update_c

The center values for each cluster in condition 1.

mu_1

The center values for each cluster in condition 2.

mu_2

The centered data from condition 1, columns should be
samples (cells) and rows should be features (genes).

pdat1

The centered data from condition 2, columns should be
samples (cells) and rows should be features (genes). The number of genes
should be the same as <code>pdat1</code>.
as in pdat1.

pdat2

The number of clusters present in the data.

ncluster

Updates the cluster membership for each iteration of SparseDC. Runs inside
<code>sparse_dc_fun</code>.

Implements the algorithm described in
Barron, M., Zhang, S. and Li, J. 2017, "A sparse differential
clustering algorithm for tracing cell type changes via single-cell
RNA-sequencing data", Nucleic Acids Research, gkx1113,
<doi:10.1093/nar/gkx1113>. This algorithm clusters samples from two
different populations, links the clusters across the conditions and
identifies marker genes for these changes. The package was designed for
scRNA-Seq data but is also applicable to many other data types, just
replace cells with samples and genes with variables. The package also
contains functions for estimating the parameters for SparseDC as outlined
in the paper. We recommend that users further select their marker genes
using the magnitude of the cluster centers.

update_c: Update Clusters

Description

Usage

Arguments

Value