Unsupervised Topological Alignment for Single-Cell Multi-Omics Integration
- Corrigendum: In the above paper,
$1_{n_x\times n_y}$ on Pages i50 and i51 should be corrected as$1_{n_x\times n_x}$ . The authors thank Dr. Chanwoo park from Seoul National University for pointing out this typo.
python >= 3.6
numpy 1.19.5
torch 1.7.0
scipy 1.4.1
torchvision 0.4.1
scikit-learn 0.23.2
umap-learn 0.3.10
UnionCom software is available on the Python package index (PyPI), latest version 0.4.0. To install it using pip, simply type:
pip3 install unioncom
- Add batch effect correction method by setting integration_type="BatchCorrect";
- Add more distances (e.g. cosine, cityblock, see sklearn.metrics.pairwise) to formulate distance matrices.
- Fix some bugs;
Each row should contain the measured values for a single cell, and each column should contain the values of a feature across cells.
>>> from unioncom import UnionCom
>>> import numpy as np
>>> data1 = np.loadtxt("./simu1/domain1.txt")
>>> data2 = np.loadtxt("./simu1/domain2.txt")
>>> type1 = np.loadtxt("./simu1/type1.txt")
>>> type2 = np.loadtxt("./simu1/type2.txt")
>>> type1 = type1.astype(np.int)
>>> type2 = type2.astype(np.int)
>>> uc = UnionCom.UnionCom()
>>> integrated_data = uc.fit_transform(dataset=[data1,data2])
>>> uc.test_LabelTA(integrated_data, [type1,type2])
>>> uc.Visualize([data1,data2], integrated_data, mode='PCA') # without datatype
>>> uc.Visualize([data1,data2], integrated_data, [type1,type2], mode='PCA') # with datatypeThe list of parameters is given below:
epoch_pd: epoch of Prime-dual algorithm (default=2000).epoch_DNN: epoch of training Deep Neural Network (default=100).epsilon: training rate of data matching matrix F (default=0.01).lr: training rate of DNN (default=0.001).batch_size: training batch size of DNN (default=100).rho: training damping term (default=10).delay: delay steps of alpha (default=0).beta: trade-off parameter of structure preserving and point matching (default=1).perplexity: perplexity of tsne projection (default=30)kmax: maximum value of knn when constructing geodesic distance matrix (default=40).output_dim: output dimension of integrated data (default=32).
The other parameters include:
log_pd: log step of Prime Dual (default=1000).log_DNN: log step of training DNN (default=10).manual_seed: random seed (default=666).distance_mode: mode of distance. ['geodesic' (suggested for multimodal integration), 'euclidean'(suggested for batch correction)] (default='geodesic').project_mode: mode of project, ['tsne', 'barycentric'] (default='tsne').integration_type: "MultiOmics" or "BatchCorrect". "BatchCorrect" needs aligned features. (default='MultiOmics')