Sparse reduced-rank regression for exploratory visualization of multimodal data sets
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Summary
Sparse reduced-rank regression can provide a valuable tool for the exploration and visualization of multimodal data sets, including Patch-seq, and is introduced as a ‘bibiplot’ visualization in order to display the dominant factors determining the relationship between transcriptomic and electrophysiological properties of neurons.
- Type
- preprint
- Published
- 2018-04-16
- Cited by
- 13
- References
- 59
- Access
- Open access
- OpenAlex
- https://openalex.org/W2797909204
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:90792329
Keywords
Visualization, Elastic net regularization, Multivariate statistics, Pattern recognition (psychology), Regression
References
- A greedy approach to sparse canonical correlation analysis
- Structured Sparse Canonical Correlation Analysis
- Sparse canonical correlation analysis from a predictive point of view
- Multivariate analyses in microbial ecology
- CCA: An R Package to Extend Canonical Correlation Analysis
- Highlighting relationships between heterogeneous biological data through graphical displays based on regularized canonical correlation analysis
- Sparse Canonical Correlation Analysis with Application to Genomic Data Integration
- Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
- Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems
- A Regularized Method for Selecting Nested Groups of Relevant Genes from Microarray Data
- Sparse Algorithms Are Not Stable: A No-Free-Lunch Theorem
- Sparse CCA using a Lasso with positivity constraints
- Sparse Canonical Correlation Analysis: New Formulation and Algorithm
- Reduced-rank regression for the multivariate linear model
- Multivariate Reduced-Rank Regression: Theory and Applications
- Neuronal cell types.
- Sparse canonical correlation analysis
- Sparse canonical methods for biological data integration: application to a cross-platform study
- Visualising associations between paired ‘omics’ data sets
- Regularization Paths for Generalized Linear Models via Coordinate Descent
Cited by
- Neocortical layer 4 in adult mouse differs in major cell types and circuit organization between primary sensory areas
- Optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization
- Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas
- Phenotypic variation within and across transcriptomic cell types in mouse motor cortex
- Sparse Bottleneck Networks for Exploratory Analysis and Visualization of Neural Patch-seq Data
- Consistent cross-modal identification of cortical neurons with coupled autoencoders
- Reduced-Rank Regression with Operator Norm Error
- Phenotypic variation of transcriptomic cell types in mouse motor cortex
- Uncovering Statistical Links Between Gene Expression and Structural Connectivity Patterns in the Mouse Brain
- In situ electro-sequencing in three-dimensional tissues
- Progress towards a cellularly resolved mouse mesoconnectome is empowered by data fusion and new neuroanatomy techniques.
- Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics
- Stability Approach to Regularization Selection for Reduced-Rank Regression
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