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main_visualization_pseudotime.py
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import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import numpy as np
import pandas as pd
import scanpy as sc
import pandas as pd
import numpy as np
import pytorch_lightning as pl
import torch as th
import itertools
import random
from dataloader import data_base
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.cluster import *
from sklearn import metrics
import warnings
warnings.filterwarnings("ignore")
sns.set_style('white', {'legend.frameon':True})
colors_palette = ['#1F77B4', '#FF7F0E', '#2CA02C', '#D62728', '#9467BD',
'#8C564B', '#E377C2', '#BCBD22', '#17BECF', '#40004B',
'#762A83', '#9970AB', '#C2A5CF', '#E7D4E8', '#F7F7F7',
'#D9F0D3', '#A6DBA0', '#5AAE61', '#1B7837', '#00441B',
'#8DD3C7', '#FFFFB3', '#BEBADA', '#FB8072', '#80B1D3',
'#FDB462', '#B3DE69', '#FCCDE5', '#D9D9D9', '#BC80BD',
'#CCEBC5', '#FFED6F', '#edf8b1', '#c7e9b4', '#7fcdbb',
'#41b6c4', '#1d91c0', '#225ea8', '#253494', '#081d58']
def get_geodesic_parameters(u, v, eps=1e-10):
if all(u) == 0:
u = np.array([eps, eps])
if all(v) == 0:
v = np.array([eps, eps])
nu = u[0]**2 + u[1]**2
nv = v[0]**2 + v[1]**2
a = (u[1]*nv - v[1]*nu + u[1] - v[1]) / (u[0]*v[1] - u[1]*v[0])
b = (v[0]*nu - u[0]*nv + v[0] - u[0]) / (u[0]*v[1] - u[1]*v[0])
return a, b
def plot_poincare_disc(x, radius=None, labels=None, title_name=None, model_name=None,
labels_name='labels', labels_order=None, labels_pos=None, labels_text=None,
file_name=None, coldict=None,
d1=4.5, d2=4.0, fs=9, ms=20,
u=None, v=None, alpha=1.0,
col_palette=plt.get_cmap("tab10"), print_labels=True,
bbox=(1.3, 0.7), leg=True, ft='png'):
idx = np.random.permutation(len(x))
df = pd.DataFrame(x[idx, :], columns=['pm1', 'pm2'])
fig = plt.figure(figsize=(d1, d2))
ax = plt.gca()
circle = plt.Circle((0, 0), radius=radius, fc='none', color='black')
ax.add_patch(circle)
ax.plot(0, 0, '.', c=(0, 0, 0), ms=4)
if title_name:
ax.set_title(title_name, fontsize=fs)
if not (labels is None):
df[labels_name] = labels[idx]
if labels_order is None:
labels_order = np.unique(labels)
if coldict is None:
coldict = dict(zip(labels_order, col_palette[:len(labels)]))
sns.scatterplot(x="pm1", y="pm2", hue=labels_name, sizes=1,
hue_order=labels_order,
palette=coldict,
alpha=alpha, edgecolor="none",
data=df, ax=ax, s=ms)
if leg:
ax.legend(fontsize=fs, loc='best', bbox_to_anchor=bbox, facecolor='white')
else:
ax.legend_.remove()
else:
sns.scatterplot(x="pm1", y="pm2",
data=df, ax=ax, s=ms)
if leg == False:
ax.legend_.remove()
if not (u is None):
a, b = get_geodesic_parameters(u, v)
circle_geo = plt.Circle((-a/2, -b/2), radius=np.sqrt(a**2/4 + b**2/4 - 1), fc='none', color='grey')
ax.add_patch(circle_geo)
fig.tight_layout()
ax.axis('off')
ax.axis('equal')
if print_labels:
if labels_text is None:
labels_list = np.unique(labels)
else:
labels_list = np.unique(labels_text)
if labels_pos is None:
labels_pos = {}
for l in labels_list:
ix_l = np.where(labels == l)[0]
if model_name == 'dhv':
Dl = poincare_distance_dhv(th.DoubleTensor(x[ix_l, :])).numpy()
if model_name == 'dv':
Dl = poincare_distance_dv(th.DoubleTensor(x[ix_l, :])).numpy()
else:
Dl = poincare_distance_poinmaps(th.DoubleTensor(x[ix_l, :])).numpy()
i = ix_l[np.argmin(Dl.sum(axis=0))]
labels_pos[l] = i
for l in labels_list:
ax.text(x[labels_pos[l], 0], x[labels_pos[l], 1], l, fontsize=fs)
plt.tight_layout()
if file_name:
if ft == 'png':
plt.savefig(file_name + '.' + ft, format=ft, dpi=600)
else:
plt.savefig(file_name + '.' + ft, format=ft)
return labels_pos
def get_projected_coordinates(u):
if u[0] == 0 or u[1] == 0:
return [np.sign(u[0]), np.sign(u[1])]
c = u[1] / u[0]
x_c = np.sign(u[0])*np.sqrt(1 / (1+c**2))
y_c = c*x_c
return [x_c, y_c]
def get_confusion_matrix(classes, true_labels_oder, true_labels, fname='', title='Confusion matrix'):
cm = np.zeros([len(np.unique(classes)), len(np.unique(true_labels))])
for il, l in enumerate(np.unique(classes)):
idx = np.where(classes == l)[0]
for it, tl in enumerate(true_labels_oder):
cm[il, it] = len(idx[np.where(true_labels[idx] == tl)[0]])
plot_confusion_matrix(cm, np.unique(classes), true_labels_oder,
normalize=True,
