-
Notifications
You must be signed in to change notification settings - Fork 0
/
README.Rmd
71 lines (55 loc) · 1.93 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# DRAGGER
<!-- badges: start -->
[![R-CMD-check](https://github.com/AEstebanMar/DRAGGER/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/AEstebanMar/DRAGGER/actions/workflows/R-CMD-check.yaml)
<!-- badges: end -->
DRAGGER provides a toolkit for drug repositioning based on variant and gene-expression data. It includes a wrapper
for a basic workflow, but a custom run can be set up with ease.
## Installation
You can install the development version of DRAGGER from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("AEstebanMar/DRAGGER")
```
## Example
DRAGGER includes a demo dataset to showcase its main use case.
```{r First few lines of demo datasets}
library(DRAGGER)
head(GWAS_demo)
head(GTEx)
head(DGIdb)
```
DRAGGER includes a wrapper function, aptly named DRAGGER, which executes the basic workflow.
Workflow is described in detail in vignettes, along with customisation options.
```{r DRAGGER_demonstration}
library(DRAGGER)
demo <- DRAGGER(GWAS_demo, GTEx, DGIdb)
head(demo)
```
Here is a preview of the type of plots included in DRAGGER, also heavily customisable.
```{r Basic_plot_demonstration}
library(DRAGGER)
demo <- suppressMessages(DRAGGER(GWAS_demo, GTEx, DGIdb))
plot_volcano(demo)
barplot_by_groups(demo, "rs_id", "candidate")
```
A small expasion of base-R chisq test is also included, and can provide insight
into DRAGGER results. The following example compares the number of RS with
a valid candidate by tissue, specifically brain vs stomach.
```{r test_chi2}
library(DRAGGER)
demo <- suppressMessages(DRAGGER(GWAS_demo, GTEx, DGIdb))
test_chi2(demo, "Tissue", "brain", "stomach",
"candidate", TRUE, FALSE)
```