---
title: "An Introduction to Harman"
author: "Jason Ross and Yalchin Oytam"
date: "`r doc_date()`"
package: "`r pkg_ver('Harman')`"
abstract: >
  Harman is a PCA and constrained optimisation based technique that maximises the removal of batch effects from datasets, with the constraint that the probability of overcorrection (i.e. removing genuine biological signal along with batch noise) is kept to a fraction which is set by the end-user.
output: 
  BiocStyle::html_document
vignette: >
  %\VignetteIndexEntry{IntroductionToHarman}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}  
---

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

# Introduction

**Harman** {noun}  
*`A threshing yard where wheat is separated from chaff`*  
Origin: Turkish and Persian

The harman package is used to remove unwanted batch (technical) effects from datasets. It was designed for microarray and deep sequencing data, but it is applicable to any high-dimensional data where the experimental variable of interest is not completely confounded with the batch variable. Harman uses a PCA and constrained optimisation based technique. This vignette is a tutorial for using Harman in a number of common scenarios and also provides insight on how to interpret results.
  
For help with Harman, use the package help system. A good place to start is typing `?harman` in the console.
Harman requires three inputs:
  1. `datamatrix`, a matrix of type numeric or integer, or a data.frame that can be coerced into such using `as.matrix()`,
  2. `expt`, a vector or factor containing the experimental grouping variable,
  3. `batch`, a vector or factor containing the batch grouping variable.

The matrix is to be constructed with features (typically microarray probes or sequencing counts) in rows and samples in columns, much like the assayData slot in the canonical  Bioconductor `eSet` object, or any object which inherits from it. The data should have normalisation and any other global adjustment for noise reduction (such as background correction) applied prior to using Harman.

The fourth parameter of interest in Harman is limit, which sets the confidence limit for batch effect removal. The default is limit=0.95. We can consider the confidence limit an inverse of the alpha level, so there is 1 - limit chance (default of 0.05, or 5%) that some of the variance from the experimental variable is being removed along with the batch variance. This user specified limit parameter allows one to tune the aggressiveness of the batch effect removal. The more samples in each batch, the more opportunity Harman has to model the batch distributions, which allows a more precise identification (and subsequent removal) of batch effects.

*The Harman package is available from Bioconductor (`r Biocpkg("Harman")`) or Github (`r Githubpkg("JasonR055/Harman")`).*
```{r, echo=FALSE, warning=FALSE}
library(knitr)
library(RColorBrewer)
```

# Transcriptome data examples

## Working with a simple transciptome microarray dataset

First off, let's load an example dataset from `r Biocexptpkg("HarmanData")` where the batches and experimental variable are balanced.
```{r}
library(Harman)
library(HarmanData)
data(OLF)
```

The olfactory stem cell study is an experiment to gauge the response of human olfactory neurosphere-derived (hONS) cells established from adult donors to ZnO nanoparticles. A total of 28 Affymetrix HuGene 1.0 ST arrays were normalised together using RMA.  
  
The data comprises six treatment groups plus a control group, each consisting of four replicates: 
```{r}
table(olf.info)
```

The `olf.info` data.frame has the expt and batch variables across `r ncol(olf.info)` columns, with each sample described in one row to give `r nrow(olf.info)` rows.
```{r}
olf.info[1:7,]
```

The data is a data.frame of normalised log2 expression values with dimensions: `r nrow(olf.data)` rows (features) x `r ncol(olf.data)` columns (samples). This data can be handed to Harman as is, or first coerced into a matrix.
```{r}
head(olf.data)
```

### Running Harman

**Okay, so let's run Harman.**
```{r runharman}
olf.harman <- harman(olf.data, expt=olf.info$Treatment, batch=olf.info$Batch, limit=0.95)
```

This creates a `harmanresults` S3 object which has a number of slots. These include the centre and rotation slots of a prcomp object which is returned after calling `prcomp(t(x))`, where x is the datamatrix supplied. These two slots are required for reconstructing the data later. The other slots are the factors supplied for the analysis (specified as expt and batch), the runtime parameters and some summary stats for Harman. Finally, there are the original and corrected PC scores.
  
```{r}
str(olf.harman)
```

### Inspecting results

Harman objects can be inspected with methods such as `pcaPlot` and `arrowPlot`, as well as the generic functions `plot` and `summary`.
```{r, fig.height=4, fig.width=7}
plot(olf.harman)
```

```{r, fig.height=5, fig.width=5}
arrowPlot(olf.harman, main='Arrowplot of correction')
```
  
Using `summary` or inspecting the stats slot we can see Harman corrected the first 7 PCs and left the rest uncorrected.
```{r}
summary(olf.harman)
```

```{r, echo=FALSE}
kable(olf.harman$stats)
```

As the confidence (limit) was 0.95, Harman will look for batch effects until this limit is met. It does this by reducing the batch means in an iterative way using a binary search algorithm until the chance that biological variance is being removed (the factor given in expt) is too high. Consider the values specified in the `confidence` column as the likelihood that separation of samples in this PC is due to batch alone and not due to the experimental variable. If this confidence value is less than limit, Harman will not make another iterative correction. For example, on PC8 the confidence is about 0.835 which is below the limit of 0.95, so Harman did not alter data on this PC. Let's check this on a plot.
```{r, fig.height=5, fig.width=5}
arrowPlot(olf.harman, 1, 8)
```
  
The horizontal arrows show us that, on PC1 the scores were shifted, but on PC8 they were not.

