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figures.py
2759 lines (2239 loc) · 120 KB
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figures.py
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import os
import sys
import numpy as np
import scipy as sp
import scipy.stats as st
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import matplotlib.colors as mcolors
import matplotlib.gridspec as gridspec
from matplotlib.font_manager import FontProperties
import matplotlib.patches as mpatches
import matplotlib.ticker as ticker
from colorsys import hls_to_rgb
import mplot as mp
import seaborn as sns
import logomaker as lm
from matplotlib.lines import Line2D
# Plot variables
FIG_DPI = 600
COLOR_1 = '#1295D8'
COLOR_2 = '#FFB511'
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_POP = C_DEL
C_PREF = LCOLOR
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
# paper style
FONTFAMILY = 'Arial'
SIZESUBLABEL = 8
SIZELABEL = 6
SIZETICK = 6
SMALLSIZEDOT = 6.
SIZELINE = 0.6
AXES_FONTSIZE = 6
AXWIDTH = 0.4
DEF_SUBLABEL = {
'family': FONTFAMILY,
'size': SIZESUBLABEL,
'weight': 'bold'
}
DEF_ERRORPROPS = {
'mew' : AXWIDTH,
'markersize' : SMALLSIZEDOT/2,
'fmt' : 'o',
'elinewidth' : SIZELINE/2,
'capthick' : 0,
'capsize' : 0
}
DEF_LABELPROPS = {
'family' : FONTFAMILY,
'size' : SIZELABEL,
'color' : BKCOLOR,
'clip_on': False
}
DEF_TEXTPROPS = {
'family' : FONTFAMILY,
'size' : SIZELABEL
}
FIGPROPS = {
'transparent' : True,
'dpi' : FIG_DPI,
#'bbox_inches' : 'tight'
}
matplotlib.rc('font', **DEF_TEXTPROPS)
FIG_DIR = './figures/'
PREF_DIR = './output/merged_preference/'
POP_DIR = './output/selection_coefficients/'
COL_SITE = 'site'
COL_AA = 'amino_acid'
COL_WT = 'WT_indicator'
COL_S = 'joint'
AA = ['A', 'R', 'N', 'D', 'C', 'E', 'Q', 'G', 'H', 'I', 'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V', '*']
NAME2NAME = {
'Flu_WSN': 'WSN',
'Flu_A549': 'PB2 A549',
'Flu_CCL141': 'PB2 CCL141',
'HIV_BG505': 'Env BG505',
'HIV_BF520': 'Env BF520',
'HIV_CD4_human': 'BF520 hu',
'HIV_CD4_rhesus': 'BF520 rhm',
'ZIKV': 'ZIKV',
'Perth2009': 'Perth2009',
'HIV_bnAbs_VRC34': 'VRC34',
'HIV_bnAbs_FP16': 'FP16-02',
'HIV_bnAbs_FP20': 'FP20-01',
'Flu_MS': 'NP MS',
'Flu_MxA': 'NP MxA',
'Flu_MxAneg': 'NP MxAneg',
'Flu_MatrixM1': 'M1',
'Flu_Aichi68C': 'Aichi68',
'Flu_PR8': 'PR8',
'WWdomain_YAP1': 'WW',
'Ubiq_Ube4b': 'Ube4b',
'HDR_Y2H_1': 'Y2H 1',
'HDR_Y2H_2': 'Y2H 2',
'HDR_E3': 'E3',
'HDR_DBR1': 'DBR1',
'Thrombo_TpoR_1': 'TpoR 1',
'Thrombo_TpoR_2': 'TpoR 2',
}
######################
# FIGURE 1 OVERVIEW AND METHODS COMPARISON
def fig_methods_comparison(fig_title='fig-1-overview.pdf'):
''' Show performance comparison across data sets '''
input_files = {
'Flu_WSN': ['WSN', 3],
'Flu_A549': ['A549', 3],
'Flu_CCL141': ['CCL141', 3],
'Flu_Aichi68C': ['Aichi68C', 2],
'Flu_PR8': ['PR8' , 3],
'Flu_MatrixM1': ['Matrix_M1', 3],
'ZIKV': ['ZIKV', 3],
'Perth2009': ['Perth2009', 4],
'Flu_MS': ['MS', 2],
'Flu_MxAneg': ['MxAneg', 2],
'HIV_BG505': ['HIV Env BG505' , 3],
'HIV_BF520': ['HIV Env BF520' , 3],
'HIV_CD4_human': ['HIV BF520 human', 2],
'HIV_CD4_rhesus': ['HIV BF520 rhesus', 2],
'HIV_bnAbs_FP16': ['HIV bnAbs FP16', 2],
'HIV_bnAbs_FP20': ['HIV bnAbs FP20', 2],
'HIV_bnAbs_VRC34': ['HIV bnAbs VRC34', 2],
'HDR_Y2H_1': ['Y2H_1', 3],
'HDR_Y2H_2': ['Y2H_2', 3],
'HDR_E3': ['E3', 6],
'WWdomain_YAP1': ['YAP1', 2],
'Ubiq_Ube4b': ['Ube4b', 2],
'HDR_DBR1': ['DBR1', 2],
'Thrombo_TpoR_1': ['TpoR', 6],
'Thrombo_TpoR_2': ['TpoR_S505N', 6]
}
print('protein\t\t\tR (pop)\tR (alt)\tR (between, Pearson/Spearman)')
pref_avg = {}
pop_avg = {}
df_ex = 0
ex_prot = 'Ubiq_Ube4b'
rps, rss = [], [] # Pearson and Spearman correlations between methods
for target_protein, info in input_files.items():
reps = info[1]
rep_list = ['rep_'+str(i+1) for i in range(reps)]
path = POP_DIR + info[0] + '_selection_coefficients.csv.gz'
df_sele = pd.read_csv(path)
# print('%s\t%d' % (target_protein, len(np.unique(df_sele[COL_SITE]))))
if target_protein==ex_prot:
df_ex = df_sele.copy(deep=True)
path = PREF_DIR + info[0] + '.csv.gz'
df_pref = pd.read_csv(path)
df_corr_pref = df_pref[[i for i in rep_list]]
