The goal of boclust is to provide a new normalization method for sparse data by a feature boosting strategy with the latent representation, especially for scRNA-seq data consisted of many zeros. Based on the normalization, a new measure of similarity is defined for the following clustering algorithm. Unlike other unsupervised cluster methods, boclust provides the suggestion K to determine the number of clusters. In this way, it may be unsuitable for low-dimentional data.

There are three major functions:

- BossaSimi: to calculate the similarity matrix and normalized data.
- BossaClust: the main function which provide an object including clustering result for shiny.
- bossa_interactive: a shiny framework to show the clustering result.

You can install boclust from github with:

```
# install.packages("devtools")
devtools::install_github("TinyOpen/boclust")
```

```
# generate sparse data from the toy model of CIDR
sparse.data <- data.frame(g.1 = c(0, 5, 0, 6, 8, 6, 7, 7),
g.2 = c(5, 0, 0, 0, 5, 7, 5, 7))
bossa.change <- BossaSimi(sparse.data, is.pca = FALSE) # with low-dimensional data, pca is uncessary
data.after <- bossa.change$U.score.non.pca # data after normalization
```

You can check after normalization, the first 4 cells which are actually from the same cluster are more closer. The seperation between the first 4 cells and the last 4 cells is large enough to get the correct clustering result.

```
d3heatmap(sparse.data) ## show heatmap of original data
d3heatmap(data.after) ## show heatmap of bossa-normalized data
```

Now, when it comes to your **high-dimentional** data, which is the target which `boclust`

is designed for. You can either use `BossaClust`

to get the final result:

```
object <- BossaClust(high.dim.data) # do normalization and clustering at the same time
bossa_interactive(object) # use shiny frame to show the result
```

Or, you can store the normalized data first, which is obtained from function `BossaSimi`

, and then do the rest work.

```
pre.object <- BossaSimi(high.dim.data)
object <- BossaClust(data = high.dim.data, data.pre = pre.object) # do normalization and clustering at the same time
bossa_interactive(object) # use shiny frame to show the result
```

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