sccross.models¶
scCross is a dDeep Learning-Based Model for integration, cross-dataset cross-modality generation and matched muti-omics simulation of single-cell multi-omics data. Our model can also maintain in-silico perturbations in cross-modality generation and can use in-silico perturbations to find key genes. Part of the sccross’ code is adapted from MIT licensed projects GLUE and SCDIFF2. Thanks for these projects:
Author: Zhi-Jie Cao Project: GLUE Ref: Cao Z J, Gao G. Multi-omics single-cell data integration and regulatory inference with graph-linked embedding[J]. Nature Biotechnology, 2022, 40(10): 1458-1466.
Author: Jun Ding Project: SCDIFF2 Ref: Ding, J., Aronow, B. J., Kaminski, N., Kitzmiller, J., Whitsett, J. A., & Bar-Joseph, Z. (2018). Reconstructing differentiation networks and their regulation from time series single-cell expression data. Genome research, 28(3), 383-395.
Functions
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Load model from file |
Submodules
Data handling utilities |
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Probability distributions |