If both dimensions were not shifted, the `arrowPlot` will default to printing **x** instead of arrows (*can't have 0 length arrows!*)
```{r, fig.height=5, fig.width=5}
arrowPlot(olf.harman, 8, 9)
```
  
We can also easily colour the PCA plot by the experimental variable to compare:
```{r, fig.height=4, fig.width=7}
par(mfrow=c(1,2))
pcaPlot(olf.harman, colBy='batch', pchBy='expt', main='Col by Batch')
pcaPlot(olf.harman, colBy='expt', pchBy='batch', main='Col by Expt')
```
  
### Reconstruct the corrected data

So, if corrections have been made and we're happy with the value of limit, then we reconstruct corrected data from the Harman adjusted PC scores. To do this, a harmanresults object is handed to the function `reconstructData()`. This function produces a matrix of the same dimensions as the input matrix filled with corrected data.
```{r}
olf.data.corrected <- reconstructData(olf.harman)
```
This new data can be over-written into something like an ExpressionSet object with a command like `exprs(eset) <- data.corrected`. An example of this is below.
  
To convince you that Harman is working as it should in reconstructing the data back from the PCA domain, let's test this principle on the original data.
```{r}
olf.data.remade <- reconstructData(olf.harman, this='original')
all.equal(as.matrix(olf.data), olf.data.remade)
```
So, the data is indeed the same *(to within the machine epsilon limit for floating point error)!*
  
### How does the new data differ from the old data?

Let's try graphically plotting the differences using `image()`. First, the rows (assays) are ordered by the variation in the difference between the original and corrected data.
```{r, fig.height=9, fig.width=7}
olf.data.diff <- abs(as.matrix(olf.data) - olf.data.corrected)

diff_rowvar <- apply(olf.data.diff, 1, var)
by_rowvar <- order(diff_rowvar)

par(mfrow=c(1,1))
image(x=1:ncol(olf.data.diff),
      y=1:nrow(olf.data.diff),
      z=t(olf.data.diff[by_rowvar, ]),
      xlab='Sample',
      ylab='Probeset',
      main='Harman probe adjustments (ordered by variance)',
      cex.main=0.7,
      col=brewer.pal(9, 'Reds'))
```

We can see that most Harman adjusted probes were from batch 3 (samples 15 to 21), while batch 1 is relatively unchanged. In batches 2 and 3, often the same probes were adjusted to a larger extent in batch 3. This suggests some probes on this array design are prone to a batch effect.

```{r cleanup1, echo=FALSE}
rm(list=ls()[grep("olf", ls())])
```

## Another simple transciptome dataset

The IMR90 Human lung fibroblast cell line data that was published in a paper by Johnson et al [doi: 10.1093/biostatistics/kxj037](doi: 10.1093/biostatistics/kxj037) comes with Harman as an example dataset.
```{r}
require(Harman)
require(HarmanData)
data(IMR90)
```

  The data is structured like so:
```{r, echo=FALSE}
kable(imr90.info)
```

It can be seen from the below table the experimental structure is completely balanced.
```{r}
table(expt=imr90.info$Treatment, batch=imr90.info$Batch)
```

### Evidence of batch effects
While there isn't glaring batch effects in PC1 and PC2, they are more apparent when plotting PC1 and PC3.
```{r, fig.height=4, fig.width=7}
par(mfrow=c(1,2), mar=c(4, 4, 4, 2) + 0.1)
imr90.pca <- prcomp(t(imr90.data), scale. = TRUE)
prcompPlot(imr90.pca, pc_x=1, pc_y=2, colFactor=imr90.info$Batch,
           pchFactor=imr90.info$Treatment, main='IMR90, PC1 v PC2')
prcompPlot(imr90.pca, pc_x=1, pc_y=3, colFactor=imr90.info$Batch,
           pchFactor=imr90.info$Treatment, main='IMR90, PC1 v PC3')
```

### Running Harman
```{r}
imr90.hm <- harman(as.matrix(imr90.data), expt=imr90.info$Treatment,
                   batch=imr90.info$Batch)
```
  
We can see the most batch correction was actually on the 3rd principal component and the data was also corrected on the 1st and 4th component.

```{r, echo=FALSE}
kable(imr90.hm$stats)
```
  
Plotting PC1 v. PC2 and PC1 v. PC3...
```{r, fig.height=4, fig.width=7}
plot(imr90.hm, pc_x=1, pc_y=2)
plot(imr90.hm, pc_x=1, pc_y=3)
```

While no batch effect was found on PC2, there are batch effects on PC1, 3 and 4. An `arrowPlot()` shows the extent of the correction:
```{r, fig.height=5, fig.width=5}
arrowPlot(imr90.hm, pc_x=1, pc_y=3)
```
  
