Awesome

Sceptic

Installation | Enviroment | Example | Input | Output | Parameter | Citation | Contact

Sceptic can perform pseudotime analysis on various types of single-cell/single-nucleus data. The model takes as input a collection of single-cell/single-nucleus data and then learns the relationship between the observed data and the associated time stamps, and finally uses the trained model to assign to each cell a real-valued pseudotime. Ideally, the pseudotimes assigned by Sceptic reflect each cell's progression along a notion of time---developmental, cell cycle, disease progression, aging---that is appropriate to the given data. Ideally, the pseudotimes assigned by Sceptic reflect each cell's progression along a notion of time---developmental, cell cycle, disease progression, aging---that is appropriate to the given data.

<img src="sceptic-schematic.jpg" alt="Sceptic schematic icon" style="float: left; margin-right: 10px;" />

Installation<a id="installation"></a>

Sceptic software is available on the Python package index (PyPI), latest version 0.0.3. To install it using pip, simply type:

$ pip install sceptic

Enviroment<a id="enviroment"></a>

Sceptic is associated with the following packages.

• python >= 3.7.7
• numpy >= 1.19.5
• pandas >= 1.3.5
• sklearn >= 1.0.2

Examples (python script) <a id="examples"></a>

We downloaded the processed scGEM dataset from UnionCom’s GitHub page.

$ python test/scGEM/scGEM.py 

The script will generate 4 outputs from Sceptic described in the section above and save it at: test/scGEM/.

Parameters of Sceptic <a id="parameter"></a>

The list of parameters is given below:

• kernel: The kernel function for sceptic SVM classfier (default=('linear', 'rbf')). Sklearn supports four kinds of kernels: linear, polynomial, rbf, sigmoid.
• C: The C parameter for rbf kernel (default=[0.1, 1, 10]). The C parameter trades off correct classification of training examples against maximization of the decision function’s margin. See more details here.
• eFold: # of folds for external cross-validation (default=5).
• iFold: # of folds for internal cross-validation (default=4).

Input<a id="input"></a>

In case the user is providing the input data:

• data_concat: the input cell by measurement matrix. (# of cells by # of measurements)
• label: processed cell time label. (# of cells by 1)
• label_list: unique list of possible cell time labels. (# of time points by 1)
• parameters: Sceptic parameter dictionary. (default={'kernel': ('linear', 'rbf'), 'C': [0.1, 1, 10]} )

Output<a id="output"></a>

When one uses sceptic.run_sceptic_and_evaluate function, several outputs are generated:

• cm: the confusion matrix for Sceptic's nested cross-validation. (# of time_points by # of time points)
• label_predicted: Sceptic's predicted discrete label for each cell. (# of cells by 1)
• pseudotime: Sceptic's predicted pseudotime (continuous) for each cell. (# of cells by 1)
• sceptic_prob: the class-proabilities for each cell. (# of cells by # of time points)

Contact<a id="contact"></a>

In case you have questions, reach out to gangliuw@uw.edu.

Citation<a id="citation"></a>

Pseudotime analysis for time-series single-cell sequencing and imaging data

If you have found our work useful, please consider citing us:

@article{li2023pseudotime,
  title={Pseudotime analysis for time-series single-cell sequencing and imaging data},
  author={Gang Li, Hyeon-Jin Kim, Sriram Pendyala, Ran Zhang, Christine M. Disteche, Jean-Philippe Vert, Xinxian Deng, Doug Fowler, and William Stafford Noble},
  doi = {10.1101/2023.11.03.565575},	
  url = {https://doi.org/10.1101%2F2023.11.03.565575},
  journal={bioRxiv},
  year={2023},
  publisher={Cold Spring Harbor Laboratory}
}