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manhattan_bulk.py
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manhattan_bulk.py
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import os
import pickle
import glob
import numpy as np
import matplotlib
matplotlib.use('Agg')
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.ticker
import pandas as pd
import scipy.stats
# def region_plotter(regions, bounds, color):
# def region_plot(*args, **kwargs):
# for p, q in regions:
# if p < bounds[0]:
# start = bounds[0]
# else:
# start = p
# if q > bounds[1]:
# end = bounds[1]
# else:
# end = q
# plt.axvspan(start, end, facecolor=color, linewidth=0, alpha=0.2)
# return region_plot
def plot_manhattan(pp_df, gene_name, gene_id, out_dir):
# print(pp_df) ####
sns.set(style="ticks", font="Roboto")
pal = sns.xkcd_palette(["dark slate blue", "blood red"])
g = sns.FacetGrid(
pp_df,
row="Cluster",
col="Source",
# hue="Causal",
# hue_kws={"marker":["o", "o", "D"]},
palette=pal,
margin_titles=True,
height=3,
aspect=2
)
# for k, v in regions.items():
# if k in annot_colormap:
# g.map(region_plotter(v, bounds, annot_colormap[k]))
g.map(
sns.scatterplot,
"Position",
"-log10 p-Value",
# size="Causal",
legend=False,
# color=".3",
linewidth=0,
# hue_order=[2, 1, 0],
# sizes={0:9, 1:12},
s=9
)
x_formatter = matplotlib.ticker.ScalarFormatter()
for i, ax in enumerate(g.fig.axes):
ax.set_xticklabels(ax.get_xticklabels(), rotation=30)
ax.xaxis.set_major_formatter(x_formatter)
# plt.subplots_adjust(top=0.9, bottom = 0.13, right = 0.96)
g.fig.suptitle(gene_name)
plt.savefig(os.path.join(out_dir, "manhattan", f"{gene_id}.svg"))
plt.clf()
plt.close()
def plot_comparison(comp_df, gene_name, gene_id, out_dir):
# print(pp_df) ####
sns.set(style="ticks", font="Roboto")
pal = sns.xkcd_palette(["dark slate blue", "blood red"])
g = sns.FacetGrid(
comp_df,
row="Cluster",
col="Source",
# hue="Causal",
# hue_kws={"marker":["o", "o", "D"]},
palette=pal,
margin_titles=True,
height=2,
aspect=1
)
# for k, v in regions.items():
# if k in annot_colormap:
# g.map(region_plotter(v, bounds, annot_colormap[k]))
g.map(
sns.scatterplot,
"Z-Score Single-Cell",
"Z-Score Bulk",
# size="Causal",
legend=False,
# color=".3",
linewidth=0,
# hue_order=[2, 1, 0],
# sizes={0:9, 1:12},
s=9
)
x_formatter = matplotlib.ticker.ScalarFormatter()
for i, ax in enumerate(g.fig.axes):
ax.set_xticklabels(ax.get_xticklabels(), rotation=30)
ax.xaxis.set_major_formatter(x_formatter)
# plt.subplots_adjust(top=0.9, bottom = 0.13, right = 0.96)
g.fig.suptitle(gene_name)
plt.savefig(os.path.join(out_dir, "comparison", f"{gene_id}.svg"))
plt.clf()
plt.close()
def analyze_locus(gene_id, plasma_data, bulk_data, gene_map, out_dir):
clusters = {
"_all": "All Cells",
"Ex": "Excitatory Neuron",
"Oligo": "Oligodendrocyte",
"Astro": "Astroglia",
"In": "Inhibitory Neuron",
"Endo": "Endothelial",
"Microglia": "Microglia",
"OPC": "Oligodendrocyte Progenitor",
"Per": "Per"
}
# print(bulk_data.keys()) ####
pp_lst = []
comp_lst = []
for clust, clust_name in clusters.items():
plasma_clust = plasma_data.get(clust)
if plasma_clust is None:
continue
# print(plasma_clust.get("run_error")) ####
# print(plasma_clust.keys()) ####
try:
for spos, z_beta, z_phi, z_bulk in zip(plasma_data["_gen"]["snp_pos"], plasma_clust["z_beta"], plasma_clust["z_phi"], bulk_data["z_beta"]):
pos = int(spos[1]) + 1
nlp_beta = -scipy.stats.norm.logsf(np.abs(z_beta)) / np.log(10) - np.log10(2)
nlp_phi = -scipy.stats.norm.logsf(np.abs(z_phi)) / np.log(10) - np.log10(2)
nlp_bulk = -scipy.stats.norm.logsf(np.abs(z_bulk)) / np.log(10) - np.log10(2)
pp_data = [
[pos, clust_name, nlp_beta, z_beta, "Single-Cell Total"],
[pos, clust_name, nlp_phi, z_phi, "Single-Cell AS"],
[pos, clust_name, nlp_bulk, z_bulk, "Bulk"],
]
pp_lst.extend(pp_data)
comp_data = [
[clust_name, z_beta, z_bulk, "Total"],
[clust_name, z_phi, z_bulk, "AS"],
]
comp_lst.extend(comp_data)
except KeyError as e:
print(e)
print(clust)
# print(plasma_clust.keys())
continue
pp_cols = [
"Position",
"Cluster",
"-log10 p-Value",
"Z-Score",
"Source"
]
pp_df = pd.DataFrame(pp_lst, columns=pp_cols)
comp_cols = [
"Cluster",
"Z-Score Single-Cell",
"Z-Score Bulk",
"Source"
]
comp_df = pd.DataFrame(comp_lst, columns=comp_cols)
gene_name = gene_map.get(gene_id.split(".")[0], gene_id)
plot_manhattan(pp_df, gene_name, gene_id, out_dir)
plot_comparison(comp_df, gene_name, gene_id, out_dir)
def analyze_list(gene_ids, genes_dir, gene_map_path, bulk_name, out_dir):
with open(gene_map_path, "rb") as gene_map_file:
gene_map = pickle.load(gene_map_file)
data_lst = []
for g in gene_ids:
gene_dir = os.path.join(genes_dir, g)
plasma_path = os.path.join(gene_dir, "combined", "plasma_i0.pickle")
bulk_path = os.path.join(gene_dir, "bulk_qtl", f"{bulk_name}_out.pickle")
try:
with open(plasma_path, "rb") as plasma_file:
plasma_data = pickle.load(plasma_file)
with open(bulk_path, "rb") as bulk_file:
bulk_data = pickle.load(bulk_file)
except (FileNotFoundError, pickle.UnpicklingError):
continue
analyze_locus(g, plasma_data, bulk_data, gene_map, out_dir)
if __name__ == '__main__':
data_path_kellis = "/agusevlab/awang/sc_kellis"
genes_dir = os.path.join(data_path_kellis, "genes_429")
gene_map_path = "/agusevlab/awang/ensembl/id_to_name.pickle"
out_dir = "/agusevlab/awang/ase_finemap_results/sc_results/kellis_429/rosmap"
gene_ids = [
"ENSG00000005059.15_3",
"ENSG00000005486.16_2",
"ENSG00000011485.14_2",
"ENSG00000029534.19_3",
"ENSG00000034510.5_2",
"ENSG00000043093.13_3",
"ENSG00000047365.11_3",
"ENSG00000049656.13_2",
"ENSG00000050748.17_3",
"ENSG00000054282.15_2",
"ENSG00000055332.16_3",
"ENSG00000058866.14_3",
"ENSG00000059145.18_3",
"ENSG00000064886.13_3",
"ENSG00000065308.4_2",
"ENSG00000067334.13_2",
"ENSG00000069424.14_2",
"ENSG00000076864.19_3",
"ENSG00000079134.11_3",
"ENSG00000079805.16_3",
"ENSG00000088205.12_2",
"ENSG00000089048.14_2",
"ENSG00000089327.14_3",
"ENSG00000091844.7_2",
"ENSG00000101361.16_3",
"ENSG00000101363.12_2",
"ENSG00000101489.19_3",
"ENSG00000101577.9_3",
"ENSG00000103275.19_3",
"ENSG00000103351.12_3",
"ENSG00000103494.12_3",
"ENSG00000104213.12_2",
"ENSG00000105278.10_2",
"ENSG00000105698.15_2",
"ENSG00000105993.14_3",
"ENSG00000106009.15_2",
"ENSG00000106049.8_2",
"ENSG00000106483.11_2",
