README.Rmd

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output: github_document
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knitr::opts_chunk$set(
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# evalGSVAsig

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The goal of evalGSVAsig is to identify which genes are contributing most to a GSVA score.

## Installation

You can install the development version of evalGSVAsig from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("lkroeh/evalGSVAsig")
```

## Example

This is a basic example which shows the format of the data:

```{r example, eval = FALSE, message=FALSE, warnings=FALSE }
library(evalGSVAsig)

#example gene list
sig <- c("GENE1", "GENE2", "GENE3")
signature_list <- c(list(sig))
names(signature_list) <- c("signature1")

#run function
output <- evalGSVAsig::GSVAsignatureRanking(eset, signature_list)

#view output
#print df of genes ordered by correlation to GSVA scores
output[[1]]

#show heatmap of ALL gene expression in relation to GSVA score
output[[2]]

#show heatmap of SIGNATURE gene expression in relation to GSVA score
output[[3]]

#get expression with GSVA scores saved in pData
output[[4]]
```

With sample data:

```{r plots, message=FALSE, warning=FALSE, results='hide' }
#with our sample data
data(signatures)
data(eset)

output <- evalGSVAsig::GSVAsignatureRanking(eset = eset, signature = signatures, metacol = 'hpv_status')
```


View tables:

```{r tables}
#This table contains all genes
head(output[[1]])
#This table contains only signature genes
head(output[[2]])
```

View heatmap that plots all signature and non-signature genes:

```{r heatmaps, dpi=300}
output[[3]] 
```

View heatmap that plots only signature genes:

```{r heatmap2, dpi=300}
output[[4]]
```