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1
Introduction
2
About me
3
Motivation behind the project
4
Single cell sequencing
5
Multiohmic study
6
Coassay experiments
7
Multiomic studies
8
Unsupervised algorithms
9
Led by
10
Overview
11
Framework
12
Benchmark
13
Quantifying Performance
14
Unsupervised
15
Supervision
16
Alignment
17
Testing
18
Cell Mapping
19
Performance
20
Feature Mapping Comparison
21
Feature Mapping Experiment
22
Conclusion
23
Thanks
Description:
Explore single-cell data integration techniques using optimal transport in this informative conference talk. Delve into the motivation behind multiomics studies and unsupervised algorithms for analyzing single-cell sequencing data. Learn about the SCOT (Single-cell multi-omics alignment with Optimal Transport) framework and its performance in benchmarking tests. Discover how this approach compares to other methods in cell mapping and feature mapping experiments. Gain insights into the latest advancements in computational genomics and their applications in integrating diverse single-cell datasets.

Single Cell Data Integration Using Optimal Transport - 2023

Computational Genomics Summer Institute CGSI
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