On PC1 the data has been shifted only a little, while on PC3 a large batch-effect signature seems to be present. The corrected data on the PC1 v. PC3 plot is still a little separated by batch. If we wish to aggressively stomp on the batch effect, (with increased risk of removing some experimental variance), we may specify something like limit=0.9.
```{r, fig.height=4, fig.width=7}
imr90.hm <- harman(as.matrix(imr90.data), expt=imr90.info$Treatment,
                   batch=imr90.info$Batch, limit=0.9)
plot(imr90.hm, pc_x=1, pc_y=3)
```
This time the samples across batches are less separated on the plot and there is also a batch effect found on PC2.
```{r, echo=FALSE}
kable(imr90.hm$stats)
```

### Reconstruct the corrected data

Again, as before we reconstuct the data. Let's also use this data to do a comparison: a PCA plot of the original data and shiny new reconstructed data.
```{r, fig.height=4, fig.width=7}
imr90.data.corrected <- reconstructData(imr90.hm)
par(mfrow=c(1,2))
prcompPlot(imr90.data, 1, 3, colFactor=imr90.hm$factors$batch, cex=1.5, main='PCA, Original')
prcompPlot(imr90.data.corrected, 1, 3, colFactor=imr90.hm$factors$batch, cex=1.5, main='PCA, Corrected')
```

```{r cleanup2, echo=FALSE}
rm(list=ls()[grep("imr90", ls())])
```

## Working with very small datasets

This example is an illustration of how Harman will have reduced power in teasing apart biological and technical variance when presented with a very small dataset (2 replicates and 2 conditions).

To illustrate, we use the toy dataset in `r Biocexptpkg("affydata")`. The `Dilution` data within relates to two sources of cRNA, A (human liver tissue) and B (Central Nervous System cell line), which have been hybridized to human array (HGU95A) in a range of proportions and dilutions.

This toy example is taken from arrays hybridized to source A at 10 μg and 20 μg. There are two replicate arrays for each generated cRNA, with each array replicate processed in a different scanner. [*For more information see Gautier et al., affy - Analysis of Affymetrix GeneChip data at the probe level*](http://bioinformatics.oxfordjournals.org/content/20/3/307.full.pdf).
```{r affydata, warning=FALSE, message=FALSE, comment=FALSE}
library(affydata, quietly = TRUE, warn.conflicts = FALSE)
data(Dilution)
Dilution.log <- Dilution
intensity(Dilution.log) <- log(intensity(Dilution))
cols <- brewer.pal(nrow(pData(Dilution)), 'Paired')
```
  
This dataset contains technical replicates of liver RNA run on two different scanners. Technical replicates are denoted as A and B samples. There are two technical replicates across two array scanners. The 10 and 20 The second 2 are also replicates. The second arrays are hybridized to twice as much RNA so the intensities should be, in general, bigger.

```{r, echo=FALSE}
kable(pData(Dilution))
```

```{r, fig.height=4, fig.width=4}
boxplot(Dilution, col=cols)
```
  
Notice that the scanner effect (A vs B) is stronger than the RNA concentration effect (10 vs 20). This certainly hints at a batch effect.

Let’s normalize the data by two methods (Loess and Quantile) and see if it removes this technical noise.
```{r normalize, fig.height=7, fig.width=9}
Dilution.loess <- normalize(Dilution.log, method="loess")
Dilution.qnt <- normalize(Dilution.log, method="quantiles")

# define a quick PCA and plot function
pca <- function(exprs, pc_x=1, pc_y=2, cols, ...) {
  pca <- prcomp(t(exprs), retx=TRUE, center=TRUE)
  if(is.factor(cols)) {
    factor_names <- levels(cols)
    num_levels <- length(factor_names)
    palette <- rainbow(num_levels)
    mycols <- palette[cols]  
  } else {
    mycols <- cols
  }
  plot(pca$x[, pc_x], pca$x[, pc_y],
       xlab=paste('PC', pc_x, sep=''),
       ylab=paste('PC', pc_y, sep=''),
       col=mycols, ...)
  if(is.factor(cols)) {
    legend(x=min(pca$x[, pc_x]), y=max(pca$x[, pc_y]),
           legend=factor_names, fill=palette, cex=0.5)
  }
}

par(mfrow=c(1,2), mar=c(4, 4, 4, 2) + 0.1)
boxplot(Dilution.loess, col=cols, main='Loess')
boxplot(Dilution.qnt, col=cols, main='Quantile')
pca(exprs(Dilution.loess), cols=cols, cex=1.5, pch=16, main='PCA Loess')
pca(exprs(Dilution.qnt), cols=cols, cex=1.5, pch=16, main='PCA Quantile')
```
  
The boxplots give the impression the batch effect has been removed. However, principal component analysis (PCA) shows the batch effect to still be the largest source of variance in the data. PC1 is dominated by batch effect (seperation of bold and pastel colours), while on PC2 the effect of RNA quantity is observed, so green (10 μg) compared with blue (20 μg).

