Package: scSTEM
Welcome!1. Introduction and installationSTEM is a tool for building single-cell level spatial transcriptomic landscapes using SC data with ST data. STEM extracts the spatial information from the gene expressions and eliminates the domain gap between spatial transcriptomics and single-cell RNA-seq data.Installationcondainstallpytorchtorchvisiontorchaudiopytorch-cuda=11.7-cpytorch-cnvidia## more info: https://pytorch.org/get-started/locally/condainstall-cconda-forgescanpypython-igraphleidenalg
condainstallseabornSince STEM is a light model, you can use the code from theSTEMfolder directly. But you can also install STEM from PYPI:pip install STEM.
To verify the installation, runpython test.pyThen we will demonstrate the workflow based on the mouse embryo data as part of our paper Figure 2.%pylabinlineimportosos.environ["CUDA_VISIBLE_DEVICES"]="3"importscanpyasscimportpandasaspdimporttorchimportscipyimporttimefromSTEM.modelimport*fromSTEM.utilsimport*2. Load and Preprocess dataFirst, we load the simulated ST data as ST data and the raw SeqFISH data as SC data. Then we normalized and log-scaled these data.scdata=pd.read_csv('./data/mousedata_2020/E1z2/simu_sc_counts.csv',index_col=0)scdata=scdata.Tstdata=pd.read_csv('data/mousedata_2020/E1z2/simu_st_counts.csv',index_col=0)stdata=stdata.Tstgtcelltype=pd.read_csv('./data/mousedata_2020/E1z2/simu_st_celltype.csv',index_col=0)spcoor=pd.read_csv('./data/mousedata_2020/E1z2/simu_st_metadata.csv',index_col=0)scmetadata=pd.read_csv('./data/mousedata_2020/E1z2/metadata.csv',index_col=0)adata=sc.AnnData(scdata,obs=scmetadata)sc.pp.normalize_total(adata)sc.pp.log1p(adata)scdata=pd.DataFrame(adata.X,index=adata.obs_names,columns=adata.var_names)stadata=sc.AnnData(stdata)sc.pp.normalize_total(stadata)sc.pp.log1p(stadata)stdata=pd.DataFrame(stadata.X,index=stadata.obs_names,columns=stadata.var_names)adata.obsm['spatial']=scmetadata[['x_global','y_global']].valuesstadata.obsm['spatial']=spcoorNext we calculate the ratio between the median total counts value of SC and ST data as the dropout rate.sc.pp.calculate_qc_metrics(adata,percent_top=None,log1p=False,inplace=True)adata.obs['n_genes_by_counts'].median()sc.pp.calculate_qc_metrics(stadata,percent_top=None,log1p=False,inplace=True)stadata.obs['n_genes_by_counts'].median()dp=1-adata.obs['n_genes_by_counts'].median()/stadata.obs['n_genes_by_counts'].median()#0.58367346938775513. Train the STEM modelWe first config the STEM model and then train it. Empirically we found by setting the sigma as half of the ST spot adjacent distance, STEM achieves the best performance.classsetting(object):passseed_all(2022)opt=setting()setattr(opt,'device','cuda:0')setattr(opt,'outf','log/Mouse_E1z2')setattr(opt,'n_genes',351)setattr(opt,'no_bn',False)setattr(opt,'lr',0.002)setattr(opt,'sigma',3)setattr(opt,'alpha',0.8)setattr(opt,'verbose',True)setattr(opt,'mmdbatch',1000)setattr(opt,'dp',dp)testmodel=SOmodel(opt)testmodel.togpu()loss_curve=testmodel.train_wholedata(400,torch.tensor(scdata.values).float(),torch.tensor(stdata.values).float(),torch.tensor(spcoor.values).float())The loss curve will be like this:4. Get embeddings and reconstruct spatial adjacencyWe first get the embeddings and build the mapping matrix
We first get the embeddings and build the mapping matrixtestmodel.modeleval()scembedding=testmodel.netE(torch.tensor(scdata.values,dtype=torch.float32).cuda())stembedding=testmodel.netE(torch.tensor(stdata.values,dtype=torch.float32).cuda())netst2sc=F.softmax(stembedding.mm(scembedding.t()),dim=1).detach().cpu().numpy()netsc2st=F.softmax(scembedding.mm(stembedding.t()),dim=1).detach().cpu().numpy()The matrixnetst2scandnetsc2stare ST-SC and SC-ST mapping matrices, respectively. In the ST-SC mapping matrix, the probability of one spot to other cells summarizes to 1. In the SC-ST mapping matrix, the probability of one cell to other spots summarizes to 1.Then we can get the spatial coordinate for every single cell.adata.obsm['spatialDA']=all_coord(pd.DataFrame(netsc2st,index=adata.obs_names,columns=stadata.obs_names),spcoor)Compared with other methods, STEM is the only method that preserves the original topology structure of all single cells.