title=title,
cmap=plt.cm.Blues, fname=fname)
return cm
def plot_confusion_matrix(cm, classes, true_labels,
normalize=False,
title='Lineage matrix',
cmap=plt.cm.Blues, fs=9, fname=''):
if normalize:
cm = cm.astype('float') / cm.sum(axis=0)[np.newaxis, :]
fig = plt.figure(figsize=(3.5, 3.5))
plt.imshow(cm, interpolation='nearest', cmap=cmap)
x_tick_marks = np.arange(len(true_labels))
y_tick_marks = np.arange(len(classes))
plt.xticks(x_tick_marks, true_labels, rotation=45, fontsize=fs)
plt.yticks(y_tick_marks, classes, fontsize=fs)
fmt = '.2f' if normalize else '.0f'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black", fontsize=fs)
plt.tight_layout()
plt.grid(False)
plt.ylabel(title, fontsize=fs)
plt.xlabel('stages', fontsize=fs)
if fname != '':
plt.savefig(fname + '.png', format='png', dpi=600)
plt.show()
plt.close()
def linear_scale(embeddings):
embeddings = np.transpose(embeddings)
sqnorm = np.sum(embeddings ** 2, axis=1, keepdims=True)
dist = np.arccosh(1 + 2 * sqnorm / (1 - sqnorm))
dist = np.sqrt(dist)
dist /= dist.max()
sqnorm[sqnorm == 0] = 1
embeddings = dist * embeddings / np.sqrt(sqnorm)
return np.transpose(embeddings)
def poincare_translation(v, x):
"""
Computes the translation of x when we move v to the center.
Hence, the translation of u with -u should be the origin.
"""
xsq = (x ** 2).sum(axis=1)
vsq = (v ** 2).sum()
xv = (x * v).sum(axis=1)
a = np.matmul((xsq + 2 * xv + 1).reshape(-1, 1),
v.reshape(1, -1)) + (1 - vsq) * x
b = xsq * vsq + 2 * xv + 1
return np.dot(np.diag(1. / b), a)
def euclidean_distance_poinmaps(x):
th.set_default_tensor_type('torch.DoubleTensor')
# print('computing euclidean distance...')
nx = x.size(0)
x = x.contiguous()
x = x.view(nx, -1)
norm_x = th.sum(x ** 2, 1, keepdim=True).t()
ones_x = th.ones(nx, 1)
xTx = th.mm(ones_x, norm_x)
xTy = th.mm(x, x.t())
d = (xTx.t() + xTx - 2 * xTy)
d[d < 0] = 0
return d
def poincare_distance_poinmaps(x):
# print('computing poincare distance...')
eps = 1e-5
boundary = 1 - eps
nx = x.size(0)
x = x.contiguous()
x = x.view(nx, -1)
norm_x = th.sum(x ** 2, 1, keepdim=True)
sqdist = euclidean_distance_poinmaps(x) * 2
squnorm = 1 - th.clamp(norm_x, 0, boundary)
x = (sqdist / th.mm(squnorm, squnorm.t())) + 1
z = th.sqrt(th.pow(x, 2) - 1)
return th.log(x + z)
def poincare_distance_dhv(x):
# print('computing poincare distance...')
import manifolds.poincare as poincare
PoincareBall = poincare.PoincareBall()
dist = PoincareBall.sqdist_xu_mobius_v2(x, x, c=1)
dist = dist.clamp(min=1e-12)
return dist
def poincare_distance_dv(x):
# print('computing poincare distance...')
import manifolds.poincare as poincare
PoincareBall = poincare.PoincareBall()
dist = PoincareBall.sqdist_xu_mobius_v2(x, x, c=1)
dist = dist.clamp(min=1e-22)
return dist
def init_scanpy_root(data, head_name, true_labels):
head_idx = np.where(true_labels == head_name)[0]
if len(head_idx) > 1:
D = pairwise_distances(data[head_idx, :], metric='euclidean')
iroot = head_idx[np.argmin(D.sum(axis=0))]
else:
iroot = head_idx[0]
return iroot
def init_scanpy_louvain(data, true_labels, k=30, n_pcs=20, computeEmbedding=True):
adata = sc.AnnData(data)
if computeEmbedding:
if n_pcs:
sc.pp.pca(adata, n_comps=n_pcs)
sc.pp.neighbors(adata, n_neighbors=k, n_pcs=n_pcs)
else:
sc.pp.neighbors(adata, n_neighbors=k)
sc.tl.louvain(adata, resolution=0.9)
louvain_labels = np.array(list(adata.obs['louvain']))
else:
louvain_labels = []
return iroot, louvain_labels
def get_scores(true_lineages, labels):
ARS = metrics.adjusted_rand_score(true_lineages, labels)
FMS = metrics.fowlkes_mallows_score(true_lineages, labels)
print(f"ARI = {ARS:.2f}")
print(f"FMS = {FMS:.2f}")
return ARS, FMS
def kMedoids(D, k, tmax=10000):
# determine dimensions of distance matrix D
m, n = D.shape
if k > n:
raise Exception('too many medoids')
# find a set of valid initial cluster medoid indices since we
# can't seed different clusters with two points at the same location
valid_medoid_inds = set(range(n))
invalid_medoid_inds = set([])
rs, cs = np.where(D == 0)
# the rows, cols must be shuffled because we will keep the first duplicate below
index_shuf = list(range(len(rs)))
np.random.shuffle(index_shuf)
rs = rs[index_shuf]
cs = cs[index_shuf]
for r, c in zip(rs, cs):
