Published August 12, 2023 | Version v1
Model Open

scDGD pretrained models

  • 1. ROR icon University of Copenhagen

Description

Each file presents a compressed collection of files for each of the pre-trained models presented in the `Deep Generative Decoder` paper (https://doi.org/10.1093/bioinformatics/btad497). Random seeds for all models was 0 and these instances were trained for 1000 epochs. For the two main models (dgd_Zheng2017 and dgd_mousebrain_1m), we also provide metadata information with cell types for the representations. In the case of the 1M mousebrain model, cell type labels were predicted using CellTypist.

Training data sources and processing are described in the corresponding paper: https://doi.org/10.1093/bioinformatics/btad49.

Files

dgd_Zheng2017.zip

Files (243.3 MB)

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md5:e081cae31017e8472b3195ee009b7c59
14.6 MB Preview Download
md5:a6b53dc612cdaeb8a5f4491c339efad5
8.0 MB Preview Download
md5:6346f9020298279af069bd5fc21a48b0
12.0 MB Preview Download
md5:7022eeed2dd1cd0745965817d45e8b4e
12.3 MB Preview Download
md5:91d39b0496b925138973863509c672b5
11.9 MB Preview Download
md5:5f7a159e7fc5f81e28d8159f09431fec
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md5:dd2d8d4cf029e4f4bc381ee0d8377374
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md5:51a91a86ecf35d223109e73c0e73a36d
13.3 MB Preview Download
md5:c4ecfd1231d8e112af6d962d467403aa
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md5:6dd91373036989553c83c8b8ab14d4f2
8.9 MB Preview Download
md5:41a3a25005aae2fecf3d1fbf59223a6a
14.7 MB Preview Download
md5:9ddbb64b3f51c48e840afdf756e92529
15.7 MB Preview Download

Additional details

References

  • Viktoria Schuster, Anders Krogh, The Deep Generative Decoder: MAP estimation of representations improves modelling of single-cell RNA data, Bioinformatics, Volume 39, Issue 9, September 2023, btad497, https://doi.org/10.1093/bioinformatics/btad497