---
title: "Statistical testing of quantitative omics data"
author: "Veit Schwämmle"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{PolySTest}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r style, echo = FALSE, results = 'asis'}
BiocStyle::markdown()
```


```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Introduction

Welcome to the PolySTest vignette. 

PolySTest is a comprehensive R package designed for the statistical testing of
quantitative omics data, including genomics, proteomics, and metabolomics 
datasets. By integrating advanced statistical methods, PolySTest addresses the 
challenges of missing features and high-dimensionality with multiple comparisons
normal in omics data. 

This package facilitates the identification of differentially abundant features
across different conditions or treatments, making it an essential tool for 
biomarker discovery and biological insight. Additionally, its robust 
visualization functions enable users with multiple views for further 
biological interpretations and comparison of the different statistical 
tests. 

## Installation


To install PolySTest and its dependencies, you can use Bioconductor's 
BiocManager.

```{r, eval=FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("PolySTest")
```

## Data Preparation


PolySTest requires quantitative omics data to be formatted as a 
`SummarizedExperiment` object, which integrates the experimental data with its 
corresponding metadata. Here’s how to prepare your data:

1. **Data Format**: Your quantitative data should be in a matrix format, 
with features (e.g., genes, proteins) as rows and samples as columns. 
Missing values can be present, but they should be represented as NA. In case of 
high missingness, it can be worth to remove features with only minimal 
non-missing values.

2. **Sample Metadata**: Accompany your data with metadata that describes each
sample, including conditions, replicates, and possible other experimental 
factors. This metadata will be crucial for defining comparisons in your 
statistical tests.

3. **Normalization**: To correct for technical variability and make your data 
comparable across samples, apply normalization methods suitable for your data 
type. For example, median normalization can adjust for differences in loading
amounts or sequencing depth:


First, we load the necessary package and example data and reduce it to 200
lines to increase processing speed. The data set consists of protein abundances
in liver samples from mice fed with four different diets. The data contains 
three replicates per diet.

```{r warning=F,message=F}
library(PolySTest)
library(SummarizedExperiment)
# Load the data file "LiverAllProteins.csv" from the PolySTest package
filename <- system.file("extdata", "LiverAllProteins.csv", 
                        package = "PolySTest")

dat <- read.csv(filename, row.names=1, stringsAsFactors = FALSE)
dat <- dat[1:200,] # Use a subset of the data for this example
```

The following line of code normalizes the data by subtracting the median of each
column from the data. This is a simple example of normalization, and you should
use the appropriate method for your data type.

```{r}
# Normalization (example)
dat <- t(t(dat) - colMedians(as.matrix(dat), na.rm = TRUE))
```

Now, create a SummarizedExperiment Object. Integrate your normalized data and 
metadata into a SummarizedExperiment object. This structured format ensures 
that PolySTest can efficiently process your data:

```{r}
sampleMetadata <- data.frame(Condition = rep(paste("Condition", 1:4), each=3),
                             Replicate = rep(1:3, each=4))

fulldata <- SummarizedExperiment(assays = list(quant = dat), 
                                 colData = sampleMetadata)
rowData(fulldata) <- rownames(dat)
metadata(fulldata) <- list(NumReps = 3, NumCond = 4)

assay(fulldata, "quant") <- dat

fulldata <- update_conditions_with_lcs(fulldata)
```



## Defining comparisons for statistical testing

Before running statistical tests, it's essential to define the comparisons 
between conditions or treatments of interest. 

When selecting comparisons, consider the scientific questions you aim to answer.
For instance, if you're exploring the effect of a specific treatment, compare 
treated samples against controls. The choice of reference condition can 
impact the interpretation of your results.

PolySTest facilitates the creation of pairwise comparisons through the 
`create_pairwise_comparisons` function. Here's how to define comparisons between
each condition and a reference condition:

```{r}
conditions <- unique(colData(fulldata)$Condition)

allComps <- create_pairwise_comparisons(conditions, 1)
```

## Running Statistical Tests with PolySTest

PolySTest offers to conduct both paired and unpaired 
statistical tests:

- **Paired Tests**: Use `PolySTest_paired` when your data involves matched 
samples, such as before-and-after treatments on the same subjects. Paired 
tests account for the inherent correlations between matched samples, offering 
more sensitivity in detecting differences.

- **Unpaired Tests**: Use `PolySTest_unpaired` for independent groups of 
samples, such as comparing different treatment groups without matching.

