Tools: tl#
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Optimal transport based method to compute the Wasserstein distance between two single single-cell experiments. |
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Annotation-free sample comparison using a Gaussian Mixture VAE and Wasserstein distance. |
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Baseline, where distances between samples are average distances between their cell group pseudobulks |
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A simple baseline, which represents samples as composition of their cell groups (for example, cell type fractions). |
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Deep generative modeling for quantifying sample-level heterogeneity in single-cell omics. |
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A dummy baseline, which represents samples as random embeddings |
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A semi-supervised conditional deep generative model from https://www.biorxiv.org/content/10.1101/2022.11.28.517803v1 |
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A simple baseline, which represents samples as pseudobulk of their gene expression |
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Method based on the matrix of pairwise Wasserstein distances between units. |
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Diffusion Earth Mover's Distance. |
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Patient representation using MOFA2 model, treating patients as samples with optional cell type views. |
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A class that loads a file to R using rpy2 and follows the same interface as other SampleRepresentation methods |
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GloScope implementation in Python for CPU and GPU |
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Base class for sample representation methods |
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Prints the basic information about the metadata and tries to guess column types |
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Test if distances are significantly different from the null distribution |
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Predict values of |
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Evaluate how well |
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Run statistical test to check if distribution of |
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Evaluate representation of |