Commit fd9057ca authored by Hanna  JULIENNE's avatar Hanna JULIENNE
Browse files

updated readme

parent 6039e59b
......@@ -19,25 +19,17 @@ Project Organization
├── models <- Trained and serialized models, model predictions, or model summaries
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
`1.0-jqp-initial-data-exploration`.
├── notebooks
├── references <- Data dictionaries, manuals, and all other explanatory materials.
├── reports
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures <- Generated graphics and figures to be used in reporting
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│   ├── __init__.py <- Makes src a Python module
│ │
│   ├── data <- Scripts to download or generate data
│   │   └── make_dataset.py
| |__ 1 generate genotype with a specified LD structure
│   ├── data
│   │   └
| |__ 1 generate_genotype.R : enerate genotype with a specified LD structure
| |__ 2 generate_Zscores.py
| | Generate estimated Zscore by simulating a continuous phenotypes from genotype and
| normal noise. then estimate beta and Zscore with a linear regression. Cohort size
......@@ -47,17 +39,24 @@ Project Organization
Can we recover a proper signal in the meta analysis using very noisy input
│ │
│   ├── features <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   ├── features
│   │   └── add_hg_37_pos.py : perform liftover to hg37 (ad GnomAD is in hg37)
│ │
│   ├── models
│ │ │
│   │   ├── Impute_simulated_signal.py
impute Zscores using simulated data : 1 mask 50 SNPs on 200, reimpute then save
│   │   └──
| impute Zscores using simulated data : mask 50 SNPs on
200, reimpute then save results
│   │   └── Imputation_strategy_simulation.py
impute Zscores using simulated data : mask 50 SNPs on
200, reimpute then save results assuming different sample size (based on hgcovid consortium or always 100)
│ │
│   └── visualization <- Scripts to create exploratory and results oriented visualizations
│   └── visualize.py
│   └── Draw_LD.R : Draw the LD matrix used to simulate Data
| |__ Draw_Imputation_quality.R : draw non imputed signal
| (intrinsic variability due to sample size)
| |__ Draw_signal_variability.R : draw imputed signal
| ( variability due to sample size + imputation error)
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
......
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment