https://github.com/23andme/pd-microbiome

HD-30486
https://github.com/23andme/pd-microbiome

Last synced: 26 days ago
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HD-30486

• Host: GitHub
• URL: https://github.com/23andme/pd-microbiome
• Owner: 23andMe
• License: mit
• Created: 2024-04-16T20:45:05.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-09-03T16:59:10.000Z (5 months ago)
• Last Synced: 2024-11-14T05:21:41.988Z (3 months ago)
• Language: R
• Size: 55.7 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# README.md

The scripts in this repo can be used to run the analyses conducted in the Stagaman *et al*. 2024 article, {TITLE} published in *Communications Medicine*.

File descriptions:

1. `.Rprofile` creates a list of directories for reading in and saving data and plots

2. `_packages_and_sources.R` 

3. `_setup.R` loads packages, sources functions, set important variables

4. `01_variable_reduction_selection.R` Conducts the variable reduction step to identify the covariates to be used in all subsequent analyses.

5. `02_Analyses` all scripts used for statistical analyses

    1. `01_required_first.R` removes low abundance and low prevalence OTUs/KOs, rarefies counts, and generates data structures to be used in differential abundances and random forest analyses

    2. `02_taxonomic_alpha.R` conducts statistical analysis of alpha-diversity in relation to PD and the covariates of interest.

    3. `03_all_beta.R` conducts statistical analysis of beta-diversity in relation to PD and the covariates of interest.

    4. `04_random_forests` scripts used for random forest classfier models

        01. `04A_covariates_split_sources.R` RF model training and testing for saliva and stool separately using only covariate data (no OTU or KO abundances).

        02. `04B_covariates_combined_sources.R` RF model training and testing for saliva and stool combined using only covariate data (no OTU or KO abundances).

        03. `04C_taxon_abunds_split_types_split_sources.R` RF model training and testing for saliva and stool separately using OTU, species, and genus abundances separately.

        04. `04D_taxon_abunds_split_types_combined_sources.R` RF model training and testing for saliva and stool combined using OTU, species, and genus abundances separately.

        05. `04E_taxon_abunds_aggregated_split_sources.R` RF model training and testing for saliva and stool separately using OTU, species, and genus abundances combined.

        06. `04F_taxon_abunds_aggregated_combined_sources.R` RF model training and testing for saliva and stool combined using OTU, species, and genus abundances combined.

        07. `04G_taxon_abunds_covariates_split_types_split_sources.R` RF model training and testing for saliva and stool separately using OTU, species, and genus abundances separately, including covariates.

        08. `04H_taxon_abunds_covariates_split_types_combined_sources.R` RF model training and testing for saliva and stool combined using OTU, species, and genus abundances separately, including covariates.

        09. `04I_taxon_abunds_covariates_aggregated_split_sources.R` RF model training and testing for saliva and stool separately using OTU, species, and genus abundances combined, including covariates.

        10. `04J_taxon_abunds_covariates_aggregated_combined_sources.R` RF model training and testing for saliva and stool combined using OTU, species, and genus abundances combined, including covariates.

        11. `04K_function_abunds_split_types_split_sources.R` RF model training and testing for saliva and stool separately using KO, module, and pathway abundances separately

        12. `04L_function_abunds_covars_split_types_split_sources.R` RF model training and testing for saliva and stool separately using KO, module, and pathway abundances separately, including covariates.

    5. `05_LDAs` scripts used for differential abundance analsyes (a.k.a linear discriminate analyses)

        1. `05A_taxon_ANCOMBC2_wCovariates.R` Differential OTU abundance analysis using ANCOMBC2, including covariates.

        2. `05B_taxon_ANCOMBC2_PDonly.R` Differential OTU abundance analysis using ANCOMBC2, without covariates.

        3. `05C_taxon_ALDEx2_wCovariates.R` Differential OTU abundance analysis using ALDEx2, including covariates.

        4. `05D_taxon_ALDEx2_PDonly.R` Differential OTU abundance analysis using ALDEx2, without covariates.

        5. `05E_function_ANCOMBC2_wCovariates.R` Differential KO abundance analysis using ANCOMBC2, including covariates.

        6. `05F_function_ANCOMBC2_PDonly.R` Differential KO abundance analysis using ANCOMBC2, without covariates.

        7. `05G_function_ALDEx2_wCovariates.R` Differential KO abundance analysis using ALDEx2, including covariates.

        8. `05H_function_ALDEx2_PDonly.R` Differential KO abundance analysis using ALDEx2, without covariates.

    6. `06_networks` scripts used to generate feature-feature (OTUs/KOs) networks

        1. `06A_between_saliva_stool_associations.R` Generates OTU-OTU and KO-KO co-abundance networks (utilizing SpeicEasi) between saliva and stool microbiomes.

        2. `06B_within_sample_associations.R` Generates OTU-OTU and KO-KO co-abundance networks (utilizing SpeicEasi) within saliva and stool microbiomes.

    7. `99_required_last.R` merges data from across different scripts in prepartion for plotting and further analysis.

6. `Helpers` scripts for custom functions and model specifications used across scripts

    1. `functions.R` custom functions

    2. `model_specs.R` model specifications