https://github.com/andquintero/bratwurst

https://github.com/andquintero/bratwurst

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README.Rmd

---
title: "Bratwurst"
author: "Daniel Huebschmann & Sebastian Steinhauser"
date: "20.07.2016, major update 09.10.2017"
output:
  BiocStyle::html_document:
    toc: yes
    keep_md: yes
---

`Bratwurst` is a software package providing functions for preprocessing,
wrappers for non-negative matrix factorization and postprocessing in `R`. This 
repo hosts the code of the Bratwurst software package.  
A detailed description 
of the software and an application to cells of the human hematopoietic system 
are available as a preprint: https://doi.org/10.1101/199547.  
Intermediate results for the analysis on hematopoietic cells are available on
zenodo: https://doi.org/10.5281/zenodo.800049.  
In this README document, we present major parts of the vignette of the package. 
First some packages have to be loaded.
  
```{r load_style, warning=FALSE, message=FALSE, results="hide"}
library(knitr)
library(ComplexHeatmap)
```

# Introduction

**NMF** (**nonnegative matrix factorization**) is a matrix decomposition 
method. It was originally described by Lee & Seung in 1999. In 2003, Brunet et 
al. applied NMF to gene expression data. In 2010, `r CRANpkg("NMF")`, an R 
package implementing several NMF solvers was published by Gaujoux et al.
NMF basically solves the problem as illustrated in the following figure
(Image taken from 
):

![NMF](vignettes/NMF.png)

Here, `V` is an input matrix with dimensions `n x m`. It is decomposed
into two matrices `W` of dimension `n x l` and `H` of dimension
`l x m`, which when multiplied approximate the original matrix `V`. `l` is
a free parameter in NMF, it is called the factorization rank. If we call the 
columns of `W` signatures, then `l` corresponds to the number of 
signatures. The decomposition thus leads to a reduction in complexity if 
`l < n`, i.e. if the number of signatures is smaller than the number of 
features, as indicated in the above figure.

In 2015, Mejia-Roa et al. introduced an implementation of an NMF-solver in 
CUDA, which lead to significant reduction of computation times by making use of
massive parallelisation on GPUs. Other implementations of
NMF-solvers on GPUs exist.

It is the pupose of the package `Bratwurst` described here to provide wrapper 
functions in R to these NMF-solvers in CUDA. Massive parallelisation not only 
leads to faster algorithms, but also makes the benefits of NMF accessible to 
much bigger matrices. Furthermore, functions for preprocessing, estimation of
the optimal factorization rank and post-hoc feature selection are provided.

# The Bratwurst package 

The main feature of the package `Bratwurst` is an S4 object called `nmf.exp`. 
It is derived from `SummarizedExperiment`, has containers for a data matrix, 
column annotation data and row annotation data and inherits 
`SummarizedExperiment`'s accessor functions `colData` and `rowData`. The matrix
to be stored in this data structure is the matrix V as described above, 
corresponding to the input matrix for the NMF-solver. `nmf.exp` furthermore has
containers for the matrices W and H which are results of the decomposition.
As NMF algorithms have to be run iteratively, an instance of the class 
`nmf.exp` can store large lists of matrices, corresponding to the results of 
different iteration steps. Accessor functions to all different containers are 
provided.

A crucial step in data analysis with NMF is the determination of the optimal 
factorization rank, i.e. the number of columns of the matrix W or 
equivalently the number of rows of the matrix H. No consensus method for an 
automatic evaluation of the optimal factorization rank has been found to date. 
Instead, the decomposition is usually performed iteratively over a range of 
possible factorization ranks and different quality measures are computed for 
every tested factorization ranks. Many quality measures have been proposed:

* The `Frobenius reconstruction error`, i.e. the Frobenius norm of the 
residuals of the decomposition: ||W x H - V||
* Criteria to assess the stability of the decomposition:

  + The `cophenetic correlation coefficient`
  + An `Amari type distance`
  + `Silhouette values` over clusters of patterns extracted iteratively at the 
  same factorization rank

The package `Bratwurst` provides functions to compute all 

# Example: leukemia data

In this README, as in the vignette for the package, we use a dataset containing 
Affymetrix Hgu6800 microarray expression data of B-ALL, T-ALL and AML samples. 
This dataset had also been used by Brunet et al. (PNAS, 2004) and Gaujoux et 
al. (BMC Bioinformatics, 2010). In Brunet et al., this dataset is called the 
__Golub__ dataset.
Preparations: load the necessary software packages:

```{r, warning=FALSE, message=FALSE}
library(Bratwurst)
library(NMF)
```

