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figs.py
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figs.py
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"""
A preamble here.
"""
############# PACKAGES #############
import sys, os
from copy import deepcopy
import numpy as np
import scipy as sp
import scipy.stats as st
import pandas as pd
import datetime as dt
import matplotlib
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.image as mpimg
import seaborn as sns
from colorsys import hls_to_rgb
import mplot as mp
############# PARAMETERS #############
# GLOBAL VARIABLES
NUC = ['A', 'C', 'G', 'T', '-']
REF = NUC[-1]
EXT = '.pdf'
# SARS-CoV-2 information
PROTEINS = ['NSP1', 'NSP2', 'NSP3', 'NSP4', 'NSP5', 'NSP6', 'NSP7', 'NSP8','NSP9', 'NSP10', 'NSP12', 'NSP13', 'NSP14',
'NSP15', 'NSP16', 'S', 'ORF3a', 'E', 'M', 'ORF6', 'ORF7a', 'ORF7b', 'ORF8', 'N', 'ORF10']
PROTEIN_LENGTHS = {'ORF3a' : 828, 'E' : 228, 'ORF6' : 186, 'ORF7a' : 365, 'ORF7b' : 132, 'S' : 3822,
'N' : 1260, 'M' : 669, 'ORF8' : 336, 'ORF10' : 117, 'NSP1' : 539, 'NSP2' : 1914,
'NSP3' : 5834, 'NSP4' : 1500, 'NSP5' : 917, 'NSP6' : 870, 'NSP7' : 249, 'NSP8' : 594,
'NSP9' : 339, 'NSP10' : 417, 'NSP12' :2795, 'NSP13' : 1803, 'NSP14' : 1582,
'NSP15' : 1038, 'NSP16' : 894}
## Code Ocean directories
#MLB_DIR = '../data/HIV'
#WFS_DIR = '../data/wfsim'
#HIV_MPL_DIR = '../data/HIV/MPL'
#SIM_MPL_DIR = '../data/simulation/MPL'
#HIV_DIR = '../data/HIV'
#SIM_DIR = '../data/simulation'
#FIG_DIR = '../results'
# GitHub directories
SIM_DIR = 'sims'
FIG_DIR = 'figures'
# Standard color scheme
BKCOLOR = '#252525'
LCOLOR = '#969696'
C_BEN = '#EB4025' #'#F16913'
C_BEN_LT = '#F08F78' #'#fdd0a2'
C_NEU = LCOLOR #'#E8E8E8' # LCOLOR
C_NEU_LT = '#E8E8E8' #'#F0F0F0' #'#d9d9d9'
C_DEL = '#3E8DCF' #'#604A7B'
C_DEL_LT = '#78B4E7' #'#dadaeb'
C_MPL = '#FFB511'
C_SL = C_NEU
PALETTE = sns.hls_palette(2) # palette1
MAIN_COLOR = PALETTE[0]
COMP_COLOR = PALETTE[1]
COLOR_1 = '#1295D8'
COLOR_2 = '#FFB511'
VAR_COLORS = sns.husl_palette(8)
C_DELTA = VAR_COLORS[0]
C_GAMMA = VAR_COLORS[1]
C_ALPHA = VAR_COLORS[2]
C_BETA = VAR_COLORS[3]
C_LAMBDA = VAR_COLORS[4]
C_EPSILON = VAR_COLORS[5]
C_D614G = VAR_COLORS[6]
C_A222V = VAR_COLORS[7]
def S2COLOR(s):
""" Convert selection coefficient to color. """
if s>0: return C_BEN
elif s==0: return C_NEU
else: return C_DEL
# Plot conventions
cm2inch = lambda x: x/2.54
SINGLE_COLUMN = cm2inch(8.8)
ONE_FIVE_COLUMN = cm2inch(11.4)
DOUBLE_COLUMN = cm2inch(18.0)
SLIDE_WIDTH = 10.5
GOLDR = (1.0 + np.sqrt(5)) / 2.0
TICKLENGTH = 3
TICKPAD = 3
# paper style
FONTFAMILY = 'Arial'
SIZESUBLABEL = 8
SIZELABEL = 6
SIZETICK = 6
SMALLSIZEDOT = 6.
SIZELINE = 0.6
AXES_FONTSIZE = 6
AXWIDTH = 0.4
## grant style
#FONTFAMILY = 'Arial'
#SIZESUBLABEL = 8
#SIZELABEL = 8
#SIZETICK = 8
#SMALLSIZEDOT = 6. * 1.3
#SIZELINE = 0.6
#AXWIDTH = 0.4
## slides style
#FONTFAMILY = 'Avenir'
#SIZESUBLABEL = 14 #18
#SIZELABEL = 14 #18
#SIZETICK = 14 #18
#SMALLSIZEDOT = 6. * 7
#SIZELINE = 1.5
#AXWIDTH = 1.0
# circle plot formatting
S_MULT = 30*SMALLSIZEDOT #40*SMALLSIZEDOT
S_CUTOFF = 0.0099
DS_NORM = 0.006
S_CUTOFF = 0.01
DS_NORM = 0.006
FIGPROPS = {
'transparent' : True,
#'bbox_inches' : 'tight'
}
DEF_BARPROPS = {
'lw' : SIZELINE/2,
'width' : 0.25,
'edgecolor' : BKCOLOR,
'align' : 'center', #other option: edge
'orientation' : 'vertical'
}
DEF_HISTPROPS = {
'histtype' : 'bar',
'lw' : SIZELINE/2,
'rwidth' : 0.8,
'ls' : 'solid',
'edgecolor' : BKCOLOR,
'alpha' : 0.5
}
DEF_ERRORPROPS = {
'mew' : AXWIDTH,
'markersize' : SMALLSIZEDOT/2,
'fmt' : 'o',
'elinewidth' : SIZELINE/2,
'capthick' : 0,
'capsize' : 0
}
DEF_LINEPROPS = {
'lw' : SIZELINE,
'ls' : '-'
}
DEF_LABELPROPS = {
'family' : FONTFAMILY,
'size' : SIZELABEL,
'color' : BKCOLOR,
'clip_on': False
}
DEF_SUBLABELPROPS = {
'family' : FONTFAMILY,
'size' : SIZESUBLABEL+1,
'weight' : 'bold',
'ha' : 'center',
'va' : 'center',
'color' : 'k',
'clip_on' : False
}
DEF_TICKLABELPROPS = {
'family' : FONTFAMILY,
'size' : SIZETICK,
'color' : BKCOLOR
}
DEF_TICKPROPS = {
'length' : TICKLENGTH,
'width' : AXWIDTH/2,
'pad' : TICKPAD,
'axis' : 'both',
'direction' : 'out',
'colors' : BKCOLOR,
'bottom' : True,
'left' : True,
'top' : False,
'right' : False
}
DEF_MINORTICKPROPS = {
'length' : TICKLENGTH-1.25,
'width' : AXWIDTH/2,
'axis' : 'both',
'direction' : 'out',
'which' : 'minor',
'color' : BKCOLOR
}
DEF_AXPROPS = {
'linewidth' : AXWIDTH,
'linestyle' : '-',
'color' : BKCOLOR
}
PARAMS = {'text.usetex': False, 'mathtext.fontset': 'stixsans', 'mathtext.default' : 'regular'}
plt.rcParams.update(PARAMS)
############# HELPER FUNCTIONS #############
def GET_CODON_START_INDEX(i):
""" Given a sequence index i, determine the index of the first nucleotide in the codon. """
if (13467<=i<=21554):
return i - (i - 13467)%3
elif (25392<=i<=26219):
return i - (i - 25392)%3
elif (26244<=i<=26471):
return i - (i - 26244)%3
elif (27201<=i<=27386):
return i - (i - 27201)%3
elif (27393<=i<=27886):
return i - (i - 27393)%3
elif ( 265<=i<=13482):
return i - (i - 265 )%3
elif (21562<=i<=25383):
return i - (i - 21562)%3
elif (28273<=i<=29532):
return i - (i - 28273)%3
elif (29557<=i<=29673):
return i - (i - 29557)%3
elif (26522<=i<=27190):
return i - (i - 26522)%3
elif (27893<=i<=28258):
return i - (i - 27893)%3
else:
return 0
def GET_LABEL(i):
""" For a SARS-CoV-2 reference sequence index i, return the label in the form
'coding region - protein number-nucleotide in codon number'.
For example, 'ORF1b-204-1'.
Should check to make sure NSP12 labels are correct due to the frame shift."""
i = int(i)
frame_shift = str(i - GET_CODON_START_INDEX(i))
if (25392<=i<26220):
return "ORF3a-" + str(int((i - 25392) / 3) + 1) + '-' + frame_shift
elif (26244<=i<26472):
return "E-" + str(int((i - 26244) / 3) + 1) + '-' + frame_shift
elif (27201<=i<27387):
return "ORF6-" + str(int((i - 27201) / 3) + 1) + '-' + frame_shift
elif (27393<=i<27759):
return "ORF7a-" + str(int((i - 27393) / 3) + 1) + '-' + frame_shift
elif (27755<=i<27887):
return "ORF7b-" + str(int((i - 27755) / 3) + 1) + '-' + frame_shift
elif ( 265<=i<805):
return "NSP1-" + str(int((i - 265 ) / 3) + 1) + '-' + frame_shift
elif ( 805<=i<2719):
return "NSP2-" + str(int((i - 805 ) / 3) + 1) + '-' + frame_shift
elif ( 2719<=i<8554):
return "NSP3-" + str(int((i - 2719 ) / 3) + 1) + '-' + frame_shift
# Compound protein containing a proteinase, a phosphoesterase transmembrane domain 1, etc.
elif ( 8554<=i<10054):
return "NSP4-" + str(int((i - 8554 ) / 3) + 1) + '-' + frame_shift
# Transmembrane domain 2
elif (10054<=i<10972):
return "NSP5-" + str(int((i - 10054) / 3) + 1) + '-' + frame_shift
# Main proteinase
elif (10972<=i<11842):
return "NSP6-" + str(int((i - 10972) / 3) + 1) + '-' + frame_shift
# Putative transmembrane domain
elif (11842<=i<12091):
return "NSP7-" + str(int((i - 11842) / 3) + 1) + '-' + frame_shift
elif (12091<=i<12685):
return "NSP8-" + str(int((i - 12091) / 3) + 1) + '-' + frame_shift
elif (12685<=i<13024):
return "NSP9-" + str(int((i - 12685) / 3) + 1) + '-' + frame_shift
# ssRNA-binding protein
elif (13024<=i<13441):
return "NSP10-" + str(int((i - 13024) / 3) + 1) + '-' + frame_shift
# CysHis, formerly growth-factor-like protein
# Check that aa indexing is correct for NSP12, becuase there is a -1 ribosomal frameshift at 13468. It is not, because the amino acids in the first frame
# need to be added to the counter in the second frame.
elif (13441<=i<13467):
return "NSP12-" + str(int((i - 13441) / 3) + 1) + '-' + frame_shift
elif (13467<=i<16236):
return "NSP12-" + str(int((i - 13467) / 3) + 10) + '-' + frame_shift
# RNA-dependent RNA polymerase
elif (16236<=i<18039):
return "NSP13-" + str(int((i - 16236) / 3) + 1) + '-' + frame_shift
# Helicase
elif (18039<=i<19620):
return "NSP14-" + str(int((i - 18039) / 3) + 1) + '-' + frame_shift
# 3' - 5' exonuclease
elif (19620<=i<20658):
return "NSP15-" + str(int((i - 19620) / 3) + 1) + '-' + frame_shift
# endoRNAse
elif (20658<=i<21552):
return "NSP16-" + str(int((i - 20658) / 3) + 1) + '-' + frame_shift
# 2'-O-ribose methyltransferase
elif (21562<=i<25384):
return "S-" + str(int((i - 21562) / 3) + 1) + '-' + frame_shift
elif (28273<=i<29533):
return "N-" + str(int((i - 28273) / 3) + 1) + '-' + frame_shift
elif (29557<=i<29674):
return "ORF10-" + str(int((i - 29557) / 3) + 1) + '-' + frame_shift
elif (26522<=i<27191):
return "M-" + str(int((i - 26522) / 3) + 1) + '-' + frame_shift
elif (27893<=i<28259):
return "ORF8-" + str(int((i - 27893) / 3) + 1) + '-' + frame_shift
else:
return "NC-" + str(int(i))
def NUMBER_NONSYNONYMOUS(linked, site_types, labels, mutant_types):
""" Given a group of mutations, find the percentage that are nonsynonymous.
Mutant_types are the types (synonymous or nonsynonymous) for the group of mutations.
site_types are the types for the linked sites."""
# Filter out linked groups if all mutations are synonymous or nonsynonymous.
linked_new = []
for i in range(len(site_types)):
if 0 < len(np.array(site_types[i])[np.array(site_types[i])=='NS']) and len(np.array(site_types[i])[np.array(site_types[i])=='NS']) < len(site_types[i]):
linked_new.append(linked[i])
linked_all = [linked_new[i][j] for i in range(len(linked_new)) for j in range(len(linked_new[i]))]
# find percentage nonsynonymous
syn = np.array(labels)[np.array(mutant_types)=='S']
num_linked = len([site for site in syn if site in linked_all])
if len(labels)==0:
return -1
else:
return (len(labels) - len(syn) + num_linked) / len(labels)
def NUMBER_NONSYNONYMOUS_NONLINKED(inferred, num_large, linked, site_types, labels_all, mutant_types):
""" Find out the percentage of the num_large largest selection coefficients that are nonsynonymous
and are not linked to some nonsynonymous mutation. """
# Filter out linked groups if all mutations are synonymous or nonsynonymous.
linked_new = []
for i in range(len(site_types)):
if 0 < len(np.array(site_types[i])[np.array(site_types[i])=='NS']) and len(np.array(site_types[i])[np.array(site_types[i])=='NS']) < len(site_types[i]):
linked_new.append(linked[i])
linked_all = [linked_new[i][j] for i in range(len(linked_new)) for j in range(len(linked_new[i]))]
# determine how many coefficients of the largest coefficients are nonsynonymous and not linked to a nonsynonymous mutation.
#large_inferred = np.sort(inferred)[-num_large:]
#indices = np.argsort(inferred)[-num_large:]
indices = np.argsort(np.absolute(inferred))[-num_large:]
large_inferred = inferred[indices]
large_mut_types, large_labels = [], []
for i in range(num_large):
large_mut_types.append(mutant_types[indices[i]])
large_labels.append(labels_all[indices[i]])
large_mut_types = np.array(large_mut_types)
#nonsyn = np.array(large_labels)[np.array(large_mut_types)=='NS']
syn = np.array(large_labels)[np.array(large_mut_types)=='S']
num_linked = len([site for site in syn if site in linked_all])
return (num_large - len(syn) + num_linked) / num_large
def CALCULATE_LINKED_COEFFICIENTS(inf_file, link_file, tv=False):
""" Finds the coefficients and the labels for the linked sites"""
# loading and processing the data
data = np.load(inf_file, allow_pickle=True)
linked_sites = np.load(link_file, allow_pickle=True) # sites that are fully linked in every region
inferred = data['selection']
if len(np.shape(inferred))==1:
error = data['error_bars']
else:
error = np.zeros(np.shape(inferred))
allele_number = data['allele_number']
labels = [GET_LABEL(i) for i in allele_number]
linked_all = [linked_sites[i][j] for i in range(len(linked_sites)) for j in range(len(linked_sites[i]))]
# finding total coefficients for linked sites and adding them together.
#if len(np.shape(inferred))==1:
if not tv:
inferred_link = np.zeros(len(linked_sites))
error_link = np.zeros(len(linked_sites))
labels_link = []
for i in range(len(linked_sites)):
labels_temp = []
for j in range(len(linked_sites[i])):
if np.any(linked_sites[i][j]==np.array(labels)):
inferred_link[i] += inferred[np.where(np.array(linked_sites[i][j])==np.array(labels))[0][0]]
error_link[i] += error[np.where(np.array(linked_sites[i][j])==np.array(labels))[0][0]]
labels_temp.append(linked_sites[i][j])
labels_link.append(labels_temp)
else:
inferred_link = np.zeros((len(inferred), len(linked_sites)))
error_link = np.zeros((len(inferred), len(linked_sites)))
labels_link = []
for i in range(len(linked_sites)):
labels_temp = []
for j in range(len(linked_sites[i])):
if np.any(linked_sites[i][j]==np.array(labels)):
inferred_link[:, i] += inferred[:, np.where(np.array(linked_sites[i][j])==np.array(labels))[0][0]]
error_link[:, i] += error[:, np.where(np.array(linked_sites[i][j])==np.array(labels))[0][0]]
labels_temp.append(linked_sites[i][j])
labels_link.append(labels_temp)
return labels_link, inferred_link, error_link
def FIND_NULL_DISTRIBUTION(file, link_file, neutral_tol=0.01):
""" Given the inferred coefficients over all time, find those that are ultimately inferred to be nearly neutral,
and then collect the inferred coefficients for these sites at every time."""
