---
title: "Introduction to the TBSignatureProfiler"
author: 
- name: Aubrey Odom
  affiliation: 
  - Program in Bioinformatics, Boston University, Boston, MA
  email: aodom@bu.edu
- name: W. Evan Johnson
  affiliation:
  - The Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA
  email: wej@bu.edu
date: '`r format(Sys.Date(), "%B %e, %Y")`'
package: TBSignatureProfiler
output: 
  BiocStyle::html_document
vignette: >
  %\VignetteIndexEntry{"Introduction to the TBSignatureProfiler"}
  %\VignetteEncoding{UTF-8}  
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---

# Introduction to the TBSignatureProfiler

Tuberculosis (TB) is the leading cause of infectious disease mortality worldwide, causing on average nearly 1.4 million deaths per year. A consistent issue faced in controlling TB outbreak is difficulty in diagnosing individuals with particular types of TB infections for which bacteria tests (e.g., via GeneXpert, sputum) prove inaccurate. As an alternative mechanism of diagnosis for these infections, researchers have discovered and published multiple gene expression signatures as blood-based disease biomarkers. In this context, gene signatures are defined as a combined group of genes with a uniquely characteristic pattern of gene expression that occurs as a result of a medical condition. To date, more than 30 signatures have been published by researchers, though most have relatively low cross-condition validation (e.g., testing TB in samples from diverse geographic and comorbidity backgrounds). Furthermore, these signatures have never been formally collected and made available as a single unified resource. 

We aim to provide the scientific community with a resource to access these aggregated signatures and to create an efficient means for their visual and quantitative comparison via open source software. This necessitated the development of the TBSignatureProfiler, a novel R package which delivers a computational profiling platform for researchers to characterize the diagnostic ability of existing signatures in multiple comorbidity settings. This software allows for signature strength estimation via several enrichment methods and subsequent visualization of single- and multi-pathway results. Its signature evaluation functionalities include signature profiling, AUC bootstrapping, and leave-one-out cross-validation (LOOCV) of logistic regression to approximate TB samples’ status. Its plotting functionalities include sample-signature score heatmaps, bootstrap AUC and LOOCV boxplots, and tables for presenting results.
 
# Installation

In order to install the TBSignatureProfiler from Bioconductor, run the following code:

```{r load profiler, eval = FALSE}
if (!requireNamespace("BiocManager", quietly=TRUE))
  install.packages("BiocManager")
BiocManager::install("TBSignatureProfiler")
```
                                                                      
# Compatibility with SummarizedExperiment objects

While the TBSignatureProfiler often allows for the form of a `data.frame` or `matrix` as input data, the most ideal form of input for this package is that of the `SummarizedExperiment` object. This is an amazing data structure that is being developed by the \emph{Bioconductor team} as part of the `r Biocpkg("SummarizedExperiment")` package, and is imported as part of the TBSignatureProfiler package. It is able to store data matrices along with annotation information, metadata, and reduced dimensionality data
(PCA, t-SNE, etc.). To learn more about proper usage and context of the `SummarizedExperiment` object, you may want to take a look at the [package vignette](https://bioconductor.org/packages/release/bioc/vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.html). A basic understanding of the `assay` and `colData` properties of a `SummarizedExperiment` will be useful for the purposes of this vignette. 

# A Quick Tutorial for the TBSignature Profiler

## Load packages

```{r setup}
suppressPackageStartupMessages({
  library(TBSignatureProfiler)
  library(SummarizedExperiment)
})
```

## Run Shiny App
This command is to start the TBSignatureProfiler shiny app. For shiny app tutorials, please go to [our website](https://compbiomed.github.io/TBSignatureProfiler-docs/index.html),
and select the tab labeled "Interactive Shiny Analysis."

```{R Run shiny app, eval = FALSE}
TBSPapp()
```

The basic functions of the shiny app are also included in the command line version of the TBSignatureProfiler, which is the focus of the remainder of this vignette.

