{"id":51120267,"url":"https://github.com/lu-m-dev/biostatistics-eda","last_synced_at":"2026-06-25T01:32:16.840Z","repository":{"id":333597347,"uuid":"1088364375","full_name":"lu-m-dev/biostatistics-eda","owner":"lu-m-dev","description":"Exploratory data analysis and visualization system for biostatistical research","archived":false,"fork":false,"pushed_at":"2026-01-20T02:03:21.000Z","size":12763,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-01-20T09:06:57.806Z","etag":null,"topics":["biostatistics","data-analysis","data-visualization","eda"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lu-m-dev.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-11-02T20:31:03.000Z","updated_at":"2026-01-20T02:03:24.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/lu-m-dev/biostatistics-eda","commit_stats":null,"previous_names":["lu-m-dev/biostatistics-eda"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/lu-m-dev/biostatistics-eda","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lu-m-dev%2Fbiostatistics-eda","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lu-m-dev%2Fbiostatistics-eda/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lu-m-dev%2Fbiostatistics-eda/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lu-m-dev%2Fbiostatistics-eda/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lu-m-dev","download_url":"https://codeload.github.com/lu-m-dev/biostatistics-eda/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lu-m-dev%2Fbiostatistics-eda/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34756205,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-24T02:00:07.484Z","response_time":106,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":["biostatistics","data-analysis","data-visualization","eda"],"created_at":"2026-06-25T01:32:16.749Z","updated_at":"2026-06-25T01:32:16.817Z","avatar_url":"https://github.com/lu-m-dev.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Exploratory Data Analysis and Visualization System for Biostatistical Research\n\n## [`data/`](./data/)\n\n_Please note: For the public repository, `data/` has been omitted to respect data privacy/licensing._\n\nThe [`data/`](./data/) directory contains the datasets used in the project. It includes two subdirectories:\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./data/original/\"\u003e\n\u003ccode\u003eoriginal/\u003c/code\u003e\n\u003c/a\u003e\nOriginal data files imported from outside this repository\n\u003c/summary\u003e\n\n- [`adni/`](./data/original/adni/)\n- [`aric/`](./data/original/aric/)\n- [`calcium/`](./data/original/calcium/)\n- [`other/`](./data/original/other/)\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/data/processed/\"\u003e\n\u003ccode\u003eprocessed/\u003c/code\u003e\n\u003c/a\u003e\nInterim data files generated manually or by a script within this repository\n\u003c/summary\u003e\n\n- [`adni/`](./data/processed/adni/)\n- [`aric/`](./data/processed/aric/)\n- [`other/`](./data/processed/other/)\n\u003c/details\u003e\n\n## [`assets/`](./assets/)\nThe [`assets/`](./assets/) directory contains final, presentation-ready tables, figures, and slides\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./assets/tables/\"\u003e\n\u003ccode\u003etables/\u003c/code\u003e\n\u003c/a\u003e\nDemographic characteristics tables, summary statistics tables, etc.\n\u003c/summary\u003e\n\n- [`adni/`](./assets/tables/adni/)\n- [`aric/`](./assets/tables/aric/)\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./assets/figures/\"\u003e\n\u003ccode\u003efigures/\u003c/code\u003e\n\u003c/a\u003e\nSaved figures in PNG (raster/pixel) and PDF (vector) formats by interactive Dash apps\n\u003c/summary\u003e\n\n- [`adni/`](./assets/figures/adni/)\n- [`aric/`](./assets/figures/aric/)\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./assets/slides/\"\u003e\n\u003ccode\u003eslides/\u003c/code\u003e\n\u003c/a\u003e\nSummary slides\n\u003c/summary\u003e\n\n\u003c/details\u003e\n\n## [`src/`](./src/)\nThe [`src/`](./src/) directory contains the source code for this project. It is organized into the following subdirectories:\n\n\u003cul\u003e\n\n### [`src/lib/`](./src/lib/)\n\nThis directory contains the library code for the project. The utility functions are organized into the following categories:\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/lib/general.py\"\u003e\n\u003ccode\u003egeneral.py\u003c/code\u003e\n\u003c/a\u003e\nGeneral utility functions\n\u003c/summary\u003e\n\n- `get_stage_list()` returns the list of names of stages stratified by biomarkers Ab42, amyloid PET, (p-Tau or t-Tau)\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/lib/stats.py\"\u003e\n\u003ccode\u003estats.py\u003c/code\u003e\n\u003c/a\u003e\nStatistical analysis\n\u003c/summary\u003e\n\n- `demographics_characteristics()` computes a summary statistics table of the study population\n- `multiple_linear_regression()` fits a multiple linear regression model and returns model statistics\n- `cluster_corr_df()` hierarchially clusters a correlation matrix\n    - `get_linkage_methods()` returns list of available linkage methods for hierarchical clustering\n    - `get_cluster_criteria()` returns list of available cluster criteria for hierarchical clustering\n- `remove_diagonal()` masks the diagonal of a square matrix with NaN\n- `fill_mirror()` fills a triangular matrix to a square matrix with its transpose\n- `mask_outlier()` returns a mask that removes outliers when applied. Outliers are determined by [Local Outlier Factor](https://scikit-learn.org/stable/auto_examples/neighbors/plot_lof_outlier_detection.html)\n- `corr_remove_outliers()` computes the outlier-removed correlation coefficient for each pair of variables; returns a correlation matrix\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/lib/dialog.py\"\u003e\n\u003ccode\u003edialog.py\u003c/code\u003e\n\u003c/a\u003e\nTkinter dialogs\n\u003c/summary\u003e\n\n- `dialog_select_directory()` prompts the user to select a directory in a dialog selection window and returns its absolute path\n- `dialog_select_file()` prompts the user to select a file in a dialog selection window and returns its absolute path\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/lib/plotly.py\"\u003e\n\u003ccode\u003eplotly.py\u003c/code\u003e\n\u003c/a\u003e\nModifications to Plotly figure objects\n\u003c/summary\u003e\n\n- `standard_layout()` configures a standard layout for plot template, axes, and fonts\n- `add_box()` draws a box plot on top of a strip plot\n- `add_pairwise_comparison()` annotates pairwise comparison results on top of a strip plot or box plot\n    - `annotation_t_test()` computes the p-value from independent-sample t-test\n    - `annotation_cohens_d()` computes Cohen's d effect size\n    - `annotation_tukey()` performs Tukey's multiple comparison post-hoc to obtain the p-value\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/lib/r_interface.py\"\u003e\n\u003ccode\u003er_interface.py\u003c/code\u003e\n\u003c/a\u003e\nInterface to R\n\u003c/summary\u003e\n\n- `tukey()` conducts Tukey's multiple comparison post-hoc in R for a single dependent variable\n- `tukey_multiple_dvs()` conducts Tukey's multiple comparison post-hoc in R sequentially for a list of dependent variables; returns a table of the resultant p-values\n\u003c/details\u003e\n\n### [`src/processing/`](./src/processing/)\n\nThis directory contains scripts that process [_original files_](./data/original/) and/or [_processed files_](./data/processed/) to [_processed files_](./data/processed/) for downstream analyses.