# loading and processing the data
data = np.load(file, allow_pickle=True)
linked_sites = np.load(link_file, allow_pickle=True) # sites that are fully linked in every region
allele_number = data['allele_number']
labels = [GET_LABEL(i) for i in allele_number]
linked_all = [linked_sites[i][j] for i in range(len(linked_sites)) for j in range(len(linked_sites[i]))]
inferred = data['selection']
traj = data['traj']
times = data['times']
mutant_sites = data['mutant_sites']
digits = len(str(len(inferred)*len(inferred[0])))
# finding total coefficients for linked sites and adding them together.
inferred_link = np.zeros((len(inferred), len(linked_sites)))
inferred_nolink = []
labels_nolink = []
for i in range(len(linked_sites)):
for j in range(len(linked_sites[i])):
if np.any(linked_sites[i][j]==np.array(labels)):
inferred_link[:,i] += inferred[:, np.where(np.array(linked_sites[i][j])==np.array(labels))[0][0]]
counter = 0
for i in range(len(labels)):
if labels[i] not in linked_all:
inferred_nolink.append(inferred[:,i])
labels_nolink.append(labels[i])
counter+=1
nolink_new = np.zeros((len(inferred_nolink[0]), len(inferred_nolink)))
for i in range(len(inferred_nolink)):
nolink_new[:,i] = inferred_nolink[i]
inferred_nolink = nolink_new
# determing sites that are ultimately inferred to be nearly neutral
inf_new = np.concatenate((inferred_link, np.array(inferred_nolink)), axis=1)
neutral_mask = np.absolute(inf_new[-1])<neutral_tol
L = len([i for i in neutral_mask if i])
inferred_neut = np.zeros((len(inf_new), L))
for i in range(len(inferred)):
inferred_neut[i] = inf_new[i][neutral_mask]
# finding the initial times at which each variant arose
### If the inferred coefficient is actually zero (not approximately zero) this is almsot definitely because the mutation has not showed up anywhere
inferred_neut = [inferred_neut[i][j] for i in range(len(inferred_neut)) for j in range(len(inferred_neut[i]))]
print(len(inferred_neut))
print(len(np.array(inferred_neut)[np.array(inferred_neut)!=0]))
inferred_neut = np.array(inferred_neut)[np.array(inferred_neut)!=0]
return inferred_neut
############# PLOTTING FUNCTIONS #############
def plot_performance(sim_file, inf_file, replicate_file, pop_size, cutoff=0.01):
# Load simulation data
sim_data = np.load(sim_file, allow_pickle=True)
simulations = sim_data['simulations']
actual = sim_data['selection_all']
pop_size = sim_data['pop_size']
traj = sim_data['traj_record']
mutant_sites = sim_data['mutant_sites']
mutant_sites_all = sim_data['mutant_sites_all']
# Load inference data
inf_data = np.load(inf_file, allow_pickle=True)
inferred = inf_data['selection']
error = inf_data['error_bars']
inferred_ind = inf_data['selection_ind']
error_ind = inf_data['errors_ind']
# Other parameters
record = np.ones(simulations)
pop_size_assumed = pop_size
selection = {}
for i in range(len(mutant_sites_all)):
selection[mutant_sites_all[i]] = actual[i]
# Functions 1
def trajectory_reshape(traj):
# reshape trajectories
traj_reshaped = []
for i in range(len(traj)):
T_temp = len(traj[i])
L_temp = len(traj[i][0])
traj_temp = np.zeros((T_temp, L_temp))
for j in range(T_temp):
traj_temp[j] = traj[i][j]
traj_reshaped.append(np.array(traj_temp))
return traj_reshaped
def find_significant_sites(traj, mutant_sites, cutoff):
# find sites whose frequency is (not?) too small
allele_number = []
for i in range(len(traj)):
for j in range(len(traj[i][0])):
if np.amax(traj[i][:, j]) > cutoff:
allele_number.append(mutant_sites[i][j])
allele_number = np.sort(np.unique(np.array(allele_number)))
return allele_number
# reorganize arrays and filter low frequency trajectories
traj = trajectory_reshape(traj)
allele_number = find_significant_sites(traj, mutant_sites, cutoff)
actual = np.array([selection[i] for i in allele_number])
L_traj = len(allele_number)
if len(actual)!=0:
s_value = np.amax(np.array(actual))
else:
s_value = 0
# Functions 2
def filter_sites(traj, error, inferred, mutant_sites, mutant_sites_all, allele_number):
# eliminate sites whose frequency is too small
traj_new = [[] for i in range(simulations)]
mutant_sites_new = [[] for i in range(simulations)]
inferred_new = np.zeros(len(allele_number))
error_bars = np.zeros(len(allele_number))
for i in range(len(traj)):
for j in range(len(traj[i][0])):
if mutant_sites[i][j] in allele_number:
