Details
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Type: Task
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Status: Closed (View Workflow)
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Priority: Major
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Resolution: Done
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Affects Version/s: None
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Fix Version/s: None
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Labels:None
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Story Points:2
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Epic Link:
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Sprint:Spring 3 2022 Jan 31 - Feb 11, Spring 4 2022 Feb 14 - Feb 25, Spring 5 2022 Feb 28 - Mar 11, Spring 6 2022 Mar 14 - Mar 25
Description
In order to improve upon existing visualizations on single cell RNA seq data, a thorough investigation needs to be conducted. The investigation begins at the Aspen Institute Lecture by Aviv Regev that highlights the importance of such visual analyses and follow the breadcrumbs to find how improvements such as interactivity, connectivity to other resources such as genome browsers etc. can be introduced to the visualizations.
To that effect, the first task is to thoroughly understand how PCA and tSNE is used in the test cases (Test Case 3) mentioned in the Aviv Regev video, break down the key features of the visualization and find other cases where PCA and tSNE are used in single cell RNA seq analysis (similar or different purposes)
Note: Karthik Raveendran mentions that when he wrote code that implements the algorithm, he reached a deeper understanding. A take-home lesson of this is: concrete implementations are ultimately what we understand, and so only by implementing an algorithm can we achieve mastery of it.