ATAC peaks near DEGs", ylabel='Foxp3 Binding',
pco = .05):
tmp = foxp3_metadata_at_all_atac_sites['treg_yuri_foxp3_bw']#.set_index('name')
treg_col, tcon_col = ['treg_yuri_foxp3_bw', 'tcon_yuri_foxp3_bw']
fig, axs = init_subplots(6, 2, space=.4, fgsz=(6, 4))
for c, (wei_col, jor_col) in enumerate(zip(*cols)):
wei_up = pval_df_all.index[(pval_df_all[wei_col] < pco) & (lfc_df_all[wei_col] > 0)]
jor_up = pval_df_all.index[(pval_df_all[jor_col] < pco) & (lfc_df_all[jor_col] > 0)]
jor_down = pval_df_all.index[(pval_df_all[jor_col] < pco) & (lfc_df_all[jor_col] < 0)]
wei_down = pval_df_all.index[(pval_df_all[wei_col] < pco) & (lfc_df_all[wei_col] < 0)]
jor_up = [x for x in jor_up if x in foxp3_count_set]
wei_up = [x for x in wei_up if x in foxp3_count_set]
jor_down = [x for x in jor_down if x in foxp3_count_set]
wei_down = [x for x in wei_down if x in foxp3_count_set]
jor_up_inds = tmp.index.isin(my_atac_meta_new['index'][my_atac_meta_new[0].isin(jor_up)])
wei_up_inds = tmp.index.isin(my_atac_meta_new['index'][my_atac_meta_new[0].isin(wei_up)])
jor_down_inds = tmp.index.isin(my_atac_meta_new['index'][my_atac_meta_new[0].isin(jor_down)])
wei_down_inds = tmp.index.isin(my_atac_meta_new['index'][my_atac_meta_new[0].isin(wei_down)])
jor_up_vals = foxp3_values_at_all_atac_sites[treg_col][jor_up_inds] - foxp3_values_at_all_atac_sites[tcon_col][jor_up_inds]
wei_up_vals = foxp3_values_at_all_atac_sites[treg_col][wei_up_inds] - foxp3_values_at_all_atac_sites[tcon_col][wei_up_inds]
jor_down_vals = foxp3_values_at_all_atac_sites[treg_col][jor_down_inds] - foxp3_values_at_all_atac_sites[tcon_col][jor_down_inds]
wei_down_vals = foxp3_values_at_all_atac_sites[treg_col][wei_down_inds] - foxp3_values_at_all_atac_sites[tcon_col][wei_down_inds]
baseline = foxp3_values_at_all_atac_sites[treg_col] - foxp3_values_at_all_atac_sites[tcon_col]
xs = np.linspace(-500, 500, jor_up_vals.shape[1])
plt.sca(axs[c])
plt.plot(xs, np.nanmean(jor_up_vals, axis=0), color='#FF9999', label=f'KO \n(n={len(jor_up_vals)})')
plt.plot(xs, np.nanmean(wei_up_vals, axis=0), color='#FF6633', label=f'Tir1 \n(n={len(wei_up_vals)})')
plt.plot(xs, np.nanmean(baseline, axis=0), color='black')
plt.legend(loc='upper left')
# p_ks = scipy.stats.ks_2samp(jor_up_vals[:, 500], wei_up_vals[:, 500])[1]
# p_t = scipy.stats.ttest_ind(jor_up_vals[:, 500], wei_up_vals[:, 500])[1]
p_t_baseline = scipy.stats.ttest_ind(baseline[:, 500], wei_up_vals[:, 500])[1]
p_t_baseline2 = scipy.stats.ttest_ind(baseline[:, 500], jor_up_vals[:, 500])[1]
# plt.text(.98, 1-.02, f'Jor/Wei_ks={format_pvalue(p_ks)}', ha='right', va='top', transform=plt.gca().transAxes)
# plt.text(.98, 1-.12, f'Jor/Wei_t={format_pvalue(p_t)}', ha='right', va='top', transform=plt.gca().transAxes)
plt.text(.98, 1-.02, f'Wei/Base={format_pvalue(p_t_baseline)}', ha='right', va='top', transform=plt.gca().transAxes)
plt.text(.98, 1-.12, f'Jor/Base={format_pvalue(p_t_baseline2)}', ha='right', va='top', transform=plt.gca().transAxes)
plt.title(col_to_name[wei_col] + " Up")
plt.sca(axs[c+3])
plt.plot(xs, np.nanmean(jor_down_vals, axis=0), color='#9999FF', label=f'KO \n(n={len(jor_down_vals)})')
