---
title: "MBQN vignette"
#date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{MBQN Package}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


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

This package contains a modified quantile normalization (QN) for preprocessing and analysis of omics or other matrix-like organized data with intensity values biased by global, columnwise distortions of intensity mean and scale. The modification balances the mean intensity of features (rows) which are rank invariant (RI) or nearly rank invariant (NRI) across samples (columns) before quantile normalization [1]. This helps to prevent an over-correction of the intensity profiles of RI and NRI features by classical QN and therefore supports the reduction of systematics in downstream analyses. Additional package functions help to detect, identify, and visualize potential RI or NRI features in the data and demonstrate the use of the modification.

## Installation
To install this package, you need R version >= 3.6.

For installation from Bioconductor run in R:

```{r bc-installation, eval = FALSE}
# if (!requireNamespace("BiocManager", quietly = TRUE))
#    install.packages("BiocManager")
BiocManager::install("MBQN")
```

## Dependencies
The core of the MBQN package uses normalizeQuantiles() from the package `limma` [2], available at https://bioconductor.org/packages/release/bioc/html/limma.html, for computation of the quantile normalization. Optionally, `normalize.quantiles()` from the package preprocessCore [3], available at https://bioconductor.org/packages/release/bioc/html/preprocessCore.html, can be used. 


To install these packages in R run: 

```{r dependencies1, eval = FALSE}
# if (!requireNamespace("BiocManager", quietly = TRUE))
#    install.packages("BiocManager")
# BiocManager::install(pkgs = c("preprocessCore","limma", "SummarizedExperiment"))
```


## Usage
The package provides two basic functions: `mbqn()` applies QN or mean/median-balanced quantile
normalization (MBQN) to a matrix. 
`mbqnNRI()` applies quantile normalization and mean/median-balanced quantile 
normalization only to selected NRI and RI features, specified by a threshold or manually. 
The input matrix may contain NAs. To run one of these functions you will need to provide an
input matrix (see Examples). The argument `FUN` is used to select between classical quantile
normalization (default), and mean or median balanced quantile normalization. The function
`mbqnGetNRIfeatures()` and `mbqnPlotRI()` can be used to check a data matrix for RI or NRI 
features. They provide a list of potential RI/NRI features together 
with their rank invariance frequency and a graphical output. 

## Examples
Example 1: Generate a distorted omics-like matrix of log2-transformed 
intensities with missing values and a single rank invariant feature:
```{r example1, eval = TRUE}
## basic example
library("MBQN")
set.seed(1234)
# data generation 
mtx <- mbqnSimuData("omics.dep")
# data distortion
mtx <- mbqnSimuDistortion(mtx)$x.mod
```
```{r figure1, fig.height = 5, fig.width = 6, fig.align = "left", fig.cap = "Fig. 1 Boxplot of the unnormalized, distorted intensity data matrix. The first feature is an RI feature (red line). It has maximum intensity for each sample!"}
plot.new()
mbqnBoxplot(mtx, irow = 1, main = "Unnormalized")
```


<br />
Apply check for rank invariant (RI) or nearly rank invariant (NRI) features to 
the data matrix and visualize result:
```{r, eval = TRUE}
res <- mbqnGetNRIfeatures(mtx, low_thr = 0.5)
```

<br />
Apply quantile normalization with and without balancing the RI feature and 
compare the intensity features: 
```{r figure2, fig.height = 5, fig.width = 6, fig.align = 'left', fig.cap = "Fig. 2 Quantile normalized intensities with balanced and unbalanced normalized RI feature. Classical quantile normalization suppresses any intensity variation of the RI feature, while the MBQN preserves its variation while reducing systematic batch effects!"}
plot.new()
mbqn.mtx <- mbqnNRI(x = mtx, FUN = median, verbose = FALSE) # MBQN
qn.mtx <- mbqnNRI(x = mtx, FUN = NULL, verbose = FALSE) # QN
mbqnBoxplot(mbqn.mtx, irow = res$ip, vals = data.frame(QN = qn.mtx[res$ip,]), main = "Normalized")
```

<br /><br />
Example 2: Visualize  the effect of normalization on
rank mixing and rank invariant intensity features by comparing the intensity distribution of unnormalized, quantile and mean/median-balanced quantile normalized data on a matrix where rows represent features, e.g. of protein
abundances/intensities, and columns represent samples. 

```{r example2, eval = TRUE}
## basic example
library("MBQN")
set.seed(1234)
mtx <- mbqnSimuData("omics.dep") 
# Alternatively: mtx <- matrix(
#            c(5,2,3,NA,2,4,1,4,2,3,1,4,6,NA,1,3,NA,1,4,3,NA,1,2,3),ncol=4)
```
<br />
Perform QN, median balanced QN, and QN with median balanced NRI feature.
```{r, eval = TRUE}
qn.mtx <- mbqn(mtx,FUN=NULL, verbose = FALSE)
mbqn.mtx <- mbqn(mtx,FUN = "median", verbose = FALSE)
qn.nri.mtx <- mbqnNRI(mtx,FUN = "median", low_thr = 0.5, verbose = FALSE)
```
<br />
Check saturation i.e. for rank invariance.
```{r, eval = TRUE}
res <- mbqnGetNRIfeatures(mtx, low_thr = 0.5)
# Maximum frequency of RI/NRI feature(s):  100 %
```

