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%matplotlib inline
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import os
import csv
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
from ete2 import Tree, TreeStyle, TextFace, faces, Face, NodeStyle, AttrFace
from Bio import Phylo
# Export matplotlib svg text as text, not path
mpl.rcParams['svg.fonttype'] = 'none'
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# First convert the nexus tree to a newick one
Phylo.convert('DUF_TREE_CONSENSUS.txt', 'nexus', 'DUF_TREE_CONSENSUS.tree', 'newick')
# Read in the Tree using ETE2
tree = Tree("DUF_TREE_CONSENSUS.tree")
# Do not color in the internal nodes
nstyle = NodeStyle()
nstyle["size"] = 0
nstyle["fgcolor"] = "black"
# Set style for nodes and round the time value
# Also re-format species name
for n in tree.traverse():
n.set_style(nstyle)
n.dist = round(n.dist, 2)
# Remove the middle underscore
n.name = " ".join(n.name.split("_"))
def layout(node):
if node.is_leaf():
# Add node name to laef nodes
N = AttrFace("name",fsize=12, ftype="Source Sans Pro", fgcolor="black", fstyle="italic")
N.margin_left = 5
faces.add_face_to_node(N, node, 0, position="aligned")
# Tree formatting
ts = TreeStyle()
ts.layout_fn = layout
ts.show_leaf_name = False
# ts.tree_width = 200
ts.draw_guiding_lines = True
ts.show_branch_length = False
ts.show_scale = False
ts.branch_vertical_margin = 15
ts.margin_right = 50
ts.draw_aligned_faces_as_table = True
# tree.render("2015-03-12-DUF1220-fig1a.svg", tree_style=ts, h=2, units="in")
tree.render("%%inline", tree_style=ts,h=2, units="in")
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# Read in the data from the excel file and use the second sheet
xls = pd.ExcelFile('2015-03-12-figure-data.xlsx')
figure1b_data = xls.parse('figure1B', index_col=None, na_values=['NA'])
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def label_point(x, y, val, ax):
'''Uses the Notes information provided in the dataframe to color certain points in the plot'''
a = pd.concat({'x': x, 'y': y, 'val': val}, axis=1)
label_num = 1
for i, point in a.iterrows():
if str(point['val']) != "nan":
if str(point['val']) == "LCA Homininae":
ax.text(point['x']+0.08, point['y']-0.2, str(label_num), name="Open Sans", size=10)
else:
ax.text(point['x']+0.08, point['y']-0.07, str(label_num), name="Open Sans", size=10)
label_num += 1
sns.set_context("paper", font_scale=1)
sns.set_style("ticks", {"axes.linewidth": 0.5, 'xtick.major.size': 5, 'ytick.major.size': 5})
plt.figure(figsize=(2, 2), dpi=300)
figure1b_data["colors"] = np.where(pd.isnull(figure1b_data).any(axis=1), '#7f7f7f', '#d62728')
sns.axlabel(r'$log_{10}$'+ '(branch length)',r'$log_{10}$'+'(scaled branch length)', name="Open Sans", size=8)
fig1b = plt.scatter(figure1b_data["log10[BRANCH LENGTH (MY)]"],
figure1b_data["log10[SCALED BRANCH LENGTH]"],
color=figure1b_data["colors"],
s=20)
plt.xticks(np.arange(0, 2.5, 0.5))
plt.yticks(np.arange(0, 3.5, 0.5))
label_point(figure1b_data["log10[BRANCH LENGTH (MY)]"], figure1b_data["log10[SCALED BRANCH LENGTH]"], figure1b_data["Notes"], plt)
sns.despine(offset=10, trim=True)
plt.savefig("2015-03-12-DUF1220-fig1b.svg", facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0,
frameon=True)
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figure1c_data = xls.parse('figure 1C', na_values=['NA'])
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sns.set_context("paper", font_scale=1)
sns.set_style("ticks", {"axes.linewidth": 0.5, 'xtick.major.size': 5, 'ytick.major.size': 5})
plt.figure(figsize=(2, 2), dpi=300)
# Only color the Homo outlier
colors = np.where(figure1c_data["Species"] == "Homo_sapiens",'#d62728', '#7f7f7f' )
fig1c = plt.scatter(figure1c_data["log10(Brain mass [mg])"],
figure1c_data["sqrt(DUF)"],
color=colors,
s=20)
sns.axlabel(r'$log10$'+'(Brainmass [mg])', r'$sqrt$'+ '(DUF1220 domain count)', name="Open Sans", size=8)
#Add the first regression line including Homo
xi = np.arange(3.3,6.5)
intercept = figure1c_data.iloc[0]["with homo"]
slope = figure1c_data.iloc[1]["with homo"]
line_with_homo = slope*xi+intercept
plt.plot(xi,line_with_homo, color='#d62728', linewidth=1)
plt.text(6.3, 15, "Homo", name="Open Sans", size=10, style="italic")
#Add the second regression line excluding Homo