traj_new[i].append(np.array(traj[i][:,j]))
mutant_sites_new[i].append(mutant_sites[i][j])
for i in range(len(allele_number)):
loc = np.where(allele_number[i] == mutant_sites_all)
inferred_new[i] = inferred[loc]
error_bars[i] = error[loc]
return traj_new, inferred_new, error_bars, mutant_sites_new
def filter_sites_ind(inferred, error):
# eliminates sites whose frequency is too small in the individually inferred coefficients
inferred_new = np.zeros((simulations, L_traj))
error_new = np.zeros((simulations, L_traj))
for sim in range(simulations):
for i in range(L_traj):
loc = np.where(mutant_sites_all == allele_number[i])
inferred_new[sim, i] = inferred[sim][loc]
error_new[sim, i] = error[sim][loc]
return inferred_new, error_new
def filter_sites_infer(traj, inferred, mutant_sites, mutant_sites_all, allele_number):
# eliminate sites whose frequency is too small
inferred_new = np.zeros(len(allele_number))
mutant_sites_new = [[] for i in range(len(traj))]
for i in range(len(traj)):
for j in range(len(traj[i][0])):
if mutant_sites[i][j] in allele_number:
mutant_sites_new[i].append(mutant_sites[i][j])
for i in range(len(allele_number)):
loc = np.where(allele_number[i] == mutant_sites_all)
inferred_new[i] = inferred[loc]
return inferred_new
def separate_sign(selection):
# finds the indices at which mutations are beneficial, neutral, and deleterious
splus, sminus, szero = {}, {}, {}
for i in selection:
if selection[i] > 0:
splus[i] = selection[i]
elif selection[i] < 0:
sminus[i] = selection[i]
else:
szero[i] = selection[i]
mutant_sites = [i for i in selection]
ind_plus, ind_minus, ind_zero = [], [], []
for i in splus:
ind_plus.append(np.where(i == mutant_sites)[0][0])
for i in sminus:
ind_minus.append(np.where(i == mutant_sites)[0][0])
for i in szero:
ind_zero.append(np.where(i == mutant_sites)[0][0])
return ind_minus, ind_zero, ind_plus
def sort_by_sign(selection, inferred):
# sorts the beneficial, deleterious, and neutral mutations
ind_minus, ind_zero, ind_plus = separate_sign(selection)
inf_plus = [inferred[i] for i in ind_plus]
inf_minus = [inferred[i] for i in ind_minus]
inf_zero = [inferred[i] for i in ind_zero]
return inf_minus, inf_zero, inf_plus
traj, inferred, error, mutant_sites = filter_sites(traj, error, inferred, mutant_sites, mutant_sites_all, allele_number)
mutant_sites_all = np.sort(np.unique([mutant_sites[j][i] for j in range(len(mutant_sites)) for i in range(len(mutant_sites[j]))]))
# plot combining the frequencies, the selection coefficients, and the histograms
indices = np.argsort(actual)[::-1]
actual = np.array(actual)[indices]
inferred = inferred[indices]
colors_inf = np.array([S2COLOR(i) for i in actual], dtype=str)
allele_number = allele_number[indices]
# Finding the different sets of actual coefficients that are all the same
actual_unique = np.unique(actual)
unique_lengths = [len(np.isin(actual, i).nonzero()[0]) for i in actual_unique]
colors_unique = np.array([S2COLOR(i) for i in actual_unique], dtype=str)
# PLOT FIGURE
## set up figure grid
w = SINGLE_COLUMN #SLIDE_WIDTH
goldh = w / 1.1
fig = plt.figure(figsize=(w, goldh))
box_traj = dict(left=0.13, right=0.90, bottom=0.60, top=0.95)
box_hist = dict(left=0.13, right=0.70, bottom=0.12, top=0.45)
## a -- individual beneficial/neutral/deleterious trajectories and selection coefficients
### trajectories
n_exs = len(inferred)
colors = [C_BEN, C_BEN, C_NEU, C_NEU, C_DEL, C_DEL]
gs = gridspec.GridSpec(n_exs, 2, width_ratios=[1, 1], wspace=0.05, **box_traj)
ax = [[plt.subplot(gs[i, 0]), plt.subplot(gs[i, 1])] for i in range(n_exs)]
legendprops = { 'loc' : 4, 'frameon' : False, 'scatterpoints' : 1, 'handletextpad' : 0.1,
'prop' : {'size' : SIZELABEL}, 'ncol' : 1 }
lineprops = { 'lw' : SIZELINE*1.5, 'linestyle' : '-', 'alpha' : 1.0 }
fillprops = { 'lw' : 0, 'alpha' : 0.3, 'interpolate' : True }
pprops = { 'xticks' : [],
'yticks' : [],
'hide' : ['top', 'bottom', 'left', 'right'],
'theme' : 'open' }
for i in range(n_exs):
pprops['colors'] = [colors[i]]
pprops['xlim'] = [ 0, 50]
pprops['ylim'] = [-0.08, 1.08]
if (i==n_exs-1):
pprops['xticks'] = [0, 10, 20, 30, 40, 50]
pprops['xlabel'] = 'Time (serial intervals)'
pprops['hide'] = ['top', 'left', 'right']
pprops['axoffset'] = 0.3
for k in range(simulations):
if allele_number[i] in mutant_sites[k]:
loc = np.where(np.array(mutant_sites[k])==allele_number[i])[0][0]
ydat = traj[k][loc]
xdat = np.arange(len(traj[k][loc]))*record[k]
else:
ydat = np.zeros(len(traj[0]))
xdat = np.arange(len(traj[0]))*record[k]
mp.line( ax=ax[i][0], x=[xdat], y=[ydat], plotprops=lineprops, **pprops)
mp.plot(type='fill', ax=ax[i][0], x=[xdat], y=[ydat], plotprops=fillprops, **pprops)
### selection coefficient estimates
dashlineprops = { 'lw' : SIZELINE * 2.0, 'ls' : ':', 'alpha' : 0.5 }
sprops = dict(lw=0, s=9., marker='o')
pprops = { 'yticks': [],
'xticks': [],
'hide' : ['top', 'bottom', 'left', 'right'],
'theme' : 'open' }
for i in range(n_exs):
pprops['xlim'] = [-0.04, 0.04]
pprops['ylim'] = [ 0.5, 1.5]
ydat = [1]
xdat = [inferred[i]]
xerr = error[i]
if (i==n_exs-1):
pprops['xticks'] = [-0.04, -0.02, 0, 0.02, 0.04]
pprops['xticklabels'] = [ ' ', r'$-2$', r'$0$', r'$2$', r'$4$']
pprops['xlabel'] = 'Inferred selection\ncoefficient, ' + r'$\hat{s}$' + ' (%)'
pprops['hide'] = ['top', 'left', 'right']
pprops['axoffset'] = 0.3
mp.plot(type='error', ax=ax[i][1], x=[xdat], y=[ydat], xerr=[xerr], colors=[colors[i]], **pprops)
ax[i][1].axvline(x=actual_unique[(-(i//2) + 2)%3], color=BKCOLOR, **dashlineprops)
### add legend
colors = [C_BEN, C_NEU, C_DEL]
colors_lt = [C_BEN_LT, C_NEU_LT, C_DEL_LT]
plotprops = DEF_ERRORPROPS.copy()
plotprops['clip_on'] = False
invt = ax[-1][-1].transData.inverted()
xy1 = invt.transform(( 0, 0))
xy2 = invt.transform((7.50, 9))
xy3 = invt.transform((3.00, 9))
xy4 = invt.transform((5.25, 9))
legend_dx1 = xy1[0]-xy2[0]
legend_dx2 = xy1[0]-xy3[0]
legend_dx = xy1[0]-xy4[0]
legend_dy = xy1[1]-xy2[1]
legend_x = 0.015
legend_y = [-4, -4 + legend_dy, -4 + (2*legend_dy)]
legend_t = ['Beneficial', 'Neutral', 'Deleterious']
for k in range(len(legend_y)):
mp.error(ax=ax[-1][-1], x=[[legend_x+legend_dx]], y=[[legend_y[k]]], edgecolor=[colors[k]], facecolor=[colors_lt[k]], plotprops=plotprops, **pprops)
ax[-1][-1].text(legend_x, legend_y[k], legend_t[k], ha='left', va='center', **DEF_LABELPROPS)
yy = -4 + (3.5*legend_dy)
mp.line(ax=ax[-1][-1], x=[[legend_x+legend_dx2, legend_x+legend_dx1]], y=[[yy, yy]], colors=[BKCOLOR], plotprops=dict(lw=SIZELINE, ls=':', clip_on=False), **pprops)
ax[-1][-1].text(legend_x, yy, 'True selection\ncoefficient', ha='left', va='center', **DEF_LABELPROPS)
yy = -4 + (5.5*legend_dy)
mp.line(ax=ax[-1][-1], x=[[legend_x+legend_dx2, legend_x+legend_dx1]], y=[[yy, yy]], colors=[BKCOLOR], plotprops=dict(lw=SIZELINE, ls='-', clip_on=False), **pprops)
ax[-1][-1].text(legend_x, yy, 'Mean of inferred\ncoefficients', ha='left', va='center', **DEF_LABELPROPS)
### bounding boxes
ax[0][0].text(box_traj['left']-0.08, box_traj['top']+0.01, 'a'.lower(), transform=fig.transFigure, **DEF_SUBLABELPROPS)
ax[0][0].text(box_hist['left']-0.08, box_hist['top']+0.03, 'b'.lower(), transform=fig.transFigure, **DEF_SUBLABELPROPS)
lineprops = { 'lw' : AXWIDTH/2., 'ls' : '-', 'alpha' : 1.0 }
pprops = { 'xlim' : [0, 1], 'xticks' : [], 'ylim' : [0, 1], 'yticks' : [],
'hide' : ['top','bottom','left','right'], 'plotprops' : lineprops }
txtprops = {'ha' : 'right', 'va' : 'center', 'color' : BKCOLOR, 'family' : FONTFAMILY,
'size' : SIZELABEL, 'rotation' : 90, 'transform' : fig.transFigure}
ax[0][0].text(box_traj['left']-0.025, box_traj['top'] - (box_traj['top']-box_traj['bottom'])/2., 'Frequency', **txtprops)
# boxprops = {'ec' : BKCOLOR, 'lw' : SIZELINE/2., 'fc' : 'none', 'clip_on' : False, 'zorder' : -100}
#
# dx = 0.005
# dxl = 0.000
# dxr = 0.001
# dy = 0.003
# ll = box_traj['left'] + dxl # left box left
# lb = box_traj['bottom'] - dy # left box bottom
# rl = (box_traj['left'] + box_traj['right'])/2. + dx + dxl # right box left
# wd = (box_traj['right'] - box_traj['left'])/2. - dxr # box width
# ht = (box_traj['top'] - box_traj['bottom']) + (2. * dy) # box height
#
# recL = matplotlib.patches.Rectangle(xy=(ll, lb), width=wd, height=ht, transform=fig.transFigure, **boxprops)
# recL = ax[0][0].add_patch(recL)
# recR = matplotlib.patches.Rectangle(xy=(rl, lb), width=wd, height=ht, transform=fig.transFigure, **boxprops)
# recR = ax[0][0].add_patch(recR)
# b -- distribution of inferred selection coefficients
rep_data = np.load(replicate_file, allow_pickle=True)
positive, negative, zero = [], [], []
