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20140222 Tokyo.R#36 RでSPADEとviSNEを使って次元削減と可視化


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Nat Biotechnol. 2013 Jun;31(6):545-52. doi: 10.1038/nbt.2594. Epub 2013 May 19.
Nat Biotechnol. 2011 Oct 2;29(10):886-91. doi: 10.1038/nbt.1991.

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20140222 Tokyo.R#36 RでSPADEとviSNEを使って次元削減と可視化

  1. 1. 変態にRを与えた結果がこれだよ…9 RでSPADEとSNEを使って次元削減と可視化 20140222 Tokyo.R#36 @ニフティ株式会社 新宿フロントタワー18F YF@Med_KU
  2. 2. 本日の内容 次元削減と可視化 SPADE Cyto Spanning tree Progression of Density normalized Events t-SNE t-distributed stochastic neighbor embedding
  3. 3. 本日の内容
  4. 4. 本日の内容 viSNE SPADE
  5. 5. 前回のTokyo.R#35で Cyto Spanning tree Progression of Density normalized Events (SPADE) n次元定量データのパターンから分化系統樹作成 新規 退会 課金厨 無課金厨 重課金厨 Nat Biotechnol. 2011 Oct 2;29(10):886-91 Science. 2011 May 6;332(6030):687-96
  6. 6. 次元削減法 線形 K-nearest neighbors algorithm (kNN) principal component analysis (PCA) linear discriminant analysis (LDA) canonical correlation analysis (CCA) feature vectors 非線形 Sammon's mapping Self-organizing map Principal curves and manifolds Autoencoders Gaussian process latent variable models Curvilinear component analysis Curvilinear distance analysis Diffeomorphic dimensionality reduction Kernel principal component analysis Isomap Locally-linear embedding(LLE) Laplacian eigenmaps Manifold alignment Diffusion maps Hessian LLE, Modified LLE Local tangent space alignment Local multidimensional scaling Maximum variance unfolding Nonlinear PCA Data-driven high-dimensional scaling Manifold sculpting RankVisu Topologically constrained isometric embedding Relational perspective map
  7. 7. 分子生物学の多次元データ Flow cytometry (FCM)
  8. 8. SPADE density-dependent down-sampling Original Down-sampling
  9. 9. SPADE minimum spanning tree 3D MST 2D MST {nnclust}
  10. 10. SPADE up-sampling Original Up-sampling
  11. 11. 309人の女の子のデータ
  12. 12. 309人の女の子のデータ
  13. 13. 309人の女の子のデータ 歳をとる 背が伸びる
  14. 14. Stochastic neighbor embedding
  15. 15. Stochastic neighbor embedding Learning rate
  16. 16. Symmetric SNE and t-SNE
  17. 17. viSNEによる可視化
  18. 18. 小規模クラスターの検出 Minimum residual disease (MRD)