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https://github.com/bigomics/playbase
Core back-end functionality and logic for OmicsPlayground
https://github.com/bigomics/playbase
Last synced: about 1 month ago
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Core back-end functionality and logic for OmicsPlayground
- Host: GitHub
- URL: https://github.com/bigomics/playbase
- Owner: bigomics
- License: other
- Created: 2022-12-20T15:50:21.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-29T12:39:12.000Z (about 1 month ago)
- Last Synced: 2024-10-29T12:47:10.734Z (about 1 month ago)
- Language: R
- Homepage:
- Size: 109 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE.md
Awesome Lists containing this project
- jimsghstars - bigomics/playbase - Core back-end functionality and logic for OmicsPlayground (R)
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# playbase
[](https://github.com/bigomics/playbase/actions/workflows/r.yml)
[](https://app.codecov.io/gh/bigomics/playbase?branch=master)
The `playbase´ package contains the core back-end functionality for the OmicsPlayground. This package allows you to run, develop, and test any essential functions used in the OmicsPlayground directly in the R console without needing to worry about the R Shiny front-end code.
## Installation
You can install the development version of playbase from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("bigomics/playbase")
```
## Data upload
The first step in any OmicsPlayground analysis is to upload data which can be used to create a `pgx` object. The `pgx` object is basically the core data structure in the OmicsPlayground upon which most analysis and plotting functions operate.
```{r example}
library(playbase)
## with two functions, we can create a pgx object that can be used in Omics Playground.
# first step is to create pgx
pgx <- playbase::pgx.createPGX(
counts = playbase::COUNTS,
samples = playbase::SAMPLES,
contrasts = playbase::CONTRASTS
)
# once pgx is created, we can compute the modules
pgx <- playbase::pgx.computePGX(
pgx = pgx
)
```
## The pgx object
The core object in playbase is the `pgx` object. This object holds the raw data and any analysis results returned from playbase modules / boards. The pgx object is simply an R list. It contains minimally the following list items:
- counts
- samples
- contrasts
A pgx object is created from these three list items via the following function:
```
my_pgx <- pgx.createPGX(counts, samples, contrasts)
```
Once a pgx object is created from these three items, the various playbase modules can operate on the pgx object to generate the analysis results relevant to that specific module.
## Playbase modules
As mentioned above, the core object in playbase is the `pgx` object. This holds all of the analysis and results derived from the raw data, as well as the raw data itself. There are various modules in playbase that take a pgx object as input, perform some analysis on the raw data in the pgx object, and then append these results to the pgx object. These modules are more-or-less independent of one another and can therefore be parallelized or run in any arbitrary order.
The core playbase modules operate on either genes or genesets.
The gene modules are as follows:
- ttest
- ttest.welch
- ttest.rank
- voom.limma
- trend.limma
- notrend.limma
- edger.qlf
- edger.lrt
- deseq2.wald
- deseq2.lrt
The geneset methods are as follows:
- fisher
- gsva
- ssgsea
- spearman
- camera
- fry
- fgsea
And extra modules are as follows:
- meta.go
- deconv
- infer
- drugs
- graph
- connectivity
- wordcloud