### Running Harman

Let’s fire up harman and try to remove this batch effect.

```{r affydata_harman1}
library(Harman)
loess.hm <- harman(exprs(Dilution.loess),
                   expt=pData(Dilution.loess)$liver,
                   batch=pData(Dilution.loess)$scanner)
qnt.hm <- harman(exprs(Dilution.qnt),
                 expt=pData(Dilution.qnt)$liver,
                 batch=pData(Dilution.qnt)$scanner)
```

If we inspect the stats on the Harman runs,

Loess-normalised data
```{r, echo=FALSE, fig.align='left'}
kable(loess.hm$stats)
```

Quantile-normalised data
```{r, echo=FALSE, fig.align='left'}
kable(qnt.hm$stats)
```

**The correction statistic is 1.0 for all dimensions, so Harman didn’t find any batch effect!**

### More aggressive settings

While our intuition tells us there is a batch effect, with the default settings (limit=0.95), harman fails to find one. *This is due to the fact there are only two replicates in each batch!* Let’s step up the limit now, to a confidence interval of 0.75. So, we are 75% sure only technical (batch) variation and not biological variation is being removed.

```{r affydata_harman2}
loess.hm <- harman(exprs(Dilution.loess),
                   expt=pData(Dilution.loess)$liver,
                   batch=pData(Dilution.loess)$scanner,
                   limit=0.75)
qnt.hm <- harman(exprs(Dilution.qnt),
                 expt=pData(Dilution.qnt)$liver,
                 batch=pData(Dilution.qnt)$scanner,
                 limit=0.75)
```
This time a batch effect is found on PC1.

Loess-normalised data
```{r, echo=FALSE}
kable(loess.hm$stats)
```

Quantile-normalised data
```{r, echo=FALSE}
kable(qnt.hm$stats)
```

### Harman plots

So what corrections did Harman make with limit=0.75? Let’s take a look…
```{r, fig.height=4, fig.width=7}
par(mfrow=c(2,2), mar=c(4, 4, 4, 2) + 0.1)
plot(loess.hm, 1, 2, pch=17, col=cols)
plot(qnt.hm, 1, 2, pch=17, col=cols)
```

### Reconstruct the corrected data
```{r affydata_reconstruct}
Dilution.loess.hm <- Dilution.loess
Dilution.qnt.hm <- Dilution.qnt
exprs(Dilution.loess.hm) <- reconstructData(loess.hm)
exprs(Dilution.qnt.hm) <- reconstructData(qnt.hm)
```

```{r}
detach('package:affydata')
```

# Working with unbalanced and confounded data

## Example dataset

The dataset (`r Biocexptpkg("bladderbatch")`) for this exercise is that discussed by Jeff Leek and others [here](http://www.nature.com/nrg/journal/v11/n10/pdf/nrg2825.pdf) and arises from this [paper](http://www.ncbi.nlm.nih.gov/pubmed/15173019). This was a bladder cancer study comparing Affymetrix HG-U133A microarray expression profiles across five groups: superficial transitional cell carcinoma (sTCC) with surrounding carcinoma *in situ* (CIS) lesions (sTCC+CIS) or without surrounding CIS lesions (sTCC-CIS), muscle invasive carcinomas (mTCC) with normal bladder mucosa from patients without a bladder cancer history (Normal) and biopsies from cystectomy specimens (Biopsy). The arrays were run on 5 different days (5 batches).
  
First, loading the data:
```{r, message=FALSE, warning=FALSE}
library(bladderbatch)
library(Harman)
# This loads an ExpressionSet object called bladderEset
data(bladderdata)
bladderEset
```

The phenotype data.
```{r, echo=FALSE}
kable(pData(bladderEset))
```

A table of batch by outcome.
```{r}
table(batch=pData(bladderEset)$batch, expt=pData(bladderEset)$outcome)
```

### Batch structure

The experimental design is very poor in controlling for a batch effect. Ideally, the five factors should be distributed across the five run dates (batches). Instead, four out of the five experimental factors are distributed across two batches. The sTCC+CIS samples are all within batch 5. In this instance, batch is completely confounded with the experimental variable, so any variation in the data will be a sum of the biological variance and technical variance. Any inference about the difference of sTCC+CIS to other groups needs to be made very guardedly. It is also worth noting the spread of the mTCC samples across batches is suboptimal. While there are 11 samples in batch 1, there is only a single sample in batch 2. Finally, batches 3 and 4 have only one sample type.
  
Let's explore the multivariate grouping of the data by generating some PCA plots, first with colouring by batch and then by experimental variable.
```{r, fig.height=4, fig.width=7}
par(mfrow=c(1,2))
prcompPlot(exprs(bladderEset), colFactor=pData(bladderEset)$batch,
    pchFactor=pData(bladderEset)$outcome, main='Col by Batch')
prcompPlot(exprs(bladderEset), colFactor=pData(bladderEset)$outcome,
    pchFactor=pData(bladderEset)$batch, main='Col by Expt')
```

The batch structures are highly unbalanced and so a batch effect is not immediately obvious to casual observation. However, a closer examination of samples that are represented well across two batches (Biopsy and Normal), shows a batch effect is certainly present. Certainly, with an older microarray technology like the Affymetrix HG-U133A, batch effects will be present.

