https://github.com/walt-lab/olink
Analysis of OLINK proteomic data to identify proteins that may be associated with brain-derived extracellular vesicles.
https://github.com/walt-lab/olink
Last synced: 5 months ago
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Analysis of OLINK proteomic data to identify proteins that may be associated with brain-derived extracellular vesicles.
- Host: GitHub
- URL: https://github.com/walt-lab/olink
- Owner: Walt-Lab
- License: gpl-3.0
- Created: 2023-10-23T19:52:53.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-03-26T14:31:13.000Z (about 1 year ago)
- Last Synced: 2025-09-05T02:33:36.252Z (10 months ago)
- Language: Roff
- Homepage:
- Size: 242 MB
- Stars: 0
- Watchers: 0
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
- Citation: CITATION.cff
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README
# OLINK Proteomic Analysis to Identify Potential Extracellular Vesicle-Associated Proteins
Analysis of OLINK proteomic data to identify proteins that may be associated with brain-derived extracellular vesicles.
### Key Features
- A dataset containing information concenrning 5416 unique proteins, collected via the OLINK HT panel using frationated human cerebrospinal fluid.
- Read OLINK parquet files and identify proteins that may be associated with extracellular vesicles using relative protein abundances in fractionated human cerebrospinal fluid.
- Overlay lists of proteins that may be associated with extracellular vesicles with single-cell RNA sequencing data and subcellular localization analysis to determine if a particular protein could be a potential cell-type specific immunocapture or validation target.
## Modules
### config.py
- Contains several global variables.
### raw_data_preprocessing.py
- Converts the raw parquet file produced by OLINK into a tidy dataframe.
- Generates graphs to display the median fractionation pattern of a protein of interest.
- Calculates the EV Association Score of a protein of interest.
Required Packages
- matplotlib.axes
- matplotlib.pyplot
- pandas
Required Documentation
- config.py
### olink_fractionation.py
- Uses fractionation patterns reported by Olink to identify proteins that may be associated with extracellular vesicles.
Required Packages
- pandas
### specificity_functions.py
- Calculates various statistical measures of specificity, including tau score, tissue specificity index, gini coefficient, Shannon entropy, specificity measure, and zscore.
Required Packages
- numpy
- pandas
- scipy
### brainrnaseq_specificity.py
- Uses data collected and made available by BrainRNA-Seq to determine proteins that are specific to a cell type of interest.
Required Packages
- requests
- numpy
- pandas
- io
- pathlib
Required Documentation
- config
- specificity_functions
### gtex_specificity.py
- Uses data made available by GTEx to determine proteins that are specific to the brain.
Required Packages
- pandas
Required Documentation
- specificity_functions
- config
### deeptmhmm_localization.py
- Uses the DeepTMHMM deep learning model to identify the most likely subcellular localization of proteins of interest.
Required Packages
- biolib
- gzip
- os
- pathlib
- pandas
### identify_targets.py
- Uses specified fractionation, localization, and cell-type specificity criteria to identify protein targets.
Required Packages
- pandas
- typing
- pathlib
Required Documentation
- raw_data_preprocessing.py
- olink_fractionation.py
- brainrnaseq_specificity.py
- deeptmhmm_localization.py
- config.py