plt.plot(xs, np.nanmean(wei_down_vals, axis=0), color='#66CCFF', label=f'Tir1 \n(n={len(wei_down_vals)})')
plt.plot(xs, np.nanmean(baseline, axis=0), color='black')
plt.title(col_to_name[wei_col] + " Down")
plt.legend(loc='upper left')
p_t_baseline = scipy.stats.ttest_ind(baseline[:, 500], wei_down_vals[:, 500])[1]
p_t_baseline2 = scipy.stats.ttest_ind(baseline[:, 500], jor_down_vals[:, 500])[1]
# plt.text(.98, 1-.02, f'Jor/Wei_ks={format_pvalue(p_ks)}', ha='right', va='top', transform=plt.gca().transAxes)
# plt.text(.98, 1-.12, f'Jor/Wei_t={format_pvalue(p_t)}', ha='right', va='top', transform=plt.gca().transAxes)
plt.text(.98, 1-.02, f'Wei/Base={format_pvalue(p_t_baseline)}', ha='right', va='top', transform=plt.gca().transAxes)
plt.text(.98, 1-.12, f'Jor/Base={format_pvalue(p_t_baseline2)}', ha='right', va='top', transform=plt.gca().transAxes)
for ax in axs:
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
def n_tfs_at_degs(cols, pval_df_all, lfc_df_all, basemean_df_all,
col_to_name, tf_df,
xlabel = "ATAC peaks near DEGs", ylabel='Foxp3 Binding', pco=0.05,
basemean_co = 20):
n = len(cols[0])
fig, axs = init_subplots(n, 1, space=.6, fgsz=(8, 4))
dict_of_vals = {}
for c, (wei_col, jor_col) in enumerate(zip(*cols)):
plt.sca(axs[c])
basemean_idx = (basemean_df_all[wei_col] > basemean_co) & (basemean_df_all[jor_col] > basemean_co)
high_basemean_genes = basemean_df_all.index[basemean_idx]
pval_df, lfc_df = pval_df_all.loc[high_basemean_genes], lfc_df_all.loc[high_basemean_genes]
wei_up = pval_df.index[(pval_df[wei_col] < pco) & (lfc_df[wei_col] > 0)]
jor_up = pval_df.index[(pval_df[jor_col] < pco) & (lfc_df[jor_col] > 0)]
both_up = wei_up.intersection(jor_up)
jor_down = pval_df.index[(pval_df[jor_col] < pco) & (lfc_df[jor_col] < 0)]
wei_down = pval_df.index[(pval_df[wei_col] < pco) & (lfc_df[wei_col] < 0)]
both_down = wei_down.intersection(jor_down)
baseline_genes = pval_df.index
jor_up_frac = np.isin(jor_up, tf_df.index).mean()
wei_up_frac = np.isin(wei_up, tf_df.index).mean()
both_up_frac = np.isin(both_up, tf_df.index).mean()
jor_down_frac = np.isin(jor_down, tf_df.index).mean()
wei_down_frac = np.isin(wei_down, tf_df.index).mean()
both_down_frac = np.isin(both_down, tf_df.index).mean()
baseline_frac = np.isin(baseline_genes, tf_df.index).mean()
jor_up_tf_count = np.isin(jor_up, tf_df.index).sum()
jor_up_no_tf_count = (~np.isin(jor_up, tf_df.index)).sum()
wei_up_tf_count = np.isin(wei_up, tf_df.index).sum()
wei_up_no_tf_count = (~np.isin(wei_up, tf_df.index)).sum()
both_up_tf_count = np.isin(both_up, tf_df.index).sum()
both_up_no_tf_count = (~np.isin(both_up, tf_df.index)).sum()
jor_down_tf_count = np.isin(jor_down, tf_df.index).sum()
jor_down_no_tf_count = (~np.isin(jor_down, tf_df.index)).sum()
wei_down_tf_count = np.isin(wei_down, tf_df.index).sum()
wei_down_no_tf_count = (~np.isin(wei_down, tf_df.index)).sum()
both_down_tf_count = np.isin(both_down, tf_df.index).sum()
both_down_no_tf_count = (~np.isin(both_down, tf_df.index)).sum()
baseline_tf_count = np.isin(baseline_genes, tf_df.index).sum()
