Knowledge Vault 6 /59 - ICML 2020
Latent Space Learning
Fabian Theis
< Resume Image >

Concept Graph & Resume using Claude 3.5 Sonnet | Chat GPT4o | Llama 3:

graph LR classDef main fill:#f9d4f9, font-weight:bold, font-size:14px classDef basics fill:#f9d4d4, font-weight:bold, font-size:14px classDef methods fill:#d4f9d4, font-weight:bold, font-size:14px classDef analysis fill:#d4d4f9, font-weight:bold, font-size:14px classDef ml fill:#f9f9d4, font-weight:bold, font-size:14px classDef future fill:#d4f9f9, font-weight:bold, font-size:14px Main[Latent Space Learning] --> A[Basic Concepts] Main --> B[Analysis Methods] Main --> C[Data Integration
and Mapping] Main --> D[Machine Learning
Approaches] Main --> E[Advanced Techniques
and Future Directions] A --> A1[Single-cell genomics:
analyze individual cell
gene expression 1] A --> A2[Transcriptome sequencing:
capture, sequence RNA
from cells 2] A --> A3[Cell lineage
analysis: study developmental
trajectories 3] A --> A4[Pseudo-temporal ordering:
arrange cells along
expression trajectory 4] A --> A5[Cell type
classification: label cells
by expression 13] A --> A6[Spatial transcriptomics:
preserve tissue gene
expression location 18] B --> B1[Latent space
learning: capture nonlinear
relationships 5] B --> B2[Batch effect
correction: remove technical
variations 12] B --> B3[Trajectory inference:
reconstruct developmental or
disease paths 16] B --> B4[Dimensionality reduction:
visualize high-dimensional data 17] B --> B5[RNA velocity:
infer future cell
states 19] B --> B6[Differential expression
analysis: identify varying
gene levels 26] C --> C1[Data integration:
combine multiple single-cell
datasets 8] C --> C2[Human Cell
Atlas: comprehensive cell
type map 9] C --> C3[Reference mapping:
align datasets to
existing atlases 14] C --> C4[Multi-omics integration:
combine molecular data
types 15] C --> C5[Cross-species comparison:
analyze cell type
similarities 23] C --> C6[Cell atlas
curation: create, maintain
reference maps 25] D --> D1[Autoencoders: compress,
reconstruct high-dimensional gene
expression data 6] D --> D2[Deep count
autoencoders: handle single-cell
RNA data 7] D --> D3[Transfer learning:
apply pre-trained models
to datasets 10] D --> D4[Single-cell architectural
surgery: adapt pre-trained
networks efficiently 11] D --> D5[Model interpretability:
explain machine learning
decisions 20] D --> D6[Generative models:
create synthetic single-cell
data 27] E --> E1[Perturbation modeling:
predict cellular drug
responses 21] E --> E2[Rare cell
type detection: identify
uncommon populations 22] E --> E3[Scalability: handle
large, complex single-cell
datasets 24] E --> E4[Uncertainty quantification:
assess prediction confidence 28] E --> E5[Model zoos:
collections of pre-trained
models 29] E --> E6[Convolutional architectures:
analyze spatial relationships
in data 30] class Main main class A,A1,A2,A3,A4,A5,A6 basics class B,B1,B2,B3,B4,B5,B6 methods class C,C1,C2,C3,C4,C5,C6 analysis class D,D1,D2,D3,D4,D5,D6 ml class E,E1,E2,E3,E4,E5,E6 future

Resume:

1.- Single-cell genomics: Technique to analyze gene expression in individual cells, providing higher resolution than traditional bulk genomics methods.

2.- Transcriptome sequencing: Process of capturing and sequencing RNA from single cells, enabling analysis of gene expression profiles.

3.- Cell lineage analysis: Studying developmental trajectories of cells using single-cell data to understand differentiation and disease processes.

4.- Pseudo-temporal ordering: Computational method to arrange cells along a trajectory based on gene expression similarities.

5.- Latent space learning: Technique to capture complex, nonlinear relationships in high-dimensional single-cell data using neural networks.

6.- Autoencoders: Neural network architectures used to compress and reconstruct high-dimensional data, revealing underlying patterns in gene expression.

7.- Deep count autoencoders: Specialized autoencoders designed to handle the unique characteristics of single-cell RNA sequencing data.

8.- Data integration: Combining multiple single-cell datasets to create comprehensive references and enable cross-study comparisons.

9.- Human Cell Atlas: Large-scale project aiming to create a comprehensive reference map of all human cell types.

10.- Transfer learning: Applying knowledge gained from pre-trained models to new datasets, improving efficiency and scalability in single-cell analysis.

11.- Single-cell architectural surgery (SCAS): Method for adapting pre-trained neural networks to integrate new datasets efficiently.

12.- Batch effect correction: Techniques to remove technical variations between different experiments while preserving biological differences.

13.- Cell type classification: Assigning cell type labels to individual cells based on gene expression patterns.

14.- Reference mapping: Aligning new single-cell datasets to existing reference atlases for annotation and comparison.

15.- Multi-omics integration: Combining different types of molecular data (e.g., RNA, protein, epigenetics) from single cells for comprehensive analysis.

16.- Trajectory inference: Computational methods to reconstruct developmental or disease progression paths from single-cell data.

17.- Dimensionality reduction: Techniques to visualize high-dimensional single-cell data in lower-dimensional spaces for analysis and interpretation.

18.- Spatial transcriptomics: Methods to analyze gene expression while preserving spatial information within tissues.

19.- RNA velocity: Technique to infer future cell states based on unspliced and spliced mRNA ratios.

20.- Model interpretability: Efforts to understand and explain the decisions made by machine learning models in single-cell analysis.

21.- Perturbation modeling: Predicting cellular responses to drugs or other perturbations using single-cell data and machine learning.

22.- Rare cell type detection: Identifying and characterizing uncommon cell populations within complex tissues.

23.- Cross-species comparison: Analyzing similarities and differences in cell types and gene expression patterns across different organisms.

24.- Scalability in single-cell analysis: Developing computational methods to handle increasingly large and complex single-cell datasets.

25.- Cell atlas curation: Process of creating, maintaining, and updating comprehensive reference maps of cell types for various tissues.

26.- Differential expression analysis: Identifying genes that are expressed at different levels between cell types or conditions.

27.- Generative models: Machine learning approaches that can create realistic synthetic single-cell data for various applications.

28.- Uncertainty quantification: Assessing and representing the confidence of predictions in single-cell analysis tasks.

29.- Model zoos: Collections of pre-trained models for single-cell data analysis, enabling efficient reuse and adaptation.

30.- Convolutional architectures: Neural network designs adapted for analyzing spatial relationships in single-cell or spatial transcriptomics data.

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