PolySTest automatically performs false discovery rate (FDR) correction on 
p-values from the statistical tests limma, rank products, Miss Test, permutation
test and t-test, and all tests but the t-test combined to the PolySTest FDR


```{r}
fulldata_unpaired <- PolySTest_unpaired(fulldata, allComps)

```


## Visualization and interpretation of results

PolySTest includes several visualizations to help interpret the results of your 
statistical tests. These visualizations not only highlight significant features
but also provide insights into the overall distribution of the data and the 
relationships between different tests:

- **P-value Distributions**: Understanding the distribution of p-values can 
help assess the tests' sensitivity and the presence of potential biological 
signals in your data.

- **Volcano Plots**: Volcano plots are a powerful way to visualize the trade-off
between magnitude of change (fold change) and statistical significance (FDR),
highlighting features of potential interest.

- **UpSet Plots**: These plots offer a comprehensive view of the overlap between
significant features identified by different statistical tests.

- **Expression Profiles and Heatmaps**: These visualizations allow for the 
detailed examination of the expression patterns of significant features, 
facilitating the identification of potential biological pathways affected by 
the conditions under study.

We now plot the pvalue distributions of limma and Miss Test for the first
comparison.

```{r fig.width=7, fig.height=4}
# Define comparisons to visualize from available ones
compNames <- metadata(fulldata_unpaired)$compNames
print(compNames)
comps <- compNames[1]

# Plotting the results
plotPvalueDistr(fulldata_unpaired,  comps, c("limma", "Miss Test"))
```

Here's an example of generating a volcano plot, marking proteins that are have 
significant changes in the first comparison with an FDR smaller than 1\%

```{r fig.width=7, fig.height=4}
# Select proteins with FDR < 0.01

sigProts <- which(rowData(fulldata_unpaired)[, paste0("FDR_PolySTest_", 
                                                      comps[1])] < 0.01)

# Volcano plot
plotVolcano(fulldata_unpaired, compNames = comps, sel_prots = sigProts, 
            testNames = c("PolySTest", "limma", "Miss Test"))
```

Now let's look into the overlap between the different statistical tests. 
An upset plot provides this information 

```{r fig.width=7, fig.height=4}
plotUpset(fulldata_unpaired, qlim=0.01)
```

PolySTest also provides a combined plot of expression changes and overlap 
between different tests. Reduce the number of features to maximally 20 to 
avoid overloaded plots.

```{r fig.width=8, fig.height=6}
plotExpression(fulldata_unpaired, sel_prots = sigProts)
```

A further comparison between tests shows how many features they provide for 
different cutoffs for the false discovery rate.

```{r fig.width=7, fig.height=4}
plotRegNumber(fulldata_unpaired)
```

For the last visualization, we create a hierarchical map (heatmap) for the 
differentially regulated proteins from the `sigProts`object.

```{r fig.width=6, fig.height=6}
plotHeatmaply(fulldata_unpaired, sel_prots = sigProts, heatmap_scale = "row")
```

## References and Further Reading

For more information on PolySTest, please refer to the PolySTest paper:  
Schwämmle V, Hagensen CE, Rogowska-Wrzesinska A, Jensen ON. PolySTest: 
Robust Statistical Testing of Proteomics Data with Missing Values Improves 
Detection of Biologically Relevant Features. Mol Cell Proteomics. 
2020;19(8):1396-1408. doi:10.1074/mcp.RA119.001777

For more tools and information, please visit https://computproteomics.bmb.sdu.dk

## Next Steps in Omics Data Analysis

With the significant features identified and visualized, the next steps usually
involve further biological interpretation and validation of the findings. Here:

1. **Pathway Enrichment Analysis**: Investigate whether the significant features
are enriched in specific biological pathways or processes.
2. **Clustering**: Group features into clusters based on their
expression patterns, which can provide insights into potential biological 
functions. We recommend using the Bioconductor package 
[VSClust](https://bioconductor.org/packages/release/bioc/html/vsclust.html) 
or its 
[Shiny web application](https://computproteomics.bmb.sdu.dk/app_direct/VSClust/)
from your lab.  
3. **Validation**: Confirm the findings from your statistical tests using
independent experimental methods and datasets.  
4. **Integration with Other Omics Layers**: Combine your findings with data from
other omics levels (e.g., transcriptomics, metabolomics) for a multi-omics analysis.  
5. **Protein Complexes**: Investigate whether the significant proteins are part
of known protein complexes and whether there are protein complex showing
strong alteration of their expression. We recommend using
our web applications 
[ComplexBrowser](https://computproteomics.bmb.sdu.dk/app_direct/ComplexBrowser)
and [CoExpresso](https://computproteomics.bmb.sdu.dk/app_direct/CoExpresso).

## Contributing and Feedback

If you have suggestions for improvements, encounter any issues, or would like to
contribute code or documentation, please visit our GitHub repository:

GitHub: https://github.com/computproteomics/PolySTest

Your input helps make PolySTest a more robust and user-friendly tool for the 
omics community."



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

```