baseline_no_tf_count = (~np.isin(baseline_genes, tf_df.index)).sum()
_, pval_jor_up = scipy.stats.fisher_exact([[jor_up_tf_count, jor_up_no_tf_count], [baseline_tf_count, baseline_no_tf_count]])
_, pval_wei_up = scipy.stats.fisher_exact([[wei_up_tf_count, wei_up_no_tf_count], [baseline_tf_count, baseline_no_tf_count]])
_, pval_both_up = scipy.stats.fisher_exact([[both_up_tf_count, both_up_no_tf_count], [baseline_tf_count, baseline_no_tf_count]])
_, pval_jor_down = scipy.stats.fisher_exact([[jor_down_tf_count, jor_down_no_tf_count], [baseline_tf_count, baseline_no_tf_count]])
_, pval_wei_down = scipy.stats.fisher_exact([[wei_down_tf_count, wei_down_no_tf_count], [baseline_tf_count, baseline_no_tf_count]])
_, pval_both_down = scipy.stats.fisher_exact([[both_down_tf_count, both_down_no_tf_count], [baseline_tf_count, baseline_no_tf_count]])
plt.bar('baseline', baseline_frac, color='lightgray', zorder=3)
plt.bar('jor_up', jor_up_frac, color='#FF9999', zorder=3, label=f'jor_up: {format_pvalue(pval_jor_up)}')
plt.bar('wei_up', wei_up_frac, color='#FF6633', zorder=3, label=f'wei_up: {format_pvalue(pval_wei_up)}')
plt.bar('both_up', both_up_frac, color='#FF0000', zorder=3, label=f'both_up: {format_pvalue(pval_both_up)}')
plt.bar('jor_down', jor_down_frac, color='#9999FF', zorder=3, label=f'jor_down: {format_pvalue(pval_jor_down)}')
plt.bar('wei_down', wei_down_frac, color='#66CCFF', zorder=3, label=f'wei_down: {format_pvalue(pval_wei_down)}')
plt.bar('both_down', both_down_frac, color='#0000FF', zorder=3, label=f'both_down: {format_pvalue(pval_both_down)}')
plt.ylabel('Fraction DEGs which are TFs')
plt.title(col_to_name[wei_col].split(".")[0])
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', frameon=False)
def n_tfs_at_degs_over_time(cols, pval_df_all, lfc_df_all, basemean_df_all,
col_to_name, tf_df,
xlabel = "ATAC peaks near DEGs", ylabel='Foxp3 Binding', pco=0.05,
basemean_co = 20):
fig, axs = init_subplots(1, 1, space=.6, fgsz=(8, 4))
dict_of_vals = {}
n = len(cols)
colors = sns.color_palette("coolwarm", n_colors=n*2)
for c, (col) in enumerate(cols):
basemean_idx = (basemean_df_all[col] > basemean_co) & (basemean_df_all[col] > basemean_co)
high_basemean_genes = basemean_df_all.index[basemean_idx]
pval_df, lfc_df = pval_df_all.loc[high_basemean_genes], lfc_df_all.loc[high_basemean_genes]
up = pval_df.index[(pval_df[col] < pco) & (lfc_df[col] > 0)]
down = pval_df.index[(pval_df[col] < pco) & (lfc_df[col] < 0)]
baseline_genes = pval_df.index
up_frac = np.isin(up, tf_df.index).mean()
down_frac = np.isin(down, tf_df.index).mean()
baseline_frac = np.isin(baseline_genes, tf_df.index).mean()
plt.bar(col_to_name[col].split(" ")[1] + ' up', up_frac, color=colors[c], zorder=3)
plt.bar(col_to_name[col].split(" ")[1] + ' down', down_frac, color=colors[2*n-1-c], zorder=3)
plt.bar('baseline', baseline_frac, color='lightgray', zorder=3)
plt.ylabel('Fraction DEGs which are TFs')
def n_foxp3_peaks_near_degs(cols, pval_df_all, lfc_df_all, basemean_df_all,
col_to_name, foxp3_count_set, full_foxp3_counts,
xlabel = "ATAC peaks near DEGs", ylabel='Foxp3 Binding', pco=0.05,
basemean_co = 20,
