From 298639b4e68d298fd14c99a459da7fc1053e9580 Mon Sep 17 00:00:00 2001 From: Tabitha Peter Date: Mon, 30 Sep 2024 20:08:45 -0500 Subject: [PATCH] update README --- README.md | 34 +++++++++++++++++++--------------- 1 file changed, 19 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 92616b30..20e4d042 100644 --- a/README.md +++ b/README.md @@ -1,31 +1,35 @@ -[![GitHub version](https://img.shields.io/static/v1?label=GitHub&message=3.2.0.0&color=blue&logo=github)](https://github.com/pbreheny/plmmr) -[![R-CMD-check](https://github.com/pbreheny/plmmr/workflows/R-CMD-check/badge.svg)](https://github.com/pbreheny/plmmr/actions) -[![Codecov test coverage](https://codecov.io/gh/pbreheny/plmmr/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pbreheny/plmmr?branch=master) + +[![GitHub version](https://img.shields.io/static/v1?label=GitHub&message=3.2.0.0&color=blue&logo=github)](https://github.com/pbreheny/plmmr) [![R-CMD-check](https://github.com/pbreheny/plmmr/workflows/R-CMD-check/badge.svg)](https://github.com/pbreheny/plmmr/actions) [![Codecov test coverage](https://codecov.io/gh/pbreheny/plmmr/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pbreheny/plmmr?branch=master) + -## plmmr +## plmmr The `plmmr` (**p**enalized **l**inear **m**ixed **m**odels in **R**) package contains functions that fit penalized linear mixed models to correct for unobserved confounding effects. -## Installation +## Installation -To install the latest version of the package: +To install the latest version of the package: -```r +``` r devtools::install_github("pbreheny/plmmr") ``` For a description of the motivation of the functions in this package (along with examples) refer to the second module of [this GWAS data tutorial](https://pbreheny.github.io/adv-gwas-tutorial/index.html) -## Note on branches +## So how fast is `plmmr`? And how well does it scale? + +To illustrate these important questions, I created a separate [GitHub repository](https://github.com/tabpeter/demo_plmmr/tree/master) that has all the scripts for a `plmmr` workflow using publicly-available genome-wide association (GWAS) data. The main takeaway: using GWAS data from a study with 1,400 samples and 800,000 SNPs, a full `plmmr` analysis will run in about half an hour using a single core on a laptop. + +Three smaller datasets ship with `plmmr`, and tutorials walking through how to analyze these data sets are documented in the [documentation site](https://pbreheny.github.io/plmmr/). While these datasets are useful for didactic purposes, they are not large enough to really highlight the computational scalability of `plmmr` -- this is what motivated the creation of the separate repository for a GWAS workflow. + +## Note on branches + +The branches of this repo are organized in the following way: -The branches of this repo are organized in the following way: +- `master` is the main (or 'head') branch. - - `master` is the main (or 'head') branch. - - - `gh_pages` is where we are keeping all the documentation for `plmmr` - - - `gwas_scale` is an **archived** branch that contains the development version of the package I used to run my dissertation analysis. Will delete this eventually. +- `gh_pages` is where we are keeping all the documentation for `plmmr` - +- `gwas_scale` is an **archived** branch that contains the development version of the package I used to run my dissertation analysis. Will delete this eventually.