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sceval_method.py
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sceval_method.py
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import torch
import numpy as np
import scib
import scanpy as sc
import scipy
import scipy.stats
import scvi
import scglue
import networkx as nx
import TOSICA
import scanpy as sc
import numpy as np
import pandas as pd
import tangram as tg
from typing import List, Tuple, Dict, Union, Optional
from ResPAN import run_respan
from itertools import chain
from anndata import AnnData
from gears import PertData, GEARS
from gears.inference import evaluate, compute_metrics, deeper_analysis, non_dropout_analysis
'''
To evaluate simulation, we used scDesign3 and Splatter, which are both designed in R. Please install R and refer their tutorials to run their codes.
'''
class scEval_bench(object):
def __init__(self, adata):
self.label = 'scGPT'
self.adata = adata # adata is raw data file for evaluating.
self.pvalue = 0.005
def bec_scvi(self, batch_key = 'batch',label_key = 'celltype', emb_name = 'X_scvi'):
adata = self.adata
adata.raw = adata # keep full dimension safe
sc.pp.highly_variable_genes(
adata,
flavor="seurat_v3",
n_top_genes=2000,
layer="counts",
batch_key="batch",
subset=True,
)
scvi.model.SCVI.setup_anndata(adata, layer="counts", batch_key="batch")
model = scvi.model.SCVI(adata, n_layers=2, n_latent=30, gene_likelihood="nb")
model.train()
adata.obsm[emb_name] = model.get_latent_representation()
return adata
def bec_respan(self, batch_key = 'batch',label_key = 'celltype'):
adata = self.adata
# pre-processing
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)
adata.X = adata.X.toarray().astype('float')
sc.pp.normalize_per_cell(adata, counts_per_cell_after=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=2000, batch_key='batch')
adata = adata[:, adata.var['highly_variable']]
# check if data is in sparse format
if isinstance(adata.X, scipy.sparse.csr.csr_matrix):
adata_new = sc.AnnData(adata.X.todense())
adata_new.obs = adata.obs.copy()
adata_new.obs_names = adata.obs_names
adata_new.var_names = adata.var_names
adata_new.obs_names.name = 'CellID'
adata_new.var_names.name = 'Gene'
del adata
adata = adata_new
adata_new = run_respan(adata, batch_key=batch_key, epoch=300, batch=1024, reduction='pca', subsample=3000, seed=999)
return adata_new
# here atac data should contain peak infromation.
def mdi_glue(self, rna, atac, batch_key = 'batch',label_key = 'celltype', emb_name = 'X_glue'):
result_folder = './'
# Data preprocessing
sc.pp.filter_cells(rna, min_genes=200)
sc.pp.filter_genes(rna, min_cells=3)
rna.layers["counts"] = rna.X.copy()
sc.pp.highly_variable_genes(rna, n_top_genes=2000, flavor="seurat_v3")
sc.pp.normalize_total(rna)
sc.pp.log1p(rna)
sc.pp.scale(rna)
sc.tl.pca(rna, n_comps=100, svd_solver="auto")
sc.pp.filter_cells(atac, min_genes=200)
sc.pp.filter_genes(atac, min_cells=3)
scglue.data.lsi(atac, n_components=100, n_iter=15)
# Graph construction
guidance = scglue.genomics.rna_anchored_guidance_graph(rna, atac)
# Configure data
scglue.models.configure_dataset(
rna, "NB", use_highly_variable=True,
use_layer="counts", use_rep="X_pca"
)
scglue.models.configure_dataset(
atac, "NB", use_highly_variable=True,
use_rep="X_lsi"
)
guidance_hvf = guidance.subgraph(chain(
rna.var.query("highly_variable").index,
atac.var.query("highly_variable").index
)).copy()
# Run GLUE
glue = scglue.models.fit_SCGLUE(
{"rna": rna, "atac": atac}, guidance_hvf,
fit_kws={"directory": result_folder}
)
glue.save("%s/glue.dill" % result_folder)
# Check integration consistency
dx = scglue.models.integration_consistency(
glue, {"rna": rna, "atac": atac}, guidance_hvf
)
print(dx)
# KNN classifier
rna.obsm[emb_name] = glue.encode_data("rna", rna)
atac.obsm[emb_name] = glue.encode_data("atac", atac)
return rna, atac
def mdi_scjoint(self, rna_path, atac_path, result_folder, subset_rna, subset_atac, rna_new_annot, stage1_lr, stage3_lr, nepoch):
from scjoint import run_scJoint
run_scJoint(rna_path, atac_path, result_folder, subset_rna, subset_atac, rna_new_annot, stage1_lr, stage3_lr, nepoch)
def cta_tosica(self, ref_data, query_data, batch_key = 'batch',label_key = 'celltype'):
TOSICA.train(ref_data, gmt_path='human_gobp', label_name=label_key, epochs=3, project = "./")
model_weight_path = './model-0.pth'
new_adata = TOSICA.pre(query_data, model_weight_path = model_weight_path, project = "./")
return new_adata
def imp_tangram(self, ref_data, query_data, overlap_gene = None,label_key = 'celltype', device='cpu'):
# Please ensure ref_data has more genes than query_data.
adata_sc = ref_data
adata_st = query_data
if overlap_gene != None:
sc.tl.rank_genes_groups(adata_sc, groupby=label_key, use_raw=False)
markers_df = pd.DataFrame(adata_sc.uns["rank_genes_groups"]["names"]).iloc[0:100, :]
markers = list(np.unique(markers_df.melt().value.values))
else:
markers = overlap_gene
tg.pp_adatas(adata_sc, adata_st, genes=markers)
ad_map = tg.map_cells_to_space(adata_sc, adata_st,
mode="cells",
# mode="clusters",
# cluster_label='cell_subclass', # .obs field w cell types
density_prior='rna_count_based',
num_epochs=500,
# device="cuda:0",
device=device
)
ad_ge = tg.project_genes(adata_map=ad_map, adata_sc=adata_sc)
return ad_ge
def pert_gears(self, batch_key = 'batch',label_key = 'celltype', device='cuda"0', save_path = './'):
pert_data = self.adata
# set up and train a model
gears_model = GEARS(pert_data, device = device)
gears_model.model_initialize(hidden_size = 64)
gears_model.train(epochs = 20)
gears_model.save_model(save_path)
gears_model.load_pretrained(save_path)
test_res = evaluate(gears_model.dataloader['test_loader'], gears_model.model, gears_model.config['uncertainty'], gears_model.device)
test_metrics, test_pert_res = compute_metrics(test_res)
return test_metrics