<br /><br />
Example 3: Apply a two-sided t-test before and after application
of different normalizations to a simulated, differentially expressed and
distorted RI feature. The feature is obtained from a simulated dataset
where each sample is distorted in mean and scale.

```{r example3, eval = TRUE}
#plot.new()
mtx <- mbqnSimuData("omics.dep", show.fig = FALSE)
mod.mtx <- mbqnSimuDistortion(mtx, s.mean = 0.05, s.scale = 0.01)
mtx2 <- mod.mtx
mod.mtx <- mod.mtx$x.mod

res <- mbqnGetNRIfeatures(mod.mtx, low_thr = 0.5)

# undistorted feature
feature1 <- mtx[1,]
# distorted feature
feature1mod = mod.mtx[1,]
# feature after normalization
qn.feature1 = mbqn(mod.mtx, verbose = FALSE)[1,]
qn.mtx = mbqn(mod.mtx,verbose = FALSE)

mbqn.mtx = mbqn(mod.mtx, FUN = "mean",verbose = FALSE)
mbqn.feature1 = mbqn(mod.mtx, FUN = "mean",verbose = FALSE)[1,]
```
<br />
Apply t-test:
```{r, eval = TRUE}
# undistorted feature
ttest.res0 <- t.test(feature1[seq_len(9)], feature1[c(10:18)],
                    var.equal =TRUE)
# distorted feature
ttest.res1 <- t.test(feature1mod[seq_len(9)], feature1mod[c(10:18)],
                    var.equal =TRUE)
# mbqn normalized distorted feature
ttest.res <- t.test(mbqn.feature1[seq_len(9)], mbqn.feature1[c(10:18)],
                    var.equal =TRUE)
```
<br />
Compare QN, MBQN and original feature.
```{r, eval = TRUE}
```
```{r figure3, fig.height = 5, fig.width = 6, fig.align = "left", fig.cap = "Fig. 3 "}
plot.new()
matplot(t(rbind(feature1 = feature1,
    mod.feature1 = (feature1mod-mean(feature1mod))/25+mean(feature1),
    qn.feature1 = (qn.feature1-mean(qn.feature1))+mean(feature1),
    mbqn.feature1 = (
        mbqn.feature1-mean(mbqn.feature1))+mean(feature1))),
    type = "b", lty = c(1,1,1), pch = "o",
    ylab = "intensity",
    xlab = "sample",
    main = "Differentially expressed RI feature",
    ylim = c(34.48,34.85))
legend(x=11,y= 34.86, legend = c("feature","distorted feature/25" ,
                                "QN feature", " MBQN feature"),pch = 1,
        col = c(1,2,3,4), lty= c(1,1,1,1), bty = "n", y.intersp = 1.5,
        x.intersp = 0.2)
legend(x = .1, y = 34.6,
        legend = paste("p-value (t-test) =",round(ttest.res1$p.value,2),
            "\np-value (t-test, mbqn) =", round(ttest.res$p.value,4)),
        bty = "n", x.intersp = 0)


if (ttest.res$p.value<0.05)
    message("H0 (=equal mean) is rejected!")


# print(mtx2$x.mod)
# print(mtx2$mx.offset)
# print(mtx2$mx.scale)
print(paste("ttest.undistorted =",ttest.res0)) 
print(paste("ttest.distorted =", ttest.res1))
print(paste("ttest.mbqndistorted =", ttest.res))
```

<!-- <br /><br /> -->
<!-- Example 4: This example downloads an LFQ intensity dataset from the PRIDE  -->
<!-- repository, normalizes the data, identifies RI/NRI features, and give graphical  -->
<!-- output. One can choose between four data sets. By default data files are stored  -->
<!-- in the current working directory currentdir/PXDxxx. -->

<!-- The following example illustrates normalization of LFQ intensity data from the PRIDE repository. -->
<!-- For illustration purpose, we use the data set "PXD001584" (file size 10.8 MB) -->

<!-- Other data examples are <br /> -->
<!-- * "PXD005138" - contains one RI feature (file size 7440.6 MB) <br /> -->
<!-- * "PXD005861" - contains one RI feature (file size 334.7 MB) <br /> -->
<!-- * "PXD006617" - contains one RI feature (file size 290.8 MB) <br /> -->

<!-- ```{r example4, eval = TRUE} -->
<!-- library("SummarizedExperiment") -->
<!-- pxd_id <- "PXD001584"    -->

