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cTPnet

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cTPnet Package

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# cTPnet
single cell Transcriptome to Protein prediction with deep neural network (cTP-net) is a transfer learning framework to impute surface protein abundances from scRNA-seq data by learning from existing single-cell multi-omic resources. While single cell RNA sequencing (scRNA-seq) is invaluable for studying cell populations, cell-surface proteins are often integral markers of cellular function and serve as primary targets for therapeutic intervention.

## Manuscript

[Surface protein imputation from single cell transcriptomes by deep neural networks](https://www.nature.com/articles/s41467-020-14391-0)

## Questions & Problems

If you have any questions or problems when using cTPnet or ctpnetpy, please feel free to open a new issue [here](https://github.com/zhouzilu/cTPnet/issues). You can also email the maintainers of the corresponding packages -- the contact information is shown under Developers & Maintainers.

## Pipeline overview

This cTPnet package includes two analysis tools: (1) **SAVERX**, we stronly recommend denoise the scRNA-seq data before impute the surface protein abundance, and (2) **cTP-net**, which impute the surface protein abundance based on previously trained model.

**Figure 1.**  *(a)* Overview of cTP-net analysis pipeline, which learns a mapping from the denoised scRNA-seq data to the relative abundance of surface proteins, capturing multi-gene features that reflect the cellular environment and related processes. *(b)* For three example proteins, cross-cell scatter and correlation of CITE-seq measured abundances vs. (1) raw RNA count, (2) SAVER-X denoised RNA level, and (3) cTP-net predicted protein abundance.

## Installation and running cTP-net
For different Seurat version, we developed separate vignette see below:

### Seurat v2
  **cTP-net R notebook** with step-by-step demonstration and rich display is available [***here***](http://raw.githack.com/zhouzilu/cTPnet/master/vignette/cTPnet_vignette_v2.html). Corresponding **Rmd script** is available [***here***](https://github.com/zhouzilu/cTPnet/blob/master/vignette/cTPnet_vignette_v2.Rmd).

### Seurat v3
  **cTP-net R notebook** with step-by-step demonstration and rich display is available [***here***](http://raw.githack.com/zhouzilu/cTPnet/master/vignette/cTPnet_vignette_v3.html). Corresponding **Rmd script** is available [***here***](https://github.com/zhouzilu/cTPnet/blob/master/vignette/cTPnet_vignette_v3.Rmd).

For a "normal" computer, the installation time is < 15mins. Given a dataset of 4000 cells, the running time of cTP-net should be less than 1 mins. No GPU required for running this algorithm.

## Work in progress
As development in available cite-seq data set, we currently trainig a model that can extend our prediction to 100 surface proteins. Also, due to additional interets in training your own cTP-net model in the computational biology field, we have decided to share our training python code [***here***](https://github.com/zhouzilu/cTPnet/blob/master/extdata/training_05152020.py). Please remember to cite our paper, and contacnt me or Nancy if for commercial usage.

## Citation

Zhou, Z., Ye, C., Wang, J. et al. Surface protein imputation from single cell transcriptomes by deep neural networks. Nat Commun 11, 651 (2020). https://doi.org/10.1038/s41467-020-14391-0

## Developers & Maintainers

* [Zilu Zhou](https://statistics.wharton.upenn.edu/profile/zhouzilu/) (zhouzilu at pennmedicine dot upenn dot edu)
  <br>
  Genomics and Computational Biology Graduate Group, University of Pennsylvania

* [Nancy R. Zhang](https://statistics.wharton.upenn.edu/profile/nzh/) (nzh at wharton dot upenn dot edu)
  <br>
  Department of Statistics, University of Pennsylvania

## Common questions:
* Error message while running demo `Error in dim(X) <- c(n, length(X)/n) : dims [product 1992] do not match the length of object[2000]`
  * Add `options(stringsAsFactors = FALSE)` before running the code.
* Windows user if you see error message `Error in py_module_import(module, convert = convert) : ModuleNotFoundError: No module named 'ctpnet'`
  * The solution is to use numpy 1.19.3. `pip install numpy==1.19.3`
  * For more information https://tinyurl.com/y3dm3h86
  * In general, we don't recommend running cTP-net on a Windows platform even though I had some success at my intital submission of the code.

cTPnet

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