df_corr_pref = df_corr_pref.dropna()
df_corr_sele = df_sele[[i for i in rep_list]]
df_corr_sele = df_corr_sele.loc[~(df_corr_sele==0).all(axis=1)]
# df_merged = pd.merge(df_pref, df_sele, on=['site', 'amino_acid'], how='inner')
df_corr_pref = df_corr_pref[rep_list]
df_corr_sele = df_corr_sele[rep_list]
corr_avg_pref = (df_corr_pref.corr().sum().sum() - reps)/(reps**2 - reps)
pref_avg[target_protein] = corr_avg_pref
corr_avg_pop = (df_corr_sele.corr().sum().sum() - reps)/(reps**2 - reps)
pop_avg[target_protein] = corr_avg_pop
# Check correlations between popDMS and alternatives
df_merge = pd.merge(df_pref, df_sele, how='left', left_on=['site', 'amino_acid'], right_on=['site', 'amino_acid']).dropna()
rp, pp = st.pearsonr(df_merge['joint'], df_merge['average'])
rs, ps = st.spearmanr(df_merge['joint'], df_merge['average'])
print('%s\t%.2f\t%.2f\t%.2f\t%.2f' % (target_protein + ''.join((2-len(target_protein)//8)*['\t']),
corr_avg_pop, corr_avg_pref, rp, rs))
rps.append(rp)
rss.append(rs)
print('average\t\t\t%.2f\t%.2f\t%.2f\t%.2f\n' % (np.mean(list(pop_avg.values())), np.mean(list(pref_avg.values())), np.mean(rps), np.mean(rss)))
# variables
w = DOUBLE_COLUMN
h = DOUBLE_COLUMN * 1.4 / GOLDR
fig = plt.figure(figsize = (w, h))
box_t = 0.75
box_b = 0.05
box_l = 0.09
box_r = 0.98
box_dx = 0.05
box_dy = box_dx * (w / h) # scale the box by the same amount along the x and y axes
box_y = (box_t - box_b - 3*box_dy)/2
box_x = box_y * (h / w) / 2
box_comp = dict(left=box_l, right=box_r, bottom=box_t-box_y, top=box_t)
box_rep1 = dict(left=box_l, right=box_l+2*box_x+1*box_dx, bottom=box_t-2*box_y-3*box_dy, top=box_t-box_y-2*box_dy)
box_rep2 = dict(left=box_l+2*box_x+2.3*box_dx, right=box_l+4*box_x+3.3*box_dx, bottom=box_t-2*box_y-3*box_dy, top=box_t-box_y-2*box_dy)
box_dr2 = dict(left=box_l+4*box_x+5.0*box_dx, right=box_r, bottom=box_t-2*box_y-3*box_dy, top=box_t-box_y-2*box_dy)
gs_comp = gridspec.GridSpec(1, 1, wspace=0, **box_comp)
gs_rep1 = gridspec.GridSpec(2, 2, hspace=box_dy, wspace=box_dx, **box_rep1)
gs_rep2 = gridspec.GridSpec(2, 2, hspace=box_dy, wspace=box_dx, **box_rep2)
gs_dr2 = gridspec.GridSpec(1, 1, wspace=0, **box_dr2)
n_sites = len(np.unique(df_ex[COL_SITE]))
box_ll = 0.09
box_tt = 0.97
box_bb = box_t+0.6*box_dy
box_rr = (box_tt - box_bb) * (h / w) * ((n_sites+1) / 21.5) + box_ll #((n_sites-1) / 21) + box_ll
box_heat = dict(left=box_ll, right=box_rr, bottom=box_bb, top=box_tt)
gs_heat = gridspec.GridSpec(1, 1, wspace=0, **box_heat)
ax_heat = plt.subplot(gs_heat[0, 0])
# PLOTS
# a -- heatmap
plot_selection(ax_heat, df_ex, legend=True)
ldx = -0.01
ldy = ldx * w/h
fig.text(box_heat['left']+ldx, (box_heat['top']+box_heat['bottom'])/2, s = 'Amino acids', rotation=90, ha='center', va='center', **DEF_LABELPROPS, transform=fig.transFigure)
fig.text((box_heat['left']+box_heat['right'])/2, box_heat['bottom']+ldy, s = 'Sites (Ube4b)', rotation=0, ha='center', va='center', **DEF_LABELPROPS, transform=fig.transFigure)
ax = plt.subplot(gs_comp[0, 0])
# sublabels
ldx = -0.05
ldy = -0.01
fig.text(box_heat['left']+ldx, box_heat['top']+ldy, s='a', **DEF_SUBLABEL, transform=fig.transFigure)
fig.text(box_comp['left']+ldx, box_comp['top']+ldy, s='b', **DEF_SUBLABEL, transform=fig.transFigure)
fig.text(box_rep1['left']+ldx, box_rep1['top']+ldy, s='c', **DEF_SUBLABEL, transform=fig.transFigure)
fig.text(box_rep2['left']+ldx, box_rep2['top']+ldy, s='d', **DEF_SUBLABEL, transform=fig.transFigure)
fig.text(box_dr2['left'] +ldx-0.01, box_dr2['top'] +ldy, s='e', **DEF_SUBLABEL, transform=fig.transFigure)
## b -- performance across data sets
pop_list = pop_avg.items()
pop_list = sorted(pop_list, key = lambda x: x[1], reverse = True)
x, y = zip(*pop_list)
ax.scatter(x, np.array(y)**2, color=C_POP, s=SMALLSIZEDOT*2)
pref_list = pref_avg.items()
x_, y_ = zip(*pref_list)
ax.scatter(x_, np.array(y_)**2, color=C_PREF, s=SMALLSIZEDOT*2)
ax.xaxis.set_ticks(x)
ax.xaxis.set_ticklabels([NAME2NAME[label] for label in x], rotation = 45, ha = 'right')
ax.set_ylim(0, 1.05)
## e -- differences in performance
ax_dr2 = plt.subplot(gs_dr2[0, 0])
