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README.Rmd
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---
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%"
)
```
# greatR <img src="man/figures/logo.png" align="right" width="120"/>
<!-- badges: start -->
[](https://cran.r-project.org/package=greatR)
[](https://lifecycle.r-lib.org/articles/stages.html)
[](https://github.com/ruthkr/greatR/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/ruthkr/greatR)
[](https://github.com/ruthkr/greatR/actions/workflows/pkgdown.yaml)
[](https://github.com/ruthkr/greatR/commits/master)
<!-- badges: end -->
`greatR` (**G**ene **R**egistration from **E**xpression **a**nd **T**ime-courses in **R**) is a tool to register (align) two sets of gene expression profiles that users wish to compare.
These gene profiles data will be referred as the **query** and the **reference** data. To match the time point ranges between those profiles, the time points of the **query** profiles will be transformed through a stretching and shifting process. This tool uses a statistical model comparison based on a Bayesian approach to evaluate the optimality of the gene expression profiles alignment.
## Package workflow
The flowchart below illustrates the workflow of the package given an input data:
```{r all-data-illustration, echo=FALSE, out.width='100%'}
knitr::include_graphics("man/figures/greatR_workflow.png")
```
More details on how to use this package are available on [function documentations](https://ruthkr.github.io/greatR/reference/index.html) and the following vignettes:
1. [Input data requirements](https://ruthkr.github.io/greatR/articles/data-requirement.html)
2. [Register data](https://ruthkr.github.io/greatR/articles/register-data.html)
3. [Process registration results](https://ruthkr.github.io/greatR/articles/process-results.html)
## Installation
You can install the stable version of `greatR` from [CRAN](https://CRAN.R-project.org) with:
```r
install.packages("greatR")
```
And the development version of `greatR` from [GitHub](https://github.com/) with:
```r
# install.packages("devtools")
devtools::install_github("ruthkr/greatR")
```
## Usage - quick start
This is a basic example which shows you how to register (align) gene expression profiles over time:
```{r load-greatR, message=FALSE, eval=FALSE}
# Load the package
library(greatR)
```
```{r register-data, message=FALSE, warning=FALSE, eval=FALSE}
# Load a data frame from the sample data
b_rapa_data <- system.file("extdata/brapa_arabidopsis_data.csv", package = "greatR") |>
utils::read.csv()
# Running the registration
registration_results <- register(
b_rapa_data,
reference = "Ro18",
query = "Col0",
scaling_method = "z-score"
)
#> ── Validating input data ────────────────────────────────────────────────────────
#> ℹ Will process 10 genes.
#> ℹ Using estimated standard deviation, as no `exp_sd` was provided.
#> ℹ Using `scaling_method` = "z-score".
#>
#> ── Starting registration with optimisation ──────────────────────────────────────
#> ℹ Using L-BFGS-B optimisation method.
#> ℹ Using computed stretches and shifts search space limits.
#> ℹ Using `overlapping_percent` = 50% as a registration criterion.
#> ✔ Optimising registration parameters for genes (10/10) [2s]
```
## Reference
Calderwood, A., Hepworth, J., Woodhouse, ... Morris, R. (2021). Comparative transcriptomics reveals desynchronisation of gene expression during the floral transition between Arabidopsis and Brassica rapa cultivars. *Quantitative Plant Biology, 2*, E4. [doi:10.1017/qpb.2021.6](https://www.cambridge.org/core/journals/quantitative-plant-biology/article/comparative-transcriptomics-reveals-desynchronisation-of-gene-expression-during-the-floral-transition-between-arabidopsis-and-brassica-rapa-cultivars/811BFDFA14F4BCC9C7F0ECC7CE103BB6)