https://github.com/cmkobel/properomics

Link tendencies across multiple omics layers using principal components

https://github.com/cmkobel/properomics

Science Score: 36.0%

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    Links to: biorxiv.org
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Link tendencies across multiple omics layers using principal components

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  • Host: GitHub
  • Owner: cmkobel
  • License: gpl-3.0
  • Default Branch: main
  • Size: 15.6 KB
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Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme License

README.md

properomics

Link tendencies across multiple omics layers using principal components.

Properomics orders samples on principal components, and correlates the ordering across several omic-layers.

This pipeline is a generalization of the workflow used in https://www.biorxiv.org/content/10.1101/2024.12.05.626740v1

Workflow

  1. Input validation
  2. Transform
  3. Filter
  4. (Optional: Statistics)
  5. Principal Component Analysis and sample ordering
  6. Correlation across omic-layers

Input

Properomics is designed to handle any type of properomic setup. This means that you need at least two independent omic layers. The omic layers can be identical on the molecular level (transcriptomics-transcriptomcs) as long as they're from different sample locations. (Needs more generalization)

metadata.tsv:

samples (n in N) x factors (f in F)

Owner

  • Name: Carl Mathias Kobel
  • Login: cmkobel
  • Kind: user
  • Company: Norges Miljø- & Biovitenskapelige Universitet

PhD-fellow in the MEMO group at NMBU.

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