comparison gss.Rmd @ 0:d0cbe6cc1f04 draft default tip

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/genomic_super_signature commit 1aadd5dce3b254e7714c2fdd39413029fd4b9b7a"

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---
title: "Analysis by GenomicSuperSignature"
date: "`r Sys.Date()`"
output:
  BiocStyle::html_document:
    toc: true
    toc_float: false
    toc_depth: 3
params: 
    val_all: val_all
    dat: dat
    RAVmodel: RAVmodel
    inputName: inputName
    numOut: numOut
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```

# RAVs best represents your dataset
The *validation* provides a quantitative representation of the relevance
between your dataset and RAVs. Below shows the top 6 validated RAVs and 
the complete result is saved as `{input_name}_validate.csv`. 

```{r}
head(params$val_all)
```

## Heatmap Table
`heatmapTable` takes validation results as its input and displays them into 
a two panel table: the top panel shows the average silhouette width (avg.sw) 
and the bottom panel displays the validation score.

`heatmapTable` can display different subsets of the validation output. For 
example, if you specify `scoreCutoff`, any validation result above that score 
will be shown. If you specify the number (n) of top validation results through 
`num.out`, the output will be a n-columned heatmap table. You can also use the 
average silhouette width (`swCutoff`), the size of cluster (`clsizecutoff`), 
one of the top 8 PCs from the dataset (`whichPC`).

Here, we print out top `r params$numOut` validated RAVs with average silhouette 
width above 0.

```{r out.height="45%", out.width="45%", message=FALSE, warning=FALSE}
heatmapTable(params$val_all, num.out = params$numOut, swCutoff = 0)
```

## Interactive Graph
Under the default condition, `plotValidate` plots validation results of all non 
single-element RAVs in one graph, where x-axis represents average silhouette 
width of the RAVs (a quality control measure of RAVs) and y-axis represents 
validation score. We recommend users to focus on RAVs with higher validation 
score and use average silhouette width as a secondary criteria. 

```{r out.height="80%", out.width="80%"}
plotValidate(params$val_all, interactive = TRUE)
```

Note that `interactive = TRUE` will result in a zoomable, interactive plot that
included tooltips, which is saved as `{input_name}_validate_plot.html` file. 

You can hover each data point for more information:    

- **sw** : the average silhouette width of the cluster   
- **score** : the top validation score between 8 PCs of the dataset and RAVs   
- **cl_size** : the size of RAVs, represented by the dot size   
- **cl_num** : the RAV number. You need this index to find more information 
about the RAV.      
- **PC** : test dataset's PC number that validates the given RAV. Because we 
used top 8 PCs of the test dataset for validation, there are 8 categories. 

If you double-click the PC legend on the right, you will enter an 
individual display mode where you can add an additional group of data 
point by single-click. 


# Prior information associated to your dataset
```{r echo=FALSE}
validated_ind <- validatedSignatures(params$val_all, num.out = params$numOut,
                                     swCutoff = 0, indexOnly = TRUE)

# In case, there are fewer validated_ind than the number of outputs user set
n <- min(params$numOut, length(validated_ind), na.rm = TRUE)
```

## MeSH terms in wordcloud
```{r out.height="60%", out.width="60%", fig.width=8, fig.height=8}
for (i in seq_len(n)) {
    set.seed(1)
    print(paste0("MeSH terms related to RAV", validated_ind[i]))
    drawWordcloud(params$RAVmodel, validated_ind[i])
}
```

## GSEA
The complete result is saved as `{input_name}_genesets_RAV*.csv`. 
```{r}
res_all <- vector(mode = "list", length = n)
for (i in seq_len(n)) {
    RAVnum <- validated_ind[i]
    RAVname <- paste0("RAV", RAVnum)
    res <- gsea(params$RAVmodel)[[RAVname]]
    res_all[[i]] <- head(res)
    names(res_all)[i] <- paste0("Enriched gene sets for RAV", validated_ind[i])
}
res_all
```

## Publication
The complete result is saved as `{input_name}_literatures_RAV*.csv`. 
```{r}
res_all <- vector(mode = "list", length = n)
for (i in seq_len(n)) {
    RAVnum <- validated_ind[i]
    res <- findStudiesInCluster(params$RAVmodel, RAVnum, studyTitle = TRUE)
    res_all[[i]] <- head(res)
    names(res_all)[i] <- paste0("Studies related to RAV", validated_ind[i])
}
res_all
```