### Running Harman

Even though the batch structures are highly unbalanced, this dataset is quite large - so, we expect Harman to be well powered to find batch effects. Let's take a look.
```{r, cache=FALSE}
expt <- pData(bladderEset)$outcome
batch <- pData(bladderEset)$batch
bladder_harman <- harman(exprs(bladderEset), expt=expt, batch=batch)
sum(bladder_harman$stats$correction < 1)
```

Harman found `r sum(bladder_harman$stats$correction < 1)` of the
`r length(bladder_harman$stats$correction)` PCs had a batch effect.
```{r}
summary(bladder_harman)
```

The `summary()` shows PC3 to be highly affected by batch. Now plotting the original and corrected data.
```{r, fig.height=4, fig.width=7}
par(mfrow=c(1,1))
plot(bladder_harman)
```
  
This time for PCs 1 and 3.
```{r, fig.height=4, fig.width=7}
plot(bladder_harman, 1, 3)
```

Now the PC scores changes displayed on an `arrowPlot`.
```{r, fig.height=5, fig.width=5}
arrowPlot(bladder_harman, 1, 3)
```

### Reconstruct the corrected data

First create a new object and then fill the exprs slot of the `r class(bladderEset)[1]` object with Harman corrected data. Alternatively, the existing object can have the exprs slot overwritten.
```{r}
CorrectedBladderEset <- bladderEset
exprs(CorrectedBladderEset) <- reconstructData(bladder_harman)
comment(bladderEset) <- 'Original'
comment(CorrectedBladderEset) <- 'Corrected'
```

## Limma analysis

Let's check the effects of Harman with a `r Biocpkg("limma")` analysis. First fitting the original data:
```{r, message=FALSE, warning=FALSE}
library(limma, quietly=TRUE)
design <- model.matrix(~0 + expt)
colnames(design) <- c("Biopsy", "mTCC", "Normal", "sTCC", "sTCCwCIS")
contrast_matrix <- makeContrasts(sTCCwCIS - sTCC, sTCCwCIS - Normal,
                                 Biopsy - Normal,
                                 levels=design)

fit <- lmFit(exprs(bladderEset), design)
fit2 <- contrasts.fit(fit, contrast_matrix)
fit2 <- eBayes(fit2)
summary(decideTests(fit2))
```

Now a linear model on the Harman corrected data:
```{r}
fit_hm <- lmFit(exprs(CorrectedBladderEset), design)
fit2_hm <- contrasts.fit(fit_hm, contrast_matrix)
fit2_hm <- eBayes(fit2_hm)
summary(decideTests(fit2_hm))
```

We can see the dramatic effect Harman has had in reducing the number of significant microarray probes. The huge reduction in the Biopsy - Normal contrast makes biological sense, about half of the biopsies were from cystectomies that were histologically normal. The Harman corrected data on the sTCCwCIS - sTCC contrast suggests that surrounding *in situ* lesions (CIS) does not overly impact the transcriptome of superficial transitional cell carcinoma (sTCC).

```{r cleanup_bladderbatch, echo=FALSE}
rm(list=ls()[!grepl("^pca$", ls())])
detach('package:bladderbatch')
```

# Methylation data example

## Example dataset

As an example, let's consider the Illumina Infinium HumanMethylation450 BeadChip data (450k array). First up, an important tip, **put M-values into Harman, not Beta-values.** Harman is designed for continuous ordinal data, not data which is constrained, such as Beta-values; which by definition are between 0 and 1. Input M-values into Harman and if Beta-values are needed downstream, convert the corrected M-values back into Beta-values using something like the `m2beta()` function in the `r Biocpkg("lumi")` package, or the `ilogit2()` function in `r Biocpkg("minfi")`.
  
M-values also have the property of having far more centrality than Beta-values. PCA is a parametric technique and so it works best with an underlying Gaussian distribution to the data. While it has been historically shown that PCA works rather well in non-parametric settings, Harman might be expected to be more sensitive if the data is more centred (M-values), rather than bimodal at the extremes (Beta-values).
  
Beta values of exactly 0 or 1 will translate to minus infinity and infinity, respectively, in M-value space. An M-value is the `logit2()` of a Beta value and  Beta-values are `ilogit2` of the M-values. These infinite values will make the internal singular value decomposition (SVD) step of Harman throw an exception. Further, infinite M-values are not commutative. If an M-value of Inf or -Inf is transformed back into a Beta-value it will have the value NaN or 0, respectively. For these reasons we do not recommend a normalisation step which creates Beta-values of exactly 0 or 1.

```{r, message=FALSE, warning=FALSE}
library(minfi, quietly = TRUE)
logit2
ilogit2
ilogit2(Inf)
ilogit2(-Inf)
```
A normalisation technique such as `preprocessIllumina()` in the `r Biocpkg("minfi")` package will give Beta-values of exactly 0 or 1, while a technique such as `preprocessSWAN()` does not. Instead it incorporates a small offset, alpha, as suggested by [Pan Du et al](http://www.biomedcentral.com/content/pdf/1471-2105-11-587.pdf). If you have a `MethySet` object, the `r Biocpkg("minfi")` package now allows a user to specify an `offset` to the Beta-value as well as a `betaThreshold` to constrain betas away from 0 or 1.
  
