scGNN: scRNA-seq Dropout Imputation via Induced Hierarchical Cell Similarity Graph

Bayesian approach to single-cell differential expression analysis

Kharchenko, P. V., Silberstein, L., and Scadden, D. T · 2014

Earlier work this paper cites.

Unbiased classification of sensory neuron types by large-scale single-cell rna sequencing

Usoskin, D., Furlan, A., Islam, S., Abdo, H., Lönnerberg, P., Lou, D., Hjerling-Leffler, J., Haeggström, J., Kharchenko, O., Kharchenko, P. V., et al · 2015

Earlier work this paper cites.

Cell types in the mouse cortex and hippocampus revealed by single-cell rna-seq

Original

Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al · 2015

Earlier work this paper cites.

Semi-supervised classification with graph convolutional networks

Original

Kipf, T. N. and Welling, M · 2016

Earlier work this paper cites.

Accounting for technical noise in differential expression analysis of single-cell rna sequencing data

Jia, C., Hu, Y., Kelly, D., Kim, J., Li, M., and Zhang, N. R · 2017

Earlier work this paper cites.

A unique microglia type associated with restricting development of alzheimer’s disease

Keren-Shaul, H., Spinrad, A., Weiner, A., Matcovitch-Natan, O., Dvir-Szternfeld, R., Ulland, T. K., David, E., Baruch, K., Lara-Astaiso, D., Toth, B., et al · 2017

Cited alongside, same era.

Single-cell sequencing of the healthy and diseased heart reveals cytoskeleton-associated protein 4 as a new modulator of fibroblasts activation

Gladka, M. M., Molenaar, B., De Ruiter, H., Van Der Elst, S., Tsui, H., Versteeg, D., Lacraz, G. P., Huibers, M. M., Van Oudenaarden, A., and Van Rooij, E · 2018

Cited alongside, same era.

Drimpute: imputing dropout events in single cell rna sequencing data

Gong, W., Kwak, I.-Y., Pota, P., Koyano-Nakagawa, N., and Garry, D. J · 2018

Cited alongside, same era.

Missing data and technical variability in single-cell rna-sequencing experiments

Hicks, S. C., Townes, F. W., Teng, M., and Irizarry, R. A · 2018

Cited alongside, same era.

Saver: gene expression recovery for single-cell rna sequencing

Huang, M., Wang, J., Torre, E., Dueck, H., Shaffer, S., Bonasio, R., Murray, J. I., Raj, A., Li, M., and Zhang, N. R · 2018

Single-cell rna-seq of rheumatoid arthritis synovial tissue using low-cost microfluidic instrumentation

Stephenson, W., Donlin, L. T., Butler, A., Rozo, C., Bracken, B., Rashidfarrokhi, A., Goodman, S. M., Ivashkiv, L. B., Bykerk, V. P., Orange, D. E., et al · 2018

Later among the works it cites.

Autoimpute: Autoencoder based imputation of single-cell rna-seq data

Talwar, D., Mongia, A., Sengupta, D., and Majumdar, A · 2018

Later among the works it cites.

Recovering gene interactions from single-cell data using data diffusion

Van Dijk, D., Sharma, R., Nainys, J., Yim, K., Kathail, P., Carr, A. J., Burdziak, C., Moon, K. R., Chaffer, C. L., Pattabiraman, D., et al · 2018

Later among the works it cites.

Deepimpute: an accurate, fast, and scalable deep neural network method to impute single-cell rna-seq data

Arisdakessian, C., Poirion, O., Yunits, B., Zhu, X., and Garmire, L. X · 2019

Later among the works it cites.

Single-cell rna-seq denoising using a deep count autoencoder

Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S., and Theis, F. J · 2019

Later among the works it cites.