pathway.pc.correlation.distance

weighted PC magnitudes e.g. scde::pagoda.pathway.wPCA() gives the weighted PC magnitudes for each gene provided; 
e.g. scde::pagoda.gene.clusters() gives the weighted PC magnitudes for de novo gene sets identified by clustering on expression

a matrix of normalized aspect patterns (rows: significant aspects, columns: cells)

numeric Number of cores to use (default=1)

n.cores

numeric Target dimensions (default=NULL)

target.ndf

Calculate correlation distance between PC magnitudes given a number of target dimensions

Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for Conos, <https://github.com/kharchenkolab/conos>. This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/pagoda2>. The size of the 'p2data' package is approximately 6 MB.

Evan Biederstedt

pathway.pc.correlation.distance: Calculate correlation distance between PC magnitudes given a number of target dimensions

Description

Usage

Arguments

Value