COEM: Cross-Modal Embedding for MetaCell Identification

Multimodal machine learning: A survey and taxonomy

Baltrušaitis, T., Ahuja, C., and Morency, L.-P · 2018

Earlier work this paper cites.

Joint profiling of chromatin accessibility and gene expression in thousands of single cells

Cao, J., Cusanovich, D. A., Ramani, V., Aghamirzaie, D., Pliner, H. A., Hill, A. J., Daza, R. M., McFaline-Figueroa, J. L., Packer, J. S., Christiansen, L., Steemers, F. J., Adey, A. C., Trapnell, C., and Shendure, J · 2018

Earlier work this paper cites.

Interpretable dimensionality reduction of single cell transcriptome data with deep generative models

Ding, J., Condon, A., and Shah, S. P · 2018

Earlier work this paper cites.

Deep generative modeling for single-cell transcriptomics

Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., and Yosef, N · 2018

Earlier work this paper cites.

Metacell: analysis of single-cell rna-seq data using k-nn graph partitions

Baran, Y., Bercovich, A., Sebe-Pedros, A., Lubling, Y., Giladi, A., Chomsky, E., Meir, Z., Hoichman, M., Lifshitz, A., and Tanay, A · 2019

Earlier work this paper cites.

Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming

Schiebinger, G., Shu, J., Tabaka, M., Cleary, B., Subramanian, V., Solomon, A., Gould, J., Liu, S., Lin, S., Berube, P., et al · 2019

Earlier work this paper cites.

Representation learning: A probabilistic perspective

Blei, D. M · 2020

Earlier work this paper cites.

Chromatin potential identified by shared single-cell profiling of rna and chromatin

Ma, S., Zhang, B., LaFave, L. M., Earl, A. S., Chiang, Z., Hu, Y., Ding, J., Brack, A., Kartha, V. K., Tay, T., Law, T., Lareau, C., Hsu, Y.-C., Regev, A., and Buenrostro, J. D · 2020

Cited alongside, same era.

Interpretable factors in scrna-seq data with disentangled generative models

Mao, H., Broerman, M. J., and Benos, P. V · 2020

Cited alongside, same era.

Learning autoencoders with relational regularization

Xu, H., Luo, D., Henao, R., Shah, S., and Carin, L · 2020

Cited alongside, same era.

Metacells untangle large and complex single-cell transcriptome networks (preprint)

Bilous, M., Tran, L., Cianciaruso, C., Gabriel, A., Michel, H., Carmona, S., Pittet, M., and Gfeller, D · 2021

Cited alongside, same era.

Single-cell multi-omic velocity infers dynamic and decoupled gene regulation

Li, C., Virgilio, M., Collins, K. L., and Welch, J. D · 2021

Cited alongside, same era.

Counterfactual invariance to spurious correlations in text classification

Veitch, V., D' Amour, A., Yadlowsky, S., and Eisenstein, J · 2021

Later among the works it cites.

Desiderata for representation learning: A causal perspective

Original

Wang, Y. and Jordan, M. I · 2021

Later among the works it cites.

Cell type and gene expression deconvolution with bayesprism enables bayesian integrative analysis across bulk and single-cell rna sequencing in oncology

Chu, T., Wang, Z., Pe’er, D., and Danko, C. G · 2022

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An optimal transport approach to deep metric learning (student abstract)

Dou, J. X., Luo, L., and Yang, R. M · 2022

Closest in time.

A deep generative model for multi-view profiling of single-cell rna-seq and atac-seq data

Li, G., Fu, S., Wang, S., Zhu, C., Duan, B., Tang, C., Chen, X., Chuai, G., Wang, P., and Liu, Q · 2022

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