intercept = figure1c_data.iloc[0]["without"]
slope = figure1c_data.iloc[1]["without"]
line_without_homo = slope*xi+intercept
plt.plot(xi,line_without_homo,color='#7f7f7f', linestyle="-", linewidth=1)
plt.xticks(np.arange(3, 7.5, 1))
sns.despine(offset=10, trim=True)
plt.savefig("2015-03-12-DUF1220-fig1c.svg", facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0,
frameon=None)
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figureS4_data = xls.parse('Figure S4', na_values=['NA'])
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sns.set_context("paper", font_scale=1)
sns.set_style("ticks", {"axes.linewidth": 0.5, 'xtick.major.size': 5, 'ytick.major.size': 5})
plt.figure(figsize=(2, 2), dpi=300)
# Only color the Homo outlier
colors = np.where(figureS4_data["Species"] == "Homo_sapiens",'#d62728', '#7f7f7f' )
fig1c = plt.scatter(figureS4_data["log10(Brain mass [mg])"],
figureS4_data["log(DUF)"],
color=colors,
s=20)
sns.axlabel(r'$log10$'+'(Brainmass [mg])', r'$log$'+ '(DUF1220 domain count)', name="Open Sans", size=8)
#Add the first regression line including Homo
xi = np.arange(3.3,6.5)
intercept = figureS4_data.iloc[0]["with homo"]
slope = figureS4_data.iloc[1]["with homo"]
line_with_homo = slope*xi+intercept
plt.plot(xi,line_with_homo, color='#d62728', linewidth=1)
plt.text(6.3, 2.3, "Homo", name="Open Sans", size=10, style="italic")
#Add the second regression line excluding Homo
intercept = figureS4_data.iloc[0]["without"]
slope = figureS4_data.iloc[1]["without"]
line_without_homo = slope*xi+intercept
plt.plot(xi,line_without_homo,color='#7f7f7f', linestyle="-", linewidth=1)
plt.xticks(np.arange(3, 7.5, 1))
sns.despine(offset=10, trim=True)
plt.savefig("2015-03-12-DUF1220-figS4.svg", facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0,
frameon=None)
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def read_domtblout(source):
'''Reads in a domtblout file and counts domain annotations'''
hits = {}
try:
with open(source, "r") as infile:
for line in infile:
# Ignore comments
if not line.startswith("#"):
fields = line.split()
target = fields[0]
if target in hits:
hits[target] += 1
else:
hits[target] = 1
return hits
except IOError:
print("File does not exit!")
def list_files(current_dir):
'''List files in the current directory'''
file_list = []
for path, subdirs, files in os.walk(current_dir): # Walk directory tree
for name in files:
f = os.path.join(path, name)
file_list.append(f)
return file_list
infolder = "2015-02-20-hmmsearch-vs-pep-all-longest-ensembl-range"
domain_counts = {"homo_sapiens": [],
"pan_troglodytes": [],
"macaca_mulatta": []}
for f in list_files(infolder):
if f.endswith("domtblout"):
version = int(f.split("-")[-1].split(".")[0][1:])
species = f.split("-")[-2]
domtblout = read_domtblout(f)
domain_counts[species].append((version, sum(domtblout.values())))
# Sort data by version
for s, counts in domain_counts.items():
counts.sort(key=lambda tup: tup[0])
sns.set_context("paper", font_scale=1.75)
sns.set_style("ticks", {"axes.linewidth": 1, 'xtick.major.size': 10, 'ytick.major.size': 10})
plt.figure(figsize=(6.5, 4), dpi=300)
sns.axlabel("Ensembl version","DUF1220 count", name="Open Sans")
# plt.scatter(*zip(*domain_counts["homo_sapiens"]))
plt.plot(*zip(*domain_counts["homo_sapiens"]),color='#d62728', linewidth=2.0,label='Homo sapiens', marker='o', markersize=5)
plt.plot(*zip(*domain_counts["pan_troglodytes"]),color='#1f77b4',linewidth=2.0, label='Pan troglodytes', marker='o', markersize=5)
plt.plot(*zip(*domain_counts["macaca_mulatta"]), color='#7f7f7f',linewidth=2.0, label='Macaca mulatta', marker='o', markersize=5)
plt.legend(loc='center right')
sns.despine(offset=20, trim=True)
plt.savefig("2015-03-12-DUF1220-figS1.svg", dpi=None, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0,
frameon=None)
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figureS2_data = xls.parse('figure S2', na_values=['NA'])
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sns.set_context("paper", font_scale=1.75)
sns.set_style("ticks", {"axes.linewidth": 0.5, 'xtick.major.size': 5, 'ytick.major.size': 5})
plt.figure(figsize=(6.5, 3), dpi=300)
onerate = figureS2_data['one-rate model']
variablerate = figureS2_data['variable rate model']
binwidth = 1