While we do not recommend use of a normalisation technique that generate exact 0 or 1 Beta-values, sometimes, we realise, only this normalised data is available. For instance, when the data has been downloaded from a public resource, pre-normalised with no raw red and green channel data. For this case, we have supplied the `shiftBetas()` helper function in Harman to resolve this problem. We shift beta values of *exactly* 0 or 1 by a very small amount (typically 1 x 10^-4^) before transformation into M-values. As an example:

```{r}
betas <- seq(0, 1, by=0.05)
betas
newBetas <- shiftBetas(betas, shiftBy=1e-4)
newBetas
range(betas)
range(newBetas)
logit2(betas)
logit2(newBetas)
```

### Loading 450K data

So, let's get underway and analyse the toy dataset supplied with minfi.
```{r loadminfidata, message=FALSE, warning=FALSE}
library(minfiData, quietly = TRUE)
baseDir <- system.file("extdata", package="minfiData")
targets <- read.metharray.sheet(baseDir)
```

Read in the files, normalise using `preprocessSWAN()` and `preprocessIllumina()` and filter out poorly called CpG sites.
```{r read450K}
rgSet <- read.metharray.exp(targets=targets)
mSetSw <- preprocessSWAN(rgSet)

mSet <- preprocessIllumina(rgSet, bg.correct=TRUE, normalize="controls",
                           reference=2)
detP <- detectionP(rgSet)
keep <- rowSums(detP < 0.01) == ncol(rgSet)
mSetIl <- mSet[keep,]
mSetSw <- mSetSw[keep,]
```

This dataset has quite a few phenotype variables of interest. The samples are paired cancer-normal samples from three people, 1 male and 2 females.
```{r}
kable(pData(mSetSw)[,c('Sample_Group', 'person', 'sex', 'status', 'Array',
                       'Slide')])
```

Doing some exploratory data analysis using MDS (considering it is methylation data), we can see that status is separated on PC1 and sex on PC2. This makes biological sense as we know cancer and gender both have large effects on DNA methylation. In cancer samples, there is typically global hypomethylation and focal hypermethylation at some CpG islands. While in the case of gender, female and male samples have very different X chromosome methylation due to imprinting, as well as some autosomal CpG site differences.
  
If the plot is recoloured by "Slide"", there is some suggestion a batch effect is intermingled with these two experimental variables. The tricky question is how to use Harman to remove the influence of Slide on the data, without also removing variance due to the experimental variables of interest?
```{r mdsplots, fig.height=5, fig.width=7}
par(mfrow=c(1, 1))
cancer_by_gender_factor <- as.factor(paste(pData(mSetSw)$sex,
                                           pData(mSetSw)$status)
                                     )

mdsPlot(mSetSw, sampGroups=cancer_by_gender_factor, pch=16)
mdsPlot(mSetSw, sampGroups=as.factor(pData(mSet)$Slide), pch=16)
```
The experiment is very small, with only `r nrow(pData(mSet))` (paired) samples. The MDS plots suggests there are two main phenotypes of influence, status and sex. The  batch variable in this case is "Slide". The sex phenotype variable, which is highly influencing the data, is spread unevenly across the two slides (batches).

```{r}
table(gender=pData(mSetSw)$sex, slide=pData(mSetSw)$Slide)
```

From the table, we can see that only slide 5723646052 has samples of male origin. If we specify a simple cancer-based model with `expt=pData(mSetSw)$status` and `batch=pData(mSetSw)$Slide`, Harman will attribute the male-specific methylation signature of the two paired male samples on slide 5723646052 as batch effect and will try and eliminate it.
  
There are two strategies here:
1. Form a compound factor by joining the status and sex factors together.
2. Generate two Harman corrections, one for status and one for sex.

As this experiment is so small, the first strategy won't be very effective. Only the factor level "F normal" is shared by both batches and for only one replicate. It's very hard for Harman to get an idea of the batch distributions.
```{r}
cancer_by_gender_factor <- as.factor(paste(pData(mSetSw)$sex,
                                           pData(mSetSw)$status)
                                     )
table(expt=cancer_by_gender_factor, batch=pData(mSetSw)$Slide)
```

In the second strategy, both levels of expt are shared across both levels of batch. This is a far more ideal way to find batch effects. Of course though, *there is confounding between sex and Slide!* So, in these cases, do not consider a batch confounded factor in downstream differential analysis.
```{r}
table(expt=pData(mSetSw)$status, batch=pData(mSetSw)$Slide)
```

### Appropriate normalisation

If the Beta-values are shifted away from 0 or 1 by a very small amount, (say 1e-7), they will generate extreme M-values. Instead a moderate correction (such as 1e-4) seems the preferred option.
```{r shift, fig.height=7, fig.width=7}
par(mfrow=c(2,2))
library(lumi, quietly = TRUE)
shifted_betas <- shiftBetas(betas=getBeta(mSetIl), shiftBy=1e-7)
shifted_ms <- beta2m(shifted_betas)  # same as logit2() from package minfi
plot(density(shifted_ms, 0.05), main="Shifted M-values, shiftBy = 1e-7",
     cex.main=0.7)

shifted_betas <- shiftBetas(betas=getBeta(mSetIl), shiftBy=1e-4)
shifted_ms <- beta2m(shifted_betas)  # same as logit2() from package minfi
plot(density(shifted_ms, 0.05), main="Shifted M-values, shiftBy = 1e-4",
     cex.main=0.7)

plot(density(beta2m(getBeta(mSetSw)), 0.05), main="SWAN normalised M-values",
     cex.main=0.7)
```

It can be seen that M-values have far more central-tendency (more Gaussian-like) than Beta-values.
```{r, fig.height=4, fig.width=7}
par(mfrow=c(1,2))
plot(density(shifted_betas, 0.1), main="Beta-values, shiftBy = 1e-4",
     cex.main=0.7)
plot(density(shifted_ms, 0.1), main="M-values, shiftBy = 1e-4",
     cex.main=0.7)
```