for sim in range(len(rep_data['inferred'])):
traj = trajectory_reshape(rep_data['traj'][sim])
inferred = rep_data['inferred'][sim]
actual = rep_data['actual'][sim]
mutants = rep_data['mutant_sites'][sim]
error = rep_data['errors'][sim]
mutant_sites_all = np.sort(np.unique(np.array([mutants[i][j] for i in range(len(mutants))
for j in range(len(mutants[i]))])))
selection = {}
for i in range(len(mutant_sites_all)):
selection[mutant_sites_all[i]] = actual[i]
allele_number = find_significant_sites(traj, mutants, cutoff)
L_traj = len(allele_number)
selection_new = {}
for i in range(L_traj):
selection_new[allele_number[i]] = selection[allele_number[i]]
actual = [selection_new[i] for i in allele_number]
mutant_sites_all = np.sort(np.unique(np.array([mutants[i][j] for i in range(len(mutants))
for j in range(len(mutants[i]))])))
inferred = filter_sites_infer(traj, inferred, mutants, mutant_sites_all, allele_number)
inf_minus, inf_zero, inf_plus = sort_by_sign(selection_new, inferred)
positive += inf_plus
negative += inf_minus
zero += inf_zero
### plot histogram
gs_hist = gridspec.GridSpec(1, 1, **box_hist)
ax_hist = plt.subplot(gs_hist[0, 0])
colors = [C_DEL, C_NEU, C_BEN]
tags = ['deleterious', 'neutral', 'beneficial']
s_true_loc = [-s_value, 0, s_value]
s_est_loc = [np.mean(negative), np.mean(zero), np.mean(positive)]
dashlineprops = { 'lw' : SIZELINE * 2.0, 'ls' : ':', 'alpha' : 0.5 }
fulllineprops = { 'lw' : SIZELINE * 2.0, 'ls' : '-', 'alpha' : 0.5 }
histprops = dict(histtype='bar', lw=SIZELINE/2, rwidth=0.8, ls='solid', alpha=0.7, edgecolor='none',
orientation='vertical')
pprops = { 'xlim': [-0.10, 0.10],
'xticks': [ -0.10, -0.075, -0.05, -0.025, 0., 0.025, 0.05, 0.075, 0.10],
'xticklabels': [r'$-10$', r'$-7.5$', r'$-5$', r'$-2.5$', r'$0$', r'$2.5$', r'$5$', r'$7.5$', r'$10$'],
'ylim': [0., 0.075],
'yticks': [0., 0.025, 0.05, 0.075],
'yticklabels': [r'$0$', r'$2.5$', r'$5$', r'$7.5$'],
'ylabel': 'Frequency (%)',
'xlabel': 'Inferred selection coefficient, ' + r'$\hat{s}$' + ' (%)',
'bins': np.arange(-0.10, 0.10+0.002, 0.002),
'combine': True,
'plotprops': histprops,
'axoffset': 0.1,
'theme': 'open' }
s_vals = [negative, zero, positive]
for i in range(len(tags)):
x = [s_vals[i]]
tprops = dict(ha='left', va='center', family=FONTFAMILY, size=SIZELABEL, rotation=0, clip_on=False)
#ax_hist.text(0.102, s_true_loc[i], r'$s_{%s}$' % (tags[i]), color=colors[i], **tprops)
ax_hist.axvline(x=s_true_loc[i], color=colors[i], **dashlineprops)
ax_hist.axvline(x=s_est_loc[i], color=colors[i], **fulllineprops)
if i<len(tags)-1: mp.hist( ax=ax_hist, x=x, colors=[colors[i]], **pprops)
else: mp.plot(type='hist', ax=ax_hist, x=x, colors=[colors[i]], **pprops)
# SAVE FIGURE
plt.savefig('%s/fig-1%s' % (FIG_DIR, EXT), dpi = 1000, facecolor = fig.get_facecolor(), edgecolor=None, **FIGPROPS)
plt.close(fig)
def plot_selection_statistics_extended(selection_file_all, selection_file_ns, label2ddr=[]):
""" Makes a plot containing the distribution of inferred coefficients and the enrichment of nonsynonymous mutations.
Combines selection_dist_plot and syn_plot functions.
correlation_plots determines whether or not the 3 plots containing the correlations are made."""
# loading and processing the data
df_sel_all = pd.read_csv(selection_file_all, comment='#', memory_map=True)
# PLOT FIGURE
## set up figure grid
w = SINGLE_COLUMN #SLIDE_WIDTH
hshrink = 0.85/0.60
goldh = w * hshrink
fig = plt.figure(figsize=(w, goldh))
box_hist = dict(left=0.15, right=0.95, bottom=0.80, top=0.95)
box_circ = dict(left=0.07, right=0.92, bottom=0.05, top=0.65)
## a -- selection coefficient histogram
### plot histogram
s_link = np.array(df_sel_all['selection coefficient'])
s_pos_log = np.log10(s_link[s_link>0])
s_neg_log = np.log10(-s_link[s_link<0])
n_hist = 15
xmin = -3
xmax = -1
x_neg = np.linspace(xmin, xmax, num=n_hist)
x_pos = np.linspace(xmin, xmax, num=n_hist)
y_neg = []
y_pos = []
y_neu = []
for i in range(len(x_neg)-1):
y_neg.append(np.sum((s_neg_log>x_neg[i]) * (s_neg_log<=x_neg[i+1])))
y_neg.append(np.sum(s_neg_log>x_neg[-1]))
y_neu.append(np.sum(s_neg_log<x_neg[0]))
for i in range(len(x_pos)-1):
y_pos.append(np.sum((s_pos_log>x_pos[i]) * (s_pos_log<=x_pos[i+1])))
y_pos.append(np.sum(s_pos_log>x_pos[-1]))
y_neu.append(np.sum(s_pos_log<x_pos[0]))
norm = 4
