SMILE

SMILE: Mutual Information Learning for Integration of Single Cell Omics Data

Citation

Xu et al. "SMILE: mutual information learning for integration of single-cell omics data". Bioinformatics

Requirements

• numpy
• scipy
• pandas
• scikit-learn
• scanpy
• pytorch

Updates

Update 06/30/2022 (Identifying shared signatures across modalities)

To identify shared signatures across modalities via SMILE, please see tutorial

|----SMILE_identify_shared_signatures_across_modalities.ipynb

Update 05/09/2022 (Using joint-profiling data as reference for integration)

##rna_X: RNA-seq data; dna_X: ATAC-seq data, rna_X and dna_X are paired data
##rna_X_unpaired: RNA-seq data; dna_X_unpaired: ATAC-seq data, rna_X_unpaired and dna_X_unpaired are unpaired data, and we wish to integrate unpaired data
##Both rna_X and dna_X are matrices in which each row represents one cell while each column stands for a feature
##each row in rna_X and dna_X is paired for training purpose
##Within modality, for example rna_X and rna_X_unpaired, data share the same feature space

## Proecessed data could be found at https://doi.org/10.5281/zenodo.7776066

from SMILE import littleSMILE
from SMILE import ReferenceSMILE_trainer
integrater = littleSMILE(input_dim_a=rna_X.shape[1],input_dim_b=dna_X.shape[1],clf_out=20)
ReferenceSMILE_trainer(rna_X,dna_X,rna_X_unpaired,dna_X_unpaired, integrater, train_epoch=1000)

Integrate RNA-seq/ATAC-seq from Granja et al 2019 with 10X multiome PBMC data as reference

|----SMILE_data_integration_withReference.ipynb

Tutorial

For quick start

##rna_X: RNA-seq data; dna_X: ATAC-seq data
##Both rna_X and dna_X are matrices in which each row represents one cell while each column stands for a feature
##each row in rna_X and dna_X is paired for training purpose 

from SMILE import SMILE
from SMILE import PairedSMILE_trainer
net = littleSMILE(input_dim_a=rna_X.shape[1],input_dim_b=dna_X.shape[1],clf_out=30)
ReferenceSMILE_trainer(X_rna_paired,X_dna_paired,X_rna_unpaired,X_dna_unpaired,net,batch_size=1024, f_temp = 0.2)

For detail

For use of SMILE in multi-source single-cell transcriptome data

|----SMILE_MouserCortex_RNAseq_example.ipynb

For use of SMILE in integration of multimodal single-cell data

|----SMILE_Celllines_RNA-ATAC-integration_example.ipynb

For screening key genes that contribute co-embedding

|----screen_factors_for_coembedding.py
Processed data can be found at https://figshare.com/articles/dataset/Mouse_skin_data_by_SHARE-seq/16620367