idroz
commented
4 years ago Hi there - thanks for trying out Ivis!
Since ivis is a general dimensionality technique, we have mostly focused our benchmarks on comparable techniques, including PCA, Isomap, UMAP, and t-SNE.
- Timings bechmarks: https://bering-ivis.readthedocs.io/en/latest/timings_benchmarks.html
- Distance benchmarks: https://bering-ivis.readthedocs.io/en/latest/comparisons.html
We have also looked internally at comparisons with scvis. If you have data, time, and willingness, would be great to see ivis benchamarked against additional techniques!
General questions about algorithm design and usage. Hi, It is a great new method to learn the low dimensional embedding of the high dimensional single cell data. But how about comparing to other scRNA-seq embedding methods? There are lots of methods for scRNA-seq dimension reduction, for example ZIFA [1], ZINB-Wave [2], DCA [3], scvi [4], scvis [5], scScope [6] etc. Most of them are zero-inflated matrix factorization analysis or denoising/zero-inflated auto-encoders. Thanks.
[1] Pierson, Emma, and Christopher Yau. "ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis." Genome biology 16.1 (2015): 241. [2] Risso, Davide, et al. "A general and flexible method for signal extraction from single-cell RNA-seq data." Nature communications 9.1 (2018): 284. [3] Eraslan, Gökcen, et al. "Single-cell RNA-seq denoising using a deep count autoencoder." Nature communications 10.1 (2019): 390. [4] Lopez, Romain, et al. "Deep generative modeling for single-cell transcriptomics." Nature methods 15.12 (2018): 1053. [5] Ding, Jiarui, Anne Condon, and Sohrab P. Shah. "Interpretable dimensionality reduction of single cell transcriptome data with deep generative models." Nature communications 9.1 (2018): 2002. [6] Deng, Yue, et al. "Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning." Nature methods 16.4 (2019): 311.