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Reduce features to "groups of genes" to score get groups feature level single per case (moGSA)
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Classroom Contents
Unsupervised Feature Learning with Matrix Decomposition - Aedin Culhane, PhD | ODSC East 2018
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- 1 Intro
- 2 Overview of Talk
- 3 Cancer Microenvironment, immune cells influence tumor progression, drug response
- 4 Many cell types
- 5 Exploratory data analysis (EDA)
- 6 Single Cell Data Analysis Pipeline
- 7 Classical Dimension Reduction Matrix Factorization approaches
- 8 Eigenvalues
- 9 Considerations when applying PCA
- 10 Correspondence Analysis
- 11 Multidimensional scaling (MDS)
- 12 Tensor Integration of 5 data sets (NC160) using multi-CIA
- 13 Reduce features to "groups of genes" to score get groups feature level single per case (moGSA)
- 14 Application of moGSA to finding PanCancer Immune subtypes
- 15 Correlation between 16 Clusters, leucocyte fraction and mutation load
- 16 Summary: multiple dataset integration
- 17 ENCODE