# if there are two points with a distance of 0...
# keep the first one for cluster init
if r < c and r not in invalid_medoid_inds:
invalid_medoid_inds.add(c)
valid_medoid_inds = list(valid_medoid_inds - invalid_medoid_inds)
if k > len(valid_medoid_inds):
raise Exception('too many medoids (after removing {} duplicate points)'.format(
len(invalid_medoid_inds)))
# randomly initialize an array of k medoid indices
M = np.array(valid_medoid_inds)
np.random.shuffle(M)
M = np.sort(M[:k])
# for i in np.unique(spectral_labels):
# idx = list(np.where(spectral_labels == i)[0])
# M[i] = random.sample(idx, 1)[0]
# create a copy of the array of medoid indices
Mnew = np.copy(M)
# initialize a dictionary to represent clusters
C = {}
for t in range(tmax):
# determine clusters, i. e. arrays of data indices
J = np.argmin(D[:, M], axis=1)
for kappa in range(k):
C[kappa] = np.where(J == kappa)[0]
# update cluster medoids
for kappa in range(k):
J = np.mean(D[np.ix_(C[kappa], C[kappa])], axis=1)
j = np.argmin(J)
Mnew[kappa] = C[kappa][j]
np.sort(Mnew)
# check for convergence
if np.array_equal(M, Mnew):
break
M = np.copy(Mnew)
else:
# final update of cluster memberships
J = np.argmin(D[:, M], axis=1)
for kappa in range(k):
C[kappa] = np.where(J == kappa)[0]
labels = np.empty((n))
for k in C.keys():
labels[list(C[k])] = str(k)
# return results
return labels
def detect_cluster(D, n_clusters=2, clustering_name='spectral', k=15, distances='Poincaré'):
if clustering_name == 'spectral':
similarity = np.exp(-D**2)
clustering = SpectralClustering(n_clusters=n_clusters, eigen_solver='arpack',
affinity='precomputed', n_neighbors=k).fit(similarity)
labels = clustering.labels_
elif clustering_name == 'kmedoids':
clustering = kMedoids(D, n_clusters, tmax=10000)
labels = clustering
else:
clustering = AgglomerativeClustering(linkage='average', n_clusters=n_clusters,
affinity='precomputed').fit(D)
labels = clustering.labels_
scores = get_scores(true_lineages, labels)
model.plot('rot', labels=labels, file_name=fout + '_' + clustering_name + '_' + distances,
title_name=f"{clustering_name} {distances}\nARI = {scores[0]:.2f}, FMS = {scores[1]:.2f}",
zoom=None, bbox=(1.1, 0.8))
title = f"{clustering_name} {distances}"
return scores, title
def get_clustering_score_table(louvain_labels, true_lineages):
nc = len(np.unique(louvain_labels))
scores = []
titles = []
scores.append(get_scores(true_lineages, louvain_labels))
titles.append('louvain')
model.plot('rot', labels=louvain_labels, file_name=fout + '_louvain',
title_name=f"louvain",
zoom=None, bbox=(1.1, 0.8))
for cname in ['spectral', 'agglomerative', 'kmedoids']:
s, t = detect_cluster(model.distances, n_clusters=nc, clustering_name=cname, k=15, distances='Poincaré')
scores.append(s)
titles.append(t)
for cname in ['spectral', 'agglomerative', 'kmedoids']:
s, t = detect_cluster(pairwise_distances(data),
n_clusters=nc, clustering_name=cname, k=15, distances='raw')
scores.append(s)
titles.append(t)
title_name_dict = {'X_pca': 'PCA',
'X_tsne': 't-SNE',
'X_umap': 'UMAP',
'X_diffmap': 'DiffusionMaps',
'X_draw_graph_fa': 'ForceAtlas2'}
for embedding_name in ['X_pca', 'X_tsne', 'X_umap', 'X_diffmap', 'X_draw_graph_fa']:
for cname in ['spectral', 'agglomerative', 'kmedoids']:
s, t = detect_cluster(pairwise_distances(adata.obsm[embedding_name]),
n_clusters=nc, clustering_name=cname, k=15, distances=title_name_dict[embedding_name])
scores.append(s)
titles.append(t)
return titles, scores
def plot_dp_on_poincare(coordinates, distances, cell=0, fs=9, title_name='None', file_name=None, d1=3.5, d2=3.1, ms=1):
fig = plt.figure(figsize=(d1, d2))
