Package 'scCAN'

scCAN

Description

This is the main function to perform sc-RNA seq data clustering clustering. scCAN is fully unsupervised scRNA-seq clustering framework that uses deep neural network and network fusion-based clustering algorithm. First, scCAN applies a non-negative autoencoder to filter scRNA-seq data. Second, the filtered data is passed to stacked Bayesian autoencoder to get multiple low-dimensional representations of input data. Subsequently, scCAN converts these compressed data into networks and unify those networks to a single graph. Then, scCAN uses a spectral clustering algorithm to obtain final clusters assignment.

Usage

scCAN(
  data,
  sparse = F,
  n.neighbors = 30,
  alpha = 0.5,
  n.iters = 10,
  ncores = 10L,
  r.seed = 1
)

Arguments

data	Gene expression matrix, with rows represent samples and columns represent genes.
sparse	Boolen variable indicating whether data is a sparse matrix. The input must be a non negative sparse matrix.
n.neighbors	Number of neighboring cells that are used to caculate the edge's weight. The number of neighbors are set n.neighbors = 30 by default.
alpha	A hyper-parameter that is used to calculate the network kernel. The value is set to alpha = 0.5 by default.
n.iters	A hyper-parameter to set the number of network fusion iterations. It is set to n.iters = 10 by default.
ncores	Number of processor cores to use.
r.seed	A parameter to set a seed for reproducibility. This values is set to r.seed = 1 by default.

Value

List with the following keys:

• cluster - A numeric vector containing cluster assignment for each sample. If do.clus = False, this values is always NULL.

• latent - A matrix representing compressed data from the input data, with rows represent samples and columns represent latent variables.

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SCE

Description

SCE dataset includes scRNA-seq data and cell type information.

Usage

SCE

Format

An object of class list of length 2.

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