label1='KO', label2='Tir1', label3='Both'):
n = len(cols[0])
fig, axs = init_subplots(n*2, 2, space=.4, fgsz=(7, 4))
dict_of_vals = {}
for c, (wei_col, jor_col) in enumerate(zip(*cols)):
basemean_idx = (basemean_df_all[wei_col] > basemean_co) & (basemean_df_all[jor_col] > basemean_co)
high_basemean_genes = basemean_df_all.index[basemean_idx]
pval_df, lfc_df = pval_df_all.loc[high_basemean_genes], lfc_df_all.loc[high_basemean_genes]
wei_up = pval_df.index[(pval_df[wei_col] < pco) & (lfc_df[wei_col] > 0)]
jor_up = pval_df.index[(pval_df[jor_col] < pco) & (lfc_df[jor_col] > 0)]
both_up = wei_up.intersection(jor_up)
jor_down = pval_df.index[(pval_df[jor_col] < pco) & (lfc_df[jor_col] < 0)]
wei_down = pval_df.index[(pval_df[wei_col] < pco) & (lfc_df[wei_col] < 0)]
both_down = wei_down.intersection(jor_down)
wei_up_counts_to_save = [(x, full_foxp3_counts.loc[x]) if x in foxp3_count_set else (x, "LOW_EXPR") for x in wei_up]
wei_down_counts_to_save = [(x, full_foxp3_counts.loc[x]) if x in foxp3_count_set else (x, "LOW_EXPR") for x in wei_down]
jor_up = [x for x in jor_up if x in foxp3_count_set]
wei_up = [x for x in wei_up if x in foxp3_count_set]
both_up = [x for x in both_up if x in foxp3_count_set]
jor_down = [x for x in jor_down if x in foxp3_count_set]
wei_down = [x for x in wei_down if x in foxp3_count_set]
both_down = [x for x in both_down if x in foxp3_count_set]
dict_of_vals[col_to_name[wei_col] + " Up"] = wei_up_counts_to_save
dict_of_vals[col_to_name[wei_col] + " Down"] = wei_down_counts_to_save
jor_up_foxp3_counts = full_foxp3_counts.loc[jor_up]
wei_up_foxp3_counts = full_foxp3_counts.loc[wei_up]
both_up_foxp3_counts = full_foxp3_counts.loc[both_up]
jor_down_foxp3_counts = full_foxp3_counts.loc[jor_down]
wei_down_foxp3_counts = full_foxp3_counts.loc[wei_down]
both_down_foxp3_counts = full_foxp3_counts.loc[both_down]
baseline = full_foxp3_counts.loc[[x for x in pval_df_all.index if x in foxp3_count_set]]
plt.sca(axs[c])
v = plot_foxp3_peaks_near_degs_as_bar(jor_up, col_to_name, label1, foxp3_count_set, full_foxp3_counts,
xlabel = "ATAC peaks near DEGs", ylabel='Foxp3 Binding',
color = '#FF9999',
linewidth = 3
)
v = plot_foxp3_peaks_near_degs_as_bar(wei_up, col_to_name, label2, foxp3_count_set, full_foxp3_counts,
xlabel = "ATAC peaks near DEGs", ylabel='Foxp3 Binding',
color = '#FF6633',
linewidth = 3
)
v = plot_foxp3_peaks_near_degs_as_bar(both_up, col_to_name, label3, foxp3_count_set, full_foxp3_counts,
xlabel = "ATAC peaks near DEGs", ylabel='Foxp3 Binding',
color = '#FF0000',
linewidth = 3
)
v = plot_foxp3_peaks_near_degs_as_bar(foxp3_count_set, col_to_name, 'degs', foxp3_count_set, full_foxp3_counts,
xlabel = "ATAC peaks near DEGs", ylabel='Foxp3 Binding',
color = 'lightgray',
linewidth = 3, plot_baseline=True
)
# sns.ecdfplot(jor_up_foxp3_counts, color='#FF9999', label=f'KO Up (n={len(jor_up_foxp3_counts)})')
# # sns.ecdfplot(wei_up_foxp3_counts, color='#FF6633', label=f'Tir1 Up (n={len(wei_up_foxp3_counts)})')
# sns.ecdfplot(both_up_foxp3_counts, color='#FF0000', label=f'Both Up (n={len(both_up_foxp3_counts)})')
# sns.ecdfplot(baseline, color='black', label=f'Baseline')
# if len(wei_up_foxp3_counts) == 0:
# continue
p_wei = scipy.stats.ranksums(wei_up_foxp3_counts, baseline)[1]
p_jor = scipy.stats.ranksums(jor_up_foxp3_counts, baseline)[1]
p_both = scipy.stats.ranksums(both_up_foxp3_counts, baseline)[1]
# plt.text(.98, .02, f'wei={format_pvalue(p_wei)}', ha='right', va='bottom', transform=plt.gca().transAxes)
# plt.text(.98, .12, f'jor={format_pvalue(p_jor)}', ha='right', va='bottom', transform=plt.gca().transAxes)
# plt.text(.98, .22, f'both={format_pvalue(p_both)}', ha='right', va='bottom', transform=plt.gca().transAxes)
plt.title(f"{col_to_name[wei_col]} Up")
plt.legend(loc = 'upper left',
# bbox_to_anchor=(1., 0),
frameon=False)
plt.sca(axs[c+n])
v = plot_foxp3_peaks_near_degs_as_bar(jor_down, col_to_name, label1, foxp3_count_set, full_foxp3_counts,
xlabel = "ATAC peaks near DEGs", ylabel='Foxp3 Binding',
color = '#9999FF',
linewidth = 3
)
v = plot_foxp3_peaks_near_degs_as_bar(wei_down, col_to_name, label2, foxp3_count_set, full_foxp3_counts,
xlabel = "ATAC peaks near DEGs", ylabel='Foxp3 Binding',
color = '#66CCFF',
linewidth = 3
)
v = plot_foxp3_peaks_near_degs_as_bar(both_down, col_to_name, label3, foxp3_count_set, full_foxp3_counts,
xlabel = "ATAC peaks near DEGs", ylabel='Foxp3 Binding',
color = '#0000FF',
linewidth = 3
)
v = plot_foxp3_peaks_near_degs_as_bar(foxp3_count_set, col_to_name, 'degs', foxp3_count_set, full_foxp3_counts,
xlabel = "ATAC peaks near DEGs", ylabel='Foxp3 Binding',
color = 'lightgray',
linewidth = 3, plot_baseline=True
)
# sns.ecdfplot(jor_down_foxp3_counts, color='#9999FF', label=f'KO Down (n={len(jor_down_foxp3_counts)})')
# sns.ecdfplot(wei_down_foxp3_counts, color='#66CCFF', label=f'Tir1 Down (n={len(wei_down_foxp3_counts)})')
# sns.ecdfplot(both_down_foxp3_counts, color='#0000FF', label=f'Both Down (n={len(both_down_foxp3_counts)})')
# sns.ecdfplot(baseline, color='black', label=f'Baseline')
plt.title(f"{col_to_name[wei_col]} Down")
p_wei = scipy.stats.ranksums(wei_down_foxp3_counts, baseline)[1]
p_jor = scipy.stats.ranksums(jor_down_foxp3_counts, baseline)[1]
p_both = scipy.stats.ranksums(both_down_foxp3_counts, baseline)[1]
# plt.text(.98, .02, f'wei={format_pvalue(p_wei)}', ha='right', va='bottom', transform=plt.gca().transAxes)
# plt.text(.98, .12, f'jor={format_pvalue(p_jor)}', ha='right', va='bottom', transform=plt.gca().transAxes)
# plt.text(.98, .22, f'both={format_pvalue(p_both)}', ha='right', va='bottom', transform=plt.gca().transAxes)
plt.legend(loc = 'lower left', bbox_to_anchor=(1, 0), frameon=False)
for ax in axs:
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
# ax.set_xlim([-.2, 10])
for ax in axs:
plt.sca(ax)
plt.xticks([-1, 0, 1, 2])
z = list(ax.get_xticklabels())
z[0].set_text("All")
ax.set_xticklabels(z)
return fig, dict_of_vals
def plot_foxp3_peaks_near_degs(degs, col_to_name, foxp3_count_set, full_foxp3_counts, ax=None, color=None,
xlabel = "ATAC peaks near DEGs", ylabel='Foxp3 Binding', linewidth=2):
ax = plt.gca() if ax is None else ax
# fig, axs = init_subplots(6, 2, space=.4, fgsz=(6, 4))
degs = [x for x in degs if x in foxp3_count_set]