## Load dataset from a SummarizedExperiment object
In this tutorial, we will work with HIV and Tuberculosis (TB) gene expression data in a `SummarizedExperiment` format. This dataset is included in the TBSignatureProfiler package and can be loaded into the global environment with `data("TB_hiv")`. The 31 samples in the dataset are marked as either having both TB and HIV infection, or HIV infection only.

We begin by examining the dataset, which contains a matrix of counts information (an "assay" in SummarizedExperiment terms) and another matrix of meta data information on our samples (the "colData"). We will also generate a few additional assays; these are the log(counts), the counts per million (CPM) reads mapped, and the log(CPM) assays. 

```{r loading data}
## HIV/TB gene expression data, included in the package
hivtb_data <- TB_hiv

### Note that we have 25,369 genes, 33 samples, and 1 assay of counts
dim(hivtb_data)

# We start with only one assay
assays(hivtb_data)
```


We now make a log counts, CPM and log CPM assay.
```{r add assays}
## Make a log counts, CPM and log CPM assay
hivtb_data <- mkAssay(hivtb_data, log = TRUE, counts_to_CPM = TRUE)

### Check to see that we now have 4 assays
assays(hivtb_data)
```

## Profile the data {.tabset}

The TBSignatureProfiler enables comparison of multiple Tuberculosis gene signatures. The package currently contains information on 34 signatures for comparison. The default signature list object for most functions here is `TBsignatures`, although a list with publication-given signature names is also available as `TBcommon`. Data frames of annotation information for these signatures, including information on associated disease and tissue type, can be accessed as `sigAnnotData` and `common_sigAnnotData` respectively.

With the `runTBSigProfiler` function, we are able to score these signatures with a selection of algorithms, including [gene set variation analysis](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-7) (GSVA) (Hänzelmann et al, 2013), [single-sample GSEA](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-7) (ssGSEA) (Barbie et al, 2009), and the [ASSIGN](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-7) pathway profiling toolkit (Shen et al, 2015). For a complete list of included scoring methods, run `?runTBsigProfiler` in the terminal.

Here, we evaluate all signatures included in the package with ssGSEA. Paraphrasing from the [ssGSEA documentation](http://software.broadinstitute.org/cancer/software/genepattern/modules/docs/ssGSEAProjection/4), for each pairing of one of the 31 samples and its gene set, ssGSEA calculates a separate enrichment score independent of the phenotypic labeling (in this case, whether a sample has HIV/TB, or HIV only). The single sample's gene expression profile is then transformed to a gene set enrichment profile. A score from the set profile represents the activity level of the biological process in which the gene set's members are coordinately up- or down-regulated.  

```{r run data}
## List all signatures in the profiler
data("TBsignatures")
names(TBsignatures)

## We can use all of these signatures for further analysis
siglist_hivtb <- names(TBsignatures)
```

```{r run profiler}
## Run the TBSignatureProfiler to score the signatures in the data
out <- capture.output(ssgsea_result <- runTBsigProfiler(input = hivtb_data,
                                                 useAssay = "log_cpm",
                                                 signatures = TBsignatures,
                                                 algorithm = "ssGSEA",
                                                 combineSigAndAlgorithm = TRUE,
                                                 parallel.sz = 1))
```


When a `SummarizedExperiment` is the format of the input data for `runTBsigprofiler`,  the returned object is also of the `SummarizedExperiment`. The scores will be returned as a part of the colData. 

Below, we subset the data to compare the enrichment scores for the Anderson_42, Anderson_OD_51, and Berry_393 signatures. 

Signature Scores
```{r show scores, message = FALSE}
## New colData entries from the Profiler
sigs <- c("Anderson_42", "Anderson_OD_51", "Berry_393")
ssgsea_print_results <- as.data.frame(colData(ssgsea_result))[, c("Disease", sigs)]
ssgsea_print_results[,2:4] <- round(ssgsea_print_results[, 2:4], 4)

DT::datatable(ssgsea_print_results)
```

## Visualization with TBSignatureProfiler Plots{.tabset}

### Heatmap with all Signatures
 

Commonly, enrichment scores are compared across signatures by means of a heatmap combined with clustering methods to group samples and/or scores. The `signatureHeatmap` function uses the information from the score data to visualize changes in gene expression across samples and signatures (or genes, if only one signature is selected). 