radius_new = np.sqrt(coordinates[:,0]**2 + coordinates[:,1]**2).max()
circle = plt.Circle((0, 0), radius=radius_new, color='black', fc="None")
cm = plt.cm.get_cmap('rainbow')
mycmap = distances
plt.gca().add_patch(circle)
plt.plot(0, 0, 'x', c=(0, 0, 0), ms=4)
if title_name:
# plt.title(title_name, fontsize=fs)
plt.scatter(coordinates[:, 0], coordinates[:, 1], c=mycmap, s=ms, cmap=cm)
plt.plot(coordinates[cell, 0], coordinates[cell, 1], 'd', c='red')
plt.plot(0, 0, 'x', c=(1, 1, 1), ms=4)
plt.axis('off')
plt.axis('equal')
plt.axis('off')
plt.axis('equal')
cb = plt.colorbar()
cb.ax.tick_params(labelsize=fs)
if file_name:
plt.savefig(file_name + '.png', format='png', dpi=600)
plt.show()
plt.close(fig)
def pseudtotime_comparison(adata, col_names, n_branchings=2):
sc.tl.dpt(adata, n_branchings=n_branchings)
diffpt = np.array(adata.obs['dpt_pseudotime'])
diffpt[diffpt == np.inf] = 1.2
model.plot('rot', labels=np.array(adata.obs['dpt_groups']),
file_name=fout + '_dpt', title_name='Poincaré map', zoom=None, bbox=(1.1, 0.8))
real_time = np.zeros(len(true_lineages))
for branch in np.unique(true_lineages):
idx = np.where(true_lineages == branch)[0]
real_time[idx] = idx - idx[0]
model.plot_pseudotime(data, col_names, true_labels, file_name=fout + '_realtime',
fs=8, idx=[], pm_pseudotime=real_time)
model.plot_pseudotime(data, col_names, true_labels, file_name=fout + '_poincare', fs=8, idx=[],
pm_pseudotime=model.distances[model.iroot])
model.plot_pseudotime(data, col_names, true_labels,
file_name=fout + '_diffuion', fs=8, idx=[], pm_pseudotime=diffpt)
return real_time, diffpt, model.distances[model.iroot]
def plotBenchamrks(adata, true_labels, fname_benchmark, pl_size=2.4, n1=2, n2=4, ms=3, fs=9, coldict=None, methods=['X_pca', 'X_umap', 'X_draw_graph_fa']):
labels_order=np.unique(true_labels)
if coldict is None:
coldict = dict(zip(labels_order, colors_palette[:len(labels_order)]))
fig, axs = plt.subplots(n1, n2, sharex=False, sharey=False, figsize=(n2*pl_size, n1*pl_size))
methods=['X_pca', 'X_tsne', 'X_umap', 'X_diffmap', 'X_draw_graph_fa', 'X_phate', 'X_pacmap', 'X_scdhmap']
title_name_dict = {'X_pca': 'PCA',
'X_tsne': 't-SNE',
'X_umap': 'UMAP',
'X_diffmap': 'DiffusionMaps',
'X_draw_graph_fa': 'ForceAtlas2',
'X_phate': 'PHATE',
'X_pacmap':'PacMAP',
'X_scdhmap':'scDHMAP'}
l=0
for i in range(n1):
for j in range(n2):
if l < len(methods):
method=methods[l]
title_name=title_name_dict[method]
axs_names=['x1', 'x2']
if method == 'X_diffmap':
x=adata.obsm[method]
else:
x=adata.obsm[method]
idx = np.random.permutation(len(x))
df = pd.DataFrame(x[idx, :], columns=axs_names)
df['labels'] = true_labels[idx]
axs[i, j].set_title(title_name, fontsize=fs)
axs[i, j].axis('equal')
axs[i, j].grid('off')
axs[i, j].set_xticks([])
axs[i, j].set_yticks([])
sns.scatterplot(x=axs_names[0], y=axs_names[1], hue='labels',
hue_order=labels_order,
palette=coldict,
alpha=1.0, edgecolor="none",
data=df, ax=axs[i, j], s=ms)
axs[i, j].set_xlabel(axs_names[0], fontsize=fs)
axs[i, j].set_ylabel(axs_names[1], fontsize=fs)
axs[i, j].legend_.remove()
fig.tight_layout()
else:
axs[i, j].axis('off')
axs[i, j].grid('off')
axs[i, j].yaxis.set_tick_params(labelsize=fs)
axs[i, j].xaxis.set_tick_params(labelsize=fs)
l += 1
fig.tight_layout()
plt.savefig(fname_benchmark + 'benchmarks.png', format='png', dpi=600, bbox_inches='tight')
plt.show()
class PoincareMaps:
def __init__(self, coordinates, model_name='dhv', cpalette=None):
self.coordinates = coordinates
self.distances = None
self.radius = np.sqrt(coordinates[:,0]**2 + coordinates[:,1]**2)
self.iroot = np.argmin(self.radius)
self.labels_pos = None
self.model_name = model_name
