Dimensionality Reduction - Lecture 11 - Deep Learning in Life Sciences (Spring 2021)

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MIT 6.874/6.802/20.390/20.490/HST.506 Spring 2021 Prof. Manolis Kellis
Guest Lecture: Joshua Welch
Deep Learning in the Life Sciences / Computational Systems Biology

0:00 Introduction
2:27 Statistical tests
6:20 Dimensionality reduction
12:00 Principal component analysis
25:55 t-SNE
33:21 t-SNE parameters
38:42 Single-cell genomics
42:57 Metagenes
42:50 LIGER and integrative nonnegative matrix factorization
50:12 iNMF optimization
1:03:50 Online learning for iNMF
1:13:35 Integrating datasets with partially overlapping features
1:16:29 Combining VAEs and GANs to generate scRNA profiles

Manolis Kellis

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I have a question, in the 29:44 we want to use low dimension to discribe the high dimension(which is alse true distribution), so the P should be high dimension and Q is low dimension. Could you please answer my question?

siduanmiao

Dimensionality Reduction - Lecture 11 - Deep Learning in Life Sciences (Spring 2021)

Dimensionality Reduction - Lecture 11 - Deep Learning in Life Sciences (Spring 2021)

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