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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-05-30T14:12:56.959Z","updated_at":"2026-01-20T16:33:30.884Z","avatar_url":"https://github.com/Walt-Lab.png","language":"Roff","funding_links":[],"categories":[],"sub_categories":[],"readme":"# OLINK Proteomic Analysis to Identify Potential Extracellular Vesicle-Associated Proteins\n\nAnalysis of OLINK proteomic data to identify proteins that may be associated with brain-derived extracellular vesicles.\n\n### Key Features\n- A dataset containing information concenrning 5416 unique proteins, collected via the OLINK HT panel using frationated human cerebrospinal fluid.\n- Read OLINK parquet files and identify proteins that may be associated with extracellular vesicles using relative protein abundances in fractionated human cerebrospinal fluid.\n- 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.\n\n## Modules\n\n### config.py\n- Contains several global variables.\n\n### raw_data_preprocessing.py\n- Converts the raw parquet file produced by OLINK into a tidy dataframe.\n- Generates graphs to display the median fractionation pattern of a protein of interest.\n- Calculates the EV Association Score of a protein of interest.\n\nRequired Packages \n- matplotlib.axes\n- matplotlib.pyplot\n- pandas\n\nRequired Documentation\n- config.py\n\n### olink_fractionation.py\n- Uses fractionation patterns reported by Olink to identify proteins that may be associated with extracellular vesicles.\n\nRequired Packages\n- pandas\n\n### specificity_functions.py\n- Calculates various statistical measures of specificity, including tau score, tissue specificity index, gini coefficient, Shannon entropy, specificity measure, and zscore.\n\nRequired Packages\n- numpy\n- pandas\n- scipy\n\n### brainrnaseq_specificity.py\n- Uses data collected and made available by BrainRNA-Seq to determine proteins that are specific to a cell type of interest.\n\nRequired Packages\n- requests\n- numpy\n- pandas\n- io\n- pathlib\n\nRequired Documentation\n- config\n- specificity_functions\n\n### gtex_specificity.py\n- Uses data made available by GTEx to determine proteins that are specific to the brain.\n\nRequired Packages\n- pandas\n\nRequired Documentation\n- specificity_functions\n- config\n\n### deeptmhmm_localization.py\n- Uses the DeepTMHMM deep learning model to identify the most likely subcellular localization of proteins of interest.\n\nRequired Packages\n- biolib\n- gzip\n- os\n- pathlib\n- pandas\n\n### identify_targets.py\n- Uses specified fractionation, localization, and cell-type specificity criteria to identify protein targets.\n\nRequired Packages\n- pandas\n- typing\n- pathlib\n\nRequired Documentation\n- raw_data_preprocessing.py\n- olink_fractionation.py\n- brainrnaseq_specificity.py\n- deeptmhmm_localization.py\n- config.py","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwalt-lab%2Folink","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwalt-lab%2Folink","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwalt-lab%2Folink/lists"}