Sceptic package
Project description
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.
Installation
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
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)
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
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
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
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
In case you have questions, reach out to gangliuw@uw.edu
.
Citation
Pseudotime analysis for time-series single-cell sequencing and imaging data (to be updated)
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}
}
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