\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/processing/adni/\"\u003e\n\u003ccode\u003eadni/\u003c/code\u003e\n\u003c/a\u003e\n\u003c/summary\u003e\n\n\u003cblockquote\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/processing/adni/bmi.ipynb\"\u003e\n\u003ccode\u003ebmi.ipynb\u003c/code\u003e\n\u003c/a\u003e\nBody Mass Index (BMI)\n\u003c/summary\u003e\n\n- Input: [`VITALS_14Jul2023.csv`](./data/original/adni/VITALS_14Jul2023.csv)\n- Output: [`bmi.csv`](./data/processed/adni/bmi.csv)\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/processing/adni/strem2.ipynb\"\u003e\n\u003ccode\u003estrem2.ipynb\u003c/code\u003e\n\u003c/a\u003e\nCSF soluble triggering receptor expressed on myeloid cells 2 (sTREM2)\n\u003c/summary\u003e\n\n- Input: [`ADNI_HAASS_WASHU_LAB_13Jul2023.csv`](./data/original/adni/ADNI_HAASS_WASHU_LAB_13Jul2023.csv)\n- Output: [`strem2.csv`](./data/processed/adni/strem2.csv)\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/processing/adni/demographics.ipynb\"\u003e\n\u003ccode\u003edemographics.ipynb\u003c/code\u003e\n\u003c/a\u003e\nBasic demographics\n\u003c/summary\u003e\n\n- Input: [`ADNIMERGE_14Jul2023.csv`](./data/original/adni/ADNIMERGE_14Jul2023.csv), [`bmi.csv`](./data/processed/adni/bmi.csv)\n- Output: [`demographics.csv`](./data/processed/adni/demographics.csv)\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/processing/adni/demographics_tau.ipynb\"\u003e\n\u003ccode\u003edemographics_tau.ipynb\u003c/code\u003e\n\u003c/a\u003e\nDemographics with tau biomarker data\n\u003c/summary\u003e\n\n- Input: [`ADNIMERGE_14Jul2023.csv`](./data/original/adni/ADNIMERGE_14Jul2023.csv), [`bmi.csv`](./data/processed/adni/bmi.csv)\n- Output: [`demographics_tau.csv`](./data/processed/adni/demographics_tau.csv)\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/processing/adni/demographics_biomarkers.ipynb\"\u003e\n\u003ccode\u003edemographics_biomarkers.ipynb\u003c/code\u003e\n\u003c/a\u003e\nDemographics with amyloid and tau biomarker data and stage assignment\n\u003c/summary\u003e\n\n- Input: [`ADNIMERGE_14Jul2023.csv`](./data/original/adni/ADNIMERGE_14Jul2023.csv), [`bmi.csv`](./data/processed/adni/bmi.csv), [`strem2.csv`](./data/processed/adni/strem2.csv)\n- Output: [`demographics_biomarkers.csv`](./data/processed/adni/demographics_biomarkers.csv)\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/processing/adni/lipidomics.ipynb\"\u003e\n\u003ccode\u003elipidomics.ipynb\u003c/code\u003e\n\u003c/a\u003e\nPlasma lipidomics, Meikle lab, longitudinal\n\u003c/summary\u003e\n\n- Input: [`ADMCLIPIDOMICSMEIKLELABLONG_13Jul2023.csv`](./data/original/adni/ADMCLIPIDOMICSMEIKLELABLONG_13Jul2023.csv), [`Lipid_Models_Final.xlsx`](./data/original/adni/Lipid_Models_Final.xlsx)\n- Output: [`lipidomics.csv`](./data/processed/adni/lipidomics.csv), [`lipidomics_total.csv`](./data/processed/adni/lipidomics_total.csv), [`lipidomics_dictionary.csv`](./data/processed/adni/lipidomics_dictionary.csv)\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/processing/adni/lipoprotein.ipynb\"\u003e\n\u003ccode\u003elipoprotein.ipynb\u003c/code\u003e\n\u003c/a\u003e\nNightingale NMR analysis of lipoproteins and metabolites\n\u003c/summary\u003e\n\n- Input: [`ADNINIGHTINGALELONG_05_24_21_27Jul2023.csv`](./data/original/adni/ADNINIGHTINGALELONG_05_24_21_27Jul2023.csv)\n- Output: [`lipoprotein.csv`](./data/processed/adni/lipoprotein.csv), [`lipoprotein_dict.csv`](./data/processed/adni/lipoprotein_dict.csv)\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/processing/adni/somascan.ipynb\"\u003e\n\u003ccode\u003esomascan.ipynb\u003c/code\u003e\n\u003c/a\u003e\nCSF proteomics SOMAscan 7000+ proteins post-QC, Cruchaga lab\n\u003c/summary\u003e\n\n- Input: [`CruchagaLab_CSF_SOMAscan7k_Protein_matrix_postQC_20230620.csv`](./data/original/adni/CruchagaLab_CSF_SOMAscan7k_Protein_matrix_postQC_20230620.csv), [`ADNI_Cruchaga_lab_CSF_SOMAscan7k_analyte_information_20_06_2023.csv`](./data/original/adni/ADNI_Cruchaga_lab_CSF_SOMAscan7k_analyte_information_20_06_2023.csv)\n- Output: [`somascan.csv`](./data/processed/adni/somascan.csv), [`somascan_dict.csv`](./data/processed/adni/somascan_dict.csv)\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/processing/adni/converters.ipynb\"\u003e\n\u003ccode\u003econverters.ipynb\u003c/code\u003e\n\u003c/a\u003e\nLongitudinal decline in cognitive status (CN to MCI, MCI to AD, or CN to AD), excluding participants diagnosed with AD at baseline\n\u003c/summary\u003e\n\n- Input: [`ADNIMERGE_14Jul2023.csv`](./data/original/adni/ADNIMERGE_14Jul2023.csv)\n- Output: [`converters.csv`](./data/processed/adni/converters.csv)\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/processing/adni/converters_to_ad.ipynb\"\u003e\n\u003ccode\u003econverters_to_ad.ipynb\u003c/code\u003e\n\u003c/a\u003e\nLongitudinal decline in cognitive status from CN or MCI to AD, excluding participants diagnosed with AD at baseline\n\u003c/summary\u003e\n\n- Input: [`ADNIMERGE_14Jul2023.csv`](./data/original/adni/ADNIMERGE_14Jul2023.csv)\n- Output: [`converters_to_ad.csv`](./data/processed/adni/converters_to_ad.csv)\n\u003c/details\u003e\n\n\u003c/blockquote\u003e\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/processing/aric/\"\u003e\n\u003ccode\u003earic/\u003c/code\u003e\n\u003c/a\u003e\n\u003c/summary\u003e\n\n\u003cblockquote\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/processing/aric/pilot.ipynb\"\u003e\n\u003ccode\u003epilot.ipynb\u003c/code\u003e\n\u003c/a\u003e\nDemographics and brain MRI data from the ARIC server for participants included in the pilot study\n\u003c/summary\u003e\n\n- Input from ARIC server: `ARIC_NP/DATA_NP/Visits/Visit 5/derive54_np.sas7bdat`, `DATA_NP/Visits/Visit 5/derive_ncs51_np.sas7bdat`, `DATA_NP/Visits/Visit 1/derive13_np.sas7bdat`\n- Input: [`all_eleigible_samples_AS2021_25v3.xlsx`](./data/original/aric/sample_selection/all_eleigible_samples_AS2021_25v3.xlsx), [`lipoproteins_6_29_23.csv`](./data/original/aric/lipoproteins_6_29_23.csv), [`dictionary.csv`](./data/processed/aric/dictionary.csv)\n- Output: [`lipoprotein_list.csv`](./data/processed/aric/lipoprotein_list.csv), [`pilot.csv`](./data/processed/aric/pilot.csv), [`demographic_characteristics.csv`](./downloads/tables/aric/demographic_characteristics.csv)\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/processing/aric/pilot_eligible.ipynb\"\u003e\n\u003ccode\u003epilot_eligible.ipynb\u003c/code\u003e\n\u003c/a\u003e\nDemographics and brain MRI data for ARIC participants eligible under the inclusion criteria\n\u003c/summary\u003e\n\n- Input from ARIC server: `ARIC_NP/DATA_NP/Visits/Visit 5/derive54_np.sas7bdat`, `DATA_NP/Visits/Visit 5/derive_ncs51_np.sas7bdat`, `DATA_NP/Visits/Visit 1/derive13_np.sas7bdat`, `DATA_NP/Visits/MultiVisit/V5_V11 Longitudinal MRI data/v5_v11_mri_derv_np_240221.sas7bdat`\n- Input: [`all_eleigible_samples_AS2021_25v3.xlsx`](./data/original/aric/sample_selection/all_eleigible_samples_AS2021_25v3.xlsx), [`ARIC_Pilot_Updated_06032022.csv`](./data/original/aric/ARIC_Pilot_Updated_06032022.csv), [`lipoproteins_6_29_23.csv`](./data/original/aric/lipoproteins_6_29_23.csv), [`dictionary.csv`](./data/processed/aric/dictionary.csv)\n- Output: [`lipoprotein_list.csv`](./data/processed/aric/lipoprotein_list.csv), [`pilot.csv`](./data/processed/aric/pilot.csv), [`demographic_characteristics.csv`](./downloads/tables/aric/demographic_characteristics.csv)\n\u003c/details\u003e\n\n\u003c/blockquote\u003e\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/processing/other/\"\u003e\n\u003ccode\u003eother/\u003c/code\u003e\n\u003c/a\u003e\n\u003c/summary\u003e\n\n\u003cblockquote\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/processing/other/davidson.ipynb\"\u003e\n\u003ccode\u003edavidson.ipynb\u003c/code\u003e\n\u003c/a\u003e\nSean Davidson HDL Proteome Watch 2023\n\u003c/summary\u003e\n\n- Input: [`HDL Proteome Watch 2023 Final.xlsx`](./data/original/other/HDL%20Proteome%20Watch%202023%20Final.xlsx)\n- Output: [`hdl_proteome_davidson.csv`](./data/processed/other/hdl_proteome_davidson.csv)\n\u003c/details\u003e\n\n\u003c/blockquote\u003e\n\n\u003c/details\u003e\n\n### [`src/analysis/`](./src/analysis/)\n\nThis directory contains Jupyter notebook files that perform analyses.