README.Rmd

---
output: github_document
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# curatedPCaData <img src="man/figures/hex.png" align="right" height="139" />

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## Overview 

`curatedPCaData` is a collection of publically available and annotated data resources
concerning prostate cancer. 

## Citation

If you use `curatedPCaData`, please consider adding the following citation:

```
@article {Laajala2023.01.17.524403,
    author = {Laajala, Teemu D and Sreekanth, Varsha and Soupir, Alex and Creed, Jordan and Halkola, Anni S and Calboli, Federico CF and Singaravelu, Kalaimathy and Orman, Michael and Colin-Leitzinger, Christelle and Gerke, Travis and Fidley, Brooke L. and Tyekucheva, Svitlana and Costello, James C},
    title = {A harmonized resource of integrated prostate cancer clinical, -omic, and signature features},
    year = {2023},
    doi = {10.1038/s41597-023-02335-4},
    URL = {https://www.nature.com/articles/s41597-023-02335-4},
    journal = {Scientific Data}
}
```

## Installation

### Bioconductor installation

In order to install the package from Bioconductor, make sure `BiocManager` is installed and then call the function to install `curatedPCaData`:

```
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("curatedPCaData")
```

### GitHub installation

A download link to the latest pre-built `curatedPCaData` tarball is available on the right-side in GitHub under [Releases](https://github.com/Syksy/curatedPCaData/releases).

You can also install `curatedPCaData` from GitHub inside R with: 

```
# install.packages("devtools")
devtools::install_github("Syksy/curatedPCaData")
```

To build the package tarball from a cloned git repo, run the following in terminal / command prompt while in the root of the project:

```
R CMD build curatedPCaData
```

It is then possible to install the self-built tarball:

```
R CMD INSTALL curatedPCaData_x.y.z.tar.gz
```

Note that building the package locally will require dependencies to be present for the R installation.

## Usage

### Vignettes

`curatedPCaData` delivers with basic vignette()s displaying the package's generic use data retrieval and basic processing in R. The vignette `overview` is intended for gaining a first-line comprehensive view into the package's contents. The intention is to display the basic functionality of the package as an `ExperimentHub` resource.

A sister package, `curatedPCaWorkflow` (GitHub [link here](https://github.com/Syksy/curatedPCaWorkflow)), serves multiple specialized vignettes that delve deeper into analysis and further processing of the data. This workflow package reproduces the results presented in Laajala et al., and provides useful insight and examples for those looking to further leverage use of the multi-omics data provided in `curatedPCaData`.

### Downloading data

The function `getPCa` is the primary means of extracting data from a cohort. It will automatically create a `MultiAssayExperiment`-object of the study:

```{r example_one, warning = FALSE, message = FALSE}
library(curatedPCaData)

mae_tcga <- getPCa("tcga")

class(mae_tcga)
names(mae_tcga)
```

### Brief examples

Simple example use of curated datasets and 'omics there-in:

```{r example_two, warning = FALSE, message = FALSE}
mae_taylor <- getPCa("taylor")
mae_sun <- getPCa("sun")

mae_tcga

mae_tcga[["gex.rsem.log"]][1:4, 1:4]

mae_tcga[["cna.gistic"]][1:4, 1:4]

colData(mae_tcga)[1:3, 1:5]

mae_taylor

mae_sun
```

For further details on the provided datasets and extra parameters for handling data extraction, please consult the `overview`-vignette.