sns.distplot(onerate, color="#d62728", bins=np.arange(min(onerate), max(onerate) + binwidth, binwidth), hist_kws={"histtype": "stepfilled", "alpha": 0.5})
sns.distplot(variablerate, color="#1f77b4", bins=np.arange(min(variablerate), max(variablerate) + binwidth, binwidth), hist_kws={"histtype": "stepfilled", "alpha": 0.5})
sns.despine()
sns.axlabel("Ln(Lh)", "Frequency", name="Open Sans")
plt.yticks(np.arange(0, 1.2, 0.2))
plt.savefig("2015-03-12-DUF1220-figS2.svg", dpi=None, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0,
frameon=None)
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figureS3_data = xls.parse('figure S3', na_values=['NA'])
figureS3_data.head()
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sns.set_context("paper", font_scale=1.75)
sns.set_style("ticks", {"axes.linewidth": 0.5, 'xtick.major.size': 5, 'ytick.major.size': 5})
plt.figure(figsize=(6.5, 3), dpi=300)
directional = figureS3_data['Non-directional model']
nondirectional = figureS3_data['Directional Model']
binwidth = 0.5
sns.distplot(directional, color="#d62728", bins=np.arange(min(directional), max(directional) + binwidth, binwidth), hist_kws={"histtype": "stepfilled", "alpha": 0.5})
sns.distplot(nondirectional, color="#1f77b4", bins=np.arange(min(nondirectional), max(nondirectional) + binwidth, binwidth), hist_kws={"histtype": "stepfilled", "alpha": 0.5})
sns.despine()
sns.axlabel("Ln(Lh)", "Frequency", name="Open Sans")
plt.yticks(np.arange(0, 0.4, 0.1))
# plt.xticks(np.arange(-32, -14, 2.5))
plt.savefig("2015-03-12-DUF1220-figS3.svg", dpi=None, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0,
frameon=None)
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# Read in the data from the excel file and use the second sheet
brain_body = pd.read_table('Brain_Body.txt', sep=' ')
brain_body.head()
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sns.set_context("paper", font_scale=1.75)
sns.set_style("ticks", {"axes.linewidth": 0.5, 'xtick.major.size': 5, 'ytick.major.size': 5})
sns.kdeplot(brain_body["Brain"]);
sns.kdeplot(brain_body["BODY"]);
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# Read in the data from the excel file and use the second sheet
prenatal_postnatal = pd.read_table('Prenatal_Postnatal.txt', sep=' ')
prenatal_postnatal.head()
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In [106]:
sns.set_context("paper", font_scale=1.75)
sns.set_style("ticks", {"axes.linewidth": 0.5, 'xtick.major.size': 5, 'ytick.major.size': 5})
sns.kdeplot(prenatal_postnatal["NeoBrain"]);
sns.kdeplot(prenatal_postnatal["PNBrain"]);
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# Read in the data from the excel file and use the second sheet
neo_cere_rob = pd.read_table('neocortex_cerebellum_rob.txt', sep=' ', encoding='utf-8')
neo_cere_rob.describe()
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In [108]:
sns.set_context("paper", font_scale=1.75)
sns.set_style("ticks", {"axes.linewidth": 0.5, 'xtick.major.size': 5, 'ytick.major.size': 5})
sns.kdeplot(neo_cere_rob["NEOCORTEX"]);
sns.kdeplot(neo_cere_rob["CEREBELLUM"]);
sns.kdeplot(neo_cere_rob["Rob"]);
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In [163]:
sns.set_context("paper", font_scale=1.2, rc={"lines.linewidth": 0.7, 'font.sans-serif': "Source Sans Pro"})
sns.set_style("ticks", {"axes.linewidth": 0.7})
plt.figure(figsize=(6.75, 5), dpi=300)
plt.subplots_adjust(left=0.1, right=0.9,
bottom=0.1, top=0.9,
hspace=0.3, wspace=0.3)
plt.subplot(222)
sns.kdeplot(brain_body["Brain"], color="#d62728")
sns.kdeplot(brain_body["BODY"], color="black")
sns.axlabel("", "Density")
sns.despine()
plt.legend().remove()
plt.subplot(223)
sns.kdeplot(prenatal_postnatal["NeoBrain"], color="black")
sns.kdeplot(prenatal_postnatal["PNBrain"], color="#d62728")
sns.despine()
sns.axlabel("Posterior mean", "Density")
plt.legend().remove()
plt.subplot(224)
sns.kdeplot(neo_cere_rob["NEOCORTEX"], color="#d62728")
sns.kdeplot(neo_cere_rob["CEREBELLUM"], color="black")
sns.kdeplot(neo_cere_rob["Rob"], color="#7f7f7f")
sns.despine()
sns.axlabel("Posterior mean", "")
plt.legend().remove()
plt.savefig("2015-04-13-DUF1220-Fig1.svg", dpi=None, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0,
frameon=None)
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