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| #!/usr/bin/env python | |
| # coding: utf-8 | |
| # # Multi omics analysis by MOFA | |
| # MOFA is a factor analysis model that provides a general framework for the integration of multi-omic data sets in an unsupervised fashion. | |
| # | |
| # This tutorial focuses on how to perform mofa in multi-omics like scRNA-seq and scATAC-seq | |
| # | |
| # Paper: [MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02015-1) | |
| # | |
| # Code: https://github.com/bioFAM/mofapy2 | |
| # | |
| # Colab_Reproducibility：https://colab.research.google.com/drive/1UPGQA3BenrC-eLIGVtdKVftSnOKIwNrP?usp=sharing | |
| # ## Part.1 MOFA Model | |
| # In this part, we construct a model of mofa by scRNA-seq and scATAC-seq | |
| # In[1]: | |
| import omicverse as ov | |
| rna=ov.utils.read('data/sample/rna_p_n_raw.h5ad') | |
| atac=ov.utils.read('data/sample/atac_p_n_raw.h5ad') | |
| # In[2]: | |
| rna,atac | |
| # We only need to add anndata to `ov.single.mofa` to construct the base model | |
| # In[4]: | |
| test_mofa=ov.single.pyMOFA(omics=[rna,atac], | |
| omics_name=['RNA','ATAC']) | |
| # In[ ]: | |
| test_mofa.mofa_preprocess() | |
| test_mofa.mofa_run(outfile='models/brac_rna_atac.hdf5') | |
| # ## Part.2 MOFA Analysis | |
| # After get the model by mofa, we need to analysis the factor about different omics, we provide some method to do this | |
| # ### load data | |
| # In[1]: | |
| import omicverse as ov | |
| ov.utils.ov_plot_set() | |
| # In[2]: | |
| rna=ov.utils.read('data/sample/rna_test.h5ad') | |
| # ### add value of factor to anndata | |
| # In[3]: | |
| rna=ov.single.factor_exact(rna,hdf5_path='data/sample/MOFA_POS.hdf5') | |
| rna | |
| # ### analysis of the correlation between factor and cell type | |
| # In[4]: | |
| ov.single.factor_correlation(adata=rna,cluster='cell_type',factor_list=[1,2,3,4,5]) | |
| # ### Get the gene/feature weights of different factor | |
| # In[5]: | |
| ov.single.get_weights(hdf5_path='data/sample/MOFA_POS.hdf5',view='RNA',factor=1) | |
| # ## Part.3 MOFA Visualize | |
| # | |
| # To visualize the result of mofa, we provide a series of function to do this. | |
| # In[6]: | |
| pymofa_obj=ov.single.pyMOFAART(model_path='data/sample/MOFA_POS.hdf5') | |
| # We get the factor of each cell at first | |
| # In[7]: | |
| pymofa_obj.get_factors(rna) | |
| rna | |
| # We can also plot the varience in each View | |
| # In[8]: | |
| pymofa_obj.plot_r2() | |
| # In[9]: | |
| pymofa_obj.get_r2() | |
| # ### Visualize the correlation between factor and celltype | |
| # In[10]: | |
| pymofa_obj.plot_cor(rna,'cell_type') | |
| # We found that factor6 is correlated to Epithelial | |
| # In[11]: | |
| pymofa_obj.plot_factor(rna,'cell_type','Epi',figsize=(3,3), | |
| factor1=6,factor2=10,) | |
| # In[24]: | |
| import scanpy as sc | |
| sc.pp.neighbors(rna) | |
| sc.tl.umap(rna) | |
| sc.pl.embedding( | |
| rna, | |
| basis="X_umap", | |
| color=["factor6","cell_type"], | |
| frameon=False, | |
| ncols=2, | |
| #palette=ov.utils.pyomic_palette(), | |
| show=False, | |
| cmap='Greens', | |
| vmin=0, | |
| ) | |
| #plt.savefig("figures/umap_factor6.png",dpi=300,bbox_inches = 'tight') | |
| # In[12]: | |
| pymofa_obj.plot_weight_gene_d1(view='RNA',factor1=6,factor2=10,) | |
| # In[18]: | |
| pymofa_obj.plot_weights(view='RNA',factor=6,color='#5de25d', | |
| ascending=True) | |
| # In[14]: | |
| pymofa_obj.plot_top_feature_heatmap(view='RNA') | |
| # In[ ]: | |