ClussCluster {ClussCluster} | R Documentation |

## Performs simultaneous detection of cell types and cell-type-specific signature genes

### Description

`ClussCluster`

takes the single-cell transcriptome data and returns an object containing cell types and type-specific signature gene sets

Selects the tuning parameter in a permutation approach. The tuning parameter controls the L1 bound on w, the feature weights.

### Usage

```
ClussCluster(x, nclust = NULL, centers = NULL, ws = NULL,
nepoch.max = 10, theta = NULL, seed = 1, nstart = 20,
iter.max = 50, verbose = FALSE)
ClussCluster_Gap(x, nclust = NULL, B = 20, centers = NULL,
ws = NULL, nepoch.max = 10, theta = NULL, seed = 1,
nstart = 20, iter.max = 50, verbose = FALSE)
```

### Arguments

`x` |
An nxp data matrix. There are n cells and p genes. |

`nclust` |
Number of clusters desired if the cluster centers are not provided. If both are provided, nclust must equal the number of cluster |

`centers` |
A set of initial (distinct) cluster centres if the number of clusters ( |

`ws` |
One or multiple candidate tuning parameters to be evaluated and compared. Determines the sparsity of the selected genes. Should be greater than 1. |

`nepoch.max` |
The maximum number of epochs. In one epoch, each cell will be evaluated to determine if its label needs to be updated. |

`theta` |
Optional argument. If provided, |

`seed` |
This seed is used wherever K-means is used. |

`nstart` |
Argument passed to |

`iter.max` |
Argument passed to |

`verbose` |
Print the updates inside every epoch? If TRUE, the updates of cluster label and the value of objective function will be printed out. |

`B` |
Number of permutation samples. |

### Details

Takes the normalized and log transformed number of reads mapped to genes (e.g., log(RPKM+1) or log(TPM+1) where RPKM stands for Reads Per Kilobase of transcript per Million mapped reads and TPM stands for transcripts per million) but NOT centered.

### Value

a list containing the optimal tuning parameter, `s`

, group labels of clustering, `theta`

, and type-specific weights of genes, `w`

.

a list containig a vector of candidate tuning parameters, `ws`

, the corresponding values of objective function, `O`

, a matrix of values of objective function for each permuted data and tuning parameter, `O_b`

, gap statistics and their one standard deviations, `Gap`

and `sd.Gap`

, the result given by `ClussCluster`

, `run`

, the tuning parameters with the largest Gap statistic and within one standard deviation of the largest Gap statistic, `bestw`

and `onesd.bestw`

### Examples

```
data(Hou_sim)
hou.dat <-Hou_sim$x
run.ft <- filter_gene(hou.dat)
hou.test <- ClussCluster(run.ft$dat.ft, nclust=3, ws=4, verbose = FALSE)
```

*ClussCluster*version 0.1.0 Index]