keys = pop_avg.keys()
dr2 = np.array([pop_avg[k]**2 - pref_avg[k]**2 for k in keys])
print('R^2 increase of >0.5 in %d out of %d data sets' % (np.sum(dr2>0.5), len(dr2)))
print('Mean (median) R^2 gain: %.2f (%.2f)' % (np.mean(dr2), np.median(dr2)))
xmin, xmax = -0.80, 0.80
ymin, ymax = 0, 5
n_bins = 35
dmax = xmax - xmin
bin_d = dmax/n_bins
bins = np.arange(xmin, xmax+2*bin_d, bin_d)
hist_y = [np.sum((bins[i]<=dr2) & (dr2<bins[i+1])) for i in range(len(bins)-1)]
hist_y.append(np.sum(dr2>=bins[-1]))
hist_props = dict(lw=AXWIDTH/2, width=0.9*dmax/n_bins, align='center', orientation='vertical', edgecolor=[BKCOLOR], alpha=0.6)
lineprops = dict(lw=SIZELINE*1.2, ls='-', alpha=1.0, drawstyle='steps-pre', clip_on=True)
fillprops = dict(lw=0, alpha=0.2, interpolate=False, step='pre', clip_on=True)
dashlineprops = dict(lw=SIZELINE*1.2, ls=':', alpha=1.0, color=BKCOLOR)
tprops = dict(ha='right', family=FONTFAMILY, size=SIZELABEL, clip_on=False)
ax_dr2.text(ymax, 0.04, 'popDMS\nmore consistent', color=BKCOLOR, rotation=0, va='bottom', **tprops)
ax_dr2.text(ymax, -0.04, 'Alternative more consistent', color=BKCOLOR, rotation=0, va='top', **tprops)
ax_dr2.axhline(y=0, **dashlineprops)
tprops['va'] = 'center'
pprops = { 'colors': [COLOR_2],
'ylim': [xmin, xmax+0.03],
'xlim': [ymin, ymax],
'yticks': [-0.80, -0.60, -0.40, -0.20, 0, 0.20, 0.40, 0.60, 0.80],
'xticks': [],
'yaxstart': xmin,
'yaxend': xmax,
'axoffset': 0,
'ylabel': 'Improvement in ' + r'$R^2$' + ' between replicates',
'xlabel': 'Number of data sets',
'theme': 'open',
'hide': ['bottom','right'] }
mp.line( ax=ax_dr2, y=[bins+(bin_d/2)], x=[hist_y], plotprops=lineprops, **pprops)
mp.plot(type='fill', ax=ax_dr2, y=[bins+(bin_d/2)], x=[hist_y], plotprops=fillprops, **pprops)
# legend
ax.text(len(x)/2, -0.30, 'Data set', ha='center', va='center', **DEF_LABELPROPS)
ax.set_ylabel('Average fraction variantion explained\nbetween replicate inferred\nmutational effects, ' + r'$R^2$', fontsize = SIZELABEL)
ax.set_yticks([0, 0.20, 0.40, 0.60, 0.80, 1.0])
ax.set_ylim(0, 1)
ax.set_xlim([-0.5, len(x)-0.5])
colors = [C_POP, C_PREF]
colors_lt = [C_POP, C_PREF]
plotprops = DEF_ERRORPROPS.copy()
plotprops['clip_on'] = False
invt = ax.transData.inverted()
xy1 = invt.transform(( 0, 0))
xy2 = invt.transform((7.50, 10))
xy3 = invt.transform((3.00, 10))
xy4 = invt.transform((5.25, 10))
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 = 0.2
legend_y = [0.12, 0.12 + 1*legend_dy]
legend_t = ['popDMS', 'Ratio/regression methods']
for k in range(len(legend_y)):
mp.error(ax=ax, x=[[legend_x+legend_dx]], y=[[legend_y[k]]], edgecolor=[colors[k]], facecolor=[colors_lt[k]], plotprops=plotprops)
ax.text(legend_x, legend_y[k], legend_t[k], ha='left', va='center', **DEF_LABELPROPS)
ax.spines['right'].set_visible(False)
ax.spines[ 'top' ].set_visible(False)
ax.tick_params(axis = 'both', which = 'major', labelsize = SIZELABEL)
# Example inference with popDMS
data_set = 'HIV Env BF520'
n_reps = 3
df_temp = pd.read_csv(POP_DIR + data_set + '_selection_coefficients.csv.gz')
pop_list = []
for rep in range(1, n_reps+1):
pop_list.append(df_temp['rep_' + str(rep)].tolist())
df_temp = pd.read_csv(PREF_DIR + data_set + '.csv.gz')
df_temp = df_temp[~(df_temp == 0).any(axis=1)]
pref_list = []
for rep in range(1, n_reps+1):
pref_list.append(df_temp['rep_' + str(rep)].tolist())
scatterprops = dict(alpha=0.2, edgecolor='none', s=SMALLSIZEDOT)
# FUTURE: MAKE A LOOP INSTEAD
ticks = []
lim = [-1.1, 1.3]
td = (lim[1] - lim[0])*0.06
n_digits = 2
for i in range(n_reps):
for j in range(i+1, n_reps):
corr = round(st.pearsonr(pop_list[i], pop_list[j])[0], n_digits)
ax2_sub = 0
if i == 0 and j == 1:
ax2_sub = plt.subplot(gs_rep1[0, 0])
ax2_sub.set_ylabel('Replicate 2', fontsize = SIZELABEL)
if i == 0 and j == 2:
ax2_sub = plt.subplot(gs_rep1[1, 0])
ax2_sub.set_ylabel('Replicate 3', fontsize = SIZELABEL)
ax2_sub.set_xlabel('Replicate 1', fontsize = SIZELABEL)
if i == 1 and j == 2:
ax2_sub = plt.subplot(gs_rep1[1, 1])
ax2_sub.set_xlabel('Replicate 2', fontsize = SIZELABEL)
ax2_sub.scatter(pop_list[i], pop_list[j], **scatterprops)