\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/analysis/adni/\"\u003e\n\u003ccode\u003eadni/\u003c/code\u003e\n\u003c/a\u003e\n\u003c/summary\u003e\n\n- [`lipidomics_tukey.ipynb`](./src/analysis/adni/lipidomics_tukey.ipynb) ANCOVA followed by Tukey post-hoc to determine which plasma lipids or biomarkers differ significantly between stages\n- [`lipidomics_boxplot.ipynb`](./src/analysis/adni/lipidomics_boxplot.ipynb) Distribution of plasma lipids or biomarkers across stages\n- [`survival.ipynb`](./src/analysis/adni/survival.ipynb) Survival analysis (Kaplan-Meier survival curve, Cox's proportional hazard model) comparing risk of conversion to AD between biomarker groups.\n- [`survival_hdl_ratio.ipynb`](./src/analysis/adni/survival_hdl_ratio.ipynb) Survival analysis comparing cognitive decline between tertiles of non-small HDL FC-to-CE ratio.\n- [`somascan_pca.ipynb`](./src/analysis/adni/somascan_pca.ipynb) Clustering of CSF proteins by PCA, followed by linear regression with dependent variable pTau\n- [`somascan_boxplot.ipynb`](./src/analysis/adni/lipidomics_tukey.ipynb) Distribution of CSF proteins across cognitive statuses\n- [`strem2_lipidomics_regression.ipynb`](./src/analysis/adni/strem2_lipidomics_regression.ipynb) Linear regression of CSF sTREM2 on plasma lipids.\n- [`strem2_lipoprotein_regression.ipynb`](./src/analysis/adni/strem2_lipoprotein_regression.ipynb) Linear regression of CSF sTREM2 on plasma lipoprotein subclasses.\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/analysis/aric/\"\u003e\n\u003ccode\u003earic/\u003c/code\u003e\n\u003c/a\u003e\n\u003c/summary\u003e\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/analysis/calcium/\"\u003e\n\u003ccode\u003ecalcium/\u003c/code\u003e\n\u003c/a\u003e\n\u003c/summary\u003e\n\n- [`calcium_all_sites.ipynb`](./src/analysis/calcium/calcium_all_sites.ipynb) Distribution of calcium measurements compared between Vista and Roche, data from all sites combined\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\n\u003ca href=\"./src/analysis/other/\"\u003e\n\u003ccode\u003eother/\u003c/code\u003e\n\u003c/a\u003e\n\u003c/summary\u003e\n\n- [`imagej_particle_results_hdl.ipynb`](./src/processing/other/imagej_particle_results_hdl.ipynb) HDL1 and HDL2 particle analysis on EM images using results exported from ImageJ\n\u003c/details\u003e\n\n\u003c/ul\u003e\n\n## Publications\nThe analysis in this repository contributed to the following publications:\n\n- Li, D.; Mantyh, W. G.; Men, L.; Jain, I.; Glittenberg, M.; An, B.; Zhang, L.; Li, L.; for the Alzheimer’s Disease Neuroimaging Initiative. sTREM2 in Discordant CSF Aβ42 and P‐tau181. _Alz \u0026 Dem Diag Ass \u0026 Dis Mo_ **2025**, _17_ (1), e70072. https://doi.org/10.1002/dad2.70072.\n\n- Li, D.; An, B.; Men, L.; Glittenberg, M.; Lutsey, P. L.; Mielke, M. M.; Yu, F.; Hoogeveen, R. C.; Gottesman, R.; Zhang, L.; Meyer, M.; Sullivan, K.; Zantek, N.; Alonso, A.; Walker, K. A. The Association of High-Density Lipoprotein Cargo Proteins with Brain Volume in Older Adults in the Atherosclerosis Risk in Communities (ARIC). _Journal of Alzheimer’s Disease_ **2025**, _103_ (3), 724–734. https://doi.org/10.1177/13872877241305806.\n\n## Data Sources\n- [Alzheimer's Disease Neuroimaging Initiative (ADNI)](https://adni.loni.usc.edu/)\n- [The Atherosclerosis Risk in Communities Study (ARIC)](https://aric.cscc.unc.edu/aric9/)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flu-m-dev%2Fbiostatistics-eda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flu-m-dev%2Fbiostatistics-eda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flu-m-dev%2Fbiostatistics-eda/lists"}