A comparison of M-values produced by the GenomeStudio-like `preprocessIllumina()` function and by `preprocessSWAN()`.
```{r, fig.height=4, fig.width=7}
par(mfrow=c(1,2))
plot(density(shifted_ms, 0.1),
     main="GenomeStudio-like M-values, shiftBy = 1e-4", cex.main=0.7)
plot(density(getM(mSetSw), 0.1), main="SWAN M-values", cex.main=0.7)
```

**From here on, we will work with SWAN normalised M-values.**

### Harman correction of M-values

For this example, status is declared as the experimental variable.  When comparing conditions with very large DNA methylation differences, such as cancer and non-neoplastic samples, the batch effect will not be so easy to observe amongst all that biological variation. This scenario changes with a more subtle phenotype of interest. More arrays in each batch will allow Harman to more easily find batch effects. Considering this experiment is so small, an aggressive setting (limit=0.65) is needed to find a batch effect across a number of PCs (PCs 2, 5 and 1 in particular). A plot shows the correction made was relatively minor.

```{r, fig.height=4, fig.width=7}
methHarman <- harman(getM(mSetSw), expt=pData(mSetSw)$status,
                     batch=pData(mSetSw)$Slide, limit=0.65)
summary(methHarman)
plot(methHarman, 2, 5)
```

### Reconstruct the corrected data

It is difficult to tease out the batch effect with such a small experimental group. Rather than further reduce the limit, let's just convert the data back.
```{r}
ms_hm <- reconstructData(methHarman)
betas_hm <- m2beta(ms_hm)
```

## Limma analysis

In downstream differential methylation analysis using `r Biocpkg("limma")` or otherwise, care must be taken to interpret the results. As Harman was run with the expt variable set to status, any other variation unrelated to status which is unbalanced across the two slides is considered as batch noise. For example, we have already shown that sex is a highly influential factor and it is unbalanced across the slides; only person of id3 is male and both the normal and cancer samples are on slide 5723646052. **Therefore, we expect Harman to attribute this gender effect to a slide effect.**

In the specification of a linear model of `model.matrix(~id + group)`, we should then expect to find no differential methylation in person id3, the male. Let's try this:

```{r limma}
library(limma)

group <- factor(pData(mSetSw)$status, levels=c("normal", "cancer"))
id <- factor(pData(mSetSw)$person)
design <- model.matrix(~id + group)
design
```

Now time to fit this design to both the original M-values and the Harman corrected M-values.
```{r fit}
fit_hm <- lmFit(ms_hm, design)
fit_hm <- eBayes(fit_hm)

fit <- lmFit(getM(mSetSw), design)
fit <- eBayes(fit)
```

Our intuition is correct. Harman has indeed squashed the variation in person id3, there are now no differentially methylated probes.
```{r limma_output}
summary_fit_hm <- summary(decideTests(fit_hm))
summary_fit <- summary(decideTests(fit))
summary_fit_hm
summary_fit
```

We also note, our ability to detect cancer-related differential methylation has been enhanced. There are now `r summary_fit_hm[1, 4] - summary_fit[1, 4]` more hypermethylated CpG probes and `r summary_fit_hm[3, 4] - summary_fit[3, 4]` more hypomethylated CpG probes.

```{r, echo=FALSE, warning=FALSE, message=FALSE}
rm(list=ls()[!grepl("^pca$", ls())])
#detach('package:minfiData', force = TRUE)
#detach('package:lumi', force = TRUE)
#detach('package:minfi', force = TRUE)
#detach('package:IlluminaHumanMethylation450kanno.ilmn12.hg19', force = TRUE)
```

# Mass Spectrophotometry data example

## Example dataset
We use the data available in the `r Biocexptpkg("msmsEDA")` library which has 14 samples across two treatments and two batches.

### Loading the mass-spec data

```{r msms, message=FALSE, warning=FALSE}
# Call dependencies
library(msmsEDA)
library(Harman)

data(msms.dataset)
msms.dataset
```

### Preprocessing

The data matrix in an MSnSet object from package `r Biocexptpkg("msmsEDA")` may have all zero rows (if it's a subset of a larger object) and some samples may have NA values, which correspond to proteins not identified in that particular sample. Principle components analysis cannot be undertaken on matrices with such features. So first we can use the wrapper function `pp.msms.data()`, which removes all zero rows and replaces NA with 0.

```{r msms_pp}
# Preprocess to remove rows which are all 0 and replace NA values with 0.
msms_pp <- pp.msms.data(msms.dataset)
```

### Batch structure

```{r, echo=FALSE}
kable(pData(msms_pp))
```


```{r}
# Create experimental and batch variables
expt <- pData(msms_pp)$treat
batch <- pData(msms_pp)$batch
table(expt, batch)
```

### Running Harman

```{r msms_harman}
# Log2 transform data, add 1 to avoid infinite values
log_ms_exprs <- log(exprs(msms_pp) + 1, 2)

# Correct data with Harman
hm <- harman(log_ms_exprs, expt=expt, batch=batch)
summary(hm)
```

The Harman result is interesting. This MS data seems to have the batch effect contained within the 1st and 14th PCs only. A marked difference compared to transcriptome microarray data and the like.