ax2_sub.text(lim[0]+td, lim[1]-td, 'R = %.2f' % corr, fontsize = SIZELABEL)
ax2_sub.set_xticks(ticks)
ax2_sub.set_yticks(ticks)
ax2_sub.set_xlim(lim)
ax2_sub.set_ylim(lim)
ax2_sub.spines['right'].set_visible(False)
ax2_sub.spines[ 'top' ].set_visible(False)
ddx = 0.033
ddy = ddx * (w / h)
fig.text(box_rep1['left']-ddx, (box_rep1['top']+box_rep1['bottom'])/2, s='popDMS', rotation=90, ha='center', va='center', **DEF_LABELPROPS, transform=fig.transFigure)
fig.text((box_rep1['left']+box_rep1['right'])/2, box_rep1['bottom']-ddy, s='popDMS', rotation=0, ha='center', va='center', **DEF_LABELPROPS, transform=fig.transFigure)
# Scatter plot of sample experiment of Enrichment ratio
ticks = []
lim = [-0.05, 1.05]
td = (lim[1] - lim[0])*0.05
for i in range(3):
for j in range(i + 1, 3):
corr = round(st.pearsonr(pref_list[i], pref_list[j])[0], n_digits)
ax3_sub = 0
if i == 0 and j == 1:
ax3_sub = plt.subplot(gs_rep2[0, 0])
ax3_sub.set_ylabel('Replicate 2', fontsize = SIZELABEL)
if i == 0 and j == 2:
ax3_sub = plt.subplot(gs_rep2[1, 0])
ax3_sub.set_ylabel('Replicate 3', fontsize = SIZELABEL)
ax3_sub.set_xlabel('Replicate 1', fontsize = SIZELABEL)
if i == 1 and j == 2:
ax3_sub = plt.subplot(gs_rep2[1, 1])
ax3_sub.set_xlabel('Replicate 2', fontsize = SIZELABEL)
ax3_sub.scatter(pref_list[i], pref_list[j], c=C_PREF, **scatterprops)
ax3_sub.text(lim[0]+td, lim[1]-td, 'R = %.2f' % corr, fontsize = SIZELABEL)
ax3_sub.set_xticks(ticks)
ax3_sub.set_yticks(ticks)
ax3_sub.set_xlim(lim)
ax3_sub.set_ylim(lim)
ax3_sub.spines['right'].set_visible(False)
ax3_sub.spines[ 'top' ].set_visible(False)
ddx = 0.033
ddy = ddx * (w / h)
fig.text(box_rep2['left']-ddx, (box_rep2['top']+box_rep2['bottom'])/2, s='Enrichment ratio', rotation=90, ha='center', va='center', **DEF_LABELPROPS, transform=fig.transFigure)
fig.text((box_rep2['left']+box_rep2['right'])/2, box_rep2['bottom']-ddy, s='Enrichment ratio', rotation=0, ha='center', va='center', **DEF_LABELPROPS, transform=fig.transFigure)
fig.savefig(FIG_DIR + fig_title, **FIGPROPS)
# show consistency b/w inferred selection coefficients and past metrics
def fig_comp_epistasis():
# plot parameters
w = DOUBLE_COLUMN
h = DOUBLE_COLUMN / 2.7
fig = plt.figure(figsize = (w, h))
box_t = 0.95
box_b = 0.12
box_l = 0.04
box_r = 0.98
box_dx = 0.06
box_y = box_t - box_b
box_x = box_y * (h / w)
box_comp = dict(left=box_l + 0*box_x + 0*box_dx, right=box_l + 1*box_x + 0*box_dx, bottom=box_b, top=box_t)
box_epi = dict(left=box_l + 1*box_x + 1*box_dx, right=box_l + 2*box_x + 1*box_dx, bottom=box_b, top=box_t)
box_hist = dict(left=box_l + 2*box_x + 2*box_dx, right=box_r, bottom=box_b+0.08, top=box_t)
# a -- compare selection coefficients and preferences in BG505
pop_file = 'output/selection_coefficients/HIV Env BG505_selection_coefficients.csv.gz'
alt_file = 'output/merged_preference/HIV Env BG505.csv.gz'
df_pop = pd.read_csv(pop_file)
df_alt = pd.read_csv(alt_file)
df_merge = pd.merge(df_pop, df_alt, how='left', left_on=['site', 'amino_acid'], right_on=['site', 'amino_acid']).dropna()
x = np.log10(df_merge['average'])
y = df_merge['joint']
gs_comp = gridspec.GridSpec(1, 1, **box_comp)
ax_comp = plt.subplot(gs_comp[0, 0])
scatterprops = dict(alpha=0.2, lw=0, s=SMALLSIZEDOT)
ddx = 0.10
ddy = 0.03
xlim = [np.min(x)-ddx, np.max(x)+ddx]
ylim = [np.min(y)-ddy, np.max(y)+ddy]
xtd = (xlim[1] - xlim[0])*0.06
ytd = (ylim[1] - ylim[0])*0.06
pprops = { 'xlim': xlim,
'xticks': [],
'ylim': ylim,
'yticks': [],
'xlabel': 'Log10 enrichment ratios (dms_tools2)',
'ylabel': 'Selection coefficients (popDMS)',
'colors': [COLOR_2],
'plotprops': scatterprops,
'axoffset': 0.1,
'theme': 'open',
'hide': ['top', 'right'] }
mp.plot(type='scatter', ax=ax_comp, x=[x], y=[y], **pprops)
corr = st.spearmanr(x, y)[0]
ax_comp.text(xlim[0]+xtd, ylim[1]-2*ytd, 'HIV-1 Env BG505\n' + r'$\rho$' + ' = %.2f' % corr, fontsize=SIZELABEL)
# b -- epistasis heatmap
## process data
epi_file = 'output/epistasis/merged_epistasis.csv.gz'
df_epi = pd.read_csv(epi_file)
df_epi['average'] = (df_epi['log2wab-log2wa-log2wb_rep1'] + df_epi['log2wab-log2wa-log2wb_rep2'])/2