Load the example data (the __Golub__ dataset is stored in the R package 
`Bratwurst`)

```{r, eval = T}
data(leukemia)
samples <- "leukemia"
```

This data was initially generated by the following commands:

```{r, eval = F}
data.path  <- file.path(getwd(), "data")
matrix.file <- list.files(data.path, "*data.txt", full.names = T)
rowAnno.file <- list.files(data.path, "micro.*anno.*txt", full.names = T)
rowAnno.bed <- list.files(data.path, ".bed", full.names = T)
colAnno.file <- list.files(data.path, "sample.*anno.*txt", full.names = T)
# Read files to summarizedExperiment
leukemia.nmf.exp <- nmfExperimentFromFile(matrix.file = matrix.file,
                                          rowAnno.file = rowAnno.file,
                                          colData.file = colAnno.file)
save(leukemia.nmf.exp, file = file.path(data.path, "leukemia.rda"))
```

Now we are ready to start an NMF analysis.

## NMF analysis

### Call wrapper function

The wrapper function for the NMF solvers in the Bratwurst package is 
`runNmfGpu`. It is called as follows:

```{r, warning=FALSE}
k.max <- 4
outer.iter <- 10
inner.iter <- 10^4

leukemia.nmf.exp<- runNmfGpuPyCuda(nmf.exp = leukemia.nmf.exp,
                                   k.max = k.max,
                                   outer.iter = outer.iter,
                                   inner.iter = inner.iter,
                                   tmp.path = "/tmp/tmp_leukemia",
                                   cpu = TRUE)
```

Depending on the choice of parameters (dimensions of the input matrix, number 
of iterations), this step may take some time. Note that the algorithm updates 
the user about the progress in the iterations.

Several getter functions are available to access the data in the generated 
`nmf.exp` object:

### `HMatrixList` 

Returns a list of matrices `H` for a specific factorization 
rank `k`. There are as many entries in this list as there were iterations in
the outer iteration. Of course the number of rows of the matrix `H` corresponds
to the chosen factorization rank.

```{r}
tmp.object <- HMatrixList(leukemia.nmf.exp, k = 2)
class(tmp.object)
length(tmp.object)
class(tmp.object[[1]])
dim(tmp.object[[1]])
```

If no value for `k` is supplied, the function returns a list of lists, one for
every iterated factorization rank.

```{r}
tmp.object <- HMatrixList(leukemia.nmf.exp)
class(tmp.object)
length(tmp.object)
class(tmp.object[[1]])
length(tmp.object[[1]])
```

### `HMatrix` 

Returns the matrix `H` for the optimal decomposition (i.e. the one 
with the minimal residual) for a specific factorization rank `k`. As in the 
previous paragraph, the number of rows of the matrix `H` corresponds to the 
chosen factorization rank.

```{r}
tmp.object <- HMatrix(leukemia.nmf.exp, k = 2)
class(tmp.object)
dim(tmp.object)
```

If no value for `k` is supplied, the function returns a list of optimal 
matrices, one for every iterated factorization rank.

```{r}
H.list <- HMatrix(leukemia.nmf.exp)
class(H.list)
length(H.list)
```

### `WMatrixList` 

Returns a list of matrices `W` for a specific factorization 
rank `k`. There are as many entries in this list as there were iterations in
the outer iteration. Of course the number of columns of the matrix `W` 
corresponds to the chosen factorization rank.

```{r}
tmp.object <- WMatrixList(leukemia.nmf.exp, k = 2)
class(tmp.object)
length(tmp.object)
class(tmp.object[[1]])
dim(tmp.object[[1]])
```

If no value for `k` is supplied, the function returns a list of lists, one for
every iterated factorization rank.

### `WMatrix`

Returns the matrix `W` for the optimal decomposition (i.e. the one 
with the minimal residual) for a specific factorization rank `k`. As in the 
previous paragraph, the number of columns of the matrix `W` corresponds to the 
chosen factorization rank.

```{r}
tmp.object <- WMatrix(leukemia.nmf.exp, k = 2)
class(tmp.object)
dim(tmp.object)
```

If no value for `k` is supplied, the function returns a list of optimal 
matrices, one for every iterated factorization rank.

```{r}
W.list <- WMatrix(leukemia.nmf.exp)
class(W.list)
length(W.list)
```


### `FrobError`

Returns a data frame with as many columns as there are iterated factorization 
ranks and as many rows as there are iterations per factorization rank.

```{r}
FrobError(leukemia.nmf.exp)
```

## Determine the optimal factorization rank

In NMF, Several methods have been described to assess the optimal factorization
rank. The Bratwurst packages implements some of them. They are computed by 
applying custom functions which subsequently update the data structure of type
`nmf.exp`.

