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main_baseline_batch_correction.py
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import os
import pytorch_lightning as pl
import torch
import numpy as np
from dataloader import data_base
import scanpy as sc
import anndata
from sklearn.manifold import TSNE
import phate
import umap
import forceatlas2
import plotly.graph_objects as go
torch.set_num_threads(2)
def up_mainfig_emb(data, ins_emb,
label, n_clusters=10, num_cf_example=2,
):
color = np.array(label)
Curve = ins_emb[:, 0]
Curve2 = ins_emb[:, 1]
ml_mx = max(Curve)
ml_mn = min(Curve)
ap_mx = max(Curve2)
ap_mn = min(Curve2)
if ml_mx > ap_mx:
mx = ml_mx
else:
mx = ap_mx
if ml_mn < ap_mn:
mn = ml_mn
else:
mn = ap_mn
mx = mx + mx * 0.2
mn = mn - mn * 0.2
layout = go.Layout(
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
width=1000,
height=1000,
autosize=False,
)
fig = go.Figure(layout=layout)
color_set_list = list(set(color.tolist()))
for c in color_set_list:
m = color == c
fig.add_trace(
go.Scatter(
mode="markers",
x=ins_emb[m, 0],
y=ins_emb[m, 1],
marker_line_width=0,
name=c,
marker=dict(
size=[3] * ins_emb.shape[0],
)
)
)
return fig
if __name__ == "__main__":
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("--gamma", type=float, default=2.0)
parser.add_argument("--n_components", type=int, default=50)
parser.add_argument('--method', type=str, default='scvi_umap', choices=['harmony_tsne', 'harmony_umap', 'harmony_phate', 'harmony_diffmap', 'harmony_forceatlas2', 'harmony_forceatlas2_v2', 'scvi_tsne', 'scvi_umap', 'scvi_phate', 'scvi_diffmap', 'scvi_forceatlas2', 'scvi_forceatlas2_v2'])
# data set param
parser.add_argument(
"--data_name",
type=str,
default="UCEPIbc",
choices=[
'UCEPIbc',
],
)
# 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")
runname = 'baseline_{}_{}'.format(
args.data_name,
args.method)
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 = data_train.data.numpy().reshape(data_train.data.shape[0], -1)
label = np.array(data_train.label)
if args.method == 'harmony_tsne' or args.method == 'scvi_tsne':
save_path = args.data_name + '_baseline' + '/path' + '_' + args.method + '_' + str(args.perplexity)
if not os.path.exists('log_' + save_path):
os.makedirs('log_' + save_path)
os.makedirs('log_' + save_path + '/result')
os.chdir(r'log_' + save_path)
latent = TSNE(perplexity=args.perplexity).fit_transform(data)
if args.method == 'harmony_umap' or args.method == 'scvi_umap':
save_path = args.data_name + '_baseline' + '/path' + '_' + args.method + '_' + str(args.perplexity) + '_' + str(args.min_dist)
if not os.path.exists('log_' + save_path):
os.makedirs('log_' + save_path)
os.makedirs('log_' + save_path + '/result')
os.chdir(r'log_' + save_path)
latent = umap.UMAP(n_neighbors=args.perplexity, min_dist=args.min_dist).fit_transform(data)
if args.method == 'harmony_phate' or args.method == 'scvi_phate':
save_path = args.data_name + '_baseline' + '/path' + '_' + args.method
if not os.path.exists('log_' + save_path):
os.makedirs('log_' + save_path)
os.makedirs('log_' + save_path + '/result')
os.chdir(r'log_' + save_path)
latent = phate.PHATE().fit_transform(data)
if args.method == 'harmony_diffmap' or args.method == 'scvi_diffmap':
save_path = args.data_name + '_baseline' + '/path' + '_' + args.method + '_' + str(args.perplexity)
if not os.path.exists('log_' + save_path):
os.makedirs('log_' + save_path)
os.makedirs('log_' + save_path + '/result')
os.chdir(r'log_' + save_path)
sadata = anndata.AnnData(X=np.array(data))
sc.pp.neighbors(sadata, n_neighbors=args.perplexity)
sc.tl.diffmap(sadata)
latent = sadata.obsm['X_diffmap'][:,1:3].copy()
if args.method == 'harmony_forceatlas2' or args.method == 'scvi_forceatlas2':
save_path = args.data_name + '_baseline' + '/path' + '_' + args.method + '_' + str(args.perplexity)
if not os.path.exists('log_' + save_path):
os.makedirs('log_' + save_path)
os.makedirs('log_' + save_path + '/result')
os.chdir(r'log_' + save_path)
sadata = anndata.AnnData(X=np.array(data))
sc.pp.neighbors(sadata, n_neighbors=args.perplexity)
sc.tl.louvain(sadata, resolution=0.9)
sc.tl.paga(sadata)
sc.pl.paga(sadata)
sc.tl.draw_graph(sadata, init_pos='paga')
latent = sadata.obsm['X_draw_graph_fr'].copy()
if args.method == 'harmony_forceatlas2_v2' or args.method == 'scvi_forceatlas2_v2':
save_path = args.data_name + '_baseline' + '/path' + '_' + args.method + '_' + str(args.perplexity)
if not os.path.exists('log_' + save_path):
os.makedirs('log_' + save_path)
os.makedirs('log_' + save_path + '/result')
os.chdir(r'log_' + save_path)
sadata = anndata.AnnData(X=np.array(data))
sc.pp.neighbors(sadata, n_neighbors=args.perplexity)
adj_matrix = sadata.obsp['connectivities']
positions = forceatlas2.forceatlas2(adj_matrix.todense())
sadata.obsm['X_forceatlas2'] = np.array(positions)
latent = np.array(positions)
np.save('latent.npy', latent)
from eval.eval_bc import Eval_all_structure_all
result_index_local, result_index_global, acc_mean, silhouette_mean = Eval_all_structure_all(data_train.sadata, data, latent, label, data_train.n_batch[0], metric_e=args.metric)