df_epi['joint'] = df_epi['popDMS_consensus_joint']
print('epistasis correlations between popDMS and regression')
print('Pearson \t%.2f,\tp=%.2e' % (st.pearsonr(df_epi['average'], df_epi['joint'])[0], st.pearsonr(df_epi['average'], df_epi['joint'])[1]))
print('Spearman\t%.2f,\tp=%.2e' % (st.spearmanr(df_epi['average'], df_epi['joint'])[0], st.spearmanr(df_epi['average'], df_epi['joint'])[1]))
sites = np.unique(list(np.unique(df_epi['site_1'])) + list(np.unique(df_epi['site_2'])))
N = len(sites)
episq = np.zeros((N, N))
smax = 0
fmax = 0
for i in range(N):
for j in range(i+1, N):
df_sub = df_epi[(df_epi['site_1']==sites[i]) & (df_epi['site_2']==sites[j])]
ssq = np.sum(np.array(df_sub['joint'])**2)
fsq = np.sum(np.array(df_sub['average'])**2)
episq[i, j] = ssq
episq[j, i] = fsq
if ssq>smax:
smax = ssq
if fsq>fmax:
fmax = fsq
for i in range(N):
for j in range(i+1, N):
episq[i, j] /= smax
episq[j, i] /= fmax
## make plot
gs_epi = gridspec.GridSpec(1, 1, **box_epi)
ax_epi = plt.subplot(gs_epi[0, 0])
rec_props = dict(height=1, width=1, ec=None, lw=0, clip_on=False, angle=45)
a = 1 / np.sqrt(2)
rec_patches = []
ticks = [1, 10, 20, 30]
tickprops = dict(family=FONTFAMILY, size=SIZELABEL, clip_on=False, rotation_mode='anchor')
for i in range(N):
for j in range(i+1, N):
cij = hls_to_rgb(0.58, 0.53 * episq[i,j] + 1. * (1 - episq[i,j]), 0.60)
cji = hls_to_rgb(0.00, 0.59 * episq[j,i] + 1. * (1 - episq[j,i]), 0.00)
recij = matplotlib.patches.Rectangle(xy=(a*(i+j-1), a*(1+j-i)), fc=cij, **rec_props)
recji = matplotlib.patches.Rectangle(xy=(a*(i+j-1), a*(1+i-j)), fc=cji, **rec_props)
rec_patches.append(recij)
rec_patches.append(recji)
if (i==0) and (j in ticks):
ax_epi.text(a*(i+j-2.5), a*(1.50+j-i), s=j, ha='center', va='bottom', rotation=45, **tickprops)
ax_epi.text(a*(i+j-3.0), a*(2.25+i-j), s=j, ha='center', va='top', rotation=-45, **tickprops)
ax_epi.text(a*(N/2-2.5-2.5), a*(1.50+N/2+2.5), s='Sites (WW domain)', ha='center', va='bottom', rotation=45, **tickprops)
ax_epi.text(a*(N/2-2.5-3.0), a*(2.25-N/2-2.5), s='Sites (WW domain)', ha='center', va='top', rotation=-45, **tickprops)
container_props = dict(height=N, width=N, ec=BKCOLOR, fc='none', lw=AXWIDTH/2, clip_on=False, angle=45)
rec = matplotlib.patches.Rectangle(xy=(a*(N-2), a*(2-N)), **container_props)
rec_patches.append(rec)
pprops = { 'colors': [BKCOLOR],
'xlim': [0, N/a],
'ylim': [-N/(2*a), N/(2*a)],
'xticks': [],
'yticks': [],
'plotprops': dict(lw=0, s=0.2*SMALLSIZEDOT, marker='o'),
'theme': 'open',
'axoffset': 0,
'hide' : ['top', 'bottom', 'left', 'right'] }
mp.plot(type='scatter', ax=ax_epi, x=[[-10]], y=[[0]], **pprops)
## legend
rec_props = dict(height=1, width=1, ec=None, lw=0, clip_on=False)
n_boxes = 14
x_box = -2
y_box = -26.5
for i in range(n_boxes+1):
t = i/n_boxes
cij = hls_to_rgb(0.58, 0.53 * t + 1. * (1 - t), 0.60)
cji = hls_to_rgb(0.00, 0.59 * t + 1. * (1 - t), 0.00)
recij = matplotlib.patches.Rectangle(xy=(x_box+i, y_box), fc=cij, **rec_props)
recji = matplotlib.patches.Rectangle(xy=(x_box+i, y_box-1), fc=cji, **rec_props)
rec_patches.append(recij)
rec_patches.append(recji)
# container_props = dict(height=2, width=n_boxes+1, ec=BKCOLOR, fc='none', lw=AXWIDTH/2, clip_on=False)
# rec = matplotlib.patches.Rectangle(xy=(x_box, y_box-1), **container_props)
# rec_patches.append(rec)
for patch in rec_patches:
ax_epi.add_artist(patch)
tprops = dict(family=FONTFAMILY, size=SIZELABEL, clip_on=False)
ax_epi.text(x_box+(n_boxes+1)/2, y_box+1.5, s='Normalized sum of\nsquared epistatic interactions', ha='center', va='bottom', **tprops)
ax_epi.text(x_box+0.5, y_box-1.5, s='0', ha='center', va='top', **tprops)
ax_epi.text(x_box+n_boxes+0.5, y_box-1.5, s='1', ha='center', va='top', **tprops)
ax_epi.text(a*(3*N/2-2.5-0.5), a*(1.50+N/2+2.5), s='popDMS', ha='center', va='bottom', rotation=-45, **tickprops)
ax_epi.text(a*(3*N/2-2.5+0.0), a*(2.25-N/2-2.5), s='Log ratio regression', ha='center', va='top', rotation=45, **tickprops)
# c -- epistasis sparsity
gs_hist = gridspec.GridSpec(1, 1, **box_hist)
ax_hist = plt.subplot(gs_hist[0, 0])