### Get Frobenius error.

The most important information about the many iterated decompositions is the
norm of the residual. In NMF this is often called the Frobenius error, as the
Frobenius norm may be used.

```{r}
leukemia.nmf.exp <- computeFrobErrorStats(leukemia.nmf.exp)
```

### Evaluate silhouette values

In 2013, Alexandrov et al. published an NMF analysis on mutational signatures.
They used an approach which a modified silhouette criterion is used to estimate 
the stability across iteration steps for one fixed factorization rank `k`.

```{r}
leukemia.nmf.exp <- computeSilhoutteWidth(leukemia.nmf.exp)
```

### Cophenetic correlation coefficient plot

```{r}
leukemia.nmf.exp <- computeCopheneticCoeff(leukemia.nmf.exp)
```

### Compute amari type distance

```{r}
leukemia.nmf.exp <- computeAmariDistances(leukemia.nmf.exp)
```

After having executed all these functions, the values of the computed measures
can be accessed with `OptKStats`:

```{r}
OptKStats(leukemia.nmf.exp)
```

These quality measures can be displayed together:

### Generate plots to estimate optimal k

```{r, warning = FALSE, message = FALSE}
gg.optK <- plotKStats(leukemia.nmf.exp)
gg.optK
```

### Generate ranked error plot.

It may also be useful to inspect the Frobenius error after ranking. This may 
give an estimation of the convergence in the parameter space of initial 
conditions.

```{r}
gg.rankedFrobError <- plotRankedFrobErrors(leukemia.nmf.exp)
gg.rankedFrobError
```



## Visualize the matrix H (exposures)

The matrices `H` may be visualized as heatmaps. We can define a meta
information object and annotate meta data:

```{r}
entity.colVector <- c("red", "blue")
names(entity.colVector) <- c("ALL", "AML")
subtype.colVector <- c("orange", "darkgreen", "blue")
names(subtype.colVector) <- c("B-cell", "T-cell", "-")
anno_col <- list(V2 = entity.colVector,
                 V3 = subtype.colVector)
heat.anno <- HeatmapAnnotation(df = colData(leukemia.nmf.exp)[, c(2:3)],
                               col = anno_col)
```

And now display the matrices `H` with meta data annotation. Bratwurst provides 
a plotting function to display the matrices `H` with meta data annotation:

```{r}
# Plot Heatmaps for H over all k
lapply(seq(2, k.max), function(k) {
  plotHMatrix(leukemia.nmf.exp, k)
})
```

## Feature selection 
### Row K-means to determine signature specific features

```{r}
### Find representative regions.
# Get W for best K
leukemia.nmf.exp <- setOptK(leukemia.nmf.exp, 4)
OptK(leukemia.nmf.exp)

signature.names <- getSignatureNames(leukemia.nmf.exp, OptK(leukemia.nmf.exp))
signature.names

FeatureStats(leukemia.nmf.exp)

leukemia.nmf.exp <- computeFeatureStats(leukemia.nmf.exp)
FeatureStats(leukemia.nmf.exp)

# You might want to add additional selection features
# such as entropy or absolute delta 
# Entropy
leukemia.nmf.exp <- computeEntropy4OptK(leukemia.nmf.exp)
FeatureStats(leukemia.nmf.exp)

leukemia.nmf.exp <- computeAbsDelta4OptK(leukemia.nmf.exp)
FeatureStats(leukemia.nmf.exp)
```

### Feature visualization

```{r}
# Plot all possible signature combinations
plotSignatureFeatures(leukemia.nmf.exp)

# Plot only signature combinations
plotSignatureFeatures(leukemia.nmf.exp, sig.combs = F)

# Try to display selected features on W matrix
sig.id <- "1000"
m <- WMatrix(leukemia.nmf.exp, k = OptK(leukemia.nmf.exp))[
  FeatureStats(leukemia.nmf.exp)[, 1] == sig.id, ]
m <- m[order(m[, 1]), ]
c <- getColorMap(m)
#m <- t(apply(m, 1, function(r) (r - mean(r))/sd(r)))

Heatmap(m, col = c, cluster_rows = F, cluster_columns = F)
```

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