if cpalette is None:
self.colors_palette = colors_palette
else:
self.colors_palette = cpalette
def find_iroot(self, labels, head_name):
head_idx = np.where(labels == head_name)[0]
if len(head_idx) > 1:
D = self.distances[head_idx, :][head_idx]
self.iroot = head_idx[np.argmin(D.sum(axis=0))]
else:
self.iroot = head_idx[0]
def get_distances(self):
if self.model_name == 'dhv':
self.distances = poincare_distance_dhv(th.DoubleTensor(self.coordinates)).numpy()
if self.model_name == 'dv':
self.distances = poincare_distance_dv(th.DoubleTensor(self.coordinates)).numpy()
else:
self.distances = poincare_distance_poinmaps(th.DoubleTensor(self.coordinates)).numpy()
def rotate(self):
self.coordinates_rotated = poincare_translation(-self.coordinates[self.iroot, :], self.coordinates)
def plot(self, pm_type='ori', labels=None,
labels_name='labels', print_labels=False, labels_text=None,
labels_order=None, coldict=None, file_name=None, title_name=None, alpha=1.0,
zoom=None, show=True, d1=4.5, d2=4.0, fs=9, ms=20, bbox=(1.3, 0.7), u=None, v=None, leg=True, ft='png'):
if pm_type == 'ori':
coordinates = self.coordinates
radius_new = np.sqrt(coordinates[:,0]**2 + coordinates[:,1]**2).max()
elif pm_type == 'rot':
coordinates = self.coordinates_rotated
radius_new = np.sqrt(coordinates[:,0]**2 + coordinates[:,1]**2).max()
if labels_order is None:
labels_order = np.unique(labels)
if not (zoom is None):
if zoom == 1:
coordinates = np.array(linear_scale(coordinates))
else:
radius = np.sqrt(coordinates[:,0]**2 + coordinates[:,1]**2)
idx_zoom = np.where(radius <= 1/zoom)[0]
coordinates = coordinates[idx_zoom, :]
coordinates = np.array(linear_scale(coordinates))
coordinates[np.isnan(coordinates)] = 0
labels = labels[idx_zoom]
self.labels_pos = plot_poincare_disc(coordinates, radius=radius_new, title_name=title_name, model_name=self.model_name,
print_labels=print_labels, labels_text=labels_text,
labels=labels, labels_name=labels_name, labels_order=labels_order, labels_pos = self.labels_pos,
file_name=file_name, coldict=coldict, u=u, v=v, alpha=alpha,
d1=d1, d2=d2, fs=fs, ms=ms, col_palette=self.colors_palette, bbox=bbox, leg=leg, ft=ft)
def detect_lineages(self, n_lin=2, clustering_name='spectral', k=15, rotated=False):
pc_proj = []
if rotated:
x = self.coordinates_rotated
else:
x = self.coordinates
for i in range(len(x)):
pc_proj.append(get_projected_coordinates(x[i]))
if clustering_name == 'spectral':
clustering = SpectralClustering(n_clusters=n_lin, eigen_solver='arpack', affinity="nearest_neighbors", n_neighbors=k).fit(pc_proj)
elif clustering_name == 'dbs':
clustering = DBSCAN(eps=1/180, min_samples=10).fit(pc_proj)
elif clustering_name == 'kmeans':
clustering = KMeans(n_clusters=n_lin).fit(pc_proj)
else:
clustering = AgglomerativeClustering(linkage='ward', n_clusters=n_lin).fit(pc_proj)
self.lineages = clustering.labels_
def detect_cluster(self, n_clusters=2, clustering_name='spectral', k=15):
if clustering_name == 'spectral':
similarity = np.exp(-self.distances**2)
clustering = SpectralClustering(n_clusters=n_clusters, eigen_solver='arpack', affinity='precomputed', n_neighbors=k).fit(similarity)
else:
clustering = AgglomerativeClustering(linkage='average', n_clusters=n_clusters, affinity='precomputed').fit(self.distances**2)
self.clusters = clustering.labels_
def plot_distances(self, cell=None, pm_type='rot', ss=10, eps=4.0, file_name=None, title_name=None, idx_zoom=None, show=False, fs=8, ms=3):
if cell is None:
cell = self.iroot
if pm_type == 'ori':
coordinates = self.coordinates
elif pm_type == 'rot':
coordinates = self.coordinates_rotated
fig = plt.figure(figsize=(5, 5))