Here, the columns of the heatmap represent samples, and rows represent signatures. Rows are split according to annotation data with associated signature disease type. As we move across the columns, we see different patterns of gene expression as indicated by the varying color and intensity of individual rectangles. In the top bar, the solid red represents a sample is HIV infected only, and solid blue indicates that the sample is both HIV and TB infected. In the gradient area of the heatmap, the scaled scores are associated with either up-regulated or down-regulated genes. A cluster of samples near the bottom of the heatmap reveals that some signatures are inversely associated with TB/HIV identification, as their phenotypic mapping for lower/higher scores is nearly the opposite of most of the other signatures.  


```{r all sigs heatmap, message = FALSE, fig.height = 9}

# Colors for gradient
colors <- RColorBrewer::brewer.pal(6, "Spectral")
col.me <- circlize::colorRamp2(seq(from = -2, to = 2, 
                                   length.out = 6), colors)

signatureHeatmap(ssgsea_result, name = "Heatmap of Signatures, 
                 ssGSEA Algorithm", 
                 signatureColNames = names(TBsignatures),
                 annotationColNames = "Disease",
                 scale = TRUE,
                 showColumnNames = TRUE,
                 choose_color = col.me)
```


### Boxplots of Scores, All Signatures

Another method of visualization for scores is that of boxplots. When multiple signatures in the input data are to be compared, the `signatureBoxplot` function takes the scores for each signature and produces an individual boxplot, with jittered points representing individual sample scores. For this specific example, it is clear that some signatures do a better job at differentiating the TB/HIV and HIV only samples than others, as seen by overlapping or separate spreads of the adjacent boxplots.

 
```{r Boxplots all, message = FALSE, results = 'hide', fig.height = 9, fig.width=15}

signatureBoxplot(inputData = ssgsea_result, 
                 name = "Boxplots of Signatures, ssGSEA", 
                 signatureColNames = names(TBsignatures),
                 annotationColName = "Disease", rotateLabels = FALSE)
```


### Compare scoring methods for a single signature

The `compareAlgs` function allows multiple scoring methods to be compared via a heatmap or boxplot with samples on the columns and methods on the rows. Here, we compare scoring methods for the "Anderson_42" signature. It seems that singscore may be the best method here, as its sample scores most closely align with the information provided by the annotation data.

An examination of the boxplot and heatmap together determine that PLAGE and the comparing Z-score methods are least helpful in correctly identifying TB from LTBI subjects - AUC scores are little more than 0.5 and subjects in different groups are falsely assigned similar scores. According to the boxplot, we see that ssGSEA has the highest predictive AUC.

```{r compareAlgs ex, warning = FALSE, message = FALSE}
# Heatmap
compareAlgs(hivtb_data, annotationColName = "Disease",
            scale = TRUE,
            algorithm = c("GSVA", "ssGSEA",
                          "singscore", "PLAGE", "Zscore"),
            useAssay = "log_counts",
            signatures = TBsignatures[1],
            choose_color = col.me, show.pb = FALSE,
            parallel.sz = 1)

# Boxplot
compareAlgs(hivtb_data, annotationColName = "Disease",
            scale = TRUE,
            algorithm = c("GSVA", "ssGSEA",
                          "singscore", "PLAGE", "Zscore"),
            useAssay = "log_counts",
            signatures = TBsignatures[1],
            choose_color = col.me, show.pb = FALSE,
            parallel.sz = 1, output = "boxplot")
```

# Additional Documentation and Tutorials

The functions presented as part of this vignette are but a limited subset of those available in the TBSignatureProfiler package. A more complete tutorial is available by going to [our website](https://compbiomed.github.io/TBSignatureProfiler-docs/index.html), and selecting the tab labeled "Command Line Analysis." 

# Session Info
```{r session info}
sessionInfo()

```
