Embedded Files
Roweis ST and Saul LK, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, 22 Dec 2000.
Fiori, L. (2020, June 6). K-means clustering using Python. Medium. Retrieved April 20, 2022, from https://medium.com/@luigi.fiori.lf0303/k-means-clustering-using-python-db57415d26e6
Study Design
Study Design
Step 1: Feature transformation/dimension reduction
t-SNE/opt-SNE
UMAP
Step 2: Clustering of cells for identifying cell populations
HDBSCAN (with UMAP)
K-means clustering
Step 3: Classification of samples based on characteristics of cell populations
Random forest
Linear SVC (support vector classifier)
Step 4: Cell-based biomarker identification and tumor burden estimate
Wilcoxon Rank Sum Test with FDR correction
An interpretable pipeline for estimating tumor burden
An interpretable pipeline for estimating tumor burden
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