circle = plt.Circle((0, 0), radius=1, color='black', fc="None")
cm = plt.cm.get_cmap('rainbow')
mycmap = np.minimum(list(self.distances[:, cell]), eps)
plt.gca().add_patch(circle)
plt.plot(0, 0, 'x', c=(0, 0, 0), ms=ms)
if title_name:
plt.title(title_name, fontsize=fs)
plt.scatter(coordinates[:, 0], coordinates[:, 1], c=mycmap, s=ss, cmap=cm)
plt.plot(coordinates[cell, 0], coordinates[cell, 1], 'd', c='red')
plt.plot(0, 0, 'x', c=(1, 1, 1), ms=ms)
plt.axis('off')
plt.axis('equal')
plt.axis('off')
plt.axis('equal')
cb = plt.colorbar()
cb.ax.tick_params(labelsize=fs)
if file_name:
plt.savefig(file_name + '.png', format='png', dpi=600)
def plot_distances_between_clusters(self, labels, pm_type='rot', eps = 4.0, file_name=None, fs=9):
if pm_type == 'ori':
poincare_coord = self.coordinates
elif pm_type == 'rot':
poincare_coord = self.coordinates_rotated
cell_list = np.unique(labels)
n = len(labels)
n_plt = len(cell_list)
# n2 = int(np.sqrt(n_plt))
n2 = 3
n1 = n_plt // n2
if n1*n2 < n_plt:
n1 += 1
if n1 == 1:
n1 = 2
if n2 == 1:
n2 = 2
f, axs = plt.subplots(n1, n2, sharey=False, figsize=(n2*3, n1*3))
l=0
for i in range(n1):
for j in range(n2):
if l < n_plt:
cell = np.random.choice(np.where(labels == cell_list[l])[0])
mycmap = self.distances[cell]
circle = plt.Circle((0, 0), radius=1, color='black', fc="None")
axs[i, j].add_patch(circle)
axs[i, j].axis('off')
axs[i, j].axis('equal')
axs[i, j].plot(0, 0, 'x', c=(0, 0, 0), ms=6)
cm = plt.cm.get_cmap('rainbow')
sc = axs[i, j].scatter(poincare_coord[:, 0], poincare_coord[:, 1], c=mycmap, s=15, cmap=cm)
axs[i, j].set_title(cell_list[l], fontsize=fs)
axs[i, j].plot(poincare_coord[cell, 0], poincare_coord[cell, 1], 'd', c='red')
else:
axs[i, j].axis('off')
axs[i, j].axis('equal')
l+=1
if file_name:
plt.savefig(file_name + '.png', format='png', dpi=600)
def plot_markers(self, data, markesnames, pm_type='rot', file_name=None, fs=8, sc=3):
if pm_type == 'ori':
poincare_coord = self.coordinates
elif pm_type == 'rot':
poincare_coord = self.coordinates_rotated
n_plt = np.size(data, 1)
# n2 = int(np.sqrt(n_plt))
n2 = 3
n1 = n_plt // n2
if n1*n2 < n_plt:
n1 += 1
if n1 == 1:
n1 = 2
if n2 == 1:
n2 = 2
f, axs = plt.subplots(n1, n2, sharey=False, figsize=(n2*sc, n1*sc))
cm = plt.cm.get_cmap('jet')
l=0
for i in range(n1):
for j in range(n2):
axs[i, j].axis('off')
axs[i, j].axis('equal')
if l < n_plt:
circle = plt.Circle((0, 0), radius=1, color='black', fc="none")
axs[i, j].add_patch(circle)
axs[i, j].plot(0, 0, 'x', c=(0, 0, 0), ms=3)
axs[i, j].axis('equal')
sc = axs[i, j].scatter(poincare_coord[:, 0], poincare_coord[:, 1], c=data[:, l], s=5, cmap=cm)
axs[i, j].set_title(markesnames[l], fontsize=fs)
plt.colorbar(sc, ax=axs[i,j])
if l == n_plt:
axs[i, j].legend(np.unique(labels), loc='center left', bbox_to_anchor=(1, 0.5), fontsize=fs)
l+=1
if file_name:
plt.savefig(file_name + '.png', format='png', dpi=600)
import argparse
parser = argparse.ArgumentParser(description="*** author")
parser.add_argument('--name', type=str, default='digits_T', )
parser.add_argument("--offline", type=int, default=0)
parser.add_argument("--project_name", type=str, default="test")
parser.add_argument("--data_path", type=str, default="./data")
parser.add_argument("--knn", type=int, default=5)
parser.add_argument("--sigma", type=float, default=1.0)
parser.add_argument("--n_components", type=int, default=10)
parser.add_argument('--method', type=str, default='pca', choices=['pca', 'tsne', 'umap', 'phate', 'diffmap', 'forceatlas2', 'ivis', 'poinmaps', 'scphere_wn', 'scdhmap'])
# data set param
parser.add_argument(
"--data_name",
type=str,
default="Olsson",
choices=[
