# integration_analysis_scripts

Scripts for multi-omics integration

## Unsupervised analysis: *integration_unsupervised.R*

This script performs unsupervised analyses (clustering) from transformed expression data (e.g., log fpkm) and methylation beta values

## Prerequisites

This R script requires the following packages:

- iClusterPlus

- gplots

- lattice

### Usage

```bash

Rscript integration_unsupervised.R [options]

```

| **PARAMETER** | **DEFAULT** | **DESCRIPTION** |

|-----------|--------------:|-------------| 

*-d* | NULL | File with somatic mutation data |

*-C* | NULL | File with copy number variation data |

*-r* | NULL | File with expression data |

*-m* | NULL | File with methylation data (beta values) |

*-k* | 2 | Minimum number of clusters |

*-K* | 6 | Maximum number of clusters |

*-c* | 2 | Number of cores |

*-o* | out | output prefix |

*-h*    |  | Show help message and exit|

For example, one can type

```bash

Rscript integration_unsupervised.R -r expression_matrix.txt -o output/

```

### Details

The script involves 3 steps

- **Data transformation** of methylation beta values, using the logit function

- **Clustering** across a range of LASSO lambda penalties and for each number of clusters *K* using iClusterPlus

- **Selection** of the best lambda value (BIC) for each *K*, and plot of the R^2 as a function of *K* to help the choice of *K*

- **Selection of the top features** differentiating the clusters

### Output

- A figure with R^2 as a function of *K*, and cluster memberships of each sample as a function of *K*

In addition, for each value of *K*:

- an .RData file with clustering results

- a heatmap with the top features for each dataset

- a .txt file with the name of the top features for each dataset

## Regression analysis for unsupervised analysis: *PCA_regression.R*

This script provides functions to perform regression analysis between variables (e.g., batch variables or clinical variables) and latent factors as obtained by PCA or group factor analysis.