if len(degs)==0:
return 0
baseline = full_foxp3_counts.loc[[x for x in foxp3_count_set]]
wei_up_foxp3_counts = full_foxp3_counts.loc[degs]
p_wei = scipy.stats.ranksums(wei_up_foxp3_counts, baseline)[1]
# plt.sca(axs[c])
if p_wei < .05:
sns.ecdfplot(wei_up_foxp3_counts, label=f'(p={format_pvalue(p_wei)})', c = color, linewidth=linewidth)
else:
sns.ecdfplot(wei_up_foxp3_counts, c = color, linewidth=linewidth)
# sns.ecdfplot(wei_up_foxp3_counts, color='#FF6633', label=f'Tir1 Up (n={len(wei_up_foxp3_counts)})')
sns.ecdfplot(baseline, color='black', linewidth=linewidth)
# if len(wei_up_foxp3_counts) == 0:
# continue
# p_jor = scipy.stats.ks_2samp(jor_up_foxp3_counts, baseline)[1]
# plt.text(.98, .02, f'wei={format_pvalue(p_wei)}', ha='right', va='bottom', transform=plt.gca().transAxes)
# plt.text(.98, .12, f'jor={format_pvalue(p_jor)}', ha='right', va='bottom', transform=plt.gca().transAxes)
# plt.title(col_to_name[wei_col] + " Up")
plt.legend(loc = 'lower right', bbox_to_anchor=(1.02, .1), frameon=False)
# plt.sca(axs[c+3])
# sns.ecdfplot(jor_down_foxp3_counts, color='#9999FF', label=f'KO Down (n={len(jor_down_foxp3_counts)})')
# sns.ecdfplot(wei_down_foxp3_counts, color='#66CCFF', label=f'Tir1 Down (n={len(wei_down_foxp3_counts)})')
# sns.ecdfplot(baseline, color='black', label=f'Baseline')
# plt.title(col_to_name[wei_col] + " Down")
# p_wei = scipy.stats.ks_2samp(wei_down_foxp3_counts, baseline)[1]
# p_jor = scipy.stats.ks_2samp(jor_down_foxp3_counts, baseline)[1]
# plt.text(.98, .02, f'wei={format_pvalue(p_wei)}', ha='right', va='bottom', transform=plt.gca().transAxes)
# plt.text(.98, .12, f'jor={format_pvalue(p_jor)}', ha='right', va='bottom', transform=plt.gca().transAxes)
# plt.legend(loc = 'lower right', bbox_to_anchor=(1.02, .1), frameon=False)
# for ax in axs:
# ax.set_xlabel(xlabel)
# ax.set_ylabel(ylabel)
# ax.set_xlim([-.2, 10])
print(1)
def plot_foxp3_peaks_near_degs_as_bar(degs, col_to_name, name, foxp3_count_set, full_foxp3_counts, ax=None, color=None,
xlabel = "ATAC peaks near DEGs", ylabel='Foxp3 Binding', linewidth=2, plot_baseline=False,
test='rankSums'):
ax = plt.gca() if ax is None else ax
# fig, axs = init_subplots(6, 2, space=.4, fgsz=(6, 4))
degs = [x for x in degs if x in foxp3_count_set]
if len(degs)==0:
return 0
baseline = full_foxp3_counts.loc[[x for x in foxp3_count_set]]
wei_up_foxp3_counts = full_foxp3_counts.loc[degs]
if test == 'rankSums':
p_wei = nonan_test(wei_up_foxp3_counts, baseline)[1]
elif test == 'fisherExact':
fisher_mat = [[(wei_up_foxp3_counts>0).sum(), (wei_up_foxp3_counts==0).sum()],
[(baseline>0).sum(), (baseline==0).sum()]
]
p_wei = scipy.stats.fisher_exact(fisher_mat)[1]
# plt.sca(axs[c])
if plot_baseline==True:
sem = baseline.sem() # Standard deviation
plt.bar(-1, baseline.mean(), color='lightgray', linewidth=linewidth, zorder=3)
plt.errorbar(-1, baseline.mean(), yerr=sem, fmt='o', color='black', capsize=5, zorder=4, markersize=0)
else:
sem = wei_up_foxp3_counts.sem() # Standard deviation
plt.bar(name, wei_up_foxp3_counts.mean(), label=f'p={format_pvalue(p_wei)}; n={len(degs)}', color = color, linewidth=linewidth,