"ENSG00000109654.14_3",
"ENSG00000111907.20_3",
"ENSG00000112697.15_2",
"ENSG00000112992.16_3",
"ENSG00000113360.16_3",
"ENSG00000113649.11_2",
"ENSG00000114120.11_3",
"ENSG00000115306.15_3",
"ENSG00000115355.15_2",
"ENSG00000115806.12_2",
"ENSG00000115827.13_2",
"ENSG00000115840.13_2",
"ENSG00000116661.9_2",
"ENSG00000116731.22_3",
"ENSG00000116962.14_2",
"ENSG00000117114.19_3",
"ENSG00000117505.12_2",
"ENSG00000118197.13_3",
"ENSG00000118217.5_3",
"ENSG00000122257.18_3",
"ENSG00000122644.12_3",
"ENSG00000123600.19_3",
"ENSG00000124222.22_3",
"ENSG00000124357.12_2",
"ENSG00000125651.13_3",
"ENSG00000125844.15_3",
"ENSG00000125868.15_2",
"ENSG00000125898.12_2",
"ENSG00000125901.5_2",
"ENSG00000129911.8_2",
"ENSG00000130522.5_3",
"ENSG00000131781.12_3",
"ENSG00000132639.12_3",
"ENSG00000133275.15_1",
"ENSG00000134986.13_3",
"ENSG00000135924.15_2",
"ENSG00000136205.16_2",
"ENSG00000137558.7_2",
"ENSG00000142327.12_3",
"ENSG00000143878.9_2",
"ENSG00000144357.16_3",
"ENSG00000144455.13_3",
"ENSG00000144567.10_3",
"ENSG00000144834.13_3",
"ENSG00000145191.12_3",
"ENSG00000146350.13_3",
"ENSG00000146963.17_3",
"ENSG00000149527.17_3",
"ENSG00000150938.9_2",
"ENSG00000152583.12_3",
"ENSG00000155545.19_2",
"ENSG00000157212.18_2",
"ENSG00000157617.16_3",
"ENSG00000158792.15_2",
"ENSG00000159228.12_2",
"ENSG00000159905.14_2",
"ENSG00000160439.15_3",
"ENSG00000162728.4_2",
"ENSG00000163075.12_2",
"ENSG00000163510.13_2",
"ENSG00000163635.17_3",
"ENSG00000163637.12_3",
"ENSG00000163714.17_3",
"ENSG00000164066.12_2",
"ENSG00000164128.6_3",
"ENSG00000164209.16_2",
"ENSG00000164402.13_3",
"ENSG00000164733.20_3",
"ENSG00000164741.14_3",
"ENSG00000166501.12_3",
"ENSG00000167378.8_3",
"ENSG00000168090.9_2",
"ENSG00000168314.17_3",
"ENSG00000168411.13_3",
"ENSG00000168491.9_2",
"ENSG00000169398.19_3",
"ENSG00000169676.5_3",
"ENSG00000169715.14_2",
"ENSG00000169851.15_3",
"ENSG00000170631.14_3",
"ENSG00000170873.18_3",
"ENSG00000171492.14_3",
"ENSG00000171621.13_2",
"ENSG00000171720.9_2",
"ENSG00000172954.13_2",
"ENSG00000173175.14_3",
"ENSG00000173852.14_3",
"ENSG00000175221.14_3",
"ENSG00000176349.11_2",
"ENSG00000176490.4_3",
"ENSG00000177335.10_2",
"ENSG00000177873.12_2",
"ENSG00000178623.11_3",
"ENSG00000178719.16_3",
"ENSG00000180228.12_2",
"ENSG00000180537.12_3",
"ENSG00000181450.17_3",
"ENSG00000181652.19_2",
"ENSG00000182108.9_3",
"ENSG00000184076.13_3",
"ENSG00000184203.7_3",
"ENSG00000184313.19_3",
"ENSG00000185658.13_3",
"ENSG00000185760.15_3",
"ENSG00000186204.14_3",
"ENSG00000186952.14_3",
"ENSG00000187189.10_3",
"ENSG00000187193.8_3",
"ENSG00000187905.10_2",
"ENSG00000188976.10_2",
"ENSG00000196569.11_2",
"ENSG00000196683.10_2",
"ENSG00000196776.14_2",
"ENSG00000197302.10_3",
"ENSG00000197928.10_3",
"ENSG00000198440.9_3",
"ENSG00000198892.6_2",
"ENSG00000204120.14_3",
"ENSG00000204257.14_3",
"ENSG00000204469.12_2",
"ENSG00000204520.12_3",
"ENSG00000205560.12_3",
"ENSG00000213020.9_3",
"ENSG00000221823.10_3",
"ENSG00000223802.7_3",
"ENSG00000227500.9_1",
"ENSG00000249087.6_2",
"ENSG00000259030.6_3",
]
bulk_name = "rosmap"
analyze_list(gene_ids, genes_dir, gene_map_path, bulk_name, out_dir)