c_neg = [hls_to_rgb(0.02, 1., 0.83)]
c_pos = [hls_to_rgb(0.58, 1., 0.60)]
for i in range(len(y_neg)):
t = np.min([(x_neg[i] + norm)/norm, 1])
c_neg.append(hls_to_rgb(0.58, 0.53 * t + 1. * (1 - t), 0.60))
t = np.min([(x_pos[i] + norm)/norm, 1])
c_pos.append(hls_to_rgb(0.02, 0.53 * t + 1. * (1 - t), 0.83))
wspace = 0.20
gs_hist = gridspec.GridSpec(1, 2, width_ratios=[1, 1], wspace=wspace, **box_hist)
hist_props = dict(lw=SIZELINE/2, width=1.5/n_hist, align='center', edgecolor=[BKCOLOR], orientation='vertical')
### negative coefficient subplot
ax_neg = plt.subplot(gs_hist[0, 0])
pprops = { 'colors': c_neg,
'xlim': [xmin-4.5/n_hist, -1][::-1],
'xticks': [-3-3/n_hist, -2, -1],
'xticklabels': ['>-0.1', '-1', '-10'],
'logy': True,
'ylim': [1e0, 1e4],
'yticks': [1e0, 1e2, 1e4],
'ylabel': 'Counts',
'hide': ['top', 'right'] }
x_neg = [[-3-2/n_hist]] + [[i] for i in x_neg]
y_neg = [[y_neu[0]]] + [[i] for i in y_neg]
mp.plot(type='bar', ax=ax_neg, x=x_neg, y=y_neg, plotprops=hist_props, **pprops)
### positive coefficient subplot
ax_pos = plt.subplot(gs_hist[0, 1])
pprops = { 'colors': c_pos,
'xlim': [xmin-4.5/n_hist, -1],
'xticks': [-3-3/n_hist, -2, -1],
'xticklabels': ['<0.1', '1', '10'],
'logy': True,
'ylim': [1e0, 1e4],
'yticks': [],
'hide': ['top', 'right', 'left'] }
x_pos = [[-3-2/n_hist]] + [[i] for i in x_pos]
y_pos = [[y_neu[1]]] + [[i] for i in y_pos]
mp.plot(type='bar', ax=ax_pos, x=x_pos, y=y_pos, plotprops=hist_props, **pprops)
## b -- circle plot
gs_circ = gridspec.GridSpec(1, 1, width_ratios=[1.0], height_ratios=[1.0], **box_circ)
ax_circ = plt.subplot(gs_circ[0, 0])
df_sel = pd.read_csv(selection_file_ns, comment='#', memory_map=True)
sig_s, sig_site_real, sig_nuc_idx = plot_circle_formatter(ax_circ, df_sel, label2ddr)
# MAKE LEGEND
ax_neg.text(box_hist['left'] + (box_hist['right']-box_hist['left'])/2, box_hist['bottom']-0.06, 'Inferred selection coefficients, ' + '$\hat{s}$' + ' (%)',
ha='center', va='top', transform=fig.transFigure, **DEF_LABELPROPS)
invt = ax_circ.transData.inverted()
xy1 = invt.transform((0,0))
xy2 = invt.transform((0,9))
coef_legend_x = 0.60
coef_legend_dx = 0.05
coef_legend_y = -0.89
coef_legend_dy = (xy1[1]-xy2[1])
txtprops = dict(ha='center', va='center', color=BKCOLOR, family=FONTFAMILY, size=SIZELABEL)
ex_s = [-0.05, -0.04, -0.03, -0.02, -0.01, 0, 0.01, 0.02, 0.03, 0.04, 0.05]
show_s = [ 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]
c_s = [C_DEL, C_BEN]
for i in range(len(ex_s)):
plotprops = dict(lw=0, marker='o', s=(np.fabs(ex_s[i]) - DS_NORM)*S_MULT if ex_s[i]!=0 else 0, clip_on=False)
mp.scatter(ax=ax_circ, x=[[coef_legend_x + i*coef_legend_dx]], y=[[coef_legend_y]], colors=[c_s[ex_s[i]>0]], plotprops=plotprops)
if show_s[i]:
ax_circ.text(coef_legend_x + i*coef_legend_dx, coef_legend_y + 0.75*coef_legend_dy, '%d' % (100*ex_s[i]), **txtprops)
ax_circ.text(coef_legend_x + len(ex_s)*coef_legend_dx/2, coef_legend_y + (2.25*coef_legend_dy), 'Inferred selection\ncoefficient, $\hat{s}$ (%)', **txtprops)
ax_neg.text( 0.05, box_hist['top']+0.01, 'a'.lower(), transform=fig.transFigure, **DEF_SUBLABELPROPS)
ax_circ.text(0.05, box_circ['top']+0.01, 'b'.lower(), transform=fig.transFigure, **DEF_SUBLABELPROPS)
# SAVE FIGURE
plt.savefig('%s/fig-2%s' % (FIG_DIR, EXT), dpi = 1000, facecolor = fig.get_facecolor(), edgecolor=None, **FIGPROPS)
plt.close(fig)
def plot_variant_selection(variant_file, trajectory_file, variant_list=[], variant_names=[], s_cutoff=0.05):
""" Map out mutations in the major variants, their aggregate inferred selection coefficients, and
frequency trajectories across regions. """
# Load data
df_var = pd.read_csv(variant_file, comment='#', memory_map=True)
df_sel = 0
if len(variant_list)>0:
df_sel = df_var[df_var.variant_names.isin(variant_list)]
temp_var_list = list(df_sel.variant_names)
temp_var_names = []
for i in range(len(temp_var_list)):
temp_var_names.append(variant_names[variant_list.index(temp_var_list[i])])
variant_list = np.array(temp_var_list)
variant_names = np.array(temp_var_names)
else:
df_sel = df_var[np.fabs(df_var.selection_coefficient)>s_cutoff]
variant_list = np.array(list(df_sel.variant_names))
variant_names = np.array(variant_list)
s_sort = np.argsort(np.fabs(df_sel.selection_coefficient))[::-1]
print('variant\tw')
for i in range(len(df_sel)):