# prepare histogram of log10 counts for sum of squared epistasis values
epi_max = 1
n_bins = 50
bin_dx = epi_max / n_bins
bins = np.arange(-bin_dx, epi_max+2*bin_dx, bin_dx)
epi_pop = np.array([episq[i, j] for i in range(N) for j in range(i+1, N)])
epi_alt = np.array([episq[j, i] for i in range(N) for j in range(i+1, N)])
hist_pop = [np.sum((bins[i]<=epi_pop) & (epi_pop<bins[i+1])) for i in range(len(bins)-2)]
hist_pop.append(np.sum(epi_pop>=bins[-2]))
hist_pop.append(0)
hist_alt = [np.sum((bins[i]<=epi_alt) & (epi_alt<bins[i+1])) for i in range(len(bins)-2)]
hist_alt.append(np.sum(epi_alt>=bins[-2]))
hist_alt.append(0)
for i in range(len(hist_pop)):
if hist_pop[i]>0:
hist_pop[i] = 1 + np.log10(hist_pop[i])
else:
hist_pop[i] = 0
for i in range(len(hist_alt)):
if hist_alt[i]>0:
hist_alt[i] = 1 + np.log10(hist_alt[i])
else:
hist_alt[i] = 0
hist_props = dict(lw=AXWIDTH/2, width=1.0*epi_max/n_bins, align='center', alpha=0.2,
orientation='vertical')#, edgecolor=[BKCOLOR])
pprops = { 'xlim': [0, epi_max+2*bin_dx],
'xticks': [ 0, 0.25, 0.5, 0.75, 1+bin_dx],
'xticklabels': [ 0, 0.25, 0.5, 0.75, 1.0],
'ylim': [0.75, 3.25],
'yticks': [0.75, 1, 2, 3, 3.25],
'yticklabels': ['', r'$1$', r'$10$', r'$10^2$', ''],
'xlabel': 'Normalized sum of\nsquared epistatic interactions',
'ylabel': 'Counts',
'colors': [COLOR_1, LCOLOR],
'plotprops': hist_props,
'axoffset': 0.1,
'theme': 'open',
'hide': ['top', 'right'] }
mp.plot(type='bar', ax=ax_hist, x=[bins+(bin_dx/2), bins+(bin_dx/2)], y=[hist_pop, hist_alt], **pprops)
lineprops = {'lw': SIZELINE, 'ls': '-', 'alpha': 1, 'drawstyle': 'steps-mid'}
pprops['plotprops'] = lineprops
mp.line(ax=ax_hist, x=[bins+(bin_dx/2), bins+(bin_dx/2)], y=[hist_pop, hist_alt], **pprops)
legend_x = 0.55
legend_d = -0.04
legend_y = 3.15
legend_dy = 0.125
plotprops = dict(mew=AXWIDTH, markersize=SMALLSIZEDOT/2, fmt='o', elinewidth=SIZELINE/2,
capthick=0, capsize=0, clip_on=False, alpha=0.6)
mp.error(ax=ax_hist, x=[[legend_x + legend_d]], y=[[legend_y]], colors=[COLOR_1], plotprops=plotprops)
ax_hist.text(legend_x, legend_y, 'popDMS', ha='left', va='center', **DEF_LABELPROPS)
mp.error(ax=ax_hist, x=[[legend_x + legend_d]], y=[[legend_y - legend_dy]], colors=[LCOLOR], plotprops=plotprops)
ax_hist.text(legend_x, legend_y - legend_dy, 'Log ratio regression', ha='left', va='center', **DEF_LABELPROPS)
ax_hist.yaxis.set_label_coords(-0.16, 0.5)
# sublabels
ddx = 0.025
ddy = ddx * (w / h)
fig.text(box_comp['left']-ddx, box_comp['top'], s = 'a', transform=fig.transFigure, **DEF_SUBLABEL)
fig.text(box_epi['left']-ddx, box_epi['top'], s = 'b', transform=fig.transFigure, **DEF_SUBLABEL)
fig.text(box_hist['left']-ddx, box_hist['top'], s = 'c', transform=fig.transFigure, **DEF_SUBLABEL)
plt.savefig(FIG_DIR + 'fig-2-comp-epistasis.pdf', **FIGPROPS)
def plot_selection(ax, df_pop, legend=True):
""" Plot selection heatmap. """
# process stored data
sites = np.unique(df_pop[COL_SITE])
df_WT = df_pop[df_pop[COL_WT]==True]
WT = [df_WT[df_WT[COL_SITE]==s].iloc[0][COL_AA] for s in sites]
s_WT = [df_WT[df_WT[COL_SITE]==s].iloc[0][COL_S] for s in sites]
s_vec = [[df_pop[(df_pop[COL_SITE]==sites[i]) & (df_pop[COL_AA]==aa)].iloc[0][COL_S] - s_WT[i] for aa in AA] for i in range(len(sites))]
s_norm = np.max(np.fabs(s_vec))
# plot selection across the protein, normalizing WT residues to zero
site_rec_props = dict(height=1, width=1, ec=None, lw=AXWIDTH/2, clip_on=False)
prot_rec_props = dict(height=len(AA), width=len(sites), ec=BKCOLOR, fc='none', lw=AXWIDTH/2, clip_on=False)
cBG = '#F5F5F5'
rec_patches = []
WT_dots_x = []
WT_dots_y = []
for i in range(len(sites)):
WT_dots_x.append(i + 0.5)
WT_dots_y.append(len(AA)-AA.index(WT[i])-0.5)
for j in range(len(AA)):
# skip WT
if AA[j]==WT[i]:
continue
# fill BG for unobserved
c = cBG
if s_vec[i][j]!=-s_WT[i]:
t = s_vec[i][j] / s_norm
if np.fabs(t)>1:
t /= np.fabs(t)
if t>0:
c = hls_to_rgb(0.02, 0.53 * t + 1. * (1 - t), 0.83)
else:
c = hls_to_rgb(0.58, 0.53 * np.fabs(t) + 1. * (1 - np.fabs(t)), 0.60)
rec = matplotlib.patches.Rectangle(xy=(i, len(AA)-1-j), fc=c, **site_rec_props)
rec_patches.append(rec)