zorder=3)
plt.errorbar(name, wei_up_foxp3_counts.mean(), yerr=sem, fmt='o', color='black', capsize=5, zorder=4, markersize=0)
plt.legend(loc = 'lower left', bbox_to_anchor=(1.02, .1), frameon=False)
return wei_up_foxp3_counts, p_wei
# plt.sca(axs[c+3])
# sns.ecdfplot(jor_down_foxp3_counts, color='#9999FF', label=f'KO Down (n={len(jor_down_foxp3_counts)})')
# sns.ecdfplot(wei_down_foxp3_counts, color='#66CCFF', label=f'Tir1 Down (n={len(wei_down_foxp3_counts)})')
# sns.ecdfplot(baseline, color='black', label=f'Baseline')
# plt.title(col_to_name[wei_col] + " Down")
# p_wei = scipy.stats.ks_2samp(wei_down_foxp3_counts, baseline)[1]
# p_jor = scipy.stats.ks_2samp(jor_down_foxp3_counts, baseline)[1]
# plt.text(.98, .02, f'wei={format_pvalue(p_wei)}', ha='right', va='bottom', transform=plt.gca().transAxes)
# plt.text(.98, .12, f'jor={format_pvalue(p_jor)}', ha='right', va='bottom', transform=plt.gca().transAxes)
# plt.legend(loc = 'lower right', bbox_to_anchor=(1.02, .1), frameon=False)
# for ax in axs:
# ax.set_xlabel(xlabel)
# ax.set_ylabel(ylabel)
# ax.set_xlim([-.2, 10])
def get_total_gene_pileup(geneset, foxp3_values_at_all_atac_sites, tmp, my_atac_meta_new, treg_col, tcon_col):
total_gene_pileup = []
for gene in geneset:
gene_atac_idx = my_atac_meta_new[0] == gene
gene_inds = my_atac_meta_new['index'][gene_atac_idx].astype(int)
gene_idx = tmp.index.isin(gene_inds)
gene_vals = foxp3_values_at_all_atac_sites[treg_col][gene_idx] - foxp3_values_at_all_atac_sites[tcon_col][gene_idx]
assert len(gene_vals.shape) == 2
one_gene_pileup = np.nanmean(gene_vals, axis=0)
total_gene_pileup.append(one_gene_pileup)
return np.asarray(total_gene_pileup)
def pileup_stat_test(p1, p2, sl = slice(490, 510), test=scipy.stats.ranksums):
p1 = np.nanmean(p1[:, sl], axis=1)
p2 = np.nanmean(p2[:, sl], axis=1)
bad1 = np.isnan(p1)
bad2 = np.isnan(p2)
p1 = p1[~bad1]
p2 = p2[~bad2]
return test(p1, p2)
def sum_foxp3_at_nearest_atac_peaks(cols, foxp3_metadata_at_all_atac_sites, foxp3_values_at_all_atac_sites,
pval_df_all, lfc_df_all, basemean_df_all, atac_bedtool, col_to_name, my_atac_meta_new,
foxp3_count_set, xlabel = "ATAC peaks near DEGs", ylabel='Foxp3 Binding',
pco = .05, basemean_co = 20):
tmp = foxp3_metadata_at_all_atac_sites['treg_yuri_foxp3_bw']#.set_index('name')
treg_col, tcon_col = ['treg_yuri_foxp3_bw', 'tcon_yuri_foxp3_bw']
fig, axs = init_subplots(6, 2, space=.4, fgsz=(6, 4))
baseline_geneset = sorted(my_atac_meta_new[0].unique())
baseline = get_total_gene_pileup(baseline_geneset, foxp3_values_at_all_atac_sites, tmp, my_atac_meta_new, treg_col, tcon_col)
# baseline = foxp3_values_at_all_atac_sites[treg_col] - foxp3_values_at_all_atac_sites[tcon_col]
for c, (wei_col, jor_col) in enumerate(zip(*cols)):
basemean_idx = (basemean_df_all[wei_col] > basemean_co) & (basemean_df_all[jor_col] > basemean_co)
high_basemean_genes = basemean_df_all.index[basemean_idx]
pval_df, lfc_df = pval_df_all.loc[high_basemean_genes], lfc_df_all.loc[high_basemean_genes]
wei_up = pval_df.index[(pval_df[wei_col] < pco) & (lfc_df[wei_col] > 0)]