"Olsson",
],
)
# baseline param
parser.add_argument(
"--metric",
type=str,
default="euclidean",
)
parser.add_argument('--perplexity', type=int, default=10)
parser.add_argument('--min_dist', type=float, default=0.1)
args = pl.Trainer.add_argparse_args(parser)
args = args.parse_args()
dataset_f = getattr(data_base, args.data_name + "Dataset")
data_train = dataset_f(
data_name=args.data_name,
knn = args.knn,
sigma = args.sigma,
n_components = args.n_components,
train=True,
datapath=args.data_path,
)
data_name = args.data_name
data = data_train.data.numpy().reshape(data_train.data.shape[0], -1)
label = np.array(data_train.label)
sadata = data_train.sadata
true_labels = np.array(sadata.obs['celltype'])
ms = 5
fs = 10
col_dict = None
dv_coord = None
sc.settings.set_figure_params(dpi=600)
#### Visualization and Structure Preservation
if data_name == 'Olsson':
sadata.obsm['X_pca'] = np.load('logs/log_Olsson_baseline/path_pca/latent.npy')
sadata.obsm['X_tsne'] = np.load('logs/log_Olsson_baseline/path_tsne_40/latent.npy')
sadata.obsm['X_umap'] = np.load('logs/log_Olsson_baseline/path_umap_40_0.7/latent.npy')
sadata.obsm['X_diffmap'] = np.load('logs/log_Olsson_baseline/path_diffmap_10/latent.npy')
sadata.obsm['X_draw_graph_fa'] = np.load('logs/log_Olsson_baseline/path_forceatlas2_10/latent.npy')
sadata.obsm['X_phate'] = np.load('logs/log_Olsson_baseline/path_phate/latent.npy')
sadata.obsm['X_pacmap'] = np.load('logs/log_Olsson_baseline/path_pacmap_40_0.6_1.5/latent.npy')
sadata.obsm['X_scdhmap'] = np.load('logs/log_Olsson_baseline/path_scdhmap/latent.npy')
dv_coord = np.load('logs/log_Olsson_dv_poin/path_euclidean_poin_dist_mobiusm_v2_dsml_100.0_0.001_-1.0_5_eu_poin_2_100_2.0_Adam_leaky_relu_5_1.0_1.0_1000.0_no_no_dmt_aug_no_0.01_0.0_0.0_100.0_500_300_100/latent.npy')
poincare_coord = np.load('logs/log_Olsson_baseline/path_poincaremaps_15_2.0_2.0/latent.npy')
dhv_coord = np.load('logs/log_Olsson_new/path_Ours/latent.npy')
# ablation
# dhv_coord = np.load('logs/log_Olsson_ablation/log_Olsson_global_loss/path_Ours/latent_5_30_0.01_0.1_1.0_10.0_10.npy')
# dhv_coord = np.load('logs/log_Olsson_ablation/log_Olsson_hnn/path_Ours/latent_10_20_0.1_0.01_1.0_0.1_10.npy')
# dhv_coord = np.load('logs/log_Olsson_ablation/log_Olsson_local_loss/path_Ours/latent_5_10_0.03_0.03_1.0_10.0_10.npy')
# dhv_coord = np.load('logs/log_Olsson_ablation/log_Olsson_data_augmentation/path_Ours/latent_20_10_0.01_0.01_1.0_10.0_10.npy')
if data_name == 'MyeloidProgenitors' or data_name == 'Krumsiek11' or data_name == 'ToggleSwitch':
root = 'root'
elif data_name == "Moignard2015":
root = "PS"
elif data_name == 'Olsson':
root = "HSPC-1"
col_dict = {'Eryth': '#1F77B4',
'Gran': '#FF7F0E',
'HSPC-1': '#2CA02C',
'HSPC-2': '#D62728',
'MDP': '#9467BD',
'Meg': '#8C564B',
'Mono': '#E377C2',
'Multi-Lin': '#BCBD22',
'Myelocyte': '#17BECF'}
elif data_name == 'Paulpca' or data_name == 'PaulPcaKnn' or data_name == 'Paul':
root = "root"
col_dict = {'12Baso': '#0570b0', '13Baso': '#034e7b',
'11DC': '#ffff33',
'18Eos': '#2CA02C',
'1Ery': '#fed976', '2Ery': '#feb24c', '3Ery': '#fd8d3c', '4Ery': '#fc4e2a', '5Ery': '#e31a1c', '6Ery': '#b10026',
'9GMP': '#999999', '10GMP': '#4d4d4d',
'19Lymph': '#35978f',
'7MEP': '#E377C2',
'8Mk': '#BCBD22',
'14Mo': '#4eb3d3', '15Mo': '#7bccc4',
'16Neu': '#6a51a3','17Neu': '#3f007d',
'root': '#000000'}
elif data_name == 'Planaria':
root = 'neoblast 1'
col_dict = {'neoblast 1': '#CCCCCC',
'neoblast 2': '#7f7f7f',
'neoblast 3': '#E6E6E6',
'neoblast 4': '#D6D6D6',
'neoblast 5': '#C7C7C7',
'neoblast 6': '#B8B8B8',
'neoblast 7': '#A8A8A8',
'neoblast 8': '#999999',
'neoblast 9': '#8A8A8A',
