Integrate PDAC with scRNA

uniPort can output a global optimal transport (OT) plan, i.e., a cell-cell correspondence matrix, that provides flexible transfer learning for deconvolution of spatial heterogeneous data using scRNA data in OT space, instead of embedding latent space.

import uniport as up
import scanpy as sc
import pandas as pd

Read microarray-based ST data of pancreatic ductal adenocarcinoma (PDAC) tissues for integration, the diameter of which stretches for 100 μm. Cell-type deconvolution was applied on 428 spots paired with 1926 single cells, measuring 19,736 genes respectively.

labels_rna = pd.read_csv('PDAC_scRNA_label.txt', sep='\t')
celltype = labels_rna['cell_type'].values
print(celltype)

['Acinar cells' 'Ductal' 'Ductal' ... 'Ductal' 'pDCs' 'RBCs']

rna = sc.read('PDAC_scRNA.txt').transpose()
spot = up.load_file('PDAC_SPOT.txt')

Add domain_id, cell_type and source obs to AnnData.

spot.obs['domain_id'] = 0
spot.obs['domain_id'] = spot.obs['domain_id'].astype('category')
spot.obs['source'] = 'SPOT'

rna.obs['cell_type'] = celltype
rna.obs['domain_id'] = 1
rna.obs['domain_id'] = rna.obs['domain_id'].astype('category')
rna.obs['source'] = 'RNA'

Concatenate SPOT and scRNA-seq with common genes using AnnData.concatenate.

adata_cm = spot.concatenate(rna, join='inner', batch_key='domain_id')

Preprocess data with common genes. Select 2,000 highly variable common genes.

Scale data using batch_scale function in uniport (modified from SCALEX).

sc.pp.normalize_total(adata_cm)
sc.pp.log1p(adata_cm)
sc.pp.highly_variable_genes(adata_cm, n_top_genes=2000, batch_key='domain_id', inplace=False, subset=True)
up.batch_scale(adata_cm)

... storing 'source' as categorical
... storing 'cell_type' as categorical

Preprocess SPOT data. Select 2,000 highly variable genes as SPOT speicifc.

sc.pp.normalize_total(spot)
sc.pp.log1p(spot)
sc.pp.highly_variable_genes(spot, n_top_genes=2000, inplace=False, subset=True)
up.batch_scale(spot)

Preprocess scRNA-seq data. Select 2,000 highly variable genes as RNA specific.

sc.pp.normalize_total(rna)
sc.pp.log1p(rna)
sc.pp.highly_variable_genes(rna, n_top_genes=2000, inplace=False, subset=True)
up.batch_scale(rna)

Integrate the SPOT and scRNA-seq data using both common and dataset-specific genes by Run function in uniport. Set save_OT=True and return a OT plan, which is a SPOT by RNA probabilistic matching matrix.

adata, OT = up.Run(adatas=[spot,rna], adata_cm=adata_cm, save_OT=True)

Dataset 0: SPOT
AnnData object with n_obs × n_vars = 428 × 2000
    obs: 'domain_id', 'source'
    var: 'highly_variable', 'means', 'dispersions', 'dispersions_norm'
    uns: 'log1p', 'hvg'
Dataset 1: RNA
AnnData object with n_obs × n_vars = 1926 × 2000
    obs: 'cell_type', 'domain_id', 'source'
    var: 'highly_variable', 'means', 'dispersions', 'dispersions_norm'
    uns: 'log1p', 'hvg'
Reference dataset is dataset 1


Data with common HVG
AnnData object with n_obs × n_vars = 2354 × 2000
    obs: 'domain_id', 'source', 'cell_type'
    var: 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'highly_variable_nbatches', 'highly_variable_intersection'
    uns: 'log1p', 'hvg'


Warning! Saving Optimal Transport plan needs extra 0.01 GB memory, please set save_OT=False if no enough memory!
float32
Size of transport plan between datasets 0 and 1: (428, 1926)

Epochs:  77%|████████████████▊     | 2554/3334 [24:20<07:26,  1.75it/s, recloss=358.15,klloss=6.82,otloss=8.00]

EarlyStopping: run 2555 epoch

Save OT plan for deconvolution.

name_idx = adata_cm[adata_cm.obs['source']=='SPOT'].obs_names
name_col = adata_cm[adata_cm.obs['source']=='RNA'].obs_names
OT_pd = pd.DataFrame(OT[0], index=name_idx, columns=name_col)
OT_pd.to_csv('/data/PDAC/OT_PDAC.txt', sep='\t')