<!-- # Download file from PRIDE to currentdir/PXDxxx -->
<!-- #getPXDfile(pxd_id) -->

<!-- # Load file -->
<!-- out <- mbqnLoadFile(pxd_id, file.pattern = "proteinGroups.txt") -->

<!-- # filter for potential contaminants and identified only by site features -->
<!-- out <- out[!rowData(out)[["ixs"]],]  -->

<!-- # extract data and feature annotation -->
<!-- mtx <- assays(out)[["data"]] -->
<!-- featureAnnotations <- rowData(out) -->

<!-- low_thr <- 0.5 -->
<!-- ylim <- NULL -->
<!-- ix <- seq_len(ncol(mtx)) -->

<!-- ix <- c(1:9,19:27) -->
<!-- mtx <- mtx[,ix] -->
<!-- ylim.qn <- ylim <- c(22.5,36) -->

<!-- #################################################################################### -->
<!-- ``` -->
<!-- ```{r figure4, fig.height = 3, fig.width = 6, fig.align = "left", fig.cap = "Fig. 4 Correctly detected and identified RI (100 %RI) and a NRI feature (75 %RI) each with a data coverage of 100% across samples. "} -->
<!-- plot.new() -->
<!-- res <- mbqnPlotAll(mtx, -->
<!--                    FUN = median, -->
<!--                    low_thr = low_thr, -->
<!--                    las = 2, -->
<!--                    type = "l", -->
<!--                    feature_index = NULL, -->
<!--                    show_nri_only = TRUE, -->
<!--                    axis.cex = 0.5, -->
<!--                    y.intersp= 0.5) -->
<!-- #dev.off() -->

<!-- # get protein name of strongest nri/ri feature -->
<!-- nri_max <- as.numeric(names(which.max(res$nri))) -->
<!-- #featureAnnotations$proteinDescription[nri_max] -->
<!-- #featureAnnotations$proteinName[nri_max] -->
<!-- #featureAnnotations$nbPeptides[nri_max] -->

<!-- colnames(mtx) <- gsub("LFQ intensity","",colnames(mtx)) -->
<!-- mbqn.mtx <- mbqn(mtx,FUN = median) -->
<!-- qn.mtx <- mbqn(mtx,FUN = NULL) -->

<!-- # Boxplot of QN intensity features, highlight RI/NRI Features -->
<!-- if(length(ylim)==0) { -->
<!--   ylim <- c(floor(min(range(mbqn.mtx, na.rm = TRUE))),ceiling(max(range(mbqn.mtx, na.rm = TRUE)))) -->
<!--   ylim.qn <- c(floor(min(range(qn.mtx, na.rm = TRUE))),ceiling(max(range(mbqn.mtx, na.rm = TRUE)))) -->
<!-- } -->

<!-- df <- data.frame(qn.mtx[as.numeric(names(res$nri)),]) -->
<!-- if(ncol(df)==1) df <- t(df) -->
<!-- rownames(df) <- paste("QN feature",names(res$nri)) -->
<!-- df2 <- data.frame(mtx[as.numeric(names(res$nri)),]) -->
<!-- if(ncol(df2)==1) df2 <- t(df2) -->
<!-- rownames(df2) <- paste("unnormal. feature",names(res$nri)) -->
<!-- colnames(df) <- colnames(df2) -->
<!-- df <- rbind(df,df2) -->
<!-- df <- as.data.frame(t(df)) -->
<!-- ``` -->
<!-- ```{r figure5, fig.height = 5, fig.width = 6, fig.align = "left", fig.cap = "Fig. 5 "} -->
<!--   mtx.nri <- mbqnNRI(mtx,FUN = median,low_thr = 0.5, verbose = FALSE) -->

<!-- plot.new() -->
<!-- mbqnBoxplot(mtx = mtx.nri, -->
<!--             irow = as.numeric(names(res$nri)), -->
<!--             ylim = ylim.qn, xlab= "", las=2, -->
<!--             ylab = "LFQ intensity", -->
<!--             vals = df,lwd = 1., -->
<!--             main = "QN with RI/NRI balanced", -->
<!--             cex.axis = 1, cex.lab = .9, cex = .9, y.intersp = 0.5) -->
<!-- ``` -->

<!-- ## Documentation -->
<!-- To view the documentation for MBQN, type in R -->
<!-- ``` -->
<!-- ??MBQN -->
<!-- ``` -->

## References
[1] Brombacher, E., Schad, A., Kreutz, C. (2020). Tail-Robust Quantile Normalization. Proteomics. <br/>
[2] Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, 
G.K. (2015). limma powers differential expression analyses for RNA-sequencing 
and microarray studies. Nucleic Acids Research 43(7), e47. <br/>
[3] Ben Bolstad (2018). preprocessCore: A collection of pre-processing 
functions. R package version 1.44.0. 
https://github.com/bmbolstad/preprocessCore.