rec = matplotlib.patches.Rectangle(xy=(0, 0), **prot_rec_props)
rec_patches.append(rec)
# add patches and plot
for patch in rec_patches:
ax.add_artist(patch)
pprops = { 'colors': [BKCOLOR],
'xlim': [0, len(sites) + 1],
'ylim': [0, len(AA) + 0.5],
'xticks': [],
'yticks': [],
'plotprops': dict(lw=0, s=0.2*SMALLSIZEDOT, marker='o', clip_on=False),
#'xlabel': 'Sites',
#'ylabel': 'Amino acids',
'theme': 'open',
'axoffset': 0,
'hide' : ['top', 'bottom', 'left', 'right'] }
mp.plot(type='scatter', ax=ax, x=[WT_dots_x], y=[WT_dots_y], **pprops)
# legend
rec_patches = []
if legend:
invt = ax.transData.inverted()
xy1 = invt.transform((0,0))
xy2 = invt.transform((0,9))
legend_dy = (xy1[1]-xy2[1])/3 # multiply by 3 for slides/poster
xloc = len(sites) + 2 + 6 # len(sites) - 6.5
yloc = 17 # -4
for i in range(-5, 5+1, 1):
c = cBG
t = i/5
if t>0:
c = hls_to_rgb(0.02, 0.53 * t + 1. * (1 - t), 0.83)
else:
c = hls_to_rgb(0.58, 0.53 * np.fabs(t) + 1. * (1 - np.fabs(t)), 0.60)
rec = matplotlib.patches.Rectangle(xy=(xloc + i, yloc), fc=c, **site_rec_props)
rec_patches.append(rec)
txtprops = dict(ha='center', va='center', color=BKCOLOR, family=FONTFAMILY, size=SIZELABEL)
ax.text(xloc+0.5, yloc-4*legend_dy, 'Mutation effects', clip_on=False, **txtprops)
ax.text(xloc-4.5, yloc+2*legend_dy, '-', clip_on=False, **txtprops)
ax.text(xloc+5.5, yloc+2*legend_dy, '+', clip_on=False, **txtprops)
xloc = len(sites) + 2 # 0
yloc = 11 # -4
c = cBG
rec = matplotlib.patches.Rectangle(xy=(xloc, yloc + 4*legend_dy), fc=c, **site_rec_props) # paper
# rec = matplotlib.patches.Rectangle(xy=(xloc, yloc + 6*legend_dy), fc=c, **site_rec_props) # slides
rec_patches.append(rec)
for patch in rec_patches:
ax.add_artist(patch)
mp.scatter(ax=ax, x=[[xloc + 0.5]], y=[[yloc + 0.5]], **pprops)
txtprops['ha'] = 'left'
ax.text(xloc + 1.5, yloc + 0.5, 'WT amino acid', clip_on=False, **txtprops)
ax.text(xloc + 1.5, yloc + 0.5 + 4*legend_dy, 'Not observed', clip_on=False, **txtprops) # paper
# ax.text(xloc + 1.3, yloc + 0.5 + 6*legend_dy, 'Not observed', clip_on=False, **txtprops) # slides
######################
# Supplementary figures
######################
# FIGURE 1 FINITE SAMPLING SIMULATION
# FIGURE 1 ARGUMENTS
# FIGURE 1 MAIN PLOT FUNCTION
def fig_finite_sampling():
FIG1_A_POS = {
'x': 0.05,
'y': 0.95
}
FIG1_B_POS = {
'x': 0.05,
'y': 0.48
}
FIG1_SIZE_X = SINGLE_COLUMN
FIG1_SIZE_Y = SINGLE_COLUMN
FIG1_SIMU_SCALE = 'linear'
FIG1_VAR = 'R'
FIG1_HSPACE = 0.4
FIG1_FINITE_NUM = 10
FIG1_MARKER_SIZE = 5
FIG1_GEN = 10
FIG1_ALPHA = 0.3
FIG1_BOX_ARG = dict(left = 0.2, right = 0.95, bottom = 0.1, top = 0.95)
FIG1_TRUE_COLOR = COLOR_2
FIG1_TRUE_ALPHA = 1.0
FIG1_TRAJECTORY_DIR = './output/simulation/WF_finite_sampling/'
FIG1_INFERENCE_DIR = './output/simulation/WF_mutational_effects/'
FIG1_WF_SIMU_FILE = './output/simulation/WF_simulation.csv'
FIG1_WF_SELECTION = FIG1_INFERENCE_DIR + '/selection_coefficients/'
FIG1_WF_LOGREG = FIG1_INFERENCE_DIR + '/log_regression/'
FIG1_WF_RATIO = FIG1_INFERENCE_DIR + '/enrichment_ratio/'
FIG1_WF_LOGRATIO = FIG1_INFERENCE_DIR + '/enrichment_ratio_log/'
# FIG1_FINITE_SIZE = '_sampling-10000/'
FIG1_NAME = 'fig-s1-sampling.pdf'
fig = plt.figure(figsize = (FIG1_SIZE_X, FIG1_SIZE_Y))
gs = fig.add_gridspec(2, 1, hspace = FIG1_HSPACE, **FIG1_BOX_ARG)
fig.text(**FIG1_A_POS, s = 'a', **DEF_SUBLABEL, transform = fig.transFigure)
fig.text(**FIG1_B_POS, s = 'b', **DEF_SUBLABEL, transform = fig.transFigure)
# Plot trajectories
ax1 = fig.add_subplot(gs[:1, 0])
ax1.set_xlabel('Generation', fontsize = SIZELABEL)
ax1.set_ylabel('Variant frequency', fontsize = SIZELABEL)
ax1.tick_params(axis = 'both', which = 'major', labelsize = SIZELABEL)
ax1.set_yscale(FIG1_SIMU_SCALE)
num_traj = 0
for traj_num in range(FIG1_FINITE_NUM):
df_trajectory = pd.read_csv(FIG1_TRAJECTORY_DIR + 'rep-%s'%traj_num + '.csv.zip', index_col=0)
for i in [FIG1_VAR]:
ax1.plot([i for i in range(FIG1_FINITE_NUM)], df_trajectory.loc[i][:FIG1_GEN], color = BKCOLOR, alpha = FIG1_ALPHA)
df_trajectory = pd.read_csv(FIG1_WF_SIMU_FILE, index_col=0)
col_list = df_trajectory.T.columns.tolist()
for i in [FIG1_VAR]:
ax1.plot(df_trajectory.columns[:FIG1_GEN], df_trajectory.T[i][:FIG1_GEN], color = FIG1_TRUE_COLOR, alpha = FIG1_TRUE_ALPHA)
# legend_fig1_a = [Line2D([0], [0], color = FIG1_FINITE_COLOR, lw = 2, label = 'Finitely sampled'),
# Line2D([0], [0], color = FIG1_TRUE_COLOR, lw = 2, label = 'True evolution')]
# ax1.legend(handles = legend_fig1_a, frameon = False, fontsize = SIZELABEL)
ax1.spines['right'].set_visible(False)
ax1.spines['top'].set_visible(False)
ax1.set_yscale('log')
ax1.set_xticks([0, 3, 6, 9])
# legend
colors = [FIG1_TRUE_COLOR, BKCOLOR]
colors_lt = [FIG1_TRUE_COLOR, LCOLOR]
plotprops = DEF_ERRORPROPS.copy()
plotprops['clip_on'] = False
invt = ax1.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 = -2.4e-4
legend_x = 0.015
legend_y = [1e-3, 1e-3 + legend_dy]
legend_t = ['True evolution', 'Finitely sampled']
for k in range(len(legend_y)):
mp.error(ax=ax1, x=[[legend_x+legend_dx]], y=[[legend_y[k]]], edgecolor=[colors[k]], facecolor=[colors_lt[k]], plotprops=plotprops)
ax1.text(legend_x, legend_y[k], legend_t[k], ha='left', va='center', **DEF_LABELPROPS)
# second panel
ax2 = fig.add_subplot(gs[1:, 0])
generation = 10
replicates = 100
sample_index = 10
generations=[1, 4, 9, 19]
finite_list = [50000]
marker = ['o', 'v', '*']
generation_ = [i + 1 for i in generations[: -1]]
for finite_sampling in finite_list:
temp = [[],[],[],[]]
for generation in generation_:
df_select = pd.read_csv(FIG1_INFERENCE_DIR + '/selection_coefficients_gen-%s_'%generation+'sampling-%s'%finite_sampling+'.csv.zip', index_col=0)
df_enrichment_regress = pd.read_csv(FIG1_INFERENCE_DIR + '/log_regression_gen-%s_'%generation+'sampling-%s'%finite_sampling+'.csv.zip', index_col=0)
df_enrichment_ratio = pd.read_csv(FIG1_INFERENCE_DIR + '/enrichment_ratio_gen-%s_'%generation+'sampling-%s'%finite_sampling+'.csv.zip', index_col=0)
df_enrichment_ratio_log = pd.read_csv(FIG1_INFERENCE_DIR + '/enrichment_ratio_log_gen-%s_'%generation+'sampling-%s'%finite_sampling+'.csv.zip', index_col=0)
enrichment_ratio_corr = df_enrichment_ratio.T.corr(method = 'pearson')
log_regression_corr = df_enrichment_regress.T.corr(method = 'pearson')
selection_coefficients_corr = df_select.T.corr(method = 'pearson')
enrichment_ratio_log_corr = df_enrichment_ratio_log.T.corr(method = 'pearson')
factor = replicates * replicates - replicates
enrichment_ratio_corr = (enrichment_ratio_corr.sum().sum() - replicates)/factor
log_regression_corr = (log_regression_corr.sum().sum() - replicates)/factor
selection_coefficients_corr = (selection_coefficients_corr.sum().sum() - replicates)/factor
enrichment_ratio_log_corr = (enrichment_ratio_log_corr.sum().sum() - replicates)/factor
temp[0].append(selection_coefficients_corr)
temp[1].append(enrichment_ratio_corr)
temp[2].append(log_regression_corr)
temp[3].append(enrichment_ratio_log_corr)
PALETTE = sns.hls_palette(3)
ax2.plot(generation_, temp[0], c = C_POP, marker = marker[finite_list.index(finite_sampling)], markersize = SMALLSIZEDOT, markeredgewidth = 0, alpha = 0.6)
ax2.plot(generation_, temp[1], c = PALETTE[0], marker = marker[finite_list.index(finite_sampling)], markersize = SMALLSIZEDOT, markeredgewidth = 0, alpha = 0.6)
ax2.plot(generation_, temp[2], c = PALETTE[1], marker = marker[finite_list.index(finite_sampling)], markersize = SMALLSIZEDOT, markeredgewidth = 0, alpha = 0.6)
ax2.plot(generation_, temp[3], c = PALETTE[2], marker = marker[finite_list.index(finite_sampling)], markersize = SMALLSIZEDOT, markeredgewidth = 0, alpha = 0.6)
ax2.set_xlabel('Generations used for inference', fontsize = SIZELABEL)
ax2.set_ylabel('Average Pearson correlation between\nreplicate inferred mutational effects', fontsize = SIZELABEL)
ax2.set_xticks([2, 5, 10])
ax2.set_xlim ([ 0, 11])
ax2.set_ylim ([-0.05, 1])
# legend_fig_1b = [Line2D([0], [0], color = '#4F94CD', lw = 2, label = 'popDMS', alpha = 0.6),
# Line2D([0], [0], color = 'red', lw = 2, label = 'Enrichment ratio', alpha = 0.6),
# Line2D([0], [0], color = 'orange', lw = 2, label = 'Log scaled ratio', alpha = 0.6),
# Line2D([0], [0], color = 'green', lw = 2, label = 'Log regression', alpha = 0.6)
# ]