'neoblast 10': '#7A7A7A',
'neoblast 11': '#6B6B6B',
'neoblast 12': '#5C5C5C',
'neoblast 13': '#4D4D4D',
'epidermis DVb neoblast': 'lightsteelblue',
'pharynx cell type progenitors': 'slategray',
'spp-11+ neurons': '#CC4C02',
'npp-18+ neurons': '#EC7014',
'otf+ cells 1': '#993404',
'ChAT neurons 1': '#FEC44F',
'neural progenitors': '#FFF7BC',
'otf+ cells 2': '#662506',
'cav-1+ neurons': '#eec900',
'GABA neurons': '#FEE391',
'ChAT neurons 2': '#FE9929',
'muscle body': 'firebrick',
'muscle pharynx': '#CD5C5C',
'muscle progenitors': '#FF6347',
'secretory 1': 'mediumpurple',
'secretory 3': 'purple',
'secretory 4': '#CBC9E2',
'secretory 2': '#551a8b',
'early epidermal progenitors': '#9ECAE1',
'epidermal neoblasts': '#C6DBEF',
'activated early epidermal progenitors': 'lightblue',
'late epidermal progenitors 2': '#4292C6',
'late epidermal progenitors 1': '#6BAED6',
'epidermis DVb': 'dodgerblue',
'epidermis': '#2171B5',
'pharynx cell type': 'royalblue',
'protonephridia': 'pink',
'ldlrr-1+ parenchymal cells': '#d02090',
'phagocytes': 'forestgreen',
'aqp+ parenchymal cells': '#cd96cd',
'pigment': '#cd6889',
'pgrn+ parenchymal cells': 'mediumorchid',
'psap+ parenchymal cells': 'deeppink',
'glia': '#cd69c9',
'goblet cells': 'yellow',
'parenchymal progenitors': 'hotpink',
'psd+ cells': 'darkolivegreen',
'gut progenitors': 'limegreen',
'branchNe': '#4292c6', 'neutrophil': '#08306b',
'branchMo': '#9e9ac8', 'monocyte': '#54278f',
'branchEr': '#fc9272', 'erythrocyt': '#cb181d',
'megakaryoc': '#006d2c', 'branchMe': '#74c476',
'proghead': '#525252', 'root': '#000000',
'interpolation': '#525252',
'Eryth': '#1F77B4',
'Gran': '#FF7F0E',
'HSPC-1': '#2CA02C',
'HSPC-2': '#D62728',
'MDP': '#9467BD',
'Meg': '#8C564B',
'Mono': '#E377C2',
'Multi-Lin': '#BCBD22',
'Myelocyte': '#17BECF',
'12Baso': '#0570b0', '13Baso': '#034e7b',
'11DC': '#ffff33',
'18Eos': '#2CA02C',
'1Ery': '#fed976', '2Ery': '#feb24c', '3Ery': '#fd8d3c', '4Ery': '#fc4e2a', '5Ery': '#e31a1c', '6Ery': '#b10026',
'9GMP': '#999999', '10GMP': '#4d4d4d',
'19Lymph': '#35978f',
'7MEP': '#E377C2',
'8Mk': '#BCBD22',
'14Mo': '#4eb3d3', '15Mo': '#7bccc4',
'16Neu': '#6a51a3','17Neu': '#3f007d',
'root': '#000000'}
elif data_name == 'UCEPIbc':
root = 'Stem'
elif data_name == 'UCIMMbc':
col_dict = {'CD4+ Activated Fos-hi': '#1f77b4',
'CD4+ Activated Fos-lo': '#ff7f0e',
'CD4+ Memory': '#2ca02c',
'CD4+ PD1+': '#d62728',
'CD69+ Mast': '#9467bd',
'CD69- Mast': '#8c564b',
'CD8+ IELs': '#e377c2',
'CD8+ IL17+': '#7f7f7f',
'CD8+ LP': '#bcbd22',
'Cycling B': '#17becf',
'Cycling Monocytes': '#aec7e8',
'Cycling T': '#ffbb78',
'DC1': '#98df8a',
'DC2': '#ff9896',
'Follicular': '#c5b0d5',
'GC': '#c49c94',
'ILCs': '#f7b6d2',
'Inflammatory Monocytes': '#c7c7c7',
'MT-hi': '#dbdb8d',
'Macrophages': '#9edae5',
'NKs': '#393b79',
'Plasma': '#637939',
'Tregs': '#8c6d31',
}
elif data_name == 'CELEGANPCA100':
root = 'Germline'
col_dict = {'ABarpaaa_lineage': '#91003f', # embryonic lineage
'Germline': '#7f2704',
# Somatic gonad precursor cell
'Z1_Z4': '#800026',
# Two embryonic hypodermal cells that may provide a scaffold for the early organization of ventral bodywall muscles
'XXX': '#fb8072',
'Ciliated_amphid_neuron': '#c51b8a','Ciliated_non_amphid_neuron': '#fa9fb5',
# immune
'Coelomocyte': '#ffff33', 'T': '#54278f',
# Exceratory
'Excretory_cell': '#004529',
'Excretory_cell_parent': '#006837',
'Excretory_duct_and_pore': '#238443',
'Parent_of_exc_duct_pore_DB_1_3': '#41ab5d',
'Excretory_gland': '#78c679',
'Parent_of_exc_gland_AVK': '#addd8e',
'Rectal_cell': '#d9f0a3',
'Rectal_gland': '#f7fcb9',
'Intestine': '#7fcdbb',
# esophagus, crop, gizzard (usually) and intestine