jor_up = pval_df.index[(pval_df[jor_col] < pco) & (lfc_df[jor_col] > 0)]
jor_down = pval_df.index[(pval_df[jor_col] < pco) & (lfc_df[jor_col] < 0)]
wei_down = pval_df.index[(pval_df[wei_col] < pco) & (lfc_df[wei_col] < 0)]
jor_up = [x for x in jor_up if x in foxp3_count_set]
wei_up = [x for x in wei_up if x in foxp3_count_set]
jor_down = [x for x in jor_down if x in foxp3_count_set]
wei_down = [x for x in wei_down if x in foxp3_count_set]
jor_up_vals = get_total_gene_pileup(jor_up, foxp3_values_at_all_atac_sites, tmp, my_atac_meta_new, treg_col, tcon_col)
wei_up_vals = get_total_gene_pileup(wei_up, foxp3_values_at_all_atac_sites, tmp, my_atac_meta_new, treg_col, tcon_col)
jor_down_vals = get_total_gene_pileup(jor_down, foxp3_values_at_all_atac_sites, tmp, my_atac_meta_new, treg_col, tcon_col)
wei_down_vals = get_total_gene_pileup(wei_down, foxp3_values_at_all_atac_sites, tmp, my_atac_meta_new, treg_col, tcon_col)
xs = np.linspace(-500, 500, jor_up_vals.shape[1])
plt.sca(axs[c])
plt.plot(xs, np.nanmean(jor_up_vals, axis=0), color='#FF9999', label=f'KO \n(n={len(jor_up_vals)})')
plt.plot(xs, np.nanmean(wei_up_vals, axis=0), color='#FF6633', label=f'Tir1 \n(n={len(wei_up_vals)})')
plt.plot(xs, np.nanmean(baseline, axis=0), color='black')
plt.legend(loc='upper left')
# p_ks = scipy.stats.ks_2samp(jor_up_vals[:, 500], wei_up_vals[:, 500])[1]
# p_t = scipy.stats.ttest_ind(jor_up_vals[:, 500], wei_up_vals[:, 500])[1]
vals = range(450, 550)
# print(wei_up_vals.shape)
p_t_baseline = pileup_stat_test(baseline, wei_up_vals)[1]
p_t_baseline2 = pileup_stat_test(baseline, jor_up_vals)[1]
# plt.text(.98, 1-.02, f'Jor/Wei_ks={format_pvalue(p_ks)}', ha='right', va='top', transform=plt.gca().transAxes)
# plt.text(.98, 1-.12, f'Jor/Wei_t={format_pvalue(p_t)}', ha='right', va='top', transform=plt.gca().transAxes)
plt.text(.98, 1-.02, f'Wei/Base={format_pvalue(p_t_baseline)}', ha='right', va='top', transform=plt.gca().transAxes)
plt.text(.98, 1-.12, f'Jor/Base={format_pvalue(p_t_baseline2)}', ha='right', va='top', transform=plt.gca().transAxes)
plt.title(col_to_name[wei_col] + " Up")
plt.sca(axs[c+3])
plt.plot(xs, np.nanmean(jor_down_vals, axis=0), color='#9999FF', label=f'KO \n(n={len(jor_down_vals)})')
plt.plot(xs, np.nanmean(wei_down_vals, axis=0), color='#66CCFF', label=f'Tir1 \n(n={len(wei_down_vals)})')
plt.plot(xs, np.nanmean(baseline, axis=0), color='black')
plt.title(col_to_name[wei_col] + " Down")
plt.legend(loc='upper left')
p_t_baseline = pileup_stat_test(baseline, wei_up_vals)[1]
p_t_baseline2 = pileup_stat_test(baseline, wei_up_vals)[1]
# plt.text(.98, 1-.02, f'Jor/Wei_ks={format_pvalue(p_ks)}', ha='right', va='top', transform=plt.gca().transAxes)
# plt.text(.98, 1-.12, f'Jor/Wei_t={format_pvalue(p_t)}', ha='right', va='top', transform=plt.gca().transAxes)
plt.text(.98, 1-.02, f'Wei/Base={format_pvalue(p_t_baseline)}', ha='right', va='top', transform=plt.gca().transAxes)
plt.text(.98, 1-.12, f'Jor/Base={format_pvalue(p_t_baseline2)}', ha='right', va='top', transform=plt.gca().transAxes)
for ax in axs:
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
return baseline, wei_down_vals