A plot rather convincingly shows that Harman was able to remove the batch effect.
```{r, fig.height=4, fig.width=7}
plot(hm)
```

### Reconstruct the corrected data

As usual we now reconstruct corrected data, but we add an extra transformation step on the end. As the data was Log2 transformed we convert it back to the original format (and subtract 1 as this was added before during the transformation into Log2 space). The corrected and transformed back data is placed into a new 'MSnSet' instance.
```{r}
# Reconstruct data and convert from Log2, removing 1 as we added it before.
log_ms_exprs_hm <- reconstructData(hm)
ms_exprs_hm <- 2^log_ms_exprs_hm - 1

# Place corrected data into a new 'MSnSet' instance
msms_pp_hm <- msms_pp
exprs(msms_pp_hm) <- ms_exprs_hm
```

```{r, echo=FALSE}
rm(list=ls()[!grepl("^pca$", ls())])
detach('package:msmsEDA')
```

# Comparison to ComBat

ComBat, available in the `r Biocpkg("sva")` package, adjusts for known batches using an empirical Bayesian framework. This involves mean and variance shrinkage on a feature by feature basis using the Bayesian model to estimate the shrinkage parameters. To allow some robustness in estimates when the number of samples is low, the model pools information across genes.

Harman uses a dimensionality reduction approach with a confidence constraint set by the user. In this case, the features by samples matrix is decomposed into principal components. Unlike ComBat, batch adjustments are not on a feature by feature basis but on a principal component by principal component basis. Each orthogonal component is examined for evidence of a batch effect and if one is found, the batch means are moved step-wise towards zero in principal component space until the confidence limit for no evidence of a batch effect is met. Finally the adjusted PC scores are transformed back into raw data space.

Fundamentally, Harman shifts feature values in a coordinated way across a batch one prinicipal component at a time, whereas ComBat shifts feature values one feature at a time by an amount informed by the batch structure and overall mean and variance of the data.

To compare the techniques, we will apply the methods to a dataset and plot the difference between the original data matrix and after correction.

### IMR90 example data
For this comparison we use the IMR90 data from the transcriptomics example earlier. This is the dataset used in the original ComBat paper by Johnson et al [doi: 10.1093/biostatistics/kxj037](doi: 10.1093/biostatistics/kxj037)

This data has 4 treatments and 3 batches.
```{r}
table(imr90.info)
```

### Applying Harman and ComBat to adjust for known batches

In accordance with the [advice](https://support.bioconductor.org/p/62874/) of Jeff Leek, for ComBat we do not include the variable of interest in specification of the model matrix.

```{r, message=FALSE, warning=FALSE}
library(HarmanData)
library(sva)

modcombat <- model.matrix(~1, data=imr90.info)
imr90.data.cb <- ComBat(dat=as.matrix(imr90.data), batch=imr90.info$Batch, mod=modcombat,
                        par.prior=TRUE, prior.plots=FALSE)

imr90.hr <- harman(imr90.data, expt = imr90.info$Treatment, batch = imr90.info$Batch)
imr90.data.hr <- reconstructData(imr90.hr)

prcompPlot(imr90.data, pc_x = 1, pc_y = 3,
           colFactor = imr90.info$Batch,
           pchFactor = imr90.info$Treatment,
           main='PC1 versus PC3')

arrowPlot(imr90.hr, pc_x = 1, pc_y = 3, main='PC1 versus PC3')
```

### Compare

Plotting the differences between matrix values as a type of heatmap. The features of the microarray (rows) are ordered by the degree of correction in Harman.
```{r}
library(RColorBrewer)

diffMap <- function(a, b, feature_order=1:nrow(a), batch, ...) {

  image(t(abs(a[feature_order, ] - b[feature_order, ])),
      col = brewer.pal(9, "Greys"),
      xaxt='n',
      yaxt='n',
      xlab='Batch',
      ylab='Ordered feature',
      ...)
  axis(1, at=seq(0, 1, length.out=length(batch)), labels=batch)
}
```

```{r, fig.height=7, fig.width=7}

probe_diffs_hr <- order(rowSums(abs(as.matrix(imr90.data - imr90.data.hr))))
probe_diffs_cb <- order(rowSums(abs(as.matrix(imr90.data - imr90.data.cb))))

diffMap(a=imr90.data, b=imr90.data.hr, feature_order=probe_diffs_hr,
        batch=imr90.info$Batch, main='Original - Harman')
```

```{r, fig.height=7, fig.width=7}
diffMap(a=imr90.data, b=imr90.data.cb, feature_order=probe_diffs_hr,
        batch=imr90.info$Batch, main='Original - ComBat')
```

```{r, fig.height=7, fig.width=7}
diffMap(a=imr90.data.hr, b=imr90.data.cb, feature_order=probe_diffs_hr,
        batch=imr90.info$Batch, main='Harman - ComBat')
```

### Concluding remarks

The feature by feature and sample by sample nature of ComBat is evident. In particular, the outlying sample in batch 3 has been subjected to quite extensive feature adjustment.
  
Harman adjusts the values of features within batches in a coordinated way. In is particular example, Harman identifies across batches a highly coordinate set of features which need correction.

Despite their differences, the Harman and Combat approaches have large overlaps in the features they identify as needing correction and the amount of feature adjustment required.